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arXiv 2024年

作者： Barton, Brandon Sagal, Jacob Feeney, Sean Grattan, George Patnaik, Pratik Oganesyan, Vadim Carr, Lincoln D. Kapit, Eliot Quantum Engineering Program Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Center for Computational Quantum Physics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Physics Colorado School of Mines 1523 Illinois St GoldenCO80401 United States Department of Computer Science Colorado School of Mines 1500 Illinois St GoldenCO80401 United States Department of Physics and Astronomy College of Staten Island City University of New York Staten IslandNY10314 United States Physics program and Initiative for the Theoretical Sciences The Graduate Center City University of New York New YorkNY10016 United States

In this work, we introduce a new iterative quantum algorithm, called Iterative Symphonic Tunneling for Satisfiability problems (IST-SAT), which solves quantum spin glass optimization problems using high-frequency oscillating transverse fields. IST-SAT operates as a sequence of iterations, in which bitstrings returned from one iteration are used to set spin-dependent phases in oscillating transverse fields in the next iteration. Over several iterations, the novel mechanism of the algorithm steers the system toward the problem ground state. We benchmark IST-SAT on sets of hard MAX-3-XORSAT problem instances with exact state vector simulation, and report polynomial speedups over trotterized adiabatic quantum computation (TAQC) and the best known semi-greedy classical algorithm. When IST-SAT is seeded with a sufficiently good initial approximation, the algorithm converges to exact solution(s) in a polynomial number of iterations. Our numerical results identify a critial Hamming radius(CHR), or quality of initial approximation, where the time-to-solution crosses from exponential to polynomial scaling in problem size. By combining IST-SAT with future classical or quantum approximation algorithms, larger gains may be achieved. The mechanism we present in this work thus presents a new path toward achieving quantum advantage in optimization. © 2024, CC BY.

关键词： Combinatorial optimization

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. MacDermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, David L. Chodera, John D. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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Cover Image, Volume 59, Issue 7

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Molecular Carcinogenesis 2020年第7期59卷

作者： Elizabeth Mitchell Sonali Jindal Tiffany Chan Jayasri Narasimhan Shamilene Sivagnanam Elliot Gray Young Hwan Chang Sheila Weinmann Pepper Schedin Department of Cell Developmental and Cancer Biology Oregon Health and Science University Portland Oregon Cancer Prevention and Control Knight Cancer Institute Oregon Health and Science University Portland Oregon Computational Biology Program Department of Cell Developmental and Cancer Biology Oregon Health and Science University Portland Oregon Department of Biomedical Engineering Oregon Center for Spatial Systems Biomedicine Oregon Health and Science University Portland Oregon Center for Health Research Kaiser Permanente Northwest Portland Oregon

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Rapid identification and monitoring of cooking oil fume-based toxic volatile organic aldehydes in lung tissue for predicting exposure level and cancer risks

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Chemosphere 2023年第1期339卷 139704页

作者： Prakasham, Karthikeyan Gurrani, Swapnil Wu, Chia-Fang Wu, Ming-Tsang Hsieh, Tusty-Jiuan Peng, Chiung-Yu Huang, Po-Chin Krishnan, Anbarasu Tsai, Pei-Chien Lin, Yu-Chia Tsai, Bongee Lin, Yuan-Chung Ponnusamy, Vinoth Kumar Kaohsiung City807 Taiwan Kaohsiung City807 Taiwan International Master Program of Translational Medicine College of Engineering and Science National United University Miaoli Taiwan Kaohsiung City807 Taiwan Department of Public Health College of Health Sciences Kaohsiung Medical University Kaohsiung City807 Taiwan Miaoli County 35053 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Kaohsiung City807 Taiwan Corporation No.392 Yucheng Rd. Zuoying District. Kaohsiung City813 Taiwan Center for Emerging Contaminants Research National Sun Yat-Sen University Kaohsiung City804 Taiwan Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung City804 Taiwan Kaohsiung Medical University Kaohsiung City807 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Department of Family Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan

Cooking oil fumes (COFs) comprised of a mixture of cancer-causing volatile organic aldehydes (VOAs), particularly trans, trans-2,4-decadienal (t,t-DDE), 4-hydroxy-hexenal (4-HHE), and 4-hydroxy-nonenal (4-HNE). Monitoring toxic VOAs levels in people exposed to different cooking conditions is vital to predicting the cancer risk. For this purpose, we developed a fast tissue extraction (FaTEx) technique combined with UHPLC-MS/MS to monitor three toxic VOAs in mice lung tissue samples. FaTEx pre-treatment protocol was developed by combining two syringes for extraction and clean-up process. The various procedural steps affecting the FaTEx sample pre-treatment process were optimized to enhance the target VOAs’ extraction efficiency from the sample matrix. Under the optimal experimental conditions, results exhibit good correlation coefficient values > 0.99, detection limits were between 0.5–3 ng/g, quantification limits were between 1–10 ng/g, and the matrix effect was © 2023 Elsevier Ltd

关键词： Diseases

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Is there any linkage between interstellar aldehyde and alcohol?

arXiv

引用

arXiv 2021年

作者： Mondal, Suman Kumar Gorai, Prasanta Sil, Milan Ghosh, Rana Etim, Emmanuel E. Chakrabarti, Sandip K. Shimonishi, Takashi Nakatani, Naoki Furuya, Kenji Tan, Jonathan C. Das, Ankan Indian Centre for Space Physics 43 Chalantika Garia Station Road Kolkata700084 India Department of Space Earth and Environment Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Chemical Sciences Federal University Wukari Katsina-Ala Road P.M.B. 1020 Taraba State Wukari Nigeria Center for Transdisciplinary Research Niigata University Nishi-ku Niigata950-2181 Japan Environmental Science Program Department of Science Faculty of Science Niigata University Nishi-ku Niigata950-2181 Japan Institute for Catalysis Hokkaido University N21W10 Kita-ku Hokkaido Sapporo001-0021 Japan Center for Computational Sciences University of Tsukuba Tsukuba305-8577 Japan Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Department of Chemistry Graduate School of Science and Engineering Tokyo Metropolitan University 1-1 Minami-Osawa Hachioji Tokyo192-0397 Japan National Astronomical Observatory of Japan Tokyo181-8588 Japan

It is speculated that there might be some linkage between interstellar aldehydes and their corresponding alcohols. Here, an observational study and astrochemical modeling are coupled together to illustrate the connection between them. The ALMA cycle 4 data of a hot molecular core, G10.47+0.03 is utilized for this study. Various aldehydes (acetaldehyde, propanal, and glycolaldehyde), alcohols (methanol and ethylene glycol), and a ketone (acetone) are identified in this source. The excitation temperatures and the column densities of these species were derived via the rotation diagram method assuming LTE conditions. An extensive investigation is carried out to understand the formation of these species. Six pairs of aldehyde-alcohol: i) methanal and methanol;ii) ethanal and ethanol;iii) propanal and 1-propanol;iv) propenal and allyl alcohol;v) propynal and propar gyl alcohol;vi) glycolaldehyde and ethylene glycol;vii) along with one pair of ketone-alcohol (acetone and isopropanol) and viii) ketene-alcohol (ethenone and vinyl alcohol) are considered for this study. Two succes sive hydrogenation reactions in the ice phase are examined to form these alcohols from aldehydes, ketone, and ketene, respectively. Quantum chemical methods are extensively executed to review the ice phase formation route and the kinetics of these species. Based on the obtained kinetic data, astrochemical modeling is employed to derive the abundances of these aldehydes, alcohols, ketone, and ketene in this source. It is seen that our model could successfully explain the observed abundances of various species in this hot molecular core. Copyright © 2021, The Authors. All rights reserved.

关键词： Acetone

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Performance of the prototype shaftless small scale horizontal wind turbine for electricity generating from industrial exhaust air system

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IOP Conference Series: Earth and Environmental science 2020年第1期463卷

作者： Wachira Puttichaem Yuttanant Boonyongmaneerat Pakpachong Vadhanasindhu Sompong Putivisutisak Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok 10330 Thailand Metallurgy and Materials Science Research Institute (MMRI) Chulalongkorn University Bangkok 10330 Thailand Faculty of Commerce and Accountancy Chulalongkorn Business School Chulalongkorn University Bangkok 10330 Thailand Advanced Computational Fluid Dynamics Research Unit Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand

Man-made or unnatural wind from the industrial exhaust air system is an alternative wind resource for countries with natural low-speed or intermittent wind such as Thailand. It has strong and consistent wind speed when compared to the natural wind, with velocity about 5 to 10 m/s at a distance of 5 cm from the exhaust air outlet. However, some negative impacts to the exhaust air system performance was observed when a conventional wind turbine was employed. The objective of this research is to feasibility study the practicality of a prototype shaftless small scale horizontal axis wind turbine (SSHWT) to generate electricity from the exhaust air of the industrial exhaust air system. Aerodynamic, blade and generator designs were addressed in this study to maximize energy output and minimize negative impacts to the performance of the original exhaust air system. The performance of SSHWT was tested with a selected industrial fan that is widely used in industrial sections. The results showed that the SSHWT could generate electricity with less negative effect to exhaust air system performance. However, it still needs further improvements caused by the voltage output is too low. By the concept design, this innovative wind turbine is compact, thus needs only small space for installation. This SSHWT has high market potential for low wind speed countries to take advantage of unnatural wind resources which are better in terms of efficiency and economy for sustainable energy development.

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Jamming of Deformable Polygons

arXiv

引用

arXiv 2018年

作者： Boromand, Arman Signoriello, Alexandra Ye, Fangfu O'Hern, Corey S. Shattuck, Mark D. Beijing Natl. Laboratory for Condensed Matter Physics and Cas Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of City University of New York New YorkNY10031 United States

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