Search

Showing total 4 results
4 results

1. Is loneliness associated with mild cognitive impairment in low- and middle-income countries?

Author

Laurie T. Butler, Ai Koyanagi, Mike Trott, Karel Kostev, Louis Jacob, Yvonne Barnett, Lauren R. Godier-McBard, Erica L Bettac, Lee Smith, Jodie Bloska, Nicola Veronese, Helen Odell-Miller, Université de Versailles Saint-Quentin-en-Yvelines - UFR Sciences de la santé Simone Veil (UVSQ Santé), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM), US National Institute on Aging through OGHA 04034785, Y1‐AG‐1005–01, YA1323–08‐CN‐0020R01 AG034479/AG/NIA NIH HHS/United StatesR21 AG034263/AG/NIA NIH HHS/United States, This paper uses data from WHO's Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the U.S. National Institute on Aging through Interagency Agreements OGHA 04034785, YA1323–08‐CN‐0020, Y1‐AG‐1005–01 and through research grants R01‐AG034479 and R21‐AG034263., Smith L., Bloska J., Jacob L., Barnett Y., Butler L., Trott M., Odell-Miller H., Veronese N., Kostev K., Bettac E.L., Godier-McBard L., and Koyanagi A.

Subjects
China, Logistic regression, multi-country, Odds, loneliness, 03 medical and health sciences, 0302 clinical medicine, mild cognitive impairment, MESH: Aged, China/epidemiology, Cognitive Dysfunction/epidemiology, Cross-Sectional Studies, Developing Countries, Humans, Loneliness, Middle Aged, Prevalence, medicine, Dementia, Cognitive Dysfunction, low- and middle-income countries, Risk factor, Cognitive impairment, Aged, 030214 geriatrics, business.industry, medicine.disease, Confidence interval, 3. Good health, Psychiatry and Mental health, Low and middle income countries, loneliness, low- and middle-income countries, mild cognitive impairment, multi-country, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Geriatrics and Gerontology, medicine.symptom, business, Demography
Abstract

Background: Loneliness may be a risk factor for mild cognitive impairment but studies on this topic are scarce, particularly from low- and middle-income countries (LMICs). Therefore, the aim of the present study was to investigate the association between loneliness and mild cognitive impairment (MCI) in six LMICs (China, Ghana, India, Mexico, Russia and South Africa). Methods: Cross-sectional, community-based, nationally representative data from the WHO Study on global AGEing and adult health (SAGE) were analyzed. The definition of MCI was based on the National Institute on Ageing-Alzheimer's Association criteria. Multivariable logistic regression analysis and meta-analysis were conducted to assess associations. Results: The final analytical sample consisted of 19,092 and 13,623 individuals aged 50–64 years (middle-aged adults) and ≥65 years (older adults), respectively. Among the middle-aged, overall, loneliness was associated with a non-significant 1.43 (95% confidence interval [CI]=0.93–2.21) times higher odds for MCI although significant associations were observed in China (OR=1.51; 95%CI=1.08–2.09) and South Africa (OR=3.87; 95%CI=1.72–8.71). As for older adults, overall, there was a significant association between loneliness and MCI (OR=1.52; 95%CI=1.12–2.07). Conclusion: In this large representative sample of middle-aged and older adults from multiple LMICs, findings suggest that loneliness is associated with MCI. It may be prudent to consider reducing loneliness in low-economic settings to aid in the prevention of MCI and ultimately dementia.

Published
2021

2. Prioritized Norms in Formal Argumentation

Author

Serena Villata, Beishui Liao, Nir Oren, Leendert van der Torre, Zhejiang University, University of Aberdeen, Individual and Collective Reasoning (ICR), University of Luxembourg [Luxembourg], Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Centre National de la Recherche Scientifique (CNRS), The research reported in this paper was partially supported by the Fundamental Research Funds for the Central Universities of China for the project Big Data, Reasoning and Decision Making, the National Research Fund Luxembourg (FNR) under grant INTER/MOBILITY/14/8813732 for the project FMUAT: Formal Models for Uncertain Argumentation from Text, and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 690974 for the project MIREL: MIning and REasoning with Legal texts., and European Project: 690974,H2020 Pilier Excellent Science,H2020-MSCA-RISE-2015,MIREL(2016)

Subjects
FOS: Computer and information sciences, Logic, Computer Science - Artificial Intelligence, Big data, [SCCO.COMP]Cognitive science/Computer science, 0102 computer and information sciences, normative multiagent systems, 01 natural sciences, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Theoretical Computer Science, Argumentation theory, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Arts and Humanities (miscellaneous), normative systems, media_common.cataloged_instance, European commission, European union, China, Mathematics, media_common, deontic logic, Computer science [C05] [Engineering, computing & technology], business.industry, Deontic logic, Public relations, Sciences informatiques [C05] [Ingénierie, informatique & technologie], 16. Peace & justice, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, Artificial Intelligence (cs.AI), 010201 computation theory & mathematics, Hardware and Architecture, Normative systems, business, formal argumentation, Social psychology, norms, Software
Abstract

To resolve conflicts among norms, various nonmonotonic formalisms can be used to perform prioritized normative reasoning. Meanwhile, formal argumentation provides a way to represent nonmonotonic logics. In this paper, we propose a representation of prioritized normative reasoning by argumentation. Using hierarchical abstract normative systems, we define three kinds of prioritized normative reasoning approaches, called Greedy, Reduction, and Optimization. Then, after formulating an argumentation theory for a hierarchical abstract normative system, we show that for a totally ordered hierarchical abstract normative system, Greedy and Reduction can be represented in argumentation by applying the weakest link and the last link principles respectively, and Optimization can be represented by introducing additional defeats capturing the idea that for each argument that contains a norm not belonging to the maximal obeyable set then this argument should be rejected., Accepted by the Journal of Logic and Computation on November 2nd, 2017

Published
2017

3. Can Innovation Help U.S. Manufacturing Firms Escape Import Competition from China?

Author

Johan Hombert, Adrien Matray, Ecole des Hautes Etudes Commerciales (HEC Paris), Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), HEC Paris Research Paper Series, and Haldemann, Antoine

Subjects
Product differentiation, International trade, Monetary economics, JEL: G - Financial Economics/G.G3 - Corporate Finance and Governance/G.G3.G31 - Capital Budgeting • Fixed Investment and Inventory Studies • Capacity, jel:L60, R&D; Innovation, Product Market Competition, Trade Shocks, JEL: O - Economic Development, Innovation, Technological Change, and Growth/O.O3 - Innovation • Research and Development • Technological Change • Intellectual Property Rights/O.O3.O33 - Technological Change: Choices and Consequences • Diffusion Processes, jel:L25, Endogeneity, China, Innovation, Stock (geology), R&D, business.industry, JEL: F - International Economics/F.F1 - Trade/F.F1.F14 - Empirical Studies of Trade, jel:G31, jel:F14, import competition, innovation, R&D tax credit, Capital expenditure, Quartile, jel:O33, Manufacturing firms, [SHS.GESTION]Humanities and Social Sciences/Business administration, Profitability index, Business, [SHS.GESTION] Humanities and Social Sciences/Business administration
Abstract

We study whether R&D-intensive firms are more resilient to trade shocks. We correct for the endogeneity of R&D using tax-induced changes to R&D cost. While rising imports from China lead to slower sales growth and lower profitability, these effects are significantly smaller for firms with a larger stock of R&D (by about half when moving from the bottom quartile to the top quartile of R&D). As a result, while firms in import-competing industries cut capital expenditures and employment, R&D-intensive firms downsize considerably less. Finally, we provide evidence that these effects are explained by R&D allowing firms to increase product differentiation.

Published
2014

4. Regional trends and drivers of the global methane budget

Author

Naveen Chandra, Akihiko Ito, Philippe Ciais, Peter A. Raymond, Jurek Müller, Ann R. Stavert, Joe R. Melton, Marielle Saunois, Phillipe Bousquet, Adrian Gustafson, Yosuke Niwa, Robert B. Jackson, Shushi Peng, Qianlai Zhuang, Hanqin Tian, Aki Tsuruta, George H. Allen, Benjamin Poulter, Joe McNorton, Bo Zheng, Yi Yin, Prabir K. Patra, Thomas Kleinen, Pierre Regnier, Peter Bergamaschi, Ronny Lauerwald, Shamil Maksyutov, Misa Ishizawa, Arjo Segers, William J. Riley, Josep G. Canadell, Zhen Zhang, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université libre de Bruxelles (ULB), This paper is the result of a collaborative international effort under the umbrella of the Global Carbon Project, a Global Research Project of Future Earth and a research partner of the World Climate Research Programme. We acknowledge primary support for the methane budget from the Gordon and Betty Moore Foundation through Grant GBMF5439 'Advancing Understanding of the Global Methane Cycle' to Stanford University (P.I. Rob Jackson, co- P.I.s Philippe Bousquet, Marielle Saunois, Josep Canadell, Gustaf Hugelius, and Ben Poulter). Josep Canadell acknowledges the support of the Australian National Environmental Science Program – Earth Systems and Climate Change. Prabir K Patra and Neveen Chandra acknowledge support from Environment Research and Technology Development Funds of the Environmental Restoration and Conservation Agency of Japan (JPMEERF20182002, JPMEERF20172001). Jurek Müller thanks for support by the Swiss National Science Foundation (#200020_172476). Peter Bergamaschi acknowledges the support of ECMWF providing computing resources under the special project 'Improve European and global CH4 and N2O flux inversions (2018-2020)'. Pierre Regnier acknowledges the support from the VERIFY project under European Union's Horizon 2020 research and innovation program grant agree-ment no. 776810. The TM5-CAMS inversions are available from https://atmos phere.copernicus.eu, Arjo Segers acknowledges the support from the Copernicus Atmosphere Monitoring Service, implemented by the European Centre for Medium-Range Weather Forecasts on be-half of the European Commission (grant no. CAMS73). William Riley acknowledges support by the US Department of Energy, Office of Science, Biological and Environmental Research, Regional and Global Climate Modeling Program through the RUBISCO Scientific Focus Area under contract DE-AC02- 05CH11231 to Lawrence Berkeley National Laboratory. The authors gratefully acknowledge those re-sponsible for the global network of atmospheric observations used in this study including Donald R Blake and Isobel Simpson, University of California Irvine, USA, Gordon Brailsford, NIWA, Cyril Crevosier, LMD, France, New Zealand, Paul Krummel and Ray Langenfelds, CSIRO, Australia, Toshinobu Machida, Yasunori Tohjima and Yukio Yoshida, NIES, Japan, Ronald Prinn, MIT, USA, Simon O’Doherty, University of Bristol, UK, Michel Ramonet, LSCE-IPSL, France, Atsushi Takizawa, JMA, Japan, Ray Weiss, Scripps Institute of Oceanography, USA and Doug Worthy, Environment Canada, Canada. We would also like to thank Lena Höglund-Isaksson, IIASA, Austria, Greet Janssens- Maenhout EC-JRC, Italy and Steven Smith, PNNL-JGCR, USA for their assistance with the anthropogenic inventory data. The authors also acknowledge the significant contribution of Goulven G. Laruelle, Department Geoscience, Environment & Society, Université Libre de Bruxelles, Brussels, Belgium who, with P. Regnier, developed the re-gionally distributed estuarine flux data set., and European Project: 776810,H2020,H2020-SC5-2017-OneStageB,VERIFY(2018)

Subjects
China, Municipal solid waste, Livestock, 010504 meteorology & atmospheric sciences, methane emissions, Oceans and Seas, 010501 environmental sciences, 7. Clean energy, 01 natural sciences, Methane, 12. Responsible consumption, bottom-up, Atmosphere, chemistry.chemical_compound, Enteric fermentation, Environmental protection, source sectors, Environmental Chemistry, Animals, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, General Environmental Science, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Global and Planetary Change, regional, Ecology, business.industry, Coal mining, Biological Sciences, Climate Action, natural emissions, Carbon project, chemistry, 13. Climate action, Greenhouse gas, top-down, Environmental science, anthropogenic emissions, business, Environmental Sciences
Abstract

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270Tg CH4 yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Full Text
View/download PDF