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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ben Jemaa, M.; Rahali, Fatma Zohra; Falleh, Hanen; Beji-Serairi, Raja; +11 Authors

    This manuscript aimed to optimise the encapsulation of Thymus capitatus essential oil into nanoemulsion. Response Surface Methodology results were best fitted into polynomial models with regression coefficient values of more than 0.95. The optimal nanoemulsion showed nanometer-sized droplets (380 nm), a polydispersity index less than 0.5, and a suitable Zeta potential (−10.3 mV). Stability results showed that nanoemulsions stored at 4 °C were stable with the lowest d3,2, PolyDispersity Index (PDI), and pH (day 11). Significant ameliorations in the capacity to neutralise DPPH radical after the encapsulation of the antimicrobial efficacy of thyme essential oil were recorded. S. typhimurium growth inhibition generated by nanoencapsulated thyme essential oil was 17 times higher than by bulk essential oil. The sensory analysis highlighted that the encapsulation of thyme essential oil improved enriched milk’s sensory appreciation. Indeed, 20% of the total population attributed a score of 4 and 5 on the scale used for milk enriched with nanoemulsion. In comparison, only 11% attributed the same score to milk enriched with bulk essential oil. The novel nanometric delivery system presents significant interest for agroalimentary industries. This study was supported by the Tunisian Ministry of Higher Education, Scientific Research, Information, and Communication Technologies and was funded under MILKQUA-H2020-PRIMA 2018—Section 2. Peer reviewed

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Archivio Istituziona...arrow_drop_down
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    Molecules
    Article . 2022
    Data sources: DOAJ
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    Molecules
    Article . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Marta Gallego-Paüls; Carles Hernandez-Ferrer; Mariona Bustamante; Xavier Basagaña; +21 Authors

    The study has received funding from the European Community’s Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 (HELIX project) and the H2020-EU.3.1.2. - Preventing Disease Programme under grant agreement no 874583 (ATHLETE project). Additionally, BiB received core infrastructure funding from the Wellcome Trust (WT101597MA) and a joint grant from the UK Medical Research Council (MRC) and Economic and Social Science Research Council (ESRC) (MR/N024397/1). INMA data collections were supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science Innovation and Technology (6-04-2014_31V-66). The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV- 1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX), and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011- 2014; “Rhea Plus”: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I+D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. MV-U and CR-A were supported by a FI fellowship from the Catalan Government (FI-DGR 2015 and #016FI_B 00272). MC received funding from Instituto Carlos III (Ministry of Economy and Competitiveness) (MS16/00128). LM is funded by a Juan de la Cierva-Incorporación fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Economía, Industria y Competitividad Background: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both SI

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    UPF Digital Repository
    Article . 2021
    License: CC BY
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    BMC Medicine
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      UPF Digital Repository
      Article . 2021
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      BMC Medicine
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Loisel, Stéphane; Piette, Pierrick; Tsai, Jason;

    AbstractModeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Astin Bulletinarrow_drop_down
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    Astin Bulletin
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2019
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Astin Bulletin
    Article . 2021 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2019
    License: arXiv Non-Exclusive Distribution
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Astin Bulletinarrow_drop_down
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      arXiv.org e-Print Archive
      Other literature type . Preprint . 2019
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      Astin Bulletin
      Article . 2021 . Peer-reviewed
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    Authors: Carmelo Quarta; Marc Claret; Lori M. Zeltser; Kevin W. Williams; +5 Authors

    Hypothalamic AgRP and POMC neurons are conventionally viewed as the yin and yang of the body’s energy status, since they act in an opposite manner to modulate appetite and systemic energy metabolism. However, although AgRP neurons’ functions are comparatively well understood, a unifying theory of how POMC neuronal cells operate has remained elusive, probably due to their high level of heterogeneity, which suggests that their physiological roles might be more complex than initially thought. In this Perspective, we propose a conceptual framework that integrates POMC neuronal heterogeneity with appetite regulation, whole-body metabolic physiology and the development of obesity. We highlight emerging evidence indicating that POMC neurons respond to distinct combinations of interoceptive signals and food-related cues to fine-tune divergent metabolic pathways and behaviours necessary for survival. The new framework we propose reflects the high degree of developmental plasticity of this neuronal population and may enable progress towards understanding of both the aetiology and treatment of metabolic disorders. International audience

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    Europe PubMed Central
    Other literature type . 2021
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    Nature Metabolism
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    Nature Metabolism
    Other literature type . Article . 2021 . Peer-reviewed
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    Article . 2021
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      Europe PubMed Central
      Other literature type . 2021
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      Nature Metabolism
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      Nature Metabolism
      Other literature type . Article . 2021 . Peer-reviewed
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      HAL-Inserm
      Article . 2021
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    Authors: Martin, Olivier; Fernandez-Diclo, Yasmil; Coville, Jérôme; Soubeyrand, Samuel;

    Insect-borne diseases are diseases carried by insects affecting humans, animals or plants. They have the potential to generate massive outbreaks such as the Zika epidemic in 2015-2016 mostly distributed in the Americas, the Pacific and Southeast Asia, and the multi-foci outbreak caused by the bacterium {\it Xylella fastidiosa} in Europe in the 2010s. In this article, we propose and analyze the behavior of a spatially-explicit compartmental model adapted to pathosystems with fixed hosts and mobile vectors disseminating the disease. The behavior of this model based on a system of partial differential equations is complementarily characterized via a theoretical study of its equilibrium states and a numerical study of its transitive phase using global sensitivity analysis. The results are discussed in terms of implications concerning the surveillance and control of the disease over a medium-to-long temporal horizon. International audience

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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2020
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    Nonlinear Analysis Real World Applications
    Other literature type . Article . 2021 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2020
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      Nonlinear Analysis Real World Applications
      Other literature type . Article . 2021 . Peer-reviewed
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    Authors: Simon Kebede Merid; Mariona Bustamante; Marie Standl; Jordi Sunyer; +13 Authors

    The research leading to these results has received funding from the European Community's Seventh Framework Program under grant agreement numbers: 211,250 (ESCAPE), and 261,357 (MeDALL), and the European Research Council (grant agreement number 757919, TRIBAL). NL was recipient of ANR-15-IDEX-02 grant. BAMSE: BAMSE was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Region Stockholm, the Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet, The Swedish Research Council Formas and the Swedish Environment Protection Agency, the Swedish Asthma and Allergy Research Foundation, the Cancer and Allergy Foundation. O. Gruzieva is supported by the Swedish Research Council for Health, Working life and Welfare (FORTE 2017-01146). GINIplus: The GINIplus study was mainly supported for the first 3 years of the Federal Ministry for Education, Science, Research and Technology (interventional arm) and Helmholtz Zentrum Munich (former GSF) (observational arm). The 4 year, 6 year, 10 year and 15 year follow-up examinations of the GINIplus study were covered from the respective budgets of the 5 study centres (Helmholtz Zentrum Munich (former GSF), Research Institute at Marien-Hospital Wesel, LMU Munich, TU Munich and from 6 years onwards also from IUF -Leibniz Research-Institute for Environmental Medicine at the University of Dusseldorf) and a grant from the Federal Ministry for Environment (IUF Dusseldorf, FKZ 20462296). Further, the 15-year follow-up examination of the GINIplus study was supported by the Commission of the European Communities, the 7th Framework Program: MeDALL project, and as well by the companies Mead Johnson and Nestle. INMA: INMA project was mainly supported by the Commission of the European Community, the 7th Framework Program: MeDALL project and by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI04/1436; PI06/0867; PI08/1151; and PI18/01142 incl. FEDER funds), and Generalitat de Catalunya-CIRIT 1999SGR 00241, Department of Health of the Basque Government (2005111093), Provincial Government of Gipuzkoa (DFG06/002), and annual agreements with the municipalities of the study area (Zumarraga, Urretxu, Legazpi, Azkoitia y Azpeitia y Beasain) Air pollution has been associated with adverse health effects across the life-course. Although underlying mechanisms are unclear, several studies suggested pollutant-induced changes in transcriptomic profiles. In this meta-analysis of transcriptome-wide association studies of 656 children and adolescents from three European cohorts participating in the MeDALL Consortium, we found two differentially expressed transcript clusters (FDR p < 0.05) associated with exposure to particulate matter < 2.5 mu m in diameter (PM2.5) at birth, one of them mapping to the MIR1296 gene. Further, by integrating gene expression with DNA methylation using Functional Epigenetic Modules algorithms, we identified 9 and 6 modules in relation to PM2.5 exposure at birth and at current address, respectively (including NR1I2, MAPK6, TAF8 and SCARA3). In conclusion, PM2.5 exposure at birth was linked to differential gene expression in children and adolescents. Importantly, we identified several significant interactome hotspots of gene modules of relevance for complex diseases in relation to PM2.5 exposure.

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    Authors: Hecham, Abdelraouf; Croitoru, Madalina; Bisquert, Pierre;

    This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features. International audience

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    Authors: Wendy M. Rauw; Lotta Rydhmer; Ilias Kyriazakis; Margareth Øverland; +7 Authors

    Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input-high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input-reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co-products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co-product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on 'protein from livestock animals' to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This research was made possible by funding from SusAn project ‘Sustainability of pig production through improved feed efficiency’ (SusPig; www.suspig-era.net), an ERA-Net co-funded under the European Union's Horizon 2020 research and innovation programme (www.era-susan.eu), under Grant Agreement no. 696231. Peer reviewed 12 Pág.

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    Article . 2020
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    Journal of the Science of Food and Agriculture
    Article . 2020 . Peer-reviewed
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    Authors: Sessa, Valentina; Assoumou, Edi; Bossy, Mireille; Carvalho, Sílvia; +1 Authors

    A big challenge of sustainable power systems is the integration of climate variability into the operational and long-term planning processes. In this paper, we focus on the run-of-river based hydropower generation on a European scale. In particular, we deal with the modeling of this form of power production based on climate variables. Translating time series of climate data (precipitation and air temperature) into time series of run-of-river based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. Indeed, this kind of electricity generation is limited by the flow of the river in which the power plants are located. Moreover, the water flow is a nonlinear function of the climate variables and the geographical characteristics of the river basins. Finally, the impact of the climate variables on the runoff may occur with a certain delay, whose determination depends on physically based phenomena (e.g., melting snow-local temperature). In this work, we first compare well-established machine learning regression algorithms to be used for modeling the run-of-river hydropower generation. Then, the technique showing to have the best performance is used for producing long-term estimates of hydropower capacity factors based on future climate scenarios for each European country.

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    Authors: Toimil, A.; Camus, P.; Losada, I. J.; Le Cozannet, G.; +3 Authors

    Abstract Developing future projections of shoreline change requires a good understanding of the driving coastal processes. These processes result primarily from the combination of mean sea level, waves, storm surges and tides, which are affected by global and regional climate change, and whose uncertainty increases with time. This paper reviews the current state of the art of methods used to model climate change-induced coastal erosion focusing on how climate change-related drivers and the associated uncertainty are considered. We identify research gaps, describe and analyse the key components of a comprehensive framework to derive future estimates of shoreline change and make suggestions for good practice. Within the scope of the review, we find that although significant progress has been made over the last decade, most of the studies limit uncertainty sampling to considering ranges of variation of forcing variables and ensembles of emissions scenarios, and applications with high level of probabilistic development remain few. Further research is necessary to fully (a) incorporate projected time series of coastal drivers into the erosion models, including bias correction; (b) sufficiently sample the uncertainty associated with each step of the top-down approach, including the consideration of different emission scenarios, inter- and intra-model variability, and multiple runs of erosion models or model ensembles; and (c) reduce uncertainty in shoreline change estimates by developing better datasets and model parameterisations, and progressing in detection and attribution.

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    Earth-Science Reviews
    Article . 2020 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ben Jemaa, M.; Rahali, Fatma Zohra; Falleh, Hanen; Beji-Serairi, Raja; +11 Authors

    This manuscript aimed to optimise the encapsulation of Thymus capitatus essential oil into nanoemulsion. Response Surface Methodology results were best fitted into polynomial models with regression coefficient values of more than 0.95. The optimal nanoemulsion showed nanometer-sized droplets (380 nm), a polydispersity index less than 0.5, and a suitable Zeta potential (−10.3 mV). Stability results showed that nanoemulsions stored at 4 °C were stable with the lowest d3,2, PolyDispersity Index (PDI), and pH (day 11). Significant ameliorations in the capacity to neutralise DPPH radical after the encapsulation of the antimicrobial efficacy of thyme essential oil were recorded. S. typhimurium growth inhibition generated by nanoencapsulated thyme essential oil was 17 times higher than by bulk essential oil. The sensory analysis highlighted that the encapsulation of thyme essential oil improved enriched milk’s sensory appreciation. Indeed, 20% of the total population attributed a score of 4 and 5 on the scale used for milk enriched with nanoemulsion. In comparison, only 11% attributed the same score to milk enriched with bulk essential oil. The novel nanometric delivery system presents significant interest for agroalimentary industries. This study was supported by the Tunisian Ministry of Higher Education, Scientific Research, Information, and Communication Technologies and was funded under MILKQUA-H2020-PRIMA 2018—Section 2. Peer reviewed

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    Molecules
    Article . 2022
    Data sources: DOAJ
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    Molecules
    Article . 2022
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    Authors: Marta Gallego-Paüls; Carles Hernandez-Ferrer; Mariona Bustamante; Xavier Basagaña; +21 Authors

    The study has received funding from the European Community’s Seventh Framework Programme (FP7/2007-206) under grant agreement no 308333 (HELIX project) and the H2020-EU.3.1.2. - Preventing Disease Programme under grant agreement no 874583 (ATHLETE project). Additionally, BiB received core infrastructure funding from the Wellcome Trust (WT101597MA) and a joint grant from the UK Medical Research Council (MRC) and Economic and Social Science Research Council (ESRC) (MR/N024397/1). INMA data collections were supported by grants from the Instituto de Salud Carlos III, CIBERESP, and the Generalitat de Catalunya-CIRIT. KANC was funded by the grant of the Lithuanian Agency for Science Innovation and Technology (6-04-2014_31V-66). The Rhea project was financially supported by European projects (EU FP6-2003-Food-3-NewGeneris, EU FP6. STREP Hiwate, EU FP7 ENV.2007.1.2.2.2. Project No 211250 Escape, EU FP7-2008-ENV- 1.2.1.4 Envirogenomarkers, EU FP7-HEALTH-2009- single stage CHICOS, EU FP7 ENV.2008.1.2.1.6. Proposal No 226285 ENRIECO, EU- FP7- HEALTH-2012 Proposal No 308333 HELIX), and the Greek Ministry of Health (Program of Prevention of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011- 2014; “Rhea Plus”: Primary Prevention Program of Environmental Risk Factors for Reproductive Health, and Child Health: 2012-15). The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium which is supported by grant PT17/0019 of the PE I+D+i 2013-2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science and Innovation and State Research Agency through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. MV-U and CR-A were supported by a FI fellowship from the Catalan Government (FI-DGR 2015 and #016FI_B 00272). MC received funding from Instituto Carlos III (Ministry of Economy and Competitiveness) (MS16/00128). LM is funded by a Juan de la Cierva-Incorporación fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Economía, Industria y Competitividad Background: Multiple omics technologies are increasingly applied to detect early, subtle molecular responses to environmental stressors for future disease risk prevention. However, there is an urgent need for further evaluation of stability and variability of omics profiles in healthy individuals, especially during childhood. Methods: We aimed to estimate intra-, inter-individual and cohort variability of multi-omics profiles (blood DNA methylation, gene expression, miRNA, proteins and serum and urine metabolites) measured 6 months apart in 156 healthy children from five European countries. We further performed a multi-omics network analysis to establish clusters of co-varying omics features and assessed the contribution of key variables (including biological traits and sample collection parameters) to omics variability. Results: All omics displayed a large range of intra- and inter-individual variability depending on each omics feature, although all presented a highest median intra-individual variability. DNA methylation was the most stable profile (median 37.6% inter-individual variability) while gene expression was the least stable (6.6%). Among the least stable features, we identified 1% cross-omics co-variation between CpGs and metabolites (e.g. glucose and CpGs related to obesity and type 2 diabetes). Explanatory variables, including age and body mass index (BMI), explained up to 9% of serum metabolite variability. Conclusions: Methylation and targeted serum metabolomics are the most reliable omics to implement in single time-point measurements in large cross-sectional studies. In the case of metabolomics, sample collection and individual traits (e.g. BMI) are important parameters to control for improved comparability, at the study design or analysis stage. This study will be valuable for the design and interpretation of epidemiological studies that aim to link omics signatures to disease, environmental exposures, or both SI

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    UPF Digital Repository
    Article . 2021
    License: CC BY
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    BMC Medicine
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    Authors: Loisel, Stéphane; Piette, Pierrick; Tsai, Jason;

    AbstractModeling policyholders’ lapse behaviors is important to a life insurer, since lapses affect pricing, reserving, profitability, liquidity, risk management, and the solvency of the insurer. In this paper, we apply two machine learning methods to lapse modeling. Then, we evaluate the performance of these two methods along with two popular statistical methods by means of statistical accuracy and profitability measure. Moreover, we adopt an innovative point of view on the lapse prediction problem that comes from churn management. We transform the classification problem into a regression question and then perform optimization, which is new to lapse risk management. We apply the aforementioned four methods to a large real-world insurance dataset. The results show that Extreme Gradient Boosting (XGBoost) and support vector machine outperform logistic regression (LR) and classification and regression tree with respect to statistic accuracy, while LR performs as well as XGBoost in terms of retention gains. This highlights the importance of a proper validation metric when comparing different methods. The optimization after the transformation brings out significant and consistent increases in economic gains. Therefore, the insurer should conduct optimization on its economic objective to achieve optimal lapse management.

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    Other literature type . Preprint . 2019
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    Astin Bulletin
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    Authors: Carmelo Quarta; Marc Claret; Lori M. Zeltser; Kevin W. Williams; +5 Authors

    Hypothalamic AgRP and POMC neurons are conventionally viewed as the yin and yang of the body’s energy status, since they act in an opposite manner to modulate appetite and systemic energy metabolism. However, although AgRP neurons’ functions are comparatively well understood, a unifying theory of how POMC neuronal cells operate has remained elusive, probably due to their high level of heterogeneity, which suggests that their physiological roles might be more complex than initially thought. In this Perspective, we propose a conceptual framework that integrates POMC neuronal heterogeneity with appetite regulation, whole-body metabolic physiology and the development of obesity. We highlight emerging evidence indicating that POMC neurons respond to distinct combinations of interoceptive signals and food-related cues to fine-tune divergent metabolic pathways and behaviours necessary for survival. The new framework we propose reflects the high degree of developmental plasticity of this neuronal population and may enable progress towards understanding of both the aetiology and treatment of metabolic disorders. International audience

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    Nature Metabolism
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    Nature Metabolism
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      Nature Metabolism
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    Authors: Martin, Olivier; Fernandez-Diclo, Yasmil; Coville, Jérôme; Soubeyrand, Samuel;

    Insect-borne diseases are diseases carried by insects affecting humans, animals or plants. They have the potential to generate massive outbreaks such as the Zika epidemic in 2015-2016 mostly distributed in the Americas, the Pacific and Southeast Asia, and the multi-foci outbreak caused by the bacterium {\it Xylella fastidiosa} in Europe in the 2010s. In this article, we propose and analyze the behavior of a spatially-explicit compartmental model adapted to pathosystems with fixed hosts and mobile vectors disseminating the disease. The behavior of this model based on a system of partial differential equations is complementarily characterized via a theoretical study of its equilibrium states and a numerical study of its transitive phase using global sensitivity analysis. The results are discussed in terms of implications concerning the surveillance and control of the disease over a medium-to-long temporal horizon. International audience

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    arXiv.org e-Print Archive
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    Nonlinear Analysis Real World Applications
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    https://doi.org/10.48550/arxiv...
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      Nonlinear Analysis Real World Applications
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    Authors: Simon Kebede Merid; Mariona Bustamante; Marie Standl; Jordi Sunyer; +13 Authors

    The research leading to these results has received funding from the European Community's Seventh Framework Program under grant agreement numbers: 211,250 (ESCAPE), and 261,357 (MeDALL), and the European Research Council (grant agreement number 757919, TRIBAL). NL was recipient of ANR-15-IDEX-02 grant. BAMSE: BAMSE was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Region Stockholm, the Strategic Research Programme (SFO) in Epidemiology at Karolinska Institutet, The Swedish Research Council Formas and the Swedish Environment Protection Agency, the Swedish Asthma and Allergy Research Foundation, the Cancer and Allergy Foundation. O. Gruzieva is supported by the Swedish Research Council for Health, Working life and Welfare (FORTE 2017-01146). GINIplus: The GINIplus study was mainly supported for the first 3 years of the Federal Ministry for Education, Science, Research and Technology (interventional arm) and Helmholtz Zentrum Munich (former GSF) (observational arm). The 4 year, 6 year, 10 year and 15 year follow-up examinations of the GINIplus study were covered from the respective budgets of the 5 study centres (Helmholtz Zentrum Munich (former GSF), Research Institute at Marien-Hospital Wesel, LMU Munich, TU Munich and from 6 years onwards also from IUF -Leibniz Research-Institute for Environmental Medicine at the University of Dusseldorf) and a grant from the Federal Ministry for Environment (IUF Dusseldorf, FKZ 20462296). Further, the 15-year follow-up examination of the GINIplus study was supported by the Commission of the European Communities, the 7th Framework Program: MeDALL project, and as well by the companies Mead Johnson and Nestle. INMA: INMA project was mainly supported by the Commission of the European Community, the 7th Framework Program: MeDALL project and by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI04/1436; PI06/0867; PI08/1151; and PI18/01142 incl. FEDER funds), and Generalitat de Catalunya-CIRIT 1999SGR 00241, Department of Health of the Basque Government (2005111093), Provincial Government of Gipuzkoa (DFG06/002), and annual agreements with the municipalities of the study area (Zumarraga, Urretxu, Legazpi, Azkoitia y Azpeitia y Beasain) Air pollution has been associated with adverse health effects across the life-course. Although underlying mechanisms are unclear, several studies suggested pollutant-induced changes in transcriptomic profiles. In this meta-analysis of transcriptome-wide association studies of 656 children and adolescents from three European cohorts participating in the MeDALL Consortium, we found two differentially expressed transcript clusters (FDR p < 0.05) associated with exposure to particulate matter < 2.5 mu m in diameter (PM2.5) at birth, one of them mapping to the MIR1296 gene. Further, by integrating gene expression with DNA methylation using Functional Epigenetic Modules algorithms, we identified 9 and 6 modules in relation to PM2.5 exposure at birth and at current address, respectively (including NR1I2, MAPK6, TAF8 and SCARA3). In conclusion, PM2.5 exposure at birth was linked to differential gene expression in children and adolescents. Importantly, we identified several significant interactome hotspots of gene modules of relevance for complex diseases in relation to PM2.5 exposure.

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    Environment International
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      Environment International
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    Authors: Hecham, Abdelraouf; Croitoru, Madalina; Bisquert, Pierre;

    This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features. International audience

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    Authors: Wendy M. Rauw; Lotta Rydhmer; Ilias Kyriazakis; Margareth Øverland; +7 Authors

    Pig production systems provide multiple benefits to humans. However, the global increase in meat consumption has profound consequences for our earth. This perspective describes two alternative scenarios for improving the sustainability of future pig production systems. The first scenario is a high input-high output system based on sustainable intensification, maximizing animal protein production efficiency on a limited land surface at the same time as minimizing environmental impacts. The second scenario is a reduced input-reduced output system based on selecting animals that are more robust to climate change and are better adapted to transform low quality feed (local feeds, feedstuff co-products, food waste) into meat. However, in contrast to the first scenario, the latter scenario results in reduced predicted yields, reduced production efficiency and possibly increased costs to the consumer. National evaluation of the availability of local feed and feedstuff co-product alternatives, determination of limits to feed sourced from international markets, available land for crop and livestock production, desired production levels, and a willingness to politically enforce policies through subsidies and/or penalties are some of the considerations to combine these two scenarios. Given future novel sustainable alternatives to livestock animal protein, it may become reasonable to move towards an added general premium price on 'protein from livestock animals' to the benefit of promoting higher incomes to farmers at the same time as covering the extra costs of, politically enforced, welfare of livestock animals in sustainable production systems. © 2020 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. This research was made possible by funding from SusAn project ‘Sustainability of pig production through improved feed efficiency’ (SusPig; www.suspig-era.net), an ERA-Net co-funded under the European Union's Horizon 2020 research and innovation programme (www.era-susan.eu), under Grant Agreement no. 696231. Peer reviewed 12 Pág.

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    Article . 2020
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    Journal of the Science of Food and Agriculture
    Article . 2020 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    DIGITAL.CSIC
    Article . 2020 . Peer-reviewed
    Data sources: DIGITAL.CSIC
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    Authors: Sessa, Valentina; Assoumou, Edi; Bossy, Mireille; Carvalho, Sílvia; +1 Authors

    A big challenge of sustainable power systems is the integration of climate variability into the operational and long-term planning processes. In this paper, we focus on the run-of-river based hydropower generation on a European scale. In particular, we deal with the modeling of this form of power production based on climate variables. Translating time series of climate data (precipitation and air temperature) into time series of run-of-river based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. Indeed, this kind of electricity generation is limited by the flow of the river in which the power plants are located. Moreover, the water flow is a nonlinear function of the climate variables and the geographical characteristics of the river basins. Finally, the impact of the climate variables on the runoff may occur with a certain delay, whose determination depends on physically based phenomena (e.g., melting snow-local temperature). In this work, we first compare well-established machine learning regression algorithms to be used for modeling the run-of-river hydropower generation. Then, the technique showing to have the best performance is used for producing long-term estimates of hydropower capacity factors based on future climate scenarios for each European country.

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    Authors: Toimil, A.; Camus, P.; Losada, I. J.; Le Cozannet, G.; +3 Authors

    Abstract Developing future projections of shoreline change requires a good understanding of the driving coastal processes. These processes result primarily from the combination of mean sea level, waves, storm surges and tides, which are affected by global and regional climate change, and whose uncertainty increases with time. This paper reviews the current state of the art of methods used to model climate change-induced coastal erosion focusing on how climate change-related drivers and the associated uncertainty are considered. We identify research gaps, describe and analyse the key components of a comprehensive framework to derive future estimates of shoreline change and make suggestions for good practice. Within the scope of the review, we find that although significant progress has been made over the last decade, most of the studies limit uncertainty sampling to considering ranges of variation of forcing variables and ensembles of emissions scenarios, and applications with high level of probabilistic development remain few. Further research is necessary to fully (a) incorporate projected time series of coastal drivers into the erosion models, including bias correction; (b) sufficiently sample the uncertainty associated with each step of the top-down approach, including the consideration of different emission scenarios, inter- and intra-model variability, and multiple runs of erosion models or model ensembles; and (c) reduce uncertainty in shoreline change estimates by developing better datasets and model parameterisations, and progressing in detection and attribution.

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    Earth-Science Reviews
    Article . 2020 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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      Earth-Science Reviews
      Article . 2020 . Peer-reviewed
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      Data sources: Crossref
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