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arXiv 2022年

作者： Sen, Jaydip Sen, Rajdeep Dutta, Abhishek Mehtab, Sidra Kherwa, Pooja Singh, Sonali Ahmed, Saheel Berry, Pranay Khurana, Sahil Akinbo, Racheal S. Daramola, Oladunni A. Ost, Kalum J. Anderson, David W. Cadotte, David W. Lainjo, Bongs Department of Data Science Praxis Business School Kolkata India School of Computing and Analytics NASHM Knowledge Campus Kolkata India Maharaja Surajmal Institute of Technology New Delhi India Federal University of Technology Akure Nigeria Hotchkiss Brain Institute CalgaryAB Canada Cumming School of Medicine CalgaryAB Canada Division of Neurosurgery Department of Clinical Neurosciences University of Calgary AB Canada Division of Neurosurgery Department of Radiology University of Calgary AB Canada Combined Orthopedic and Neurosurgery Spine Program University of Calgary AB Canada Cybermatic International MontréalQC Canada

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence. © 2022, CC BY-SA.

关键词： Learning algorithms

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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Spin-tunable thermoelectric performance in monolayer chromium pnictides

arXiv

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arXiv 2021年

作者： Muntini, Melania S. Suprayoga, Edi Wella, Sasfan A. Fatimah, Iim Yuwana, Lila Seetawan, Tosawat Cahaya, Adam B. Nugraha, Ahmad R.T. Hasdeo, Eddwi H. Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Research Center for Quantum Physics National Research and Innovation Agency Tangerang Selatan15314 Indonesia Center of Excellence on Alternative Energy Research and Development Institution Sakon Nakhon Rajabhat University Sakon Nakhon47000 Thailand Program of Physics Faculty of Science and Technology Sakon Nakhon Rajabhat University Sakon Nakhon47000 Thailand Department of Physics Faculty of Mathematics and Natural Sciences Universitas Indonesia Depok16424 Indonesia Department of Physics and Material Science University of Luxembourg L-1511 Luxembourg

Historically, finding two-dimensional (2D) magnets is well known to be a difficult task due to instability against thermal spin fluctuations. Metals are also normally considered poor thermoelectric (TE) materials. Combining intrinsic magnetism in two dimensions with conducting properties, one may expect to get the worst for thermoelectrics. However, we will show this is not always the case. Here, we investigate spin-dependent TE properties of monolayer chromium pnictides (CrX, where X = P, As, Sb, and Bi) using first-principles calculations of electron- and phonon-energy dispersion, along with Boltzmann transport formalism under energy-dependent relaxation time approximation. All the CrX monolayers are dynamically stable and they also exhibit half metallicity with ferromagnetic ordering. Using the spin-valve setup with antiparallel spin configuration, the half metallicity and ferromagnetism in monolayer CrX enable manipulation of spin degrees of freedom to tune the TE figure of merit (ZT). At optimized chemical potential and operating temperature of 500 K, the maximum ZT values (≈ 0.22, 0.12, and 0.09) with the antiparallel spin-valve setup in CrAs, CrSb, and CrBi improve up to almost twice the original values (ZT ≈ 0.12, 0.08, and 0.05) without the spin-valve configuration. Only in CrP, which is the lightest species and less spin-polarized among CrX, the maximum ZT (≈ 0.34) without the spin-valve configuration is larger than that (≈ 0.19) with the spin-valve one. We also find that, at 500 K, all the CrX monolayers possess exceptional TE power factors of about 0.02-0.08 W/m.K2, which could be one of the best values among 2D conductors. © 2021, CC BY.

关键词： Chromium

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Correction to: Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization

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Health care management science 2023年第4期26卷 747页

作者： Shima Azizi Özge Aygül Brenton Faber Sharon Johnson Renata Konrad Andrew C Trapp Business Analytics and Information Systems Peter J. Tobin College of Business St. John's University Queens NY USA. azizis@stjohns.edu. Data Science Program Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. Biomedical Engineering Worcester Polytechnic Institute 100 Institute Road Worcester MA USA. The Business School Worcester Polytechnic Institute 100 Institute Road Worcester MA USA.

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Medical Management and Device-Based Therapies in Chronic Heart Failure

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Journal of the Society for Cardiovascular Angiography and Interventions 2023年第6期2卷 101206-101206页

作者： Nguyen, Andrew H. Hurwitz, Madelyn Abraham, Jacob Blumer, Vanessa Flanagan, M. Casey Garan, A. Reshad Kanwar, Manreet Kataria, Rachna Kennedy, Jamie L.W. Kochar, Ajar Hernandez-Montfort, Jaime Pahuja, Mohit Shah, Palak Sherwood, Matthew W. Tehrani, Behnam N. Vallabhajosyula, Saraschandra Kapur, Navin K. Sinha, Shashank S. Inova Schar Heart and Vascular Institute Inova Fairfax Medical Campus Falls Church VA United States School of Medicine University of Virginia Charlottesville VA United States Center for Cardiovascular Analytics Research & Data Science Providence-St. Joseph Health Portland Oregon United States Division of Cardiology Beth Israel Deaconess Medical Center Boston Massachusetts United States Cardiovascular Institute at Allegheny Health Network Pittsburgh PA United States Lifespan Cardiovascular Institute Warren Alpert Medical School of Brown University Providence Rhode Island United States Division of Cardiology Brigham & Women's Hospital Harvard Medical School Boston Massachusetts United States Advanced Heart Disease Program Baylor Scott & White Health Temple Texas United States Department of Cardiology University of Oklahoma Health Science Center Oklahoma City OK United States Section of Cardiovascular Medicine Department of Medicine Wake Forest University School of Medicine Winston-Salem NC United States The CardioVascular Center Tufts Medical Center Boston Massachusetts United States

Heart failure (HF) remains a major cause of morbidity and mortality worldwide. Major advancements in optimal guideline-directed medical therapy, including novel pharmacological agents, are now available for the treatment of chronic HF including HF with reduced ejection fraction and HF with preserved ejection fraction. Despite these efforts, there are several limitations of medical therapy including but not limited to: delays in implementation and/or initiation;inability to achieve target dosing;tolerability;adherence;and recurrent and chronic costs of care. A significant proportion of patients remain symptomatic with poor HF-related outcomes including rehospitalization, progression of disease, and mortality. Driven by these unmet clinical needs, there has been a significant growth of innovative device-based interventions across all HF phenotypes over the past several decades. This state-of-the-art review will summarize the current landscape of guideline-directed medical therapy for chronic HF, discuss its limitations including barriers to implementation, and review device-based therapies which have established efficacy or demonstrated promise in the management of chronic HF. © 2023 The Author(s)

关键词： devices heart failure medical management

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Outcomes of Patients Supported on Impella 5.5 for More Than 14 Days: A Cardiogenic Shock Working Group (CSWG) Analysis

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The Journal of Heart and Lung Transplantation 2025年第4期44卷 S270-S270页

作者： N. Uriel M. Kanwar S. Li K. John Q. Kong A. Carnicelli S. Sinha A. Garan J. Hernandez Montfort J. Abraham D. Burkhoff N. Kapur Columbia University Irving Medical Center New York NY Cardiovascular Institute at Allegheny Health Network Pittsburgh PA Institute for Advanced Cardiac Care Medical City Healthcare Dallas TX The Cardiovascular Center Tufts Medical Center Boston MA Medical University of South Carolina Charleston SC Inova Heart and Vascular Institute Inova Fairfax Campus Falls Church VA Beth Israel Deaconess Medical Center Boston MA Baylor Scott & White Health Advanced Heart Failure Program Clinic Temple TX Center for Cardiovascular Analytics Research and Data Science Providence Heart Institute Portland OR Cardiovascular Research Foundation

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Enhancing Suicide Risk Prediction With Polygenic Scores in Psychiatric Emergency Settings: Prospective Study

JMIR Bioinformatics and Biotechnology

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JMIR Bioinformatics and Biotechnology 2024年第1期5卷 e58357页

作者： Lee, Younga Heather Zhang, Yingzhe Kennedy, Chris J. Mallard, Travis T. Liu, Zhaowen Vu, Phuong Linh Feng, Yen-Chen Anne Ge, Tian Petukhova, Maria V. Kessler, Ronald C. Nock, Matthew K. Smoller, Jordan W. Psychiatric & Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Psychiatry Harvard Medical School Boston MA United States Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA United States Center for Precision Psychiatry Massachusetts General Hospital Boston MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Harvard College Harvard University Cambridge MA United States Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei Taiwan Department of Health Care Policy Harvard Medical School Boston MA United States Department of Psychology Harvard University Cambridge MA United States Mental Health Research Program Franciscan Children’s Brighton MA United States Department of Psychiatry Massachusetts General Hospital Boston MA United States

Background: Despite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. Objective: This study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. Methods: Study participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. Results: Of the 333 patients (n=178, 53.5% male;mean age 36.8, SD 13.6 years;n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination

关键词： electronic health record genomics genotypes machine learning polygenic risk score predictive algorithms suicide attempt suicide risk prediction

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A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

arXiv

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arXiv 2018年

作者： Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon Analytics and Data Science Ph.D. Program Kennesaw State University United States Analytics and Data Science Institute Kennesaw State University United States Ermas Consulting Inc. United States

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost benefit analysis

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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