Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1373/jalm.2016.022517 | DOI Listing |
bioRxiv
October 2024
Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by .
View Article and Find Full Text PDFAm J Hum Genet
November 2024
Department of Clinical and Biomedical Sciences, Medical School, University of Exeter, St Luke's Campus, Magdalen Road, Exeter EX1 2LU, UK. Electronic address:
As more patients receive genome-wide sequencing, the number of individuals diagnosed with multiple monogenic conditions is increasing. We sought to investigate the relative phenotypic contribution of dual diagnoses using both manual curation and computational approaches. First, we computed 1,003,236 semantic similarity scores for all possible pairs of 1,417 genes in the Developmental Disorder Gene2Phenotype (DDG2P) database using Human Phenotype Ontology terms.
View Article and Find Full Text PDFJ Clin Densitom
October 2024
Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON, Canada; KITE Research Institute, University Health Network, Toronto, Canada; Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada. Electronic address:
PLoS One
August 2024
Faculty of Education and Psychology, ELTE Eötvös Loránd University, Budapest, Hungary.
Computational thinking (CT) is a set of problem-solving skills with high relevance in education and work contexts. The present paper explores the role of key cognitive factors underlying CT performance in non-programming university students. We collected data from 97 non-programming adults in higher education in a supervised setting.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!