1 Abstract Interactions between proteins and other biomolecules underlie nearly all biological processes, yet designing such interactions de novo remains challenging. Capturing their specific interactions and co-optimizing sequence and structure are difficult and often require extensive computation. We present Protein Hunter, a fast, fine-tuning-free framework for de novo protein design. Starting from an all-X sequence, we find diffusion-based structure prediction models hallucinate reasonable looking structures that can be further improved through iterative sequence re-design and structure re-prediction. This lightweight strategy achieves high AlphaFold3 in silico success rates across both unconditional and conditional generation tasks, including binders to proteins, cyclic peptides, small molecules, DNA, and RNA. Protein Hunter also supports multi-motif scaffolding and partial redesign, providing a general and efficient platform for de novo protein design across diverse molecular targets. <<<

Hui Wang, Yuxin Chen, Jun Lv, Xiaoting Cheng, Qi Cao, Daqi Wang, Longlong Zhang, Biyun Zhu, Min Shen, Chunxin Xu, Mengzhao Xun, Zijing Wang, Honghai Tang, Shaowei Hu, Chong Cui, Luoying Jiang, Yanbo Yin, Luo Guo, Yi Zhou, Lei Han, Ziwen Gao, Jiajia Zhang, Sha Yu, Kaiyu Gao, Jinghan Wang, Bing Chen, Wuqing Wang, Zheng-Yi Chen, Huawei Li, Yilai Shu <<<

AbstractGene therapy is a promising approach for hereditary deafness. We recently showed that unilateral AAV1-hOTOF gene therapy with dual adeno-associated virus (AAV) serotype 1 carrying human OTOF transgene is safe and associated with functional improvements in patients with autosomal recessive deafness 9 (DFNB9). The protocol was subsequently amended and approved to allow bilateral gene therapy administration. Here we report an interim analysis of the single-arm trial investigating the safety and efficacy of binaural therapy in five pediatric patients with DFNB9. The primary endpoint was dose-limiting toxicity at 6 weeks, and the secondary endpoint included safety (adverse events) and efficacy (auditory function and speech perception). No dose-limiting toxicity or serious adverse event occurred. A total of 36 adverse events occurred. The most common adverse events were increased lymphocyte counts (6 out of 36) and increased cholesterol levels (6 out of 36). All patients had bilateral hearing restoration. The average auditory brainstem response threshold in the right (left) ear was >95 dB (>95 dB) in all patients at baseline, and the average auditory brainstem response threshold in the right (left) ear was restored to 58 dB (58 dB) in patient 1, 75 dB (85 dB) in patient 2, 55 dB (50 dB) in patient 3 at 26 weeks, and 75 dB (78 dB) in patient 4 and 63 dB (63 dB) in patient 5 at 13 weeks. The speech perception and the capability of sound source localization were restored in all five patients. These results provide preliminary insights on the safety and efficacy of binaural AAV gene therapy for hereditary deafness. The trial is ongoing with longer follow-up to confirm the safety and efficacy findings. Chinese Clinical Trial Registry registration: ChiCTR2200063181. <<<

Victor R. Ambros, Martin Chalfie, Aric L. Daul, Andrew Z. Fire, David H. Hall, H. Robert Horvitz, Craig C. Mello, Gary Ruvkun, Nathan E. Schroeder, Paul W. Sternberg, Ann E. Rougvie <<<

Experimental organisms such as the nematode Caenorhabditis elegans are fundamental to biological discovery. The success of C. elegans research has been greatly enabled by infrastructure that allows thousands of scientists to share and access research materials and unpublished information efficiently. Here, we celebrate the worm by interweaving vignettes describing four Nobel Prize–winning discoveries with descriptions of how the major NIH-supported research resources—the Caenorhabditis Genetics Center, WormBase, and WormAtlas—provide invaluable support for all C. elegans research. The synergy between investigation and the availability of shared resources for the C. elegans community is a paradigm for all model organism research, and the continued support of such community research resources will be essential for maximizing impactful discoveries in the future. <<<

Hojae Choi, Brian Coventry, Magnus Bauer, Preetham Venkatesh, Anqi Chen, Donghyo Kim, Asim K. Bera, Alex Kang, Hannah Nguyen, Emily Joyce, Bhanumathi Shankaran, Tuscan Rock Thompson, Jacob Merle Gershon, Alexander F. Shida, Gyu Rie Lee, Donald Hilvert, Samuel J. Pellock, David Baker <<<

Abstract Despite advances in de novo enzyme design, success has been largely limited to low energy barrier model reactions. Amide bonds such as those linking amino acids along the peptide backbone are stable for hundreds of years in neutral aqueous solution because of the high energy barrier to hydrolysis 1 . Here we describe the use of a new deep learning method, RFD2-MI 2 , to de novo design enzymes which utilize an activated cysteine nucleophile to hydrolyze the polypeptide backbone in a sequence-dependent manner, achieving rate enhancements over the background reaction ( k cat / k uncat ) of up to 3 × 10 7 . The generated designs have folds very different from the proteases in nature (TM score < 0.50), and crystal structures are very close to the design models (Cα RMSDs < 1.2 Å), highlighting the accuracy of the design methodology. Our approach has broad utility for advancing the design of novel proteases for both biotechnical and medical applications. <<<

The intricate neurofluid dynamics and balance is essential in preserving the structural and functional integrity of the brain. Key among these forces are: hemodynamics, such as heartbeat-driven arterial and venous blood flow, and hydrodynamics, such as cerebrospinal fluid (CSF) circulation. The delicate interplay between these dynamics is crucial for maintaining optimal homeostasis within the brain. Currently, the widely accepted framework for understanding brain functions is the Monro-Kellie’s doctrine, which posits a constant sum of intracranial CSF, blood flow and brain tissue volumes. However, in recent decades, there has been a growing interest in exploring the dynamic interplay between these elements and the impact of external factors, such as daily changes in body position. CSF circulation in particular plays a crucial role in the context of neurodegeneration and dementia, since its dysfunction has been associated with impaired clearance mechanisms and accumulation of toxic substances. Despite the implementation of various invasive and non-invasive imaging techniques to investigate the intracranial hemodynamic or hydrodynamic properties, a comprehensive understanding of how all these elements interact and are influenced by body position remains wanted. Establishing a comprehensive overview of this topic is therefore crucial and could pave the way for alternative care approaches. In this review, we aim to summarize the existing understanding of intracranial hemodynamic and hydrodynamic properties, fundamental for brain homeostasis, along with factors known to influence their equilibrium. Special attention will be devoted to elucidating the effects of body position shifts, given their significance and remaining ambiguities. Furthermore, we will explore recent advancements in imaging techniques utilized for real time and non-invasive measurements of dynamic body fluid properties in-vivo. <<<

An autonomous robotic nut tightening system for a serial manipulator equipped with a parallel gripper is proposed. The system features a hierarchical motion-primitive-based planner and a control-switching scheme that alternates between force and position control. Extensive simulations demonstrate the system's robustness to variance in initial conditions. Additionally, the proposed controller tightens threaded screws 14% faster than the baseline while applying 40 times less contact force on manipulands. For the benefit of the research community, the system's implementation is open-sourced. <<<

Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives <<<

Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins. <<<

Jason Guo, Roger Liang, Andrew Chung, Zhijie Li, Boyuan Li, Eric Chen, Lin Li, Jingjing Wang, Meng-Hsiung Hsieh, Ivy Xiangyi Fang, Benjamin Kroger, Yunguan Wang, Min Zhu, Xiongzhao Ren, Greg Mannino, Yuemeng Jia, Yonglong Wei, Stephen Moore, Daniel J. Siegwart, Stephen S. Chung, Zixi Wang, Tripti Sharma, Suman Komjeti, Yi Han, Purva Gopal, Guanghua Xiao, Tao Wang, Hao Zhu <<<

The origin of cancer is poorly understood because premalignant cells are rarely followed in their native environments. While the spatial compartmentalization of metabolic functions is critical for proper liver function, it is unknown if cancers arise from some zones but not others, and if there are metabolic determinants of cancer risk. Zone-specific, mosaic introduction of Ctnnb1 and Arid2 mutations, commonly co-mutated genes in hepatocellular carcinoma (HCC), showed that position and metabolic context determine clone fates. Ctnnb1/Arid2 -driven cancers were much more likely to arise in zone 3. The zone 3 genes Gstm2 and Gstm3 were required for efficient HCC initiation, in part through inhibition of ferroptosis. In the liver, the zonal determinants of HCC development can reveal metabolic vulnerabilities of cancer. <<<

How are systems supporting high-level cognition organized in the human brain? We hypothesize that cognitive processes involved in understanding people and places are implemented by distinct neural systems with parallel anatomical organization. We test this hypothesis using precision neuroimaging of individual human brains on diverse tasks involving perception and cognition in the domains of familiar people, places, and objects. We find that thinking about people and places elicits responses in distinct areas of high-level association cortex within the default mode network, spanning the frontal, parietal, and temporal lobes. Person- and place-preferring brain regions are systematically spatially adjacent across cortical zones. These areas have strongly domain-specific response profiles across visual, semantic, and episodic tasks and are specifically functionally connected to other parts of association cortex with like domain preference. Social and spatial networks remain anatomically separated at the apex of a unimodal-to-transmodal gradient across cortex and include regions with anatomical connections to the hippocampal formation. These results demonstrate the existence of parallel, domain-specific networks reaching the cortical apex. <<<

Li Wang, Cheng Wang, Juan A. Moriano, Songcang Chen, Guolong Zuo, Arantxa Cebrián-Silla, Shaobo Zhang, Tanzila Mukhtar, Shaohui Wang, Mengyi Song, Lilian Gomes de Oliveira, Qiuli Bi, Jonathan J. Augustin, Xinxin Ge, Mercedes F. Paredes, Eric J. Huang, Arturo Alvarez-Buylla, Xin Duan, Jingjing Li, Arnold R. Kriegstein <<<

Abstract Propranolol (PROP) is a nonselective β-adrenergic receptor antagonist used to treat hypertension and cardiac arrhythmias. Oral administration of PROP has recently emerged as a new treatment modality for hemangiomas. However, the side effects of PROP at the cellular level have not been adequately described. The present study investigates and highlights the mechanisms of coupling of the drugs cyclosporin-A (CyA) and PROP on cell proliferation and the occurrence of apoptosis. It also relays the antioxidant effect of PROP on human umbilical vein endothelial cells (HUVECs). HUVECs were treated with CyA and PROP. At 24 hours after treatment, the levels of reactive oxygen species (ROS), cell proliferation, and apoptosis were determined using the ROS kit, MTT assay, and Annexin V staining. In addition, the related proteins of phospho-p38 mitogen-activated protein kinase were determined by western blotting. Subsequently, HUVECs pretreated with CyA or PROP were treated with the p38 inhibitor (SB203580). Finally, the ROS level, cell proliferation, and apoptosis were measured again in both active HUVECs and HUVECs, in which the p38 proteins were inhibited. The combination of CyA and PROP reversed the effect of CyA on cell viability, reduced the ROS level and the cell apoptosis induced by PROP. Moreover, inhibition of p38 protein catalase activity immediately stopped the effect of CyA–propranolol in HUVECs. The effect of the CyA–propranolol combination on HUVECs is associated with the p38 pathway changes, which is proven to be a potential chemotherapeutic agent that minimizes the side effects of PROP in hemangioma therapy. <<<

Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis, Ryan Pachauri, Thomas Tumiel, Federico Barbero, Johan Obando-Ceron, Jiaxin Shi, Satinder Singh, Shaobo Hou, Nenad Tomašev, Tom Zahavy <<<

While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on chess engine search statistics to overcome some of those shortcomings. The rewards are designed to enhance a puzzle's uniqueness, counter-intuitiveness, diversity, and realism. Our RL approach dramatically increases counter-intuitive puzzle generation by 10x, from 0.22\% (supervised) to 2.5\%, surpassing existing dataset rates (2.1\%) and the best Lichess-trained model (0.4\%). Our puzzles meet novelty and diversity benchmarks, retain aesthetic themes, and are rated by human experts as more creative, enjoyable, and counter-intuitive than composed book puzzles, even approaching classic compositions. Our final outcome is a curated booklet of these AI-generated puzzles, which is acknowledged for creativity by three world-renowned experts. <<<

Jiaming Li, Mengmeng Jiang, Qiaoran Wang, Zikai Zheng, Jianghua Shen, Jingyi Li, Muzhao Xiong, Yandong Zheng, Xiaoyong Lu, Yusheng Cai, Yanling Fan, Lingling Geng, Qianzhao Ji, Qianqian Peng, Shuhui Sun, Yuanyuan Wang, Zijuan Xin, Kaowen Yan, Yuanhan Yang, Jun Yu, Haoteng Yan, Ding Ai, Yongping Bai, Yan Bi, Xiu-Wu Bian, Pengcheng Bu, Jian-Ping Cai, Chun-Mei Cao, Feng Cao, Zhongwei Cao, Renjie Chai, Piu Chan, Chang Chen, Cheng-Shui Chen, Chunying Chen, Di Chen, Hou-Zao Chen, Lin Chen, Quan Chen, Xiao Chen, Xiaochun Chen, Yu Chen, Zi-Jiang Chen, Weimin Ci, Zhe Dai, Qiurong Ding, Birong Dong, Jiahong Dong, Jian-Gao Fan, Shiqing Feng, Xin Feng, Yun Feng, Xiaobing Fu, Xiaolong Fu, Feng Gao, Jiangang Gao, Qiang Gao, Shaorong Gao, Yonghao Gu, Youfei Guan, Feifan Guo, Jing-Dong J. Han, Haiping Hao, Jihui Hao, Fuchu He, Jinhan He, Ming He, Mingguang He, Qiyang He, Zhiying He, Zuhong He, Huashan Hong, Jiaxu Hong, Shengping Hou, Cheng Hu, Ping Hu, Zhibin Hu, Canhua Huang, Jun Huang, Kai Huang, Pengyu Huang, Xunming Ji, Yong Ji, Shunji Jia, Hong Jiang, Wenjian Jiang, Lingjing Jin, Zi-Bing Jin, Shenghong Ju, Zhenyu Ju, Qing-Peng Kong, Wei Kong, Wei-Jia Kong, Xiangqing Kong, Guanghua Lei, Geng-Lin Li, Ji Li, Jian Li, Mengfeng Li, Rong Li, Wei Li, Wei Li, Xiao-Jun Li, Xin Li, Qingfeng Liang, Zhen Liang, Haotian Lin, Baohua Liu, Cai-Yue Liu, Changsheng Liu, Feng Liu, Jianfeng Liu, Jun-Ping Liu, Ke Liu, Lin Liu, Pingsheng Liu, Qiang Liu, Qiang Liu, Tiemin Liu, Wenwen Liu, Xingguo Liu, Yajun Liu, Yong Liu, Youhua Liu, Youshuo Liu, Zhili Liu, Xiao Long, Yao Lu, Jian Luo, Xianghang Luo, Chunhong Ma, Shuai Ma, Xinran Ma, Jianhua Mao, Zhiyong Mao, Shyh-Chang Ng, Guangjun Nie, Yuyu Niu, Yaojin Peng, Jun Pu, Jieyu Qi, Li Qiang, Jie Qiao, Yingying Qin, Aijuan Qu, Jing Qu, Jie Ren, Ruibao Ren, Xiong Z. Ruan, Anbing Shi, Haibo Shi, Jie Shi, Kwok-Fai So, Moshi Song, Weihong Song, Zhou Songyang, Jiacan Su, Aijun Sun, Liang Sun, Qiang Sun, Yi Eve Sun, Yu Sun, Peifu Tang, Qi-Qun Tang, Yi Tang, Jun Tao, Ling Tao, Mei Tian, Xiao-Li Tian, Ye Tian, Xiaolin Tong, Cong-Yi Wang, Haibo Wang, Hongmei Wang, Huating Wang, Jianan Wang, Jianwei Wang, Jianwei Wang, Jiqiu Wang, Liheng Wang, Lin Wang, Miao Wang, Qiang Wang, Si Wang, Sijia Wang, Songlin Wang, Wengong Wang, Xiaoming Wang, Xiaoning Wang, Yan Wang, Yan-Jiang Wang, Yuan Wang, Yunfang Wang, Zhenning Wang, Xiawei Wei, Jianping Weng, Haitao Wu, Jihong Wu, Xiaohuan Xia, Yang Xia, Andy Peng Xiang, Guozhi Xiao, Junjie Xiao, Yichuan Xiao, Zhi-Xiong Jim Xiao, Zhengwei Xie, Wei Xiong, Aimin Xu, Hua Xu, Lingyan Xu, Ming Xu, Liying Yan, Jiayin Yang, Jichun Yang, Liu Yang, Yun-Gui Yang, Ze Yang, Zhenglin Yang, Hongjie Yao, Jing Ye, Chengqi Yi, Fan Yi, Honghua Yu, Yang Yu, Zhengrong Yu, Ti-Fei Yuan, Jirong Yue, Rui Yue, Chen Zhang, Chunxiang Zhang, Cuntai Zhang, Feng Zhang, Hongbo Zhang, Hongjia Zhang, Huijie Zhang, Jie Zhang, Jingjing Zhang, Licheng Zhang, Lingqiang Zhang, Luoying Zhang, Qingjiong Zhang, Wei Zhang, Weiping J. Zhang, Xin Zhang, Xuan Zhang, Yong Zhang, Yun-Wu Zhang, Zhanjun Zhang, Zhuohua Zhang, Bing Zhao, Guoguang Zhao, Jiajun Zhao, Meng Zhao, Tongbiao Zhao, Jialin C. Zheng, Junke Zheng, Zhuozhao Zheng, Huixia Zhou, Lili Zhou, Xiangtian Zhou, Yongsheng Zhou, Zhongjun Zhou, Lan Zhu, Yizhun Zhu, Zhiming Zhu, Wenjuan Zhuang, Weiguo Zou, Weiqi Zhang, Gang Pei, Guang-Hui Liu <<<

Variational quantum algorithms (VQAs) are leading strategies to reachpractical utilities of near-term quantum devices. However, the no-cloningtheorem in quantum mechanics precludes standard backpropagation, leading toprohibitive quantum resource costs when applying VQAs to large-scale tasks. Toaddress this challenge, we reformulate the training dynamics of VQAs as anonlinear partial differential equation and propose a novel protocol thatleverages physics-informed neural networks (PINNs) to model this dynamicalsystem efficiently. Given a small amount of training trajectory data collectedfrom quantum devices, our protocol predicts the parameter updates of VQAs overmultiple iterations on the classical side, dramatically reducing quantumresource costs. Through systematic numerical experiments, we demonstrate thatour method achieves up to a 30x speedup compared to conventional methods andreduces quantum resource costs by as much as 90\% for tasks involving up to 40qubits, including ground state preparation of different quantum systems, whilemaintaining competitive accuracy. Our approach complements existing techniquesaimed at improving the efficiency of VQAs and further strengthens theirpotential for practical applications. <<<

Shuanghao Bai, Wenxuan Song, Jiayi Chen, Yuheng Ji, Zhide Zhong, Jin Yang, Han Zhao, Wanqi Zhou, Wei Zhao, Zhe Li, Pengxiang Ding, Cheng Chi, Haoang Li, Chang Xu, Xiaolong Zheng, Donglin Wang, Shanghang Zhang, Badong Chen <<<

Embodied intelligence has witnessed remarkable progress in recent years,driven by advances in computer vision, natural language processing, and therise of large-scale multimodal models. Among its core challenges, robotmanipulation stands out as a fundamental yet intricate problem, requiring theseamless integration of perception, planning, and control to enable interactionwithin diverse and unstructured environments. This survey presents acomprehensive overview of robotic manipulation, encompassing foundationalbackground, task-organized benchmarks and datasets, and a unified taxonomy ofexisting methods. We extend the classical division between high-level planningand low-level control by broadening high-level planning to include language,code, motion, affordance, and 3D representations, while introducing a newtaxonomy of low-level learning-based control grounded in training paradigmssuch as input modeling, latent learning, and policy learning. Furthermore, weprovide the first dedicated taxonomy of key bottlenecks, focusing on datacollection, utilization, and generalization, and conclude with an extensivereview of real-world applications. Compared with prior surveys, our work offersboth a broader scope and deeper insight, serving as an accessible roadmap fornewcomers and a structured reference for experienced researchers. All relatedresources, including research papers, open-source datasets, and projects, arecurated for the community athttps://github.com/BaiShuanghao/Awesome-Robotics-Manipulation. <<<