Remote sensing depicts riparian vegetation responses to water stress in a humid Atlantic region

Riparian areas in the Cantabrian Atlantic ecoregion (northwest Portugal) play a key role in soil formation and conservation, regulation of nutrient and water cycle, creation of landscape aesthetic value and the preservation of biodiversity. The maintenance of their ecological integrity is crucial given the ever increase in multiple anthropogenic (water demand and agriculture) and climatic pressures (droughts and extreme events). We developed a transferable remote sensing approach, taking advantage of the latest freely available technologies (Sentinel-2 and Copernicus Land products), to detect intra-annual and inter-annual changes in riparian vegetation productivity at the river basin scale related to water stress. This study has used the normalized difference vegetation index (NDVI) to investigate riparian vegetation productivity dynamics on three different vegetation types (coniferous, broadleaved and grassland) over the past 5 years (2015-2019). Our results indicated that inter-annual seasonality differed between drier (2017) and wetter (2016) years. We found that intra-annual dynamics of NDVI were influenced by the longitudinal river zonation. Our model ranked first (r2m = 0.73) showed that the productivity of riparian vegetation during the dry season was positively influenced by annual rainfall and by the type of riparian vegetation. The emergent long lags between climatic variation and riparian plant productivity provides opportunities to forecast early warnings of climatically-driven impacts. In addition, the different average productivity levels among vegetation types should be considered when assessing climatic impacts on riparian vegetation. Future applications of Sentinel 2 products could seek to distinguish riparian areas that are likely to be more vulnerable to changes in the annual water balance from those that are more resistant under longer-term changes in climate.Contrato-Programa UIDB/04050/2020. ERA4CS/0004/2016. CLIMALERT: Climate Alert Smart System for Sustainable Water and Agriculture, an ERA-NET initiated by JPI Climate (ERA4CS programme) co-funded by the EU commission (Grant Agreement 690462) and FCT (ERA4CS/0004/2016). This work was supported by the “Contrato-Programa” UIDB/04050/2020 funded by national funds through the FCT I.P. (GP

Optimal Uncertainty Quantification

We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and the assumptions/information set are brought to the forefront. This framework, which we call \emph{Optimal Uncertainty Quantification} (OUQ), is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. Although OUQ optimization problems are extremely large, we show that under general conditions they have finite-dimensional reductions. As an application, we develop \emph{Optimal Concentration Inequalities} (OCI) of Hoeffding and McDiarmid type. Surprisingly, these results show that uncertainties in input parameters, which propagate to output uncertainties in the classical sensitivity analysis paradigm, may fail to do so if the transfer functions (or probability distributions) are imperfectly known. We show how, for hierarchical structures, this phenomenon may lead to the non-propagation of uncertainties or information across scales. In addition, a general algorithmic framework is developed for OUQ and is tested on the Caltech surrogate model for hypervelocity impact and on the seismic safety assessment of truss structures, suggesting the feasibility of the framework for important complex systems. The introduction of this paper provides both an overview of the paper and a self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository Research Papers). See SIAM Review for higher quality figure

Lack of Association between the Tagging SNP A+930→G of SOCS3 and Type 2 Diabetes Mellitus: Meta-Analysis of Four Independent Study Populations

BACKGROUND: The suppressor of cytokine signalling 3 (SOCS3) provides a link between cytokine action and their negative consequences on insulin signalling. Thus SOCS3 is a potential candidate gene for type 2 diabetes (T2DM). METHODOLOGY/PRINCIPAL FINDINGS: Based on HapMap we identified the polymorphism A+930-->G (rs4969168) as a haplotype tagging SNP (htSNP) sufficiently covering the genetic variation of the whole gene. We therefore examined the association between rs4969168 within SOCS3 and T2DM in three independent study populations; one prospective case-cohort study and two cross-sectional study populations. Due to the low frequency of individuals being homozygous for the polymorphism a dominant model of inheritance was assumed. The case-cohort study with 2,957 individuals (764 of them with incident T2DM) showed no effect of the polymorphism on diabetes risk (hazard ratio (95%CI): 0.86 (0.66-1.13); p = 0.3). Within the MeSyBePo-study population 325 subjects had T2DM from a total of 1,897 individuals, while the second cross-sectional cohort included 851 cases of T2DM within a total of 1653 subjects. According to the results in the prospective study, no association with T2DM was found (odds ratio (95%CI): 0.78 (0.54-1.12) for MesyBepo and 1.13 (0.90-1.42) for the Leipzig study population). There was also no association with metabolic subtraits such as insulin sensitivity (p = 0.7), insulin secretion (p = 0.8) or the hyperbolic relation of both, the disposition index (p = 0.7). In addition, no evidence for interaction with BMI or sex was found. We subsequently performed a meta-analysis, additionally including the publicly available data from the T2DM-subcohort of the WTCCC (n = 4,855). The overall odds ratio within that meta-analysis was 0.96 (0.88-1.06). CONCLUSIONS/SIGNIFICANCE: There is no strong effect of the common genetic variation within the SOCS3 gene on the development of T2DM

Bias in protein and potassium intake collected with 24-h recalls (EPIC-Soft) is rather comparable across European populations

Purpose: We investigated whether group-level bias of a 24-h recall estimate of protein and potassium intake, as compared to biomarkers, varied across European centers and whether this was influenced by characteristics of individuals or centers. Methods: The combined data from EFCOVAL and EPIC studies included 14 centers from 9 countries (n = 1,841). Dietary data were collected using a computerized 24-h recall (EPIC-Soft). Nitrogen and potassium in 24-h urine collections were used as reference method. Multilevel linear regression analysis was performed, including individual-level (e.g., BMI) and center-level (e.g., food pattern index) variables. Results: For protein intake, no between-center variation in bias was observed in men while it was 5.7% in women. For potassium intake, the between-center variation in bias was 8.9% in men and null in women. BMI was an important factor influencing the biases across centers (p <0.01 in all analyses). In addition, mode of administration (p = 0.06 in women) and day of the week (p = 0.03 in men and p = 0.06 in women) may have influenced the bias in protein intake across centers. After inclusion of these individual variables, between-center variation in bias in protein intake disappeared for women, whereas for potassium, it increased slightly in men (to 9.5%). Center-level variables did not influence the results. Conclusion: The results suggest that group-level bias in protein and potassium (for women) collected with 24-h recalls does not vary across centers and to a certain extent varies for potassium in men. BMI and study design aspects, rather than center-level characteristics, affected the biases across center

Traditional old dietary pattern of castellana grotte (Apulia) is associated with healthy outcomes

Background: There is still room for further studies aimed at investigating the most widespread diets in the Mediterranean area. The objective of the study is to analyze the relation of food group intake to clinical chemical indicators of health, and also to compare the food group intake with healthy well-known diet indices. Methods: Lifestyle, dietary, and clinical data collected in 2005/2006 and 2012/2018 from Castellana Grotte, located in the rural area of Apulia, were analyzed. The study populations included newly recruited subjects at each time period (n = 1870) as well as subjects examined twice and compared over time regarding health indicators (n = 734). Diet was assessed through a validated food frequency questionnaire. Three healthy diet indices were calculated and related to 29 food groups. We also performed prospective regression of food group consumption with health indicators. Results: The diet over the time period of observation was very stable and consisted of a high proportion of vegetables, fruit and grains. No major changes in body mass index (BMI) and blood pressure were observed. Consumption of low-fat dairy, juices, olive oil, and water were related to reductions in weight gain, systolic blood pressure, high-density lipoprotein (HDL)-cholesterol and cholesterol (total and HDL) levels, in that order. Over the time periods we observed only a slight decrease of adherence to the Meddietscore. The correlations of the healthy diet indices with food groups revealed some differences among the indices, mostly regarding the intake of fruit and vegetables. Conclusions: The dietary pattern of Apulia is in line with many principles of a healthy diet and the cohort population seems to be less liable to undergo a transition to a westernized diet

Dietary behavior : An interdisciplinary conceptual analysis and taxonomy

The preparation of this paper was supported by the DEterminants of DIet and Physical ACtivity (DEDIPAC) knowledge hub. This work was supported by the Joint Programming Initiative “Healthy Diet for a Healthy Life.” The funding agencies supporting this work are (in alphabetical order of participating Member State): France: Institut National de la Recherche Agronomique (INRA); Germany: Federal Ministry of Education and Research (BMBF); Italy: Ministry of Education, University and Research/Ministry of Agriculture Food and Forestry Policies; Norway: The Research Council of Norway, Division for Society and Health; and The United Kingdom: The Medical Research Council (MRC).Peer reviewedPublisher PD