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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

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Research Square

Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

Ten quick tips for deep learning in biology

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

High temperature structure detection in ferromagnets

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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

Characterization of TOF-PET detectors for a second generation radiofrequency-penetrable PET insert for simultaneous PET/MRI

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Characterization of TOF-PET detectors for a second generatio...

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Qian Dong Brian J. Lee Chen-Ming Chang Ilaria Sacco Ronald D. Watkins Peter Fischer Emily Anaya Craig S. Levin Departments of Electrical Engineering and Radiology Stanford University Stanford CA USA Departments of Mechanical Engineering and Radiology Stanford University Stanford CA USA Departments of Applied Physics and Radiology Stanford University Stanford CA USA Department of Radiology Stanford University Stanford CA USA Department of Computer Science Heidelberg University Mannheim Germany Departments of Radiology Electrical Engineering Bioengineering and Physics Stanford University and Molecular Imaging Program at Stanford (MIPS) Stanford CA USA

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Simultaneous PET/MRI combines two powerful and complementary modalities, providing multiparametric information to assess the anatomic as well as biochemical basis of disease. However, the high cost of the current commercial integrated PET/MRI systems limit the long-term potential, accessibility, and availability of PET/MRI. A portable PET insert provides a cost- effective alternative to achieve simultaneous PET/MRI for the sites that are already equipped with MR systems. Therefore, we are developing a radiofrequency (RF)-penetrable MR-compatible time-of-flight (TOF)-PET insert that can acquire simultaneous PET/MR data using the MR systems built-in body coil for RF transmission. In this study, we characterized performance of two detector modules in the MR bore under four different MRI conditions: B 0 only, echo planar imaging (EPI), gradient echo (GRE), and fast spin echo (FSE), each running for 15 minutes. The one-hour study results for coincidence timing resolution, energy resolution, coincidence count rate and ASIC chip temperature of two detector modules demonstrate the PET performance stability as well as the MR compatibility of the PET detectors under different MRI sequences. The global coincidence timing and energy resolution achieved was 264.21 ± 4.82 ps FWHM and 13.66 ± 0.08 % FWHM, respectively.

关键词： Detectors Clocks Energy resolution Magnetic resonance imaging Timing Positron emission tomography Radio frequency

OPTIMADE, an API for exchanging materials data

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arXiv 2021年

作者： Andersen, Casper W. Armiento, Rickard Blokhin, Evgeny Conduit, Gareth J. Dwaraknath, Shyam Evans, Matthew L. Fekete, Ádám Gopakumar, Abhijith Gražulis, Saulius Merkys, Andrius Mohamed, Fawzi Oses, Corey Pizzi, Giovanni Rignanese, Gian-Marco Scheidgen, Markus Talirz, Leopold Toher, Cormac Winston, Donald Aversa, Rossella Choudhary, Kamal Colinet, Pauline Curtarolo, Stefano Di Stefano, Davide Draxl, Claudia Er, Suleyman Esters, Marco Fornari, Marco Giantomassi, Matteo Govoni, Marco Hautier, Geoffroy Hegde, Vinay Horton, Matthew K. Huck, Patrick Huhs, Georg Hummelshøj, Jens Kariryaa, Ankit Kozinsky, Boris Kumbhar, Snehal Liu, Mohan Marzari, Nicola Morris, Andrew J. Mostofi, Arash A. Persson, Kristin A. Petretto, Guido Purcell, Thomas Ricci, Francesco Rose, Frisco Scheffler, Matthias Speckhard, Daniel Uhrin, Martin Vaitkus, Antanas Villars, Pierre Waroquiers, David Wolverton, Chris Wu, Michael Yang, Xiaoyu École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Materials Design and Informatics Unit Department of Physics Chemistry and Biology Linköping University Sweden Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Theory of Condensed Matter Group Cavendish Laboratory 19 J.J. Thomson Avenue CambridgeCB3 0HE United Kingdom Lawrence Berkeley Labs BerkeleyCA United States Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Department of Physics King's College London London United Kingdom Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Institute of Biotechnology Life Science Center Vilnius University Saulétekio av. 7 VilniusLT-10257 Lithuania Institute of Computer Science Faculty of Mathematics and Informatics Vilnius University Naugarduko g. 24 VilniusLT-03225 Lithuania Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 Berlin14195 Germany Center for Autonomous Materials Design Duke University DurhamNC27708 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Humboldt-Universität zu Berlin Institut für Physik and IRIS Adlershof Berlin12489 Germany École Polytechnique Fédérale de Lausanne Sion1951 Switzerland Trieste34136 Italy Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Ansys 300 Rustat House 62 Clifton Rd CambridgeCB1 7EG United Kingdom De Zaale 20 Eindhoven5612 AJ Netherlands Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Argonne National Laboratory LemontIL60439 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Los AltosCA940

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. © 2021, CC BY.

关键词： Specifications

Unsupervised extraction of phenotypes from cancer clinical notes for association studies

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

Evaluating the impact of COVID-19 pandemic lockdown on education in Nigeria: Insights from teachers and students on virtual/online learning

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Bulletin of the National Research Centre 2021年第1期45卷 76页

作者： Osamudiamen Ebohon Anayochukwu Chukwunonso Obienu Francis Irabor Frank Iwebuke Amadin Ehimwenma Sheena Omoregie Biochemistry Program Department of Biological and Chemical Sciences Faculty of Natural and Applied Sciences Michael and Cecilia Ibru University P.M.B. 100 Agbarha-Otor Delta State Nigeria. Department of Information Technology Bayelsa Medical University Yenagoa Bayelsa State Nigeria. Department of Computer Science University of Benin P.M.B 1154 Benin City Nigeria. Malaria Research Molecular Biology and Toxicology Unit Department of Biochemistry Faculty of Life Sciences University of Benin P.M.B 1154 Benin City Nigeria.

BACKGROUND:As in many countries worldwide, due to the COVID-19 pandemic lockdown, higher institutions in Nigeria closed in March 2020 and only began reopening in October of the same year. As a result of the lockdown, some higher institutions had to quickly move from the traditional face-to-face teaching method to virtual learning. This study aimed to investigate the impact of COVID-19 lockdown on education in Nigeria and also provide recommendations that may be useful in developing remote teaching contingency strategies. Five-point Likert-scale questionnaire targeting students and teachers separately was designed to get feedbacks from both students and teachers on their experiences, issues and successes. The questionnaires were divided into five categories: virtual classrooms, course learning outcomes, alternative method of assessment, impact of online teaching and satisfaction. RESULTS:A total of 703 students and 60 teachers from five different local universities participated in this study. All participants (> 50%) had difficulties with Internet connection. Students (67%) as well as teachers (59%) agree that they had limited interactions with one another and this negatively influence student's satisfaction ( < 0.01). While students were split on the most appropriate method of assessment, teachers (63%) believe assignments and oral examinations are more suitable for online teaching. Many teachers (66%) admitted that it was difficult assessing students' abilities and performance. Some students (> 40%) were concerned about the number of assignments given. Most teachers (84%) believe there is an increase in tendency for examination malpractice when assessment was conducted virtually. Students had significantly ( < 0.05) higher marks in all courses during online assessment compared to previous session involving face-to-face teaching. About 83% of teachers admitted it was difficult explaining complex scientific concepts. CONCLUSION:Based on the results of this study, we

关键词： COVID-19 Coronavirus Education Lockdown Nigeria Online teaching Pandemic Survey Virtual learning

The effect of additive chemicals on synthesis of bismuth nanoparticles

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Materials Today: Proceedings 2018年第6期5卷 14057-14062页

作者： K. Petsom A. Kopwitthaya M. Horphathum J. Kaewkhao N. Sangwaranatee Center of Excellence in Glass Technology and Materials Science (CEGM) Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand Physics Program Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand National Electronics and Computer Technology Center Pathum Thani 12120 Thailand Applied Physics Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok 10300 Thailand

Bismuth nanostructures are of interest due to unique properties such as strong diamagnetism, high magnetoresistance under magnetic field, as well as biocompatibility compared with lead. Previous study have reported a relationship of obtained nanostructures and synthesized conditions including pH, temperature and time during nucleation. Most of them confront difficulties on size controlling, particularly in large volume synthesis. Therefore, the goal of this work is to simplify the synthesis method by studying an effect of additive chemicals which are introduced to a growth solution before nanocrystal formation. Ascorbic acid, dioctyl sulfosuccinate sodium salt (AOT) and ethanol were chosen to slow down the reaction or disturb the micellar template. The study has shown that size of nanoparticles can be easily tuned by adding different amount of aforementioned chemicals. According to different role in nanocrystal forming mechanism, more ascorbic acid tends to increase diameter of obtained particles. Oppositely, more AOT has shrunk synthesized particles due to packed micelles. Loose micelles are found in ethanol experiment leading to bigger particles in size. In addition, crystalline structure exhibited the diffraction plane at (012), (104), and (110) with X-Ray diffraction (XRD) analysis. Field emission scanning electron microscopy (FE-SEM) was used to determine morphology of nanoparticles, and also size distribution was confirmed by nanoparticle tracking analysis (NTA). We also found the quench of nanocrystal formation in the presence of high concentration of ethyl alcohol.

关键词： Ascorbic acid Docusate sodium salt Ethanol Room temperature synthesis

Low-temperature electron-phonon interaction of quantum emitters in hexagonal boron nitride

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arXiv 2019年

作者： Grosso, Gabriele Moon, Hyowon Ciccarino, Christopher J. Flick, Johannes Mendelson, Noah Toth, Milos Aharonovich, Igor Narang, Prineha Englund, Dirk R. Advanced Science Research Center City University of New York Photonics Initiative New YorkNY United States Physics Program Graduate Center City University of New York New YorkNY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States School of Mathematical and Physical Sciences University of Technology Sydney UltimoNSW2007 Australia Center for Computational Quantum Physics Flatiron Institute New YorkNY United States

关键词： Electron-phonon interactions