Child–Adult Differences in Using Dual-Task Paradigms to Measure Listening Effort

The purpose of the project was to investigate the effects modifying the secondary task in a dual-task paradigm to measure objective listening effort. To be specific, the complexity and depth of processing were increased relative to a simple secondary task

Child–Adult Differences in Using Dual-Task Paradigms to Measure Listening Effort

The purpose of the project was to investigate the effects modifying the secondary task in a dual-task paradigm to measure objective listening effort. To be specific, the complexity and depth of processing were increased relative to a simple secondary task

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma

We describe the landscape of somatic genomic alterations of 66 chromophobe renal cell carcinomas (ChRCCs) based on multidimensional and comprehensive characterization, including mitochondrial DNA (mtDNA) and whole genome sequencing. The result is consistent that ChRCC originates from the distal nephron compared to other kidney cancers with more proximal origins. Combined mtDNA and gene expression analysis implicates changes in mitochondrial function as a component of the disease biology, while suggesting alternative roles for mtDNA mutations in cancers relying on oxidative phosphorylation. Genomic rearrangements lead to recurrent structural breakpoints within TERT promoter region, which correlates with highly elevated TERT expression and manifestation of kataegis, representing a mechanism of TERT up-regulation in cancer distinct from previously-observed amplifications and point mutations

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc