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Genetic diversity and population structure of cowpea mutant collection using SSR and ISSR molecular markers.
Sci Rep
December 2024
Laboratoire Campus de Biotechnologies Végétales, Département de Biologie Végétale, Faculté des Sciences et Techniques, Université Cheikh Anta Diop, Dakar-Fann, Dakar, 10700, Senegal.
Cowpea is a seed legume, important for food and nutritional security in Africa's arid and semi-arid zones. Despite its importance, cowpea is experiencing a loss of genetic diversity due to climate change. Therefore, this study aimed to evaluate the genetic variability of 33 cowpea mutant collections using 20 SSR and 13 ISSR markers.
View Article and Find Full Text PDFHuman migration from the Levant and Arabia into Yemen since Last Glacial Maximum.
Sci Rep
December 2024
College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates.
While a broad consensus about the first successful migration modern humans out of Africa seems established, the peopling of Arabia remains somewhat enigmatic. Identifying the ancestral populations that contributed to the gene pool of the current populations inhabiting Arabia and the impact of their contributions remains a challenging task. We investigate the genetic makeup of the current Yemeni population using 46 whole genomes and 169 genotype arrays derived from Yemeni individuals from all geographic regions across Yemen and 351 genotype arrays derived from neighboring populations providing regional context.
View Article and Find Full Text PDFEnhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation.
Sci Rep
December 2024
Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions.
View Article and Find Full Text PDFMultivariate stochastic modeling for transcriptional dynamics with cell-specific latent time using SDEvelo.
Nat Commun
December 2024
School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
Recently, RNA velocity has driven a paradigmatic change in single-cell RNA sequencing (scRNA-seq) studies, allowing the reconstruction and prediction of directed trajectories in cell differentiation and state transitions. Most existing methods of dynamic modeling use ordinary differential equations (ODE) for individual genes without applying multivariate approaches. However, this modeling strategy inadequately captures the intrinsically stochastic nature of transcriptional dynamics governed by a cell-specific latent time across multiple genes, potentially leading to erroneous results.
View Article and Find Full Text PDFQuantifying the regulatory potential of genetic variants via a hybrid sequence-oriented model with SVEN.
Nat Commun
December 2024
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, 100871, Beijing, China.
Deciphering how noncoding DNA determines gene expression is critical for decoding the functional genome. Understanding the transcription effects of noncoding genetic variants are still major unsolved problems, which is critical for downstream applications in human genetics and precision medicine. Here, we integrate regulatory-specific neural networks and tissue-specific gradient-boosting trees to build SVEN: a hybrid sequence-oriented architecture that can accurately predict tissue-specific gene expression level and quantify the tissue-specific transcriptomic impacts of structural variants across more than 350 tissues and cell lines.
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