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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia

IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： I Nurlaila A A Hidayat B Pardamean Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated.

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (vol 53, pg 403, 2021)

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NATURE GENETICS 2021年第6期53卷 935-935页

作者： Granja, Jeffrey M. Corces, M. Ryan Pierce, Sarah E. Bagdatli, S. Tansu Choudhry, Hani Chang, Howard Y. Greenleaf, William J. Department of Genetics Stanford University School of Medicine Stanford CA USA Program in Biophysics Stanford University Stanford CA USA Center for Personal Dynamic Regulomes Stanford University Stanford CA USA Program in Cancer Biology Stanford University School of Medicine Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Department of Applied Physics Stanford University Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Gladstone Institute of Neurological Disease Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Department of Biochemistry Faculty of Science Cancer and Mutagenesis Unit King Fahd Center for Medical Research King Abdulaziz University Jeddah Saudi Arabia

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR;https://***/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

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Author Correction: Functional genomics data: privacy risk assessment and technological mitigation

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Nature reviews. Genetics 2022年第4期23卷 259页

作者： Gamze Gürsoy Tianxiao Li Susanna Liu Eric Ni Charlotte M Brannon Mark B Gerstein Computational Biology and Bioinformatics Program Yale University New Haven CT USA. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. Molecular Cellular and Developmental Biology Yale University New Haven CT USA. Statistics and Data Science Yale University New Haven CT USA. Department of Biology Stanford University Stanford CA USA. Computational Biology and Bioinformatics Program Yale University New Haven CT USA. mark@***. Molecular Biophysics and Biochemistry Yale University New Haven CT USA. mark@***. Statistics and Data Science Yale University New Haven CT USA. mark@***. Computer Science Yale University New Haven CT USA. mark@***.

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data gaps and opportunities for modeling cancer health equity

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Journal of the National Cancer Institute - Monographs 2023年第62期2023卷 246-254页

作者： Trentham-Dietz, Amy Corley, Douglas A. Del Vecchio, Natalie J. Greenlee, Robert T. Haas, Jennifer S. Hubbard, Rebecca A. Hughes, Amy E. Kim, Jane J. Kobrin, Sarah Li, Christopher I. Meza, Rafael Neslund-Dudas, Christine M. Tiro, Jasmin A. Department of Population Health Sciences and Carbone Cancer Center School of Medicine and Public Health University of Wisconsin-Madison Madison WI United States Division of Research Kaiser Permanente Northern California Oakland CA United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA United States Marshfield Clinic Research Institute Marshfield WI United States Division of General Internal Medicine Massachusetts General Hospital Boston MA United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Population and Data Sciences University of Texas Southwestern Medical Center Dallas TX United States Department of Health Policy and Management Center for Health Decision Science Harvard T.H. Chan School of Public Health Boston MA United States Healthcare Delivery Research Program Division of Cancer Control & Population Sciences National Cancer Institute National Institutes of Health Rockville MD United States Department of Integrative Oncology British Columbia (BC) Cancer Research Institute Vancouver BC Canada Department of Public Health Sciences and Henry Ford Cancer Henry Ford Health Detroit MI United States Department of Public Health Sciences University of Chicago Biological Sciences Division University of Chicago Medicine Comprehensive Cancer Center Chicago IL United States

Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: ***@***.

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Disparities in stage at diagnosis for liver cancer in China

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Journal of the National Cancer Center 2023年第1期3卷 7-13页

作者： Tianhao Shan Xianhui Ran Huizhang Li Guoshuang Feng Siwei Zhang Xuehong Zhang Lei Zhang Lingeng Lu Lan An Ruiying Fu Kexin Sun Shaoming Wang Ru Chen Li Li Wanqing Chen Wenqiang Wei Hongmei Zeng Jie He National Central Cancer Registry National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital Department of Cancer PreventionInstitute of Cancer Research and Basic Medical SciencesChinese Academy of SciencesHangzhouChina Big Data Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Channing Division of Network Medicine Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA Department of Nutrition T.H.Chan School of Public HealthHarvard UniversityBostonUSA China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anChina Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health CentreAlfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineMonash UniversityMelbourneAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhouChina Department of Chronic Disease Epidemiology Yale School of Public HealthYale Cancer CenterNew HavenUSA Department of Thoracic Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Background:The stage at diagnosis is a major factor in making treatment strategies and cancer control ***,the stage distribution for liver cancer in China was not well *** this multi-center hospital-based study,we aimed to identify the distribution and factors associated with stage at diagnosis for liver cancer in ***:We included patients diagnosed with primary liver cancer in 13 hospitals of 10 provinces covering various geographic and socioeconomic populations during 2016-2017 in *** stage distribution overall,and by sex and age at diagnosis were *** used logistic regression to identify the factors associated with stage Ⅲ-Ⅳ *** further compared these estimates with data from the ***:We included 2,991 patients with known stage at diagnosis in *** proportion of patients diagnosed with stageⅠ,Ⅱ,Ⅲ,and Ⅳ was 17.5%,25.6%,29.3%,and 27.6%,*** proportion of stage Ⅲ-Ⅳ cases was higher in women[65.1%vs 54.9%,adjusted odds ratio(OR)=1.5,95%CI:1.2,1.8]and those≥60 years(61.6%vs 52.8%,OR=1.4,95%CI:1.2,1.6).We found an increased risk of stage Ⅲ-Ⅳ among drinkers and those without a family history of *** to the USA,our study population had a substantially higher proportion of stage Ⅲ-Ⅳ cases(56.9%vs 45.6%).Conclusion:The disparities in liver cancer stage at diagnosis among different populations within China,and between China and the USA,imply the necessity for improving cancer awareness and early detection for liver cancer in China.

关键词： Liver cancer Stage at diagnosis Disparity China USA

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Results from the CsI Calorimeter onboard the 2023 ComPair Balloon Flight

arXiv

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arXiv 2024年

作者： Shy, Daniel Woolf, Richard S. Sleator, Clio Phlips, Bernard Grove, J. Eric Wulf, Eric A. Johnson-Rambert, Mary Davis, Mitch Kong, Emily Caligiure, Thomas Crosier, A. Wilder Bolotnikov, Aleksey Cannady, Nicholas Carini, Gabriella A. Caputo, Regina Fried, Jack Ghosh, Priyarshini Griffin, Sean Hays, Elizabeth Herrmann, Sven Kierans, Carolyn Kirschner, Nicholas Liceaga-Indart, Iker McEnery, Julie Mitchell, John Moiseev, A.A. Parker, Lucas Dellapenna, Alfred Perkins, Jeremy S. Sasaki, Makoto Schoenwald, Adam J. Smith, Lucas D. Valverde, Janeth Wasti, Sambid Zajczyk, Anna U.S. Naval Research Laboratory 4555 Overlook Ave SW WashingtonDC20375 United States Technology Service Corporation ArlingtonVA22202 United States Naval Research Enterprise Internship Program U.S. Naval Research Laboratory WashingtonDC20375 United States Brookhaven National Laboratory UptonNY11973 United States NASA Goddard Space Flight Center GreenbeltMD United States Center for Space Sciences and Technology University of Maryland Baltimore County 1000 Hilltop Circle BaltimoreMD21250 United States Center for Research and Exploration in Space Science and Technology NASA/GSFC GreenbeltMD20771 United States Wisconsin IceCube Particle Astrophysics Center University of Wisconsin-Madison 222 W Washington Ave Unit 500 MadisonWI53703 United States The Department of Physics The George Washington University 725 21st NW WashingtonDC20052 United States Catholic University of America 620 Michigan Ave NE WashingtonDC20064 United States University of Maryland at College Park College ParkMD20742 United States Space Remote Sensing and Data Science Los Alamos National Laboratory Los AlamosNM87545 United States

The ComPair gamma-ray telescope is a technology demonstrator for a future gamma-ray telescope called the All-sky Medium Energy Gamma-ray Observatory (AMEGO). The instrument is composed of four subsystems, a double-sided silicon strip detector, a virtual Frisch grid CdZnTe calorimeter, a CsI:Tl based calorimeter, and an anti-coincidence detector (ACD). The CsI calorimeter’s goal is to measure the position and energy deposited from high-energy events. To demonstrate the technological readiness, the calorimeter has flown onboard a NASA scientific balloon as part of the GRAPE-ComPair mission and accumulated around 3 hours of float time at an altitude of 40 km. During the flight, the CsI calorimeter observed background radiation, Regener-Pfotzer Maximum, and several gamma-ray activation lines originating from aluminum. © 2024, CC BY-NC-ND.

关键词： Gamma rays

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EMBRACING DIGITAL AND COMPUTATIONAL PATHOLOGY DURING RESIDENCY TRAINING: PERSPECTIVES FROM TWO FORMER ANATOMIC PATHOLOGY RESIDENTS

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Journal of Pathology Informatics 2022年 13卷 100042-100042页

作者： Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston Massachusetts United States Cancer Program Broad Institute of Harvard and MIT Cambridge Massachusetts United States Department of Biomedical Informatics Harvard Medical School Boston Massachusetts United States Cancer Data Science Program Dana-Farber Cancer Institute Boston Massachusetts United States

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PATHOLOGY RESIDENT EXPERIENCE USING A LOW-COST 3D PRINTED MICROSCOPE FOR PORTABLE DIGITAL PATHOLOGY

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Journal of Pathology Informatics 2022年 13卷 100043-100043页

作者： Williamson, Drew F.K. Chen, Tiffany Y. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston Massachusetts United States Cancer Program Broad Institute of Harvard and MIT Cambridge Massachusetts United States Department of Biomedical Informatics Harvard Medical School Boston Massachusetts United States Cancer Data Science Program Dana-Farber Cancer Institute Boston Massachusetts United States

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Deep learning-based computational pathology predicts origins for cancers of unknown primary

arXiv

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arXiv 2020年

作者： Lu, Ming Y. Zhao, Melissa Shady, Maha Lipkova, Jana Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States

Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor3. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up of pathology, radiology, endoscopy, laboratory tests, and clinical correlation in an attempt to determine the primary origin. Such exploration is not only time and resource consuming, but it might significantly delay administration of the suitable treatment. Despite extensive diagnostic work-ups the primary may never be determined in many cases. Recent work has focused on using genomics and transcriptomics for identification of tumor origins4–6. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (κ=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used

关键词： Diseases

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