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arXiv 2021年

作者： Bianchin, Gianluca Dall'Anese, Emiliano Poveda, Jorge I. Jacobson, David Carlton, Elizabeth J. Buchwald, Andrea G. Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO United States VanaData BoulderCO United States Department of Environmental and Occupational Health Colorado School of Public Health University of Colorado Anschutz CO United States Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz CO United States

Since the early months of 2020, non-pharmaceutical interventions (NPIs) - implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance - have been the primary means to control SARS-CoV-2 transmission. We seek to identify how risk tolerance and vaccination rates impact the rate at which a population can return to pre-pandemic contact behavior. To this end, we develop a novel feedback control method for data-driven decision-making to identify optimal levels of NPIs across geographical regions in order to guarantee that hospitalizations will not exceed a given risk tolerance. Results are shown for the state of Colorado, and they suggest that: coordination in decision-making across regions is essential to maintain the daily number of hospitalizations below the desired limits;increasing risk tolerance can decrease the number of days required to discontinue NPIs, at the cost of an increased number of deaths;and if vaccination uptake is less than 70%, at most levels of risk tolerance, return to pre-pandemic contact behaviors before the early months of 2022 may newly jeopardize the healthcare system. Copyright © 2021, The Authors. All rights reserved.

关键词： Vaccines

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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Transcutaneous cervical vagus nerve stimulation lengthens exhalation in the context of traumatic stress

Transcutaneous cervical vagus nerve stimulation lengthens ex...

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2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Gazi, Asim H. Sundararaj, Srirakshaa Harrison, Anna B. Gurel, Nil Z. Wittbrodt, Matthew T. Shah, Amit J. Vaccarino, Viola Bremner, J. Douglas Inan, Omer T. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States College of Sciences Georgia Institute of Technology AtlantaGA United States David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Anesthesiology Northwestern University School of Medicine ChicagoIL United States Department of Epidemiology Rollins School of Public Health AtlantaGA United States Department of Psychiatry and Behavioral Sciences Emory University School of Medicine AtlantaGA United States

ISBN: (纸本)9781665403580

Transcutaneous electrical stimulation of the vagus nerve is believed to deliver afferent signaling to the brain that, in turn, yields downstream changes in peripheral physiology, including cardiovascular and respiratory parameters. While the effects of transcutaneous cervical vagus nerve stimulation (tcVNS) on these parameters have been studied broadly, little is known regarding the specific effects of tcVNS on exhalation time and the spontaneous respiration cycle. By understanding such effects, tcVNS could be used to counterbalance sympathetic hyperactivity following distress by enhancing vagal tone through parasympathetically favored modulation of inspiration and expiration - specifically, lengthened expiration relative to inspiration. We thus investigated the effects of tcVNS on respiration timings by decomposing the respiration cycle into inspiration and expiration times and incorporating state-of-the-art respiration quality assessment algorithms for respiratory effort belt and electrocardiogram derived respiration signals. This enabled robust estimation of respiration timings from quality measurements alone. We thereby found that tcVNS increases expiration time minutes after stimulation, compared to a sham control (N = 26). This suggests that tcVNS could counteract sympathovagal imbalance, given the relationship between expiration and heightened vagal tone. © 2021 IEEE

关键词： Signal processing

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

arXiv

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arXiv 2025年

作者： Jabin, Md. Asaduzzaman Jiang, Hanqi Li, Yiwei Kaggwa, Patrick Douglass, Eugene Sekandi, Juliet N. Liu, Tianming School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA30602 United States School of Computing College of Engineering University of Georgia AthensGA30602 United States Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA30602 United States Department of Pharmaceutical and Biomedical Sciences College of Pharmacy University of Georgia AthensGA30602 United States Global Health Institute College of Public Health University of Georgia AthensGA30602 United States

Chronic diseases, including diabetes, hypertension, asthma, HIV/AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized Video-LLaVA-based multimodal large vision language model (LVLM) aimed at visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features—such as the clear visibility of the patient’s face, medication, water intake, and the act of ingestion—and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability. © 2025, CC BY-NC-ND.

关键词： Fetal monitoring

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Microplastics and nanoplastics in environment: Sampling, characterization and analytical methods

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Groundwater for Sustainable Development 2024年 26卷

作者： Mahapatra, Sarbanee Maity, Jyoti Prakash Singha, Shuvendu Mishra, Tanisha Dey, Gobinda Chandra Samal, Alok Banerjee, Pritam Biswas, Chhanda Chattopadhyay, Sourasis Patra, Rasmi Ranjan Patnaik, Sneha Bhattacharya, Prosun Environmental Science laboratory Department of Chemistry School of Applied Sciences KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Agricultural Chemistry National Taiwan University Taipei106319 Taiwan Department of Environmental Science University of Kalyani Nadia West Bengal 741235 India Department of Environmental Science Policy and Management University of California University Avenue and Oxford St BerkeleyCA94720 United States Research & Development KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science & Application Odisha University of Agriculture & Technology Odisha Bhubaneswar India School of Public Health KIMS Medical College KIIT University Bhubaneswar751024 India Department of Healthcare Administration Asia University ROC Taiwan KTH-International Groundwater Arsenic Research Group Department of Sustainable Development Environmental Science and Engineering KTH Royal Institute of Technology Teknikringen 10B StockholmSE-100 44 Sweden

Microplastics (MPs) and nanoplastics (NPs) have gained increasing attention in environmental research due to their ubiquitousness and potential impacts on natural environments and human health as per the UN Sustainable Development Goals (SDGs), particularly SDG-14 to address global threats where at least 12 SDGs, directly/indirectly impacts. Present review is undertaken to highlight the process of breakdown of diverse groups of plastic products in soil, surface water, and groundwater under the influence of different factors (UV, light, heat, microbe, etc.), which are mobilized as MPs/NPs to the surface water, groundwater, air, soil, and living organisms by different natural and anthropogenic processes. Review also highlights a comprehensive overview of the methodology for sampling, characterization, and analysis for these minuscule plastic particles (PPs) in various environmental samples, encompassing surface/subsurface water, sediments, soils, and biological organisms. The collection, extraction, and characterization of MPs/NPs, typically employ filtration processes, wherein a known volume of water is passed through a fine mesh to capture MPs/NPs from water samples. Sediment/soil samples require sieving and density separation techniques to isolate PPs from the surrounding matrix. Biological samples require digestion steps to remove organic matter, leaving behind plastics for analysis. Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM) are commonly utilized to determine the polymer composition, size, shape, and surface characteristics of PPs. Quantification involves several approaches, visual counting, image analysis, and spectroscopic techniques. Abundance of MPs/NPs in the respective environmental samples (water, air, soil, etc.) can be determined by comparing the richness (i.e., number/mass) of plastics to the volume or weight of the original sample. Such comprehensive analytical methodologies contribute to

关键词： Sustainable development

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Ultra Low Voltage Variable Gain Cherry-Hooper Amplifier with 1MHz Constant Bandwidth, 1.7μW Power Dissipation and ±0.2V Supply 62

Ultra Low Voltage Variable Gain Cherry-Hooper Amplifier with...

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62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019

作者： Rico-Aniles, Hector Daniel Ramirez-Angulo, Jaime Lopez-Martin, Antonio J. Rocha, Jose Miguel DIaz-Sanchez, Alejandro Carvajal, Ramon G. VLSI Laboratory Klipsch School of Electrical and Computer Engineering Las Cruces United States Dept. of Electrical and Electronics Engineering Public Univerity of Navarra Navarra Spain Electronics Engineering Department Universidad de Sevilla Seville Spain

ISBN: (纸本)9781728127880

An ultra-low-voltage low-power amplifier is introduced. The design is based on the Cherry-Hooper architecture and operates with a ±0.2V supply and ±190mV linear input range. It has an approximately constant bandwidth of 1MHz with a power consumption of 1.7μW. The low-voltage operation is achieved using floating batteries that keep the gates of all differential pairs at a constant value close to the upper rail with large input signal variations. It has a figure of merit of 2.61 MHzpF/μW for a nominal gain A=15.6. © 2019 IEEE.

关键词： Bandwidth

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Association Between Sex and Race and Ethnicity and IV Sedation Use in Patients Receiving Invasive Ventilation

CHEST Critical Care

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CHEST Critical Care 2024年第4期2卷 100100-100100页

作者： Walker, Sarah L. Angriman, Federico Burry, Lisa Celi, Leo Anthony Fiest, Kirsten M. Gichoya, Judy Johnson, Alistair Liu, Kuan Mehta, Sangeeta Roman-Sarita, Georgiana Seyyed-Kalantari, Laleh Vu, Thanh-Giang T. Whitlock, Elizabeth L. Tomlinson, George Yarnell, Christopher J. Institute of Health Policy Management and Evaluation University of Toronto Toronto ON Canada Interdepartmental Division of Critical Care Medicine University of Toronto Toronto ON Canada Leslie Dan Faculty of Pharmacy University of Toronto Toronto ON Canada University Health Network Toronto ON Canada Sinai Health Toronto ON Canada Department of Critical Care Medicine Scarborough Health Network Toronto ON Canada Scarborough Health Network Research Institute Toronto ON Canada Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada Department of Critical Care Medicine University of Calgary Calgary AB Canada Department of Radiology and Biomedical Informatics Emory University Atlanta GA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA Department of Anesthesia & Perioperative Care University of California San Francisco San Francisco CA Department of Anesthesiology & Pain Medicine University of Washington Seattle WA United States Department of Computer Science University of Toronto Toronto ON Canada Department of Respiratory Therapy University Health Network Toronto ON Canada

Background: IV sedation is an important tool for managing patients receiving invasive ventilation, yet excess sedation is harmful, and dosing could be influenced by implicit bias. Research Question: What are the associations between sex or race and ethnicity and sedation practices? Study Design and Methods: We performed a retrospective single-center cohort study of adults receiving invasive ventilation for ≥ 24 hours using the Medical Information Mart for Intensive Care Version IV (2008-2019) database from Boston, Massachusetts. We used a repeated-measures design (4-hour intervals) to study the association between sex (female or male) or race and ethnicity (Asian, Black, Hispanic, White) and sedation outcomes. Sedation outcomes included sedative use (propofol, benzodiazepine, dexmedetomidine) and minimum sedation score. We categorized sedative use as follows: no sedative and then lowest, second, third, and highest quartiles of sedative dose. We adjusted for covariates with multilevel Bayesian proportional odds modeling and reported ORs with 95% credible intervals (CrIs). Results: We studied 6,764 patients: 43% female;3.5% Asian, 12% Black, 4.5% Hispanic, and 80% White. Benzodiazepines were administered to 2,334 patients (36%). Black patients received benzodiazepines less often and at lower doses than White patients (more benzodiazepine: OR, 0.66;95% CrI, 0.49-0.92). Propofol was administered to 3,865 patients (57%). Female patients received propofol less often and at lower doses than male patients (more propofol: OR, 0.72;95% CrI, 0.61-0.86). Dexmedetomidine was administered to 1,439 patients (21%), and use was similar across sex or race and ethnicity. Female patients were less sedated than male patients (deeper sedation: OR, 0.71;95% CrI, 0.62-0.81), and Black patients were more sedated than White patients (more sedated: OR, 1.28;95% CrI, 1.05-1.55). Interpretation: Among patients receiving invasive ventilation for at least 24 hours, IV sedation and attained sedati

关键词： Bayesian modeling deep sedation differences in treatment by race or ethnicity differences in treatment by sex mechanical ventilation

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No COVID-19 climate silver lining in the US power sector

arXiv

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arXiv 2020年

作者： Luke, Max Somani, Priyanshi Cotterman, Turner Suri, Dhruv Lee, Stephen J. Nera Economic Consulting BostonMA United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal Karnataka India Department of Engineering and Public Policy Carnegie Mellon University PittsburghPA United States Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Karnataka India Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

Recent studies conclude that the global coronavirus (COVID-19) pandemic decreased power sector CO2emissions globally and in the United States. In this paper, we analyze the statistical significance of CO2emissions reductions in the U.S. power sector from March through December 2020. We use Gaussian process (GP) regression to assess whether CO2emissions reductions would have occurred with reasonable probability in the absence of COVID-19 considering uncertainty due to factors unrelated to the pandemic and adjusting for weather, seasonality, and recent emissions trends. We find that monthly CO2emissions reductions are only statistically significant in April and May 2020 considering hypothesis tests at 5% significance levels. Separately, we consider the potential impact of COVID- 19 on coal-fired power plant retirements through 2022. We find that only a small percentage of U.S. coal power plants are at risk of retirement due to a possible COVID-19-related sustained reduction in electricity demand and prices. We observe and anticipate a return to pre-COVID-19 CO2emissions in the U.S. power sector. Copyright © 2020, The Authors. All rights reserved.

关键词： Coal

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