Modeling based insights into mechanical dysfunction in esophageal motility disorders [0.03%]
基于模型的食管运动障碍机械功能异常研究新见解
Guy Elisha,Sourav Halder,Xinyi Liu et al.
Guy Elisha et al.
Esophageal motility arises from the continuous coupling between enteric neural activity and the organ's mechanical response, yet the structure of this coupling remains poorly understood. Esophageal motility disorders represent mechanical dy...
V-Cornea: A computational model of corneal epithelium homeostasis, injury, and recovery [0.03%]
V-角膜：角膜上皮稳态、损伤和恢复的计算模型
Joel Vanin,Michael Getz,Catherine Mahony et al.
Joel Vanin et al.
Purpose: To develop a computational model that addresses limitations in current ocular irritation assessment methods, particularly regarding long-term effects, and recovery patterns following chemical exposure or trauma t...
Abnormal vasculature reduces overlap between drugs and oxygen in a tumour computational model: Implications for therapeutic efficacy [0.03%]
异常血管减少肿瘤计算模型中药物和氧气的重叠：对治疗效果的影响
Romain Enjalbert,Jakub Köry,Timm Krüger et al.
Romain Enjalbert et al.
The tumour microvasculature is abnormal, and as a consequence oxygen and drug transport to the tumour tissue is impaired. The abnormal microvasculature contributes to tumour tissue hypoxia, as well as to varying drug penetration depth in th...
Long-term perceptual priors drive confidence bias that favors prior-congruent evidence [0.03%]
长期知觉先验驱动偏向一致先验证据的信心偏差
Marika Constant,Elisa Filevich,Pascal Mamassian
Marika Constant
According to the Bayesian framework, both our perceptual decisions and confidence about those decisions are based on the precision-weighted integration of prior expectations and incoming sensory information. While it is generally assumed th...
Nishant Joshi,Sven van Der Burg,Tansu Celikel et al.
Nishant Joshi et al.
Neuronal classification based on morphology, electrophysiology, and molecular markers is often considered static. Here, we challenge this view, showing that functional classification depends on input patterns. Using single-cell recordings f...
DAGFormer: A graph-based domain adaptation approach for single-cell cancer drug response prediction [0.03%]
基于图的域适应方法DAGFormer用于单细胞药物响应预测
Fen Yan,ZhiHua Du,Yu-An Huang
Fen Yan
Developing computational methods for single-cell drug response prediction deepens our understanding of tumor heterogeneity and uncovers resistance mechanisms critical to improving cancer therapy. However, current approaches struggle to full...
ASPEN: Robust detection of allelic dynamics in single cell RNA-seq [0.03%]
ASPEN：单细胞RNA测序中等位基因动态学的稳健检测
Veronika Petrova,Muqing Niu,Thomas S Vierbuchen et al.
Veronika Petrova et al.
Single-cell RNA-seq data from F1 hybrids provides a unique framework for dissecting complex regulatory phenomena, but allelic measurements are limited by technical noise due to low counts. Here, we present ASPEN, a statistical method for mo...
Zero-shot deep learning for the annotation of unknown eDNA sequences using co-occurrences and phylogenetic embeddings [0.03%]
基于共现和系统发育嵌入的未知eDNA序列标注的零样本深度学习方法
Steven Stalder,Théophile Sanchez,Michele Volpi et al.
Steven Stalder et al.
The advent of environmental DNA (eDNA) metabarcoding marks a transformative era in large-scale biodiversity monitoring. However, the analysis of eDNA datasets is limited by incomplete reference databases and the increasing volume of data re...
Democratising high performance computing for bioinformatics through serverless cloud computing: A case study on CRISPR-Cas9 guide RNA design with Crackling Cloud [0.03%]
一种基于无服务器云计算的生物信息学高性能计算民主化研究：带有Crackling Cloud的CRISPR-Cas9向导RNA设计案例分析
Jacob Bradford,Divya Joy,Mattias Winsen et al.
Jacob Bradford et al.
Organisations are challenged when meeting the computational requirements of large-scale bioinformatics analyses using their own resources. Cloud computing has democratised large-scale resources, and to reduce the barriers of working with la...
Stable individualized brain computing model informed by spatiotemporal co-activity patterns [0.03%]
基于时空协同作用模式的稳定个性化脑计算模型
Lan Yang,Jiayu Lu,Xinran Wu et al.
Lan Yang et al.
Accurate simulation of the brain's intrinsic dynamic activity is essential for understanding human cognition and behavior and developing personalized brain disease therapies. Traditional neurodynamics models depend on structural connectivit...