A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding

The modulation effect of longitudinal acupuncture on resting state functional connectivity in knee osteoarthritis patients

Recent advances in brain imaging have contributed to our understanding of the neural activity associated with acupuncture treatment. In this study, we investigated functional connectivity across longitudinal acupuncture treatments in older patients with knee osteoarthritis (OA). Over a period of 4 weeks (six treatments), we collected resting state functional magnetic resonance imaging (fMRI) scans from 30 patients before and after their first, third and sixth treatments. Clinical outcome showed a significantly greater pain subscore on the Knee Injury and Osteoarthritis Outcome Score (KOOS) (indicative of improvement) with verum acupuncture than with sham acupuncture. Independent component analysis (ICA) of the resting state fMRI data showed that the right frontoparietal network (rFPN) and the executive control network (ECN) showed enhanced functional connectivity (FC) with the rostral anterior cingulate cortex/medial prefrontal cortex, a key region in the descending pain modulatory system, in the verum groups as compared to the sham group after treatments. We also found that the rFPN connectivity with the left insula is (1) significantly associated with changes in KOOS pain score after treatments, and (2) significantly enhanced after verum acupuncture treatments as compared to sham treatment. Analysis of the acupuncture needle stimulation scan showed that compared with sham treatment, verum acupuncture activated the left operculum/insula, which also overlaps with findings observed in resting state analysis. Our results suggest that acupuncture may achieve its therapeutic effect on knee OA pain by modulating functional connectivity between the rFPN, ECN and the descending pain modulatory pathway. Clinical trial number: NCT0107939

Merged consensus clustering to assess and improve class discovery with microarray data

<p>Abstract</p> <p>Background</p> <p>One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced.</p> <p>Results</p> <p>Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, <it>Drosophila melanogaster</it>.</p> <p>Conclusions</p> <p>Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, <it>clusterCons</it>, is freely available at CRAN and sourceforge under the GNU public licence.</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

A large-scale study on the effects of sex on gray matter asymmetry

Research on sex-related brain asymmetries has not yielded consistent results. Despite its importance to further understanding of normal brain development and mental disorders, the field remains relatively unexplored. Here we employ a recently developed asymmetry measure, based on the Dice coefficient, to detect sex-related gray matter asymmetries in a sample of 457 healthy participants (266 men and 191 women) obtained from 5 independent databases. Results show that women’s brains are more globally symmetric than men’s (p < 0.001). Although the new measure accounts for asymmetries distributed all over the brain, several specific structures were identified as systematically more symmetric in women, such as the thalamus and the cerebellum, among other structures, some of which are typically involved in language production. These sex-related asymmetry differences may be defined at the neurodevelopmental stage and could be associated with functional and cognitive sex differences, as well as with proneness to develop a mental disorder

An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae

Background: Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. Conclusions/Significance: YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.This work was supported by grants from the N.S.F. (IIS-0325116, EIA-0219061), N.I.H. (GM06779-01,GM076536-01), Welch (F-1515), and a Packard Fellowship (EMM). These agencies were not involved in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.Cellular and Molecular Biolog

The Collagen Chaperone HSP47 Is a New Interactor of APP that Affects the Levels of Extracellular Beta-Amyloid Peptides

Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive decline of cognitive function that represents one of the most dramatic medical challenges for the aging population. Aβ peptides, generated by processing of the Amyloid Precursor Protein (APP), are thought to play a central role in the pathogenesis of AD. However, the network of physical and functional interactions that may affect their production and deposition is still poorly understood. The use of a bioinformatic approach based on human/mouse conserved coexpression allowed us to identify a group of genes that display an expression profile strongly correlated with APP. Among the most prominent candidates, we investigated whether the collagen chaperone HSP47 could be functionally correlated with APP. We found that HSP47 accumulates in amyloid deposits of two different mouse models and of some AD patients, is capable to physically interact with APP and can be relocalized by APP overexpression. Notably, we found that it is possible to reduce the levels of secreted Aβ peptides by reducing the expression of HSP47 or by interfering with its activity via chemical inhibitors. Our data unveil HSP47 as a new functional interactor of APP and imply it as a potential target for preventing the formation and/or growth amyloid plaques

Exploration of Shared Genetic Architecture Between Subcortical Brain Volumes and Anorexia Nervosa

In MRI scans of patients with anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknown whether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlying AN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlations ranged from − 0.10 to 0.23 (all p > 0.05). There were some signs of an inverse concordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune system relevant genes, in particular DRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain- and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN

Measurement of charged-particle event shape variables in inclusive root(s)=7 TeV proton-proton interactions with the ATLAS detector

The measurement of charged-particle event shape variables is presented in inclusive inelastic pp collisions at a center-of-mass energy of 7 TeV using the ATLAS detector at the LHC. The observables studied are the transverse thrust, thrust minor, and transverse sphericity, each defined using the final-state charged particles' momentum components perpendicular to the beam direction. Events with at least six charged particles are selected by a minimum-bias trigger. In addition to the differential distributions, the evolution of each event shape variable as a function of the leading charged-particle transverse momentum, charged-particle multiplicity, and summed transverse momentum is presented. Predictions from several Monte Carlo models show significant deviations from data