Short structural variants (SSVs) are short genomic variants (<50 bp) other than SNPs. It has been suggested that SSVs contribute to many human complex traits. However, high-throughput analysis of SSVs presents numerous technical challenges. In order to facilitate the discovery and assessment of SSVs, we have developed a prototype bioinformatics tool, "SSV evaluation system," which is a searchable, annotated database of SSVs in the human genome, with associated customizable scoring software that is used to evaluate and prioritize SSVs that are most likely to have significant biological effects and impact on disease risk. This new bioinformatics tool is a component in a larger strategy that we have been using to discover potentially important SSVs within candidate genomic regions that have been identified in genome-wide association studies, with the goal to prioritize potential functional/causal SSVs and focus the follow-up experiments on a relatively small list of strong candidate SSVs. We describe our strategy and discuss how we have used the SSV evaluation system to discover candidate causal variants related to complex neurodegenerative diseases. We present the SSV evaluation system as a powerful tool to guide genetic investigations aiming to uncover SSVs that underlie human complex diseases including neurodegenerative diseases in aging.
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http://dx.doi.org/10.1002/humu.23023 | DOI Listing |
Comput Struct Biotechnol J
December 2024
Department of Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-University of Halle-Wittenberg, Halle (Saale) 06120, Germany.
Reliable in silico prediction of fragment binding modes remains a challenge in current drug design research. Due to their small size and generally low binding affinity, fragments can potentially interact with their target proteins in different ways. In the current study, we propose a workflow aimed at predicting favorable fragment binding sites and binding poses through multiple short molecular dynamics simulations.
View Article and Find Full Text PDFACS Energy Lett
January 2025
Department of Chemistry and Centre for Processable Electronics, Molecular Sciences Research Hub, Imperial College London, London W12 0BZ, U.K.
Antisolvent treatment is used in the fabrication of perovskite films to control grain growth during spin coating. We study widely incorporated aromatic hydrocarbons and aprotic ethers, discussing the origin of their performance differences in 2D/3D Sn perovskite (PEAFASnI) solar cells. Among the antisolvents that we screen, diisopropyl ether yields the highest power conversion efficiency in solar cells.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
April 2024
New York State Psychiatric Institute and Department of Psychiatry, Columbia University Vagelos College of Physicians & Surgeons, New York, NY, United States.
Background: Depression is a major public health concern for adolescents, who exhibit low rates of connection to care despite significant needs. Although barriers to help-seeking such as stigma are well documented, interventions to address stigma and to increase help-seeking behavior are insufficient. Dissemination of short videos in social media offer a promising approach, but designing effective stimuli requires better insight into adolescents' perspectives of their own experiences, barriers, and possible interventions.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
August 2024
Department of Occupational Therapy Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Background: Restricted and repetitive behavior (RRB) is a core symptom of autism spectrum disorder (ASD). The structure of RRB subcategories and their relationship with atypical sensory processing in Japan are not well understood. This study examined subcategories of the RRB in Japanese children with ASD and explored their relationship with sensory processing.
View Article and Find Full Text PDFHeliyon
January 2025
BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
Deformable image registration is a cornerstone of many medical image analysis applications, particularly in the context of fetal brain magnetic resonance imaging (MRI), where precise registration is essential for studying the rapidly evolving fetal brain during pregnancy and potentially identifying neurodevelopmental abnormalities. While deep learning has become the leading approach for medical image registration, traditional convolutional neural networks (CNNs) often fall short in capturing fine image details due to their bias toward low spatial frequencies. To address this challenge, we introduce a deep learning registration framework comprising multiple cascaded convolutional networks.
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