Are mice good models for human neuromuscular disease? Comparing muscle excursions in walking between mice and humans

The mouse is one of the most widely used animal models to study neuromuscular diseases and test new therapeutic strategies. However, findings from successful pre-clinical studies using mouse models frequently fail to translate to humans due to various factors. Differences in muscle function between the two species could be crucial but often have been overlooked. The purpose of this study was to evaluate and compare muscle excursions in walking between mice and humans

Quantification and analysis of icebergs in a tidewater glacier fjord using an object-based approach

Tidewater glaciers are glaciers that terminate in, and calve icebergs into, the ocean. In addition to the influence that tidewater glaciers have on physical and chemical oceanography, floating icebergs serve as habitat for marine animals such as harbor seals (Phoca vitulina richardii). The availability and spatial distribution of glacier ice in the fjords is likely a key environmental variable that influences the abundance and distribution of selected marine mammals; however, the amount of ice and the fine-scale characteristics of ice in fjords have not been systematically quantified. Given the predicted changes in glacier habitat, there is a need for the development of methods that could be broadly applied to quantify changes in available ice habitat in tidewater glacier fjords. We present a case study to describe a novel method that uses object-based image analysis (OBIA) to classify floating glacier ice in a tidewater glacier fjord from high-resolution aerial digital imagery. Our objectives were to (i) develop workflows and rule sets to classify high spatial resolution airborne imagery of floating glacier ice; (ii) quantify the amount and fine-scale characteristics of floating glacier ice; (iii) and develop processes for automating the object-based analysis of floating glacier ice for large number of images from a representative survey day during June 2007 in Johns Hopkins Inlet (JHI), a tidewater glacier fjord in Glacier Bay National Park, southeastern Alaska. On 18 June 2007, JHI was comprised of brash ice ([Formula: see text] = 45.2%, SD = 41.5%), water ([Formula: see text] = 52.7%, SD = 42.3%), and icebergs ([Formula: see text] = 2.1%, SD = 1.4%). Average iceberg size per scene was 5.7 m2 (SD = 2.6 m2). We estimate the total area (± uncertainty) of iceberg habitat in the fjord to be 455,400 ± 123,000 m2. The method works well for classifying icebergs across scenes (classification accuracy of 75.6%); the largest classification errors occur in areas with densely-packed ice, low contrast between neighboring ice cover, or dark or sediment-covered ice, where icebergs may be misclassified as brash ice about 20% of the time. OBIA is a powerful image classification tool, and the method we present could be adapted and applied to other ice habitats, such as sea ice, to assess changes in ice characteristics and availability

Prediction of susceptibility to major depression by a model of interactions of multiple functional genetic variants and environmental factors

Major depressive disorder (MDD) is the most common psychiatric disorder and the second overall cause of disability. Even though a significant amount of the variance in the MDD phenotype is explained by inheritance, specific genetic variants conferring susceptibility to MDD explain only a minimal proportion of MDD causality. Moreover, genome-wide association studies have only identified two small-sized effect loci that reach genome-wide significance. In this study, a group of Mexican-American patients with MDD and controls recruited for a pharmacogenetic study were genotyped for nonsynonymous single-nucleotide polymorphisms (nsSNPs) and used to explore the interactions of multiple functional genetic variants with risk-classification tree analysis. The risk-classification tree analysis model and linkage disequilibrium blocks were used to replicate exploratory findings in the database of genotypes and phenotypes (dbGaP) for major depression, and pathway analysis was performed to explore potential biological mechanisms using the branching events. In exploratory analyses, we found that risk-classification tree analysis, using 15 nsSNPs that had a nominal association with MDD diagnosis, identified multiple increased-MDD genotype clusters and significant additive interactions in combinations of genotype variants that were significantly associated with MDD. The results in the dbGaP for major depression disclosed a multidimensional dependent phenotype constituted of MDD plus significant modifiers (smoking, marriage status, age, alcohol abuse/dependence and gender), which then was used for the association tree analysis. The reconstructed tree analysis for the dbGaP data showed robust reliability and replicated most of the genes involved in the branching process found in our exploratory analyses. Pathway analysis using all six major events of branching (PSMD9, HSD3B1, BDNF, GHRHR, PDE6C and PDLIM5) was significant for positive regulation of cellular and biological processes that are relevant to growth and organ development. Our findings not only provide important insights into the biological pathways underlying innate susceptibility to MDD but also offer a predictive framework based on interactions of multiple functional genetic variants and environmental factors. These findings identify novel targets for therapeutics and for translation into preventive, clinical and personalized health care

Judgment of Learning Accuracy in High-functioning Adolescents and Adults with Autism Spectrum Disorder

This study explored whether adults and adolescents with autism spectrum disorder (ASD) demonstrate difficulties making metacognitive judgments, specifically judgments of learning. Across two experiments, the study examined whether individuals with ASD could accurately judge whether they had learnt a piece of information (in this case word pairs). In Experiment 1, adults with ASD demonstrated typical accuracy on a standard ‘cue-alone’ judgment of learning (JOL) task, compared to age- and IQmatched neurotypical adults. Additionally, in Experiment 2, adolescents with ASD demonstrated typical accuracy on both a standard ‘cue-alone’ JOL task, and a ‘cue-target’ JOL task. These results suggest that JOL accuracy is unimpaired in ASD. These results have important implications for both theories of metacognition in ASD and educational practise

The C313Y Piedmontese mutation decreases myostatin covalent dimerisation and stability

<p>Abstract</p> <p>Background</p> <p>Myostatin is a key negative regulator of muscle growth and development, whose activity has important implications for the treatment of muscle wastage disorders. Piedmontese cattle display a double-muscled phenotype associated with the expression of C313Y mutant myostatin. <it>In vivo</it>, C313Y myostatin is proteolytically processed, exported and circulated extracellularly but fails to correctly regulate muscle growth. The C313Y mutation removes the C313-containing disulphide bond, an integral part of the characteristic TGF-β cystine-knot structural motif.</p> <p>Results</p> <p>Here we present <it>in vitro </it>analysis of the structure and stability of the C313Y myostatin protein that reveals significantly decreased covalent dimerisation for C313Y myostatin accompanied by a loss of structural stability compared to wild type. The C313Y myostatin growth factor, processed from full length precursor protein, fails to inhibit C2C12 myoblast proliferation in contrast to wild type myostatin. Although structural modeling shows the substitution of tyrosine causes structural perturbation, biochemical analysis of additional disulphide mutants, C313A and C374A, indicates that an intact cystine-knot motif is a major determinant in myostatin growth factor stability and covalent dimerisation.</p> <p>Conclusions</p> <p>This research shows that the cystine-knot structure is important for myostatin dimerisation and stability, and that disruption of this structural motif perturbs myostatin signaling.</p

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal