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arXiv 2024年

作者： Newby, Eli Shi, Wenlong Jiao, Yang Albert, Reka Torquato, Salvatore Department of Physics Pennsylvania State University University ParkPA16802 United States Materials Science and Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Disordered hyperuniform many-particle systems are recently discovered exotic states of matter, characterized by a complete suppression of normalized infinite-wavelength density fluctuations, as in perfect crystals, and lack of conventional long-range order, as in liquids and glasses. In this work, we begin a program to quantify the structural properties of nonhyperuniform and hyperuniform networks. In particular, large two-dimensional (2D) Voronoi networks (graphs) containing approximately 10,000 nodes are created from a variety of different point configurations, including the antihyperuniform hyperplane intersection process (HIP), nonhyperuniform Poisson process, nonhyperuniform random sequential addition (RSA) saturated packing, and both non-stealthy and stealthy hyperuniform point processes. We carry out an extensive study of the Voronoi-cell area distribution of each of the networks through determining multiple metrics that characterize the distribution, including their higher-cumulants (i.e., skewness 1 and excess kurtosis 2). We show that the HIP distribution is far from Gaussian, as evidenced by a high skewness (γ1 = 3.16) and large positive excess kurtosis (γ2 = 16.2). The Poisson (with γ1 = 1.07 and γ2 = 1.79) and non-stealthy hyperuniform (with γ1 = 0.257 and γ2 = 0.0217) distributions are Gaussian-like distributions, since they exhibit a small but positive skewness and excess kurtosis. The RSA (with γ1 = 0.450 and γ2 = -0.0384) and the highest stealthy hyperuniform distributions (with γ1 = 0.0272 and γ2 = -0.0626) are also non-Gaussian because of their low skewness and negative excess kurtosis, which is diametrically opposite non-Gaussian behavior of the HIP. The fact that the cell-area distributions of large, finite-sized RSA and stealthy hyperuniform networks (e.g., with N ≈ 10, 000 nodes) are narrower, have larger peaks, and smaller tails than a Gaussian distribution implies that in the thermodynamic limit the distributions should exhibit compact suppo

关键词： Gaussian distribution

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Comparison of volume transport in the Halmahera Sea between la Nina 2011 and El Nino 2015 events based on numerical model 2

Comparison of volume transport in the Halmahera Sea between ...

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2nd Maritime science and Advanced Technology: Marine science and Technology in Framework of The Sustainable Development Goals, MSAT 2019

作者： Riza, Muhammad Putri, Mutiara R. Mandang, Idris Department of Earth Science Faculty of Earth Science and Technology Bandung Institute of Technology Jalan Ganesha No 10 Bandung40132 Indonesia Hydro-Atmosphere Environment Research Group Physical Oceanography and Computational Modeling Laboratory Program Study of Physics Faculty of Mathematics and Natural Sciences Mulawarman University Jalan Barong Tongkok No 04 Samarinda75123 Indonesia

Variations of volume transport in the Halmahera Sea are strongly influenced by the El Nino Southern Oscillation (ENSO). Based on the Southern Oscillation Index (SOI), in 2011 La Nina event took place with a strength of 3.02, while in 2015 El Nino occurred with a strength of -2.6. This paper discusses the variation of transport volume caused by the ENSO phenomenon based on the results of the Regional Ocean Model System (ROMS). On the Halmahera Sea at a latitude of 0.3°S with a width of 67 km and a depth of down to 200 m, net volume transport always moves southward. The largest volume transport in La Nina 2011 occurred in September-October, which was -8.9 Sv. Meanwhile, in El Nino 2015 the largest volume transport occurred in July-August, which was equal to -4.9 Sv. The cross correlation coefficient between volume transport and SOI in 2011 and 2015 was r = 0.55 and r = 0.61 respectively, where these results indicate a strong relationship. © Published under licence by IOP Publishing Ltd.

关键词： Numerical models

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Challenges in Developing Materials for Microreactors: A Case-Study of Yttrium Dihydride in Extreme Conditions

SSRN

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SSRN 2023年

作者： Tunes, Matheus Araujo Parkison, Darren Huang, Yuqing Chancey, M.R. Vogel, Sven C. Mehta, Vedant K. Torrez, Michael A. Luther, E.P. Valdez, James A. Wang, Yongqiang Yu, Jianguo Cinbiz, Mahmut Nedim Shivprasad, Aditya P. Kohnert, Cailtin A. Montanuniversität Leoben Austria Materials Science and Technology Division Los Alamos National Laboratory United States Department of Nuclear Engineering University of California Berkeley United States Department of Nuclear Engineering North Carolina State University United States Irradiated Fuels and Materials Idaho National Laboratory United States Program Project Interface Division Los Alamos National Laboratory United States Sigma Division Los Alamos National Laboratory United States Computational Mechanics and Materials Idaho National Laboratory United States Materials Science and Technology Division Oak Ridge National Laboratory United States

The development of microreactor technology presents an efficient solution for providing portable electricity, catering to both human space exploration needs within our solar system and supplying power to remote Earth-bound areas. The miniaturization of nuclear reactors poses immediate new challenges for materials science with respect to the capability for controlling nuclear reactions via thermalization of highly-energetic neutrons. In a microreactor, neutron moderation takes place in compact geometries, therefore new moderator materials are required to exhibit high moderating power per unit of volume. This challenge is currently being addressed through the advancement of hydrogen technology and materials science, particularly focusing on development of transition metal hydrides, but to date, research on their irradiation response, specifically regarding phase stability, hydrogen in-lattice retention, and their dependence on irradiation temperature and dose, is limited. Herein, we present a detailed investigation on the response of yttrium dihydride (\ce{YH2}) - the most suitable moderator candidate with respect to its high operational temperature - to heavy ion irradiation. A combination of multiple experimental techniques indicate that \ce{YH2} is stable up to an irradiation dose of 2 dpa and below 800$^{\circ}$C, identified as a critical temperature for \ce{YH2}. Our study unravels that voids emerge as the major form of radiation damage. Below the critical temperature no phase change, precipitation or amorphization was observed to occur. Experimental results from ion beam and neutron scattering techniques with concomitant density functional theory calculations have allowed us to elaborate and propose new strategies to enhance the metal hydride performance in extreme environments. © 2023, The Authors. All rights reserved.

关键词： Hydrides

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Local and global measures of the shear moduli of jammed disk packings

arXiv

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arXiv 2023年

作者： Zhang, Shiyun Jin, Weiwei Wang, Dong Xu, Ding Zhang, Jerry Shattuck, Mark D. O’Hern, Corey S. Department of Physics University of Science and Technology of China Anhui Hefei230026 China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Strain-controlled isotropic compression gives rise to jammed packings of repulsive, frictionless disks with either positive or negative global shear moduli. We carry out computational studies to understand the contributions of the negative shear moduli to the mechanical response of jammed disk packings. We first decompose the ensemble-averaged, global shear modulus as hGi = (1 − F−)hG+i + F−hG−i, where F− is the fraction of jammed packings with negative shear moduli and hG+i and hG−i are the average values from packings with positive and negative moduli, respectively. We show that hG+i and h|G−|i obey different power-law scaling relations above and below pN2 ∼ 1. For pN2 > 1, both hG+iN and h|G−|iN ∼ (pN2)β, where β ∼ 0.5 for repulsive linear spring interactions. Despite this, hGiN ∼ (pN2)β0 with β0 & 0.5 due to the contributions from packings with negative shear moduli. We show further that the probability distribution of global shear moduli P(G) collapses at fixed pN2 and different values of p and N. We calculate analytically that P(G) is a Gamma distribution in the pN2 1 limit. As pN2 increases, the skewness of P(G) decreases and P(G) becomes a skew-normal distribution with negative skewness in the pN2 1 limit. We also partition jammed disk packings into subsystems using Delanunay triangulation of the disk centers to calculate local shear moduli. We show that the local shear moduli defined from groups of adjacent triangles can be negative even when G > 0. The spatial correlation function of local shear moduli C(~r) displays weak correlations for pn2sub −2, where nsub is the number of particles within each subsystem. However, C(~r) begins to develop long-ranged spatial correlations with four-fold angular symmetry for pn2sub & 10−2 © 2023, CC BY-NC-ND.

关键词： Elastic moduli

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What we should learn from pandemic publishing

arXiv

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arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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The pH Effects on SARS-CoV and SARS-CoV-2 Spike Proteins in the Process of Binding to hACE2

Research Square

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Research Square 2021年 rs3rs-871118页

作者： Xie, Yixin Guo, Wenhan Lopez-Hernadez, Alan Teng, Shaolei Li, Lin Computational Science Program University of Texas at El Paso El PasoTX United States Department of Biology Howard University Washington DC United States Department of Physics University of Texas at El Paso El PasoTX United States

COVID-19 has been threatening human health since the late 2019, which has significant impact on human health and economy. Understanding the SARS-CoV-2 and other coronaviruses is important to develop effective treatments for COVID-19 and other coronaviruses-caused diseases. In this work, we applied multi-scale computational approaches to study the electrostatic features of spike (S) proteins for SARS-CoV and SARS-CoV-2. From our results, we found that SARS-CoV and SARS-CoV-2 have similar charge distributions and electrostatic features when binding with the human angiotensin-converting enzyme 2 (hACE2). The energy pH-dependence calculations revealed that the complex structures of hACE2 and the S proteins of SARS-CoV/SARS-CoV-2 are stable at pH values ranging from 7.5 to 9. Molecular dynamics simulations were performed using NAMD to investigate the hydrogen bonds between S proteins and hACE2. From the MD simulations it was found that SARS-CoV-2 has four pairs of essential hydrogen bonds (high occupancy, >80%), while SARS-CoV has three pairs, which indicates the SARS-CoV-2 S protein has relatively more robust binding strategy than SARS-CoV S protein. Four key residues forming essential hydrogen bonds from SARS-CoV-2 are identified, which are potential drug targets for COVID-19 treatments. The findings in this study shed lights on the current and future treatments for COVID-19 and other coronaviruses-caused diseases. © 2021, CC BY.

关键词： Coronavirus

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Improving debris flow evacuation alerts in Taiwan using machine learning

arXiv

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arXiv 2022年

作者： Tsai, Yi-Lin Irvin, Jeremy Chundi, Suhas Ng, Andrew Y. Field, Christopher B. Kitanidis, Peter K. Department of Civil and Environmental Engineering Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Woods Institute for the Environment Stanford University StanfordCA United States Interdisciplinary Environmental Studies Program Stanford University StanfordCA United States Department of Earth System Science Stanford University StanfordCA United States Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States

Taiwan has the highest susceptibility to and fatalities from debris flows worldwide. The existing debris flow warning system in Taiwan, which uses a time-weighted measure of rainfall, leads to alerts when the measure exceeds a predefined threshold. However, this system generates many false alarms and misses a substantial fraction of the actual debris flows. Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time. We found that a random forest model performed the best among the five models and outperformed the existing system in Taiwan. Furthermore, we identified the rainfall trajectories strongly related to debris flow occurrences and explored trade-offs between the risks of missing debris flows versus frequent false alerts. These results suggest the potential for machine learning models trained on hourly rainfall data alone to save lives while reducing false alerts. Copyright © 2022, The Authors. All rights reserved.

关键词： Rain

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Exploring the mutation tendency of a specific region in the virus taxonomic groups using data mining techniques

Exploring the mutation tendency of a specific region in the ...

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IEEE International Conference on Big Data

作者： Mikyung Je Myeongji Cho Yoon Jung Kim Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Division of Bio IT Honam National Institute of Biological Resources Mokpo Republic of Korea Lab. of Computational Virology & Viroinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665480468

The GDNQ motif is mainly found in nonsegmented (-)ssRNA viruses and plays a role in the catalytic and polymerase activity pathways. However, in some recently reported dsRNA mycoviruses, the GDNQ motif of the (-)ssRNA virus group is found instead of the GDD motif commonly found in the dsRNA virus group. Therefore, in this study, data mining techniques were used to explore the mutation tendency of a specific region in the virus taxonomic groups. Viral protein sequences were obtained from the NCBI Virus data repository, and the performance of several known string-searching algorithms (BF, KMP, and BM) was evaluated to establish an optimal strategy for extracting useful information from sequence big data. Then, the pattern-matching performance of the algorithm was tested to detect whether the conserved region sequence was a common motif type or a mutated motif type. As a result of the analysis, it was confirmed that the BF method produced the fastest results. However, the BM method recorded higher accuracy. Through the virus mutation tendency derived from the analysis results, it was confirmed that 7.1% of dsRNA viruses had a mutated motif type based on the total data analyzed and that it was relatively recent that ssRNA virus motif variants began to be discovered in dsRNA viruses. It is expected that additional in-depth analysis of the host range and infection route of viruses with the mutation will help understand the evolution of dsRNA and ssRNA mycoviruses.

关键词： Proteins Big Data Data mining Polymers Viruses (medical)

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computational Prediction of Functional Effects for Cancer Related Genetic Sequence Variants

Computational Prediction of Functional Effects for Cancer Re...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bofei Wang Jonathon Mohl Ming-Ying Leung Computational Science program The University of Texas at El Paso El Paso USA The University of Texas at El Paso El Paso USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Rapid advances in next generation sequencing (NGS) technologies provide many opportunities to identify associations between genetic sequence variants (GSVs) and diseases, which may lead to better clinical diagnosis and treatments. The goal of this project is to efficiently identify candidate GSVs that may be associated with cancer using prostate data as an example. For this purpose, we analyzed the NGS data of 503 patients with prostate cancer (PrCa), which are in variant call format (VCF) files in The Cancer Genome Atlas project released through the National Cancer Institute Genomic Data Commons. We proposed a systematic workflow to first split the NGS data by chromosome for efficient preprocessing, then compare the statistical properties of GSVs between cancer and normal samples, and finally identify the GSVs with statistically significant associations with PrCa. Two popular tools FATHMM and PROVEAN were also employed to predict functional effects of GSVs. These results provided a list of 104 high-scoring nonsynonymous GSVs that deserve further investigation for possible pathogenicity. One particular GSV on this list, located within the SPOP gene on chromosome 17, is also significantly associated with PrCa. Methods presented in this paper will be implemented into the OncoMiner pipeline, enabling their application to various types of cancers.

关键词： Bioinformatics Cancer Genomics Tumors Tools Sequential analysis Encoding

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Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome

arXiv

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arXiv 2022年

作者： Callahan, Tiffany J. Stefanski, Adrianne L. Kim, Jin-Dong Baumgartner, William A. Wyrwa, Jordan M. Hunter, Lawrence E. Department of Biomedical Informatics Columbia University New YorkNY United States Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO United States Database Center for Life Science Research Organization of Information and Systems Kashiwa Japan Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO United States

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations. © 2022, CC BY.

关键词： Diseases

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