Core Insights - The article highlights significant advancements in various fields of research published in the journal Cell, with a notable contribution from Chinese scholars, indicating a strong presence in cutting-edge scientific research [1]. Group 1: Measles Virus Research - A study by Zhang Heqiao and Roger Kornberg's team elucidated the structure of the measles virus polymerase complex and its interaction with non-nucleoside inhibitors, laying the groundwork for rational antiviral drug design [3][4]. Group 2: AI in Protein Engineering - The research team led by Gao Caixia developed a novel AI protein engineering simulation method called AiCE, which integrates structural and evolutionary constraints, enabling efficient protein evolution simulation and functional design without the need for specialized AI model training [7]. Group 3: Vertebrate Genomics - The team from Zhejiang University introduced a high-throughput, sensitive single-nucleus ATAC sequencing technology (UUATAC-seq) to create chromatin accessibility maps, and developed the Nvwa model for predicting cis-regulatory elements, revealing the conserved syntax of vertebrate regulatory sequences [10][11]. Group 4: Primate Brain Research - A study identified cell type-specific enhancers in the macaque brain, establishing tools for understanding primate brain structure and diseases, which could enhance insights into cognitive functions [15]. Group 5: Peripheral Nerve Imaging - Researchers from the University of Science and Technology of China pioneered a high-speed, subcellular resolution imaging technique for whole-mouse peripheral nerves, providing a detailed peripheral nerve atlas and new tools for studying nerve regulation and disease mechanisms [19]. Group 6: Primate Prefrontal Cortex Connectivity - A study reconstructed the whole-brain connectivity network of the macaque prefrontal cortex at the single-neuron level, revealing refined axon targeting and arborization, which is crucial for understanding complex cognitive functions in primates [23]. Group 7: Optogenetics in Drug Discovery - The research led by Felix Wong developed an optogenetics platform for discovering selective modulators of the integrated stress response, identifying compounds that enhance cell death without toxicity, and demonstrating antiviral activity in a herpes simplex virus mouse model [27][28]. Group 8: Engineering Yeast Behavior - A study from Imperial College London established engineering principles for yeast, enabling programmable multicellular behaviors, transforming yeast from a "single-cell factory" to a "multicellular system chassis" [33][34].

Group 1 - Kunlun Wanwei released and open-sourced Skywork-R1V 3.0, achieving a score of 76.0 in the comprehensive multimodal evaluation MMMU, surpassing closed-source models like Claude-3.7-Sonnet (75.0) and GPT-4.5 (74.4), nearing the level of human junior experts (76.2) [1] - Hugging Face announced the release and open-sourcing of the small parameter model SmolLM3, which supports six languages and features a 128k context window, enabling deep and non-deep reasoning modes [1] - Zhejiang University developed a deep learning AI model named "Nuwa CE" for genomic prediction design, achieving over 90% accuracy in predicting phenotypic changes due to mutations in genomic regulatory regions, with results published in the journal Cell [1] Group 2 - Hugging Face's desktop robot Reachy Mini is now available for order, featuring two versions: Reachy Mini Wireless priced at $449 (approximately 3224 RMB) and Reachy Mini Lite at $299 (approximately 2147 RMB), both designed for developers [1][2] - Both versions of Reachy Mini are open-source DIY kits, comparable in size to a plush toy, equipped with screens and antenna structures, allowing users to program via Python and access over 1.7 million AI models and 400,000 datasets through the Hugging Face Hub [2]

郭国骥团队自主开发了超高通量、超灵敏度的单核染色质可及性测序技术，在这一技术基础上构建了覆 盖小鼠、鸡、守宫、蝾螈和斑马鱼五种代表性脊椎动物的全组织调控元件图谱，形成优质"数据库"，并 开发出多任务深度学习AI模型"女娲CE"，实现从基因组到细胞图谱的直接预测。 "基于大量优质的数据，'女娲CE'模型在多项指标上超越现有的基因组AI模型。"郭国骥介绍，"女娲 CE"能够预测基因组调控元件发生突变之后对各种细胞类型带来的表型变化，经检测准确率超过90%。 江大学郭国骥教授团队开发出一款用于基因组预测设计的深度学习AI模型"女娲CE"，能够以超过90% 的准确率预测基因组调控区域发生突变之后带来的表型变化，并结合疾病表型设计出相应的治疗位点。 8日，相关成果发表于国际学术期刊《细胞》。据介绍，基因组由DNA组成，不仅包含蛋白质的编码序 列，还包含大量不编码蛋白质的调控序列。这两类序列的协同作用，共同决定了生物体的复杂表型特 征。 "基于'女娲CE'预测出的一个镰刀型贫血症治疗性基因位点，我们对该位点进行修改，使得胎儿血红蛋 白表达量得到提升。"郭国骥表示，"女娲CE"系列模型将帮助研究人员更好地理解遗传病发生的复 ...