To improve performance and reduce power, processor designers employ advances that shrink feature sizes, lower voltage levels, reduce noise margins, and increase clock rates. However, these advances make processors more susceptible to transient faults that can affect correctness. While reliable systems typically employ hardware techniques to address soft-errors, software techniques can provide a lower-cost and more flexible alternative. This paper presents a novel, software-only, transient-fault-detection technique, called SWIFT. SWIFT efficiently manages redundancy by reclaiming unused instruction-level resources present during the execution of most programs. SWIFT also provides a high level of protection and performance with an enhanced control-flow checking mechanism. We evaluate an implementation of SWIFT on an Itanium 2 which demonstrates exceptional fault coverage with a reasonable performance cost. Compared to the best known single-threaded approach utilizing an ECC memory system, SWIFT demonstrates a 51% average speedup.

Autism spectrum disorders (ASD) are characterized by both phenotypic and genetic heterogeneity. Our analysis of functional networks perturbed in ASD suggests that both truncating and non-truncating de novo mutations contribute to autism, although there is a strong bias against truncating mutations in early embryonic development. We find that functional mutations are preferentially observed in genes likely to be haploinsufficient. Multiple cell types and brain areas are affected, but the impact of ASD mutations appears to be strongest in the cortical neurons and the medium spiny neurons of the striatum, implicating corticostriatal brain circuits. In females, truncating ASD mutations on average impact genes with 50–100% higher brain expression levels compared to males. Our study also suggests that truncating de novo mutations play a smaller role in the etiology of high-functioning ASD cases. Overall, we find that stronger functional insults usually lead to more severe intellectual, social and behavioral ASD phenotypes.

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS309 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Despite the successful identification of several relevant genomic loci, the underlying molecular mechanisms of schizophrenia remain largely unclear. We developed a computational approach (NETBAG+) that allows an integrated analysis of diverse disease-related genetic data using a unified statistical framework. The application of this approach to schizophrenia-associated genetic variations, obtained using unbiased whole-genome methods, allowed us to identify several cohesive gene networks related to axon guidance, neuronal cell mobility, synaptic function and chromosomal remodeling. The genes forming the networks are highly expressed in the brain, with higher brain expression during prenatal development. The identified networks are functionally related to genes previously implicated in schizophrenia, autism and intellectual disability. A comparative analysis of copy number variants associated with autism and schizophrenia suggests that although the molecular networks implicated in these distinct disorders may be related, the mutations associated with each disease are likely to lead, at least on average, to different functional consequences.

Even as modern computing systems allow the manipulation and distribution of massive amounts of information

In conclusion, patients with BMS showed significantly higher levels of cortisol in UWS and of 17β-estradiol in SWS compared with controls.

Summary Vaccinia virus capping enzyme is a heterodimer of D1 (844-aa) and D12 (287-aa) polypeptides that executes all three steps in m7GpppRNA synthesis. The D1 subunit comprises an N-terminal RNA triphosphatase (TPase)–guanylyltransferase (GTase) module and a C-terminal guanine-N7-methyltransferase (MTase) module. The D12 subunit binds and allosterically stimulates the MTase module. Crystal structures of the complete D1•D12 heterodimer disclose the TPase and GTase as members of the triphosphate tunnel metalloenzyme and covalent nucleotidyltransferase superfamilies, respectively, albeit with distinctive active site features. An extensive TPase-GTase interface clamps the GTase nucleotidyltransferase and OB domains in a closed conformation around GTP. Mutagenesis confirms the importance of the TPase-GTase interface for GTase activity. The D1•D12 structure complements and rationalizes four decades of biochemical studies of this enzyme (the first capping enzyme to be purified and characterized) and provides new insights to the origins of the capping systems of other large DNA viruses.

Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora.In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.