Inferring Meaningful Communities from Topology-Constrained Correlation Networks

Community structure detection is an important tool in graph analysis. This can be done, among other ways, by solving for the partition set which optimizes the modularity scores . Here it is shown that topological constraints in correlation graphs induce over-fragmentation of community structures. A refinement step to this optimization based on Linear Discriminant Analysis (LDA) and a statistical test for significance is proposed. In structured simulation constrained by topology, this novel approach performs better than the optimization of modularity alone. This method was also tested with two empirical datasets: the Roll-Call voting in the 110th US Senate constrained by geographic adjacency, and a biological dataset of 135 protein structures constrained by inter-residue contacts. The former dataset showed sub-structures in the communities that revealed a regional bias in the votes which transcend party affiliations. This is an interesting pattern given that the 110th Legislature was assumed to be a highly polarized government. The -amylase catalytic domain dataset (biological dataset) was analyzed with and without topological constraints (inter-residue contacts). The results without topological constraints showed differences with the topology constrained one, but the LDA filtering did not change the outcome of the latter. This suggests that the LDA filtering is a robust way to solve the possible over-fragmentation when present, and that this method will not affect the results where there is no evidence of over-fragmentation.     

Extracting Labeled Topological Patterns from Samples of Networks

An advanced graph theoretical approach is introduced that enables a higher level of functional interpretation of samples of directed networks with identical fixed pairwise different vertex labels that are drawn from a particular population. Compared to the analysis of single networks, their investigation promises to yield more detailed information about the represented system. Often patterns of directed edges in sample element networks are too intractable for a direct evaluation and interpretation. The new approach addresses the problem of simplifying topological information and characterizes such a sample of networks by finding its locatable characteristic topological patterns. These patterns, essentially sample-specific network motifs with vertex labeling, might represent the essence of the intricate topological information contained in all sample element networks and provides as well a means of differentiating network samples. Central to the accurateness of this approach is the null model and its properties, which is needed to assign significance to topological patterns. As a proof of principle the proposed approach has been applied to the analysis of networks that represent brain connectivity before and during painful stimulation in patients with major depression and in healthy subjects. The accomplished reduction of topological information enables a cautious functional interpretation of the altered neuronal processing of pain in both groups.     

Diversity of Archaea Communities within Contaminated Sand Samples from Johnston Atoll

A molecular 16S rRNA gene (SSU rDNA) analysis was performed for the determination of Archaea communities in polycyclic aromatic hydrocarbon (PAH)- and polychlorinated biphenyl (PCB)-contaminated sand samples obtained from Johnston Atoll. The objective of this study was to investigate Archaea community structure and phylogenetic diversity in a PAH- and PCB-contaminated marine environment that may potentially be intrinsically bioremediating these compounds. The clones obtained from this analysis were equally represented between the Crenarchaeota and Euryarchaeota phyla. This isolated marine environment is predominantly reef habitat, suggesting that the xenobiotic compounds introduced over time influenced the community structure of autochthonous Archaea. Phylogenetic diversity within these samples suggests that the resident Archaea populations were only distantly related to cultivated taxa and cloned sequences found in the public domain from both marine and terrestrial origins.     

海南五指山热带雨林区Bt菌株的分离及其新型cry基因鉴定

五指山原始热带雨林区位于海南岛中部,是海南岛地区面积最大的热带原始雨林。我们依据不同的海拔高度对五指山原始雨林区进行了系统的取样,共采集了234份土壤样品,采用醋酸钠-高温处理的方法分离出各类产芽孢杆菌886株,并通过显微镜观察鉴定出产伴胞晶体蛋白的Bt菌株21株,其Bt菌株分离率为2.3%。为了进一步挖掘五指山丰富的Bt杀虫基因资源,还利用PCR方法结合测序技术对21株Bt分离株的cry基因进行了鉴定,鉴定结果显示cry1、cry4、cry39、cry40、cry31和cry46等基因型存在,表明五指山热带Bt菌株含有丰富cry基因资源,这将为为进一步开展Bt杀虫基因的分离克隆打下基础。     

Reconstructing visual experiences from brain activity evoked by natural movies

Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3-5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6-8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.     

Identifying Cytochrome P450 Functional Networks and Their Allosteric Regulatory Elements

Cytochrome P450 (CYP) enzymes play key roles in drug metabolism and adverse drug-drug interactions. Despite tremendous efforts in the past decades, essential questions regarding the function and activity of CYPs remain unanswered. Here, we used a combination of sequence-based co-evolutionary analysis and structure-based anisotropic thermal diffusion (ATD) molecular dynamics simulations to detect allosteric networks of amino acid residues and characterize their biological and molecular functions. We investigated four CYP subfamilies (CYP1A, CYP2D, CYP2C, and CYP3A) that are involved in 90% of all metabolic drug transformations and identified four amino acid interaction networks associated with specific CYP functionalities, i.e., membrane binding, heme binding, catalytic activity, and dimerization. Interestingly, we did not detect any co-evolved substrate-binding network, suggesting that substrate recognition is specific for each subfamily. Analysis of the membrane binding networks revealed that different CYP proteins adopt different membrane-bound orientations, consistent with the differing substrate preference for each isoform. The catalytic networks were associated with conservation of catalytic function among CYP isoforms, whereas the dimerization network was specific to different CYP isoforms. We further applied low-temperature ATD simulations to verify proposed allosteric sites associated with the heme-binding network and their role in regulating metabolic fate. Our approach allowed for a broad characterization of CYP properties, such as membrane interactions, catalytic mechanisms, dimerization, and linking these to groups of residues that can serve as allosteric regulators. The presented combined co-evolutionary analysis and ATD simulation approach is also generally applicable to other biological systems where allostery plays a role.     

Degradation of Methylmercury by Bacteria Isolated from Environmental Samples

A total of 207 bacterial cultures, isolated from environmental samples, was screened for ability to degrade methylmercury. Of these, 30 were found positive for aerobic demethylation. Twenty-two of these were shown to be facultative anaerobes and 21 of these degraded methylmercury anaerobically. All positive species volatilized methylmercury aerobically, and methane was produced as a degradation product. Although methylmercury degradation was complete in most cases, material balances indicated some of the inorganic mercury formed was not volatilized and is presumed bound to the cells. All positive isolates were tolerant to at least 0.5 mug of methylmercury per ml, and the extent of volatilization of mercury increased with concentration to the threshold value. The results indicate that demethylating species are prevalent in the environment and may be important in suppressing the methylmercury content of sediments.     

Mapping Yoruba Networks: Power and Agency in the Making of Transnational Communities

Mapping Yoruba Networks: Power and Agency in the Making of Transnational Communities . Kamari Maxine Clarke. Durham, NC: Duke University Press, 2004. 345 pp.     

Microbial Communities and Exopolysaccharides from Polynesian Mats

Microbial mats present in two shallow atolls of French Polynesia were characterized by high amounts of exopolysaccharides associated with cyanobacteria as the predominating species. Cyanobacteria were found in the first centimeters of the gelatinous mats, whereas deeper layers showing the occurrence of the sulfate reducers Desulfovibrio and Desulfobacter species as determined by the presence of specific biomarkers. Exopolysaccharides were extracted from these mats and partially characterized. All fractions contained both neutral sugars and uronic acids with a predominance of the former. The large diversity in monosaccharides can be interpreted as the result of exopolymer biosynthesis by either different or unidentified cyanobacterial species. Received July 25, 2000; accepted October 21, 2000     

Isolation and Characterization of Bluetongue Virus Recovered from Blood Samples by Immunoaffinity Purification

An immunoaffinity chromatography (IAC) method was optimized for the selective capture of bluetongue virus (BTV) from blood samples and isolation of the virus in cell culture. The antibody against BTV core particles (lacking the outer capsid proteins VP2 and VP5) was used for the optimization of IAC technique. The antibody against BTV core particle was conjugated with Protein A-virus complex and the complex was dissociated using elution buffer (4 M MgCl₂ with 75 mM HEPES, pH 6.5). The optimized IAC method specifically purified the BTV without capturing other commonly infecting small ruminant’s viruses like gaotpox virus (GTPV), sheeppox virus (SPPV), Peste des petits ruminants virus (PPRV) and Foot and mouth disease virus (FMDV). The blood samples (n?=?22), positive for BTV antigen in sandwich-ELISA were attempted for virus isolation in the BHK-21 cell using the optimized IAC method. A total of seven BTV were isolated by selective capturing of the virion particles. The isolated viruses were characterized by RNA-PAGE, sequence analysis and serum neutralization test (SNT). Electropherotypic analysis of viral dsRNA in the RNA-PAGE revealed the presence of ten dsRNA segments characteristic of BTV. Out of seven isolates, four isolates were identified as BTV-1 and three isolates were identified as BTV-16 based on nucleotide sequences of segment-2. Phylogenetic analysis of segment-2 nucleotide sequence segregated BTV-1 and BTV-16 isolates to monophyletic cluster at close proximity to other eastern topotype. In SNT, hyperimmune serum (HIS) against BTV-1 neutralized the four BTV-1 isolates up to a titer?>?256 and HIS against BTV-16 neutralized the three BTV-16 isolates up to a titer?>?128. The IAC technique will be useful for the selective capture of BTV from mixed infection (BTV with other small ruminant’s viruses) and isolation from blood sample having low viral load by enrichment.     

Evaluating Communities of Practice and Knowledge Networks: A Systematic Scoping Review of Evaluation Frameworks

Communities of Practice (CoPs) are increasingly considered a part of ecohealth and other sectors such as health care, education, and business. However, there is little agreement on approaches to evaluate the influence and effectiveness of CoPs. The purpose of this review was to understand what frameworks and methods have been proposed or used to evaluate CoPs and/or knowledge networks. The review searched electronic databases in interdisciplinary, health, education, and business fields, and further collected references and forward citations from relevant articles. Nineteen articles with 16 frameworks were included in the synthesis. The purposes of the evaluation frameworks varied; while some focused on assessing the performance of CoPs, several frameworks sought to learn about CoPs and their critical success factors. Nine of the frameworks had been applied or tested in some way, most frequently to guide a case study. With limited applications of the frameworks, strong claims about generalizability could not be made. The review results can inform the development of tailored frameworks. However, there is a need for more detailed and targeted CoP evaluation frameworks, as many imperative CoP evaluation needs would be unmet by the available frameworks.     

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.     

Identifying normal embryos from reciprocal translocation carriers by whole chromosome haplotyping

正Reciprocal translocation is a chromosomal structural abnormality that arises when two non-homologous chromosomes rearrange and attach with each other, an incidence that occurs in about 1/500to 1/625 newborns (Mackie and Scriven, 2002). This event typically does not lead to any significant loss of genetic material, thus reciprocal translocation carriers do not exhibit any severe abnormal