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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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Selected social and lifestyle correlates of brain health markers: the Cross-Cohort Collaboration Consortium

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第4期21卷 e70148页

作者： Grasset, Leslie Bis, Joshua C. Frenzel, Stefan Kojis, Daniel Simino, Jeannette Yaqub, Amber Beiser, Alexa Berr, Claudine Bressler, Jan Bülow, Robin DeCarli, Charles S. Fohner, Alison E. Harrington, Laura B. Helmer, Catherine Ikram, M Arfan Lemaitre, Rozenn N. Lopez, Oscar L. Longstreth, W.T. Neitzel, Julia Odden, Michelle C. Palta, Priya Schmidt, Carsten O. Talluri, Rajesh Vernooij, Meike W. Völzke, Henry Voortman, Trudy Whalen, Quest Wittfeld, Katharina Grabe, Hans J. Mosley, Thomas H. Psaty, Bruce M. Wolters, Frank J. Seshadri, Sudha Dufouil, Carole University Bordeaux Inserm Bordeaux Population Health Research Center Bordeaux France Cardiovascular Health Research Unit Department of Radiology & Nuclear Medicine Erasmus University of Washington Seattle WA United States Department of Psychiatry and Psychotherapy Greifswald University Medicine Greifswald Germany Framingham Heart Study Framingham MA United States Boston University School of Public Health Boston MA United States Department of Data Science John D. Bower School of Population Health University of Mississippi Medical Center Jackson MS United States Memory Impairment and Neurodegenerative Dementia (MIND) Center University of Mississippi Medical Center Jackson MS United States Department of Epidemiology Erasmus University Medical Center Rotterdam Netherlands Department of Biostatistics Boston University School of Public Health Boston MA United States Department of Neurology Boston School of Medicine Boston MA United States INM Univ Montpellier INSERM Montpellier France Human Genetics Center School of Public Health University of Texas Health Science Center at Houston Houston TX United States Institute of Diagnostic Radiology and Neuroradiology Greifswald University Medicine Greifswald Germany Department of Neurology and Center for Neuroscience University of California at Davis Sacramento CA United States Imaging of Dementia and Aging (IDeA) Laboratory Department of Neurology University of California-Davis Davis CA United States Department of Epidemiology University of Washington Seattle WA United States Cardiovascular Health Research Unit University of Washington Seattle WA United States Kaiser Permanente Washington Health Research Institute Seattle WA United States Department of Epidemiology University of Washington School of Public Health Seattle WA United States Department of Health Systems Science Kaiser Permanente Bernard J. Tyson School of Medicine Pasadena CA United States Cardiovascular Health Research Unit Department

INTRODUCTION: To investigate the associations of education level, marital status, and physical activity with dementia risk and brain MRI markers. METHODS: data from six community-based samples from the Cross-Cohort Collaboration Consortium were analyzed. Self-reported education level, marital status, and physical activity at age 60 to 75 years were harmonized. Subsamples of participants with brain MRI markers at time of exposure were selected. Associations with dementia risk and cross-sectional MRI markers were meta-analyzed. RESULTS: Higher education level was associated with lower dementia risk (hazard ratio [HR] = 0.65, 95% confidence interval [CI] = 0.59;0.72 vs low level) but not significantly with brain MRI markers. Compared with being unmarried, being married was only associated with higher total brain and hippocampal volumes. Being physically active was associated with lower dementia risk (HR = 0.73, 95% CI = 0.52;1.04), as well as larger total brain volume and smaller white matter hyperintensity volume. DISCUSSION: This study provides further evidence regarding the contribution of education level and physical activity to dementia resilience. HIGHLIGHTS: Education level, marital status, and physical activity are thought to contribute to resilience against ADRD. We used random-effects meta-analysis to summarize results from six community-based samples from the CCC. In this cross-cohort meta-analysis, higher education level and being physically active were associated with lower risk of dementia. In cross-sectional analyses, being married was associated with larger TBV and HV, while being physically active was associated with larger TBV and lower WMHV. © 2025 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

关键词： cohorts dementia meta‐analysis MRI resilience

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Monitoring mangrove dynamics and evaluating future afforestation potential in the Egyptian Red Sea

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年 26卷

作者： Abou Samra, Rasha M. Almazroui, Mansour Li, Wenzhao El-Askary, Hesham Environmental Sciences Department Faculty of Science Damietta University PO Box 34517 New Damietta City Egypt Center of Excellence for Climate Change Research Department of Meteorology King Abdulaziz University Jeddah Saudi Arabia Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich NR4 7TJ United Kingdom Earth Systems Science and Data Solutions Lab Chapman University Orange 92866 CA United States Schmid College of Science and Technology Chapman University Orange 92866 CA United States Department of Environmental Sciences Faculty of Science Alexandria University Moharem Bek Alexandria 21522 Egypt

Mangrove forests are vital for ecosystem services and coastal management but face stressors from anthropogenic activities and climate change. This study estimates the extent and afforestation potential of mangroves along the Egyptian Red Sea coast from 1984 to 2022 using NDVI derived from Landsat-5 and Sentinel-2 data in Google Earth Engine. Aboveground biomass (AGB), belowground biomass (BGB), carbon (C) stock, and CO₂ sequestration potential were evaluated using Sentinel-2 and elevation data. Future afforestation suitability (2020–2050) under SSP2-4.5 and SSP5-8.5 scenarios was assessed with the MaxEnt model. Mangrove area increased from 0.95 km² in 1984 to 1.46 km² in 2022, with Landsat-5 classification accuracy between 89 and 94 % and Sentinel-2 at 92–96 %. Mean AGB and BGB were 63.48 Mg/ha and 18.41 Mg/ha, while mean C stock and CO₂ sequestration were 40.94 Mg/ha and 150.25 Mg/ha, respectively, below global averages. Under SSP2-4.5, 18.60 km² and 21.40 km² were identified as highly and moderately suitable for afforestation, decreasing to 12.34 km² and 12.50 km² under SSP5-8.5. Under the SSP2-4.5 scenario, high-suitability areas for mangroves would be predominantly located along the northern coast, with fewer areas identified along the southern coast. Under the SSP5-8.5 scenario, high-suitability areas are expected to shift and concentrate only in the northern regions. Significant predictors under SSP2-4.5 included sea water velocity, bathymetry, and air temperature, while SSP5-8.5 highlighted bathymetry, sea surface temperature, and salinity. This study provides insights into mangrove dynamics, biomass potential, and afforestation opportunities, offering critical guidance for conservation and restoration strategies. © 2025 The Authors

关键词： Carbon stock Mangroves MaxEnt model NDVI Species distribution

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Impact of Iron Deficiency on the Growth and Bioelectrical Profile of Different Gut Bacteria

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MicrobiologyOpen 2025年第3期14卷 e70015页

作者： Elisa Quarta Marwane Bourqqia-Ramzi David Muñoz-Rodriguez María Teresa García-Esteban Antonio Murciano-Cespedosa Álvaro Mateos González Francisco José Conejero-Meca Juan Lombardo-Hernandez Jesús Mansilla-Guardiola Simona Baroni Simonetta Geninatti Crich Stefano Geuna Luca Munaron Deborah Chiabrando Celia Herrera-Rincon Department of Biodiversity Ecology & Evolution and Modeling Data Analysis & Computational Tools for Biology Research Group Biomathematics Unit Faculty of Biological Sciences Complutense University of Madrid Madrid Spain. Department of Molecular Biotechnology and Health Sciences Molecular Biotechnology Center "Guido Tarone" University of Torino Turin Italy. Department of Genetic Physiology and Microbiology Unit of Microbiology Faculty of Biological Sciences Complutense University of Madrid Madrid Spain. Department of Clinical and Biological Sciences University of Torino Turin Italy. Department of Life Science and Systems Biology (DBIOS) University of Torino Turin Italy.

SCOPE:Iron deficiency (ID) is the most common nutritional deficiency worldwide, impacting gut bacteria's metabolism and cellular biochemistry, but its effects on the microbiota-gut-brain axis (MGB) are poorly understood. Early-life ID-related dysbiosis is linked to neurodevelopmental impairments like autism and attention deficit hyperactivity disorder. Studying ID's impact on bacterial signaling can guide interventions to target MGB in iron-deficient populations. This study examined the responses of Escherichia coli (E. coli) and Limosilactobacillus reuteri (L. reuteri) to in-vitro ID conditions using the iron chelator 2,2'-Bipyridyl (BP). METHODS AND RESULTS:We assessed and modeled their growth and cultivability and explored their bioelectric profiles using the voltage-sensitive dye DiBAC4(3). Results showed differential responses: L. reuteri's growth and cultivability were unaffected by BP, while E. coli's growth rate and cultivability decreased under ID. Additionally, we created a deterministic mathematical model that demonstrated a decrease in the population's average reproduction rate in E. coli under ID. Only E. coli exhibited an altered bioelectric profile, marked by increased cell depolarization in ID conditions, which was largely rescued upon the addition of a saturating concentration of iron. CONCLUSION:These findings highlight specific bioelectrical responses in gut bacteria to ID. Understanding this variability is crucial for deciphering the microbiota's role in health and disease, particularly concerning nutritional iron imbalance and bacterial signaling in the MGB.

关键词： bioelectricity membrane potential microbiota‐gut‐brain axis nutritional deficiency

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Large amplitude bidirectional anisotropy of cosmic-ray intensity observed with world-wide networks of ground-based neutron monitors and muon detectors in November, 2021

arXiv

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arXiv 2022年

作者： Munakata, Kazuoki Kozai, M. Kato, C. Hayashi, Y. Kataoka, R. Kadokura, A. Tokumaru, M. Mendonça, R.R.S. Echer, E. Dal Lago, A. Rockenbach, M. Schuch, N.J. Bageston, J.V. Braga, C.R. Al Jassar, H.K. Sharma, M.M. Duldig, M.L. Humble, J.E. Sabbah, I. Evenson, P. Mangeard, P.-S. Kuwabara, T. Ruffolo, D. Sáiz, A. Mitthumsiri, W. Nuntiyakul, W. Kóta, J. Department of Physics Shinshu University Nagano Matsumoto390-8621 Japan Polar Environment Data Science Center Joint Support-Center for Data Science Research Research Organization of Information and Systems Tachikawa Tokyo190-0014 Japan National Institute of Polar Research Tachikawa Tokyo190-8518 Japan Department of Polar Science School of Multidisciplinary Sciences The Graduate University for Advanced Studies SOKENDAI Tachikawa Tokyo190-8518 Japan Institute for Space-Earth Environmental Research Nagoya University Aichi Nagoya464-8601 Japan São José dos Campos12227-010 Brazil Southern Space Coordination National Institute for Space Research P.O. Box 5021 RS Santa Maria97110-970 Brazil George Mason University 4400 University Drive FairfaxVA22030 United States Physics Department Kuwait University P.O. Box 5969 Safat13060 Kuwait School of Natural Sciences University of Tasmania HobartTAS7001 Australia Department of Natural Sciences College of Health Sciences Public Authority of Applied Education and Training Kuwait City72853 Kuwait Bartol Research Institute Department of Physics and Astronomy University of Delaware 217 Sharp Laboratory NewarkDE19716 United States Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Lunar and Planetary Laboratory University of Arizona TucsonAZ85721 United States

We analyze the cosmic-ray variations during a significant Forbush decrease observed with worldwide networks of ground-based neutron monitors and muon detectors during November 3-5, 2021. Utilizing the difference between primary cosmic-ray rigidities monitored by neutron monitors and muon detectors, we deduce the rigidity spectra of the cosmic-ray density (or omnidirectional intensity) and the first- and second-order anisotropies separately, for each hour of data. A clear two-step decrease is seen in the cosmic-ray density with the first ∼ 2% decrease after the interplanetary shock arrival followed by the second ∼ 5% decrease inside the magnetic flux rope (MFR) at 15 GV. Most strikingly, a large bidirectional streaming along the magnetic field is observed in the MFR with a peak amplitude of ∼ 5% at 15 GV which is comparable to the total density decrease inside the MFR. The bidirectional streaming could be explained by adiabatic deceleration and/or focusing in the expanding MFR, which have stronger effects for pitch angles near 90◦, or by selective entry of GCRs along a leg of the MFR. The peak anisotropy and density depression in the flux rope both decrease with increasing rigidity. The spectra vary dynamically indicating that the temporal variations of density and anisotropy appear different in neutron monitor and muon detector data. © 2022, CC BY.

关键词： Cosmic rays

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Multilevel delayed acceptance MCMC with an adaptive error model in PyMC3

arXiv

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arXiv 2020年

作者： Lykkegaard, Mikkel Bue Scheichl, Robert Mingas, Grigorios Fox, Colin Dodwell, Tim J. Centre for Water Systems Institute for Data Science Artificial Intelligence University of Exeter EX4 4QF United Kingdom Institute for Applied Mathematics Interdisciplinary Center for Scientific Computing Ruprecht-Karls-Universität Heidelberg Heidelberg69120 Germany Alan Turing Institute NW1 2DB United Kingdom Department of Physics University of Otago Dunedin9016 New Zealand Institute for Data Science and Artificial Intelligence University of Exeter EX4 4QF United Kingdom

Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications. Multilevel Delayed Acceptance (MLDA) with an Adaptive Error Model (AEM) is a novel approach, which alleviates this problem by exploiting a hierarchy of models, with increasing complexity and cost, and correcting the inexpensive models on-the-fly. The method has been integrated within the open-source probabilistic programming package PyMC3 and is available in the latest development version. In this paper, the algorithm is presented along with an illustrative example. © 2020, CC BY.

关键词： Open source software

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Generating eco-friendly ionic liquids with enhanced CO 2 solubility using language models

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2025年第1期3卷

作者： Adroit T.N. Fajar Guillaume Lambard Md. Amirul Islam Bidyut B. Saha Zakiah D. Nurfajrin Kevin Septioga Center for Energy Systems Design (CESD) International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) Kyushu University 744 Motooka Fukuoka 819-0395 Japan Data-driven Materials Design Group Center for Basic Research on Materials National Institute for Materials Science Namiki 1-1 Tsukuba 305-0044 Japan International Institute for Carbon-Neutral Energy Research (WPI-I2CNER) Kyushu University 744 Motooka Fukuoka 819-0395 Japan Department of Applied Chemistry Graduate School of Engineering Kyushu University 744 Motooka Fukuoka 819-0395 Japan

This study presents a viable approach for designing eco-friendly ionic liquids (ILs) with enhanced CO 2 solubility using language models, specifically GPT-2 in conjunction with SMILES-X. The GPT-2 model was fine-tuned on a relatively small, unlabeled IL dataset and subsequently used to generate diverse IL structures. SMILES-X models, trained on IL datasets labeled with CO 2 solubility and eco-toxicity values, were employed to predict the properties of the generated ILs. Trends observed in the predicted IL properties were validated using density functional theory (DFT) and COSMO-RS calculations. The GPT-2 model was then fine-tuned iteratively, with the training data updated by including the top generated ILs from previous cycles. This iterative process led to a gradual improvement in the properties of the generated ILs. It was also observed, however, that continuously adding curated generated ILs to the training data eventually caused the model to produce correct but unrealistic IL structures. These findings highlight both the potential and limitations of language models in designing novel chemicals. Additionally, the CO 2 adsorption capacity of a surrogate IL was experimentally measured, demonstrating the potential of this approach in advancing decarbonization technologies.

关键词： Generative model GPT Prediction SMILES Decarbonization

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Informal caregivers' attitudes and compliance towards a connected health platform for home care support: Insights from a long-term exposure

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Gerontechnology 2019年第4期18卷 231-242页

作者： Guisado-Fernández, Estefanía Giunti, Guido Mackey, Laura Silva, Paula Alexandra Blake, Catherine Caulfield, Brian Insight Centre for Data Analytics University College Dublin 3rd floor O'Brien Science Building East Dublin Ireland M3S Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Department of Computer Science Center for Informatics and Systems of the University of Coimbra (CISUC) University of Coimbra Coimbra Portugal

Background When designing Connected Health (CH) solutions for home care, it is vital to focus on usability and user experience to ensure that technologies are easy to use and meet users' expectations and needs. Generally, the usability and user experience tests are conducted during short-term exposure, which does not allow a true understanding of how the technology can help with the home caring tasks. Research aim We aim to investigate informal caregivers' feedback on the utility and usability of a CH monitoring platform for People with Dementia (PwD) during a period of extended use in the natural living context, and to understand how this was related to compliance patterns. Methods Informal caregiver's feedback about the CH platform, usability, and the impact of short-term versus long-term exposure were investigated through semi-structured individual interviews at the beginning and end of a 6-month deployment in the home care setting. Informal caregivers' compliance with the CH platform was analysed from their daily platform utilization during the deployment time. Results 11 informal caregivers agreed to participate. There was a change in the participants' opinions about the CH platform between the short-term and the long-term exposure feedback. Their initial impressions about what the CH platform could offer them to improve their delivery of home care for the PwD did not correspond with what they found that the CH platform could provide them following the long-term exposure. If at the beginning they saw the CH platform as a helpful tool to facilitate home care delivery and to improve their self-efficacy, after the deployment they expressed that because of the way the platform was designed it was mainly conceived for dementia research benefit and not to fulfill their caring needs. Compliance with the CH platform was quite low and similar between all participants. Conclusions Most contemporary CH studies are not conducted in real-life settings and without enough dur

关键词： Connected health Dementia Informal caregiver Usability User experience

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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