Results 171 to 180 of about 375,599 (291)

SDS‐CRISPR for Single‐Nucleotide Variant Detection

Advanced Science, EarlyView.
Structure‐disruption‐sensitive CRISPR (SDS‐CRISPR) converts structural instability into single‐nucleotide precision, thereby overcoming mismatch tolerance in canonical Cas12a and enabling versatile diagnostics across DNA, RNA, and microRNA targets. When applied to rapid IDH1 mutation detection for glioma genotyping and integrated with lateral‐flow ...
Xin Guan, Chong Guo, Jiongyu Zhang, Rui Yang, Yerramsetti Ramachandra, Chengyu Hou, Minjie Pei, Shuo Zhang, Kurt T. Schalper, Xingye Liu, Qian Wu, Ketan R. Bulsara, Changchun Liu   +12 more
wiley   +1 more source

Profile-guided Hybrid Approach for block-wise missing data handling in multi-omics: a breast cancer case study. [PDF]

BioData Min
Abdelaziz EH, Amin E, Ismail R, Mabrouk M.   +3 more
europepmc   +1 more source

Fosl2 Regulates FSH‐Dependent Follicle Maturation Through Feedback Amplification of FSH/FSHR Signaling

Advanced Science, EarlyView.
This study identifies a FOSL2‐driven positive feedback loop that amplifies FSH/FSHR signaling. During FSH‐dependent follicle maturation, FSH induces Fosl2 expression via the cAMP‐PKA‐CREB cascade. FOSL2 in turn binds the promoters of Fshr and estrogen‐biosynthesis genes to enhance their transcription, thereby increasing Fshr mRNA level and amplifying ...
Hongru Shi, Chaoli Chen, Zaohong Ran, Jianning Liao, Zian Wu, Xiaodong Wang, Yongheng Zhao, Wenkai Ke, Bowen Tan, Yun Liu, Youqiang Su, Wei Ren, Xiang Li, Changjiu He   +13 more
wiley   +1 more source

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation

Advanced Science, EarlyView.
This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean‐Samuel Dupré, Manuel Schröter, J. Gray Camp, Fernando Cardes, Julian Bartram, Andreas Hierlemann   +10 more
wiley   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Authors' reply to: Interpretable multi-task prediction in rhabdomyolysis: promise and caveats. [PDF]

EClinicalMedicine
Liu C, Shi J, Wang F, Li D, Luo Y, Yang B, Zhao Y, Zhang L, Yang D, Jin H, Song J, Guo X, Fan H, Lv Q.   +13 more
europepmc   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood. [PDF]

Stat Med
Xu C, Briollais L, Andrulis IL, Bull SB.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Large-Scale Genotype-Based Trait Imputation With Multi-Ancestry GWAS Data. [PDF]

Genet Epidemiol
Ren J, Pan W.
europepmc   +1 more source