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arXiv 2020年

作者： Mei, Zhe Grigas, Alex T. Treado, John D. Corres, Gabriel Melendez Vuorte, Maisa Sammalkorpi, Maria Regan, Lynne Levine, Zachary A. O’Hern, Corey S. Department of Chemistry Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Biology UPR Humacao Puerto RicoHumacao00792 Puerto Rico Department of Chemistry and Materials Science School of Chemical Engineering Aalto University Aalto Finland Department of Bioproducts and Biosystems School of Chemical Engineering Aalto University Aalto Finland Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Department of Pathology Yale School of Medicine New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Globular proteins undergo thermal fluctuations in solution, while maintaining an overall well-defined folded structure. In particular, studies have shown that the core structure of globular proteins differs in small, but significant ways when they are solved by x-ray crystallography versus solution-based NMR spectroscopy. Given these discrepancies, it is unclear whether molecular dynamics (MD) simulations can accurately recapitulate protein conformations. We therefore perform extensive MD simulations across multiple force fields and sampling techniques to investigate the degree to which computer simulations can capture the ensemble of conformations observed in experiments. By analyzing fluctuations in the atomic coordinates and core packing, we show that conformations sampled in MD simulations both move away from and sample a larger conformational space than the ensemble of structures observed in NMR experiments. However, we find that adding inter-residue distance restraints that match those obtained via Nuclear Overhauser Effect measurements enables the MD simulations to sample more NMR-like conformations, though significant differences between the core packing features in restrained MD and the NMR ensemble remain. Given that the protein structures obtained from the MD simulations possess smaller and less dense protein cores compared to those solved by NMR, we suggest that future improvements to MD forcefields should aim to increase the packing of hydrophobic residues in protein cores. © 2020, CC BY.

关键词： Molecular dynamics

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Minimal blow-up initial data in critical fourier-herz spaces for potential navier-stokes singularities

arXiv

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arXiv 2018年

作者： Li, Jingyue Miao, Changxing Zheng, Xiaoxin Graduate School of China Academy of Engineering Physics Beijing100088 China Institute of Applied Physics and Computational Mathematics P.O. Box 8009 Beijing100088 China School of Mathematics and Systems Science Beihang University Beijing100191 China

关键词： Viscous flow

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Interface-driven unusual anomalous Hall effect in MnxGa/Pt bilayers: No correlation with chiral spin structures

arXiv

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arXiv 2019年

作者： Meng, Kangkang Zhu, Lijun Jin, Zhenhu Liu, Enke Zhao, Xupeng Malik, Iftikhar Ahmed Fu, Zhenguo Wu, Yong Miao, Jun Xu, Xiaoguang Zhang, Jinxing Zhao, Jianhua Jiang, Yong Beijing Advanced Innovation Center for Materials Genome Engineering School of Materials Science and Engineering University of Science and Technology Beijing Beijing China Cornell University IthacaNY14853 United States Department of Applied Physics Graduate School of Engineering Tohoku University 6-6-05 Aoba-yama Sendai980-8579 Japan Institute of Physics Chinese Academy of Sciences Beijing China State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing China Department of Physics Beijing Normal University Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

The effects of spin-orbit coupling and symmetry breaking at the interface between a ferromagnet and heavy metal are particularly important for spin-based information storage and computation. Recent discoveries suggest they can create chiral spin structures (e.g. skyrmions), which have often been identified through the appearance of the bump/dip features of Hall signals, the so-called topological Hall effect (THE). In this work, however, we have present an unusual anomalous Hall effect (UAHE) in MnxGa/Pt bilayers and demonstrated that the features extremely similar to THE can be generated without involving any chiral spin structures. The low temperature magnetic force microscopy has been used to explore the magnetic field-dependent behavior of spin structures, and the UAHE as a function of magnetic field does not peak near the maximal density of magnetic bubbles. The results unambiguously evidence that the UAHE in MnxGa/Pt bilayers shows no correlation with chiral spin structures but is driven by the modified interfacial properties. The bump/dip features of Hall signals cannot be taken as an unambiguous signature for the emergence of chiral spin structures, and a wealth of underlying and interesting physics need explored. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

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Pharmacoinformatics and cellular studies of algal peptides as novel functional molecules to modulate type-2 diabetes markers

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Clinical Nutrition ESPEN 2024年 63卷 1119-1119页

作者： Nurkolis, F. Kurniawan, R. Basrowi, R.W. Sundjaya, T. Taslim, N.A. Hardinsyah, H. Tjandrawinata, R.R. Tallei, T.E. Kim, B. Tsopmo, A. Department of Biological Sciences Faculty of Sciences and Technology State Islamic University of Sunan Kalijaga (UIN Sunan Kalijaga) Yogyakarta Editorial Board of Journal of Indonesian Specialized Nutrition Jakarta Graduate from School of Medicine Faculty of Medicine Hasanuddin University Makassar Diabetes Connection Care Eka Hospital Bumi Serpong Damai Tangerang Magister Occupational Medicine Department of Community Medicine Faculty of Medicine Universitas Indonesia Indonesia Health Collaborative Center (HCC) Jakarta Indonesia Departement of Epidemiology Faculty of Public Health Universitas Indonesia Jakarta Division of Clinical Nutrition Department of Nutrition Faculty of Medicine Hasanuddin University Makassar Division of Applied Nutrition Faculty of Human Ecology Department of Community Nutrition IPB University Bogor Biotechnology Faculty of Biotechnology Atma Jaya Catholic University of Indonesia Jakarta Biology Faculty of Mathematics and Natural Sciences Universitas Sam Ratulangi Manado Indonesia Pathology College of Korean Medicine Kyung Hee University Seoul Republic Of Korea Food Science and Nutrition Program Department of Chemistry Carleton University Ottawa Canada

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Model of virus therapy and chemotherapy for cancer

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IOP Conference Series: Earth and Environmental science 2019年第1期364卷

作者： E Safitri T Tarmizi M Ramli S Wahyuni M Ikhwan Mathematics Graduate Study Program Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Mathematics Universitas Syiah Kuala Banda Aceh 23111 Indonesia Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia

This article discusses the mathematical model of changes in the number of cancer cells as a result of the intervention. Here the effect of combination treatment, in the form of virus injection and chemotherapy on tumor cells, is presented in detail. There are two different tumor cells, uninfected tumor cells and infected tumor cells. Model simulations with different parameters were performed for 700 d. The initial values for the population of virus-infected tumor cells are divided into two categories. The number of tumor cells injected with the virus is ½ (half) and all tumor cells that are not infected. Based on the simulation results, it was concluded that the more the number of tumor cells injected with the virus, the faster the decrease in the number of tumor cell populations. It was also concluded that the reduced population of tumor cells reduces the amount of chemotherapy concentration used.

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A light in the dark: searching for electromagnetic counterparts to black hole-black hole mergers in LIGO/Virgo O3 with the Zwicky Transient Facility

arXiv

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arXiv 2022年

作者： Graham, Matthew J. McKernan, Barry Ford, K.E. Saavik Stern, Daniel Djorgovski, S.G. Coughlin, Michael Burdge, Kevin B. Bellm, Eric C. Helou, George Mahabal, Ashish A. Masci, Frank J. Purdum, Josiah Rosnet, Philippe Rusholme, Ben California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Department of Science CUNY Borough of Manhattan Community College 199 Chambers Street New YorkNY10007 United States Department of Astrophysics American Museum of Natural History Central Park West New YorkNY10028 United States Physics Program CUNY Graduate Center 365 5th Avenue New YorkNY10016 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States DIRAC Institute Department of Astronomy University of Washington 3910 15th Avenue NE SeattleWA98195 United States IPAC California Institute of Technology 1200 E. California Blvd PasadenaCA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Université Clermont Auvergne CNRS IN2P3 LPC Clermont-FerrandF-63000 France

The accretion disks of active galactic nuclei (AGN) are promising locations for the merger of compact objects detected by gravitational wave (GW) observatories. Embedded within a baryon-rich, high density environment, mergers within AGN are the only GW channel where an electromagnetic (EM) counterpart must occur (whether detectable or not). Considering AGN with unusual flaring activity observed by the Zwicky Transient Facility (ZTF), we describe a search for candidate EM counterparts to binary black hole (BBH) mergers detected by LIGO/Virgo in O3. After removing probable false positives, we find nine candidate counterparts to BBH mergers during O3 (seven in O3a, two in O3b) with a p-value of 0.0019. Based on ZTF sky coverage, AGN geometry, and merger geometry, we expect ≈ 3(NBBH/83)(fAGN/0.5) potentially detectable EM counterparts from O3, where NBBH is the total number of observed BBH mergers and fAGN is the fraction originating in AGN. Further modeling of breakout and flaring phenomena in AGN disks is required to reduce our false positive rate. Two of the events are also associated with mergers with total masses > 100M☉, which is the expected rate for O3 if hierarchical (large mass) mergers occur in the AGN channel. Candidate EM counterparts in future GW observing runs can be better constrained by coverage of the Southern sky as well as spectral monitoring of unusual AGN flaring events in LIGO/Virgo alert volumes. A future set of reliable AGN EM counterparts to BBH mergers will yield an independent means of measuring cosmic expansion (H0) as a function of redshift. © 2022, CC BY.

关键词： Gravity waves

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Rational design of stealthy hyperuniform two-phase media with tunable order

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Physical Review E 2018年第2期97卷 023311-023311页

作者： Robert A. DiStasio, Jr. Ge Zhang Frank H. Stillinger Salvatore Torquato Department of Chemistry and Chemical Biology Cornell University Ithaca New York 14853 USA Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Disordered stealthy hyperuniform materials are exotic amorphous states of matter that have attracted recent attention because of their novel structural characteristics (hidden order at large length scales) and physical properties, including desirable photonic and transport properties. It is therefore useful to devise algorithms that enable one to design a wide class of such amorphous configurations at will. In this paper, we present several algorithms enabling the systematic identification and generation of discrete (digitized) stealthy hyperuniform patterns with a tunable degree of order, paving the way towards the rational design of disordered materials endowed with novel thermodynamic and physical properties. To quantify the degree of order or disorder of the stealthy systems, we utilize the discrete version of the τ order metric, which accounts for the underlying spatial correlations that exist across all relevant length scales in a given digitized two-phase (or, equivalently, a two-spin state) system of interest. Our results impinge on a myriad of fields, ranging from physics, materials science and engineering, visual perception, and information theory to modern data science.

关键词： Pattern formation

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High temperature structure detection in ferromagnets

arXiv

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arXiv 2018年

作者： Cao, Yuan Neykov, Matey Liu, Han Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure. We give matching upper and lower minimax bounds under which testing this problem is possible/impossible respectively. Our results reveal that a key quantity called graph arboricity drives the testability of the problem. On the computational front, under a conjecture of the computational hardness of sparse principal component analysis, we prove that, unless the signal is strong enough, there are no polynomial time linear tests on the sample covariance matrix which are capable of testing this problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Ferromagnetism

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