检索结果-内蒙古大学图书馆

Molecular architecture of mammalian pyruvate dehydrogenase complex

学校读者我要写书评

暂无评论

Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

Metallic and ferromagnetic carbon allotrope discovered using molecular precursors

学校读者我要写书评

暂无评论

Physical Review Research 2025年第1期7卷 013048-013048页

作者： Hong Fang Joel Therrien Puru Jena Department of Physics Rutgers University-Camden 530 Federal Street Camden New Jersey 08102 USA Center for Computational and Integrative Biology Rutgers University Camden New Jersey 08103 USA Department of Materials Science and Engineering Rutgers University-New Brunswick Piscataway New Jersey 08854 USA Department of Electrical and Computer Engineering University of Massachusetts Lowell Massachusetts 01854 USA Department of Physiscs Virginia Commonwealth University Richmond Virginia 23284 USA

Although carbon materials are known for their versatile structures and properties, a crystalline phase of carbon being both metallic and ferromagnetic is yet to be found. Here, using an organic molecular precursor 3,3-dimethyl-1-butene (C6H12) and a comprehensive theoretical study, we have discovered a metastable crystalline carbon phase that is both metallic and ferromagnetic under ambient conditions. The new carbon structures, produced by stacking puckered layers of 4,6-membered rings, are found to exhibit exotic electronic, magnetic, and mechanical properties originating from a special sp2−sp3 bonding topology. These findings open the prospect of realizing the metallic and ferromagnetic carbon allotrope in practice.

关键词： Crystalline materials

Anisotropic exchange spin model to investigate the Curie temperature dispersion of L10-FePt magnetic nanoparticles

学校读者我要写书评

暂无评论

Journal of Applied Physics 2025年第18期137卷

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic exchange spin model that accounts for magnetic anisotropy and evaluated the Curie temperature ( T c ) dispersion due to finite-size effects in L1 0 -FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L1 0 -FePt nanoparticles near their T c and rapidly cooling them. However, variations in T c caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of T c dispersion critical. In this study, we constructed atomistic Landau-Lifshitz-Gilbert models to explicitly incorporate the spin exchange anisotropy of L 1 0 -FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on T c dispersion. As a result, (1) the T c dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70 % of the experimentally observed T c dispersion can be attributed to particle-size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. © 2025 Author(s).

关键词： Magnetic nanoparticles

Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhang, Jiancheng Li, Xiangting Guo, Xiaolu You, Zhaoyi Böttcher, Lucas Mogilner, Alex Hoffmann, Alexander Chou, Tom Xia, Mingtao Department of Electrical and Computer Engineering University of California RiversideCA United States Department of Computational Medicine University of California Los AngelesCA United States Institute for Quantitative and Computational Biosciences University of California Los Angeles Los AngelesCA United States Ray and Stephanie Lane Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA United States Department of Computational Science and Philosophy Frankfurt School of Finance and Management Frankfurt am Main Germany Laboratory for Systems Medicine Department of Medicine University of Florida FL32610 United States Courant Institute of Mathematical Sciences New York University NY10012 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Mathematics University of Houston HoustonTX77004 United States

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions ("intrinsic noise") and (ii) heterogeneity of cellular states across different cells that are influenced by external factors ("extrinsic noise"). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NFκB signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical. © 2025, CC BY.

关键词： Stochastic systems

Anisotropic Exchange Spin Model to Investigate the Curie Temperature Dispersion of Finite-Size L10-FePt Magnetic Nanoparticles

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Ochiai, Kohei Tsuyama, Tomoyuki Shimizu, Sumera Zhang, Lei Watanabe, Jin Kudo, Fumito Zhu, Jian-Gang Okuno, Yoshishige Resonac Corporation Research Center for Computational Science and Informatics 8 Ebisu-cho Kanagawa-ku Kanagawa. Yokohama221-8517 Japan Resonac Hard Disk Corporation Research & Development Center 5-1 Yawatakaigan-dori Chiba Ichihara290-0067 Japan Data Storage Systems Center Carnegie Mellon University PittsburghPA15213 United States Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA15213 United States Materials Science and Engineering Department Carnegie Mellon University PittsburghPA15213 United States

We developed an anisotropic spin model that accounts for magnetic anisotropy and evaluated the Curie temperature (Tc) dispersion due to finite size effects in L10-FePt nanoparticles. In heat-assisted magnetic recording (HAMR) media, a next-generation magnetic recording technology, high-density recording is achieved by locally heating L10-FePt nanoparticles near their Tc and rapidly cooling them. However, variations in Tc caused by differences in particle size and shape can compromise recording stability and areal density capacity, making the control of Tc dispersion critical. In this study, we constructed atomistic LLG models to explicitly incorporate the spin exchange anisotropy of L10-FePt, based on parameters determined by first-principles calculations. Using this model, we evaluated the impact of particle size on Tc dispersion. As a result, (1) the Tc dispersion critical to the performance of HAMR can be reproduced, whereas it was previously underestimated by isotropic models and (2) approximately 70% of the experimentally observed Tc dispersion can be attributed to particle size effects. This research highlights the role of exchange anisotropy in amplifying finite-size effects and underscores the importance of size control in HAMR media. Copyright © 2025, The Authors. All rights reserved.

关键词： Curie temperature

Biomedical data and AI

学校读者我要写书评

暂无评论

Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词：

MedMimic: A Physician-Inspired Multimodal Fusion Framework for Early Diagnosing Fever of Unknown Origin

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Chen, Minrui Zhou, Yi Jiang, Huidong Zhu, Yuhan Zou, Guanjie Chen, Minqi Tian, Rong Saigo, Hiroto Department of Information Science and Technology Kyushu University Fukuoka Japan Department of Nuclear Medicine West China Hospital Sichuan University Chengdu China Department of Computer Science Institute of Science Tokyo Yokohama Japan Center for Advanced Intelligence Project RIKEN Tokyo Japan Department of Computational Biology and Medical Sciences University of Tokyo Tokyo Japan Department of Thoracic Surgery Institute of Thoracic Oncology West China Hospital Sichuan University Chengdu China Department of Electrical Engineering and Computer Science Kyushu University Fukuoka Japan

Fever of unknown origin (FUO) poses a significant diagnostic challenge, often requiring extensive evaluations. While recent efforts integrating clinical features with 18F-FDG PET/CT imaging have improved diagnostic accuracy, conventional feature extraction methods based on manually defined features often lack robustness and generalizability. Furthermore, the scarcity of annotated 18F-FDG PET/CT datasets limits the effective training of deep learning *** overcome these challenges, we propose Medical Mimicry (MedMimic), a framework inspired by real-world diagnostic processes. MedMimic leverages pre-trained models—including DINOv2, Vision Transformer, and ResNet-18—to transform high-dimensional 18F-FDG PET/CT imaging data into low-dimensional, semantically meaningful feature tensors. A learnable self-attention-based fusion network then integrates these imaging features with clinical data, producing compact yet discriminative representations for downstream classification. Our dataset was collected from Sichuan University West China Hospital, one of China’s top-tier medical institutions, and comprised 607 consecutive FUO patients who underwent 18F-FDG PET/CT. After excluding cases that did not meet the inclusion criteria, 416 patient admissions from January 2017 to December 2023 were included in the study. Our proposed multimodal fusion classification network (MFCN) achieved macro-averaged area under the receiver operating characteristic curve (macro-AUROC) values ranging from 0.8654 to 0.9291 across all seven decomposed tasks, outperforming competing machine learning (ML) and single-modality deep learning (DL) methods. Additionally, ablation studies validated the effectiveness of the proposed MFCN model. Furthermore, we employed a five-fold cross-validation strategy to ensure the robustness of our results. By integrating the strengths of pre-trained large models and deep learning, MedMimic offers a novel perspective and an effective solution for disease classifica

关键词： Data fusion

Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Lotfinia, Mahshad Tayebiarasteh, Arash Samiei, Samaneh Joodaki, Mehdi Arasteh, Soroosh Tayebi Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany Department of Computer Engineering Hamedan University of Technology Hamedan Iran Quantitative Cell Dynamics and Translational Systems Biology University Hospital RWTH Aachen Aachen Germany Institute for Computational Genomics Joint Research Center for Computational Biomedicine University Hospital RWTH Aachen Aachen Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Urology Stanford University StanfordCA United States Department of Radiology Stanford University StanfordCA United States

Reliable artificial intelligence (AI) models for medical image analysis often depend on large and diverse labeled datasets. Federated learning (FL) offers a decentralized and privacy-preserving approach to training but struggles in highly non-independent and identically distributed (non-IID) settings, where institutions with more representative data may experience degraded performance. Moreover, existing large-scale FL studies have been limited to adult datasets, neglecting the unique challenges posed by pediatric data, which introduces additional non-IID variability. To address these limitations, we analyzed n=398,523 adult chest radiographs from diverse institutions across multiple countries and n=9,125 pediatric images, leveraging transfer learning from general-purpose self-supervised image representations to classify pneumonia and cases with no abnormality. Using state-of-the-art vision transformers, we found that FL improved performance only for smaller adult datasets (P © 2025, CC BY.

关键词： Federated learning

Tree Reconstruction Guarantees from CRISPR-Cas9 Lineage Tracing Data Using Neighbor-Joining 29th

学校读者我要写书评

暂无评论

Tree Reconstruction Guarantees from CRISPR-Cas9 Lineage Tra...

29th International Conference on Research in computational Molecular biology, RECOMB 2025

作者： Prillo, Sebastian An, Kevin Wu, Wilson Kristanto, Ivan Jones, Matthew G. Song, Yun S. Yosef, Nir Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Systems Immunology Weizmann Institute of Science Rehovot7610001 Israel Department of Statistics University of California Berkeley BerkeleyCA94720 United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA94720 United States Center for Personal Dynamic Regulomes Stanford University StanfordCA94305 United States

ISBN: (纸本)9783031902512

CRISPR-Cas9 based lineage tracing technologies have enabled the reconstruction of single-cell phylogenies from transcriptional readouts. However, developing tree-reconstruction algorithms with theoretical guarantees in this setting is challenging. In this work, we derive a reconstruction algorithm with theoretical guarantees using Neighbor-Joining (NJ) on distances that are moment-matched to estimate the true tree distances. We develop a series of tools to analyze this algorithm and prove its theoretical guarantees. Empirically, we show on both simulated lineage tracing data and on real data from a mouse model of lung cancer the improved performance of our method as compared to the traditional use of NJ. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词：