Nature computational science最新文献_第5页

Cover runners-up of 2024 2024 年封面亚军

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Nature computational science Pub Date : 2024-12-20 DOI: 10.1038/s43588-024-00758-6

引用次数: 0

Simulation and assimilation of the digital human brain 数字人脑的模拟和同化

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Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00731-3

Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium

{"title":"Simulation and assimilation of the digital human brain","authors":"Wenlian Lu, Xin Du, Jiexiang Wang, Longbin Zeng, Leijun Ye, Shitong Xiang, Qibao Zheng, Jie Zhang, Ningsheng Xu, Jianfeng Feng, the DTB Consortium","doi":"10.1038/s43588-024-00731-3","DOIUrl":"10.1038/s43588-024-00731-3","url":null,"abstract":"Here we present the Digital Brain (DB)—a platform for simulating spiking neuronal networks at the large neuron scale of the human brain on the basis of personalized magnetic resonance imaging data and biological constraints. The DB aims to reproduce both the resting state and certain aspects of the action of the human brain. An architecture with up to 86 billion neurons and 14,012 GPUs—including a two-level routing scheme between GPUs to accelerate spike transmission in up to 47.8 trillion neuronal synapses—was implemented as part of the simulations. We show that the DB can reproduce blood-oxygen-level-dependent signals of the resting state of the human brain with a high correlation coefficient, as well as interact with its perceptual input, as demonstrated in a visual task. These results indicate the feasibility of implementing a digital representation of the human brain, which can open the door to a broad range of potential applications. The Digital Brain platform is capable of simulating spiking neuronal networks at the neuronal scale of the human brain. The platform is used to reproduce blood-oxygen-level-dependent signals in both the resting state and action, thereby predicting the visual evaluation scores.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"890-898"},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the path toward brain-scale simulations 迈向大脑尺度模拟之路

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Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00743-z

Felix Wang, James B. Aimone

引用次数: 0

Spatial modeling algorithms for reactions and transport in biological cells 生物细胞中反应和运输的空间建模算法。

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Nature computational science Pub Date : 2024-12-19 DOI: 10.1038/s43588-024-00745-x

Emmet A. Francis, Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg, Christopher T. Lee, Marie E. Rognes, Padmini Rangamani

{"title":"Spatial modeling algorithms for reactions and transport in biological cells","authors":"Emmet A. Francis, Justin G. Laughlin, Jørgen S. Dokken, Henrik N. T. Finsberg, Christopher T. Lee, Marie E. Rognes, Padmini Rangamani","doi":"10.1038/s43588-024-00745-x","DOIUrl":"10.1038/s43588-024-00745-x","url":null,"abstract":"Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales. Spatial Modeling Algorithms for Reactions and Transport (SMART) is a software package that allows users to simulate spatially resolved biochemical signaling networks within realistic geometries of cells and organelles.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"76-89"},"PeriodicalIF":12.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting emergence of crystals from amorphous precursors with deep learning potentials 预测具有深度学习潜力的非晶态前体晶体的出现。

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Nature computational science Pub Date : 2024-12-18 DOI: 10.1038/s43588-024-00752-y

Muratahan Aykol, Amil Merchant, Simon Batzner, Jennifer N. Wei, Ekin Dogus Cubuk

{"title":"Predicting emergence of crystals from amorphous precursors with deep learning potentials","authors":"Muratahan Aykol, Amil Merchant, Simon Batzner, Jennifer N. Wei, Ekin Dogus Cubuk","doi":"10.1038/s43588-024-00752-y","DOIUrl":"10.1038/s43588-024-00752-y","url":null,"abstract":"Crystallization of amorphous precursors into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to the synthesis and development of new materials in the laboratory. Reliably predicting the outcome of such a process would enable new research directions in these areas, but has remained beyond the reach of molecular modeling or ab initio methods. Here we show that candidates for the crystallization products of amorphous precursors can be predicted in many inorganic systems by sampling the local structural motifs at the atomistic level using universal deep learning interatomic potentials. We show that this approach identifies, with high accuracy, the most likely crystal structures of the polymorphs that initially nucleate from amorphous precursors, across a diverse set of material systems, including polymorphic oxides, nitrides, carbides, fluorides, chlorides, chalcogenides and metal alloys. This study introduces a2c, a computational method that leverages machine learning and atomistic simulations to predict the most likely crystallization products upon annealing of amorphous precursors. The a2c tool was demonstrated on a variety of materials, including oxides, nitrides and metallic glasses, and can assist researchers in discovering synthesis pathways for materials design.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 2","pages":"105-111"},"PeriodicalIF":12.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00752-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An integrative data-driven model simulating C. elegans brain, body and environment interactions 模拟秀丽隐杆线虫大脑、身体和环境相互作用的综合数据驱动模型。

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Nature computational science Pub Date : 2024-12-16 DOI: 10.1038/s43588-024-00738-w

Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang

{"title":"An integrative data-driven model simulating C. elegans brain, body and environment interactions","authors":"Mengdi Zhao, Ning Wang, Xinrui Jiang, Xiaoyang Ma, Haixin Ma, Gan He, Kai Du, Lei Ma, Tiejun Huang","doi":"10.1038/s43588-024-00738-w","DOIUrl":"10.1038/s43588-024-00738-w","url":null,"abstract":"The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body–environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body–environment model. The brain model was built by multicompartment models with realistic morphology, connectome and neural population dynamics based on experimental data. Simultaneously, the body–environment model used a lifelike body and a three-dimensional physical environment. Through the closed-loop interaction between the two submodels, BAAIWorm reproduced the realistic zigzag movement toward attractors observed in C. elegans. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, BAAIWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment. BAAIWorm is an integrative data-driven model of C. elegans that simulates interactions between the brain, body and environment. The biophysically detailed neuronal model is capable of replicating the zigzag movement observed in this species.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 12","pages":"978-990"},"PeriodicalIF":12.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s43588-024-00738-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A simulated C. elegans with biophysically detailed neurons and muscle dynamics 模拟秀丽隐杆线虫的生物物理细节神经元和肌肉动力学。

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Nature computational science Pub Date : 2024-12-16 DOI: 10.1038/s43588-024-00740-2

引用次数: 0

Author Correction: Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS 作者更正：使用MMIDAS对单细胞数据集进行离散细胞类型和连续类型特异性变异性的联合推断。

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Nature computational science Pub Date : 2024-12-12 DOI: 10.1038/s43588-024-00759-5

Yeganeh Marghi, Rohan Gala, Fahimeh Baftizadeh, Uygar Sümbül

引用次数: 0

Generative language models exhibit social identity biases 生成语言模型表现出社会身份偏见。

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Nature computational science Pub Date : 2024-12-12 DOI: 10.1038/s43588-024-00741-1

Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek

{"title":"Generative language models exhibit social identity biases","authors":"Tiancheng Hu, Yara Kyrychenko, Steve Rathje, Nigel Collier, Sander van der Linden, Jon Roozenbeek","doi":"10.1038/s43588-024-00741-1","DOIUrl":"10.1038/s43588-024-00741-1","url":null,"abstract":"Social identity biases, particularly the tendency to favor one’s own group (ingroup solidarity) and derogate other groups (outgroup hostility), are deeply rooted in human psychology and social behavior. However, it is unknown if such biases are also present in artificial intelligence systems. Here we show that large language models (LLMs) exhibit patterns of social identity bias, similarly to humans. By administering sentence completion prompts to 77 different LLMs (for instance, ‘We are…’), we demonstrate that nearly all base models and some instruction-tuned and preference-tuned models display clear ingroup favoritism and outgroup derogation. These biases manifest both in controlled experimental settings and in naturalistic human–LLM conversations. However, we find that careful curation of training data and specialized fine-tuning can substantially reduce bias levels. These findings have important implications for developing more equitable artificial intelligence systems and highlight the urgent need to understand how human–LLM interactions might reinforce existing social biases. Researchers show that large language models exhibit social identity biases similar to humans, having favoritism toward ingroups and hostility toward outgroups. These biases persist across models, training data and real-world human–LLM conversations.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 1","pages":"65-75"},"PeriodicalIF":12.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective quantum error correction by AI 人工智能有效的量子纠错。

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Nature computational science Pub Date : 2024-12-11 DOI: 10.1038/s43588-024-00755-9

Jie Pan

引用次数: 0