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Received — 10 September 2026 ⏭

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1

✇Cell Death Discovery nature.com science feeds
By: Wen-Lin Cheng · Yufang Shi · Quan Zhang · Zhiwei Cai · Fei-Xiang Jiang · Xiangjie Kong · Jian-Lei Cao · Li Peng · Ming Chen · Tao He · Hairong Wang
31 August 2026 at 08:00

Cell Death Discovery, Published online: 31 August 2026; doi:10.1038/s41420-026-03309-2

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1
Received — 27 May 2026 ⏭

HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhi (Leo) · Wang · Botao He · Kelin Yu · Seungjae Lee · Ruohan Gao · Furong Huang · Yiannis Aloimonos
26 May 2026 at 12:00
arXiv:2605.24934v1 Announce Type: cross Abstract: Human egocentric video captures rich manipulation demonstrations without any robot hardware, yet transferring these skills to robots remains challenging due to the embodiment gap between human and robot in both visual appearance and kinematics. We present HumanEgo, a framework that bridges the embodiment gap by lifting each human demonstration to an entity-level representation of hand-object interaction, and training a flow matching policy with dense auxiliary objectives that amplify supervision from every trajectory. HumanEgo is robot-data-free, hardware-agnostic, data-efficient, and zero-shot human-to-robot transferable. With only 30 minutes of human videos per task, HumanEgo achieves 92.5% average success across four real-world tasks (75% with just 15 minutes), outperforms matched-time robot teleoperation by 41%, and robustly transfers zero-shot across novel robots, cameras, and environments.

Refined immune-based molecular subtypes of gastric cancer: Integrating mismatch repair status and tumor microenvironment for enhanced immunotherapy prediction

✇Omics in Gastric
By: Ke Shen · Yanqiang Zhang · Zhiqin Wang · Ruixing Xu · Jintao He · Mengya Zhou · Yiming Lu · Danni Zhu · Yizhou Wei · Weiwei Zhu · Qing Yang · Zhiyuan Xu · Can Hu · Siwei Pan · Xiangdong Cheng
18 May 2026 at 18:00

Chin J Cancer Res. 2026 Apr 30;38(2):234-251. doi: 10.21147/j.issn.1000-9604.2026.02.09.

ABSTRACT

OBJECTIVE: Gastric cancer (GC) is heterogeneous, and current mismatch repair (MMR)-based classifications incompletely predict response to immune checkpoint inhibitors (ICIs).

METHODS: RNA sequencing (RNA-seq) and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes (R1-R4) by integrating MMR status, survival, and tumor microenvironment (TME) features. Multi-omics profiling and pathway analysis defined subtype biology. External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction (NTP). Immune response-associated genes were identified from responder vs. non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry (mIHC).

RESULTS: R1 showed the best prognosis and highest immunotherapy response with objective response rate (ORR) 54.5%, while R4 had the worst prognosis. R2 represented an immune-unresponsive deficient mismatch repair (dMMR) subset, and R3 captured an immune-active proficient mismatch repair (pMMR) subgroup with moderate therapy sensitivity. Multi-omics integration revealed subtype-specific pathways (e.g., ECM remodeling in R1, metabolic reprogramming in R2). Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response. Eight immune response-associated genes (e.g., CXCL10, CXCL11, ELN, GAD1, IL32, MT1E, OR2I1P, SLC3A1) were identified and validated by mIHC for predictive relevance.

CONCLUSIONS: This immune-based molecular framework refines risk stratification beyond conventional MMR categories, identifies ICI-sensitive subsets among both dMMR and pMMR tumors, and proposes candidate biomarkers for patient selection.

PMID:42147371 | PMC:PMC13171420 | DOI:10.21147/j.issn.1000-9604.2026.02.09

Received — 17 March 2026 ⏭

Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziwei Wang · Zhentao He · Xingyi He · Hongbin Wang · Tianwang Jia · Jingwei Luo · Siyang Li · Xiaoqing Chen · Dongrui Wu
16 March 2026 at 12:00
arXiv:2603.12296v1 Announce Type: cross Abstract: Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a compelling way to mitigate data scarcity and enhance model capacity. This survey provides a comprehensive review of brain signal generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, and key applications. We systematically categorize existing generative algorithms into four types: knowledge-based, feature-based, model-based, and translation-based approaches. Furthermore, we benchmark existing brain signal generation approaches across four representative BCI paradigms to provide an objective performance comparison. Finally, we discuss the potentials and challenges of current generation approaches and prospect future research on accurate, data-efficient, and privacy-aware BCI systems. The benchmark codebase is publicized at https://github.com/wzwvv/DG4BCI.
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