Results 141 to 150 of about 4,446,595 (349)
High‐resolution spatiotemporal transcriptomic data from male and female spinach inflorescences across four key stages first reveal the sex differentiation initiates at the four‐leaf stage and is governed by epigenetic regulation via the SpMSI1‐SpHDT2 complex.
Chen You+10 more
wiley +1 more source
In this study, a comprehensive single‐cell transcriptome atlas of river and swamp buffalo, encompassing 12 tissues and 397,011 cells, is constructed. Differential expression analyses identify metabolic and secretory tissues and cell types that mediate the divergence of milk production.
Dongmei Dai+13 more
wiley +1 more source
Phylogenetic position ofRickettsia tsutsugamushiand the relationship among its antigenic variants by analyses of 16S rRNA gene sequences [PDF]
Norio Ohashi+3 more
openalex +1 more source
Self‐amplifying (saRNA), linear (linRNA), and circular (circRNA) mRNAs are compared under standardized conditions using lipid nanoparticles (LNPs) and pABOL polymer. saRNA achieved superior expression, while linRNA and circRNA performance varied based on untranslated region elements and delivery method.
Irafasha C. Casmil+12 more
wiley +1 more source
Structural Basis of GABAB Receptor Activation during Evolution
This study explores the structural and functional mechanisms of the drosophila GABAB receptor, a key role in neurotransmission. Using cryo‐EM, the research reveals how the receptor's activation differs from its human counterpart, highlighting unique evolutionary features.
Guofei Hou+14 more
wiley +1 more source
The study on the complete mitochondrial genome of Acanthopsetta nadeshnyi and its phylogenetic position. [PDF]
Chae JY+5 more
europepmc +1 more source
The phylogenetic position of the New Zealand batfly,Mystacinobia zelandica(Mystacinobiidae; Oestroidea) inferred from mitochondrial 16S ribosomal DNA sequence data [PDF]
Dianne Gleeson+2 more
openalex +1 more source
Biomolecular Interaction Prediction: The Era of AI
This review offers a thorough examination of recent progress in deep learning for predicting biomolecular interactions, including those involving proteins, nucleic acids, and small molecules. It covers data processing strategies, representative model architectures, and evaluation metrics, while highlighting current methodological limitations.
Haoping Wang, Xiangjie Meng, Yang Zhang
wiley +1 more source