More similarities than differences: An international comparison of CVD mortality and risk factors in women

In this article we describe global cardiovascular risk factor trends in women, both physiological and behavioral, in order to improve the understanding of cardiovascular health of women. Our aim in presenting this information is to inform interventions and policies to improve the cardiovascular health of women. Although differences are apparent between developing and developed countries, a range of commonalities exist that allow a global approach to improving women's health. A multifaceted approach considering physiological, social, economic, and political determinants is critical to improve the cardiovascular health outcomes of women

Optimal placement of fuses and switches in active distribution networks using value-based MINLP

Contingency conditions in distribution networks create financial losses for different parts of the system including electricity customers, electricity retailers, distributed generation (DG) units, etc. Therefore, protective device allocation methods have been introduced in recent years to enhance the reliability of the power system. In this study, a new formulation is proposed to find the optimal places of sectionalizing switches and fuses while taking the financial loss of both electricity customers and DG units into account. The current method has the flexibility to consider DG effect on any location of the network and its islanded operation in case of contingencies. Moreover, the uncertainty in load and renewable generation is taken into account using stochastic programming. The results demonstrate that the DG units and their financial loss can change the results of switch and fuse placement dramatically when there are no tie switches in the network. Furthermore, it is found that this method can decrease the total reliability costs by 3.86% when high penetration of DG units is introduced into a modified Roy Billinton test system (RBTS). The problem is modeled as a mixed-integer nonlinear (MINLP) formulation and is handled using BARON solver in GAMS environment

Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust insitu plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis— i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration’s Surface Biology and Geology mission

The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what level of priority should stakeholders commit to in order to obtain these data? Before fully addressing these questions a remaining challenge is the complex nature of spatiotemporal yield variation. Here, a methodological framework is presented to separate the spatial and temporal components of crop yield variation at the subfield level. The framework can also be used to quantify the benefits of different data types on the predicted crop yield as well to better understand the connection of that data to underlying mechanisms controlling yield. Here, fine-resolution (10 m) datasets were assembled for eight 64 ha field sites, spanning a range of climatic, topographic, and soil conditions across Nebraska. Using Empirical Orthogonal Function (EOF) analysis, we found the first axis of variation contained 60–85 % of the explained variance from any particular field, thus greatly reducing the dimensionality of the problem. Using Multiple Linear Regression (MLR) and Random Forest (RF) approaches, we quantified that location within the field had the largest relative importance for modeling crop yield patterns. Secondary factors included a combination of vegetation condition, soil water content, and topography. With respect to predicting spatiotemporal crop yield patterns, we found the RF approach (prediction RMSE of 0.2−0.4 Mg/ha for maize) was superior to MLR (0.3−0.8 Mg/ha). While not directly comparable to MLR and RF the EOF approach had relatively low error (0.5–1.7 Mg/ha) and is intriguing as it requires few calibration parameters (2–6 used here) and utilizes the climate-based aridity index, allowing for pragmatic long-term predictions of subfield crop yield

Molecular mechanisms related to colistin resistance in enterobacteriaceae

Colistin is an effective antibiotic for treatment of most multidrug-resistant Gram-negative bacteria. It is used currently as a last-line drug for infections due to severe Gram-negative bacteria followed by an increase in resistance among Gram-negative bacteria. Colistin resistance is considered a serious problem, due to a lack of alternative antibiotics. Some bacteria, including Pseudomonas aeruginosa, Acinetobacter baumannii, Enterobacteriaceae members, such as Escherichia coli, Salmonella spp., and Klebsiella spp. have an acquired resistance against colistin. However, other bacteria, including Serratia spp., Proteus spp. and Burkholderia spp. are naturally resistant to this antibiotic. In addition, clinicians should be alert to the possibility of colistin resistance among multidrug-resistant bacteria and development through mutation or adaptation mechanisms. Rapidly emerging bacterial resistance has made it harder for us to rely completely on the discovery of new antibiotics; therefore, we need to have logical approaches to use old antibiotics, such as colistin. This review presents current knowledge about the different mechanisms of colistin resistance. © 2019 Aghapour et al

O-C Study of 545 Lunar Occultations from 13 Double Stars

International audienceIn this article, we have studied the reports of lunar occultations by this project observation's teams (named APTO) in comparison with other observations of the objects. Thirteen binary stars were selected for this study. All the previous observations of these stars were also collected. Finally, an analysis of O-C of all reports were performed

Non-participation in population-based disease prevention programs in general practice

<p>Abstract</p> <p>Background</p> <p>The number of people with a chronic disease will strongly increase in the next decades. Therefore, prevention of disease becomes increasingly important. The aim of this systematic review was to identify factors that negatively influence participation in population-based disease prevention programs in General Practice and to establish whether the program type is related to non-participation levels.</p> <p>Methods</p> <p>We conducted a systematic review in Pubmed, EMBASE, CINAHL and PsycINFO, covering 2000 through July 6th 2012, to identify publications including information about characteristics of non-participants or reasons for non-participation in population-based disease prevention programs in General Practice.</p> <p>Results</p> <p>A total of 24 original studies met our criteria, seven of which focused on vaccination, eleven on screening aimed at early detection of disease, and six on screening aimed at identifying high risk of a disease, targeting a variety of diseases and conditions. Lack of personal relevance of the program, younger age, higher social deprivation and former non-participation were related to actual non-participation. No differences were found in non-participation levels or factors related to non-participation between the three program types. The large variation in non-participation levels within the program types may be partly due to differences in recruitment strategies, with more active, personalized strategies resulting in higher participation levels compared to an invitation letter.</p> <p>Conclusions</p> <p>There is still much to be gained by tailoring strategies to improve participation in those who are less likely to do so, namely younger individuals, those living in a deprived area and former non-participants. Participation may increase by applying more active recruitment strategies.</p