MICA*063N has one nucleotide different from MICA*027 at position 184 (C→T) in codon 62 (CAG→TAG), resulting in a premature stop codon in exon 2.
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A parallel CNN architecture for sport activity recognition based on minimal movement data.
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Henan College of Transportation, Zhengzhou, 450000, Henan, China.
Novel Human Activity Recognition (HAR) methodologies, which are built upon learning algorithms and employ ubiquitous sensors, have achieved remarkable precision in the identification of sports activities. Such progress benefits all age groups of humanity, and in the future, AI will be used to address difficult problems in scientific research. A novel approach is introduced in this article to utilize motion sensor data in order to categorize and distinguish various categories of sports activities.
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Shaanxi Key Laboratory of Natural Products & Chemical Biology, College of Chemistry & Pharmacy, Northwest A&F University, Yangling, Shaanxi, 712100, China.
Marine cyclopianes are a family of diterpenoid with novel carbon skeleton and diverse biological activities. Herein, we report our synthetic and chemical proteomics studies of cyclopiane diterpenes which culminate in the asymmetric total synthesis of conidiogenones C, K and 12β-hydroxy conidiogenone C, and identification of Immunity-related GTPase family M protein 1 (IRGM1) as a cellular target. Our asymmetric synthesis commences from Wieland-Miescher ketone and features a sequential intramolecular Pauson-Khand reaction and gold-catalyzed Nazarov cyclization to rapidly construct the 6-5-5-5 tetracyclic skeleton.
View Article and Find Full Text PDFHaplotype-Resolved Genotyping and Association Analysis of 1,020 β-Thalassemia Patients by Targeted Long-Read Sequencing.
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View Article and Find Full Text PDFA comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer.
Brief Bioinform
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Department of Microbiology and Immunology, University at Buffalo, The State University of New York, 955 Main Street, Buffalo, New York, NY 14203, United States.
Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels.
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