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In silico bacteria evolve robust cooperation via complex quorum-sensing strategies (vol 10, 8628, 2020)yy

SCIENTIFIC REPORTS 2020年第1期10卷 8628-8628页

作者： Wang, Yifei Rattray, Jennifer B. Thomas, Stephen A. Gurney, James Brown, Sam P. School of Biological Sciences Georgia Institute of Technology Atlanta 30332 GA USA. The Institute for Data Engineering and Science (IDEaS) Georgia Institute of Technology Atlanta 30332 GA USA. Center for Microbial Dynamics and Infection Georgia Institute of Technology Atlanta 30332 GA US School of Biological Sciences Georgia Institute of Technology Atlanta 30332 GA USA Center for Microbial Dynamics and Infection Georgia Institute of Technology Atlanta 30332 GA USA Graduate Program in Quantitative Biosciences (QBioS) Georgia Institute of Technology Atlanta 30332 GA USA

Many species of bacteria collectively sense and respond to their social and physical environment via 'quorum sensing' (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or 'spandrels' of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between defined ecological challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal/low response) and generalized reciprocity (signal auto-regulation) strategists - that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results given defined ecological challenges with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.

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Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648-e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

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College Community-Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association

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JMIR mHealth and uHealth 2025年 13卷 e51707页

作者： Garrett I Ash Selene S Mak Adrian D Haughton Madilyn Augustine Phillip O Bodurtha Robert S Axtell Beatrice Borsari Jason J Liu Shaoke Lou Xin Xin Lisa M Fucito Sangchoon Jeon Matthew Stults-Kolehmainen Mark B Gerstein Program in Computational Biology & Bioinformatics Yale University New Haven CT United States. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT United States. Center for Pain Research Informatics Medical Comorbidities and Education Center (PRIME) VA Connecticut Healthcare System West Haven CT United States. Center for the Study of Healthcare Innovation Implementation & Policy (CSHIIP) VA Greater Los Angeles Healthcare system Los Angeles CA United States. Department of Health and Movement Sciences Programs Southern Connecticut State University New Haven CT United States. Department of Psychiatry Yale School of Medicine New Haven CT United States. Center for Outcomes Research and Evaluation Yale New Haven Hospital New Haven CT United States. Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States. Yale Cancer Center New Haven CT United States. Smilow Cancer Hospital Yale New Haven Hospital New Haven CT United States. Yale School of Nursing Orange CT United States. Center for Weight Management Yale New Haven Hospital New Haven CT United States. Department of Biobehavioral Sciences Teachers College Columbia University New York NY United States. Department of Computer Science Yale University New Haven CT United States. Department of Statistics and Data Science Yale University New Haven CT United States. Section of Biomedical Informatics and Data Science Yale University New Haven CT United States.

BACKGROUND:College students are vulnerable to setting long-term trajectories of low physical activity (PA) but are reachable via mobile health fitness tracking (eg, mobile health step counting) and interpersonal support tailored to the college community. However, no studies have statistically isolated the appeal and influence of these intra- and interpersonal components in college-based PA interventions. OBJECTIVE:This study retrospectively examined a college-based PA promotion program at a northeast US public university during the COVID-19 pandemic to (1) test the impact of student status on the use of intervention components and (2) determine whether such use was associated with successful retention and goal achievement in the program. METHODS:The university used a commercial platform for a 30-day PA promotion program during April 2021 with intrapersonal (step-tracker syncing, education, self-monitoring, and motivational messaging) and interpersonal (friend interactions and team games) components. App use was operationalized as intrapersonal (frequency of opening app, education, and self-monitoring) and interpersonal (friends made in-app and team affiliation and size). RESULTS:Campus-wide emails elicited sign-up by 156 undergraduate students, 57 graduate students, and 126 faculty and staff members. Objective 1 yielded the following results: undergraduates used the app less frequently (median 0.8, IQR 0.4-1.7 times per day) than other groups (graduate students: median 1.4, IQR 0.7-2.7 times per day; P=.01; faculty: median 1.3, IQR 0.7-2.7 times per day, H=14.5; P=.001) but made the same number of friends (median 1-2) and teammates (median 8-9; P=.77 for friends and P=.93 for teammates). Objective 2 yielded the following results: most participants (313/335, 93.4%; 95% CI 90%-96%) were retained for the first 7 days, but by 30 days, retention dropped, most notably for undergraduate students (82/154, 53.2%; 95% CI 45%-61%), followed by graduate students (39/56, 70%; 95

关键词： education faculty goals mobile apps social networks students universities

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Handling Missing Values in Multivariate Time Series Classification*

Handling Missing Values in Multivariate Time Series Classifi...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Julia Friend Alec Hauck Sruthi Kurada Thomas Hartvigsen Cansu Sen Elke A. Rundensteiner Oberlin College Valparaiso University Advanced Math and Science Academy Charter School Marlborough Data Science Program Worcester Polytechnic Institute Worcester Polytechnic Institute

ISBN: (数字)9781538693742

ISBN: (纸本)9781538693759

Multivariate time series data are littered with missing values across many domains. Often the patterns of missing data points reflect conscious decisions by the data collector, and thus may contain correlations that can be used to more accurately classify the data. Previous works propose machine learning models that are capable of detecting these correlations in order to achieve highly accurate results. We review the literature and identify a rich variety of strategies along the machine learning pipeline for handling missing values, including forward imputation, appending additional missing indicators, and others. We also study different deep learning methods for handling time series data, namely GRU and LSTM cells. We then design an experimental study for investigating the relative effectiveness of different state-of-the-art methods for handling missing values to more fully comprehend the intricacies of missing data. For the experimental evaluation study, we utilize MIMIC-III, a publicly available critical care dataset composed of Electronic Health Records (EHR) for over 58,000 hospital admissions collected at Beth Israel Deaconess Medical Center from 2001 to 2012. In particular, our experimental study compares variety of missingness-aware machine learning methods for predicting patient mortality, a benchmark task for this database. We validate in general that extracting information from missing values indeed improves predictive accuracy, and we observe that GRU and LSTM cells perform comparably with the forward imputation method further impacting the resulting accuracy for both models.

关键词： Correlation Hospitals Time series analysis Pipelines MIMICs Predictive models Reliability

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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A streamlined encoder/decoder architecture for melody extraction

arXiv

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arXiv 2018年

作者： Hsieh, Tsung-Han Su, Li Yang, Yi-Hsuan Data Science Degree Program National Taiwan University Taiwan Research Center for IT Innovation Academia Sinica Taiwan Institute of Information Science Academia Sinica Taiwan

Melody extraction in polyphonic musical audio is important for music signal processing. In this paper, we propose a novel streamlined encoder/decoder network that is designed for the task. We make two technical contributions. First, drawing inspiration from a state-of-The-Art model for semantic pixelwise segmentation, we pass through the pooling indices between pooling and un-pooling layers to localize the melody in frequency. We can achieve result close to the state-of-The-Art with much fewer convolutional layers and simpler convolution modules. Second, we propose a way to use the bottleneck layer of the network to estimate the existence of a melody line for each time frame, and make it possible to use a simple argmax function instead of ad-hoc thresholding to get the final estimation of the melody line. Our experiments on both vocal melody extraction and general melody extraction validate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Harsawardana Reza Rahutomo Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Teddy Suparyanto Don Bosco Surya Atmaja Bayu Samoedro Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.

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Factors Associated with Clinical Trial Participation in Adult Hematopoietic Stem Cell Transplantation (HSCT) Recipients: A Center for International Blood and Marrow Transplant Research (CIBMTR) Analysis

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Transplantation and Cellular Therapy 2025年第2期31卷 S493-S493页

作者： Gray, Tamryn Fowler, RN, PhD, MPH Brazauskas, Ruta, PhD Patel, Dr. Jinalben, MPH Rangarajan, Hemalatha, MD Michelis, Fotios V., MD, PhD Battiwalla, Minoo, MD, MS Lehmann, Leslie E., MD Stefanski, Dr. Heather E., MD, PhD El-Jawahri, Areej R., MD Saber, Wael School of Nursing UNC Chapel Hill Chapel Hill NC Division of Biostatistics Data Science Institute Medical College of Wisconsin Milwaukee WI CIBMTR® (Center for International Blood and Marrow Transplant Research) Department of Medicine Medical College of Wisconsin Milwaukee WI Department of Pediatric Hematology Oncology Blood and Marrow Transplantation Nationwide Children's Hospital Columbus OH Princess Margaret Cancer Centre Toronto ON Canada Sarah Cannon Transplant & Cellular Therapy Program at TriStar Centennial/HCA Healthcare Nashville TN Center for Stem Cell Transplantation Division of Hematologic Malignancy Department of Medical Oncology Dana-Farber Cancer Institute Harvard Medical School Boston MA Department of Pediatrics DIvision of BMT and CT University of Minnesota Minneapolis MN Massachusetts General Hospital Boston MA

Topic Significance & Study Purpose/Background/RationaleOnly 3-5% of adults with cancer enroll in clinical trials nationwide. While clinical trial participation in solid tumors is well-studied, little is known about clinical trial participation in the context of HSCT. Methods, Intervention, & AnalysisRetrospective cohort study to: (1) describe clinical trial participation rates by HSCT type; (2) examine associations between patient, disease, and transplant-related characteristics and trial participation; and (3) explore the association between trial participation and overall survival (OS). Multivariable logistic and Cox regression models were used to examine associations and were stratified by transplant center and HSCT year to adjust for trial availability. Findings & InterpretationWe analyzed CIBMTR data collected between 2013 and 2019 that included 84,151 adult HSCT recipients (autologous: n=51,133; allogeneic: n=33,018). The proportion of patients who participated in a clinical trial was 16.2% in allogeneic HSCT and 2.8% in autologous HSCT. In allogeneic HSCT, older age (ages 60-69: OR = 1.92, p=0.0003), and age ≥ 70 (OR = 2.19, p<0.0001), Black race (OR = 1.35, p=0.0002), cord blood transplant (OR = 3.19, p<0.0001), MMUD (OR = 2.04, p=0.0017), intermediate disease risk (OR = 1.23, p = 0.007), a diagnosis of sickle cell disease (OR = 2.64, p <0.0001) were associated with higher odds of participation. In contrast, a diagnosis of severe aplastic anemia (OR = 0.32, p <0.0001), receipt of peripheral blood stem cells (OR = 0.46, P<0.0001) and haploidentical donors (OR= 0.49, P<0.0001) were associated with lower odds of participation. In autologous HCT, ≥3 comorbidities were associated with lower odds of participation (3: OR = 0.65, p = 0.0003; 4: OR = 0.54, p < 0.0001; 5≥: OR = 0.53, p <0.0001). Compared to Hodgkin disease, plasma cell disorders/MM (OR 1.74, p = 0.0009) had higher odds of participation. Clinical trial participation was not associated with OS for allog

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Improving prostate cancer detection with breast histopathology images

arXiv

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arXiv 2019年

作者： Khan, Umair Akhtar Hasan Stürenberg, Carolin Gencoglu, Oguzhan Sandeman, Kevin Heikkinen, Timo Rannikko, Antti Mirtti, Tuomas Top Data Science Ltd. Helsinki Finland University of Helsinki Faculty of Medicine Department of Pathology Research Program in Systemic Oncology Helsinki Finland Department of Laboratory Medicine Department of Pathology Skäne University Hospital Malmö Sweden Helsinki University Hospital Department of Urology and Research Program in Systemic Oncology Faculty of Medicine University of Helsinki Helsinki Finland

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as *** show that the proposed crosscancer approach outperforms transfer learning from ImageNet dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Urology

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Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers

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BMC Digital Health 2023年第1期1卷 1-1页

作者： Thomas Wetere Tulu Ching Long Chan Chun Hei Wu Kei Hang Katie Chan Tsz Kin Wan Peter Yat Ming Woo Cee Zhung Steven Tseng Asmir Vodencarevic Cristina Menni Department of Biomedical Sciences City University of Hong Kong Hong Kong SAR China Computational Data Science Program Addis Ababa University Addis Ababa Ethiopia Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China Department of Epidemiology and Center for Global Cardiometabolic Health School of Public Health Brown University Providence RI USA Department of Neurosurgery Kwong Wah Hospital Hong Kong SAR China Department of Medicine and Geriatrics Kwong Wah Hospital Hong Kong SAR China Innovative Medicines Novartis Pharma GmbH 90429 Nuremberg Germany Department of Twin Research King’s College London London UK

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