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Local two-sample testing over graphs and point-clouds by random-walk distributions

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arXiv 2020年

作者： Landa, Boris Qu, Rihao Chang, Joseph Kluger, Yuval Program in Applied Mathematics Yale University Department of Pathology Yale University School of Medicine Department of Statistics and Data Science Yale University Interdepartmental Program in Computational Biology and Bioinformatics Yale University Department of Immunology Yale School of Medicine

Rejecting the null hypothesis in two-sample testing is a fundamental tool for scientific discovery. Yet, aside from concluding that two samples do not come from the same probability distribution, it is often of interest to characterize how the two distributions differ. Given samples from two densities f1and f0, we consider the task of localizing occurrences of the inequality f1> f0. To avoid the challenges associated with high-dimensional space, we propose a general hypothesis testing framework where hypotheses are formulated adaptively to the data by conditioning on the combined sample from the two densities. We then investigate a special case of this framework where the notion of locality is captured by a random walk on a weighted graph constructed over this combined sample. We derive a tractable testing procedure for this case employing a type of scan statistic, and provide non-asymptotic lower bounds on the power and accuracy of our test to detect whether f1> f0in a local sense. Furthermore, we characterize the test's consistency according to a certain problem-hardness parameter, and show that our test achieves the minimax detection rate for this parameter. We conduct numerical experiments to validate our method, and demonstrate our approach on two real-world applications: detecting and localizing arsenic well contamination across the United States, and analyzing two-sample single-cell RNA sequencing data from melanoma *** Codes 62G10 Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

International Workshop on Applications of Probability and Statistics to Biology,July 11-13,2019--In Honor of Professor Minping Qian’s 80th Birthday

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Quantitative Biology 2020年第2期8卷 177-186页

作者： Minghua Deng Jianfeng Feng Hong Qian Lin Wan Fengzhu Sun Center for Quantitative Biology Peking UniversityBeijing 100871China LMAM Center for Statistical ScienceSchool of Mathematical SciencesPeking UniversityBeijing 100871China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China School of Mathematical Sciences Fudan UniversityShanghai 200433China Centre for Computational Systems Biology Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence at Fudan University Ministry of EducationShanghai 200433China Department of Computer Science University of WarwickCoventryCV47ALUnited Kingdom Department of Applied Mathematics University of WashingtonSeattleWA 98195USA NCMIS Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA

The International Workshop on Applications of Probability and Statistics to Biology(APSB)was successfully held in Shanghai,China,July 11-13,*** workshop was hosted by the Institute of science and Technology for Brain-inspired Intelligence(ISTBI)at Fudan University,and in honor of the 80th birthday of *** Qian of Peking *** of the twenty eight speakers were former students or close collaborators of ***;and there were over eighty participants from all over China and United States.

关键词： Probability speakers ping

Chemical evolution of some selected complex organic molecules in low-mass star-forming regions

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arXiv 2023年

作者： Bhat, Bratati Kar, Rumela Mondal, Suman Kumar Ghosh, Rana Gorai, Prasanta Shimonishi, Takashi Tanaka, Kei E.I. Furuya, Kenji Das, Ankan Institute of Astronomy Space and Earth Science 1 Bidhan Sishu Sarani Kolkata700054 India Department of Metallurgical Engineering and Material Sciences IIT Bombay Powai Maharashtra Mumbai400076 India S. N. Bose National Center for Basic Sciences JD-Block Salt Lake Kolkata700098 India Indian Centre for Space Physics 43 Chalantika Garia Station Road Kolkata700084 India Department of Space Earth & Environment Chalmers University of Technology GothenburgSE-412 96 Sweden Center for Transdisciplinary Research Niigata University Niigata Nishi-ku950-2181 Japan Environmental Science Program Department of Science Faculty of Science Niigata University Niigata Nishi-ku950-2181 Japan Department of Earth and Planetary Sciences Tokyo Institute of Technology Meguro Tokyo152-8551 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan National Astronomical Observatory of Japan Tokyo181-8588 Japan

The destiny of complex organic molecules (COMs) in star-forming regions is interlinked with various evolutionary phases. Therefore, identifying these species in diversified environments of identical star-forming regions would help to comprehend their physical and chemical heritage. We identified multiple COMs utilizing the Large program ‘Astrochemical Surveys At IRAM’ (ASAI) data, dedicated to chemical surveys in Sun-like star-forming regions with the IRAM 30 m telescope. It was an unbiased survey in the millimeter regime, covering the prestellar core, protostar, outflow region, and protoplanetary disk phase. Here, we have reported some transitions of seven COMs, namely, methanol (CH3OH), acetaldehyde (CH3CHO), methyl formate (CH3OCHO), ethanol (C2H5OH), propynal (HCCCHO), dimethyl ether (CH3OCH3), and methyl cyanide (CH3CN) in some sources L1544, B1-b, IRAS4A, and SVS13A. We found a trend among these species from the derived abundances using the rotational diagram method and MCMC fit. We have found that the abundances of all of the COMs, except for HCCCHO, increase from the L1544 (prestellar core) and peaks at IRAS16293-2422 (class 0 phase). It is noticed that the abundance of these molecules correlate with the luminosity of the sources. The obtained trend is also visible from the previous interferometric observations and considering the beam dilution effect. Copyright © 2023, The Authors. All rights reserved.

关键词： Molecules

Multimodal AI predicts clinical outcomes of drug combinations from preclinical data

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arXiv 2025年

作者： Huang, Yepeng Su, Xiaorui Ullanat, Varun Liang, Ivy Clegg, Lindsay Olabode, Damilola Ho, Nicholas John, Bino Gibbs, Megan Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA United States Program in Biological and Biomedical Sciences Harvard Medical School BostonMA United States Harvard College CambridgeMA United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca GaithersburgMD United States Clinical Pharmacology and Quantitative Pharmacology Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Program in Computational Biology Carnegie Mellon University PittsburghPA United States Imaging and Data Analytics Clinical Pharmacology & Safety Sciences R&D AstraZeneca WalthamMA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative CambridgeMA United States

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the development of precision therapies. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21,842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference—a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance. Copy

关键词： Diseases

Cool and gusty, with a chance of rain: Dynamics of multiphase CGM around massive galaxies in the Romulus simulations

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arXiv 2023年

作者： Saeedzadeh, Vida Jung, S. Lyla Rennehan, Douglas Babul, Arif Tremmel, Michael Quinn, Thomas R. Shao, Zhiwei Sharma, Prateek Mayer, Lucio O’Sullivan, E. Loubser, S. Ilani Department of Physics and Astronomy University of Victoria 3800 Finnerty Road VictoriaBCV8P 1A1 Canada Research School of Astronomy & Astrophysics Australian National University CanberraACT2611 Australia Center for Computational Astrophysics Flatiron Institute 162 5th Ave New YorkNY10010 United States School of Physics University College Cork College Road CorkT12 K8AF Ireland Astronomy Department University of Washington Box 351580 SeattleWA98195-1580 United States Department of Astronomy School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Department of Physics Joint Astronomy Program Indian Institute of Science Bangalore560012 India Institute for Computational Science University of Zürich Winterthurerstrasse 190 Zürich8057 Switzerland Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Centre for Space Research North-West University Potchefstroom2520 South Africa Indian Institute of Science Bangalore560012 India

Using high-resolution Romulus simulations, we explore the origin and evolution of the circumgalactic medium (CGM) in the region 0.1 ≤ R/R500 ≤ 1 around massive central galaxies in group-scale halos. We find that the CGM is multiphase and highly dynamic. Investigating the dynamics, we identify seven patterns of evolution. We show that these are robust and detected consistently across various conditions. The gas cools via two pathways: (1) filamentary cooling inflows and (2) condensations forming from rapidly cooling density perturbations. In our cosmological simulations, the perturbations are mainly seeded by orbiting substructures. The condensations can form even when the median tcool/tff of the X-ray emitting gas is above 10 or 20. Strong amplitude perturbations can provoke runaway cooling regardless of the state of the background gas. We also find perturbations whose local tcool/tff ratios drop below the threshold but which do not condense. Rather, the ratios fall to some minimum value and then bounce. These are weak perturbations that are temporarily swept up in satellite wakes and carried to larger radii. Their tcool/tff ratios decrease because tff is increasing, not because tcool is decreasing. For structures forming hierarchically, our study highlights the challenge of using a simple threshold argument to infer the CGM’s evolution. It also highlights that the median hot gas properties are suboptimal determinants of the CGM’s state and dynamics. Realistic CGM models must incorporate the impact of mergers and orbiting satellites, along with the CGM’s heating and cooling cycles. Copyright © 2023, The Authors. All rights reserved.

关键词： Condensation

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Layer-dependent optically-induced spin polarization in InSe

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arXiv 2022年

作者： Nelson, Jovan Stanev, Teodor K. Lebedev, Dmitry LaMountain, Trevor Tyler Gish, J. Zeng, Hongfei Shin, Hyeondeok Heinonen, Olle Watanabe, Kenji Taniguchi, Takashi Hersam, Mark C. Stern, Nathaniel P. Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Material Science & Engineering Northwestern University EvanstonIL60208 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Material Science Division Argonne National Laboratory LemontIL60439 United States Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL60208 United States

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for opto-electronics due to its high electron mobility, tunable direct bandgap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules suggesting potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photo-luminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g-factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab-initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for opto-spintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material. Copyright © 2022, The Authors. All rights reserved.

关键词： Spin dynamics

Discovery of dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors using an artificial intelligence model and their effects on tau and tubulin dynamics

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Biomedicine and Pharmacotherapy 2024年 181卷 117688-117688页

作者： Chen, Jun-Hong Tu, Huang-Ju Lin, Tony Eight Peng, Zhao-Xiang Wu, Yi-Wen Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Lee, Hsueh-Yun Pan, Shiow-Lin HuangFu, Wei-Chun Hsu, Kai-Cheng Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong Guangdong Shenzhen China Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Pharmacy Taipei Medical University Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The dual-specificity tyrosine-phosphorylation-regulated kinase 1 A (DYRK1A) presents a promising therapeutic target for neurological diseases. However, current inhibitors lack selectivity, which can lead to unexpected side effects and increase the difficulty of studying DYRK1A. Therefore, identifying selective inhibitors targeting DYRK1A is essential for reducing side effects and facilitating neurological disease research. This study aimed to discover DYRK1A inhibitors through a screening pipeline incorporating a deep neural network (DNN) model. Herein, we report an optimized model with an accuracy of 0.93 on a testing set. The pipeline was then performed to identify potential DYRK1A inhibitors from the National Cancer Institute (NCI) library. Four novel DYRK1A inhibitors were identified, and compounds NSC657702 and NSC31059 were noteworthy for their potent inhibition, with IC₅₀ values of 50.9 and 39.5 nM, respectively. NSC31059 exhibited exceptional selectivity across 70 kinases. The compounds also significantly reduced DYRK1A-induced tau phosphorylation at key sites associated with the pathology of neurodegenerative diseases. Moreover, they promoted tubulin polymerization, suggesting a role in microtubule stabilization. Cytotoxicity assessments further confirmed the neuronal safety of the compounds. Together, the results demonstrated a promising screening pipeline and novel DYRK1A inhibitors as candidates for further optimization and development. © 2024 The Authors

关键词： Deep Learning DYRK1A Tau

Bacterial biofilms use chiral branches to escape crowded environments by tracking oxygen gradient

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arXiv 2022年

作者： Basaran, Mustafa Yüce, Tevfik Can Yaman, Yusuf Ilker Vetter, Roman Kocabas, Askin Department of Physics Koç University Sarıyer Istanbul34450 Turkey Bio-Medical Sciences and Engineering Program Koç University Sarıyer Istanbul34450 Turkey Computational Physics for Engineering Materials ETH Zurich Zurich8093 Switzerland Koç University Surface Science and Technology Center Koç University Sarıyer Istanbul34450 Turkey Koç University Research Center for Translational Medicine Koç University Sarıyer Istanbul34450 Turkey

Bacterial biofilms collectively develop distinct and ordered structures, including fibers, bundles, and branches. Often, it is unclear how these structural motifs convey specific advantages to bacterial strains under challenging conditions. In oxygen-limited environments, dense bacterial aggregates generally deplete oxygen, which leads to arrest of bacterial growth. However, we observed that biofilm-forming Bacillus subtilis could use branching patterns to escape from these crowded regions by tracking the oxygen gradient. The process depends on the chain-forming ability of the biofilm. As a result of collective branching triggered by bending and mechanical buckling, bacteria can extend from the oxygen-depleted biofilm core to the oxygen available periphery. Remarkably, these bacterial branches are strongly chiral and curve in a clockwise direction. Our analysis revealed that the surface friction and axial rotation of the twisting cell wall break left-right symmetry on solid surfaces and drive chiral bending of bacterial chains. We further observed that the chirality of individual branches could propagate across a large scale and shape the macroscopic morphology of the colony under a limited spatiotemporal growth profile. Taken together, our results provide new insights into how simple physical interactions lead to bacterial collaboration and promote the survival of biofilm-forming strains in challenging environments. © 2022, CC0.

关键词： Biofilms