Optimised diets for achieving One Health: A pilot study in the Rhine-Ruhr Metropolis in Germany

Dietary changes are needed to align the global food systems with the planetary boundaries and contribute to Sustainable Development Goals. We employed a Life Cycle Assessment (LCA) framework, extended with indicators on human health and animal welfare, to assess 2020 food consumption data of a pilot sample collected through an online survey in the Rhine-Ruhr Metropolis (Germany). Feasible optimisation scenarios representing alternative sustainable choices towards overarching environmental, societal and policy goals were explored. Meat and meat products contributed most to overall environmental impacts (e.g., climate change, terrestrial acidification), and fish and seafood to animal welfare loss (e.g., animal lives lost, animal life years suffered). Sodium intake was the most contributing risk factor for life minutes lost. The combined optimisation scenario reduces 55% of greenhouse gas emissions, improves human health indicators by 25% and reduces animal welfare loss substantially (by 52-97%). This is possible with a shift towards flexitarian and vegetarian dietary scenarios. These optimisations deliver improvements across One Health dimensions with marginal changes in dietary scenarios and align with the sustainability goals of the EU Green Deal. Working with regional data can offer advantages in obtaining more realistic baseline dietary information to promote localised dietary shifts. While this research has limitations regarding sample representativeness, it can serve as a case study to encourage sustainable consumption in the Rhine-Ruhr region

The rank reversal problem in multi-criteria decision making : a literature review

Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field

Structure and membrane interactions of the homodimeric antibiotic peptide homotarsinin.

Antimicrobial peptides (AMPs) from amphibian skin are valuable template structures to find new treatments against bacterial infections. This work describes for the first time the structure and membrane interactions of a homodimeric AMP. Homotarsinin, which was found in Phyllomedusa tarsius anurans, consists of two identical cystine-linked polypeptide chains each of 24 amino acid residues. The high-resolution structures of the monomeric and dimeric peptides were determined in aqueous buffers. The dimer exhibits a tightly packed coiled coil three-dimensional structure, keeping the hydrophobic residues screened from the aqueous environment. An overall cationic surface of the dimer assures enhanced interactions with negatively charged membranes. An extensive set of biophysical data allowed us to establish structure-function correlations with antimicrobial assays against Gram-positive and Gram-negative bacteria. Although both peptides present considerable antimicrobial activity, the dimer is significantly more effective in both antibacterial and membrane biophysical assays.PMC524437

Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data

The Atlantic Forest is one of the most threatened biodiversity hotspots and environmental impacts has made its landscape fragmented and heterogeneous. The heterogeneity of the fragments is a challenge for the characterization and quantification of forest resources, such as the stock of biomass and carbon. Methodologies based on remote sensing have been used, to improve these estimates without compromising execution costs. The objective was to estimate, with high spatial resolution passive remote sensing, the aboveground carbon stock in fragments of different successional stages of the Atlantic Forest. Forests were classified into initial, intermediate, and advanced successional stages. In each stratum, 10 plots (20x50 m) were established, and the carbon stock was calculated by adjusted Schumacher and Hall model. The reflectances of the blue, green, red, and near-infrared bands and vegetation indices (VIs) were obtained in the dry and rainy seasons, from MSI/Sentinel-2 images, with a resolution of 10 m. Artificial Neural Networks (ANN), with different combinations of variables, were trained and validated with simulated reflectance values. Carbon was estimated by ANN with the best performance in training and validation. The average carbon stock in the initial, intermediate, and advanced strata was 24.99, 35.79 and 82.28 Mg ha−1, respectively, with a general average of 47.68 Mg ha−1. The carbon estimates were better with the ANN trained with the reflectances of the rainy season. The addition of VIs did not improve ANN performance. The simulated spectral data were consistent and adequate to validate the selected ANN. The total carbon stock, modeled was 41,962.15 Mg, ranging from 6.68 to 108.29 Mg ha−1, with an average of 48.70 Mg ha−1. The carbon stock in the advanced stratum is more than three times that observed in the initial stratum, and they were efficiently estimated using high-resolution multispectral data, obtained in the rainy season, as inputs