High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies

Characteristic Evolution and Matching

I review the development of numerical evolution codes for general relativity based upon the characteristic initial value problem. Progress in characteristic evolution is traced from the early stage of 1D feasibility studies to 2D axisymmetric codes that accurately simulate the oscillations and gravitational collapse of relativistic stars and to current 3D codes that provide pieces of a binary black hole spacetime. Cauchy codes have now been successful at simulating all aspects of the binary black hole problem inside an artificially constructed outer boundary. A prime application of characteristic evolution is to extend such simulations to null infinity where the waveform from the binary inspiral and merger can be unambiguously computed. This has now been accomplished by Cauchy-characteristic extraction, where data for the characteristic evolution is supplied by Cauchy data on an extraction worldtube inside the artificial outer boundary. The ultimate application of characteristic evolution is to eliminate the role of this outer boundary by constructing a global solution via Cauchy-characteristic matching. Progress in this direction is discussed.Comment: New version to appear in Living Reviews 2012. arXiv admin note: updated version of arXiv:gr-qc/050809

Drug-microbiota interactions and treatment response: Relevance to rheumatoid arthritis

Knowledge about associations between changes in the structure and/or function of intestinal microbes (the microbiota) and the pathogenesis of various diseases is expanding. However, interactions between the intestinal microbiota and different pharmaceuticals and the impact of these on responses to treatment are less well studied. Several mechanisms are known by which drug-microbiota interactions can influence drug bioavailability, efficacy, and/or toxicity. This includes direct activation or inactivation of drugs by microbial enzymes which can enhance or reduce drug effectiveness. The extensive metabolic capabilities of the intestinal microbiota make it a hotspot for drug modification. However, drugs can also influence the microbiota profoundly and change the outcome of interactions with the host. Additionally, individual microbiota signatures are unique, leading to substantial variation in host responses to particular drugs. In this review, we describe several known and emerging examples of how drug-microbiota interactions influence the responses of patients to treatment for various diseases, including inflammatory bowel disease, type 2 diabetes and cancer. Focussing on rheumatoid arthritis (RA), a chronic inflammatory disease of the joints which has been linked with microbial dysbiosis, we propose mechanisms by which the intestinal microbiota may affect responses to treatment with methotrexate which are highly variable. Furthering our knowledge of this subject will eventually lead to the adoption of new treatment strategies incorporating microbiota signatures to predict or improve treatment outcomes

Non-hexagonal neural dynamics in vowel space

Are the grid cells discovered in rodents relevant to human cognition? Following up on two seminal studies by others, we aimed to check whether an approximate 6-fold, grid-like symmetry shows up in the cortical activity of humans who "navigate" between vowels, given that vowel space can be approximated with a continuous trapezoidal 2D manifold, spanned by the first and second formant frequencies. We created 30 vowel trajectories in the assumedly flat central portion of the trapezoid. Each of these trajectories had a duration of 240 milliseconds, with a steady start and end point on the perimeter of a "wheel". We hypothesized that if the neural representation of this "box" is similar to that of rodent grid units, there should be an at least partial hexagonal (6-fold) symmetry in the EEG response of participants who navigate it. We have not found any dominant n-fold symmetry, however, but instead, using PCAs, we find indications that the vowel representation may reflect phonetic features, as positioned on the vowel manifold. The suggestion, therefore, is that vowels are encoded in relation to their salient sensory-perceptual variables, and are not assigned to arbitrary gridlike abstract maps. Finally, we explored the relationship between the first PCA eigenvector and putative vowel attractors for native Italian speakers, who served as the subjects in our study

Beyond the Jaynes-Cummings model: circuit QED in the ultrastrong coupling regime

In cavity quantum electrodynamics (QED), light-matter interaction is probed at its most fundamental level, where individual atoms are coupled to single photons stored in three-dimensional cavities. This unique possibility to experimentally explore the foundations of quantum physics has greatly evolved with the advent of circuit QED, where on-chip superconducting qubits and oscillators play the roles of two-level atoms and cavities, respectively. In the strong coupling limit, atom and cavity can exchange a photon frequently before coherence is lost. This important regime has been reached both in cavity and circuit QED, but the design flexibility and engineering potential of the latter allowed for increasing the ratio between the atom-cavity coupling rate and the cavity transition frequency above the percent level. While these experiments are well described by the renowned Jaynes-Cummings model, novel physics is expected in the ultrastrong coupling limit. Here, we report on the first experimental realization of a superconducting circuit QED system in the ultrastrong coupling limit and present direct evidence for the breakdown of the Jaynes-Cummings model.Comment: 5 pages, 3 figure

A mild alkali treated jute fibre controlling the hydration behaviour of greener cement paste

To reduce the antagonistic effect of jute fibre on the setting and hydration of jute reinforced cement, modified jute fibre reinforcement would be a unique approach. The present investigation deals with the effectiveness of mild alkali treated (0.5%) jute fibre on the setting and hydration behaviour of cement. Setting time measurement, hydration test and analytical characterizations of the hardened samples (viz., FTIR, XRD, DSC, TGA and free lime estimation) were used to evaluate the effect of alkali treated jute fibre. From the hydration test, the time (t) required to reach maximum temperature for the hydration of control cement sample is estimated to be 860 min, whilst the time (t) is measured to be 1040 min for the hydration of a raw jute reinforced cement sample. However, the time (t) is estimated to be 1020 min for the hydration of an alkali treated jute reinforced cement sample. Additionally, from the analytical characterizations, it is determined that fibre-cement compatibility is increased and hydration delaying effect is minimized by using alkali treated jute fibre as fibre reinforcement. Based on the analyses, a model has been proposed to explain the setting and hydration behaviour of alkali treated jute fibre reinforced cement composite

Systematizing Confidence in Open Research and Evidence (SCORE)

Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral sciences; expert and machine generated estimates of credibility; and, evidence of reproducibility, robustness, and replicability to validate the estimates. Beyond the primary research objective, the data and artifacts generated from this program will be openly shared and provide an unprecedented opportunity to examine research credibility and evidence

The Effect of Epstein-Barr Virus Latent Membrane Protein 2 Expression on the Kinetics of Early B Cell Infection

Infection of human B cells with wild-type Epstein-Barr virus (EBV) in vitro leads to activation and proliferation that result in efficient production of lymphoblastoid cell lines (LCLs). Latent Membrane Protein 2 (LMP2) is expressed early after infection and previous research has suggested a possible role in this process. Therefore, we generated recombinant EBV with knockouts of either or both protein isoforms, LMP2A and LMP2B (Δ2A, Δ2B, Δ2A/Δ2B) to study the effect of LMP2 in early B cell infection. Infection of B cells with Δ2A and Δ2A/Δ2B viruses led to a marked decrease in activation and proliferation relative to wild-type (wt) viruses, and resulted in higher percentages of apoptotic B cells. Δ2B virus infection showed activation levels comparable to wt, but fewer numbers of proliferating B cells. Early B cell infection with wt, Δ2A and Δ2B viruses did not result in changes in latent gene expression, with the exception of elevated LMP2B transcript in Δ2A virus infection. Infection with Δ2A and Δ2B viruses did not affect viral latency, determined by changes in LMP1/Zebra expression following BCR stimulation. However, BCR stimulation of Δ2A/Δ2B cells resulted in decreased LMP1 expression, which suggests loss of stability in viral latency. Long-term outgrowth assays revealed that LMP2A, but not LMP2B, is critical for efficient long-term growth of B cells in vitro. The lowest levels of activation, proliferation, and LCL formation were observed when both isoforms were deleted. These results suggest that LMP2A appears to be critical for efficient activation, proliferation and survival of EBV-infected B cells at early times after infection, which impacts the efficient long-term growth of B cells in culture. In contrast, LMP2B did not appear to play a significant role in these processes, and long-term growth of infected B cells was not affected by the absence of this protein. © 2013 Wasil et al

Combined SVM-CRFs for Biological Named Entity Recognition with Maximal Bidirectional Squeezing

Biological named entity recognition, the identification of biological terms in text, is essential for biomedical information extraction. Machine learning-based approaches have been widely applied in this area. However, the recognition performance of current approaches could still be improved. Our novel approach is to combine support vector machines (SVMs) and conditional random fields (CRFs), which can complement and facilitate each other. During the hybrid process, we use SVM to separate biological terms from non-biological terms, before we use CRFs to determine the types of biological terms, which makes full use of the power of SVM as a binary-class classifier and the data-labeling capacity of CRFs. We then merge the results of SVM and CRFs. To remove any inconsistencies that might result from the merging, we develop a useful algorithm and apply two rules. To ensure biological terms with a maximum length are identified, we propose a maximal bidirectional squeezing approach that finds the longest term. We also add a positive gain to rare events to reinforce their probability and avoid bias. Our approach will also gradually extend the context so more contextual information can be included. We examined the performance of four approaches with GENIA corpus and JNLPBA04 data. The combination of SVM and CRFs improved performance. The macro-precision, macro-recall, and macro-F1 of the SVM-CRFs hybrid approach surpassed conventional SVM and CRFs. After applying the new algorithms, the macro-F1 reached 91.67% with the GENIA corpus and 84.04% with the JNLPBA04 data

Design options, Implementation Issues and Evaluating Success of Ecologically Engineered Shorelines

Human population growth and accelerating coastal development have been the drivers for unprecedented construction of artificial structures along shorelines globally. Construction has been recently amplified by societal responses to reduce flood and erosion risks from rising sea levels and more extreme storms resulting from climate change. Such structures, leading to highly modified shorelines, deliver societal benefits, but they also create significant socioeconomic and environmental challenges. The planning, design and deployment of these coastal structures should aim to provide multiple goals through the application of ecoengineering to shoreline development. Such developments should be designed and built with the overarching objective of reducing negative impacts on nature, using hard, soft and hybrid ecological engineering approaches. The design of ecologically sensitive shorelines should be context-dependent and combine engineering, environmental and socioeconomic considerations. The costs and benefits of ecoengineered shoreline design options should be considered across all three of these disciplinary domains when setting objectives, informing plans for their subsequent maintenance and management and ultimately monitoring and evaluating their success. To date, successful ecoengineered shoreline projects have engaged with multiple stakeholders (e.g. architects, engineers, ecologists, coastal/port managers and the general public) during their conception and construction, but few have evaluated engineering, ecological and socioeconomic outcomes in a comprehensive manner. Increasing global awareness of climate change impacts (increased frequency or magnitude of extreme weather events and sea level rise), coupled with future predictions for coastal development (due to population growth leading to urban development and renewal, land reclamation and establishment of renewable energy infrastructure in the sea) will increase the demand for adaptive techniques to protect coastlines. In this review, we present an overview of current ecoengineered shoreline design options, the drivers and constraints that influence implementation and factors to consider when evaluating the success of such ecologically engineered shoreline