Can Geographical Factors Determine the Choices of Farmers in the Ethiopian Highlands to Trade in Livestock Markets?

Proximity and affiliation to the local market appear to be two of the most relevant factors to explain farmer's choices to select a particular trading point. Physical barriers may limit the options , especially in developing countries. A network of villages linked by traders/farmer-traders sharing livestock markets was built with field data collected in 75 villages from 8 kebelles in the Wassona Werna wereda of the Ethiopian Highlands. Two exponential random graph models were fitted with various geographical and demographic attributes of the nodes (dyadic independent model) and three internal network structures (dyadic dependent model). Several diagnostic methods were applied to assess the goodness of fit of the models. The odds of an edge where the distance to the main market Debre Behran and the difference in altitude between two connected villages are both large increases significantly so that villages far away from the main market and at different altitude are more likely to be linked in the network than randomly. The odds of forming an edge between two villages in Abamote or Gudoberet kebelles are approximately 75% lower than an edge between villages in any other kebelles (p<0.05). The conditional log-odds of two villages forming a tie that is not included in a triangle, a 2-star or a 3-star is extremely low, increasing the odds significantly (p<0.05) each time a node is in a 2-star structure and decreasing it when a node is in a 3-star (p<0.05) or in a triangle formation (p<0.05)), conditional on the rest of the network. Two major constraining factors, namely distance and altitude, are not deterrent for the potential contact of susceptible small ruminant populations in the Highlands of Ethiopia

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Cognitive function in a randomized trial of evolocumab

Inga Stuķēna as well as a complete list of investigators is provided in the Supplementary Appendix, available at NEJM.org. https://www.nejm.org/doi/suppl/10.1056/NEJMoa1701131/suppl_file/nejmoa1701131_appendix.pdf Funding Information: (Funded by Amgen; EBBINGHAUS ClinicalTrials.gov number, NCT02207634.) Supported by Amgen. We thank Sarah T. Farias, Ph.D., at UC Davis Health for providing the English-language and translated versions of the Everyday Cognition (ECog) tool. Publisher Copyright: Copyright © 2017 Massachusetts Medical Society.BACKGROUND: Findings from clinical trials of proprotein convertase subtilisin–kexin type 9 (PCSK9) inhibitors have led to concern that these drugs or the low levels of low-density lipoprotein (LDL) cholesterol that result from their use are associated with cognitive deficits. METHODS: In a subgroup of patients from a randomized, placebo-controlled trial of evolocumab added to statin therapy, we prospectively assessed cognitive function using the Cambridge Neuropsychological Test Automated Battery. The primary end point was the score on the spatial working memory strategy index of executive function (scores range from 4 to 28, with lower scores indicating a more efficient use of strategy and planning). Secondary end points were the scores for working memory (scores range from 0 to 279, with lower scores indicating fewer errors), episodic memory (scores range from 0 to 70, with lower scores indicating fewer errors), and psychomotor speed (scores range from 100 to 5100 msec, with faster times representing better performance). Assessments of cognitive function were performed at baseline, week 24, yearly, and at the end of the trial. The primary analysis was a noninferiority comparison of the mean change from baseline in the score on the spatial working memory strategy index of executive function between the patients who received evolocumab and those who received placebo; the noninferiority margin was set at 20% of the standard deviation of the score in the placebo group. RESULTS: A total of 1204 patients were followed for a median of 19 months; the mean (±SD) change from baseline over time in the raw score for the spatial working memory strategy index of executive function (primary end point) was −0.21±2.62 in the evolocumab group and −0.29±2.81 in the placebo group (P<0.001 for noninferiority; P=0.85 for superiority). There were no significant between-group differences in the secondary end points of scores for working memory (change in raw score, −0.52 in the evolocumab group and −0.93 in the placebo group), episodic memory (change in raw score, −1.53 and −1.53, respectively), or psychomotor speed (change in raw score, 5.2 msec and 0.9 msec, respectively). In an exploratory analysis, there were no associations between LDL cholesterol levels and cognitive changes. CONCLUSIONS: In a randomized trial involving patients who received either evolocumab or placebo in addition to statin therapy, no significant between-group difference in cognitive function was observed over a median of 19 months.publishersversionPeer reviewe

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models

Across the sciences, the statistical analysis of networks is central to the production of knowledge on relational phenomena. Because of their ability to model the structural generation of networks, exponential random graph models are a ubiquitous means of analysis. However, they are limited by an inability to model networks with valued edges. We solve this problem by introducing a class of generalized exponential random graph models capable of modeling networks whose edges are valued, thus greatly expanding the scope of networks applied researchers can subject to statistical analysis

Gene conversion in human rearranged immunoglobulin genes

Over the past 20 years, many DNA sequences have been published suggesting that all or part of the V&lt;sub&gt;H&lt;/sub&gt; segment of a rearranged immunoglobulin gene may be replaced in vivo. Two different mechanisms appear to be operating. One of these is very similar to primary V(D)J recombination, involving the RAG proteins acting upon recombination signal sequences, and this has recently been proven to occur. Other sequences, many of which show partial V&lt;sub&gt;H&lt;/sub&gt; replacements with no addition of untemplated nucleotides at the V&lt;sub&gt;H&lt;/sub&gt;–V&lt;sub&gt;H&lt;/sub&gt; joint, have been proposed to occur by an unusual RAG-mediated recombination with the formation of hybrid (coding-to-signal) joints. These appear to occur in cells already undergoing somatic hypermutation in which, some authors are convinced, RAG genes are silenced. We recently proposed that the latter type of V&lt;sub&gt;H&lt;/sub&gt; replacement might occur by homologous recombination initiated by the activity of AID (activation-induced cytidine deaminase), which is essential for somatic hypermutation and gene conversion. The latter has been observed in other species, but not in human Ig genes, so far. In this paper, we present a new analysis of sequences published as examples of the second type of rearrangement. This not only shows that AID recognition motifs occur in recombination regions but also that some sequences show replacement of central sections by a sequence from another gene, similar to gene conversion in the immunoglobulin genes of other species. These observations support the proposal that this type of rearrangement is likely to be AID-mediated rather than RAG-mediated and is consistent with gene conversion

Deeper knowledge of shallow waters: reviewing the invertebrate fauna of southern African temporary wetlands

Temporary lentic wetlands are becoming increasingly recognised for their collective role in contributing to biodiversity at the landscape scale. In southern Africa, a region with a high density of such wetlands, information characterising the fauna of these systems is disparate and often obscurely published. Here we provide a collation and synthesis of published research on the aquatic invertebrate fauna inhabiting temporary lentic wetlands of the region. We expose the poor taxonomic knowledge of most groups, which makes it difficult to comment on patterns of richness and endemism

Erroneous attribution of relevant transcription factor binding sites despite successful prediction of cis-regulatory modules

<p>Abstract</p> <p>Background</p> <p><it>Cis</it>-regulatory modules are bound by transcription factors to regulate gene expression. Characterizing these DNA sequences is central to understanding gene regulatory networks and gaining insight into mechanisms of transcriptional regulation, but genome-scale regulatory module discovery remains a challenge. One popular approach is to scan the genome for clusters of transcription factor binding sites, especially those conserved in related species. When such approaches are successful, it is typically assumed that the activity of the modules is mediated by the identified binding sites and their cognate transcription factors. However, the validity of this assumption is often not assessed.</p> <p>Results</p> <p>We successfully predicted five new <it>cis</it>-regulatory modules by combining binding site identification with sequence conservation and compared these to unsuccessful predictions from a related approach not utilizing sequence conservation. Despite greatly improved predictive success, the positive set had similar degrees of sequence and binding site conservation as the negative set. We explored the reasons for this by mutagenizing putative binding sites in three <it>cis</it>-regulatory modules. A large proportion of the tested sites had little or no demonstrable role in mediating regulatory element activity. Examination of loss-of-function mutants also showed that some transcription factors supposedly binding to the modules are not required for their function.</p> <p>Conclusions</p> <p>Our results raise important questions about interpreting regulatory module predictions obtained by finding clusters of conserved binding sites. Attribution of function to these sites and their cognate transcription factors may be incorrect even when modules are successfully identified. Our study underscores the importance of empirical validation of computational results even when these results are in line with expectation.</p

Hostility, Physical Aggression and Trait Anger as Predictors for Suicidal Behavior in Chinese Adolescents: A School-Based Study

Purpose: This study explored the extent to which trait aggression is associated with suicidal behavior in a nationwide school-based sample of adolescents. Methods: A nationwide sample of 14,537 high school students in urban areas of China was recruited. Information concerning suicide ideation, plans, attempts, trait aggression and other risk factors was collected by a self-reported questionnaire. Multivariate regression analyses were employed to predict suicidal behavior. Results: Approximately 18.5 % of students reported suicide ideation, 8.7 % reported suicide plans, and 4.1 % reported attempts during the past one year. Hostility and trait anger had a significant positive association with suicidal ideation. Hostility and physical aggression were positively related to suicide plans. Hostility had a positive correlation with suicide attempts, while trait anger was inversely associated with suicide attempts. Conclusions: This study suggests that hostility, physical aggression and trait anger may be able to be used to predict suicidal behavior among adolescents. Suicide prevention programs should target at attenuating the severity of hostility, anger and physical aggression. But teachers and parents should also give close attention to students with low trait anger

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

<p>Abstract</p> <p>Background</p> <p>We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely <it>social</it>, as revealed based on the local <it>sociality </it>parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: <it>a priori </it>knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.</p> <p>Results</p> <p>We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced <it>social </it>proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. <it>Intrinsically disordered </it>are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity.</p> <p>Conclusions</p> <p>The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.</p