tag:blogger.com,1999:blog-91056526975274562862024-03-19T07:19:22.161+00:00Systems and Signals Group<a href="https://www.imperial.ac.uk/people/nick.jones/research.html"> Imperial Group page link</a> <a href="https://twitter.com/sys_sig"> @sys_sig</a>Unknownnoreply@blogger.comBlogger55125tag:blogger.com,1999:blog-9105652697527456286.post-27168772624500796082023-02-07T12:56:00.002+00:002023-02-07T12:56:38.253+00:00 Stochastic Survival of the Densest: defective mitochondria could be seen as altruistic to understand their expansion<p><span style="font-family: Arial; font-size: 11pt; text-align: justify; white-space: pre-wrap;">With age, our skeletal muscles (e.g. muscle of our legs and arms) work less well. In some people, there is a substantial loss of strength and functionality called sarcopenia. This has a knock-on effect on the health of elderly people. If we want to delay skeletal muscle ageing, so that we can stay active and healthy for longer, we need to understand the mechanism behind it.</span></p><span id="docs-internal-guid-f0fe89e7-7fff-3e4c-601d-53e5a87544e7"><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">The spread of a type of mitochondrial mutants, deletions, has been causally implicated in the ageing of skeletal muscle. But how do these mutations expand, replacing normal (wildtype) mitochondrial DNA? One could think that they have a replicative fitness advantage, therefore they outcompete wildtypes. However, no definitive explanation of this supposed faster replication is known for non-replicating cells, despite numerous proposals. We might even expect that these mutants are actively eliminated since they are disadvantageous to the cell.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">In a recent </span><a href="https://www.pnas.org/doi/10.1073/pnas.2122073119" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">PNAS paper</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">, (free version </span><a href="https://www.biorxiv.org/content/10.1101/2020.09.01.277137v2" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">here</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">) we unveil a new evolutionary mechanism, termed </span><span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">stochastic survival of the densest, </span><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">that accounts for the expansion of mitochondrial deletions in skeletal muscle. We do not assume a replicative advantage for deletions, and even allow the possibility of a higher degradation rate for mutants compared to non-mutants. Our stochastic model predicts a noise-driven </span><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">clonal wave of advance</span><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> of mitochondrial mutants, recapitulating experimental observation qualitatively and quantitatively. </span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="border: none; display: inline-block; height: 300px; overflow: hidden; width: 624px;"><img height="300" src="https://lh5.googleusercontent.com/Lw5dWyJ_PdV7zwOlrMekIydl-4zYQz_nZTOgTfwwUZJI08ILnrOGSwhcKaQLjCh9qsqDhOfNI3cw0z_MF5BVToo9GmCRbYOODuGimSxQJkYGK2qKXVSiu8GSXMKNLyUGTQcWVX1Fy7CvSwY3_9vnNKk" style="margin-left: 0px; margin-top: 0px;" width="624" /></span></span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 9pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Stochastic survival of the densest accounts for the expansion of mitochondrial DNA mutants in aged skeletal muscle through a noise-driven wave of advance of denser mutants.</span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 9pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">A) Dysfunctional mtDNA mutants expand in muscle fibres with age, occupying macroscopic regions where strength and functionality are lost. B) A stochastic model of a spatially extended system predicts a travelling wave of denser mutants (e.g. the mitochondrial deletions in skeletal muscles), according to a novel evolutionary mechanism termed </span><span style="font-family: Arial; font-size: 9pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">stochastic survival of the densest.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">A key reason behind mutants’ expansion is that they can live at higher densities (or carrying capacity) in the muscle fibres. This, together with the stochastic nature of biological processes, gives rise to the surprising wave-like expansion. Remarkably, if you take the noise away, the effect disappears: it is a truly stochastic phenomenon. We are very excited about this new mechanistic understanding: the clonal expansion of deletions has been the subject of intense research for over 30 years. Moreover, this progress allows principled suggestions of therapies that might slow down skeletal muscles ageing. </span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">We are possibly even more excited about the evolutionary implications of our work. We believe that stochastic survival of the densest is a previously unrecognised mechanism of evolution that can account for other counterintuitive phenomena. We essentially showed that, in the presence of noise, a species can take over a system because the more of it there is the faster </span><span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">all</span><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> species in the system replicate. This conforms to one of the strict definitions of altruism. Therefore, the model can also account for the spread of altruism: a species can win a competition because it is altruistic. The model we use to show the effect is one of the simplest models for populations (generalised stochastic Lotka-Volterra) and we think that the effect is robust, and will be reproduced by a variety of similar models and in a range of geometries/topologies</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Mitochondrial deletion mutants are bad for our muscles and our health, but in order to understand (and counteract) their expansion we might consider them an altruistic species that, driven by noise, outcompetes wildtype mitochondria. Ferdinando and Nick</span></p><br /><br /><br /></span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-47605356817339406032023-02-07T12:54:00.000+00:002023-02-07T12:54:19.778+00:00Community detection in graphons<p></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: justify;"><span class="s1" style="color: #1b1b1b; font-family: Calibri; font-kerning: none; font-size: 16px; text-align: left;">Graphs are one way to represent data sets as they arise in the social sciences, biology, and many other research domains. In these graphs, the nodes represent entities (e.g., people) and edges represent connections between them (e.g., friendships). For the analysis of graphs, a vast selection of mathematical and computational techniques has been developed. For example, Google uses <a href="https://en.wikipedia.org/wiki/PageRank">eigenvectors</a> to quantify the importance of webpages in a graph that represents the word-wide web.</span></div></div><p></p><p class="p4" style="color: #244084; font-family: "Calibri Light"; font-size: 21.3px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">Community detection</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">One field of interest in the analysis of graphs is the detection of so-called communities. These are groups of nodes that are strongly connected internally but only sparsely to nodes in other groups. A common example in the social sciences are friendship groups. Community-detection methods can be used to cluster large graph-structured data sets and so provide insights into many complex systems. For example, one can use the community structure to investigate how <a href="https://doi.org/10.1073/pnas.1018985108">brain</a> function is organised</span><span class="s1" style="font-kerning: none;">. In these biological graphs, the different communities represent different parts of the brain, each of which fulfilling different functions. Many algorithms to identify communities exist, the most popular of which is modularity maximisation.</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p5" style="color: #244084; font-family: "Calibri Light"; font-size: 17.3px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">Graphons</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">As the scale of these data sets increase, the graphs that represent them get larger, too. This makes the development of computationally-efficient tools for their analysis a pressing issue. One way to investigate these large, finite objects is to abstract them with idealized infinite objects. In this context, <a href="https://doi.org/10.48550/arXiv.1611.00718">graphons</a> have emerged as one way of looking of infinite-sized graphs.<span class="Apple-converted-space"> </span></span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">One way to motivate graphons is to look at graphs that grow in size (i.e., the number of nodes) by iteratively adding nodes and edges. In Fig. 1, we show graphs of varying size in a so-called pixel image. Each pixel image visualises the edges in a graph with <i>n</i> nodes. The <i>n^2</i> pixels in each image represent a single pair of nodes and are RED if an edge is connecting the pair of nodes and WHITE if not. With growing network size <i>n</i>, we observe that the pixels get smaller, making the discrete image resemble a continuous density plot. The graphon, as shown on the very right, is the continuous limiting object of such growing graphs as <i>n</i> goes to infinity. This intuitive notion of convergence can be concretised and proven by looking at subgraph <a href="https://doi.org/10.1090%2Ftran%2F7543">counts</a></span><span class="s1" style="font-kerning: none;">.</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p7" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; min-height: 19px; text-align: justify;"><span class="s1" style="font-kerning: none;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjdWaMwpJ48-zAoOjdBGr95LbLWX5CCjwX_T66YNyb6mhfQeXp56dGGKpvA_KSCdbIkXyDRklxgaE-EEDKwvdLZt8mBOD8tzsEW1D9ygunBT6U2xT3XQwtvCnYzDWUw-lJ_dXh5CWN4UA2CvkI2Wg_pbNv9ayDwjfKVpItY61JfB_-aKoOx_1fZ0VHtuQ" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="106" data-original-width="451" height="94" src="https://blogger.googleusercontent.com/img/a/AVvXsEjdWaMwpJ48-zAoOjdBGr95LbLWX5CCjwX_T66YNyb6mhfQeXp56dGGKpvA_KSCdbIkXyDRklxgaE-EEDKwvdLZt8mBOD8tzsEW1D9ygunBT6U2xT3XQwtvCnYzDWUw-lJ_dXh5CWN4UA2CvkI2Wg_pbNv9ayDwjfKVpItY61JfB_-aKoOx_1fZ0VHtuQ=w400-h94" width="400" /></a></div><br /><br /><p></p><p class="p7" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; min-height: 19px; text-align: justify;"><br /></p><p class="p5" style="color: #244084; font-family: "Calibri Light"; font-size: 17.3px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">Defining community detection for graphons</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">In our paper, which was just published in SIAM Applied Mathematics "<a href="https://doi.org/10.1137/22M1492003">Modularity maximization for graphons</a>"</span><span class="s1" style="font-kerning: none;"> (free version <a href="https://doi.org/10.48550/arXiv.2101.00503">here</a>) joint with Michael Schaub, we explore how one can define and compute these communities for graphons. To achieve this, we define a modularity function for graphons, which is an infinite-sized limit for the well-established modularity function for graphons. The modularity function for graphs is defined as a function of a double-sum over all pairs of edges. The core idea is, that in the infinite-size limit (i.e, for graphons) this double sum approaches a double integral, which may simplify the computational burden of community detection.</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;">In our paper, we explore a selection of synthetic graphons and compute their community structure. Surprisingly (at least for us), the continuous form of graphons allows us to analytically compute the optimal community structure for some of these --- something that is usually not possible for graphs. Lastly, we outline a computational pipeline that would allow us to detect community structure in a privacy-preserving way (see Fig. 2). Florian and Nick.</span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEi3UdliYKCMsmeiOnVeznt3eGneRJ0q9-VA42ZHK4gAUyEfUfg5EOVYSQX4ZBnqU6NUAVEIizZs9yjr67Q_2g08Cg2Pn99HK9SOdwRMN2rlR82FovRjRjAsgxqN95HQVpGnC0MeLCEfn-F-BRr5-W4BYMMJcpIZRzkhd7_iTv8xa9JHoujXg-kgQswxlA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="262" data-original-width="451" height="186" src="https://blogger.googleusercontent.com/img/a/AVvXsEi3UdliYKCMsmeiOnVeznt3eGneRJ0q9-VA42ZHK4gAUyEfUfg5EOVYSQX4ZBnqU6NUAVEIizZs9yjr67Q_2g08Cg2Pn99HK9SOdwRMN2rlR82FovRjRjAsgxqN95HQVpGnC0MeLCEfn-F-BRr5-W4BYMMJcpIZRzkhd7_iTv8xa9JHoujXg-kgQswxlA" width="320" /></a></div><br /><p></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p><p class="p3" style="font-family: Calibri; font-size: 16px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span class="s1" style="font-kerning: none;"> </span></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-56354274564454931172023-01-05T11:21:00.000+00:002023-01-05T11:21:58.768+00:00Can misinformation really impact people’s intent to get vaccinated?<p><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Health misinformation, especially that related to vaccines, is </span><a href="https://doi.org/10.1016/j.vaccine.2010.02.052" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">not a new phenomenon</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">. Growth of the internet in the last two decades has led to an exponential rise in use of online social media platforms. Consequently, many people rely on information available online to inform their health decisions. While social media platforms have improved access to all kinds of information for their users, </span><a href="https://doi.org/10.1126/science.aap9559" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">false information has been shown to diffuse faster and deeper</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> than true information. Therefore, it’s not surprising that the COVID-19 pandemic has been accompanied by an </span><a href="https://www.who.int/news/item/23-09-2020-managing-the-covid-19-infodemic-promoting-healthy-behaviours-and-mitigating-the-harm-from-misinformation-and-disinformation" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">“infodemic”</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">: an excessive spread of false or misleading information, both online and offline, that has eroded trust in scientific evidence and expert advice, and undermined the public health response. Susceptibility to online misinformation regarding the pandemic has been </span><a href="https://doi.org/10.1098/rsos.201199" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">negatively associated</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> with compliance of public health guidance, and willingness to get vaccinated. However, it is not clear if it is </span><span style="font-family: "Times New Roman"; font-size: 12pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">exposure </span><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">to misinformation that lowers vaccination intent, or simply that those who are unwilling to vaccinate </span><span style="font-family: "Times New Roman"; font-size: 12pt; font-style: italic; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">believe </span><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">in (mis)information that justifies their stance on vaccination.</span></p><span id="docs-internal-guid-8c184574-7fff-24f6-d5b0-3df2efc97746"><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-indent: 36pt;"><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">This begs the question, can exposure to misinformation actually lower people’s intent to get vaccinated? Given the hidden underlying factors that can induce a correlation between belief in misinformation and unwillingness to vaccinate, we </span><a href="https://doi.org/10.1038/s41562-021-01056-1" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">conducted a randomized controlled experiment</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> in September 2020 in the UK and the USA on a representative sample of 4,000 people from each country. Everyone was asked about their intention to get a COVID-19 vaccine to protect themselves. Two-thirds of the people were then exposed to five recently circulating pieces of online misinformation about COVID-19 vaccines, whereas one-third were exposed to five pieces of factually correct information to serve as a control group. Following this, people were asked again about their vaccination intent. This allowed us to quantify the causal impact of exposure to misinformation, relative to factual information, on vaccination intent. Before exposure, 54.1% of respondents in the UK and 42.5% in the USA reported that they would “definitely” accept a COVID-19 vaccine, while 6.0% and 15.0% said they would “definitely not” accept it. Unfortunately, even this brief exposure to misinformation induced a decline in intent of 6.2 percentage points in the UK and 6.4 percentage points in the USA among those who stated that they would definitely accept a vaccine. Interpreting these results in the context of vaccination coverage rates required to achieve herd immunity—which </span><a href="https://doi.org/10.1016/j.jinf.2020.03.027" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">ranges from anywhere between 55% to 85%</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> depending on the country and infection rate—suggests that </span><a href="https://thehill.com/opinion/healthcare/564062-covid-19-misinformation-is-a-public-health-hazard-we-need-to-start?rl=1" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">misinformation could impede efforts to successfully fight the pandemic</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">.</span></p><div align="left" dir="ltr" style="margin-left: 0pt;"><table style="border-collapse: collapse; border: none; table-layout: fixed; width: 468pt;"><colgroup><col></col></colgroup><tbody><tr style="height: 0pt;"><td style="overflow-wrap: break-word; overflow: hidden; padding: 5pt 5pt 5pt 5pt; vertical-align: top;"><p dir="ltr" style="line-height: 1.2; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: "Times New Roman"; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><span style="border: none; display: inline-block; height: 343px; overflow: hidden; width: 610px;"><img height="343" src="https://lh4.googleusercontent.com/rNC_oy2Ay827sPo4XV2-dnybJRgGMrc2RukPUCENxe-b2hWJBsE0I67jFDR9bixZ-gUpxpZWSPoA56r3AeO5ZVTZyqD6gMKpPcfx6Kuel0hHtg8cjQpzzu-VaUtcITB51X8r1StL28UqddfsqJM6Zry3agBeoUlzDUo9O65Xg_0BxQY7lm-wZDG4va-t" style="margin-left: 0px; margin-top: 0px;" width="610" /></span></span></p></td></tr><tr style="height: 0pt;"><td style="overflow-wrap: break-word; overflow: hidden; padding: 5pt 5pt 5pt 5pt; vertical-align: top;"><p dir="ltr" style="line-height: 1.2; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: "Times New Roman"; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Randomized controlled experiment reveals that exposure to misinformation can reduce the willingness to vaccinate oneself against COVID-19, which may be detrimental to goals of achieving herd immunity.</span></p></td></tr></tbody></table></div><br /><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">Since now we know that COVID-19 misinformation can indeed reduce people’s willingness to vaccinate, </span><a href="https://www.hhs.gov/sites/default/files/surgeon-general-misinformation-advisory.pdf" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">what can be done</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> about it? We must understand the how and why of it. There exist a </span><a href="https://www.theguardian.com/world/2021/jul/17/covid-misinformation-conspiracy-theories-ccdh-report" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">small minority of organized actors</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> who supply much of this misinformation online, but the vast majority of </span><a href="https://www.scientificamerican.com/article/most-people-dont-actively-seek-to-share-fake-news/" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">people do not actively seek to spread misinformation</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">. They are simply trying to make an informed decision while faced with a deluge of information, and false narratives that exploit people’s </span><a href="https://doi.org/10.1177%2F14614448211011451" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">fear and anxieties</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> simply become more likely to be shared ahead. In </span><a href="https://doi.org/10.1038/s41562-021-01056-1" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">our research</span></a> (in Nature Human Behaviour)<span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">, we found that scientific-sounding misinformation that purported a direct link between the COVID-19 vaccine and adverse effects were associated more strongly with a decline in intent. This extends the scope of the problem beyond online misinformation, and towards </span><a href="https://www.tandfonline.com/doi/full/10.1080/10410236.2020.1838096" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">understanding and addressing vaccine hesitancy</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> more broadly. Individually, we must understand the concerns of our peers, and think about </span><a href="https://doi.org/10.1038/s41586-021-03344-2" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">veracity over emotions before sharing anything</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"> with them, online or offline. Collectively, we must foster a </span><a href="https://doi.org/10.1038/s41577-020-0380-8" style="text-decoration-line: none;"><span style="color: #1155cc; font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;">deeper public understanding of vaccination</span></a><span style="font-family: "Times New Roman"; font-size: 12pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;">, which can be brought about by clearer scientific communication, and rebuilding trust in institutions. Sahil</span></span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-54450636624554673442023-01-04T14:16:00.001+00:002023-01-04T14:18:39.816+00:00 Inference and influence of network structure using snapshot social behavior without network data<p><span style="font-family: Arial; font-size: 12px; text-align: justify;"><span style="color: #111111;">In a more and more polarized world, the role of underling social structure driving social influence in opinions and behaviours is still unexplored. In this study we developed a method, the kernel-Blau-Ising model (KBI), to uncover how people are influenced/connected based on their socio-demographic coordinates (e.g., income, age, education, postcode), and tested the model in the EU referendum and two London Mayoral elections.</span></span></p><div class="separator" style="clear: both; text-align: center;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEjNnb2heOvWNf4EDgMMzpbkuj39hnbzd-a85OtApsJKwsl6mB0kFsPV6QB4f6DpASWgrU5CI1PvXEOC3i_Jn9A_cCr8y3qIBcj_OGbn29ie9F7lGtm7kHu9OBF1bppvb9BhbJh8fCMs2GamLlqTM9Zrmru0e13Iiwd3HfXt0viw80byfVGEYNvJJvWQ_A" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="495" data-original-width="1400" height="224" src="https://blogger.googleusercontent.com/img/a/AVvXsEjNnb2heOvWNf4EDgMMzpbkuj39hnbzd-a85OtApsJKwsl6mB0kFsPV6QB4f6DpASWgrU5CI1PvXEOC3i_Jn9A_cCr8y3qIBcj_OGbn29ie9F7lGtm7kHu9OBF1bppvb9BhbJh8fCMs2GamLlqTM9Zrmru0e13Iiwd3HfXt0viw80byfVGEYNvJJvWQ_A=w635-h224" width="635" /></a></div><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">Outline of the KBI methodology (high res image </span><a href="https://www.science.org/doi/10.1126/sciadv.abb8762" style="font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">here</a><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">). Input data consist of aggregated behavioral data for different geographical areas and sociodemographic variables (age, income, education, etc.) associated to those areas (from census data). (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">A</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) Heatmap of (hypothetical) behavioral data in Greater London, in this case electoral outcomes, where red represents 100% votes to Labour and blue represents 100% votes to Conservatives. (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">B</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) Probability distribution of behavioral outcomes in (A). (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">C</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) Blau space representation of the behavioral outcomes spanned by sociodemographic characteristics (e.g., age and income). (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">D</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) Blau space representation of KBI approach using input data in and learning parameters: the External Fields, which account for the general trends, e.g., older people are more likely to vote Conservatives than younger people, and the network that connects the population according to their distances in the Blau space and their homophilic preferences. Once the model parameters are learnt, we can further estimate how changes and interventions affect behavioral outcomes. Examples of potential network-sensitive intervention strategies: how changes to income distribution (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">E</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) and homophilic preferences (</span><b style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">F</b><span style="color: #111111; font-family: Helvetica; font-size: 8px; letter-spacing: 0.2px; text-align: justify;">) can reduce behavioral polarization.</span></div><p class="p5" style="font-family: Helvetica; font-size: 16.5px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; min-height: 20px; text-align: justify;"><span style="color: #111111;"><br /></span></p><p class="p7" style="font-family: Arial; font-size: 12px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; text-align: justify;"><span style="color: #111111;"><span class="s2">Despite using no social network data, we discover established signatures of homophily,</span> the tendency to befriend those similar to oneself—the stronger homophily is, the more social segregation. We found consistent geographical segregation for the three elections, while education was a strong segregation factor for the EU Referendum, it wasn’t for Mayoral Elections, however, age and income were. The model can be used to explore how reducing inequalities or encouraging mixing among groups can reduce social polarization. <span class="s3">You can read about our work "Inference of a universal social scale and segregation measures using social connectivity kernels" free in the journal Science Advances <a href="https://www.science.org/doi/10.1126/sciadv.abb8762" target="_blank">here</a>. Antonia and Nick</span></span></p><p class="p8" style="color: #333333; font-family: Arial; font-size: 10px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; min-height: 11px; text-align: justify;"><br /></p><p class="p9" style="color: #333333; font-family: Arial; font-size: 10px; font-stretch: normal; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; min-height: 11px;"><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-20401964725448815802020-10-29T20:06:00.002+00:002020-10-29T20:06:52.698+00:00catch22 features of signals<p class="MsoNormal"> <span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">By taking repeated measurements over time we can study the
dynamics of our environment – be it the mean temperature of the UK by month,
the daily opening prices of stock markets, or the heart rate of a patient in
intensive care. The resulting data consists of an ordered list of single
measurements and we will call it ‘time series’ from now on. Time series can be
long (many measurements) and complex and in order to facilitate exploitation of
the gathered data we often want to summarise the captured sequences. For
example, we might collapse the 12 monthly mean temperatures for each of the
past 100 years to a yearly average. This would enable us to remove the effects
of the seasons, reduce 12 yearly measurements to 1 and thereby let us quickly
compare the temperatures across many years without studying each monthly measurement.
Taking the average value of a time-series is a very simple example of a so
called ‘time series feature’, an operation that takes an ordered series of
measurements as an input and gives back a single figure that quantifies one
particular property of the data. By constructing a set of appropriate features,
we can compare, distinguish and group many time series quickly and even
understand in what aspects (i.e., features) two time series are similar or
different.</span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Over the past decades, thousands of such time-series features have
been developed across different scientific and industrial disciplines, many of
which are much more sophisticated than an average over measurements. But which
features should we choose from this wealth of options for a given data set of
time series? Do features exist that can characterise and meaningfully distinguish
sequences from a wide range of sources? </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">We here propose a selection procedure that tailors feature-sets to
given collections of time-series datasets and that can identify features which are
generally useful for many different sequence types. The selection is based on the
rich collection of 7500+ diverse candidate features previously gathered in the
comprehensive ‘highly comparative time-series analysis’ (<i style="mso-bidi-font-style: normal;"><a href="https://github.com/benfulcher/hctsa">hctsa</a>)</i> toolbox (paper <a href="https://doi.org/10.1016/j.cels.2017.10.001">here</a>) from
which we automatically curate a small, minimally redundant feature subset based
on single-feature performances on the given collection of time-series
classification tasks. </span></p>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img alt="" height="224" 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oNzdjLndEWlUkzWxFu5vhPiSm3V2nhZiDUOtUWQgnV8PO1Rvzfkxdma9YmSdMuscwcZoaVUry0qsVmb8qxbVl9VdcmKOtMO/hiKLZPOgJbo5fmxikU4mCD+R/yShMQHa4fB064+f2vK2+33KJBiaePd2VQdPOxgs2Mm1OfKrfsYeAmmSoow4D8mOAUgYl787Gc/c2X7IpXTh5uIj8e3WvdIt5y7G9axn7199C1WUlFme3cfZnGRBENMSDfffLPT+ggk8HwmoYaLcOI+0tiYoIGEmckr28U3+5lUACGmU8plJWjVCp/qHgFwDQU53plKzZavTbRVuQnWrX2pxpR9qsEo9HO79femolhbsDTFMtIrLLtHkcUIRFetT7DXP1eCeWyMjRhYZN27brSilELrPWCgnl3vsIBB3Xex43uZVwQmUDEEZhg4n3a8tZpnwEwDV7GoecSObUF+e+5Fs68/2bHzajs6Q54AHwzNRXPCKwYMGOCsTF5JvJUrVzoegplyvPKQg/lPbeNa3+3wKgQmvrGIhdIO69uWf1z4R2D3rkPC3mhIzZD8KxzCmEYpjTVv3jz7wx/+4GzooXpA7g0SnEeYMyiQ7JW7YlIhvSHRUwkC3+LnksrLN+eReef537WPQJVAbfpPKfbIGx1sxrxky+qtqvsdyi3r8zLba3C+jRuZZ4OyCu2T6Rn2f690lhmhQibFKluyKsFyN8XZT4sT9XIjsFTZySeVSMApllbfJuxMpPY72LE9zLv//Oc/RrWPuXPnGvl+wZF9O9bitkczF8OhEQa2mhy6DGrgIfX+nRDZqnf17kdzH4hgftJJJzlhiHxTzOfffPON0wxZuBkCsMJNgB8Ro8Q7hMMSEe7++e2FfwRCguH333/vfDNXX3210+6QuiZNmuSAMVQX2O9JbOTeAKSsQUfxWkyomBoIoPEI/0+ygiNCTmLUHU3EGgEybENF8iZ+APh67db7m3agxrRR3UKT/B8jta+oJM5+mJ9iC5YkWlFBjM2enWSzrXoZpZU/i7X+3YptaU6i3f1UZ3vnc6kVtVKMLVqRrKjcZBsJNkYZIUgRno5GCPidddZZrocIUyHnSwP7D7Nj3voSfwMHsAlPQzjHRIoPmYLbLOiMpsicYBX7hjxD5hLtYnGoLeiFY8I555pwyFr1pUplHv2/71+2I7LHWFbbrtjFwnK/IcGQZGuSSJkIgBxRoVRkD0VMRMykmFUhJuhll13mwJQIQFIzaIPK/4EUapJRgo1lVhbrs3dgVXltL9CkXS/neZoc2miinaR5NgTMKnVPG0iWFRB2kamsIW0E3keT/Jbgm5hQab26lOm+SfbdFsVk/bScDQk2Z1GyzV1SDZB19WvVCrOvppgNk6Whm1aykRU7aohnu3DhQsMMhuaGxQHJnHk1YsSIehVYrs/NEOA1e/Zsf5WB+gxWMx+DawXggo/w3I488kiXEoPg3RCC91BSjRUnmGOYy1n5xKfoH4F1hbl22Ue3W0FpsR3db7+wdnizirRtm0jkgB8+m6+++srGjRtX52SBWXnV/PntrSHHbyS5YCDc9mqb/xLglWiS3vrkkzaNvEZpjk4T1PZKcevZmrz/fv55e1elsV5XfphmcfWJaJJwcz6eVsk+/pZJdpNepIdefdXmycTCtmIx1w9lZnlL7ShUrLodXcMdz3n6VKjfU8Uon9Nq64tl4n1UzvsSUjloc/Mx7lo6zvWT60aCCJJR1xTvqPUfq2z00Hw79dCNdubxG238AfkCiAqZiyqssDLOPpuhMlDq/vDBxc48Wld3JOsYPdat1VDA6zqvKfaRTsHco+oLQhmgiIAEEIazTikMMZRA1hT36F9jx0aACGIEbkykgBh/E2VMUYa6CBD1ABOLAzmrCOe4b5hT8CWAlW2hqCEaZ6h2/G3hGYEf1y+xE1+9zjokZ9jjE/5ovTO6hE0rpIfi7jWJElSYqlg/rqkIrZCJ/rkqzAzSxH/hrbdsLyWztpc0+JJMtP0F0MGeAcVU2gJpEU/q2FJN9mMF2sOUrP3f//3P5istZIQiD0coMvYxgdk6md2uUd4iExyt8K1PP7UZeqGyFK5/oXwP+KUee+MNt0LGcVo38XXtn0LBAfVpsszGvZVjlqL8IlbR6C6N8kjVSt2ol+oDOfd7qY3TtFyMc+KLydZN2h8nhJOLtUppDlqbfdvDtYtNpeUxll+UUH2MtnFcuvyAo3fZZGOGbbIZ8h8O6lVkhWVx9sbHGfbJF2nWpXO5kx9qC7hNz1QwgJZxO/CAGOvd02z+PLOuAkYVWdG4bNuN5v4LqZ2UHNJ1iCBkuRwq0WRlZTXILBZ8PwhvBEa4Zxa80/87akYAczkxCwjot91225Z+HaR3FHDDf4g7JvA54pYh5YsIVHx+F1xwgeNnRMjzN24aIlERvNAMawM9ABRA9awTWy7u/2jyEZi0ZKpdMeluO3PIIXbFqJMtMW6zMhTGnoQEQ2zo/wNQBIpI59DFF1/sko7DeO1tmooRiHTXtUbIF7CbAOxpARxAlSrtbZImfF85s7eZtOLeBZqo/xNQsv0UgdFCvRyrZDqZKYA8UwEYT6iuJZP9Z0qI3VOFlnWg8zkyudsKZM+TY/6R556zJxXG/7200UNkLinTC3CPFpc9XL/RbA9WIm2+TLQJ0v4AwgNVqX691th7QMx6FyVoL9A1z5B0GYeWuD0gFMgVyPe3er1ewvhKm704xfp0LVbgi2oZqmvlZbFWJYRPSKq0tbnxNk3aXielQ5SUxroo0mk/pWt9xzZKlYi39RtinVRUJoV1+Yrqx7hqdcjHuWWcBwyIsVNPMRvQT5vUF1a4SUresjuqfvBMWRWAPC40QtIg2IY2t808qEevKawMBa5hRzuYZBvSXj0u6R8SphHg/QUQn9N7ynuLEANAUSELYGPtQN7TQOJ5Y2onTxpelpOTY99JyEYLxMJAnVlSv7A6ENxHoYVQhHuIOUIblOXzqXlG4LEZb9itU5+1G/Y+x44fOM7iYyNjiQvJPTFDkCTvFZyF+RDqHmlKlKSWLqmtna7vrVINyCTIPFlDcREzS9P2o8aOtUdeecUeFHj/TBLiYplDV2jyvymQTJcmhzaXLq7flv7DTHUTSXrB9lNlmyy9BLsLeKmBOkfh++30onFMd0WwJcnEmqF+sFwUbRTKZzFLL1i6GDNtdFDJslRt55rODFybOhYwaOS7r5Vv7+Nv021IdpGt3yiTckacbSqMt5kKjpm9ONUKi+KsrzS+YgHgPIFl5toKW7MxXlGi6Tb3pyRbsjTkIwu4SuifbTtUWa/eMVYs3P1pYZXtsWeM7b1XtSU69BnNu5UFU4lgZp045uEq1aNFQzjwwAPtxBNPrPcKEjDFp7TCOVI+0YeY7YOZZ/PeqX/1ukaAGrLkKqPJAU5ElMIbyDfNzs7eRiP02gEwWdgXIOV4/I34GplTaIO4cfA/kr/Kdkr/BRNCEjyPaFZPIQg+xv87siNQrjJrN3/xiL3x05f24CG/tb26hS+nMFTPQ3JWpK6nn37aTSKkLKSjv/71r85WH6qRsG2T5MeSTc4sIVCaLUa2lIAaCnNrcrLYb4WOqRBjA7SK9Q1AHSuTiU6yeyU9HiaNrkCT/xxphu+ognw3mTS/0LJPOapA7xGrX3yitJHdJSV+J/PoXtIaKQIwQS8cQDddTBezywZpgDDiAn0A5aECzwkC3yKdj0YYr2NK9VsD5DW93W8iQ+PkC1T3xZT5xCjaVlVmliXbSx9lWo6A74Dd46xr+3JbtS7OCopjZI6Nc5/CwhoiQY3rZbYvs8HD8qx7tziZQTPsxxmxuhez/feLsV/9UkEzMpNinhXOu+vXaCBKNmD6ovgxvh0IrY7yWATSBGp47PMSo0nbQXDDt8S8hfkxl4lI/frrr51vCNOoD4aMWssiTJujRo1yn/r0HB7CvICYE7zPgKJnEQAMMb3WRV5+9I5aIupq099XvxHILd5kV350l60uXGtPH3GT9WvX3VnC6nd2w44KCYZMFFb3ZuKwwOX999/vkl8phxUx0rUAl/014fETYvb8j7S9PjJPnH744dZNEtpYVSTpIVMqUj6RH0mSEPuK4T2qkGu0wAsFgLtL8n9I5tE75R8Yrso3gyU9jtF5C6T5cU6CEGiIti2WieQhAf4QAdzJula2rvPE229bhjS/05TEmyypcq7MLNRCHaZ2iEIdL7B8Qn7FHpt9hpUCwU2AoV68GgRugawQGfFYcgRCANG2VGUZbSqsd3cVRVc06MKfEq1f9xKB+ibr063UMtNUgSMnQdpppa3pn2gfTU1TXFClzKmK4u1SahnyA372TarrQp/+mwR6y+2k4yoFen1s/k+x9tQzVQKRGDvjVLNePaIbAAPHhfSee+65x81BGBlzj6AawDCQkPzvvPNO218+XDQIcmMffvhhJ8BRWBsgJCQfgCS/FW3Bp9Y/Ah7oYVJlziAYsTr9HrIIYRqluPb2CBD0gXB7oxT+/Qs3LreL3rvVshQg86SAsL0CZpqCQoKhd2EmFGYKJGns75GuwwdQnSTgQ9OiQsi9gekV2paNJIdKpX4BbPH6Dfjtjj+QbahaAqZfn3tuNUDxt46bIEbp9quNBJ2zp87ZUwDpEIT2dMy+AmE+jmhLn8vxJegct51tOvZAaZ5bjqn+5c7fvNGKyuPsx4XJllcQL/De5FahX6eKMT8uSbMenUrty5mp8gcm2vCEQhUglwCQUGWrN8XbC++1c0BIO9/NS7Ups4qtXVqFfTs30ZasTrAMgeLRYzfY+L032sb8WHt3coZ9vzDFdm1TYkeMybWhu66x4Xsut6wBnS0ru5+Yfqr1V/fHHyQtUBbiPnULwd6dRM03YIg/iJUA0ApCEdL/h1rdhMV3yTeDnpG/97zzznPBMaT4AKRERGM2wyQGY/SYnPcdqm1/W3SNAEI5zxnfH34//IV810bMGawLnIdFgedPTiGBcghF2zN9MjfQDPn41HQjMHnZ93bZpDvtuH5j7eq9zrSkCATK1HY3IZ80oce33nqrm0gAIj6xcePG1dZG+LYDOAImR9739loPPM7T0LxvAVnIttju7fPaD2zH2+a1431722v7Fq7S7EZVfFkrcyf1QzcWxtmT73S0257oYiOHFVuXTNU6lCIZn5AhU6sS55eWuWOe/6jdllYLVFLth59SZY6Nta9nJNuatXEalhj7+sdU22dooQPHz35ItdWr4m2jAm0O3n+NJbXdaPOX9pB/sa/9tCjR4bwWo1AuXXWQTM5aBbFKNqiNKtQnCu7vKrkCE2pzE7lfFHan3BYBNDA/QA6rhUcIaaRh4E/ERIo5H/Moq0vgN4KREZJPbVysCQCjR6zMQnQhfiPMb77p1BuZ6PtGuyM6lIWeebYsKYeAwzOvTcPDN0iNZY+YPxdddJH3Z72+aR+TPMDoU+RH4Jkf37M/f/W4/X6PM+yUIQfLihcSniLWkRpXg6HAKJCkCF+GiORbtGhRrRMvYr1raQ07M6iUShWOpXZsmaJD56lO6OPvtLelivhcuyHNdhkQL5Nvud33Yrr9OC9xmztMSJQvUectWZZgTyzdyvS9g6b9kGwLVFatRH7E3LxqZKvUCfnlnVRRpr3FxsVbmaJVaQMTLbJF7z7VVloF5NVJ4H28sKJ8s/xQ58FNsJOKI4AUEaWAIUwp2FfodYM5i9AG0/LMY94+VqHARBbM0ABHhDzfh+iNVPR+4/vD9wu4kVvI71z587FW1QaGjb0b5hFaJdfy5lZj2/TPDz0CLMH096+esOfmfmT3HnClje2lVBnHxEIfH6mtNcAQMwJrGZLzx8oBmKKQtm+66aaQfWCiIJlzTLZ8cYHEuUQAYmqlzl/rpirLVVTo+1Pa2vtfZ1hxSYwAKlG+yQRbro8jFF/5+tblxtmy5TWG3hKTqi29ZVq1KhSlplU5sPKAsFO3EhszttzOOjVZPrMklyqBVSdAAQrVTIvYhhkLfyBAhpaHlB6o2QXeBJoDHwCP89AmqaDEObUtVExbRAtyjE/RPQKYPOEfL7/8svP7IRwhxFx66aUR6zhzCT810aS+1SBiw2ybSgvt2o/vsbnyEz41/kYb3LFPxANlarubGhwZ6YsABKQuJiBg5y3HFNwIEvlHitjkeHwxSPIkuEKAIwsDeyuVn3766XbEEUcEN9Fy/65WzLb2X5YUzNsFAsGXJmW4Ci+DBxTbWcestyS5vO57oYMN7ltqI/sV2yylSCSnVil/0ZRHWWU9uqlShiJGM1RYu0SRpZsElsHUt3eZZfcptYWLE2y9okzbdiy1Y47NsX33jLdOnVPE2KuL4wSf11L/hgkxB//97387EMQ8Rp1bwC2YMKkCgphFYZBEoaJZ8hvGGYoIpEFIC9YYQx3rb2veEeAZkf5www032OeqPoUgA69hjkSKPL6HkMW1axPEInX9naHdJXmr7Vfv36qKMm3svwqU6Zy6bXBcU49BDTBk4mFvp+IHeT1Iz4AhuVrBRKjy8yqR5lUGAQhP0LJMMCyc1ZQ5gplNUs7fp6roEgiGUeOYBtSI9pTGVl9ar6T3T77LcBoeMToQKRP5yhF884tMpQPES8ursleUKtExrcwyU8ptr6FFykusUipHsk35LsXat6uw/UYXqGpOru07PM+ef7eDKuW0t7YZFdazR5WtXRcrgWIr4rLIb6+uZRYnbXuxzKgdO8RZr+5dLSExTto75sHqfrSW/1nLkBXnSbhG6GIueikUwQBGFKlHgOLEiRO9P2v9pi3ahun5FN0jwPOHl9x7770u1Ynnj8+QQiCRsjhxDdrG1+gDYePnx8biPJu1brGN7DLQUuOT7JuVs+xXH95hh/bew/4w+lxL1rbmphpgSIemKy8PXwuh6UwITKdE4gUTUhN2dY4B3EjWx98IGGLaIHiByC2OIYEawlfzhtITiAqLbWZNEffYipXJtk4BLyP6F0o9rx9jJAq0myJD26TGuQBW7kvvjuUVVVrndtUBQJgqO2hleoprJ8kX2E35f0tWJdq0mUkuyCZfEaElArulKxPt6ZUd7C2tN7hWgTJJii7t26tU6SWqOqPll/I2xcoEGGNLZVadv6xSEZYLbeyEFGlNWcojTLHpP5gib/XMvqeYQLWfsFK3kZ1lirCsDoqhfy2NPv74Y3v00Ueddoi5ivl1uCKNg4GwofeFVkjlEd8E1tARbLrzsDIhkGMaveKKK5rswgQSohggMIVr3jVZ56PoQpWKGLzm43/bo7PesImjL7AhHfrYHyc/bFfudqKdM2xCkwfK1DY0IcEQ5oMklpWV5fyGSGahyhHBSJgo+AYh/sYsxTYWZMXMRcQeNSbRIAnKYVLhB3L5YpvPq61zEd0u8NJt2SIBzrwlyTain2yW2lYfSk8tt713rSkcVCqApk+3IlujKjNrVsfaNeesUnrFRlugyNDpc1Jt6ZpES5UpVO+2igfEWKHAkKJq+Yo4zS+s1gKXr4x3kaAjdimxkYNL1bdE+2FOohUIPNtkqHpGl84SPtKtc6cUe/gRsylfCgy7VGncY+wIZaUQBAOkt5fFgeGVgu7+JpKUFLuW4k8kYhBLA7UnTznlFFcEAmsDcy0cAOYxWAIwwtFefeaNf0zDRgCegpmckmxo/QjdWABY1JcUrEgRwj7Cuw+GjRthakHP37jMKuVHum3aC9Yrrb3dsf+ldmCf3RUoU9Ml1LirNfzskGCYnZ1tl1xyiZuABMcwKQ5UGaxgwu9CFYdXVA4N4ORv6vhhFgVQvagvtEpAkA8mB0xgaI0krTc3ARJxcfXTCLf2Vai5OQNk6zYJAzKV9utZYucfmWPfzEqz3bLzrVIRpRVKi6gQ6GW2qbSRg4rtw7VpLhCmVBpfkkynSaVVNkDnrdgcVENqBaXaWN0+RcW5Bw8ptBSVbcselCqQTTOK6SxZWiUtvNo8mrMqxqZqSabOHVXMO1XbBIIVuiXAECugqhpZdyXcjx1jek6BPY7e3+SQYUGA6VH9aINu+phjjgmbhI5myDz3gTB654DXM9wxJMoTTexF/yJMw28iSfgk/QCaxo+w2H71eytm1COlrVtxYliHfuKX2hFFFBIM6R8geMcddzgfIJIR5oJgYhuJzVQKwZTKgr4QkagENRDwgM8QxoNfMZDQNlsN4XNkUUGUuzLKrfHw9ZHQEyuTaqzyDQuk+a1aFy+/IgdVA9RGBcqs3JBkXdoU21Fj8uwn5R0uWVb9SEiy79W13Lr1yrF+u6y0nn26WLfu/RT9WL1/6rcxMjVr5Ym5MsdKM9xfyfXHHx8j8KwGQHeRzf8BiMkCXSrRtBTaWwXSPQsCpQGpKYrgFS5zFYIaheh9U2n0zwgEaGIXeFZUGsIFgxBTW6pNuO4IvsfHp4aNwLJNOZYigWVTSbEtKlgr4bzC+rft6T7RBoTcYUgwhElQwoqcHkCL5ZyQkCiWG0ieyRPJPZBIjobw8fBpPaQXI1CrF7aUSPObsyBVEaIpjlGvzU+wz79Ps4VLkyzjBa3E0aFYdUdT3GoTS5ZuK8kmSiNNSyy3YrVRoKWYUrRaRby2ZaRWWgetVdhv4ArrPXC1ioR3UV3WPqo3Gm9pmxPiWddURfitd5apaHGMnXPW9qrMtBwgZL5gWmcOkjCP/5AAGpghAQ3hIMxrzOeoCeQKx0210jYAQwRvqhKxYDi/sSzBZxCYIkGAoFfkm/gJ34KwY6M8I2eBnfr2n1V3RCuNqBpWSXmZzFXlrsRabDPkENan9yHBEMcxQTBMAqQvkpNZBmWnJuFgfnG8W0qpgsjTzdjCuoMLlyfbsjVJKugdax9MzbBPp1SH88+em2QD+pfampw4FwATPH6FWpniRyXRx8im2UnRpf16l6oWa5mS56URZq+07gNypPX1sJKVWVpaSlVlAoRUFOue4gNypVi/vqb0jODWW/bfFHqgjuQHWmD5qquusrdVNxZrBWb4cAAYBcBpm5JvkTa3tewn0by9x0TKAr3ELPz+9793LhuACj8iq09EihD0MdEjfIXLGhGpvkZju/PkI1ydn2Nri/Pt1jEX2TEqrzZ93Xwb13M3S46LztzekGBIDg+M4u9//7tjFAQZnHPOOdE45k3WJ0KEWGPwwynpSqhX2MtmMHTmUP1OTqhQVXziUbciVhvVFt1vZL5AMsZe+CDT5QeqSIwkJAWzKMK0l/ILe3YoswG9SixVx04SkO7Wv0CVNnItpesmK43to0CjXs7PWqIC/FhiA4kSamXariwB5x8M3NfSfzPnnnjiCZdOgb+QgCxMZeEKcwdUsXyEA1hb+lhHc/+pNMOKI4AfFgL+BpzQDM844wwXmxCp/iMkMT98MNz+CKMJxkjjG9i+l4sO/XHdIiuVVpiQkGzjeo/SqhM9bYD2RTPVAEOCFnBSU7PxJC1+S9QdGuLOzjQAoj5dSuyCY9dt45QDn9blx9vXs9ootaHEFCRqy9fE27r1Wmvx4Fy77txl9t3sNPvsuzTbuD5O41ipRe61rJJch4kyi25SbiLFu7+dk2zLlT+4bm/VQ+zcxtrEZFubtlq6aWOMrV5DaTdFvi42rXBh1rZm3nk0z7EG9c3zDSGdE0WKPzqcPkMYHZVtfEbXoMfTZCehmcGLqE2KWRtrAZYrYhT4JlCvMYTmSeTyokWLXB50oLDl+wvrN7I/CAgnvPYHWUFL7JkJN9onS761f814zfJkFqWsWkmFmJdTEuCW0Us1wJDKHbfddpszi+67774ulJ1JQfUHt4ht9N5LxHu2XqtLTJ2Tbv17F1vf3spZ2GwujS2psvUKhpmzPM2tS7jb4BLLyatQWHac/evZbvalimrPmlMdgFSyOYCG3MFvf0ix7xNU7m5zWgU3QCpF7GSz/tkCX3208pAKb1fjr6qTWTulTHiu2+ieWo1/HNQljRRh9p88ebJL99nZBb1IjXE42mU9UZ7T7bff7nKdsRgQx5CVleWi0rd3jcBUHM6j2AJapkcEUqF5UikrsCiIt9//3v4IrCxYp5gGpZSVFNi579xie3cZYr8ccqjd/cOrlitfIc9gq71s++011xE1wPCPf/yjnXbaabZgwQJnRvI65vICvT920u8SARgRn5uUF7hJAMaagphJ8wribNrcFHvyjfa2Vtqf3KzWs1e5FuSNt8ICaYS51RGkwcOGT7lcwTMeJeq8DqowVaq0jVnzq1w6hpQXl0oh5dwFzAwcII1SJnciSRVQJ/9ide4gv32q/wiQq+ZHktZ/vJrrSICLCkNEF1O8oz4BMwjvpGJQoxbwI0+VAKwHH3zQASnLfSFoERg4Vot1syABZSVh2oGaIb/9wJntP/keaR1kDlXNSVWZOVBFtv829lc2e+1Ce3Dmm1qdJ9baJbVpERaYGmDIrWMW5eNTwAgofaJKyLdmY4K9O0W2ysq2LsG9TMC1aIWWYlK9Ua98Gis/kFDfLrNcL1iMdRTIFcqvV5S/tb0UzZ3OXSts5ao4U61aS1cZtpG7aT6VxtmsGVWWtyHGFs1XXqC0wySBH8D50SS1wbGKKF2wUObYFdXmUwCZbQl6msQUULJRro4WSwS3PPDAA858BWPDVIZ/iPzUcAW7EBjm55C1jCkCICGM11cgp8weuc98AFIWdgYYqbuMtevJJ5+0AQMG2MEHH+zapLoNoBlMpIjxwZyOud6n0CPwXc58LYKuIiR6Vw/P2sfaJKbavj1H2NMymbIe4ZCOWa64SOizo2drC2aZTTCIAplqUpUdhY9uUADNBmmBJx60wfYbkWczF6ZpTcJEe+DV5C1AyPEylVt+rmqVqgg3ITXr1spuLhALJKrQrFghtU7Hdu9VYQOUHtG+rZZj0na0PUiCqV7EKmuTrsi2zkrj0D4Js45WLDfLkfsSX+LqVTKnKtgXABwx3CxTptSWCoaA3/333+8CJJDoySmDIT300ENupfusrKwWIWVWPyX//3COAJobPj7mCADJiiOhNDci4U8++WQXEY9ghSmUMpH4oQFUgA2ARNuE+PtAFRUJ1Aq5BtVnAEI+PoUegWdmv2u/++Jh21Am5iRet0mmUsqvxcUk2AG9dw99UpRu9cGwlgeDdoeWRjoDmtdipU7c9Gh3e+GNTBvSr41ddnqircxJUFpFggJcqocxQRpcZ61VGBunWqJL4lRUWMnCqk9arPUHqQYTSFSJYf7QdpyO7z9ApeykQVICFvArFnjuPbrKxu4b47TBXQVyVJBR/QJHU6eazZmn89W/7GyzUZp3iUpjrG4v8Eot7zcVi0iyJ8cQfx4a3AsvvOCCuYJNWQ29OwLDYJBElYZiqA1t1z8v/CPAM5+tVaop1E1kMc8rS0LR9gp1A2ge0YZHgF6ghaE2wANsiWAOVXDEa2tn/S5XAv2tU560d5d8Y5cMO9JuVZm1jaVF1r1NJ4v3pPkWNjg1wBBzAhGlTDhvMvHbK8/Wwu6vQd3lFVq2Osle+bSt1H/qh1LDNMk+nCxbpIiC20++1c76dStRjdFYrR5RbkVKmmeJpRQlzc/cvGgvWtt6Fd8OpBStSdirb4kVF1XZkvnUcVU0qjRH+fD14rGkUzW4DRxkNnRIjPMPKobARZ+iKXoaH/OtjbozYmR1viFA2BqI6E6kesykMDDmHmYvpHqiSQOl98bcL/4n1toEcLmGT9E7AgTRUOeYZ0XFK54X86Su5wbvYv6g3VEBC6GHpH18kARP1SdYhvbrukb0jlhke5Yrk+jvPr7PluavsXsO/LUrvL1b5wGynqkSlvIIm3qF+nDdbQ0wzJaaQfQWlWOGDRvmrsPEw1wVTEw4EqEpuYbNnVxE0jIg9sFsWAqKKC6qRSDttwSSsmZdO5XZSQexxI/+0IaFAsf5+rz3cbr10IoVvzlztQ3OKrKPpmSqWkyqW6Giv+qLzlAlmrxaAmaoQ7rHXmvt9FPm2Y9zO9uT/+1v69eY7TLU7I/X6zq6llLfbOFPpgCAalCcL+1vvWqRQq4v1T/pkpGzmCxttLUA4eZb02obezkG9s4777gIwuOPP97GjRsX1vJbfgUab7Sj/xvNjAT7lStXuqXg0OTgRwRABUaGBt4JWh3BNhyHZQFhigAaKhuRqgGw1kXwPNrwtUJzyy09NustO3HAAdYzo6tdNelO65zc1h489HfWNb2j40WHZe9T13C2iH01wJAJ9I9//MMF0GyvwgP2e4CQlAuc03/729/chGMCkRtGzdITTzzRSVesZxgIhtEucZFE372zbKWQkKezFtO98TxpdV062bg98uy4/dbJh5hgu/YrkPQZY7kFsQ4QS6UNhqIMmUsPPXyNTThsjkw0bW38ob2deVMVx8ToVVZN+ajy47tI1GRlYWgIjW+0wVCEnKIhd8eH2t+St2ESfe2119zSYER9vvrqqy7yj+V78AeFgzytAaENxudT9I4AzwdzJcD2/vvvO+sAfj74U21giDZIpKhHCOmkjLHoQH0AztMsEeR3dnrg+1ftqZlv2AdLplqiqscc13+sXbXHqZaRuNln00oGqAYYcl8DBw50uTcwDMogDR8uh1UIwgSxePFiu/zyy50kjxaIpgg44qDGtEFZLUDTq2BDm5yzVqaKGJkhopfEILe6GSwhptL6dC2xXXsXWPGmGHvqzY42a0mKDc0uditErC+ItwqB2WL5EIMpTsW6+w3YaIcdPNcWL+lk33w72MbtH6cyU+YKa5PQz8oS+CchvtACNVS1EukbrZGYH1988YX95S9/cXPPAypqUhL1h1awI6ZS5uikSZNcEAWmsUBfUWscv9Z4TzzvrKwsF/354YcfOpMnkaA7mnCPAF4fIGQMmXccvyNzrTWOPfeUrioysQkptrIo12792UV2+tDD3AK9re1+a4AhdvWbb77ZRV2xmjQmKxjQxIkTnXQWOABITXy8pGUmmidJ4ZOBeZHDQ0LrLbfc4iIC2f+lmN3CRYu0okONywc2H12/tVYhaxBuKkmw4vIqSyzU6usyiRaXxltGWrlbk3DuwkTda817StfSTX37V9iyFb21GG+WElTVTp7SLQoEhhKuJHsYqRbi29uYQqNrAJqmNzAgSm3dddddLtoPzZAKIVQbIRIwlEXBizBk/sHE8AlhmUAow3zPmoiYvABan1reCPAMKQby0ksvOX5CrdLHH3/czj33XBs0aFDEbghNlI8nkEXsQlHccIUi/Zaqxmh5ZZnt2XmgHT1gbKsEQh5BDc798ssv2y9+8QsnhWHm3Geffdyq9EhkrCcXSEjZmCmo7g7jwQSBfR5GBIDifyR8GTBkImN64JxTTj3VPpO0XhEityew/Wj7nSLT6Yi+BTZI2mCc9Lcc5RzuvkuRdWhTbmvWxdl+w8vtPy93sFVaoDdFaRVDtHZh/+wSS2vbxjp0VMFf3dBIKdk/LTKNr7RIfYvP2z57VUeJohlSHGOzgrj19rWhxrate1vdr0svvdSZw6hFSVg8Oa833XST0wSCGRPWB5YaIzSenLKpCrN99NFHnVBGTtnZZ59txx57rLNQBJ/b6gauld4Qwg4RxliojtRSLQg78BFSbiIJhgj0XHdnNaXnKzr0xskP2jtLv7MSzS0iSANrL7e26VYDDPH9PfPMM44JEUhDojNmK29ZpsABQNqmfilaHzZ9VrJHcsfnePXVV9s4OcMuvPBCt40IwUCTQ0ULlNI7ZZTb+NEbdR9mc5ckOTMm5s0yfVJVZ3T83rmWmV5pH6rgdq/OhTZ67CJrp8T6VWt3syWLWYamOmIU/2DfbJ0jU+da5QoiE8hVK81nM+gFIF+8rK67jzJp6Dpe2uPOQFgUqA6CEAWALZIVAcZH0EugmRNND8BE82P9TIjV0M8//3wXyMXcO+6445x2iLk0kGjX0yQDt/u/o28E8P8Ry0CUO3MB6xKCN+6ZSBLXxTKxMwpRy/LW2JUKlGkj8+iojn3sq2XfK3cwkqPd/G3XAEO0PwDtO9X7+uUvf+m0OyTr4LUM6Trm0UMPPdQFxmDKyJJdHykK/w4T6aKLLrLx48c7BlafMkrNPxy19ECL8+qN0D9QSiXZViXblyrMzaoR2ryFEuUbHNC92Dq1L7Q+g5ZZTHtph6qluHFjolZgqPYBtmlTJQEjxoGflGdLrGvRXV0O4O3QoUqfgAttuWLr+8H8wfdMwANmMMxU+JiJBMRHhLXBY07M07POOsv5qRHMYJLkD3IcwhlVZjCpYbVgHnrmfEYNsyuBOlgueEahzK9RNboSTF2EVeCEa0gHOb9nz2rpqiHnN/E5CDw8UyJHsVjhuuFZo/UTzxBJQlhiDnnzLZLXiqa2v131o1358d32s27D7Hd7n2VTVs6yexJfsJMHH9zqgmYCx70GGD777LOuTh/LN3kLqdYFZEyUwEnJ3zAiiN9ZAshoJdaYBMDi9YlRfmBgwEx1n1VFpiROpdaImqqyof0LWX3JZixMsclawDdDyy6JH7uybOQjvvRZO/vv2+3kbC6WRlxuR0/oKLNxBy07Uw2aHIutFG0SYph2HRYa5DR0DmhRaMoUrbozEXll5BR202KNaIIwP4AMgSuUyQqtz9vOt+e3xhLBuZmZmc7fGDiGMFiAtsWsV/fnP5u9+07gLTTsN376Y48z03veEogao2j/CNdEkmK6hBCQLrjgAjdHInEfzCOCAZlbWMuiXlgK0yC8tuBTm/jFY3b+sPF2ztAjLE3BM4dk7eU+YbpE1DZTAwxJcEayJpqPb1IjWlSdUqfF1W+8i1Ra7a2pmfbjomQbOyrfendXdjtrMHnYo+8yGcsXLE1U6bUUm7tYds34GPtqZrotWJFq7TLKBKKxMpFW2MzF0mAmx9uiJVoDTQz8yScGmVY0sWwV1F65Sr7B+ZhS+R3j6ojK+hySuHS8tEES7V952+z555WHOEQl2TJibHh12mfI81rLRgQo6pCSsnP33Xc7gQqfEZodABmKKWEy48O5ACgRzJhVAUJMq2wPJoASIY9UjVD7g49v9r81p8JCjEULClyDBx199NEuLoF0CnKf8ePxfBGOIkXMiZ7SoBGWQs25SF23Odu9V1VkHp31jt2499l2WN/Rrq5oc/anqa9dAwyRhHbbbTcj0RktceLEiS4KD00RZhTNxLJIq2WSdJpXTf63tevaV6YVJ177rK3dcGdXmUIwgyrx/ewVtnCFKlRskEYRp2qk2pZXFG+TZ6bZ/CXJNnlWurVrU2F5xXLeK0hmhVawd4W4VZC7SKvWU8gbklDpSqgtWaZ6ogLEdUqap/Raosyiq1aaTftW5k9piRxXg7SNAJtvp5l9NUVLOv2ov+er6IECcg4/JMYl2jdrNI1ucbcRVdajZ4wD7Rr9D8MG/NTUJ0UyBwjJJ8PaUBtoYcKHaaE9UimJc4mKxqdUWx4abfnRpWF4WBFuAisBK0qQI4hpG/M5gg8gSTWaSBJzpLY5F8nrNnXbRZL4JypQZuqaOXavKsqM6jKwxZZUa8zY1QBDHMYwFT5I6HyoJBOuZOfGdLauc8GVDVpk96WP2lmRVozYjEuhTxFDZ7WJd7/JdPupHfrKW5lWnB/j1gv8Yb4qXihYhhQItMGly6WqicgHTNTivGUVsc5fKOVCL6i0kc7lNmdxgrVrr8AYaXFtBXpDBsVYp44mH5dWXega4xblzc6SeWepSVsREMrfWE6kfy2AuEU73XwI5lI+7vBQ5+i4JiGNgQPxCPcBaTzQ/F7XvZG+4xGgyNqb2yOA0DOnbu9Yf3/zjQDCzLhx41zuMxphVlaWA0MvYj2SPSNqFWrNJftW5q+z3066S7nSlfbgIb+3rLbdxHr0ku+EVAMMCUgINguw3Em0E4+vY2aZnXf02mpmXdfz1L5yaYb9VEt06owUmVtibI/dimzMiALr3qXclq7tZBu0FmGx1hrME7B6VCkAYJtHitewFaukIeoDlctqk92/yjLbxKh0VHWk6JLlAssu8g/K7dhdEaNEhA4aLO1KyzWx5FIoQpuUtdpeeNns3vvlV9zF7IrLYlxaRqjjm35bXYPb+N7A6AhuIbcM0CLv8IQTTnCBMMFzs6FXwwdFu+Fqr6H98M/b/gh4uX7kFi5cuNDNCYRz1l6l5mikCIUAn3Vr1A4/XPyNvbPoS/tCwTF7dR1sv9/nHOuQXK0cRGo8o73dGuy4NrNStN8I/SNAJS1lc3RKXR0mKlTHHjFuvV2ntQj/+kBX69ahzFISlTwv7bCirNLapVe5wJX1abGWu67GMIVsHXDM16oTuXItonnKZWWFWsNwwfxqjW62TJ4dtdbg3LmmKvxmEw7X2oNamimYFMSmZWXMjj+aMnAC0W7V+YnBx7XWv/EXYh7DTwhozdWAka5z/fXXb1OVpjH3j5ZBMj95aj4gNmYkI38uQTOkVGAapQgIvmDM55EmAJdCD60NDMu0xtxfv37SPv7pSztx8CE+EG6eSNvl8kuXLo16X+E2L8V2zHfsLpQfsEDRn2s2xtt732Ro1foYe+OjDBW9rrDjx+Xa3kMK7bTD1koCjbXnP2hr3/yYZstXJ9icBQlupYiRIwusSDbLxSuSLWelNEstt9RGQtW4Q8zGHxzjimd3EIgRLdqvr1a5WKJit99oCab9BXLaTkoG8RCsO1gXYeoFUOMV6VqlCjh1mn7raqgF7UMTpCAzvr/s7GzHiMaMGWMzZsxwq1fAEAPzVRt6a/ghKSgRjrYa2gf/vPqNgKeh5SosG2Ci2DYRwhT7aKzVCivEzJkzFfGda3vuuadr1+sVc5Go0tZGT6jo9tT1S6wsPtGyMnu02FUmwv1caoAheTzTp093k46JQuUYEub5EJnX4kga4BYSuJQqTeGrmW3s8+/TbaU0vs+/UskX0aaCGHvspfb27Lsq+aWI0oSUSjvz0LVKni+11EQtBSNVMv9ncVaifasUYLM8r8oOPajIdh2aYhuVOvHN11X2a5kyMSASgXqwgBHiXZK/X+Ycs14yk8qlVW9yr6GAs/W9jrUPAVoa1WRuvPFGN9/I9YLp4bfBhxgu8GItQ4pJEKkYmMhfe8/8Pc01AswBqs/w7AFGIkn5DXg1hvAZY3Yl+hiTOaZ5Cja0ViqtKLP7vvufPTX7PUtQHpkiK2x14XoJ5/WwprXWQQm4rxpgiHmKupAs4cQqEzAKbPYtypSkwBae7/KcZPvw63RpgIm2WMsvxck8CrDkSzMk6nT5agXClG9FS4CrKL/67zse7WwffpvptLxKmTwzU8stSQA5d0mcSn6lOZPsknnV6RKAXPduMS4neuOGavDqpnzg3n1ktpWPUAKm0wb59mn7I0BB7d21lhULumIiI4qZKMJwghZh+gTehLPN7d+Zf8SOjgCARVQxQjppXwDgfvvt50CMUnzMi1BEVDwVjFgwgLnEosAffPCBlkZb44QtLAyYXpkHRM7TfrD/EZ7XovheqIHYvG1DUZ7d8tVjtjhvld027lK7QkEz6wo3Wl5pgYJndiZxu/ZBqgGG48aNM9aRY3FVJgIJp+eee26tLWBKYFLCtIYOHVrjOMwMTEq0Sqo5RJKAunV5ifb9vDTbd3iefIAVWnuwTAEtVdZFPkEv7CNGuYixCg0t0xz4bFob+8+z7Wp0K1MJ9X27lSgtIt66dC239h3yLTZzva0t5tg0B3Al0vjy8mUGXVadSzjjB9M4VDfVvq2iStvGODBkqhE96s+5GsMccgP+IGrcegnxMDbmWTgJzXDKlCmOMaJt+BSdI0AaBb5d0ryoU/vjj3K8izChA2ihiBQMSrdRmo9lnDClkrCPJYC0HaoZYenCHA/fIg0H3zEgG0hEk3JNAJNayy2VFm5cbn/8/EFLkVn0ljEXWWZSunhftQm4EsbkkxuBkFyAZGTC08nXqksygkkBnP/9738d2DHpKCWx5koAAEAASURBVLLsEQwMkytl3ZiYmCLCSh660ah+k6owe3GKPf9hW+vTo9j69S60g7UOoREww7GgUgCV6u9uikD98Kt05QgqwnOwKp90LbPJ36XaVWestk1K1bjvxU4qtp1nxx8zx2LaJtqgIX20MK/SONYoqGVUlR17lNIm0s3eekeFPY5VBOnaauCbMEEmU7UJD8fv164dlW7ohE/bGwEEMUxWSOoAFQKVNyfDZapHMIPJ1TW/t9dPf3/kR4DnxGK8aICAFr5e/IaAExarUMQ5HE/+KcE2aIVUrKHIN2XdAEECqLBAUHZywYIFrk3mmgeIzLnVq1e7+QcgtyQiTWKqSqpBBWVF9s9vnrYRWon+0pEnWHctxkte4YDM7jZvzXwb2jFbOdUhYaAl3XJY+lrrKDAxAoEt1NWoAPHKK6+4KD+A7uc//7mddtppbsJyPJOQcGgkuMCcLiYan0aRTndpDvp2oCM0m7M0ya64s5fN+jHJ1mxKsAeuWWTpqg4DCHIMH/IEHYFL2pWgUmy7Diw1FvPt06nIhvUrtlihao78ife80Fk5lolKrpfDr91AO3RcprXXChSnnayKMppr1AsF7LgVPqw4TxFufnMtSMq19ektf6GS1Pnt0/ZHAEsE2gBBNPgJASw+RJaGi2grKyvLtRuuNv12IjMCCNXwEjQ7NDXMpgAiVbKoEhNMACDPNlvaI8CH0E75PZ45fA2wxPqAiRyz6x577BHS8gDgYqEINp8GXy/a/v56xSz7xQe32sK81dYtta1dMfIkO32XQ6x9crV2m5KQZLePu9wuULm10T12tTatbJHehj6PWsGwPg0iMRFkQwksJicJ+0xapDd8j0899ZRb9untt9/eEvgAgD722GNuVYIErSjQUNpUFGcvfNDOcnITN/sCq+y7+ak2bXq1Kfa9j9Ptlk7drE/HEgGxUEjg1K93sR07Vgl8ASqiB2RUpNmQn6Cgmgqj7RU5iUqT2IxophUqynu6QBnWIEwS6OlW5W8M6D1gqJSIXYYEbNv8U0Oj+6+53d8SegRgQtddd50zk0bKp4cpjShCCnWHKygn9N34Wxs7AjwfiqkTVAUBhpg9iXQPBYYcg7CNAM43c4iUMcye3hJzmOA9gneFmgNsQwijDY5pKZRTtNFKlPRcoHqQF484zs7c5VBrm1xdL9q7h6yMrsbHp60jEBIMMQ9gqiKqz8vr2XrK1l/eREHy8pbDwXSBJEfVmhdffNGVUKLQLgD561//2pktWAXjYzmzyxthfkjRkkkH7ZWnKjFb1a3snqX21hcZlqtoz+y+pXbaIeuMZZdIlo/XYUk6JxAIuRM0xT5dS61Ncpn17lxi++22yT7+NsMOG7PJYlIS7d9Pt7VDJlTZtVerILnmjgRTm/I1aRFVWpxXmqG0S4hrBGCs2+b/1/ARqKs4fMNb3Xqmxyy3bvF/ResIAGrwk//973+uHikmToRtAmPqIsAMTRAApKbp008/7Qo3wKv4e3vE+ZESxrZ37YbuzyspsM+WT7d1CoyJjVNVLGmDCb4ZtF7DGRIMSTbF0fzqq6867Q7n8oQJE1xJpMBWmSiEvLP+ISYJpC/ORRNkiZX77rvP5e9glyc0GvCESGQFNGFIDSVy73orwMX5AmlEglt3LZ90qyJFn9bKEb/7+Srba1dlwCsVolx1Q5etSVJhbSEXAl7AZSmxlplSZj2kQU74Wa7zHca3ybUNGQV24eV9rI/emX32rgZCLuN1uUf3GOvfT6ZadaFIeYZJ8gv6c44Rahw1lRSO9knAVyiNoHF34J8d7hGAbyBMY84kGpRnR/3kumolcwzLyyGoeyXdOB7L1YgRI2qtWRvYd0CY81sKLZFZ9JYvH7XPVsywAqVRKETGpU0EsLuWcivN0s+QYIjNHecyEhU+vyeffNKmTZvmUi3Q7jxQw/aOXxEtkgVWr7nmGmdOIASaCYgTm8l08cUXu4kcaGpoDBBuGSlWmAigFAHk+D03WlJMhY0eLCCU6bNKx6xVcv2rn7a148ZtsF7dit32gNOspzTCY/bbqGovpbY8v8h6D99kGd0GSotNconzBMAEEoBIce899oiRmViFt6epZNrwat9g4HH+7x0fAXw5Dz/8sJtHWCWYQ8ylcJOnGTYV+Ia7/ztTe/ANNEHSH9AQEaQxlZ599tm1RqjDo7yl5BgrtEHmUn2Ja+L2aSmL+05bNcdu+PIhG9Gxn1028ni7Y9pzNndTzma534fD+jz3kGCImfTaa691EaIs4YSGB1ESC2YVyJywvVMjMJBOP/30LX9ipggspLxlRwR+xCiIpmu7UjvxoA1u1Qm3HJOuA2TGKZ0ipNlfO1OUVN+pQ6kt3pRv8wqKrN+ggaoc089WrJQmuDlAJrC7gKH3YTvtSkmWSSbwqMb/5hrlUmYrBeo7CyFgEYRFFDLmdUztzDGqxaANwNTCQZjyW5LUH457bqltkP5Azil856GHHnK+QqxWCOBUJ4oEISQRdMMn2gWmtxZ+Ybd+84wd0XdvtwbhVyuU4yWtFh8OJlKFn0ViiFpdmyHZN6bOQw45xDEgJKT33nvP/Y3mF+3Egr1JWn4p0BQKGrrpEApTBDiVmviLcvNtfkmxDRk2VBGhvWq9TYAPTTGUtgh4hZPSFDkuK7RlttwUpwYNB/Nv3Lhx7jN//nxXcYRcVoIowgWGJGN//vnnLsCrpfmFGjSoLfgkng9mT4QX5sH69eudUI5gjhWK0myBVqdw3CrtEX3KJ9xth6N/tEEKxSMzXrf/zn7fzt/1SDum3xhLT0y1vbRC/YmDDtLqOqU2pvswI3rUp+2PQEgwpDIDSasE0EBPPPGES4+gIk2LoABQArwInslRpOgT73S2cw5fbd07ydFHYqFQ0v0vZGs/oL+NkuMP03BdpHdDEYjV2hptR5L0jtvIEZG8QvS2jRnsr3/9q9MCmIcwRM88H45eo23uv//+LsAiHO35bURuBPDrohWSbvPnP//Z+XnRFskdJEfwvPPOC/tzRBvkGlw3GjXD/NIiu+2b/9rXWnXiei3GO6bniC2L8XZSOsUtYy+K3ANppS2HBEMkL9Il+ECAIBJZiwBDpwJuRkOBVal8htMXptpbX7W1eCUWnnxgjmym2o8/sarCChMLLKF7L2vbM8UB4/aeMwBIsEwguasFAHDgPv93w0bgrbfecsnWzLvDDjvMmcdWrFhh/fv394NeGjakLfYswJBKWMQrAH4QQXijRo2KWGUYtEHyoxGawimEheMhrMhfazdNfsjytULAX/a72IZ17Ku1Vh3jC0fzO20bIcGQkOU333zT8P3h8yOSiwToaCdWnc9R+bRyFeMG2Sg8s2hNol3+r17KKUuyzu3LbdHKZEtPlr8opsyWV2ywlZVplpCsXCJZVmvMJ52/PYyTi8tkWbWBA/xcwnDODzT0H374wZnB+CYvEO1wR01WlF175JFHXLrPOeecs6XEWzj76rcV2RHAHDp58mQnEGEqhxDUQ5V/DFdP0AYRxAjcoWpNtADiDzkL7IbPH7I+GZ3s+n3Otd4ZWuvNp7CMQEgwhOngH/z666+dbZ6JsFKVHEijiGYq1EK8b07OtHUblV8kQQkgW7AiyWYLCKHc/Dj762Nd7YixK63XsBxLULTYgp92s6xeSZrs7pBt/mORjsMOViXSbfNVtzmG8zBn+hTeESCVBzMVaTqvv/66ixwkwToUU4JxEQaP4AbBwCjugC+J5GwCb9iGj/Coo45qcblj4R3ZltcawU7wJOqMUtqxKQihC62Qz44KYJHq3/uLv7a/TXnKDuozys4fdpR1Tq1ZUzlS194Z2g0JhphJr7zySgeABDMgIZGwGs0E8BWXyemtlSWSFdnJeoXzlidbYUm8pSu/MC9Pyy9pRfsf5sTbiaevshUb2tjaeSNUji1R5dtMvimVTAuKmyFIprMveDXLY6fsFgAGAAJ21KUE1Ej7CSTmKjV02YdvkULO/E1lpBNOOMEBKiH1aBdEJPrU8kaAoCmijIkgpRayV0oPYKytAk047pK5xqe5wZAFCJ6c+Y49PPMNO2fI4Xb8oHGW4ZdQC8cj3qaNkGAI0yDHi4mG3Zz1w9AMs7Oztzk52v7ATIpPz/n1Eiqtc9sya68KNGP2jLc3P2hTvT2z0jI7dBM49rb27RJcRRkpEdIkou1udu7+UG0E/xDFlmGE3idwVNAYSL3gg9UC0CTYi3QgTGhYN4455hhnYiNEnjqUnmYJg2uI2TXw+v7vphkBnh1RoyTR/+IXv3AX5TkGC0bh7g0CFQIZ86q5ALFIJdXunPqMfbJ0ul2z5xl2QO9Rlhy3rUAY7vveWdsLCYbYyPGzUBIL5kJI8ymnnBLVYyQMtM7tyuykg9cJDUmXiLGN8h+uXB9v7dIr7P1P2zjAK8rVuoYJ/eygA5QeIQ1yxSqZUudXiZnSgk/RMgIUfkfrYw6S+AxwBVeLgSFeeOGF7ji0B0yllOpi/rIiBQwMUCQ6mnMR8Lw2MMF++eWXbj9CX3Mxu2gZ72juB6DHHKCSFfwI8zcWK3JP+R0JAgABYOZdU4BhmQodF1WUWGp8slw8ce6W1hRusD9Nftj4vmXML2xEl4GqwxzCnxOJAdgJ2wwJhlRuIPEZ0xPSN9IRYegtgkhQ178yBdFMn59u02an2PKCMktOL7PS9Qm6l1h7+x0ttSQQ7KJao3IJuIV3Nfed5tgi7nEn6CSgRX4rDBCgw0JBNCFMMJiYox7BuPhAtIFGSTRqMNgxp9E8iU5tCmbn9c//3vERQOhh9ZJx48a5VSsQkgAphJ9IEfMFQawpoklZgf7vU5605+ZNsiu1zNJJgw+ylfk59sfP/mOdUjPtb/tfbH0ztYK4TxEdgZBgSP0/FtMEDPG7QB6DCe4NycsslolPhvJISOUQDApGhq8RZnbBBRdYVlaW2xfx/8QLk5Q+MXZkrnXtt8y+XVFhg2d1sGlTEmz3favshutjrGd3AaaWKZs6TWAoX6NP0TUCy5Ytc8ESmMYCKx6F6iWrp2C9gGkClpQORKoHDImEDgZC2mAb628yJznPp+gdAZ4PGiB8hGWcWFIJP3A4llaCryEYMYeCtUx4WKCgFakR2liSbx+vmGmz1y20yaormp6Qao/MestGd9/Ffjn8OOuSpiXkfIr4CIQEQxgKSc8AGBMExjFx4kRX8T2wRwDgv//97y2Fc2+++WYXvIBZIzc31/7+9787hkZ+2J/+9CcXGu0xJi/yL7C9cP4u10SeI5PZSkmPQ5X7sN++6da3j9nVv42xrN7VV4IH7jJYgTMSunSLPkXRCPTu3dslWJNvSC1KzJ4XXXSRyy8L7iaFl9EcAD+CKu69917jPEyonBuKYIJEnPoU/SOAzxABh1iGq666ysUvfPLJJ443IbA3lNAsqXXKwuPwOQqB46MON20o3mTPz/nQ5m5YYicOPMD2VlWYQHbDXEwQM4qNS7H3ln5r36yZY5ePPFFVZA5wq9KHuz9+e6FHICQYEq116623Ommb0zBJYC4IJqQpymX985//dDZ9CnoDojAyAPHyyy93dv1JkyY5yYvzkeA/0PJNrCUXp/D5SFCFJteigkLTEqA2QqtvtG/fyX4un7O6YR0CrGyAoYRM94lEP/w2Gz4CFHi47bbbttSGRDusDdgIkvEoOzvbzUfv79q+EcaiudRWbf3eGbfDMxBcMFuSfE/C/SL5gbFK1UWch28YoAP4OJ58VfzRnpkVwf/ggw92oBjcnie413WN+uybte4ne372u/bZyhmWmpRuwzv1t9SE5C2nbk2YVyS8ll36074X2KFZe1lKfHVK2JYD/R8RHYGQYMiEueeee9wExNxEagXSU/CSKUwwPkwsCP8MAAnBaIjeu/vuu13R5d/97ndOw2SCsS9R52z19LhTwvOf2o8V4+xJEIUAuY0c75BcTnI+yyQaOTdDePq/k7cC83rhhRdc5PIrr7ziAmKQnAlyQbhCyAoHoUVS79Kn6B8B+AXRwh999JHT9nl2AGOgEBR4F/CgWbNmuZKSgN3555/vcqYp8s3fWBmovUwiP5ogVgX8x/C9QMLyxXwEVBtjyWIR3dG9RlpachvbS0EwyUrnCqRi1RDdpKjR8spyO2PwIbZv9119IAwcoCb6HRIMOyrbHBDz6M4773SAGAyGTBCYEz5GmBWTkHOZPEwkzKZoif/5z3+2RPExkYkC6y9/TZWOCxsJBAtKy2yF0K63giKSxei2yl7VIBgR8A3bDfgNMQJogMcdd5yT4s8991w3n/AZzZ07N6y+PeYnfklMrD5F9wggQAN+p556qqtVC98hNqE2nyEpEeSb8sH0icb37bff2vjx4906rViwML0yv8hfJXcR3hYMeMw5hH2CtwDMhlKPNp3t7F3GW64W3u3Xrsc2pdMWbFhmN37xkO2S0c2uHXWije050tom1VHlo6Gd8M/b7giEBEPC01966SUHbkjlk2Rf3Jvq1EGEQxvp7KabbnIS13777ecm1C233OICb6j4waT7zW9+4xYG/tWvfrWlBSZZuKhKL0uemNt3G3MtUQETvdWvUAT26nZ8iuIRAPhYt+5f//qXsyRg0kKAysnJsb/97W9OmwuH+QpTG2UHadun6B4BhGyWa8JSgE94+fLl9uijj7p1UhG2gwmzKMAHf0LggQeh9WGpwnqFdkiZPjROFjHnGwF+2DCt5L2Z4Hu0Q1lAwLAxNGPtQrvpi4ftKxXVvm6vs+y84UdZYmy8zV2/xK746C7bvcsAu2S3E6xbmkpe+dRsIxASDOkNUVREWUGEtI8ePdr9DvwPSeroo492BbyZcOR0cR4L/pKeQUQqEjjMi4kZCQII1+na0zfmWbtuXVQndGhIU5q6I9+AaXJHohd+m+EcgeHDh7vIZMzvSOyez9rLHQzHtVjxnHKDMLpgjSAc7ftthG8E4C1UJCINhg85hxQCIV4hFBhyZfiQFyAF/0HI4m9Ajg8ACGGJYL6FIgSlcAhLqwrW2SoV184pzrMlheusuLzUvls31+7+7gWtQTjaTh9yiLVP9k32oZ5BU24LCYZMkKWqT4bWxySiGgimzVD+GvYH1izlb8LZIW/Vi0jdEOsQrpJJZGbeJusmDWLIkMG1Tl4JhNofqZ5Ut4sEi4TJ+NVFvKieH4LxaghxLQhGHg5NqSF9iNQ5gN7JJ58cqeZdu5jY0BTCwewi2lG/cQdcCEXUqEXQZr7j7z3iiCNqHR0AD+sT7wkaHmZO8laxcLF4uVfwu9YGtIN3M5zvVpwpYlSWqXd/+sKenfuhjew00M4bdsQ2wTR19cffF9kRCAmGmCRIbCWij0n1/PPP26efftpkK9bX+5Y1WWM7dLRsTfR+8iGEc+LWuw+bD0QDxmmPCefII4+s8/RFioTjxaawwfbWT6QhfCCs5UY05aBBg9wz+fDDD530SyBAbZoNYeNo9zAONCFApjG+jzpvqoXtZFxgioxHc86bFjZszdJdQAmrE+lZ06dPd0I52lxdAVCexoegjuBDviqmUlI0KNe3PdMnc4LrNlRYDRyoHumdrId8hQukHX685Bv7bvVsO27AODtNwTKBUaWB5/i/m34EQoIhkwcfDRMPBszv7Wk7Tdl1ALpAF0yTtNg1RMrH9vqC2QU/KA5yKuGTwIufiqWCABu2UTAAYOMFovoOoEX5Ls4htwlBgfQQ7xj8pZ7JDYc9UijXOeAA5QpJMkWYINAIPxVjirCBdghxHNfGmY+2Qh8Ye/6GCWBifvzxx53JEECjPaLceLEB1oEDBzrNnchf+sj40Eei7ziGftFPgBPJGGEHqZnKLGxfuHChuz+u5ZmPtjeGTbmf8eE+8O+Eax6iMTBejJVP0T8CgBMWJ4qx14fwDTKfPcJKddlll3l/1uubuca72lhhaUiHLLtxz7PsjrgkW5y3yi4ecbwdLvOoX1qtXo+hyQ4KCYYwV9Z+I88Lho1fcN99922yTtV1oXIxr7kyi67Ry7GnfAfVlv+6zth2HxoceY4vv/yyHX744S7Slco5bOMe0YIxnVH8mSALwIdVEADEd9991/lO0erQuvA5AUwc88ADDzjgI1w7S1VNCNWmwsnDDz/sziWyDf8GkWzeMkK8ZDBjAPaxxx5zQMlxACGF0YnMBUQBKAQU/LD4zzgPbRFABeg4Bv8JAAdwQBQ64NmRH0o7+EvQhug7YwAhEHz88ccufeGss86KWpMhz4PxJMcsXGDIePoBNG4a+P+FGAHeSwIJEUT53VhAzFR+4dHZ+1q3Nh0VMKNKHz5F3QiEBEMYJxUeWMYJeu211xwDjVQQzJZRoSxDPJK6PuXypQUJ7WXytc3KzbMcHTF8+K6WJqltRwlAQOsD4DFnsm4eJkRCtwEyAAagIXmbY/DvkXMJ+KH9IZniO50yZYo7BmADhChsjraBxgYQnXHGGU7D428i2hg7wJPKPF5QEX3nJSO6DdDCFDpmzBin7WFyRUPElAeokrqCdAsw8nISRECyMNfn+QDmACH7uRZAzgdfC1ot1+DeARX6ieTMh3tB8EEjDeUT3tHxDefxPA80du7p2GOPdfcTrvYZK8YWU1s4TGHh6pffTnSMAO8lgibvTjjmR/f0jta9/5jouDm/FyFHICQYTp061QECkjP0xhtvOO0D5htJqhD4fTEtQ2sQxtmBI3PlC9OGzYBYKC1npoAwX5rR7iNHOObekL4wsWGuVMoBqKidysTH5HnxxRc7LRAA5API8WE/GhnmT8/XBLgASuwHXDjekyDx4WFaRpNDE+M6aDfkz5Gy4p3HN+exD6DFJIuZE3PqqFGjnI8WDREND0DlOhD9oR+AOAyd34AjLy8mX0CTbZwLsCPcwPy5d47Bj8IxtMO1uAf6Ek2Eifepp55ySdFI6BMnTrQ//OEPW/rd2L7SJho5YxEOZtfY/vjnR9cI8D4g1PLOIZTyjvrUukcgJBhiVgMs0GSwmTMpYLYRI2mEsPnn3u9oZ17bx5KTquxPV62yq09Zrmx5mRK1b6UmZbFAbA9phIBMQ4n7IArt9ttvdwBIHhsmUAoA45cDGAjbxvSJSY6XAYBh+R9MmTNmzHBBF2hkjA/alZcCgMkUX2KWzKQsNQNg4rtDO2NhUoAH8MNP56UMwIgBLnyMaI0EBuDbQ/O84447nFaI85+8KCoB8VwANLQ++oyWS7g5mihaH4E1EL5KtFvAFXAGFBk3rotplJf9XCUd8zfbAcZoIqKZSbLGf4um+5e//GWLjzUc/UTip/pIbcFH4bhGS2mjsqpSRaKXWH5pfqPmAe9prKZR19RO1iujZvnGljIe9JP3AbcGFhsfCFvSk2t4X2PEFEOqBJhJ8Z8BBgcddJAdf/zxYZsUMOZfaZHOX8tEOVxAU1VZYT8uTrVzbs62r79N1kRUAe1Bpfb+XXOsaycVU05tYxUyE1aIqYfLlMd9oR3gO2IIACTMl4AMvwE5Xgg+ABighYZF0AWOfF4Qtgcew2/a4ps20OgwwwGw3DPbES5oBwbP3x6xDTDkePZxPtoc/QCUaYMP1/VeTu6BYwBjjuPa3BPtegID7QZeB+YfeIx3b14/ouWbgg0Ub0CYYOwIoAG8+GCq5n4bQwgr+IwR/ALHp6Ft6tHYAYr0/1TLg4WD/nCz2Z+vD2hJJnt74/WADQ38qbliJ5xoUru3NFBQVmxHvXS1fbTyB6GZ9jeUNP8yND9/M+oUu2H0eQ1tJSrOQ3BF2AQMCTrzrQdR8Vgi2omQmiFXhLFSkYFvog/RPmBMkaAYwXGf7sU2YcxGgSFJ0Gb77ZFvSWkF9u2GIhvcd6ADkXAaKgAQgBCCGQJSfCDv2/2h/7wXASDm5fDI287f3m+PsQa2AXh54MSxoRg5baOteAQABgaLBIKgdwz3wMcjrh0cbh5KeAg8xuu310a0fBMsQz1bBASIyFeEC3ynnjDQmL4yVty797wa01ZrOHdrtutWAa013Fdj7oF5Fq3vR2Puyz839AiEBEOCRTAJQjAf/FWYDUMRmgVLqyC9UwcwkLnAvPA/Yj6EudVFqYmV9usTV1lqsirfFMfbSYcvsWm5a61t156WkNI4LaCu6/r7onMEmE/4qjEPM8fIB/ztb38btiAaJH+sH5i7fVMpcwAQDAcQhqON6JiTuDn4+IAYHc8j0r0ICYYwIPxk5OnAKPCDAWrBCduAHb4w0hQAy0kK1b/66qtdnzE5sgwUgSQwNqIBAytG1JhgqqKdkV5h15y5wtbLRDh1ba6qyvS1obsM2QZgIz0gfvvRMQJvvvmmC2654oorXMATQlagdt3YXsLkKNIdDi2zsX3xz4/OEUAI4+PTzjECW60jAfcLkyBQAyBk2RNy4zCTBhP+KJjWH//4R5s4caIzpxJxiWmVKEc0RoJALrnkEpe3x/nso+0N8r1J5NqmSfnxbfmGQvtyTZ5lDRisOqO7+EC4zQjtPH8QsEQhAnI7WVWANAv8o+Ei5nY0Bg6F6/78dho3AvApLGJ88NX71PpHIKRmiK+KBXuZEEQ3MhmCtUKGxgvg8IIQSFkgwARfnJdagESPFM6xSFm0ReTkHCWXx4khBVKMwDFN0Z3D4xOsZ4+GL5kS2Kb/u2WOAJGzpKQQeYvfk6Al5lCgH3VH7gwrxvvvv+/aJH/UI+Z4oGnf2+5/79wjwJwg9xg/vm892DnmwrZoFHTPTIjafIUc6k0SmJQXHQkgQjAtT5L3mBimUT5UO/nys8+sQpolhCFiTUmpdRo4wNoquq+t2+r/tzOPAHOPKGbM696cqm08PCHLCyYCRJlz+Ko9oONvNE3mrG/6qm0k/e2BI8DcqeHOCTzA/92qRqBOMNzencJ8KN1G9ZVs5bQRpUhqAonl48aNc4wIfyLVVIIjUT2JvEKS+ffrN9h6geQ+iubc0fJq2+ujv79ljgBRu5S4e+utt5xmiIR+zTXXbFkRxbsr/Nv33XefK612oFIuMM1TPg9BjOAY5ica4bnKqWQVFvzaHkB6bfjfO/cI4Lb5TMI5AYBZWVk792DsxHe/rdNuBwcCbZAlVTBjkahOKDyMBukbqfy6665zxaZhbOedd942rRNzViTTFUC4UaC6xx67N6i82jaN+n+0mhHAZ8gCvwAYqwwQmOWlwng3iYaH4IXARfUeBCzyB0855RT7xz/+4fJkOefMM890gpqX++mdz1wNFzBKlttSLclrvzHf6lrTEtnyEF+N+aidJu+763jo/5gTHjFfiHMIJKwIFLjAJ02gX3NSU1ssmtoXGu3Xa5RmyMSB2Vx11VXbzCFMWxA1NW+88cZt9nl/lMp3+J38i8P6D7C9VFVme6Yw7zz/u/lHwNPqI9kTSqXht8YfjR+aBGgWmcb/7BEmrAsuuMClXyCY8bKxCGxWVpbz9bANZuct08R2zid4BqZIsBcMKByAqGastLr+ude9Rn3nb4pR/1iIVs0IXWKk6W5l641oWg3Gqq1K3Xt142Yk3Zds1DowebqBuMYEi6i8YEyC5W/Ic2MbCET0mOeFkMz4R5K4LjWGKdyAhQBhinSxBx980JnPKchPqUm0QaxZlGJkbnCOR8wlhCfM67URxSugwCjnwG3MMa7PPMRnTb+4d9r03iGi7lEeKK2IQoEFLVjo865P/+CTtOcRlhGuj0JCWwA6fzPvsdwxt7meJ/hxT2xHeKSaFpWqavPD834wPoG5ysSCYP2jDdolD5j+8t5xH2zn2pzDGDIGbGNcqNhFPWj+DkXcC/nZgf2hD9wP98y7yvVRrrgeY0hFMc6jn9wb1/T6QDUulsmrr6k7srMy1B1v3lamTt//6KPWQ9GCdN6TirhJHnhDGRTn0RYDQ3J7fQciuKu0w8AywXiwPOiGkNcOE4e+1DYRttc27fBC8aEvjWEojA/9oS+0xaTaEeL4E044wRUy58WIBDGRWeCXUnm8CAhVlAVk0gc/C2/u0A/65t0PxwWON4yYD8R2wJZjGNtwUG+l4nZDDmxkczyO1UtNUdpb8MpiBOJVRx7V+G7qXmN071UBjTNe3UoK7ZiKno1uP1btL587zf7wyo/btMU1mCsso0TuciQJ4YlVZ7AaUKwBhsrKLn215uno0aNdPeJsuXUoig+TJueUUoaB/eL94jwqH1EsPtQcgTHDI2DOvNvcI9v4ZhvABFjAsHlvAQZ4Cd+cxznwKb7ZzzZWrQkEA2+cuP4iLdfG/OUd4Bxo8eLFblzZxr1w75RrBCCY45zH9egT53ggyfvP9YiWDcVLOI+l3XhmfPibD6vj4LLgevBqsgayJEjwzbtE3wF2vmmfMeD95T7hpfTL67t3b3zTNsIAwAe4Bl6P4Dn6QBsE1uH64Hq0j3BLDWaOAeDZxhjzmz7STn2pWcCQgcLP46E7Dwrib/xC1157rSvIXN+bCD4OCQoz2bnyE20v2T/43MC/eTi8VEhPXtHywP31/c2EIx8TTZm6ow0lJDk0JCoD8VI3lHipWOWC+4JBNISYnEzcSBEFtAFEBBpeIiY/UmOoF4kXG2bCPl4kcmKZA7ycXqRzcD8J4kJqDCfpcmEhXt9KvRJom1uIxnfgxd5yXqgfvG9iGoEU55hr/RlH4LnBv3mfqXcaikIx3lDHNWYbDBJtEOAAnAAAvhGsYJqABUCHKR4ip5r9gfMZho/Ah2aHsBWKqXpzMVAYC9zmMXSPvwW34QEU394+2vKODx4D2mZf4P7gbcx5r7/ecV7btMdvtnvb6nM9zvPa8q7nbfOux99e2xzj9cHbxjcfwMpri3MCKbht9gVvC/w7sG3vngK3ecJA4DXq+t0sYEiHmZR8AokBBCQpHs0gN5Q4n7UYmdCBJoUdbQ/zGi8KEkZgebUdbQeJk8r3MHbaaiix0sQ+++zjpK7GFE4nZJxCCp75oaH9ieR5SPRId88995wDOnzOjCNzJ5hY9JexgdGy8si9997r8hMvvPDCWs3vtBOoNQa32dx/M/sb8QrU3X3G0IFf3Yc1Zq8q6Dbm9EafC9P05gqMMpA5w1sC+Qs8IlAr9C7O9sbwD68d/7tljECzgGFtQ8MEzsrKqm13vbfDFENN7no3sPlAXgSkyMYSTBezSWMJMOXTWMKMgMYUzYTZCh8DQVissoG5asiQISEBLDBvkFVIsApEG332pdkPs8z2VHnfPfQJO0mDkRlDUWlFppwUM5kBw0XSRWz5phx7a+EX1iezm+3fc4QlxW/1W4XrOuFsxwNANBEEWbRFLCsIkWiBrGPaEPJMjrxDHtg2pJ36nINWilUJpQEtF/4YScLMiiuC1TpCmWojcW3MqDyjQK08EtehTYRr7o9roRAEU9xEUfBG/29/BJp7BJ599lmnTVP0gYpI//vf/1wVJJhapJlQJO69ezcVgE8xe+YFs0mfmI3eG79lGK8kAVCSpMn+Z3b33dXfm9cjbcxVAMKcgg0ueAcgnLz8B3tmznvWN7O7dUyNjL+4Mf31zoXBwtwBLdYGJQ6BalmY0DGPsjTYjs4jBDSqceH3wm+1o+d7favPN2Z/1loluAefJcybuR8pwhVBKhM+SE/wjLRWjLZ+/fXXu/gFXEiRBnvqbeOTRTkJpXT5YBip2eW326gRgOGwwC8vJLmDMAV8Pp4zv1GNN8PJq9eYUVRpvFzGs+dp7c6XzA45IIzWSjEWRYKYOL3ZuHFmCk5TZIHU0D0afLellWX24PRX7LGZApE1s+2gXnvYmF7DLS0+2e6b/rKN6NTf2iZHzm/c4I7rREAQhoc1ASaLZQbTO35yzOqhgAxtEl8zGhmaEcEo33//vQuwwb2BxYlgDTRNylOGg3mzbid9hUFzbYCPQBG0FwqeABIEreBD9wJLGjMuXA/BgL6jIRNRy280T+IH6AP+VGIkwgGGBOHQNuNNPifF8blftvF+ewE4XDsc44nAwjhBPKtJyivGGsDzI6DKs/Zlh7Cc+GDYmJnlnxuxEeDlxyRFbVIiyM4++2zHGAJ9PRG7eJgafvFVs3YKXn3vI7P7HzZ7XgAoN7SdoGpw33xrtinfbNCARlwMk6i0B9fo3/6mCzxv9vrrJqlBqDve7GFdVL5hcYcdvki51hh9/6cpNnP9QvvnuMtsfPa+lpqYZBuLC6x/u17WNjHV3lw02cb2CA0sO3zBJjgBZlubnxgAIhWDmAXADtMk1gkYNoIYUaFYKfiGscNMG8O8Mdm9/fbb7nrEJdAvopsxi7IdNwbaGgFzaLGAYmPmPhGzLF7+lNaxJIqW+73hhhvcN+vWonkCGoAwAoCnTTf0sWCOZCzx+bNuKBGmBEcyZvSB673yyitO8KBvBATW9mzq0wdA/q677nIBj6y9C/D+f3v3AaVXVe0BfCMkSDVCABGEAIaigIiARKSIERsKSAeRUB4gPAICFppSImXRFVjS8aF5IJFHEViCi46G3gTphNB770Te/t3LZSXjTDIz30zKzDlrfcl897v3nHP3Kf/dz8iRIyvgP/XUUyttAMB1ULp+8b1oa3KaZjZDIrL0WAbIwHN8wPUrVARUGmxFPCdthB1NBJ6ETns3cTm8bLfddhXXoR76aOo1QbVsSRwqOnJgUQ/1gMEzUO6d2EnFxLzjjjvivvvuq4K42+MsmzYF8NrEOc0IAG/qsZB4ceLGqFm22GKLDvuDa7MoSEUkIp9m8ExWmVl43xpccVMd0UefqD7GpQcpGuCWmyJHLE7NdcHtHI6ml4K+bIET2wOnl751th/zpVl2919Ezu/EpRMiHnok4le/jvjsYhE7b5/azJMj1v1mC9JhSs3JykdOSAc+ZuW/itxhIk9Fjjj22MgJFrmbR4oVne3yR/dN+GBC3Pbs/TF8kZVj4MwDqutvT3gvTrj13Fh/6Jqx0qc/H3e/MC7Gv/ZMLDp363b1jxqeRn9Qq9ovbNbmHnWaPcHeQ1Ly3Toi1UxurXW2+xg99TftsY/b/4QTkQ59ACPfB21zEGtlfZJ2vWPTnlATtkH7rr3TnmSP0wZ1rPdspQBbe7z2FPsez36hNcDIGbni08eOHVvd16qXsbEj2TdjY+8E6NrwXpL9G9dG5Uw6blt61yLbtrWJvuOMjj766GqzIzJDdQUBBVzTl5uIAAowdlRwFSeffHJ1PJQB9LfSgKRUXABSe4C3o0JcF5TrmCkDY4I0BZGJ379NWwxg7ai4T9/1d/jw4VWbTT36w4B/6623VrlZcZ/S1LVXTFr9btQVwB6HqADJq1L0pwoAtrhZbXZUAPioUaOqlHlUj00hbQFDXCc1jMXAxlJKz1Fg2MoRe46MGJcg+NTTiUkJgsstG3H3vRHzprC2Qgptb6XfS7eLjSaZtkoKTOkld55IzxCTP1KEiTyivctSoZCIu194OJ5/85VYYtDC8bfxN3/UvYSJGDzHoMqZZrZZBsZX05HmjYxP7AsFw7raaqtVx9Z5H4ymjRzz7Dd/23DZG0kxzabb3XcnAKyd6QMbD2lrk1RGjdeo+XhQC/8hydkjWymAb4MNNqgYffWQnKiOvUcDsttss03Vpx122KHl9mh2AHtj1qCy5IyIjv5HX+1K0EKSaxV87YVo1Tj+kEwbJ0qMBMl06623jjXWWCMcC9eeFDpNJEOA5zQCHdI5nRZUDUwAgQ2c04TzFElEuCZ5JtsWEqRBRQD1mGDc6tWD6ENSCtpvv/0qorf38k19beuhIhkzZkxVD25CG8BCOILvHRXcF7AymfVbm4BLfww+QCXO/yVVWUR29bVX2CrcbwIDVXp89SpA1KIhMapXGEnbEJWJ66ReMdma+5vfgKHf0JnEajyAobpLaZ0C55wXccllEV9bPeKQAyP22jfBb/lakPvCh8O+/ndbaCc35jwwNJITikzDE7l71RJiqtzSoJX62XS2EJuRY9+V8kHe/Oo7b8RtT98fGyy5Zlw+/qY45uazY6vPfTMee/35eODFx2P9JdaMgR8bECsusFRXqp7u77We7AWKtWufsqYxshhka29y+0hXX1Bb2lDsYfYZ1xqbIbBq77SgrrbT3E8abdojGQEI34G8dQ/4acV6qpDI0FTBUNhfmr2wcYRrVSKcuK/Gqinas48q2rUnA1w25I7KNJMMDXjDXSFIQxTEMjiNStAkaQja9iVcV49ngZT6mjr9zwANXA844ICqDpxBe2XievyuvqYeEiXpi4oEeJBEgVN7xcQyATyrP+oByv42yXF/ruFASXN05u0Vi69RaaireT/3et9GJdBwq5OT6Br7R1tjeLPA1dH81hGd2+tjuTYpBfLQlfjnv+prN9+e0l/+vf/P0lZ4SsSNt0ZsvWWq7dPEd8A+KazV1oBJK+jMt2Rgkmup78zk+FX4BNvgXnuld05uYiunKJrMUSZy7XaQ4swzfSyWnmfRuOmZexIU34yj0l742ntvxG5XHBun3nlBjFj2O7HQXPN1prcz3D3mv3XnQ7KxRiTLsP7tR42k0ZMvpg3tSsOGUabOoxVik+xp71H7UwP29iAaJ+pEyS2030hVPfV+3qt5P8IK7RatGQGBace+1pOlaU+dTrzxXsxJTEEcp6ZUpgkYAgciMqAx8Yi0jWoA8Uw6Ni6DJwvMkJTw2iuAglSkHs8BKmI5ogANgHNEcs+yUTiAuCOpZ+J69IfKFJekfdwGwMBVXXTRRZVqk7qzvYLz8BxwAoo4L+oNdeoTcZ2xnDQGWElk7RVcjT5p2z3eDaiZyOgBDNkKqZFNYNcmV7yHjz74X33aoIZVFxUGqbqnF8Pk+tTXfpslhbCTEp/GXFg7zZD+xiSvc9apEZf+NeKOu9Kk94uUAFoJE8WEpY0nPYpq0Mu5ljmz6lCK1ICk3jxixAipQFoi71wD54h1Fl0lfnX9KakYnSl+OWy7OOPb+8UJw/eKFeZfsqW6p+eHSUucPQADrQw7OkmJrevbyXRYMz1Z7BX8BrShPWpKa1MfgEVPr0fCAVWifRDYO2TB3iuRPQ/bngYn+zgTFWYbDaknmWR22mmnah+0r/ZkQUMqXoVWcc8996y0jP5v73D6tm3PlBvkB20vTo3vwIJKk90MWOyyyy7V4ONUJF8WfwIQ3HfwwQd/pNdu2zdA6MgewGcwEQPYUmtSIWoDYVwzoYFIe0U9DLsGS7s77rhjlcYJKJssQIQhm3eZvnZUGL3PPPPMCqAAjMkHHEmW3JXF8nhfC0tf23PxVTcuClcKqBwtg7PipoxDdY3h2dBRtW688caTTQ7A7kr1bJFbCGKtqF8YtTEQaON9AXVPT9CO6NSXr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style="margin-left: auto; margin-right: auto;" width="640" /></td></tr><tr><td class="tr-caption" style="text-align: center;"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">Figure 1: The selected 22 features perform only slightly worse
than the full (pre-filtered) set of 4,791. <b style="mso-bidi-font-weight: normal;">A</b>
Scatter of classification accuracy in each dataset, error bars signify standard
deviation across folds. <b style="mso-bidi-font-weight: normal;">B</b> Mean
execution times for time series of length 10,000. <b style="mso-bidi-font-weight: normal;">C</b> Near-linear scaling of computation time with time-series length.</span></td></tr></tbody></table><p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"></span><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span><p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">By applying our pipeline to a standard library of 93
classification problems in the data-mining literature (UEA/UCR), we compiled a
set of 22 features (<i style="mso-bidi-font-style: normal;">catch22</i>) that we
then implemented in C and wrapped for R, Python, and Matlab. The 22 resulting
features individually possess discriminative power and only do ~10% worse than
the full <i style="mso-bidi-font-style: normal;">hctsa</i> feature set on the
considered data at a highly (1000-fold) reduced computation time, see Fig. 1.</span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">As the UEA/UCR-datasets mainly consists of short, aligned, and
normalised time series, the features are especially suited to these
characteristics. The selection pipeline may be applied to other collections of
time-series datasets with different properties to generate new, different
feature sets and can further be adapted to performance metrics other than
classification accuracy to select features for analyses such as clustering,
regression, etc.</span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">See full paper for all the details here for free (</span><a href="http://link.springer.com/article/10.1007/s10618-019-00647-x"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">http://link.springer.com/article/10.1007/s10618-019-00647-x</span></a><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;">) under the title "catch22: CAnonical Time-series CHaracteristics Selected through highly comparative time-series analysis" in the journal Data Mining and Knowledge Discovery. The <i style="mso-bidi-font-style: normal;">catch22</i> feature set is on GitHub (</span><a href="https://github.com/chlubba/catch22"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman";">https://github.com/chlubba/catch22</span></a><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: "Times New Roman";">). Carl, Ben, Nick</span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
<p class="MsoNormal"><span style="mso-bidi-font-family: Calibri; mso-bidi-theme-font: minor-latin;"> </span></p>
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{page:WordSection1;}</style></p>Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-9105652697527456286.post-40680360060339600392020-10-29T19:31:00.001+00:002020-10-29T19:31:27.270+00:00Universal approaches to measuring social distances and segregation<p>How people form connections is a fundamental question in the social sciences. Peter Blau offered a powerful explanation: people connect based on their positions in a social space. Yet a principled measure of social distance remains elusive. Based on a social network model, we develop a family of intuitive segregation measures formalising the notion of distance in social space. </p><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><img height="452" src="https://lh4.googleusercontent.com/W4FEJmTaxz2E5XWJntJPrcTvNHv5P5YVCRgdj10PAQEI8ilBqr0TwzHTxDajhhar-pO31MwsTGhXKNpzsn5QJQLC4vHBgE5VCfqgKqY-t7YS-7htM99jajTqCoRWTTkjmeSmpv_f" style="margin-left: auto; margin-right: auto; margin-top: 0px;" width="602" /></td></tr><tr><td class="tr-caption" style="text-align: center;">The Blau space metric we learn from connections between individuals
offers an intuitive explanation for how people form friendships: the
larger the distance, the less likely they are to share a bond. It can
also be employed to visualise the relative positions of individuals in
the social space: a map of society.</td></tr></tbody></table><p class="MsoNormal">Using US and UK survey data, we show that the social fabric is relatively stable across time. Physical separation and age have the largest effect on social distance with implications for intergenerational mixing and isolation in later stages of life. You can read about our work "Inference of a universal social scale and segregation measures using social connectivity kernels" free <a href="https://arxiv.org/pdf/2008.05337.pdf">here</a> and in the journal Royal Society Interface <a href="https://royalsocietypublishing.org/doi/10.1098/rsif.2020.0638">here</a>. Till and Nick.</p>Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-9105652697527456286.post-87944019531915730952020-10-13T14:08:00.000+01:002020-10-13T14:08:57.419+01:00Predicting and controlling rat feeding<p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgO5JbLkcnw8X6pmhgyjMZYODGbLA1JAycFykGabwIjbbAT1STMhDctRxl_vteLnvtOX3r9cUEDd2hoW8_hrkGtCTFhhD-s9-v6YDJ2FP2F-MIF-k9AXIzdNhi-oPVh3cfoliPbUBywV563/s2220/journal.pbio.3000482.g001.tif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="763" data-original-width="2220" height="219" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgO5JbLkcnw8X6pmhgyjMZYODGbLA1JAycFykGabwIjbbAT1STMhDctRxl_vteLnvtOX3r9cUEDd2hoW8_hrkGtCTFhhD-s9-v6YDJ2FP2F-MIF-k9AXIzdNhi-oPVh3cfoliPbUBywV563/w640-h219/journal.pbio.3000482.g001.tif" width="640" /></a></div><br /><div class="separator" style="clear: both; text-align: center;"><br /></div><br />We made a rather surprising discovery: that a stochastic model of rat feeding was quite predictive. Using a slowly and smoothly varying quantity (stomach fullness) we were then able to make good predictions on when the next feeding bout would be. Given the parameters of this model fit to diverse rats we were then able to study the effects of various drugs and explore in silico approaches to lowering food intake.<p></p><p>You can read a more extensive article discussing this paper on the Imperial news <a href=" https://www.imperial.ac.uk/news/194266/behavioral-interventions-anorectic-drugs-how-reduce/">website</a>. The article is available for free in the journal PLoS Biology <a href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000482">here</a>. Tom, Kevin and Nick</p><p><br /></p>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-74744826481774170462020-10-13T08:56:00.001+01:002020-10-13T14:12:42.455+01:00Finding communities in time-series<p><span style="font-family: Arial; font-size: 14.6667px; text-align: justify; white-space: pre-wrap;">In many complex systems, the exact relationship between entities is unknown and unobservable. Instead, we may observe interdependent signals from the nodes, such as time series. Current methods for detecting communities (i.e. nodes that are more closely related to one another than to the rest of the network) when edges are unobservable typically involve a complicated process: choose a measure to assess the similarity between pairs of time series, convert the similarity matrix to a (weighted) network, and, finally, infer community structure. This approach is computationally expensive and each step of the three-stage process computes point estimates, making it difficult to distinguish genuine structure from noise.</span></p><span id="docs-internal-guid-a7ab57fa-7fff-774e-7f13-2be842e96891"><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwBATM_FqrVJ1RJP16vKoraI_S3nmG8AylIb4u64TIHyNfjqWpnqWBU1CImyTLdKdJ1R4MfFr7k5sHJukTsMRg0F7KlH3REPsVqexbdts0pOeVRxUybvX5PWZqDiFf8IuVSDHa7IBbj356/" style="margin-left: auto; margin-right: auto;"><img data-original-height="763" data-original-width="1050" height="291" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwBATM_FqrVJ1RJP16vKoraI_S3nmG8AylIb4u64TIHyNfjqWpnqWBU1CImyTLdKdJ1R4MfFr7k5sHJukTsMRg0F7KlH3REPsVqexbdts0pOeVRxUybvX5PWZqDiFf8IuVSDHa7IBbj356/w400-h291/image.png" width="400" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">Discovering clusters in financial time series </td></tr></tbody></table><div style="text-align: justify;"><br /></div><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span style="font-family: Arial; font-size: 14.6667px; white-space: pre-wrap;">We propose a Bayesian hierarchical model for multivariate time series data that provides an end-to-end community detection algorithm and propagates uncertainties directly from the raw data to the community labels. Our approach naturally supports multiscale community detection and enables community detection even for short time series. We uncover salient communities in both financial returns time series of S&P100 stocks and climate data in the United States. You can read the article for free </span><a href="https://advances.sciencemag.org/content/6/4/eaav1478" style="font-family: Arial; font-size: 14.6667px; white-space: pre-wrap;">here</a><span style="font-family: Arial; font-size: 14.6667px; white-space: pre-wrap;"> in Science Advances under the title Community detection in networks without observing edges. This was fun work with our collaborators Leto Peel and Renaud Lambiotte. Till and Nick</span></p><div><span style="font-family: Arial; font-size: 11pt; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre-wrap;"><br /></span></div></span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-40197197339295817302020-05-25T14:47:00.002+01:002020-05-25T14:47:50.040+01:00Why are fungal networks so widespread?<div dir="ltr" style="text-align: left;" trbidi="on">
You can read about our recent paper on fungal networks in <a href="https://natureecoevocommunity.nature.com/users/384165-dr-luke-heaton/posts/why-are-fungal-networks-so-widespread">this</a> blog article in <a href="https://natureecoevocommunity.nature.com/users/384165-dr-luke-heaton/posts/why-are-fungal-networks-so-widespread">Nature Evolution and Ecology</a>. Confusingly the article is available for free in <a href="https://www.nature.com/articles/s41467-020-16072-4">Nature Communications</a>.<br />
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Luke, Mark and Nick<br />
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-44549780189592509642019-07-25T16:32:00.000+01:002019-07-25T16:32:40.649+01:00Mitochondrial networks and Ageing in the Variance<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-size: medium;"><span style="font-family: "arial" , "helvetica" , sans-serif;">Mitochondrial DNA (mtDNA) populations within our cells encode vital energetic machinery. MtDNA is housed within mitochondria, cellular compartments lined by two membranes, that lead a very dynamic life. Individual mitochondria can fuse when they meet, and fused mitochondria can fragment to become individual smaller mitochondria, all the while moving throughout the cell. The reasons for this dynamic activity remain unclear (we’ve compared hypotheses about them before <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4672710/" style="color: #4d469c; text-decoration-line: none;">here</a> and <a href="https://www.cell.com/molecular-plant/fulltext/S1674-2052(18)30337-X" style="color: #4d469c; text-decoration-line: none;">here</a>, with blog articles <a href="hhttps://systems-signals.blogspot.com/2015/04/the-function-of-mitochondrial-networks.html" style="color: #4d469c; text-decoration-line: none;">here</a>). But what influence do these physical mitochondrial dynamics have on the genetic composition of mtDNA populations?</span></span></div>
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<span style="font-size: medium;"><span style="font-family: "arial" , "helvetica" , sans-serif;">MtDNA populations can, naturally or as a result of gene therapies, consist of a mixture of different mtDNA types. Typically, different cells will have different proportions of, say, type A and type B. For example, one cell may be 20% type A, another cell may be 40% type A, and a third may be 70% type A. This variability matters because when a certain threshold (often around 60%) is crossed for some mtDNA types, we get devastating diseases.</span></span></div>
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<span style="font-size: medium;"><span style="font-family: "arial" , "helvetica" , sans-serif;">We previously showed <a href="https://www.cell.com/ajhg/fulltext/S0002-9297(16)30397-4" style="color: #4d469c; text-decoration-line: none;">mathematically</a> (<a href="https://systems-signals.blogspot.com/2016/11/evolution-energetics-noise.html" style="color: #4d469c; text-decoration-line: none;">blog</a>) and <a href="https://www.nature.com/articles/s41467-018-04797-2" style="color: #4d469c; text-decoration-line: none;">experimentally</a> (<a href="https://systems-signals.blogspot.com/2018/09/aging-in-variance-increasing-variation.html" style="color: #4d469c; text-decoration-line: none;">blog</a>) that this cell-to-cell variability in mtDNA proportions (often called “heteroplasmy variance” and sometimes referred to via the “mtDNA bottleneck”) is expected to increase linearly over time. However, this analysis pictured mtDNAs as individual molecules, outside of their mitochondrial compartments. When mitochondria fuse to form larger compartments, their mtDNA is more protected: smaller mitochondria (and their internal mtDNA) are subject to greater degradation. More degradation means more replication, and more opportunities for the fraction of a particular type of mtDNA to change per unit time. In a new paper <a href="https://www.genetics.org/content/early/2019/07/10/genetics.119.302423" style="color: #4d469c; text-decoration-line: none;">here</a> in Genetics, we show (using a mathematical tour de force by Juvid) that this protection can dramatically influence cell-to-cell mtDNA variability. Specifically, the rate of heteroplasmy variance increase is scaled by the proportion of mitochondria that exist in a fragmented state. (It turns out that it's the proportion of itochondria that are fragmented that's important -- not whether the rate of fission-fusion is fast or slow).</span></span></div>
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<span style="font-size: medium;"><span style="font-family: "arial" , "helvetica" , sans-serif;">This has knock-on effects for how the cell can best get rid of low-quality mutant mtDNA. In particular, if mitochondria are allowed to fuse based on their quality (“selective fusion”), we show that intermediate rates of fusion are best for removing mutants. Too much fusion, and all mtDNA is protected; too little, and good mtDNA cannot be sorted from bad mtDNA using the mitochondrial network. This mechanism could help explain why we see different levels of mitochondrial fusion in different conditions. More broadly, this link between mitochondrial physics and genetics (which we’ve also speculated about <a href="https://www.frontiersin.org/articles/10.3389/fgene.2018.00718/full" style="color: #4d469c; text-decoration-line: none;">here</a> (<a href="https://systems-signals.blogspot.com/2019/02/how-mitochondria-can-vary-and.html" style="color: #4d469c; text-decoration-line: none;">blog</a>) and <a href="https://www.cell.com/molecular-plant/fulltext/S1674-2052(18)30337-X" style="color: #4d469c; text-decoration-line: none;">here</a>) suggests one way that selective pressures and tradeoffs could influence mitochondrial dynamics, giving rise to the wide variety of behaviours that remain unexplained. Juvid, Nick, and Iain</span></span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-21827003912573293682019-07-19T08:43:00.000+01:002019-07-19T08:43:39.120+01:00The cell's power station policy<div dir="ltr" style="text-align: left;" trbidi="on">
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Our cells are filled with populations of mitochondrial DNA (mtDNA) molecules, which encode vital cellular machinery that supports our energy requirements. The cell invests energy in maintaining its mtDNA population, like us using electricity-powered tools to help maintain our power stations. Our cellular power stations can vary in quality (for example, mutations can damage mtDNA), and are subject to random influences. How should the cell best invest energy in controlling and maintaining its power stations? And can we use this answer to design better therapies to address damaged mtDNA?</div>
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<span style="font-family: arial, helvetica, sans-serif; font-size: medium;">In a new paper "Energetic costs of cellular and therapeutic control of stochastic mitochondrial DNA populations" free <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007023" style="color: #4d469c; text-decoration-line: none;">here</a> in PLoS Computational Biology, we attempt to answer this question using mathematical modelling, linking with genetic experiments done by our excellent collaborators at Cambridge (</span><span style="font-family: arial, helvetica, sans-serif; font-size: medium;">Payam Gammage, Lindsey Van Haute and Michal Minczuk). We first expand a mathematical model for how diverse mtDNA populations within cells change over time – building new power stations and decommissioning old ones, under the “governance” of the cell. We then produce an “energy budget” for the cellular “society” – describing the costs of building, decommissioning, and maintaining different power stations, and the corresponding profits of energy generation.</span></div>
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<span style="font-family: arial, helvetica, sans-serif; font-size: medium;">We find some surprising results. First, it can get harder to maintain a good energy budget in a tissue (a collection of individual cellular “societies”) over time, even if demands stay the same and average mtDNA quality doesn’t change. This is because the cell-to-cell variability in mtDNA quality does increase, carrying with it an added energetic challenge. This increased challenge could be a contributing factor to the collection of problems involved in ageing.</span></div>
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<span style="font-family: arial, helvetica, sans-serif;">An overview of our approach. A mathematical model for the processes and "budget" involved in controlling mtDNA populations makes a general set of biological predictions and explains gene-therapy observations</span></div>
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<span style="font-family: arial, helvetica, sans-serif; font-size: medium;">Next, we found that cells with only low-quality mtDNA can perform worse than cells with a mix of low- and high-quality mtDNA. This is because low-quality mtDNA may consume less cellular resource, although global efficiency is decreased. Linked to this, removal of low-quality mtDNA (decommissioning bad power stations) alone is not always the best strategy to improve performance. Instead, jointly elevating low- and high-quality mtDNA levels, avoiding this detrimental mixed regime, is the best strategy for some situations. These insights may help explain some of the negative effects recently observed in cells with mixed mtDNA populations.</span></div>
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<span style="font-family: arial, helvetica, sans-serif;">Our theory suggests that mixed mtDNA populations may do worse than pure ones, even if the pure population is a low-functionality mutant. Image from Hanne's post <a href="http://imperialmitochondriacs.blogspot.com/2019/07/energetic-costs-of-cellular-and.html" style="color: #4d469c; text-decoration-line: none;">here</a></span> </div>
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<span style="font-family: arial, helvetica, sans-serif; font-size: medium;">We identified how best to control cellular mtDNA populations across the full range of possible populations, and used this insight to link with exciting gene therapies where low-quality mtDNA is preferentially removed through an experimental intervention (using so-called “endonucleases” to cut particular mtDNA molecules). We found that strong, single treatments will be outperformed by weaker, longer-term treatments, and identified how the mtDNA variability we know is present can practically effect the outcome of these therapies. We hope that the principles found in this work both add to our basic understanding of ageing and mixed (“heteroplasmic”) mitochondrial populations, and may inform more efficient therapeutic approaches in the future. Iain, Hanne, Nick</span></div>
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<span style="font-family: arial, helvetica, sans-serif; font-size: medium;">(Hanne's also written a post about this paper, you can read it </span><a href="http://imperialmitochondriacs.blogspot.com/2019/07/energetic-costs-of-cellular-and.html" style="color: #4d469c; font-family: arial, helvetica, sans-serif; font-size: large; text-decoration-line: none;">here</a><span style="font-family: arial, helvetica, sans-serif; font-size: medium;">)</span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-13361991148418303162019-02-27T20:52:00.003+00:002019-02-27T20:54:54.673+00:00Guessing the spreading time of rumours<div dir="ltr" style="text-align: left;" trbidi="on">
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<b style="mso-bidi-font-weight: normal;"><u><span lang="EN-US" style="background: white; color: #222222;"></span></u></b><span lang="EN-US" style="background: white; color: #222222;"></span><span lang="EN-US"> Social scientists are fascinated by social influence. That is, how people's beliefs, opinions and actions are influenced by others. This is relevant for understanding voting, health behaviour or opinions on issues like vaccination and climate change (topics our group is interested in). Mathematically inclined social scientists often interpret social influence using network theory. Networks or graphs are used to
represent systems consisting of many individual units, known as <i style="mso-bidi-font-style: normal;">nodes</i>, and the interactions between
them, which are referred to as <i style="mso-bidi-font-style: normal;">edges</i>
or <i style="mso-bidi-font-style: normal;">links</i><i>.</i> In social
networks the nodes represent people and the links represent social ties such as
friendships.</span></div>
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<span lang="EN-US">Given a particular graph there are tools for modelling how opinions and beliefs
can <a href="https://arxiv.org/abs/0710.3256" target="_blank">spread through a graph</a>. However, in practice we often don’t know the
structure of the social network itself. This could be because: i) the data we
would like is unavailable ii) privacy concerns about social network data mean we can't share it even if we have it iii) the data
exists but is full of errors or omissions. Fortunately, we know a lot about the structure of social networks
from decades of past research by social scientists and statisticians. For
example, many social networks are known to be <i style="mso-bidi-font-style: normal;">homophilous </i>- this means that people who are similar to each other
are more likely to share a social connection (e.g many of your friends are
probably a similar age to you).</span></div>
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<span lang="EN-US">Inspired by this, we consider a simple
mathematical model for homophilous networks known as a Random Geometric Graph
(RGG). In an RGG the nodes are assigned random positions in a (unit) box. Nodes are
connected to all the nodes which are within a set distance (see figure), which we
refer to as the <i style="mso-bidi-font-style: normal;">connection radius</i>.
Positions of nodes may represent the positions of individuals in geographic
space or in some “social space” where the coordinate axis might represent
attributes such as age, income and education level. Since social networks are
<a href="https://www.annualreviews.org/doi/abs/10.1146/annurev.soc.27.1.415" target="_blank">homophilous</a> we will expect those who are closer together in “social space” to
share a social tie.</span></div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgm5qnjsTllFHZS_PBnk6jr4T8po0TK9IkBiV9Dops4Qkbi9p7JUlImO4jUaCNdeORqzgcykBvb_jc7HazSsvThxvoSu0odYvj2tbdT4R6vUgLYcFA2AwybcUDUPb05ukWDlFXuwX9CcPcQ/s1600/rgg.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="480" data-original-width="640" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgm5qnjsTllFHZS_PBnk6jr4T8po0TK9IkBiV9Dops4Qkbi9p7JUlImO4jUaCNdeORqzgcykBvb_jc7HazSsvThxvoSu0odYvj2tbdT4R6vUgLYcFA2AwybcUDUPb05ukWDlFXuwX9CcPcQ/s320/rgg.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span lang="EN-US">Example of a Random Geometric
Graph with 100 nodes and a connection radius of 0.2.</span></td></tr>
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<span lang="EN-US">One basic question we can ask about a
network is: “how long does it take something to spread across it?” We refer to
this as the diffusion timescale. The diffusion timescale in a graph is indicative
of how well connected the graph is and governs how quickly we might expect a
disease, rumour or the adoption of a new behaviour to spread through it (or
even how long it will take a <a href="https://people.maths.ox.ac.uk/maini/PKM%20publications/384.pdf" target="_blank">zombie</a> apocalypse to take hold<span style="mso-spacerun: yes;"></span>). In our recent research we focus on the
question:</span></div>
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<i style="mso-bidi-font-style: normal;"><span lang="EN-US">“If
we do not know the network (but perhaps know some of its properties) how
precisely can we know the diffusion timescale?”</span></i></div>
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<span lang="EN-US">We show that different RGGs drawn at random
with the same number of nodes and connection radius can have very different
diffusion timescales. This implies that if we don’t have a good grasp of the
graph structure then it could be difficult to predict the outcomes of
processes such as the spread of an opinion through a social network. Or alternatively we can gain lots of extra information about diffusion timescales if we happen to know the social co-ordinates of individuals. On the
other hand, we do find some classes of RGGs where the diffusion time scale is
very predictable given only knowledge of the number of nodes and the connection
radius. </span></div>
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<span lang="EN-US">Our work helps put limitations on how
accurately we can forecast the outcome of processes on networks given the
available data (which is always imperfect). Future work may involve asking the
same questions for real world datasets. In addition, most of our new results
were obtained through computer simulations, meaning that there is also scope
for more theory. </span></div>
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<span lang="EN-US">You can read about our research in the
paper </span><span lang="EN-US"><span lang="EN-US">“<i style="mso-bidi-font-style: normal;">Large
algebraic connectivity fluctuations in spatial network ensembles imply a
predictive advantage from node location information</i>” for free </span> <a href="https://arxiv.org/pdf/1805.06797.pdf" target="_blank">here</a> or for not-free <a href="https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.052316" target="_blank">here</a> in Physical Review E. Matt and Nick.</span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-60243412033395146502019-02-27T20:28:00.000+00:002019-02-27T20:28:14.448+00:00How mitochondria can vary, and consequences for human health<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-size: small;">Mitochondria are components of
the cell which are involved in generating “energy currency”
molecules called ATP across much of complex life. Since many
mitochondria exist within single cells (often hundreds or thousands),
it is possible for the characteristics of individual mitochondria to
vary within cells, and within tissues. This variation of
mitochondrial characteristics can affect biological function and
human health. </span>
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<span style="font-size: small;">Since mitochondria possess
their own, small, circular, DNA molecules (mtDNA), we can split
mitochondrial characteristics into two categories: genetic and
non-genetic. In our review, we discuss a number of aspects in which
mitochondria vary, from both genetic and non-genetic perspectives. </span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEht8PqESf8VRL1Wf-9qxgHWYCwoMsuKBUeoC_4Rt9bdbFIQ9A4L_-Kdpm1mKCOGtJvNS6e2QcqBDe9wYo1vz1nCyUJSy71WRZsvCMqlJ0ZrKA_DIC54HMMgAqS0TkD_SbUW27zLvqyvXkw/s1600/blog.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1180" data-original-width="997" height="400" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEht8PqESf8VRL1Wf-9qxgHWYCwoMsuKBUeoC_4Rt9bdbFIQ9A4L_-Kdpm1mKCOGtJvNS6e2QcqBDe9wYo1vz1nCyUJSy71WRZsvCMqlJ0ZrKA_DIC54HMMgAqS0TkD_SbUW27zLvqyvXkw/s400/blog.png" width="336" /></a></div>
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<span style="font-size: small;">
</span></div>
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<span style="font-size: small;">In terms of mitochondrial
genetics, the amount of mtDNA per cell is variable. When a cell
divides, its daughters receive a share of its parents mtDNA, but the
split isn’t precisely 50/50, so cell division can cause variability
in the number of mtDNAs per cell. As mtDNAs are replicated and
degraded over time, errors in the copying process may give rise to
mtDNA mutations, which may spread throughout a cell. Factors such as:
the total amount, the rate of degradation/replication, the mean
fraction of mutants, and the extent of fragmentation in the
mitochondrial network, can all influence how variable the fraction of
mutated mtDNAs becomes through time (<a href="https://arxiv.org/abs/1809.01882">see
here</a> for a preview of some upcoming work on this topic). The
total amount, and mutated fraction of mtDNAs, are implicated in
diseases such as neurodegeneration, as well as the ageing process.</span></div>
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<span style="font-size: small;">Apart from genetic variations,
there are many non-genetic features of mitochondria which also vary
within and between cells. Changes in mtDNA sequence
can change the amino-acid sequence of the proteins encoded by mtDNA,
causing structural changes in the molecular machines which generate
ATP. The shape of the membranes of mitochondria are also highly
variable, and respond to mitochondrial activity through quantities
such as pH, where mitochondrial activity itself may depend on mtDNA
sequence. The previous two examples (mitochondrial protein and
membrane structure) demonstrate how the genetic state of mitochondria
may influence their non-genetic characteristics. Mitochondrial
non-genetic characteristics may also influence the genetic state: for
instance, mitochondrial membrane potential can influence the
probability of a mitochondria being degraded, along with its mtDNA. </span>
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<span style="font-size: small;"><span style="text-decoration: none;"><span style="font-weight: normal;">The
inter-dependence of genetic and non-genetic characteristics
demonstrate the complex feedback loops linking these two aspects of
mitochondrial physiology. We suggest here that, since changes in
mitochondrial genetics occur more slowly than most physical aspects
of mitochondrial physiology, understanding mitochondrial genetics may
be especially important in explaining phenomena such as ageing, which
appear</span></span></span><span style="font-size: small;"><span style="text-decoration: none;"><span style="font-weight: normal;">s</span></span></span><span style="font-size: small;"><span style="text-decoration: none;"><span style="font-weight: normal;">
to be closely related to mitochondrial heterogeneity. </span></span></span>You
can freely access our work, which has recently been published in
Frontiers in Genetics, as “Mitochondrial Heterogeneity”
<a href="https://www.frontiersin.org/articles/10.3389/fgene.2018.00718/full">https://www.frontiersin.org/articles/10.3389/fgene.2018.00718/full</a>
Juvid, Iain and Nick.
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<span style="font-size: small;"> </span>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-32664727001832836332018-10-20T17:13:00.001+01:002018-10-20T17:21:58.644+01:00Mutated islands of brain matter from development might be common in the human population<div dir="ltr" style="text-align: left;" trbidi="on">
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You are made up of a lot of cells, and so is your brain. You were also derived from a single cell: the union of a sperm and an egg. In order for your body to grow from a single cell into an adult human, a massive amount of cell division must occur, which means that the DNA inside your cells must also be replicated intensively. In copying all of this DNA, “spelling mistakes” can sometimes be made. If that mistake occurs early enough in development, all of the subsequent cells which are copied from the mutant parent also receive this mistake, which potentially gives rise to large islands of mutated cells (called “somatic mosaicism”). If a copying error occurs at a particularly important base of DNA, this could potentially cause disease in the tissue once you have fully developed into an adult. </div>
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Inherited mutations in certain genes are known, in rare cases, to cause neurodegenerative disease (such as Alzheimer's and Parkinson's disease). We wondered whether non-heritable “spelling mistakes” in these disease-causing genes is common enough in the human population to potentially explain the more common forms of neurodegenerative disease. </div>
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Our experimental collaborators at the University of Cambridge went searching for mutated chunks of brain matter in post-mortem samples of brains from 54 human individuals. Using genetic sequencing technology, they found evidence for these mutated islands of grey matter. However, none of these samples were pathological themselves, since only a small fraction of the brain per individual was sampled. This provided an opportunity for mathematical modelling of how the brain develops, so that we may predict the prevalence of pathological mutations in human brains, given the experimental data. </div>
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Our mathematical model is incredibly simple (and crude -- others have developed much more sophisticated approaches): we assume that, in order to grow a brain, you take the initial cell from which you were derived, and double it repeatedly until the mass of cells corresponds to the number of cells in the brain (this is called a binary tree). Each copying event is called a “generation”, and corresponds to a row of the tree below. If a mutation occurs whilst copying the DNA of a particular cell at a particular generation, then a fixed fraction of all the subsequent daughters will also be mutated, generating a mutant region. Repeatedly simulating brain development using a computer allows us to gather statistics about the probability of an individual harbouring pathological islands of brain matter. </div>
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<tr><td class="tr-caption" style="text-align: justify;">Mathematical modelling of brain development reveals that islands of pathologically mutated cells are potentially common in the population. Left: We modelled neurodevelopment as a simple binary tree, where an initial cell doubles repeatedly until the final adult brain is created. DNA copying errors are carried forward into daughter cells. Right: A typical simulated individual. Coloured circles represent islands of pathologically mutated cells in the adult human brain. Whole brain area (black circle) is not to scale with the mutated regions (coloured circles). The mutated regions are really tiny proportionately. </td></tr>
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Neurodevelopment is, of course, much more complicated than a series of doubling events. Amongst other effects, regions spatially re-arrange themselves, cells die, and cell division isn't always symmetric (i.e. daughter cells may not always be capable of dividing themselves). We explored several modifications to the simple model above, and found that our extrapolations were surprisingly robust. We argue that, once the developing human brain consists of about 1 million cells, as long as each daughter cell gives rise to roughly the same number of daughter cells in subsequent divisions, and that spatial mixing of the brain isn't too strong, every individual is expected to harbour about 1 pathologically mutated island of cells consisting of about 10,000 to 100,000 cells. The basic idea is that if those 1 million cells replicate once then they are really likely to have a pathological mutation crop up in one of those 1 million divisions. Larger regions may also occur, but are rarer, and conversely, smaller regions are more common (see the right panel of the figure above). This kind of argument suggests that for a whole range of possible ways in which our brain develops we’re likely to have islands of mutation. </div>
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We also discuss an observation which emerges from the tree-structure of neurodevelopment which may allow us to directly estimate the mutation rate from a simple back-of-the-envelope calculation. Any particular experiment will have a certain detection sensitivity, in that it will be able to detect mutations common to a minimum number of cells in a sample, and no fewer (in our case this was ~0.5% of cells in a sample). Because of the tree structure of neurodevelopment, the most common mutations observed will occur at exactly the detection sensitivity: larger mutated islands become exponentially rarer, whereas smaller mutations are too small to be measurable. </div>
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Now consider cutting a whole brain up into a number of equally-sized chunks. As the size of the chunks increases (where we are able to detect mutations affecting 0.5% of each chunk), each chunk is tuning into a mutation event which affects more cells, and therefore higher up in the neurodevelopmental tree. But, the number of mutated cells from any particular generation in the tree is a constant: mutations high up in the tree are larger, but also rarer, and these two effects precisely balance each other. Therefore, regardless of how large each chunk is, the total number of cells which you expect to be able to detect is independent of chunk size. The total number of detectably mutated cells does, of course, depend on the mutation rate and the total number of bases that are sequenced. Furthermore, we may say that the total number of detectably mutated cells equals: (number of mutated chunks) x (number of mutated cells per mutated chunk): this argument itself is also independent of the size of each chunk, only depending on the detection sensitivity and the fraction of detectably mutated chunks across the whole experiment. Therefore, we may equate the total number of mutations from any given generation, and the total number of detectably mutated, to write down the mutation rate entirely independently of the size of each brain chunk. Another way of putting the above argument is that (for very simple models of the brain we describe) we expect that the quantity: (the fraction of chunks containing mutation) x (sensitivity of the detection technique), is an invariant directly linked to the mutation rate (specifically to the number of mutations expected in a single replication of the sequences studied). This doesn't depend on the size of the chunks or the size of the brain. As such, if our experiment had half the sensitivity to mutated cells per brain chunk (so 1% instead of 0.5%), we’d have had to measure twice as many bits of brain to obtain a similar number of detectably mutated brain chunks. It's obviously crude but helpful for insight -- and we're order-of-magnitude enthusiasts (see <a href="http://www.sanjoymahajan.com/works.htm" target="_blank">this</a> and <a href="https://www.weizmann.ac.il/plants/Milo/cbn" target="_blank">this</a> and <a href="https://www.nature.com/news/my-digital-toolbox-back-of-the-envelope-biology-1.17140" target="_blank">this</a>) </div>
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Overall, our results suggest that pathologically mutated islands of brain matter are potentially possessed by all of us. These islands may potentially be sources of protein aggregates, which could spread in the brain and cause neurodegeneration; perhaps they’re regions which could be thought of as randomly triggering pathology sometime over our lives with the rate of triggering proportional to the size of the region. Future work is required to verify this, by direct observation of pathologically mutated islands, and mechanistic studies to quantify how large an island is “large enough” to have a high chance of inducing disease within a human lifespan. </div>
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You can freely access our work, which has recently been published in Nature Communications, as "High prevalence of focal and multi-focal somatic genetic variants in the human brain" <a href="https://www.nature.com/articles/s41467-018-06331-w">https://www.nature.com/articles/s41467-018-06331-w</a> Juvid, Nick and our friends in the Department of Clinical Neurosciences at the University of Cambridge especially Mike, Wei and Patrick.</div>
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Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-9105652697527456286.post-24402175543153452018-10-09T19:24:00.003+01:002018-10-09T19:24:38.819+01:00Towards fully automated remote ecosystem monitoring <div dir="ltr" style="text-align: left;" trbidi="on">
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Natural ecosystems around the world are being impacted by
human activity at an ever-increasing rate. However, we still don’t fully
understand the true extent of our actions on these complex systems, limiting
our ability to develop sustainable, well informed best practices.</div>
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Much of the problem is in collecting sufficient amounts of
data from environments which are often difficult for scientists to access and
survey thoroughly (e.g. polar regions, tropical rainforests, savannas).
Therefore, we have been working on methods of fully-automating ecosystem
monitoring in a cost-effective way, that will provide huge amounts of data on
the health of a remote ecosystem over long time periods, with minimal effort
required by field scientists to maintain the system.</div>
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Our first step towards this goal has been to develop a
device that continuously records data from a variety of sensors (microphones,
cameras, humidity sensors etc.) and uploads the data to the internet directly
from the field using a standard mobile phone internet connection. The device is
also powered by a solar panel setup, meaning that battery replacements are
unnecessary. In theory, once initially set up, this device can sit out in a
remote field site indefinitely, with the data sent straight to scientists almost
instantaneously. Mobile phone connections are patchy at best in remote locations so a key challenge was to have a low-power system that could opportunistically exploit the available mobile signal.</div>
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<span style="font-family: "Calibri",sans-serif; font-size: 11.0pt; line-height: 107%; mso-ansi-language: EN-GB; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">The kit is cheap and open source so you can make your own and you're welcomed to have an explore: <a class="linkBehavior" href="http://www.rpi-eco-monitoring.com/">http://www.rpi-eco-monitoring.com</a> and, if you subscribe, you can read more in a recent New Scientist <a href="https://www.newscientist.com/article/2179850-ai-eavesdrops-on-borneos-rainforests-to-check-on-biodiversity/" target="_blank">article</a>. You can read our full paper for free in Methods
in Ecology and Evolution for more details - <span class="MsoHyperlink"><a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13089">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13089 </a></span></span> </div>
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Of particular interest to our group has been using audio to identify calling
animals in the tropical forests of Borneo. We work at the SAFE project site in
Sabah where ecologists from around the world investigate the effect of logging
and the oil palm industry on the biodiversity of these ancient rainforests. A
growing network of 12 monitoring devices are currently scattered around the
SAFE landscape (see <a href="https://www.imperial.ac.uk/people/r.ewers" target="_blank">Rob Ewers</a>'s work) in areas varying from old-growth forest (with almost no impact
from humans) to oil palm plantations. </div>
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<tr><td class="tr-caption" style="text-align: center;"><b style="mso-bidi-font-weight: normal;">A real-time acoustic
monitoring unit, deployed in the tropical forests of Sabah, Borneo at the SAFE
project site. Data used from these devices helps investigate the effect of the
oil palm industry on the biodiversity found in the region.</b></td></tr>
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We are developing algorithms using a wide array of machine
learning techniques that will automatically listen to the masses of audio from
these monitoring devices in Borneo and give us a real-time measure of the
biodiversity in the different forest locations. With a finer scale
understanding of the full human impact on these fragile ecosystems we can help
inform better sustainable practices for the oil-palm industry to minimise their
damage on the threatened species of this region. Sarab, Lorenzo, Rob and Nick</div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-78771264223505888922018-09-23T17:48:00.001+01:002018-09-23T17:48:55.650+01:00Aging in the Variance: increasing variation between egg-cells with age<div dir="ltr" style="text-align: left;" trbidi="on">
DNA in mitochondria, the powerhouses of the cell, is passed down from mother to child. But there are many mitochondria in each cell, and these mitochondria may have different genetic features. If a mother carries a mixture of mitochondrial DNA (mtDNA) types, this can make it hard to say which features their children will inherit. For mothers carrying a disease-causing mtDNA mutation, this makes family planning and clinical therapies challenging.<br /><br />In particular, the role of a mother's age has long been a mystery. Is the probability of a child inheriting a particular mtDNA feature higher when mothers are younger or older? An answer to this question could help plan clinical strategies to improve fertility and prevent the inheritance of deadly mitochondrial disease.<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEis_eWLaUNEyBdICYxJ6wE3Bubpbe65PbcV4gXi2760REjsQtSom-hWfn9DlaqcntLKjoetk-7ac2OkOGHlHsIC8lawu3zCkpY7m2QaWs7VwnZtZWBvcynNm8C62apa_ko-O23eRBSS3wE2/s1600/natcomms.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="389" data-original-width="400" height="311" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEis_eWLaUNEyBdICYxJ6wE3Bubpbe65PbcV4gXi2760REjsQtSom-hWfn9DlaqcntLKjoetk-7ac2OkOGHlHsIC8lawu3zCkpY7m2QaWs7VwnZtZWBvcynNm8C62apa_ko-O23eRBSS3wE2/s320/natcomms.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Joerg Burgstaller and colleagues (see our blog entry <a href="http://systems-signals.blogspot.com/2014/06/evolutionary-competition-within-our.html" target="_blank">here</a>) previously made a type of mouse that
contains two (apparently benignly differing) types of mitochondria (and their genetic material mtDNA) in
every cell. This means that their egg-cells that make the next
generation also have both types of mtDNA present. Our latest paper
investigates how the proportion of the two types of mtDNA varies within
the ovary, finding that cells become more and more variable with time.</td></tr>
</tbody></table>
<br />To address this, we worked with our excellent collaborators with a combination of maths, statistics, and experiment. Our collaborators used cutting-edge technology to reveal the proportions of two-types of mtDNA in the egg cells of mother mice at a wide range of ages, and in the litters of offspring the mothers produced. This experimental work was the largest-scale study of mammalian mtDNA that we're aware of, involving thousands of observations throughout lifetimes and between generations. In concert, we developed a mathematical model describing the changes to, and inheritance of, mtDNA from mother to offspring. We combined the model and data to learn how different biological processes affect mtDNA through and between generations.<br /><br />We found that the cell-to-cell variability in the proportions of the two types of mtDNA dramatically increased as mothers aged. This means that the probability of inheriting more extreme -- both lower and higher -- levels of a genetic feature increases for older mothers. We also found that different mtDNA mixtures were inherited in different ways - with some mtDNA types favoured for inheritance and some disfavoured. We used our findings to create a way to predict how the risk that offspring would inherit disease-causing mtDNA features changes over time. Moving forward, we're aiming to harness these powerful ways of using large datasets to describe and predict the dynamics of mtDNA inheritance in humans, and to learn what it is about these mtDNA types that predicts their evolution across generations. You can read the article “Large-scale genetic analysis reveals mammalian mtDNA heteroplasmy dynamics and variance increase through lifetimes and generations” for free in Nature Communications <a href="https://www.nature.com/articles/s41467-018-04797-2" target="_blank">here</a>. Iain, Joerg and Nick</div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-38169055425256674452018-01-08T20:10:00.000+00:002018-01-08T20:10:44.164+00:00How cells adapt to progressive increase in mitochondrial mutation<div dir="ltr" style="text-align: left;" trbidi="on">
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<span lang="EN-GB"><span style="color: white;">Mitochondria produce the cell's major
energy currency: ATP. If mitochondria become dysfunctional, this can be
associated with a variety of devastating diseases, from Parkinson's disease to
cancer. Technological advances have allowed us to generate huge volumes of data
about these diseases. However, it can be a challenge to turn these large,
complicated, datasets into basic understanding of how these diseases work, so
that we can come up with rational treatments.<o:p></o:p></span></span></div>
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<span lang="EN-GB"><span style="color: white;">We were interested in a dataset (see <a href="http://www.pnas.org/content/111/38/E4033.long"><span class="InternetLink">here</span></a>)
which measured what happened to cells as their mitochondria became
progressively more dysfunctional. A typical cell has roughly 1000 copies of
mitochondrial DNA (mtDNA), which contains information on how to build some of
the most important parts of the machinery responsible for making ATP in your
cells. When mitochondrial DNA becomes mutated, these instructions accumulate
errors, preventing the cell's energy machinery from working properly. Since
your cells each contain about 1000 copies of mitochondrial DNA, it is
interesting to think about what happens to a cell as the fraction of mutated
mitochondrial DNA (called 'heteroplasmy') gradually increases.<span style="mso-spacerun: yes;"> </span>We used maths to try and explain how a cell
attempts to cope with increasing levels of heteroplasmy, resulting in a wealth
of hypotheses which we hope to explore experimentally in the future.<o:p></o:p></span></span></div>
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<span lang="EN-GB" style="color: white;">The central idea arising from our analysis
of this large dataset is that cells seem to attempt to maintain the number of normal
mtDNAs per cell volume as heteroplasmy initially increases from 0% mutant. We
suggest they do this by shrinking their size. By getting smaller, cells are
able to reduce their energy demands as the fraction of mutant mtDNA increases,
allowing them to balance their energy budget and maintain energy supply =
demand. However, cells can only get so small and eventually the cell must
change its strategy. At a critical fraction of mutated mtDNA (h* in the cartoon
above), we suggest that cells switch on an alternative energy production mode
called glycolysis. This causes energy supply to increase, and as a result,
cells grow larger in size again. These ideas, as well as experimental proposals
to test them, are freely available in the Biochemical Journal "Mitochondrial DNA Density Homeostasis Accounts for a Threshold Effect in a Cybrid Model of a Human Mitochondrial Disease". Juvid, Iain and Nick</span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-23930002631022026212017-08-08T08:47:00.002+01:002017-08-08T08:49:27.691+01:00What we learn from the learning rate<div dir="ltr" style="text-align: left;" trbidi="on">
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<span class="s1">Cells need to sense their environment in order to survive. For example, some cells measure the concentration of food or the presence of signalling molecules. We are interested in studying the physical limits to sensing with limited resources, to understand the challenges faced by cells and to design synthetic sensors.</span></div>
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<span class="s1"><br />
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<span class="s1">We have recently published a <span class="s2"><a href="http://iopscience.iop.org/article/10.1088/1742-5468/aa71d4"><span style="color: #b4a7d6;">paper</span></a></span><span style="color: magenta;"> </span>'What we learn from the learning rate' (<span class="s2"><span style="color: #b4a7d6;">free version</span></span>) where we explore the interpretation of a metric called 'the learning rate' that has been used to measure the quality of a sensor (e.g <span class="s2"><a href="http://iopscience.iop.org/article/10.1088/1367-2630/16/10/103024"><span style="color: #b4a7d6;">here</span></a></span>). Our motivation is that in this field a number of metrics (a metric is a number you can calculate from the properties of the sensor that, ideally, tells you how good the sensor is) have been applied to make some statement about the quality of sensing, or limits to sensory performance. For example, a limit of particular interest is the energy required for sensing. However, it is not always clear how to interpret these metrics. We want to find out what the learning rate means. If one sensor has a higher learning rate than another what does that tell you? </span></div>
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<span class="s1">The learning rate is defined as the rate at which changes in the sensor increase the information the sensor has about the signal. The information the sensor has about the signal is how much your uncertainty about the state of the signal is reduced by knowing the state of the sensor (this is known as the mutual information). From this definition, it seems plausible that the learning rate could be a measure of sensing quality, but it is not clear. Our approach is a test to destruction – challenge the learning rate in a variety of circumstances, and try to understand how it behaves and why.</span></div>
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<span class="s1"><br />
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<span class="s1">To do this we need a framework to model a general signal and sensor system. The signal hops between discrete states and the sensor also hops between discrete states in a way that follows the signal. A simple example is a cell using a surface receptor to detect the concentration of a molecule in its environment.</span></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgqAOfUstm_qwwDkjaIKK78FoCIV6Efg8PYnby4xjtZp3iCgziY3G9Msxsl6Og6vTy07ZRp0V9n13Gqy8Sev9fJksb_9ZTaeeDAeA0EIM_MQN92RISIHrzP4jZK_4CW_QHDpvEFPPOYPlsZ/s1600/statesblog.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="421" data-original-width="702" height="238" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgqAOfUstm_qwwDkjaIKK78FoCIV6Efg8PYnby4xjtZp3iCgziY3G9Msxsl6Og6vTy07ZRp0V9n13Gqy8Sev9fJksb_9ZTaeeDAeA0EIM_MQN92RISIHrzP4jZK_4CW_QHDpvEFPPOYPlsZ/s400/statesblog.png" width="400" /></a></div>
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<span class="s1">The figure shows such a system. The circles represent the states and the arrows represent transitions between the states. The signal is the concentration of a molecule in the cell’s environment. It can be in two states; high or low, where high is double the concentration of low. The sensor is a single cell surface receptor, which can be either unbound or bound to a molecule. Therefore, the joint system can be in four different states. The concentration jumps between its states with rates that don’t depend on the state of the sensor. The receptor becomes unbound at a constant rate and is bound at a rate proportional to the molecule concentration. </span></div>
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<span class="s1">We calculated the learning rate for several systems, including the one above, and compared it to the mutual information between the signal and the sensor (the<span style="color: blue;"> <a href="https://en.wikipedia.org/wiki/Mutual_information" target="_blank"><span style="color: #b4a7d6;">mutual information</span></a></span> is a refined measure of correlation). We found that in the simplest case, shown in the figure, the learning rate essentially reports the correlation between the sensor and the signal and so it is showing you the same thing as the mutual information. In more complicated systems the learning rate and mutual information show qualitatively different behaviour. This is because the learning rate actually reflects the rate at which the sensor must change in response to the signal, which is not, in general, the equivalent to the strength of correlations between the signal and sensor. Therefore, we do not think that the learning rate is useful as a general metric for the quality of a sensor. Rory, Nick and Tom </span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-86279482868252844352017-07-18T09:44:00.000+01:002018-02-27T20:33:53.398+00:00Looplessness<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="color: white;"><b style="text-align: justify;">Complex</b><span style="text-align: justify;"> (adj.): 1.
Consisting of many different and connected parts. ‘A complex network of water
channels’.</span></span></div>
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<span style="color: white;"><o:p></o:p></span></div>
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<span style="color: white;"><i>Oxford English Dictionary</i><o:p></o:p></span></div>
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<span style="color: white;"><span style="mso-bidi-font-family: "Liberation Serif"; mso-fareast-font-family: "Liberation Serif";">‘</span>Complex
systems’ – like cells, the brain or human society – are often defined as those
whose interesting behaviour emerges from the interaction of many connected
elements. A simple but particularly useful representation of almost any complex
system is therefore as a network (aka a graph). When the connections (edges)
between elements (nodes) have a direction, this takes the form of a directed
network. For example, to describe <a href="https://www.blogger.com/null" style="mso-comment-date: 20170717T1427; mso-comment-reference: Office_1;">interactions</a> in an ecosystem, ecologists use directed
networks called food webs, in which each species is a node and directed edges
(usually drawn as arrows) go from prey to their predators. The last two decades
have witnessed a lot of research into the properties of networks, and how their
structure is related to aspects of complex systems, such as their dynamics or
robustness. In the case of ecosystems, it has long been thought that their
remarkable stability – in the sense that they don’t tend to succumb easily to
destructive avalanches of extinctions – must have something to do with their
underlying architecture, especially given May’s paradox: mathematical models
predict that ecosystems should become more unstable with increasing size and
complexity, but this doesn’t seem to happen to, say, rainforests or coral
reefs.<o:p></o:p></span></div>
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<span style="color: white;"><br /></span></div>
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<span style="color: white;"><b>Trophic coherence</b><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">In 2014 we proposed a solution to
May’s paradox: the key structural property of ecosystems was a food-web feature
called “<a href="http://www.pnas.org/content/111/50/17923.long" target="_blank">trophic coherence</a>”. Ecologists classify species by trophic level in
the following way. Plants (nodes with no in-coming edges) have level one,
herbivores (species which only have in-coming edges from plants) are at level
two, and, in general, the level of any species is defined as the average level
of its prey, plus one. Thus, if the network in the top left-hand corner of the
figure below represented a food web, the nodes at the bottom would be plants
(level 1) the next ones up would be herbivores (level 2), the next, primary
carnivores (level 3) and so on. In reality, though, food webs are never quite
so neatly organised, and many species prey on various levels, making food webs
a bit more like the network in the top right-hand corner. Here, most species
have a fractional trophic level. In order to measure this degree of order,
which we called trophic coherence, we attributed to each directed edge a
“trophic difference”, the difference between the levels of the predator and the
prey, and looked at the statistical distribution of differences over all the
edges in the whole network. We called the standard deviation of this
distribution an “incoherence parameter”, <i>q,</i> because a perfectly coherent
network like the one on the left has <i>q</i>=0, while a more incoherent one
like that on the right has <i>q</i>>0 – in this case, <i>q</i>=0.7. <o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">It turns out that the trophic
coherence of food webs is key to their stability, and when we simulated (i.e.
generated in the computer) networks with varying levels of coherence, we found
that, for sufficiently coherent ones, the relationship between size and stability
is inverted. Although there are plenty of caveats to this result – not least
the question how one should measure stability – this suggests a solution to
May’s paradox. Since then, further research has shown that trophic coherence
affects other structural and dynamical properties of networks – for instance,
whether a cascade of activity will propagate through a neural network (example papers <a href="https://arxiv.org/abs/1603.00670" target="_blank">here</a>, <a href="https://arxiv.org/abs/1603.03767" target="_blank">here</a> and <a href="https://arxiv.org/abs/1609.04318" target="_blank">here</a>!). But all these results were somewhat anecdotal, since we didn’t have a
mathematical theory relating trophic coherence to other network features. This
is what we set out to do in our most <a href="https://arxiv.org/abs/1505.07332" target="_blank">recent paper</a>.<o:p></o:p></span></div>
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAesNPDWhO8r35Aw21dhfmXcWdxuk7u9AVh_bnv52w8HCMFIREOlep3yWZs7128ItuNSucVEmyp0DNSO68FdG5HNdUjQoiUt2VhYr-zSqFTEtYnSsqKKbzWDufmTgY_VTUg-JvhbWHQmeD/s1600/blognets.png" imageanchor="1" style="margin-left: auto; margin-right: auto; text-align: center;"><span style="color: white;"><img border="0" data-original-height="537" data-original-width="494" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAesNPDWhO8r35Aw21dhfmXcWdxuk7u9AVh_bnv52w8HCMFIREOlep3yWZs7128ItuNSucVEmyp0DNSO68FdG5HNdUjQoiUt2VhYr-zSqFTEtYnSsqKKbzWDufmTgY_VTUg-JvhbWHQmeD/s1600/blognets.png" /></span></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="color: white; font-size: small;"><b style="font-family: "times new roman", serif; text-align: justify;">Figure. </b><span style="font-family: "times new roman" , serif; text-align: justify;">Four directed networks, plotted so that the height of each node on the vertical axis is proportional in each case to its trophic level. The top two are synthetic networks, generated in a computer with the ‘preferential preying model’, which allows the user to tune trophic coherence [1,3]. Thus, they both have the same numbers of nodes and edges, but the one on the left is perfectly coherent (</span><i style="font-family: "times new roman", serif; text-align: justify;">q</i><span style="font-family: "times new roman" , serif; text-align: justify;">=0) while the one on the right is more incoherent (</span><i style="font-family: "times new roman", serif; text-align: justify;">q</i><span style="font-family: "times new roman" , serif; text-align: justify;">=0.7). The bottom two are empirically derived: the one on the left is the Ythan Estuary food web, which is significantly coherent (it has </span><i style="font-family: "times new roman", serif; text-align: justify;">q</i><span style="font-family: "times new roman" , serif; text-align: justify;">=0.42, which is about 15% of its expected </span><i style="font-family: "times new roman", serif; text-align: justify;">q</i><span style="font-family: "times new roman" , serif; text-align: justify;">) and belongs to the loopless regime; the one on the right is a representation of the </span><i style="font-family: "times new roman", serif; text-align: justify;">Chlamydia pneumoniae</i><span style="font-family: "times new roman" , serif; text-align: justify;"> metabolic network, which is singificantly incoherent (</span><i style="font-family: "times new roman", serif; text-align: justify;">q</i><span style="font-family: "times new roman" , serif; text-align: justify;">=8.98, or about 162% of the random expectation) and sits in the loopful regime. The top two networks are reproduced from the SI of Johnson </span><i style="font-family: "times new roman", serif; text-align: justify;">et al</i><span style="font-family: "times new roman" , serif; text-align: justify;">, </span><i style="font-family: "times new roman", serif; text-align: justify;">PNAS</i><span style="font-family: "times new roman" , serif; text-align: justify;">, 2014 [1], while the bottom two are from the SI of Johnson & Jones, </span><i style="font-family: "times new roman", serif; text-align: justify;">PNAS</i><span style="font-family: "times new roman" , serif; text-align: justify;">, 2017 [5].</span></span></td></tr>
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<span style="color: white;"><br /></span></div>
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<span style="color: white;"><br /></span></div>
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<span style="color: white;"><b>Looplessness</b><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">In statistical physics one thinks
about systems in terms of ensembles – the sets of all possible systems which
satisfy certain constraints – and this method has also been used in graph
theory. For example, the Erdős-Rényi ensemble comprises all possible networks with
given numbers of nodes <i>N</i> and edges <i>L</i>, while the configuration
ensemble also specifies the degree sequence (the degree of a node being its
number of neighbours). We defined the “coherence ensemble” as the set of all
possible directed networks which not only have given <i>N</i>, <i>L</i> and
degree sequences (each node has two degrees in directed networks, one in and
one out) but also specified trophic coherence. This allows us to derive
equations for the expected values of various network properties as a function
of trophic coherence; in other words, these are the values we should expect to
measure in a network given its trophic coherence (and other specified
constraints) if we had no other knowledge about its structure.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">Many network properties are
heavily influenced by cycles – that is, paths through a network which begin and
end at the same node. For example, in a food web you might find that eagles eat
snakes, which eats squirrels, which eat eagles (probably in egg form), thus
forming a cycle of length three. These cycles (properly called ‘directed
cycles’ in directed networks), or loops, are related to various structural and
dynamical features of complex systems. For example, feedback loops can
destabilise ecosystems, mediate self-regulation of genes, or maintain neural
activity in the brain. Furthermore, it had been reported that certain kinds of
network – in particular, food webs and gene regulatory networks – often had
either no cycles at all, or only a small number of quite short cycles. This was
surprising, because in (arbitrarily large) random networks the number of cycles of length <i>l</i> grows exponentially with <i>l</i>, so it was assumed that there must be some
evolutionary reason for this “looplessness”. We were able to use our coherence
ensemble approach to derive the probability with which a randomly chosen path
would be a cycle, as a function of <i>q.</i> From there we could obtain
expected values for the number of cycles of length <i>l</i>, and for other
quantities related to stability (in particular, for the adjacency matrix
eigenspectrum, which captures the total extent of feedback in a system). It
turns out that the number of cycles does indeed depend on length exponentially,
but via a factor τ which is a function of trophic coherence. For sufficiently
coherent networks, <i>τ</i> is negative, and hence the expected number of
cycles of length <i>l</i> falls rapidly to zero. In fact, such networks have a
high chance of being completely acyclic. Thus, our theory predicts that
networks can belong to either of two regimes, depending on the “loop exponent” <i>τ</i>:
a loopful one with lots of feedback, or a loopless one in which networks are
either acyclic or have just a few short cycles. A comparison with a large set
of networks from the real world – including networks of species, genes,
metabolites, neurons, trading nations and English words –<span style="mso-spacerun: yes;"> </span>shows that this is indeed so, and almost all
of them are very close to our expectations given their trophic coherence.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">Our theory can also be used to
see how close quantities such as trophic coherence, or mean trophic level, are
to what would be our random expectations, given just <i>N</i>, <i>L</i> and the
degree sequences, for any real directed network. We found, for example, that in
our dataset the food webs tended to be very coherent, while networks derived
from metabolic reactions were significantly incoherent (see the bottom two
networks in the figure: the one on the left is a food web and the one on the
right is a metabolic network). Our gene regulatory networks are interesting in
that, while often quite coherent in absolute terms, they are in fact very close
to their random expectation.<o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><b>Open questions</b><o:p></o:p></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;"><br /></span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="color: white;">This work leaves open many new
questions. Why are some networks significantly coherent, and others incoherent?
We can guess at the mechanism behind food-web coherence: the adaptations which
allow a given predator, say a wolf, to hunt deer are also useful for catching
prey like goats or elk, which have similar characteristics because they, in
turn, have similar diets – i.e. trophic levels. This correlation between
trophic levels and node function might be more general. For example, we have
shown that in a network of words which are concatenated in a text, trophic
level serves to identify syntactic function, and something similar may occur in
networks of genes or metabolites. If edges tend to form primarily between nodes
with certain functions, this might induce coherence or incoherence. Some
networks, like the artificial neural networks used for “deep learning”, are
deliberately coherent, which suggests another question: how does coherence affect
the performance of different kinds of system? Might there be an optimal level
of trophic coherence for neural networks? And how might it affect financial,
trade, or social networks, which can, in some sense, be considered human
ecosystems? We hope topics such as these will attract the curiosity of other
researchers who can make further inroads. You can read our paper <span style="font-size: 13.3333px;">“Looplessness in networks is linked to trophic coherence” for <a href="https://arxiv.org/abs/1505.07332" target="_blank">free here</a> and also in the journal <a href="http://www.pnas.org/content/114/22/5618.abstract" target="_blank">PNAS</a>. Sam and Nick.</span></span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-55604304807697241142017-07-13T13:55:00.002+01:002017-07-13T13:55:35.541+01:00Mitochondrial heterogeneity, metabolic scaling and cell death<div dir="ltr" style="text-align: left;" trbidi="on">
<br />
<div style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-family: "arial" , sans-serif;"><span style="font-size: small;"><span style="font-weight: normal;">Juvid Aryaman, Hanne Hoitzing, Joerg P. Burgstaller, Iain G. Johnston and Nick S. Jones</span></span></span></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-family: "arial" , sans-serif;"><span style="font-size: small;"><span style="font-weight: normal;"><br /></span></span></span></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<a href="http://onlinelibrary.wiley.com/doi/10.1002/bies.201700001/full"><span style="font-family: "arial" , sans-serif;"><span style="font-size: small;"><span style="font-weight: normal;">http://onlinelibrary.wiley.com/doi/10.1002/bies.201700001/full</span></span></span></a></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-family: "arial" , sans-serif;"><span style="font-size: small;"><span style="font-weight: normal;">Cells
need energy to produce functional machinery, deal with challenges,
and continue to grow and divide -- these activities and others are
collectively referred to as "cell physiology". Mitochondria
are the dominant energy sources in most of our cells, so we'd expect
a strong link between how well mitochondria perform and cell
physiology. Indeed, when mitochondrial energy production is
compromised, deadly diseases can result -- as we've written about
<a href="http://mitomaths.blogspot.co.uk/2016/01/therapies-for-mtdna-disease-models-and.html">before</a>.<br /><br />The
details of this link -- how cells with different mitochondrial
populations may differ physiologically -- is not well understood. A
recent article shed new light on this link by looking at a measure of
mitochondrial functionality in cells of different sizes. They found
what we'll call the "mitopeak" -- mitochondrial
functionality peaks at intermediate cell sizes, with larger and
smaller cells having less functional mitochondria. The subsequent
interpretation was that there is an “optimal”, intermediate, size
for cells. Above this size, it was suggested that a proposed
universal relationship between the energy demands of organisms (from
microorganisms to elephants) and their size predicts the reduction in
the function of mitochondria. Smaller cells, which result from a
large cell having divided, were suggested to have inherited their
parent's low mitochondrial functionality. Cells were predicted to
“reset” their mitochondrial activity as they initially grow and
reach an “optimal” size.<br /><br />We were interested in the
mitopeak, and wondered if scientifically simpler hypotheses could
account for it. Using mathematical modelling, our idea was to use the
observation that as a cell becomes larger in volume, the size of its
mitochondrial population (and hence power supply) increases in
concert. We considered that a cell has power demands which also track
its volume, as well as demands which are proportional to surface area
and power demands which do not depend on cell size at all (such as
the energetic cost of replicating the genome at cell division, since
the size of a cell's genome does not depend on how big the cell is).
Assuming that power supply = demand in a cell, then bigger cells may
more easily satisfy e.g. the constant power demands. This is because
the number of mitochondria increases with cell volume yet the
constant demands remain the same regardless of cell size. In other
words, if a cell has more mitochondria as it gets larger, then each
mitochondrion has to work less hard to satisfy power demand. <br /><br />To
explain why the smallest cells also have mitochondria which do not
appear to work hard, we suggested that some smaller cells could be in
the process of dying. If smaller cells are more likely to die, and if
dying cells have low mitochondrial functionality (both of these ideas
are biologically supported), then, by combining this with the power
supply/demand picture above, the observed mitopeak naturally emerges
from our mathematical model. <br /><br />As an alternative model, we also
suggested that the mitopeak could come entirely from a nonlinear
relationship between cell size and cell death, with mitochondrial
functionality as a passive indicator of how healthy a cell is. This
indicates the existence of multiple hypotheses which could explain
this new dataset.</span></span></span></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<span style="font-family: "arial" , sans-serif;"><span style="font-size: small;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgs637B7V1z3plVF-iZ1qael4jG7itenvPHdakF6yu41gnvRPXfQtUGOsm-ePbb9okxVMHaVslo7_0Ely_-qc8KO8unZXerrx8fvWeMm-vAk3mJTprnGSG3yp6-Z0HbvqNb_xzw9psJbwk/s1600/graphical_abstract_image.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="610" data-original-width="668" height="365" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgs637B7V1z3plVF-iZ1qael4jG7itenvPHdakF6yu41gnvRPXfQtUGOsm-ePbb9okxVMHaVslo7_0Ely_-qc8KO8unZXerrx8fvWeMm-vAk3mJTprnGSG3yp6-Z0HbvqNb_xzw9psJbwk/s400/graphical_abstract_image.png" width="400" /></a></span></span></div>
<br />
<div style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: small;">Interestingly,
we also found that the mitopeak could be an alternative to one aspect
of a model we <a href="http://mitomaths.blogspot.co.uk/2016/01/how-cellular-power-stations-might.html">used
some time ago</a> to explain a different dataset, looking at the
physiological influence of mitochondrial variability. Then, we
modelled the activity of mitochondria as a quantity that is inherited
identically by each daughter cell from its parent, plus some noise --
noting that this was a guess at the true behaviour because we didn't
have the data to make a firm statement. We needed this relationship
because observed functionality varied comparatively little between
sister cells but substantially across a population. The mitopeak
induces this variability without needing random inheritance of
functionality, and may thus be the refined picture we've been looking
for. These ideas, and suggestions for future strategies to explore
the link between mitochondria and cell physiology in more detail, are free in our new BioEssays article "Mitochondrial heterogeneity, metabolic scaling and cell death" <a href="http://onlinelibrary.wiley.com/doi/10.1002/bies.201700001/full">here</a>.
Juvid, Nick, and Iain.</span></span></div>
</div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-40629696107762543942017-01-19T13:12:00.000+00:002017-01-19T13:12:21.250+00:00Using (mutual) information as a chemical battery<div dir="ltr" style="text-align: left;" trbidi="on">
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Biological systems at many scales exploit information to extract energy from their environment. In chemotaxis, single-celled organisms use the location of food molecules to navigate their way to more food; humans use the fact that food is typically found in the cafeteria. Although the general idea is clear, the fundamental physical connection between information and energy is not yet well-understood. In particular, whilst energy is inherently physical, information appears to be an abstract concept, and relating the two consistently is challenging. To overcome this problem, we have designed two microscopic machines that can be assembled out of naturally-occurring biological molecules and exploit information in the environment to perform chemical work.</div>
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<tr><td class="tr-caption" style="text-align: center;">Using chemical correlations as a battery.</td></tr>
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The basic idea behind the machines is simple, and makes use of pre-existing biology. We use an enzyme that can take a small phosphate group from one molecule and attach it to another – a process known as phosphorylation. Phosphorylation is the principal signaling mechanism within a cell, as enzymes called kinases use phosphate molecules to activate other proteins. In addition to signalling, phosphates are one of the cell’s main stores of energy; chains of phosphate bonds in ATP (the cell’s fuel molecule) act as batteries. By ‘recharging’ ATP through phosphorylation, we store energy in a useful format; this is effectively what mitochondria do via a long series of biochemical reactions. In reality energy is stored both in the newly-formed phosphate bond and in the fact that the concentration of ATP has changed. We are only interested in the effects due to concentration so we set up the model to ignore the contribution from bond formation. This can trivially be put back in, as we explain in the Supplementary Material.</div>
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The machines we consider have three main components: the enzyme, the ‘fuel’ molecule that acts as a source of phosphates to charge ATP, and an activator for the enzyme, all of which are sitting in a solution of ATP and its dephosphorylated form ADP. Fuel molecules can either be charged (i.e. have a phosphate attached) or uncharged (without phosphate). When the enzyme is bound to an activator, it allows transfer of a phosphate from a charged fuel molecule to an ADP, resulting in an uncharged fuel molecule and ATP. The reverse reaction is also possible.</div>
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In order to systematically store energy in ATP, we want to activate the enzyme when a charged fuel molecule is nearby. This is possible if we have an excess of charged fuel molecules, or if charged fuel molecules are usually located near activators. In the second case, we're making use of information: the presence of an activator is informative about the possible presence of a charged fuel molecule. This is a very simple analogue of the way that cells and humans use information as outlined above. Indeed, mathematically, the 'mutual information' between the fuel and activator molecules is simply how well the presence of an activator indicates the presence of a charged fuel molecule. This mutual information acts as an additional power supply that we can use to charge our ATP-batteries. We analyse the behaviour of our machines in environments containing information, and find that they can indeed exploit this information, or expend chemical energy in order to generate more information.</div>
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A nice feature of our designs is that they are completely free-running, or autonomous. Like living systems, they can operate without any external manipulation, happily converting between chemical energy and information on its own. There’s still a lot to do on this subject; we have only analysed the simplest kind of information structure possible and have yet to look at more complex spatial or temporal correlations. In addition, our system doesn’t learn, but relies on ‘hard-coded’ knowledge about the relation between fuel and activators. It would be very interesting to see how machines that can learn and harness more complex correlation structures would behave. You can read about this paper <a href="https://arxiv.org/abs/1604.05474" target="_blank">here</a> for free or in <a href="http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.028101" target="_blank">Physical Review Letters</a> under the title 'Biochemical machines for the interconversion of mutual information and work'. Tom, Nick, Pieter Rein, Tom</div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-70786227520567609032016-11-24T20:41:00.000+00:002016-11-25T07:49:11.533+00:00Evolution, Energetics & Noise<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="font-family: "arial" , "helvetica" , sans-serif;">Mitochondrial DNA (mtDNA) contains instructions for building important cellular machines. We have populations of mtDNA inside each of our cells -- almost like a population of animals in an ecosystem. Indeed, mitochondria were originally independent organisms, that billions of years ago were engulfed by our ancestor's cells and survived -- so the picture of mtDNA as a population of critters living inside our cells has evolutionary precedent! MtDNA molecules replicate and degrade in our cells in response to signals passed back and forth between mitochondria and the nucleus (the cell's "control tower"). Describing the behaviour of these population given the random, noisy environment of the cell, the fact that cells divide, and the complicated nuclear signals governing mtDNA populations, is challenging. At the same time, experiments looking in detail at mtDNA inside cells are difficult -- so predictive theoretical descriptions of these populations are highly valuable.</span><span style="font-family: "arial" , "helvetica" , sans-serif;"> </span><br />
<span style="font-family: "arial" , "helvetica" , sans-serif;"><br />Why should we care about these cellular populations? MtDNA can become mutated, wrecking the instructions for building machines. If a high enough proportion of mtDNAs in a cell are mutated, our cells struggle and we get diseases. It only takes a few cells exceeding this "threshold" to cause problems -- so understanding the cell-to-cell distribution of mtDNA is medically important (as well as biologically fascinating). Simple mathematical approaches typically describe only average behaviours -- we need to describe the variability in mtDNA populations too. And for that, we need to account for the random effects that influence them. </span><span style="font-family: "arial" , "helvetica" , sans-serif;"> </span><br />
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<tr><td class="tr-caption" style="text-align: center;">In our cells, signals from the "control tower" nucleus lead to the replication (orange) and degradation (purple) of mtDNA. These processes affect mtDNA populations that may contain normal (blue) and mutant (red) molecules. Our mathematical approach -- extending work addressing a similar but simpler system -- describes how the total number of machines, and the proportion of mutants, is likely to behave and change with time and as cells divide.</td></tr>
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<span style="font-family: "arial" , "helvetica" , sans-serif;"><span style="font-size: xx-small;"> </span><br />In the past, we have used a branch of maths called stochastic processes to answer questions about the random behaviour of mtDNA populations. But these previous approaches cannot account for the "control tower" -- the nucleus' control of mtDNA. To address this, we've developed a mathematical tradeoff -- we make a particular assumption (which we show not to be unreasonable) and in exchange are able to derive a wealth of results about mtDNA behaviour under all sorts of different nuclear control signals. Technically, we use a rather magical-sounding tool called "Van Kampen's system size expansion" to approximate mtDNA behaviour, then explore how the resulting equations behave as time progresses and cells divide.<br /><br />Our approach shows that the cell-to-cell variability in heteroplasmy (the potentially damaging proportion of mutants in a cell) generally increases with time, and surprisingly does so in the same way regardless of how the control tower signals the population. We're able to update a decades-old and commonly-used expression (often called the Wright formula) for describing heteroplasmy variance, so that the formula, instead of being rather abstract and hard to interpret, is directly linked to real biological quantities. We also show that control tower attempts to decrease mutant mtDNA can induce more variability in the remaining "normal" mtDNA population. We link these and other results to biological applications, and show that our approach unifies and generalises many previous models and treatments of mtDNA -- providing a consistent and powerful theoretical platform with which to understand cellular mtDNA populations. The article is in the American Journal of Human Genetics <a href="http://www.cell.com/ajhg/fulltext/S0002-9297%2816%2930397-4">here</a> and a preprint version can be viewed <a href="http://biorxiv.org/content/early/2016/08/30/072363">here</a>. Crossed from <a href="http://mitomaths.blogspot.co.uk/" target="_blank">here</a>.</span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-39095588368233639032016-11-24T19:57:00.000+00:002016-11-24T19:57:55.500+00:00The largest survey of opinions on vaccine confidence<div dir="ltr" style="text-align: left;" trbidi="on">
<span style="font-family: inherit;"><span style="font-size: 16px;">Monitoring trust in immunisation programmes is essential if we are to identify areas and socioeconomic groups that are prone to vaccine-scepticism, and also if we are to forecast these levels of mistrust. Identification of vaccine-sceptic groups is especially important as clustering of non-vaccinators in social networks can serve to disproportionately lower the required vaccination levels for collective (or herd) immunity. To investigate these regions and socioeconomic groups, we performed a large-scale, data-driven study on attitudes towards vaccination. The survey — which we believe to be the largest on attitudes to vaccinations to date with responses from 67,000 people from 67 countries — was conducted by WIN Gallup International Association and probed respondents’ vaccine views by asking them to rate their agreement with the following statements: “vaccines are important for children to have”; “overall I think vaccines are safe”; “overall I think vaccines are effective”; and “vaccines are compatible with my religious beliefs”.</span><br style="font-size: 16px;" /><br style="font-size: 16px;" /><span style="font-size: 16px;">Our results show that attitudes vary by country, socioeconomic group, and between survey questions (where respondents are more likely to agree that vaccines are important than safe). Vaccine-safety related sentiment is particularly low in the European region, which has seven of the ten least confident countries, including France, where 41% of respondents disagree that vaccines are safe. Interestingly, the oldest age group — who may have been more exposed to the havoc that vaccine-preventable diseases can cause — hold more positive views on vaccines than the young, highlighting the association between perceived danger and pro-vaccine views. Education also plays a role. Individuals with higher levels of education are more likely to view vaccines as important and effective, but higher levels of education appear not to influence views on vaccine safety.</span></span><br />
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<tr><td class="tr-caption" style="font-size: 12.8px;"><span style="background-color: white; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 12px; text-align: left;">Vaccine World map of percentage negative ("tend to disagree" or "strongly agree") survey responses to the statement "overall I think vaccines are safe"</span></td></tr>
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<span style="font-size: 16px;"></span><span style="font-family: inherit;"><br style="font-size: 16px;" /><span style="font-size: 16px;">Our study, "The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey" can be read for free in the journal EBioMedicine <a href="http://www.ebiomedicine.com/article/S2352-3964(16)30398-X/fulltext" target="_blank">here</a> </span><span style="font-size: 16px;">with a commentary <a href="http://www.ebiomedicine.com/article/S2352-3964(16)30404-2/fulltext" target="_blank">here</a>. You can find other treatments in <a href="http://www.sciencemag.org/news/2016/09/france-most-skeptical-country-about-vaccine-safety" target="_blank">Science magazine</a>, New Scientist, Financial Times, Le Monde and Scientific American. Alex, Iain, and Nick.</span></span></div>
Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-9105652697527456286.post-5155809921835629882016-08-31T14:09:00.000+01:002016-08-31T14:09:02.747+01:00Understanding the strength and correlates of immunisation programmes<div dir="ltr" style="text-align: left;" trbidi="on">
Childhood vaccinations are vital for the protection of children against dreadful diseases such as measles, polio, and diphtheria. In addition to providing personal protection, vaccines can also suppress epidemic outbreaks if a sufficiently large proportion of the population has immunity status – this “herd immunity” is important for society as many individuals are unable to vaccinate for medical reasons. Over the past half a century, public health organisations have made concerted efforts to vaccinate every child worldwide. However, notwithstanding the substantial improvements to vaccine coverage rates across the globe over the past few decades, there are still millions of unvaccinated children worldwide. The majority of these children live in countries where large numbers of the populations live in deprived, rural regions with poor access to healthcare. However, a number of children are denied vaccines because of parental attitudes and beliefs (which are often influenced by the media, religious groups, or anti-vaccination groups) – such hesitancy has been responsible for recent outbreaks in developing (e.g. Nigeria, Pakistan, Afghanistan) and developed (e.g. USA, UK) countries alike. Monitoring vaccine coverage rates, summarising recent vaccination behaviours, and understanding the factors which drive vaccination behaviour are thus key to our understanding vaccine acceptance, and can allow immunisation programmes to be more effectively tailored.<br />
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To understand these pertinent issues, we used machine learning tools on publicly-available vaccination and socioeconomic data (which can be found <a href="http://www.gapminder.org/" target="_blank">here</a> and the World Health Organization’s <a href="http://www.who.int/immunization/monitoring_surveillance/routine/coverage/en/" target="_blank">websites</a>). We used <a href="http://www.gaussianprocess.org/gpml/chapters/RW2.pdf" target="_blank">Gaussian process regression</a> to forecast vaccine coverage rates and used the predictive distributions over forecasted coverage rates to introduce a quantitative marker summarising a country’s recent vaccination trends and variability: this summary is termed the Vaccine Performance Index. Parameterisations of this index can then be used to identify countries which are likely (over next few years) to have vaccine coverage rates far from those required for herd immunity levels or that are displaying worrying declines in rates and to assess which countries will miss immunisation goals set by global public health bodies. We find that these poorly-performing countries were mostly located in South-East Asia and sub-Saharan Africa though, surprisingly, a handful of European countries also perform poorly.<br />
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To investigate the factors associated with vaccination coverage, we sought links between socioeconomic factors with vaccine coverage and found that countries with higher levels of births attended by skilled health staff, gross domestic product, government health spending, and higher education levels have higher vaccination coverage levels (though these results are region-dependent).<br />
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Our vaccine performance index could aid policy makers’ assessments of the strength and resilience of immunisation programmes. Further, identification of socioeconomic correlates of vaccine coverage points to factors to address to improve vaccination coverage. You can read further in our freely available paper – which is in collaboration with the London School of Hygiene and Tropical Medicine (Heidi Larson and David Smith) and IIT Delhi (Sumeet Agarwal) – in the open-access journal Lancet Global Health under the title “<a href="http://thelancet.com/journals/langlo/article/PIIS2214-109X(16)30167-X/fulltext" target="_blank">Forecasted trends in vaccination coverage and correlations with socioeconomic factors: a global time-series analysis over 30 years</a>” and there is another free article unpacking it under the title "<a href="http://thelancet.com/journals/langlo/article/PIIS2214-109X(16)30185-1/fulltext" target="_blank">Global Trends in Vaccination Coverage</a>". Alex, Iain, Nick.<br />
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-9105652697527456286.post-7462862497002312672016-01-10T14:27:00.000+00:002016-08-15T13:16:38.484+01:00Energetic arguments constraining complex fungal systems<div dir="ltr" style="text-align: left;" trbidi="on">
Fungi are ubiquitous and ecologically important organisms that grow over the resources they consume. Fungi decompose everything from dead trees to dung, but whatever substrate they consume, fungi are obliged to spend energy on growth, reproduction, and substrate digestion. Many fungi also recycle their own biomass to fuel further growth. Within this overall framework, each fungal species adopts a different strategy, depending on the relative investment in growth, recycling, digestion and reproduction. Collectively, these strategies determine ecologically critical rates of carbon and nutrient cycling, including rates of decomposition and CO2 release. Crucially, a given fungus will encounter more of a resource if it increases its growth rate, and it will obtain energy from that resource more rapidly if it increases its investment in transporters and digestive enzymes. However, any energy that is expended on growth or resource acquisition cannot be spent on spore production, so fungi necessarily confront trade-offs between these three essential processes.<br />
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To understand these trade-offs we developed an energy budget model which uses a common energy currency to systematically explore how different rates of growth, recycling, and investments in resource acquisition affect the amount of energy available for reproduction, and how those trade-offs are affected by characteristics of the resource environment. Our model helps to explain the complex range of strategies adopted by various fungi. In particular, it shows that recycling is only beneficial for fungi growing on recalcitrant, nutrient-poor substrates, and that when the timescale of reproduction is large compared to the time required for the fungus to double in size, the total energy available for reproduction will be maximal when a very small fraction of the energy budget is spent on reproduction. You can read about this free under the title "<a href="http://www.journals.uchicago.edu/doi/10.1086/684392" target="_blank">Energetic Constraints on Fungal Growth</a>" and it appears in the glamorously titled American Naturalist. Luke, Mark and Nick</div>
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