Results 101 to 110 of about 34,284 (266)

Divergent Responses of Bacterial Communities to Permafrost Degradation and Their Associations With Carbon Across Vertical Profiles

Advanced Science, EarlyView.
Bacterial α‐diversity decreases, but stochasticity and community stability increase across the 15 m‐depth vertical profiles and along the degraded gradient within the active layer. The abundance and interaction of core taxa mainly control community stability in the active and permafrost layers, respectively.
Shengyun Chen, Yuzheng Gu, Ali Bahadur, Enyan Liu, Tonghua Wu, Xiaofan Zhu, Yuanqiang Zou, Hewei Liang, Peijie Wei, Linwei Wu, Qingbai Wu, Peizhi Yang, Hongyan Yu, Yunfeng Yang   +13 more
wiley   +1 more source

CDK4/6 Inhibition Induces CD8+ T Cell Antitumor Immunity via MIF‐Induced Functional Orchestration of Tumor‐Associated Macrophages

Advanced Science, EarlyView.
CDK4/6 inhibition promotes CD8+ T cell expansion through tumor‐macrophage crosstalk by activating HIF‐1α and enhancing MIF‐CD44/CD74 signaling. This reprograms TAMs to boost MHC‐I antigen presentation, and CDK4/6 inhibitor‐trained M1 TAM supernatant therapy synergizes with low‐dose PD‐1 blockade to restore antitumor immunity.
Lin He, Yuzhong Peng, Lat‐lun Leong, Jingbo Zhou, Dongyang Tang, Weilu Wang, Xiaoran Wu, Josh haipeng Lei, Yongqin Ye, Yangyang Feng, Yunfeng Qiao, Xiangpeng Chu, Di Mu, Qi Zhao, Tzuming Liu, Yan Chen, Paul Kwonghang Tam, Chu‐Xia Deng   +17 more
wiley   +1 more source

Heuristically Adaptive Diffusion‐Model Evolutionary Strategy

Advanced Science, EarlyView.
Building on the mathematical equivalence between diffusion models and evolutionary algorithms, researchers demonstrate unprecedented control over evolutionary optimization through conditional diffusion. By training diffusion models to associate parameters with specific traits, they can guide evolution toward solutions exhibiting desired behaviors ...
Benedikt Hartl, Yanbo Zhang, Hananel Hazan, Michael Levin   +3 more
wiley   +1 more source

Classifier Adaptive Fusion: Deep Learning for Robust Outdoor Vehicle Visual Tracking

IEEE Access, 2019
Deep auto-encoder (DAE) models have been successfully used in object tracking due to its strong capability of feature representation. However, single deep auto-encoder model would not be robust enough to represent the appearance model of outdoor vehicle ...
Jing Xin, Xing Du, Yaqian Shi
doaj   +1 more source

Telomere‐to‐Telomere Genomes Reveal that Multiscale Evolution Shapes the Largest Metabolic Arsenal of Diaporthe Fungi

Advanced Science, EarlyView.
This study presents the first telomere‐to‐telomere genomes and population resources for Diaporthe pathogens, uncovering the largest known fungal repertoire of secondary metabolite gene clusters. Structural variations and horizontal gene transfer drive cluster diversification, while specific rapidly evolving clusters control virulence, offering novel ...
Kainan Li, Chen Zhang, Zhichao Zhang, Jiaheng Cheng, Qiuyan Gao, Long Gao, Xiaolin Zhao, Wei Yan, Yuanchao Wang, Wenwu Ye   +9 more
wiley   +1 more source

Recognition of cancer mediating genes using MLP-SDAE model

Systems and Soft Computing
This article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques.
Sougata Sheet, Ranjan Ghosh, Anupam Ghosh   +2 more
doaj   +1 more source

Correlated Variational Auto-Encoders

, 2019
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the correlations between data points, which might be crucial for learning latent representations from dataset where a priori ...
Tang, Da, Liang, Dawen, Jebara, Tony, Ruozzi, Nicholas   +3 more
openaire   +2 more sources

ITGB1 Regulates Triple‐Negative Breast Cancer Development by Modulating the Tumor Microenvironment

Advanced Science, EarlyView.
ABSTRACT Tumorigenesis and metastasis are frequently attributed to the intricate interplay between cancer cells and the tumor microenvironment (TME). Comprehending the mechanisms and key regulators of cancer‐immune crosstalk in the TME is imperative for developing efficacious immunotherapy.
Nuozi Song, Siqi Chen, Lei Wang, Jessica Dang, Xu Cao, Stephanie Singh, Lu Yang, Jinhui Wang, Steven T. Rosen, Yingyu Wang, Chun‐Wei D. Chen, Cheng Zhang, Mingye Feng   +12 more
wiley   +1 more source

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Advanced Science, EarlyView.
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu, Kexing Li, Yuge Wang, Hongyu Li, Hongyu Zhao   +4 more
wiley   +1 more source

BGN/MDK Axis in the Melanoma Tumor Microenvironment Strengthens Tumor Malignancy by Modulating Cancer Cells and Cancer‐Associated Fibroblasts Crosstalk

Advanced Science, EarlyView.
This study reveals that m6A regulators cooperatively upregulate BGN in melanoma, promoting malignancy. Within the tumor microenvironment, CAFs show highest BGN expression. The BGN/MDK axis mediates cancer‐stroma crosstalk, driving normal fibroblast (NF) activation and enhancing the pro‐tumor effect of CAFs, highlighting a promising therapeutic target ...
Hao‐ze Shi, Ming‐yang Wu, Jin‐quan Liu, Cuicui Tian, Li Ma, Xue‐mei Zhou, Ze‐Hao Sun, Zhi‐yong Xu, Run‐dong Zhang, Shan‐yuan Ye, Li‐ming Huang, Yan Wang, Jing‐shu Xiong, Wen‐bo Bu, Xian‐feng Cheng, Jian‐fang Sun, Hao Chen   +16 more
wiley   +1 more source