Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

Analysis of skeletal muscle function in the C57BL6/SV129 syncoilin knockout mouse

Syncoilin is a 64-kDa intermediate filament protein expressed in skeletal muscle and enriched at the perinucleus, sarcolemma, and myotendinous and neuromuscular junctions. Due to its pattern of cellular localization and binding partners, syncoilin is an ideal candidate to be both an important structural component of myocytes and a potential mediator of inherited myopathies. Here we present a report of a knockout mouse model for syncoilin and the results of an investigation into the effect of a syncoilin null state on striated muscle function in 6–8-week-old mice. An analysis of proteins known to associate with syncoilin showed that ablation of syncoilin had no effect on absolute expression or spatial localization of desmin or alpha dystrobrevin. Our syncoilin-null animal exhibited no differences in cardiotoxin-induced muscle regeneration, voluntary wheel running, or enforced treadmill exercise capacity, relative to wild-type controls. Finally, a mechanical investigation of isolated soleus and extensor digitorum longus indicated a potential differential reduction in muscle strength and resilience. We are the first to present data identifying an increased susceptibility to muscle damage in response to an extended forced exercise regime in syncoilin-deficient muscle. This study establishes a second viable syncoilin knockout model and highlights the importance of further investigations to determine the role of syncoilin in skeletal muscle

A Corpus of Potentially Contradictory Research Claims from Cardiovascular Research Abstracts

Background: Research literature in biomedicine and related fields contains a huge number of claims, such as the effectiveness of treatments. These claims are not always consistent and may even contradict each other. Being able to identify contradictory claims is important for those who rely on the biomedical literature. Automated methods to identify and resolve them are required to cope with the amount of information available. However, research in this area has been hampered by a lack of suitable resources. We describe a methodology to develop a corpus which addresses this gap by providing examples of potentially contradictory claims and demonstrate how it can be applied to identify these claims from Medline abstracts related to the topic of cardiovascular disease. Methods A set of systematic reviews concerned with four topics in cardiovascular disease were identified from Medline and analysed to determine whether the abstracts they reviewed contained contradictory research claims. For each review, annotators were asked to analyse these abstracts to identify claims within them that answered the question addressed in the review. The annotators were also asked to indicate how the claim related to that question and the type of the claim. Results: A total of 259 abstracts associated with 24 systematic reviews were used to form the corpus. Agreement between the annotators was high, suggesting that the information they provided is reliable. Conclusions: The paper describes a methodology for constructing a corpus containing contradictory research claims from the biomedical literature. The corpus is made available to enable further research into this area and support the development of automated approaches to contradiction identification

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

Activity and Habitat Use of Chimpanzees (Pan troglodytes verus) in the Anthropogenic Landscape of Bossou, Guinea, West Africa

Many primate populations inhabit anthropogenic landscapes. Understanding their long-term ability to persist in such environments and associated real and perceived risks for both primates and people is essential for effective conservation planning. Primates in forest–agricultural mosaics often consume cultivars to supplement their diet, leading to potentially negative encounters with farmers. When crossing roads, primates also face the risk of encounters with people and collision with vehicles. Chimpanzees (Pan troglodytes verus) in Bossou, Guinea, West Africa, face such risks regularly. In this study, we aimed to examine their activity budget across habitat types and the influence of anthropogenic risks associated with cultivated fields, roads, and paths on their foraging behavior in noncultivated habitat. We conducted 6-h morning or afternoon follows daily from April 2012 to March 2013. Chimpanzees preferentially used forest habitat types for traveling and resting and highly disturbed habitat types for socializing. Wild fruit and crop availability influenced seasonal habitat use for foraging. Overall, chimpanzees preferred mature forest for all activities. They showed a significant preference for foraging at >200 m from cultivated fields compared to 0–100 m and 101–200 m, with no effect of habitat type or season, suggesting an influence of associated risk. Nevertheless, the chimpanzees did not actively avoid foraging close to roads and paths. Our study reveals chimpanzee reliance on different habitat types and the influence of human-induced pressures on their activities. Such information is critical for the establishment of effective land use management strategies in anthropogenic landscapes

Extragalactic Radio Continuum Surveys and the Transformation of Radio Astronomy

Next-generation radio surveys are about to transform radio astronomy by discovering and studying tens of millions of previously unknown radio sources. These surveys will provide new insights to understand the evolution of galaxies, measuring the evolution of the cosmic star formation rate, and rivalling traditional techniques in the measurement of fundamental cosmological parameters. By observing a new volume of observational parameter space, they are also likely to discover unexpected new phenomena. This review traces the evolution of extragalactic radio continuum surveys from the earliest days of radio astronomy to the present, and identifies the challenges that must be overcome to achieve this transformational change.Comment: To be published in Nature Astronomy 18 Sept 201

Destabilization of the Dystrophin-Glycoprotein Complex without Functional Deficits in α-Dystrobrevin Null Muscle

α-Dystrobrevin is a component of the dystrophin-glycoprotein complex (DGC) and is thought to have both structural and signaling roles in skeletal muscle. Mice deficient for α-dystrobrevin (adbn−/−) exhibit extensive myofiber degeneration and neuromuscular junction abnormalities. However, the biochemical stability of the DGC and the functional performance of adbn−/− muscle have not been characterized. Here we show that the biochemical association between dystrophin and β-dystroglycan is compromised in adbn−/− skeletal muscle, suggesting that α-dystrobrevin plays a structural role in stabilizing the DGC. However, despite muscle cell death and DGC destabilization, costamere organization and physiological performance is normal in adbn−/− skeletal muscle. Our results demonstrate that myofiber degeneration alone does not cause functional deficits and suggests that more complex pathological factors contribute to the development of muscle weakness in muscular dystrophy