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Publisher Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2023年第1期55卷 166页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Paul Jongjoon Choi Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

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High temperature structure detection in ferromagnets

arXiv

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arXiv 2018年

作者： Cao, Yuan Neykov, Matey Liu, Han Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Statistics & Data Science Carnegie Mellon University PittsburghPA United States Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

This paper studies structure detection problems in high temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of independent Rademacher variables, versus the alternative that the underlying graph contains a subgraph of a certain structure. We give matching upper and lower minimax bounds under which testing this problem is possible/impossible respectively. Our results reveal that a key quantity called graph arboricity drives the testability of the problem. On the computational front, under a conjecture of the computational hardness of sparse principal component analysis, we prove that, unless the signal is strong enough, there are no polynomial time linear tests on the sample covariance matrix which are capable of testing this problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Ferromagnetism

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Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

npj metabolic health and disease

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npj metabolic health and disease 2024年第1期2卷 15页

作者： Annalise Schweickart Richa Batra Bryan J Neth Cameron Martino Liat Shenhav Anru R Zhang Pixu Shi Naama Karu Kevin Huynh Peter J Meikle Leyla Schimmel Amanda Hazel Dilmore Kaj Blennow Henrik Zetterberg Colette Blach Pieter C Dorrestein Rob Knight Suzanne Craft Rima Kaddurah-Daouk Jan Krumsiek Tri-Institutional Program in Computational Biology & Medicine Weill Cornell Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine Institute for Computational Biomedicine Englander Institute for Precision Medicine New York NY USA. Department of Neurology Mayo Clinic Rochester MN USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Microbiology New York University Grossman School of Medicine New York NY USA. Department of Biostatistics and Bioinformatics Duke University Durham NC USA. Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS) Hobart TAS Australia. Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC Australia. Baker Department of Cardiovascular Research Translation and Implementation La Trobe University Bundoora VIC Australia. Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA. Department of Psychiatry and Neurochemistry University of Gothenburg Gothenburg Sweden. Duke Molecular Physiology Institute Duke University Durham NC USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Departments of Pediatrics Computer Science and Engineering Bioengineering University of California San Diego La Jolla CA USA. Department of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston Salem NC USA. Duke Institute of Brain Sciences Duke University Durham NC USA. Department of Medicine Duke University Durham NC USA.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

关键词： Diseases Metabolomics

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MoSi2N4single-layer: A novel two-dimensional material with outstanding mechanical, thermal, electronic, optical, and photocatalytic properties

arXiv

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arXiv 2020年

作者： Bafekry, A. Faraji, M. Hoat, Do M. Fadlallah, M.M. Shahrokhi, M. Shojaei, F. Gogova, D. Ghergherehchi, M. Department of Physics University of Guilan Rasht41335-1914 Iran Department of Physics University of Antwerp Groenenborgerlaan 171 AntwerpB-2020 Belgium Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu Ankara06560 Turkey Computational Laboratory for Advanced Materials and Structures Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Physics Faculty of Science Benha University Benha13518 Egypt University Lyon ENS de Lyon CNRS Universit Claude Bernard Lyon 1 Laboratoire de Chimie UMR 5182 LyonF-69342 France Department of Chemistry Faculty of Sciences Persian Gulf University Bushehr75169 Iran Department of Physics University of Oslo P.O. Box 1048 Blindern Oslo Norway College of Electronic and Electrical Engineering Sungkyun Kwan University Suwon Korea Republic of

Very recently, the two-dimensional (2D) form of MoSi2N4has been successfully fabricated [Hong et al., Sci. 369, 670 (2020)]. Motivated by theses recent experimental results, herein we investigate the structural, mechanical, thermal, electronic, optical and photocatalytic properties using hybrid density functional theory (HSE06-DFT). Phonon band dispersion calculations reveal the dynamical stability of MoSi2N4monolayer structure. Furthermore, the mechanical study confirms the stability of MoSi2N4monolayer. As compared to the corresponding value of graphene, we find the Young's modulus decreases by ∼ 30% while the Poisson's ratio increases by ∼ 30%. In addition, its work function is very similar to that of phosphorene and MoS2 monolayers. The electronic structure investigation shows the MoSi2N4monolayer is an indirect bandgap semiconductor. We have determined the bandgap using the HSE06 (GGA) is 2.35 (1.79) eV, which is an overestimated (underestimated) value of the experimental bandgap (1.99 eV). The thermoelectric study shows a good thermoelectric performance of the MoSi2N4monolayer with a figure of merit slightly larger than unity at high temperatures. The optical analysis using the RPA method constructed over HSE06 shows that the first absorption peak of the MoSi2N4monolayer for in-plane polarization is located in the visible range of spectrum, i.e. it is a promising candidate for advancing optoelectronic nanodevices. The photocatalytic study indicates the MoSi2N4monloayer can be a promising photocatalyst for water splitting as well as and CO2 reduction. In summary, the fascinating MoSi2N4monloayer is a promising 2D material in many applications due to its unique physical properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Quantum processor-inspired machine learning in the biomedical sciences

arXiv

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arXiv 2019年

作者： Li, Richard Y. Gujja, Sharvari Bajaj, Sweta R. Gamel, Omar E. Cilfone, Nicholas Gulcher, Jeffrey R. Lidar, Daniel A. Chittenden, Thomas W. Department of Chemistry University of Southern California Los AngelesCA United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA United States Computational Statistics and Bioinformatics Group Advanced Artificial Intelligence Research Laboratory Genuity Science BostonMA United States Complex Biological Systems Alliance MedfordMA United States Cancer Genetics Group Genuity Science BostonMA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Department of Physics and Astronomy University of Southern California Los AngelesCA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA United States

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data;namely "Ising-type" methods whose objective function is formulated identically to simulated annealing and quantum annealing, as well as a restricted Boltzmann machine. Here we show for the first time the efficacy of multiple Ising-type ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using Ising-type ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. © 2019, CC BY.

关键词： Machine learning

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Domain morphology and mechanics of the H/T′ transition metal dichalcogenide monolayers

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Physical Review Materials 2018年第11期2卷 114002-114002页

作者： Joel Berry Songsong Zhou Jian Han David J. Srolovitz Mikko P. Haataja Department of Materials Science and Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Mechanical and Aerospace Engineering Princeton University Princeton New Jersey 08544 USA Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Materials Science and Engineering City University of Hong Kong Hong Kong SAR People's Republic of China Princeton Institute for the Science and Technology of Materials (PRISM) Princeton University Princeton New Jersey 08544 USA Program in Applied and Computational Mathematics (PACM) Princeton University Princeton New Jersey 08544 USA

The properties of two-dimensional (2D) transition metal dichalcogenide (TMD) monolayers can be dynamically controlled via strain-induced displacive structural transformations between semiconducting (H) and metallic or semimetallic (T′) crystal structures. The shapes, symmetries, and kinetics of crystalline domains generated during these transformations and the mechanical response of transforming monolayers are of fundamental and applied interest in, e.g., phase change memory devices and the study of topologically protected edge states in quantum spin Hall insulating T′ crystals. We quantitatively characterize T′ domain morphologies during H→T′ transformations in both flat and bendable TMD monolayers using a combination of first principles and continuum calculations. Wulff constructions for MoTe2 and MoS2 show that T′ domains within much larger T′ domains are either rhombi of fixed proportions (if nonmisfitting) or rectangles whose aspect ratio AR increases with domain size L0 (if misfitting). Isolated T′ domains within much larger H domains undergo a morphological crossover from compact to elongated shapes at L0≈100–200 nm if the sheet is constrained to be flat or L0≳2μm if the sheet is free to bend. This crossover is driven by a competition between anisotropic interfacial energy and elastic misfit energy, and its position can be tuned via the monolayer-substrate interaction strength. It is shown that the aspect ratio AR obeys a scaling law AR∼L02/3. Stress-strain response characterized as a function of strain orientation reveals extreme anisotropy in the effective elastic modulus through H/T′ coexistence. Ferroelastic multidomain T′−WTe2 monolayers are found to exhibit two to three regimes of reversible mechanical response, and localized buckling in freely suspended T′ monolayers is shown to qualitatively alter T′ domain symmetries.

关键词： Bending Domain walls Mechanical deformation Microstructure Structural phase transition 2-dimensional systems Transition metal dichalcogenides Density functional theory Phase-field modeling

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Model reduction with memory and the machine learning of dynamical systems

arXiv

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arXiv 2018年

作者： Ma, Chao Wang, Jianchun Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China Beijing Institute of Big Data Research Beijing100871 China

The well-known Mori-Zwanzig theory tells us that model reduction leads to memory effect. For a long time, modeling the memory effect accurately and efficiently has been an important but nearly impossible task in developing a good reduced model. In this work, we explore a natural analogy between recurrent neural networks and the Mori-Zwanzig formalism to establish a systematic approach for developing reduced models with memory. Two training models-a direct training model and a dynamically coupled training model-are proposed and compared. We apply these methods to the Kuramoto-Sivashinsky equation and the Navier-Stokes equation. Numerical experiments show that the proposed method can produce reduced model with good performance on both short-term prediction and long-term statistical properties. Copyright © 2018, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Correction: Metabolic modulation to improve MSC expansion and therapeutic potential for articular cartilage repair

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Stem cell research & therapy 2024年第1期15卷 403页

作者： Ching Ann Tee Daniel Ninio Roxby Rashidah Othman Vinitha Denslin Kiesar Sideeq Bhat Zheng Yang Jongyoon Han Lisa Tucker-Kellogg Laurie A Boyer Critical Analytics for Manufacturing Personalised-medicine (CAMP) Interdisciplinary Research Group Singapore-MIT Alliance for Research and Technology (SMART) Centre 1 Create Way Enterprise Wing #04-13/14 Singapore 138602 Republic of Singapore. NUS Tissue Engineering Program Life Sciences Institute National University of Singapore 27 Medical Drive DSO (Kent Ridge) Building Level 4 Singapore 117510 Republic of Singapore. Department of Bioresources University of Kashmir Hazratbal Srinagar 190006 India. Department of Orthopaedic Surgery National University of Singapore 1E Kent Ridge Road NUHS Tower Block 11 Singapore 119288 Republic of Singapore. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 50 Vassar St Cambridge MA 02139 USA. Department of Biological Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA. Critical Analytics for Manufacturing Personalised-medicine (CAMP) Interdisciplinary Research Group Singapore-MIT Alliance for Research and Technology (SMART) Centre 1 Create Way Enterprise Wing #04-13/14 Singapore 138602 Republic of Singapore. tuckernus@***. Cancer and Stem Cell Biology and Centre for Computational Biology Duke-NUS Medical School 8 College Rd Singapore 169857 Republic of Singapore. tuckernus@***. Critical Analytics for Manufacturing Personalised-medicine (CAMP) Interdisciplinary Research Group Singapore-MIT Alliance for Research and Technology (SMART) Centre 1 Create Way Enterprise Wing #04-13/14 Singapore 138602 Republic of Singapore. lboyer@mit.edu. Department of Biological Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA. lboyer@mit.edu. Department of Biology Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA. lboyer@mit.edu.

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Lysosomal dysfunction and inflammatory sterol metabolism in pulmonary arterial hypertension

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science (New York, N.Y.) 2025年第6732期387卷 eadn7277页

作者： Harvey, Lloyd D. Alotaibi, Mona Tai, Yi-Yin Tang, Ying Kim, Hee-Jung J Kelly, Neil J. Sun, Wei Woodcock, Chen-Shan C Arshad, Sanya Culley, Miranda K. El Khoury, Wadih Xie, Rong Al Aaraj, Yassmin Zhao, Jingsi Hafeez, Neha Rao, Rashmi J. Jiang, Siyi Negi, Vinny Kirillova, Anna Perk, Dror Watson, Annie M. St Croix, Claudette M. Stolz, Donna B. Lee, Ji Young Cheng, Mary Hongying Zhang, Manling Detmer, Samuel Guzman, Edward Manan, Rajith S. Saggar, Rajan Haley, Kathleen J. Waxman, Aaron B. Okawa, Satoshi Schwantes-An, Tae-Hwi Pauciulo, Michael W. Wang, Bing Webb, Amy Chauvet, Caroline Anderson, Daniel G. Nichols, William C. Desai, Ankit A. Lafyatis, Robert Nouraie, S Mehdi Wu, Haodi McDonald, Jeffrey G. Cheng, Susan Bahar, Ivet Bertero, Thomas Benza, Raymond L. Jain, Mohit Chan, Stephen Y. Medical Scientist Training Program University of Pittsburgh Pittsburgh PA United States Center for Pulmonary Vascular Biology and Medicine Heart Lung Blood Vascular Medicine Institute University of Pittsburgh Pittsburgh PA United States Division of Cardiology University of Pittsburgh School of Medicine Pittsburgh PA United States Division of Pulmonary and Critical Care Medicine University of California San Diego La Jolla CA United States Department of Medicine University of California La Jolla San Diego CA United States Department of Pharmacology University of California La Jolla San Diego CA United States Division of Cardiology Veterans Affairs Pittsburgh Healthcare System Pittsburgh PA United States Center for Biologic Imaging University of Pittsburgh Pittsburgh PA United States Laufer Center for Physical and Quantitative Biology Stony Brook University Stony Brook NY United States Department of Biochemistry and Cell Biology Stony Brook University Stony Brook NY United States Department of Chemistry Massachusetts Institute of Technology Boston MA United States Institute of Medical Engineering and Science Massachusetts Institute of Technology Boston MA United States School of Engineering and Applied Sciences Harvard University Cambridge MA United States Department of Chemical Engineering Massachusetts Institute of Technology Boston MA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles (UCLA) Los Angeles CA USA Department of Pathology David Geffen School of Medicine UCLA Los Angeles CA United States Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States Department of Computational and Systems Biology University of Pittsburgh Pittsburgh PA United States McGowan Institute for Regenerative Medicine University of Pittsburgh Pittsburgh PA United States Department of Medical and Molecular G

Vascular inflammation regulates endothelial pathophenotypes, particularly in pulmonary arterial hypertension (PAH). Dysregulated lysosomal activity and cholesterol metabolism activate pathogenic inflammation, but their relevance to PAH is unclear. Nuclear receptor coactivator 7 (NCOA7) deficiency in endothelium produced an oxysterol and bile acid signature through lysosomal dysregulation, promoting endothelial pathophenotypes. This oxysterol signature overlapped with a plasma metabolite signature associated with human PAH mortality. Mice deficient for endothelial Ncoa7 or exposed to an inflammatory bile acid developed worsened PAH. Genetic predisposition to NCOA7 deficiency was driven by single-nucleotide polymorphism rs11154337, which alters endothelial immunoactivation and is associated with human PAH mortality. An NCOA7-activating agent reversed endothelial immunoactivation and rodent PAH. Thus, we established a genetic and metabolic paradigm that links lysosomal biology and oxysterol processes to endothelial inflammation and PAH.

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Quantifying the trade-offs between energy consumption and salt removal rate in membrane-free cation intercalation desalination

arXiv

引用

arXiv 2018年

作者： Liu, Sizhe Smith, Kyle C. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Study University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Electrochemical desalination devices that use redox-active cation intercalation electrodes show promise for desalination of salt-rich water resources with high water recovery and low energy consumption. While previous modeling and experiments used ion-exchange membranes to maximize charge efciency, here a membrane-free alternative is evaluated to reduce capital cost by using a porous diaphragm to separate Na1+xNiFe(CN)6 electrodes. Two-dimensional porous-electrode modeling shows that, while charge eficiency losses are inherent to a diaphragm-based architecture, charge eficiency values approaching the anion transference number (61% for NaCl) are achievable for diaphragms with suFIciently low salt conductance. Closed-form equations are thereby derived that relate charge eficiency to the non-dimensional Pfeclet and Damkohler numbers that enable the selection of current and ow velocity to produce a desired degree-of-desalination. Simulations using these conditions are used to quantify the tradeo_s between energy consumption and salt removal rate for diaphragmbased cells operated at a range of currents. The simulated distributions of reactions are shown to result from the local salt concentration variations within electrodes using di_usion-potential theory. We also simulate the cycling dynamics of various ow configurations and show that ow-through electrodes exceed the degree-of-desalination compared with ow-by and ow-behind configurations due to solution stagnation within electrodes. Copyright © 2018, The Authors. All rights reserved.

关键词： Desalination

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