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arXiv 2020年

作者： Masuda, Naoki Porter, Mason A. Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Department of Mathematics University of California Los Angeles Los AngelesCA United States

Suppose that you’re going to school and arrive at a bus stop. How long do you have to wait before the next bus arrives? Surprisingly, it is longer — possibly much longer — than what the bus schedule suggests intuitively. This phenomenon, called the waiting-time paradox, has a purely mathematical origin. Different buses arrive with different intervals, leading to this paradox. In this article, we look at the waiting-time paradox, explain why it happens, and explore some of its implications (beyond the possibility of being late for school). Copyright © 2020, The Authors. All rights reserved.

关键词： Buses

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Minimization-Aware Recursive K∗ (MARK∗ ): A Novel, Provable Algorithm that Accelerates Ensemble-Based Protein Design and Provably Approximates the Energy Landscape 1

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23rd International Conference on Research in computational Molecular Biology, RECOMB 2019

作者： Jou, Jonathan D. Holt, Graham T. Lowegard, Anna U. Donald, Bruce R. Department of Computer Science Duke University DurhamNC United States Computational Biology and Bioinformatics Program Duke University DurhamNC United States Department of Biochemistry Duke University Medical Center DurhamNC United States Department of Chemistry Duke University DurhamNC United States

ISBN: (数字)9783030170837

ISBN: (纸本)9783030170820

Protein design algorithms that model continuous sidechain flexibility and conformational ensembles better approximate the in vitro and in vivo behavior of proteins. The previous state of the art, iMinDEE- A∗ - K∗, computes provable Ε -approximations to partition functions of protein states (e.g., bound vs. unbound) by computing provable, admissible pairwise-minimized energy lower bounds on protein conformations and using the A∗ enumeration algorithm to return a gap-free list of lowest-energy conformations. iMinDEE-A ∗ - K∗ runs in time sublinear in the number of conformations, but can be trapped in loosely-bounded, low-energy conformational wells containing many conformations with highly similar energies. That is, iMinDEE- A∗ - K∗ is unable to exploit the correlation between protein conformation and energy: similar conformations often have similar energy. We introduce two new concepts that exploit this correlation: Minimization-Aware Enumeration and Recursive K∗. We combine these two insights into a novel algorithm, Minimization-Aware Recursive K∗ (MARK∗ ), that tightens bounds not on single conformations, but instead on distinct regions of the conformation space. We compare the performance of iMinDEE- A∗ - K∗ vs. MARK∗ by running the BBK∗ algorithm, which provably returns sequences in order of decreasing K∗ score, using either iMinDEE- A∗ - K∗ or MARK∗ to approximate partition functions. We show on 200 design problems that MARK∗ not only enumerates and minimizes vastly fewer conformations than the previous state of the art, but also runs upÂ to two orders of magnitude faster. Finally, we show that MARK∗ not only efficiently approximates the partition function, but also provably approximates the energy landscape. To our knowledge, MARK∗ is the first algorithm to do so. We use MARK∗ to analyze the change in energy landscape of the bound and unbound states of the HIV-1 capsid protein C-terminal domain in complex with camelid V H H, and measure the change in conformati

关键词： Proteins

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Predicting permeability via statistical learning on higher-order microstructural information

arXiv

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arXiv 2020年

作者： Röding, Magnus Ma, Zheng Torquato, Salvatore RISE Research Institutes of Sweden Göteborg41276 Sweden Department of Physics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Quantitative structure-property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure-property relationships. We make the data and code used publicly available to fac

关键词： Forecasting

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Generative models of simultaneously heavy-tailed distributions of interevent times on nodes and edges

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Physical Review E 2020年第5期102卷 052303-052303页

作者： Elohim Fonseca dos Reis Aming Li Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260 USA Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan

Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied. The literature supports that such heavy-tailed distributions are present for interevent times associated with both individual nodes and individual edges in networks. However, the simultaneous presence of heavy-tailed distributions of interevent times for nodes and edges is a nontrivial phenomenon, and its origin has been elusive. In the present study, we propose a generative model and its variants to explain this phenomenon. We assume that each node independently transits between a high-activity and low-activity state according to a continuous-time two-state Markov process and that, for the main model, events on an edge occur at a high rate if and only if both end nodes of the edge are in the high-activity state. In other words, two nodes interact frequently only when both nodes prefer to interact with others. The model produces distributions of interevent times for both individual nodes and edges that resemble heavy-tailed distributions across some scales. It also produces positive correlation in consecutive interevent times, which is another stylized observation for empirical data of human activity. We expect that our modeling framework provides a useful benchmark for investigating dynamics on temporal networks driven by non-Poissonian event sequences.

关键词： Network structure Social networks Stochastic networks Network Models

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The transit timing and atmosphere of hot jupiter HAT-P-37b

arXiv

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arXiv 2021年

作者： A-Thano, Napaporn Jiang, Ing-Guey Awiphan, Supachai Rattanamala, Ronnakrit Su, Li-Hsin Hengpiya, Torik Sariya, Devesh P. Yeh, Li-Chin Shlyapnikov, A.A. Gorbachev, Mark A. Rublevski, Alexey N. Mannaday, Vineet Kumar Thakur, Parijat Sahu, D.K. Mkrtichian, David Griv, Evgeny Department of Physics and Institute of Astronomy National Tsing-Hua University Hsinchu30013 Taiwan National Astronomical Research Institute of Thailand 260 Moo 4 Donkaew Mae Rim Chiang Mai50180 Thailand PhD Program in Astronomy Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Physics and General Science Faculty of Science and Technology Nakhon Ratchasima Rajabhat University Nakhon Ratchasima30000 Thailand Regional Observatory for the Public Songkhla 79/4 Moo 4 Khao Roop Chang Muang District Songkhla90000 Thailand Institute of Computational and Modeling Science National Tsing-Hua University Hsinchu30013 Taiwan Crimean Astrophysical Observatory 298409 Nauchny Crimea 495009 India Indian Institute of Astrophysics Bangalore560034 India Department of Physics Ben-Gurion University Beer-Sheva84105 Israel

The transit timing variation (TTV) and transmission spectroscopy analyses of the planet HAT-P-37b, which is a hot Jupiter orbiting an G-type star, were performed. Nine new transit light curves are obtained and analysed together with 21 published light curves from the literature. The updated physical parameters of HAT-P-37b are presented. The TTV analyses show a possibility that the system has an additional planet which induced the TTVs amplitude signal of 1.74 ± 0.17 minutes. If the body is located near the 1:2 mean motion resonance orbit, the sinusoidal TTV signal could be caused by the gravitational interaction of a sub-Earth mass planet with mass of 0.06 MO. From the analysis of an upper mass limit for the second planet, the Saturn mass planet with orbital period less than 6 days is excluded. The broad-band transmission spectra of HAT-P-37b favours a cloudy atmospheric model with an outlier spectrum in B-filter. Copyright © 2021, The Authors. All rights reserved.

关键词： Planets

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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle

arXiv

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arXiv 2020年

作者： Yu, Haijun Tian, Xinyuan Weinan, E. Li, Qianxiao NCMIS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics National University of Singapore Singapore119077 Singapore Institute of High Performance Computing A*STAR Singapore138632 Singapore

We propose a systematic method for learning stable and physically interpretable dynamical models using sampled trajectory data from physical processes based on a generalized Onsager principle. The learned dynamics are autonomous ordinary differential equations parameterized by neural networks that retain clear physical structure information, such as free energy, diffusion, conservative motion and external forces. For high dimensional problems with a low dimensional slow manifold, an autoencoder with metric preserving regularization is introduced to find the low dimensional generalized coordinates on which we learn the generalized Onsager dynamics. Our method exhibits clear advantages over existing methods on benchmark problems for learning ordinary differential equations. We further apply this method to study Rayleigh-Bénard convection and learn Lorenz-like low dimensional autonomous reduced order models that capture both qualitative and quantitative properties of the underlying dynamics. This forms a general approach to building reduced order models for forced dissipative *** Codes 76E30, 34D20, 68T05/07, 82C35 Copyright © 2020, The Authors. All rights reserved.

关键词： Ordinary differential equations

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Publisher Correction: Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition

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Scientific reports 2019年第1期9卷 18996页

作者： Panupong Mahalapbutr Nitchakan Darai Wanwisa Panman Aunchan Opasmahakul Nawee Kungwan Supot Hannongbua Thanyada Rungrotmongkol Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Program in Biotechnology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. Department of Chemistry Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand. Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand. Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***. Molecular Sensory Science Center Faculty of Science Chulalongkorn University Bangkok 10330 Thailand. t.rungrotmongkol@***.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Assessing individual head and neck squamous cell carcinoma patient response to therapy through integration of functional and genomic data

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Scientific reports 2025年第1期15卷 19742页

作者： Daniel Bottomly Chase Mathieson Myles Vigoda Sophia Jeng Nathaniel Evans Ashley Anderson Aurora Blucher Aletha Lesch Christina Zheng Ted Laderas James Jacobs Molly Kulesz-Martin Shannon McWeeney OHSU Knight Cancer Institute Portland OR 97239 USA. OHSU School of Dentistry Portland OR 97239 USA. Michigan State University College of Osteopathic Medicine East Lansing USA. Program in Biomedical Sciences Anesthesiology and Perioperative Medicine School of Medicine Oregon Health & Science University Portland OR 97239 USA. Recursion Pharmaceuticals Salt Lake City UT USA. Department of Dermatology Oregon Health & Science University Portland OR 97239 USA. Fred Hutch Cancer Center Seattle WA USA. Randall Children's Cancer and Blood Disorders Program at Legacy Emmanuel Portland OR USA. Department of Cell Developmental and Cancer Biology Oregon Health & Science University Portland OR 97239 USA. OHSU Knight Cancer Institute Portland OR 97239 USA. mcweeney@ohsu.edu. Division of Bioinformatics and Computational Biology Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland OR 97239 USA. mcweeney@ohsu.edu. Division of Oncological Sciences Oregon Health & Science University Portland OR 97239 USA. mcweeney@ohsu.edu.

Even though head and neck squamous cell carcinoma (HNSCC) is the seventh most common cancer worldwide, there are only two PD-1 targeted immunotherapies (pembrolizumab and nivolumab) and one tumor intrinsic EGFR targeted therapy (cetuximab) that are FDA approved for treatment of HNSCC. Taking advantage of a high throughput inhibitor assay and computational tools originally showing success in leukemia, we designed and employed HNSCC-specific inhibitor panels that capture the diversity of aberrational pathways in HNSCC to test viable cells derived from patients' HNSCC tumors. This provides a functional context to the multi-omic readouts conducted on these samples (mutations, protein expression and copy number alterations). In addition to generating these deeply characterized functional genomics datasets, we also developed additional visual analytics that have the potential to provide greater insight into HNSCC drug response patterns and potentially aid precision oncology tumor boards in evaluation and assessment of effective targeted therapeutic agents.

关键词： Antitumor drug screening assays HNSCC Multi-Omics

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