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

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

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

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Cell 2025年第12期188卷 3310-3328.e27页

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Emergency Department Decision Support using Clinical Pseudo-notes

arXiv

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arXiv 2024年

作者： Lee, Simon A. Jain, Sujay Chen, Alex Ono, Kyoka Rudas, Akos Fang, Jennifer Chiang, Jeffrey N. Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California at Los Angeles Los AngelesCA90095 United States Department of Statistics and Data Science University of California Los Angeles United States Department of Natural Sciences International Christian University Mitaka Tokyo Japan LA Health Services Enterprise Clinical Informatics Los AngelesCA United States Harbor-UCLA Medical Center Department of Emergency Medicine TorranceCA United States University of California Los Angeles Department of Emergency Medicine Los AngelesCA United States Department of Neurosurgery University of California Los AngelesCA90095 United States

In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using "pseudo-notes", mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several decision support tasks within the Emergency Department across multiple hospital systems. Our findings indicate that MEME outperforms traditional machine learning, EHR-specific foundation models, and general LLMs, highlighting its potential as a general and extendible EHR representation strategy. © 2024, CC BY.

关键词： Embeddings

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A pragmatic approach to estimating average treatment effects from EHR data: The effect of prone positioning on mechanically ventilated COVID-19 patients

arXiv

引用

arXiv 2021年

作者： Izdebski, Adam Thoral, Patrick J. Lalisang, Robbert C.A. McHugh, Dean M. Gommers, Diederik Cremer, Olaf L. Bosman, Rob J. Rigter, Sander Wils, Evert-Jan Frenzel, Tim Dongelmans, Dave A. de Jong, Remko Peters, Marco A.A. Kamps, Marlijn J.A. Ramnarain, Dharmanand Nowitzky, Ralph Nooteboom, Fleur G.C.A. de Ruijter, Wouter Urlings-Strop, Louise C. Smit, Ellen G.M. Mehagnoul-Schipper, D. Jannet Dormans, Tom de Jager, Cornelis P.C. Hendriks, Stefaan H.A. Achterberg, Sefanja Oostdijk, Evelien Reidinga, Auke C. Festen-Spanjer, Barbara Brunnekreef, Gert B. Cornet, Alexander D. van den Tempel, Walter Boelens, Age D. Koetsier, Peter Lens, Judith Faber, Harald J. Karakus, A. Entjes, Robert de Jong, Paul Rettig, Thijs C.D. Arbous, Sesmu Fleuren, Lucas M. Dam, Tariq A. Tonutti, Michele de Bruin, Daan P. Elbers, Paul W.G. Cinà, Giovanni Pacmed Amsterdam Netherlands Department of Intensive Care Medicine Laboratory for Critical Care Computational Intelligence Amsterdam Medical Data Science Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Institute of Logic Language and Computation University of Amsterdam Netherlands Department of Intensive Care Erasmus Medical Center Rotterdam Netherlands Intensive Care UMC Utrecht Utrecht Netherlands ICU OLVG Amsterdam Netherlands Department of Anesthesiology and Intensive Care St. Antonius Hospital Nieuwegein Netherlands Department of Intensive Care Franciscus Gasthuis & Vlietland Rotterdam Netherlands Department of Intensive Care Medicine Radboud University Medical Center Nijmegen Netherlands Department of Intensive Care Medicine Amsterdam UMC Amsterdam Netherlands Intensive Care Bovenij Ziekenhuis Amsterdam Netherlands Intensive Care Canisius Wilhelmina Ziekenhuis Nijmegen Netherlands Intensive Care Catharina Ziekenhuis Eindhoven Eindhoven Netherlands Department of Intensive Care ETZ Tilburg Tilburg Netherlands Intensive Care HagaZiekenhuis Den Haag Netherlands Intensive Care Laurentius Ziekenhuis Roermond Netherlands Department of Intensive Care Medicine Northwest Clinics Alkmaar Netherlands Intensive Care Reinier de Graaf Gasthuis Delft Netherlands Intensive Care Spaarne Gasthuis Haarlem en Hoofddorp Netherlands Intensive Care VieCuri Medisch Centrum Venlo Netherlands Intensive care Zuyderland MC Heerlen Netherlands Department of Intensive Care Jeroen Bosch Ziekenhuis Den Bosch Netherlands Intensive Care Albert Schweitzerziekenhuis Dordrecht Netherlands ICU Haaglanden Medisch Centrum Den Haag Netherlands ICU Maasstad Ziekenhuis Rotterdam Rotterdam Netherlands ICU SEH BWC Martiniziekenhuis Groningen Netherlands Intensive Care Ziekenhuis Gelderse Vallei Ede Netherlands Department of Intensive Care Ziekenhuisgroep Twente Almelo Netherlands FRCP Department of Intensive Care Medisch Spectrum Twente Enschede Netherla

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 intensive Care patients. © 2021, CC BY.

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

引用

Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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Spatial reasoning from natural language instructions for robot manipulation

arXiv

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arXiv 2020年

作者： Venkatesh, Sagar Gubbi Biswas, Anirban Upadrashta, Raviteja Srinivasan, Vikram Talukdar, Partha Amrutur, Bharadwaj Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bangalore560012 India Department of Electrical and Communication Engineering Indian Institute of Science Bangalore560012 India Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language. We propose a pipelined architecture of two stages to perform spatial reasoning on the text input. All the objects in the scene are first localized, and then the instruction for the robot in natural language and the localized co-ordinates are mapped to the start and end co-ordinates corresponding to the locations where the robot must pick up and place the object respectively. We show that representing the localized objects by quantizing their positions to a binary grid is preferable to representing them as a list of 2D co-ordinates. We also show that attention improves generalization and can overcome biases in the dataset. The proposed method is used to pick-and-place playing cards using a robot arm. © 2020, CC BY-NC-ND.

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Cover Feature: The Configuration Identification of Guanine Molecules Adsorbed on the Si(100) Surface by Using XPS Shake-up Satellites and NEXAFS (ChemPhysChem 20/2024)

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ChemPhysChem 2024年第20期25卷

作者： Shuyi Wang Jingdong Guo Hujun Shen Xiong Li Chao Cen Shaohong Cai Mingsen Deng College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Guizhou Provincial Key Laboratory of Computational Nano-material Science Guizhou Education University Guiyang 550018 China School of Information Guizhou University of Finance and Economics Guiyang 550025 China Moutai Institute Renhuai 564507 China National Engineering Research Center for Compounding and Modification of Polymer Materials Guiyang 550014 China

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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

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Journal of Molecular Biology 2025年第15期437卷 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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Novel Metabolomic and Adiposity Markers Enhance Prediction of Obstructive Sleep Apnea: A Prospective Analysis from the China Human Phenobank

SSRN

引用

SSRN 2025年

作者： Wu, Yudan Ba, Yihan Chen, Chen Wang, Qi Chen, Qinsheng Zhang, Jinxian Chen, Longlong Zhao, Yining Shi, Biru Ye, Lisong Chen, Wei Wang, Chengyan Hu, Zixin Zhang, Jun Feng, Kai Wang, Yan Liu, Yuehua Qi, Yuan Tang, Huiru Luo, Jingchun Tian, Mei State Key Laboratory of Genetic Engineering School of Life Sciences Human Phenome Institute Zhangjiang Fudan International Innovation Center Fudan University Shanghai201203 China Department of Endocrinology and Metabolism Zhongshan Hospital Fudan University Shanghai200032 China Department of Orthodontics Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Chinese Academy of Sciences Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health University of the Chinese Academy of Sciences Chinese Academy of Sciences Shanghai200031 China Fudan University Shanghai201203 China Department of Radiology Huashan Hospital State Key Laboratory of Medical Neurobiology National Center for Neurological Disorders Fudan University Shanghai200040 China Department of Preventive Dentistry Shanghai Stomatological Hospital School of Stomatology Fudan University Shanghai200001 China Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases Fudan University Shanghai200433 China Shanghai Academy of Artificial Intelligence for Science Shanghai200232 China

Background: Obstructive sleep apnea (OSA) prevalence is increasing among Asian populations, yet effective biomarker-based screening tools remain underdeveloped. Metabolic dysregulation and adiposity patterns show potential as novel biomarkers for OSA risk stratification, but their predictive utility has not been comprehensively evaluated in Chinese populations. We aimed to identify key metabolic and adiposity biomarkers for OSA and subsequently develop an integrated prediction model to enhance risk assessment in a Chinese ***: In this prospective cohort study (2018-2023), we enrolled 972 adults (aged 20-60 years) from the China Human Phenobank. All participants underwent overnight polysomnography, sleep questionnaires, metabolomic profiling (1,840 metabolites), and DXA-derived body fat distribution analysis (58 adiposity parameters). OSA was defined according to international clinical criteria as apnea hypopnoea index (AHI) ≥5/h accompanied by characteristic symptoms (excessive daytime sleepiness, fatigue, or impaired cognition) or associated comorbidities. Participants were randomly allocated to training (n=777) and test (n=195) cohorts. We employed a multi-staged feature selection approach integrating differential analysis, correlation filtering, and machine learning algorithms (mRMR and LASSO) to identify key biomarkers. Selected features were integrated into a predictive model combining clinical, metabolomic, and adiposity data. Metabolic pathway enrichment analysis identified biological mechanisms underlying OSA *** identified two critical fat distribution markers (visceral adipose tissue volume and gynoid fat) and ten metabolic signatures (including specific TAG-FAs, lipoprotein subtypes, and steroid derivatives) significantly associated with OSA. The gender-specific fat distribution patterns and protective effects of gynoid fat distribution provided new insights into OSA pathophysiology. Notably, we identified H2FC, a specific H

关键词： Risk assessment

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