Googling Social Interactions: Web Search Engine Based Social Network Construction

Social network analysis has long been an untiring topic of sociology. However, until the era of information technology, the availability of data, mainly collected by the traditional method of personal survey, was highly limited and prevented large-scale analysis. Recently, the exploding amount of automatically generated data has completely changed the pattern of research. For instance, the enormous amount of data from so-called high-throughput biological experiments has introduced a systematic or network viewpoint to traditional biology. Then, is “high-throughput” sociological data generation possible? Google, which has become one of the most influential symbols of the new Internet paradigm within the last ten years, might provide torrents of data sources for such study in this (now and forthcoming) digital era. We investigate social networks between people by extracting information on the Web and introduce new tools of analysis of such networks in the context of statistical physics of complex systems or socio-physics. As a concrete and illustrative example, the members of the 109th United States Senate are analyzed and it is demonstrated that the methods of construction and analysis are applicable to various other weighted networks.     

Identifying and Evaluating Field Indicators of Urogenital Schistosomiasis-Related Morbidity in Preschool-Aged Children

BackgroundSeveral studies have been conducted quantifying the impact of schistosome infections on health and development in school-aged children. In contrast, relatively little is known about morbidity levels in preschool-aged children (≤5 years) who have been neglected in terms of schistosome research and control. The aim of this study was to compare the utility of available point-of-care (POC) morbidity diagnostic tools in preschool versus primary school-aged children (6–10 years) and determine markers which can be used in the field to identify and quantify Schistosoma haematobium-related morbidity.Conclusions/SignificancePreschool-aged children in endemic areas can be effectively screened for schistosome-related morbidity using the same currently available diagnostic tools applicable to older children. UACR for detecting albuminuria is recommended as the best choice for rapid assessment of morbidity attributed to S. haematobium infection in children in the field. The use of dipstick microhaematuria and proteinuria as additional indicators of schistosome-related morbidity would improve the estimation of disease burden in young children.     

ReacKnock: Identifying Reaction Deletion Strategies for Microbial Strain Optimization Based on Genome-Scale Metabolic Network

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.     

基于支持向量机的蛋白质相互作用识别

支持向量机（SVM）是广泛应用于各个领域的分类算法,包括生物信息学。本研究应用SVM作为蛋白质相互作用的分类算法,所用蛋白质相互作用数据下载于墨尼黑生物信息学中心的酿酒酵母数据集,包含有6736条蛋白质,其中相互作用的有4837对,不相互作用的有9674对。提取蛋白质主要结构的电荷和等电位点特征,并应用SVM分类算法对此进行了分类。结果显示,分类的正确率在60％左右,但是较系统发育谱法还是获得了较高的分类正确率。     

Comparing the Ability of Anthropometric Indicators in Identifying Metabolic Syndrome in HIV Patients

Background

Highly active antiretroviral therapy (HAART) can cause side effects in HIV patients, as the metabolic syndrome. Early identification of risk for development of cardiovascular diseases using available reliable and practical methods is fundamental. On this basis, the aim of this study was to compare the effectiveness of anthropometric indicators to identify metabolic syndrome in HIV patients on HAART.

Methods

It is a cross-sectional study. A number of 280 stable HIV patients were studied. It measured weight, height, waist circumference (WC), hip circumference (HP), thigh circumference (TC) and calculated body mass index (BMI), body adiposity index (BAI), waist to hip ratio (WHR) and waist to thigh ratio (WTR). There was also a performance of biochemical tests of lipid profile and fasting glucose. Systemic blood pressure was measured. The criteria proposed by the National Cholesterol Education Program III (NCEP-ATP III) to metabolic syndrome classification was used. Individuals were divided in groups with or without metabolic alterations and their anthropometric indicators were compared. Receiver operating characteristic (ROC) curves were designed for each anthropometric indicator using the metabolic syndrome classification to identify sensitivity and specificity.

Results

WC was a good tool to identify each metabolic disorder separately: total cholesterol (only females, p<0.05), triglycerides (only males, p<0.001), HDL cholesterol (p<0.05), LDL cholesterol (p<005) and fasting glycemic (p<005). WC also showed the best performance to identify metabolic syndrome in both genders (areas under the curve (AUCs): 0.79 and 0.76 for male and female, respectively), while BAI proved to be an inadequate indicator (AUCs: 0.63 and 0.67 for males and females), respectively, in this population.

Conclusions

The central adiposity measure (WC) had the best performance to identify metabolic syndrome, and it is a convenient, cheap and reliable tool that can be used in clinical practice routinely to prevent cardiovascular complications in HIV patients.     

Large Neural Network Simulations on Multiple Hardware Platforms

To efficiently simulate very large networks of interconnected neurons, particular consideration has to be given to the computer architecture being used. This article presents techniques for implementing simulators for large neural networks on a number of different computer architectures. The neuronal simulation task and the computer architectures of interest are first characterized, and the potential bottlenecks are highlighted. Then we describe the experience gained from adapting an existing simulator, SWIM, to two very different architectures–vector computers and multiprocessor workstations. This work lead to the implementation of a new simulation library, SPLIT, designed to allow efficient simulation of large networks on several architectures. Different computer architectures put different demands on the organization of both data structures and computations. Strict separation of such architecture considerations from the neuronal models and other simulation aspects makes it possible to construct both portable and extendible code.     

Identifying Network Motifs that Buffer Front-to-Back Signaling in Polarized Neutrophils

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Role of Nematodes in Soil Health and Their Use as Indicators

The composition of nematode communities (plant-parasitic and free-living) may be used as bioindicators of soil health or condition because composition correlates well with nitrogen cycling and decomposition, two critical ecological processes in soil. Maturity and trophic diversity indices withstand statistical rigor better than do abundances, proportions, or ratios of trophic groups. Maturity indices respond to a variety of land-management practices, based largely on inferred life history characteristics of families. Similarity indices may be more useful than diversity indices because they reflect taxon composition. Improving existing indices or developing alternative indices refined by a greater understanding of the biology of key taxa may enhance the utility of nematodes as bioindicators.     

Identifying the Main Mosquito Species in China Based on DNA Barcoding

Mosquitoes are insects of the Diptera, Nematocera, and Culicidae families, some species of which are important disease vectors. Identifying mosquito species based on morphological characteristics is difficult, particularly the identification of specimens collected in the field as part of disease surveillance programs. Because of this difficulty, we constructed DNA barcodes of the cytochrome c oxidase subunit 1, the COI gene, for the more common mosquito species in China, including the major disease vectors. A total of 404 mosquito specimens were collected and assigned to 15 genera and 122 species and subspecies on the basis of morphological characteristics. Individuals of the same species grouped closely together in a Neighborhood-Joining tree based on COI sequence similarity, regardless of collection site. COI gene sequence divergence was approximately 30 times higher for species in the same genus than for members of the same species. Divergence in over 98% of congeneric species ranged from 2.3% to 21.8%, whereas divergence in conspecific individuals ranged from 0% to 1.67%. Cryptic species may be common and a few pseudogenes were detected.     

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

Introduction

The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics.

Methods

We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students’ beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network.

Results

Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics.

Conclusions

This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student’s perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.     

Bayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Network

Wireless Sensor Network monitor and control the physical world via large number of small, low-priced sensor nodes. Existing method on Wireless Sensor Network (WSN) presented sensed data communication through continuous data collection resulting in higher delay and energy consumption. To conquer the routing issue and reduce energy drain rate, Bayes Node Energy and Polynomial Distribution (BNEPD) technique is introduced with energy aware routing in the wireless sensor network. The Bayes Node Energy Distribution initially distributes the sensor nodes that detect an object of similar event (i.e., temperature, pressure, flow) into specific regions with the application of Bayes rule. The object detection of similar events is accomplished based on the bayes probabilities and is sent to the sink node resulting in minimizing the energy consumption. Next, the Polynomial Regression Function is applied to the target object of similar events considered for different sensors are combined. They are based on the minimum and maximum value of object events and are transferred to the sink node. Finally, the Poly Distribute algorithm effectively distributes the sensor nodes. The energy efficient routing path for each sensor nodes are created by data aggregation at the sink based on polynomial regression function which reduces the energy drain rate with minimum communication overhead. Experimental performance is evaluated using Dodgers Loop Sensor Data Set from UCI repository. Simulation results show that the proposed distribution algorithm significantly reduce the node energy drain rate and ensure fairness among different users reducing the communication overhead.     

Evaluating Network Analysis Indicators of Ecosystem Status in the Gulf of Alaska

This is the first study on the emergent properties for empirical ecosystem models that have been validated by time series information. Ecosystem models of the western and central Aleutian Islands and Southeast Alaska were used to examine indices of ecosystem status generated from network analysis and incorporated into Ecopath with Ecosim. Dynamic simulations of the two ecosystems over the past 40 years were employed to examine if these indices reflect the dissimilar changes that occurred in the ecosystems. The results showed that the total systems throughput (TST) and ascendancy (A) followed the climate change signature (Pacific decadal oscillation, PDO) in both ecosystems, whereas the redundancy (R) followed the inverse trend. The different trajectories for important species such as Steller sea lions (Eumetopias jubatus), Atka mackerel (Pleurogrammus monopterygius), pollock (Theragra chalcograma), herring (Clupea pallasii), Pacific cod (Gadus macrocephalus) and halibut (Hippoglossus stenolepis) were noticeable in the Finn cycling index (FCI), entropy (H) and average mutual information (AMI): not showing large change during the time that the Stellers sea lions, herring, Pacific cod, halibut and arrowtooth flounder (Atheresthes stomias) increased in Southeast Alaska, but showing large declines during the decline of Steller sea lions, sharks, Atka mackerel and arrowtooth flounder in the Aleutians. On the whole, there was a change in the emergent properties of the Aleutians around 1976 that was not seen in Southeast Alaska. Conversely, the emergent properties of both systems showed a change around 1988, which indicated that both systems were unstable after 1988.     

Multiple Ornaments in Male Northern Cardinals, Cardinalis cardinalis, as Indicators of Condition

Investigations of male ornaments in the context of sexual selection have tended to focus on single ornaments, although many species of birds possess multiple ornaments. Understanding the evolution of multiple ornaments requires knowledge of correlations among ornaments in the same individual and the extent to which ornament expression reflects individual condition and behavior. Variation in four male ornaments in socially monogamous, biparental northern cardinals (Cardinalis cardinalis) was related to body size, indices of condition, level of paternal care, and reproductive success. Redness of breast plumage positively predicted body size and negatively predicted nestling feeding rate. Bill color predicted current body condition, with birds with redder bills in better condition. Birds with smaller black face masks had greater reproductive success. These results are consistent with the hypothesis that different ornaments in male cardinals provide information on different aspects of condition and behavior.     

Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.     

The Role of Spatial Configuration in Multiple Identity Tracking

Background

The simultaneous tracking and identification of multiple moving objects encountered in everyday life requires one to correctly bind identities to objects. In the present study, we investigated the role of spatial configuration made by multiple targets when observers are asked to track multiple moving objects with distinct identities.

Methodology/Principal Findings

The overall spatial configuration made by the targets was manipulated: In the constant condition, the configuration remained as a virtual convex polygon throughout the tracking, and in the collapsed condition, one of the moving targets (critical target) crossed over an edge of the virtual polygon during tracking, destroying it. Identification performance was higher when the configuration remained intact than when it collapsed (Experiments 1a, 1b, and 2). Moreover, destroying the configuration affected the allocation of dynamic attention: the critical target captured more attention than did the other targets. However, observers were worse at identifying the critical target and were more likely to confuse it with the targets that formed the virtual crossed edge (Experiments 3–5). Experiment 6 further showed that the visual system constructs an overall configuration only by using the targets (and not the distractors); identification performance was not affected by whether the distractor violated the spatial configuration.

Conclusions/Significance

In sum, these results suggest that the visual system may integrate targets (but not distractors) into a spatial configuration during multiple identity tracking, which affects the distribution of dynamic attention and the updating of identity-location binding.     

Identifying the Role of E2 Domains on Alphavirus Neutralization and Protective Immune Responses

Background

Chikungunya virus (CHIKV) and other alphaviruses are the etiologic agents of numerous diseases in both humans and animals. Despite this, the viral mediators of protective immunity against alphaviruses are poorly understood, highlighted by the lack of a licensed human vaccine for any member of this virus genus. The alphavirus E2, the receptor-binding envelope protein, is considered to be the predominant target of the protective host immune response. Although envelope protein domains have been studied for vaccine and neutralization in flaviviruses, their role in alphaviruses is less characterized. Here, we describe the role of the alphavirus E2 domains in neutralization and protection through the use of chimeric viruses.

Methodology/Principal Findings

Four chimeric viruses were constructed in which individual E2 domains of CHIKV were replaced with the corresponding domain from Semliki Forest virus (SFV) (ΔDomA/ΔDomB/ΔDomC/ ΔDomA+B). Vaccination studies in mice (both live and inactivated virus) revealed that domain B was the primary determinant of neutralization. Neutralization studies with CHIKV immune serum from humans were consistent with mouse studies, as ΔDomB was poorly neutralized.

Conclusions/Significance

Using chimeric viruses, it was determined that the alphavirus E2 domain B was the critical target of neutralizing antibodies in both mice and humans. Therefore, chimeric viruses may have more relevance for vaccine discovery than peptide-based approaches, which only detect linear epitopes. This study provides new insight into the role of alphavirus E2 domains on neutralization determinants and may be useful for the design of novel therapeutic technologies.