Analytic structure factors and pair-correlation functions for the unpolarized homogeneous electron gas

We propose a simple and accurate model for the electron static structure factors (and corresponding pair-correlation functions) of the 3D unpolarized homogeneous electron gas. Our spin-resolved pair-correlation function is built up with a combination of analytic constraints and fitting procedures to quantum Monte Carlo data, and, in comparison to previous attempts (i) fulfills more known integral and differential properties of the exact pair-correlation function, (ii) is analytic both in real and in reciprocal space, and (iii) accurately interpolates the newest, extensive diffusion-Monte Carlo data of Ortiz, Harris and Ballone [Phys. Rev. Lett. 82, 5317 (1999)]. This can be of interest for the study of electron correlations of real materials and for the construction of new exchange and correlation energy density functionals.Comment: 14 pages, 5 figures, submitted to Phys. Rev.

Knowledge Hub on the Integrated Assessment of Chemical Contaminants and their Effects on the Marine Environment

In a time of environmental awareness, spurred on by the possibility that our world is threatened by climate change, it is important to remember that there are other anthropogenic pressures, which are also essential for addressing the protection of the marine and coastal environment. Pollution is a global, complex issue that contributes to biodiversity loss and poor environmental health and comes from the production and release of many of the synthetic chemicals that we use in our daily lives. Chemical contaminants are often underrepresented as a major contributor of environmental deterioration. The Joint Programming Initiative Healthy and Productive Seas and Oceans (JPI Oceans) established in 2018 the JPI Oceans Knowledge Hub on the integrated assessment of chemical contaminants and their effects on the marine environment. The purpose of the Knowledge Hub was to provide recommendations on how to improve the methodological basis for marine chemical status assessment. The work has resulted in the following policy paper which focuses on improving the efficiency and implementation of integrated assessment methodology of effects of chemicals of emerging concern. Substantial additional knowledge of biological effects is needed to achieve Good Environmental Status (GES) of our oceans and coastal areas. The Knowledge Hub is represented by highly skilled scientists and policy makers, appointed by the JPI Oceans Management Board, to ensure that the recommendations provided are useful for policy making

A ROC analysis-based classification method for landslide susceptibility maps

[EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. 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Prev Vet Med 45:23–41Günther A, Reichenbach P, Malet JP, van den Eeckhaut M, Hervás J, Dashwood C, Guzzetti F (2013) Tier-based approaches for landslide susceptibility assessment in Europe. Landslides 10:529–546. https://doi.org/10.1007/s10346-012-0349-1Günther A, Van Den Eeckhaut M, Malet J-P, Reichenbach P, Hervás J (2014) Climate-physiographically differentiated Pan-European landslide susceptibility assessment using spatial multi-criteria evaluation and transnational landslide information. Geomorphology 224:69–85Gupta RP, Kanungo DP, Arora MK, Sarkar S (2008) Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps. Int J Appl Earth Obs Geoinf 10(3):330–341. https://doi.org/10.1016/j.jag.2008.01.003Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. 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The Weyl double copy from twistor space

The Weyl double copy is a procedure for relating exact solutions in biadjoint scalar, gauge and gravity theories, and relates fields in spacetime directly. Where this procedure comes from, and how general it is, have until recently remained mysterious. In this paper, we show how the current form and scope of the Weyl double copy can be derived from a certain procedure in twistor space. The new formalism shows that the Weyl double copy is more general than previously thought, applying in particular to gravity solutions with arbitrary Petrov types. We comment on how to obtain anti-self-dual as well as self-dual fields, and clarify some conceptual issues in the twistor approach

Poorly controlled type 2 diabetes is accompanied by significant morphological and ultrastructural changes in both erythrocytes and in thrombin-generated fibrin: implications for diagnostics

We have noted in previous work, in a variety of inflammatory diseases, where iron dysregulation occurs, a strong tendency for erythrocytes to lose their normal discoid shape and to adopt a skewed morphology (as judged by their axial ratios in the light microscope and by their ultrastructure in the SEM). Similarly, the polymerization of fibrinogen, as induced in vitro by added thrombin, leads not to the common ‘spaghetti-like’ structures but to dense matted deposits. Type 2 diabetes is a known inflammatory disease. In the present work, we found that the axial ratio of the erythrocytes of poorly controlled (as suggested by increased HbA1c levels) type 2 diabetics was significantly increased, and that their fibrin morphologies were again highly aberrant. As judged by scanning electron microscopy and in the atomic force microscope, these could be reversed, to some degree, by the addition of the iron chelators deferoxamine (DFO) or deferasirox (DFX). As well as their demonstrated diagnostic significance, these morphological indicators may have prognostic value.Biotechnology and Biological Sciences Research Council (grant BB/L025752/1) as well as the National Research Foundation (NRF) of South Africa.http://www.cardiab.com/hb201

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Estimating mortality and disability in Peru before the COVID-19 pandemic: a systematic analysis from the Global Burden of the Disease Study 2019

Background: Estimating and analyzing trends and patterns of health loss are essential to promote efficient resource allocation and improve Peru’s healthcare system performance. Methods: Using estimates from the Global Burden of Disease (GBD), Injuries, and Risk Factors Study (2019), we assessed mortality and disability in Peru from 1990 to 2019. We report demographic and epidemiologic trends in terms of population, life expectancy at birth (LE), mortality, incidence, prevalence, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by the major diseases and risk factors in Peru. Finally, we compared Peru with 16 countries in the Latin American (LA) region. Results: The Peruvian population reached 33.9 million inhabitants (49.9% women) in 2019. From 1990 to 2019, LE at birth increased from 69.2 (95% uncertainty interval 67.8–70.3) to 80.3 (77.2–83.2) years. This increase was driven by the decline in under-5 mortality (−80.7%) and mortality from infectious diseases in older age groups (+60 years old). The number of DALYs in 1990 was 9.2 million (8.5–10.1) and reached 7.5 million (6.1–9.0) in 2019. The proportion of DALYs due to non-communicable diseases (NCDs) increased from 38.2% in 1990 to 67.9% in 2019. The all-ages and age-standardized DALYs rates and YLLs rates decreased, but YLDs rates remained constant. In 2019, the leading causes of DALYs were neonatal disorders, lower respiratory infections (LRIs), ischemic heart disease, road injuries, and low back pain. The leading risk factors associated with DALYs in 2019 were undernutrition, high body mass index, high fasting plasma glucose, and air pollution. Before the COVID-19 pandemic, Peru experienced one of the highest LRIs-DALYs rates in the LA region. Conclusion: In the last three decades, Peru experienced significant improvements in LE and child survival and an increase in the burden of NCDs and associated disability. The Peruvian healthcare system must be redesigned to respond to this epidemiological transition. The new design should aim to reduce premature deaths and maintain healthy longevity, focusing on effective coverage and treatment of NCDs and reducing and managing the related disability