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arXiv 2024年

作者： Johnson, Ruth Pasaniuc, Bogdan Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA90095 United States Department of Pathology Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States Institute of Precision Health University of California Los Angeles Los AngelesCA90095 United States

Precision medicine aims to create biomedical solutions tailored to specific factors that affect disease risk and treatment responses within the population. The success of the genomics era and recent widespread availability of electronic health records (EHR) has ushered in a new wave of genomic biobanks connected to EHR databases (EHR-linked biobanks). This perspective aims to discuss how race, ethnicity, and genetic ancestry are currently utilized to study common disease variation through genetic association studies. Although genetic ancestry plays a significant role in shaping the genetic landscape underlying disease risk in humans, the overall risk of a disease is caused by a complex combination of environmental, sociocultural, and genetic factors. When using EHR-linked biobanks to interrogate underlying disease etiology, it is also important to be aware of how the biases associated with commonly used descent-associated concepts such as race and ethnicity can propagate to downstream analyses. We intend for this resource to support researchers who perform or analyze genetic association studies in the EHR-linked biobank setting such as those involved in consortium-wide biobanking efforts. We provide background on how race, ethnicity, and genetic ancestry play a role in current association studies, highlight considerations where there is no consensus about best practices, and provide transparency about the current shortcomings. © 2024, CC BY.

关键词： Genome

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An ensemble machine learning model generates a focused screening library for the identification of CDK8 inhibitors

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Protein science 2024年第6期33卷 e5007页

作者： Lin, Tony Eight Yen, Dyan HuangFu, Wei-Chun Wu, Yi-Wen Hsu, Jui-Yi Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Pan, Shiow-Lin Yang, Chia-Ron Huang, Wei-Jan 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 TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong (Shenzhen) Guangdong Shenzhen China Biomedical Translation Research Center Academia Sinica Taipei Taiwan Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CDK8-focused screening library. The ensemble model consists of six different algorithms optimized for CDK8 inhibitor classification. The models were trained using a CDK8-specific fragment library along with molecules containing CDK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CDK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CDK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CDK8 inhibitors, with one compound producing an IC₅₀ value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CDK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CDK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CDK8. © 2024 The Authors. Protein science published by Wiley Periodicals LLC on behalf of The Protein Society.

关键词： CDK8 fragment machine learning molecular docking structure-based virtual screening

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*** - A plug-and-play solution for online systems medicine and network-based drug repurposing

arXiv

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arXiv 2023年

作者： Maier, Andreas Hartung, Michael Abovsky, Mark Adamowicz, Klaudia Bader, Gary D. Baier, Sylvie Blumenthal, David B. Chen, Jing Elkjaer, Maria L. Garcia-Hernandez, Carlos Helmy, Mohamed Hoffmann, Markus Jurisica, Igor Kotlyar, Max Lazareva, Olga Levi, Hagai List, Markus Lobentanzer, Sebastian Loscalzo, Joseph Malod-Dognin, Noel Manz, Quirin Matschinske, Julian Mee, Miles Oubounyt, Mhaned Pico, Alexander R. Pillich, Rudolf T. Poschenrieder, Julian M. Pratt, Dexter Pržulj, Nataša Sadegh, Sepideh Saez-Rodriguez, Julio Sarkar, Suryadipto Shaked, Gideon Shamir, Ron Trummer, Nico Turhan, Ugur Wang, Ruisheng Zolotareva, Olga Baumbach, Jan Institute for Computational Systems Biology University of Hamburg Hamburg Germany Division of Orthopaedic Surgery Schroeder Arthritis Institute Data Science Discovery Centre Osteoarthritis Research Program Krembil Research Institute UHN Toronto Canada Data Science Discovery Centre for Chronic Diseases Krembil Research Institute University Health Network 60 Leonard Avenue 5KD-407 TorontoONM5T 0S8 Canada Department of Molecular Genetics University of Toronto TorontoON Canada The Donnelly Centre University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada The Lunenfeld-Tanenbaum Research Institute Mount Sinai Hospital TorontoON Canada TUM School of Life Sciences Technical University of Munich Munich Germany Erlangen91052 Germany Department of Medicine University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurology Odense University Hospital Odense Denmark Institute of Clinical Research University of Southern Denmark Odense Denmark Institute of Molecular Medicine University of Southern Denmark Odense Denmark Barcelona08034 Spain Institute for Advanced Study Lichtenbergstrasse 2a GarchingD-85748 Germany Technical University of Munich Germany National Institute of Diabetes Digestive and Kidney Diseases BethesdaMD20892 United States Departments of Medical Biophysics and Computer Science University of Toronto Toronto Canada Institute of Neuroimmunology Slovak Academy of Sciences Bratislava Slovakia Heidelberg69120 Germany Heidelberg Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg69117 Germany Blavatnik School of Computer Science Tel-Aviv University Tel-Aviv Israel Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany Department of Medicine Bri

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce ***, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, *** turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, *** has been successfully integrated with 21 computational systems medicine tools. Available at https://***, *** has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research. © 2023, CC BY.

关键词： Data visualization

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Hyperuniformity order metric of Barlow packings

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Physical Review E 2019年第2期99卷 022111-022111页

作者： T. M. Middlemas F. H. Stillinger S. Torquato Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University New Jersey 08544 USA

The concept of hyperuniformity has been a useful tool in the study of density fluctuations at large length scales in systems ranging across the natural and mathematical sciences. One can rank a large class of hyperuniform systems by their ability to suppress long-range density fluctuations through the use of a hyperuniformity order metric Λ¯. We apply this order metric to the Barlow packings, which are the infinitely degenerate densest packings of identical rigid spheres that are distinguished by their stacking geometries and include the commonly known fcc lattice and hcp crystal. The “stealthy stacking” theorem implies that these packings are all stealthy hyperuniform, a strong type of hyperuniformity, which involves the suppression of scattering up to a wave vector K. We describe the geometry of three classes of Barlow packings, two disordered classes and small-period packings. In addition, we compute a lower bound on K for all Barlow packings. We compute Λ¯ for the aforementioned three classes of Barlow packings and find that, to a very good approximation, it is linear in the fraction of fcc-like clusters, taking values between those of least-ordered hcp and most-ordered fcc. This implies that the value of Λ¯ of all Barlow packings is primarily controlled by the local cluster geometry. These results highlight the special nature of anisotropic stacking disorder, which provides impetus for future research on the development of anisotropic order metrics and hyperuniformity properties.

关键词： Classical statistical mechanics

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Global trends and biases in biodiversity conservation research

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Cell Reports Sustainability 2024年第5期1卷

作者： Caldwell, Iain R. Hobbs, Jean-Paul A. Bowen, Brian W. Cowman, Peter F. DiBattista, Joseph D. Whitney, Jon L. Ahti, Pauliina A. Belderok, Roy Canfield, Sean Coleman, Richard R. Iacchei, Matthew Johnston, Erika C. Knapp, Ingrid Nalley, Eileen M. Staeudle, Timo M. Láruson, Áki Jarl College of Arts Society and Education James Cook University 1 James Cook Drive Douglas 4811 QLD Australia School of Biological Sciences The University of Queensland Brisbane 4069 QLD Australia Hawaiʻi Institute of Marine Biology University of Hawai‘i at Mānoa Kāne‘ohe 96744 HI United States Biodiversity and Geosciences Program Queensland Museum Tropics Townsville 4810 QLD Australia College of Science and Engineering James Cook University Townsville 4811 QLD Australia Australian Museum Research Institute Australian Museum Sydney 2010 NSW Australia National Oceanic and Atmospheric Administration Pacific Islands Fisheries Science Center Honolulu 96818 HI United States Division of Integrated Science and Engineering California Department of Water Resources Sacramento 95814 CA United States Department of Marine Biology and Ecology Rosenstiel School of Marine Atmospheric and Earth Science University of Miami Miami 33149 FL United States College of Natural and Computational Sciences Hawaiʻi Pacific University Makapuʻu Campus 41-202 Kalanianaʻole Hwy Waimānalo 96795 HI United States University of Hawaiʻi Sea Grant College Program University of Hawaiʻi at Mānoa Honolulu 96822 HI United States Aquaculture and Fisheries Group Wageningen University & Research Wageningen 6708 Netherlands Institute of Life and Environmental Sciences University of Iceland Reykjavík 102 Iceland Marine and Freshwater Research Institute Hafnarfjörður 200 Iceland

Efforts to conserve biodiversity have been hampered by long-standing biases, including a disproportionate focus on particular taxa and ecosystems with minimal attention to underlying genetic diversity. We assessed whether these biases have persisted over the past four decades by analyzing trends in 17,502 research articles published in four top conservation-focused journals. Overall, we found that historical biases in conservation biology research remain entrenched. Despite increasing numbers of conservation articles published each decade from 1980 to 2020, research effort has increasingly focused on the same suite of taxa. Surprisingly, some of the most-studied species in these conservation articles had low conservation risk, including several domesticated animals. Animals and terrestrial ecosystems are consistently over-represented while plants, fungi, and freshwater ecosystems remain under-represented. Strategically funding investigations of understudied species and ecosystems will ensure more effective conservation effort across multiple levels of biodiversity, alleviate impediments to biodiversity targets, and ultimately prevent further extinctions. © 2024 The Author(s)

关键词： bibliometry biodiversity conservation GBIF historical trends IUCN research bias threatened species

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Phase Transformations, Microstructural Refinement and Defect Evolution Mechanisms in Al-Si Alloys under Non-Hydrostatic Diamond Anvil Cell Compression

SSRN

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SSRN 2020年

作者： Liu, Tingkun Olszta, Matthew Gwalani, Bharat Park, Changyong Mathaudhu, Suveen Devaraj, Arun Physical and Computational Science Directorate Pacific Northwest National Laboratory Energy and Environment Directorate Pacific Northwest National Laboratory High Pressure Collaborative Access Team X-ray Science Division Argonne National Laboratory Material Science and Engineering Program University of California Riverside Mechanical Engineering Department University of California Riverside

Non-hydrostatic compression of materials using a diamond anvil cell (DAC) can transform equilibrium microstructure of an alloy to novel, potentially metastable states. In this study, in situ synchrotron X-ray diffraction (XRD) during compression up to 24 GPa and subsequent ex situ, high resolution analytical electron microscopy (AEM) after decompression of an Al-Si alloy provided insight into the crystallographic changes during the compression as well as microstructural refinement, and defect structures caused by such a high pressure compression-decompression process. Pressure resolved in-situ synchrotron XRD was used to detail the phase transformation pathway of the eutectic Si phase in Al-Si alloy, from Si-I → Si-XI → Si-V during compression, and a final transformation predominantly to Si-III after decompression. Using scanning and transmission electron microscopy (S/TEM), site specific analysis of the alloy immediately underneath the anvil contact surface demonstrated a highly complex microstructure. A narrow region of thick amorphous Al oxide interspersed with nanocrystalline grains was found at the top surface. Underneath this Al oxide, while the majority the eutectic Si was transformed into highly-deformed, polycrystalline (PC) Si-III, a complex intermediate layer was discovered at the interface between Al and Si, comprised of a small fraction of Al nanocrystals and a majority of nanocrystalline Si-I. This combination of pressure resolved in-situ synchrotron XRD coupled with subsequent high resolution, electron microscopy resolved the phase transformation as well as non-equilibrium microstructures in a metallic alloy induced by a non-hydrostatic high pressure compression followed by decompression. © 2020, The Authors. All rights reserved.

关键词： Silicon alloys

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Design and Performance Evaluation of a P4 based Load Balancer 8

Design and Performance Evaluation of a P4 based Load Balance...

引用

8th International Conference on Innovation, Communication and Engineering, ICICE 2019

作者： Hsu, Shih-Jung Ke, Chih-Heng Chen, Yeong-Sheng Hung, Cheng-Feng Lo, Yu-Wen Master Degree Program of Computational Intelligence and Applications National Quemoy University Taiwan National Taipei University of Education Department of Computer Science Taiwan Master of Information Technology and Application National Quemoy University Taiwan National Quemoy University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728158396

Being increasingly dense and mature in layout, Internet has become an infrastructure for a state. The traditional single server can no longer bear the network traffic in rapid growth. For example, once the booking service for concert tickets is made available to the public, we are always prompted when clicking into the booking system that the server is busy. And when the system is finally restored normal, the tickets have been sold out. The load issue puzzling the ticket system also occurs in the transportation section every time when a long holiday is coming. Even though the system is updated on a yearly basis, net- work congestion still happens frequently. Therefore, there is still great space for the development of the ways and mechanisms of load balance in traditional multi-servers. Evaluation of the mech- anism and efficacy of load balance through P4Switch is proposed in this paper in the hope to achieve more efficient server load balance. © 2019 IEEE.

关键词： loadbalance openflow p4switch sdn

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Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence

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Physical Review Fluids 2020年第5期5卷 054606-054606页

作者： Chenyue Xie Jianchun Wang Weinan E Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Department of Mathematics Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation (LES) of turbulence. The input features are based on the first-order derivatives of the filtered velocity field at different spatial locations. The correlation coefficients of SGS forces predicted by the spatial artifical neural network (SANN) models with reasonable spatial stencil geometry can be made larger than 0.99 in an a priori analysis, and the relative error of SGS forces can be made smaller than 15%, much smaller than that of the traditional gradient model. In a posteriori analysis, a detailed comparison is made on the results of LES using the SANN model, implicit large eddy simulation (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) at grid resolution of 643. It is shown that the SANN model performs better than the ILES, DSM, and DMM models in the prediction of the spectrum and other statistical properties of the velocity field, as well as the instantaneous flow structures. These results suggest that artificial neural network with consideration of spatial characteristics is a very effective tool for developing advanced SGS models in LES of turbulence.

关键词： Compressible flows Turbulence Artificial neural networks

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Genome-wide methylation profiling reveals differentially methylated genes in blood DNA of small-cell lung cancer patients

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Precision Clinical Medicine 2022年第3期5卷 192-194页

作者： Yanqi He Calvin Wei Zhifu Sun Julie M.Cunningham Liang Wang Zong Wei Ping Yang Division of Epidemiology Department of Quantitative Health SciencesMayo ClinicScottsdaleAZ 85259USA Department of Respiratory and Critical Care Medicine West China HospitalSichuan UniversityChengdu 610041China Department of Physiology and Biomedical Engineering Mayo ClinicScottsdaleAZ 85259USA School of Molecular Science Arizona State UniversityTempeAZ 85281USA Division of Computational Biology Department of Quantitative Health SciencesMayo ClinicRochesterMN 55905USA Genomic Shared Resource Mayo Clinic Cancer CenterRochesterMN 55905USA Program of Tumor Biology Moffitt Cancer CenterTampaFL 33612USA

Dear Editor,Small-cell lung cancer(SCLC),one of the most-deadly malignant tumors,accounts for∼15%of all lung cancers.1,2 Although highly responsive to the standard chemoradiotherapy,the recurrence rate of SCLC is nearly unity,with median survival time ranging from 2–6 *** all SCLC patients are inoperable,leading to the difficulty in obtaining adequate biopsy tissue for research.3 Consequently,there is an urgent need to find noninvasive biomarkers to predict the risk and provide early diagnosis of *** aimed to identify potential epigenomic markers for SCLC development in blood DNA and reveal differentially methylated genes through a multi-omics approach,from genome-wide DNA methylation profiling,followed by pathway enrichment analyses and target gene validation utilizing Gene Expression Omnibus(GEO)and Oncomine microarray databases,to TCGA PanCancer *** is known that virtually all patients with SCLC are victims of tobacco smoking,4 but it is unknown whether stopping smoking before SCLC diagnosis would influence the epigenetic biomarkers under *** here report a study that identified aberrantly methylated genes between current and former smokers among SCLC patients,revealing a set of candidate biomarkers in peripheral blood DNA for better stratifying patients with high risk.

关键词： patients diagnosis cancer

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Generation and structural characterization of Debye random media

arXiv

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arXiv 2020年

作者： Ma, Zheng Torquato, Salvatore 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

In their seminal paper on scattering by an inhomogeneous solid, Debye and coworkers proposed a simple exponentially decaying function for the two-point correlation function of an idealized class of two-phase random media. Such Debye random media, which have been shown to be realizable, are singularly distinct from all other models of two-phase media in that they are entirely defined by their one- and two-point correlation functions. To our knowledge, there has been no determination of other microstructural descriptors of Debye random media. In this paper, we generate Debye random media in two dimensions using an accelerated Yeong-Torquato construction algorithm. We then ascertain microstructural descriptors of the constructed media, including their surface correlation functions, pore-size distributions, lineal-path function, and chord-length probability density function. Accurate semi-analytic and empirical formulas for these descriptors are devised. We compare our results for Debye random media to those of other popular models (overlapping disks and equilibrium hard disks), and find that the former model possesses a wider spectrum of hole sizes, including a substantial fraction of large holes. Our algorithm can be applied to generate other models defined by their two-point correlation functions, and their other microstructural descriptors can be determined and analyzed by the procedures laid out here. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

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