Individual Differences in Learning Social and Non-Social Network Structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between non-social bits of information? Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. We examined individual differences in the ability to learn community structure of networks composed of social versus non-social stimuli. Although participants were able to learn community structure of both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of non-social community structure. Taken together, our results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

SpliceMiner: a high-throughput database implementation of the NCBI Evidence Viewer for microarray splice variant analysis

BACKGROUND: There are many fewer genes in the human genome than there are expressed transcripts. Alternative splicing is the reason. Alternatively spliced transcripts are often specific to tissue type, developmental stage, environmental condition, or disease state. Accurate analysis of microarray expression data and design of new arrays for alternative splicing require assessment of probes at the sequence and exon levels. DESCRIPTION: SpliceMiner is a web interface for querying Evidence Viewer Database (EVDB). EVDB is a comprehensive, non-redundant compendium of splice variant data for human genes. We constructed EVDB as a queryable implementation of the NCBI Evidence Viewer (EV). EVDB is based on data obtained from NCBI Entrez Gene and EV. The automated EVDB build process uses only complete coding sequences, which may or may not include partial or complete 5' and 3' UTRs, and filters redundant splice variants. Unlike EV, which supports only one-at-a-time queries, SpliceMiner supports high-throughput batch queries and provides results in an easily parsable format. SpliceMiner maps probes to splice variants, effectively delineating the variants identified by a probe. CONCLUSION: EVDB can be queried by gene symbol, genomic coordinates, or probe sequence via a user-friendly web-based tool we call SpliceMiner (). The EVDB/SpliceMiner combination provides an interface with human splice variant information and, going beyond the very valuable NCBI Evidence Viewer, supports fluent, high-throughput analysis. Integration of EVDB information into microarray analysis and design pipelines has the potential to improve the analysis and bioinformatic interpretation of gene expression data, for both batch and interactive processing. For example, whenever a gene expression value is recognized as important or appears anomalous in a microarray experiment, the interactive mode of SpliceMiner can be used quickly and easily to check for possible splice variant issues

SpliceCenter: A suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies

<p>Abstract</p> <p>Background</p> <p>Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant.</p> <p>Results</p> <p>A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript.</p> <p>Conclusion</p> <p>SpliceCenter <url>http://discover.nci.nih.gov/splicecenter</url> provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists.</p

RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis

<p>Abstract</p> <p>Background</p> <p>The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.</p> <p>Results</p> <p>We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.</p> <p>Conclusions</p> <p>RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.</p

Potential cellular and biochemical mechanisms of exercise and physical activity on the ageing process

Exercise in young adults has been consistently shown to improve various aspects of physiological and psychological health but we are now realising the potential benefits of exercise with advancing age. Specifically, exercise improves cardiovascular, musculoskeletal, and metabolic health through reductions in oxidative stress, chronic low-grade inflammation and modulating cellular processes within a variety of tissues. In this this chapter we will discuss the effects of acute and chronic exercise on these processes and conditions in an ageing population, and how physical activity affects our vasculature, skeletal muscle function, our immune system, and cardiometabolic risk in older adults

The Somatic Genomic Landscape of Glioblastoma

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies