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Inversion tillage is a commonly applied soil cultivation practice in Europe, which often has been blamed for deteriorating topsoil stability and organic carbon (OC) content. In this study, the potential to reverse these negative effects in the topsoil by alternative agricultural management practices are evaluated in seven long-term experiments (running from 8 to 54 years the moment of sampling) in five European countries (Belgium, Czech Republic, Hungary, Italy and UK). Topsoil samples (0–15 cm) were collected and analysed to evaluate the effects of conservation tillage (reduced and no tillage) and increased organic inputs of different origin (farmyard manure, compost, crop residues) combined with inversion tillage on topsoil stability, soil aggregates and, within these, OC distribution using wet sieving after slaking. Effects from the treatments on the two main components of organic matter, i.e. particulate (POM) and mineral associated (MAOM), were also evaluated using dispersion and size fractionation. Reduced and no-tillage practices, as well as the additions of manure or compost, increased the aggregates mean weight diameter (MWD) (up to 49 % at the Belgian study site) and topsoil OC (up to 51 % at the Belgian study site), as well as the OC corresponding to the different aggregate size fractions. The incorporation of crop residues had a positive impact on the MWD but a less profound effect both on total OC and on OC associated with the different aggregates. A negative relationship between the mass and the OC content of the microaggregates (53–250 µm) was identified in all experiments. There was no effect on the mass of the macroaggregates and the occluded microaggregates (mM) within these macroaggregates, while the corresponding OC contents increased with less tillage and more organic inputs. Inversion tillage led to less POM within the mM, whereas the different organic inputs did not affect it. In all experiments where the total POM increased, the total soil organic carbon (SOC) was also affected positively. We concluded that the negative effects of inversion tillage on topsoil can be mitigated by reducing the tillage intensity or adding organic materials, optimally combined with non-inversion tillage methods. ispartof: Soil vol:8 issue:2 pages:621-644 status: Published online

<p>The long-term sustainable exploitation of geothermal resources requires cautious planning and regulation. Exploitation in excess of natural recharge can result in reservoir pressure decline, causing a decrease in production rates. Furthermore, such “overexploitation” of geothermal reservoirs may lead to compaction and land subsidence. Understanding of such phenomena is critical for the assessment of societal-environmental risks, but can also be used for optimization by constraining reservoir processes and properties.</p><p>Excessive thermal water volumes have been extracted from porous sedimentary rocks in the Hungarian part of the Pannonian Basin. Thermal water production in Hungary increased significantly from the early 70’s. Regional-scale overexploitation of geothermal reservoirs resulted in basin-scale pressure drop in the Upper Pannonian sediments, leading to compaction and ground subsidence.</p><p>We investigated surface deformation at the Szentes geothermal filed, SE Hungary, where the largest pressure decline occurred. We obtained data from the European Space Agency’s ERS and Envisat satellites to estimate the ground motions for the periods of 1992-2000 and 2002-2010. We applied inverse geomechanical modelling to understand the compaction behaviour of the reservoir system and to estimate the subsurface properties. We constrained the model parameters using the Ensemble Smoother with Multiple Data Assimilation, which allowed us to incorporate large amounts of surface movement observations in a computationally efficient way. The model requires pressure time series as input parameters, therefore, the lack of regular pressure measurements in geothermal wells of Szentes resulted in significant uncertainties. Still, we managed to identify a potential delay in pressure drop and subsidence, implying a time-decay compaction behaviour of the reservoir system,  and we arrived at realistic estimates for the compaction coefficient of the reservoir. The improved parametrization enables better forecasting of the reservoir behaviour and facilitates the assessment of future subsidence scenarios. This study thus demonstrates the effectiveness of InSAR-based ground motion data and inverse geomechanical modelling for the monitoring of geothermal reservoirs and the establishment of a sustainable production scheme.</p>

AbstractBackgroundThe increasing prevalence of antimicrobial resistance (AMR) is a significant threat to global health. The widespread use of antibiotics is increasingly shortening the time it takes for resistant strains to develop. More and more multi-drug-resistant bacterial strains cause life-threatening infections and the death of tens of thousands of people each year. Beyond disease control animals are often given antibiotics for growth promotion or increased feed efficiency, which further increase the chance of the development of multi-resistant strains. After the consumption of unprocessed animal products, these strains may meet the human bacteriota. Among the foodborne and the human populations, antimicrobial resistance genes (ARGs) may be shared by horizontal gene transfer. This study aims to test the presence of antimicrobial resistance genes in milk metagenome, investigate their genetic position and their linkage to mobile genetic elements.ResultsWe have analyzed raw milk samples from public markets sold for human consumption. The milk samples contained genetic material from various bacterial species and the detailed analysis uncovered the presence of several antimicrobial resistance genes. The samples contained complete ARGs influencing the effectiveness of acridine dye, cephalosporin, cephamycin, fluoroquinolone, penam, peptide antibiotics and tetracycline. One of the ARGs, PC1 beta-lactamase may also be a mobile element that facilitates the transfer of resistance genes to other bacteria, e.g. to the ones living in the human gut.ConclusionBesides the animal products’ antibiotic residuals, their potentially transmissible antimicrobial resistance gene content may also contribute to the development of human pathogenic bacteria’s antimicrobial resistance.

Research collaborations are continuously emerging catalyzed by online platforms, where people can share their codes, calculations, data and results. These virtual research platforms are innovative, community oriented, flexible and secure as required by modern scientific approaches. A wide range of open source and commercial solutions are available in this field emphasizing the relevant aspects of such a platform differently. In this paper we present our open source and modular platform, KOOPLEX, which combines the key concepts of dynamic collaboration, customizable research environment, data sharing, access to datahubs, reproducible research and reporting. It is easily deployable and scalable to serve more users or access large computational resources. Comment: 22 pages, 4 figures, 6 tables

2.AbstractA possible way to tackle the crisis of antimicrobial resistance development is a strict policy when prescribing antibiotics. Thus, it is important that prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR.This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes (ARG). We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of any qnrS gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 92% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work goes further than the typical predictions that use machine learning as a black box model concept. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.3.Impact statementWhole genome sequencing has become the standard approach to study molecular epidemiology of bacteria. However, the application of WGS in the clinical microbiology laboratory as part of individual patient diagnostics still requires significant steps forward, in particular with respect to prediction of antibiotic susceptibility based on DNA sequence. Whilst the majority of studies of prediction of susceptibility have used a binary outcome (susceptible/resistant), a quantitative prediction of susceptibility, such as the MIC, will allow for earlier detection of trends in increasing resistance as well as the flexibility to follow potential adjustments in definitions of susceptible (wild type) and resistant (non-wild type) categories (breakpoints/ epidemiological cut-off values).4.Data summaryIn this study, 704 E. coli genomes combined with MIC measurement for ciprofloxacin were analysed (24). Paired-end sequencing was performed on all isolates and the results were stored in FASTQ format. The isolates originated from five countries, Denmark, Italy, USA, UK, and Vietnam. The MIC distribution for these isolates is depicted in Table 1. Out of 704, 266 E. coli genomes had no country metadata available and were used as an independent test set. All data were deposited in the AMR Data Hub (24) which consists of raw sequencing data, ciprofloxacin minimum inhibitory concentrations, and additional metadata such as the origin of the samples.TABLE 1The collected and used data in the analysis grouped by country and MIC values.Publicly available sequencing data was used from projects PRJEB21131, PRJNA266657, PRJNA292901, PRJNA292904, PRJNA292902, PRJDB7087, PRJEB21880, PRJEB21997, PRJEB14086 and PRJEB16326.Download and analysis scripts are available at https://github.com/patbaa/AMR_ciprofloxacin. iTOL phylogenetic tree is available at https://itol.embl.de/tree/14511722611491391569485969.The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

The computational cost as well as the probabilistic skill of ensemble forecasts depends on the spatial resolution of the numerical weather prediction model and the ensemble size. Periodically, e.g. when more computational resources become available, it is appropriate to reassess the balance between resolution and ensemble size. Recently, it has been proposed to investigate this balance in the context of dual-resolution ensembles, which use members with two different resolutions to make probabilistic forecasts. This study investigates whether statistical post-processing of such dual-resolution ensemble forecasts changes the conclusions regarding the optimal dual-resolution configuration. Medium-range dual-resolution ensemble forecasts of 2-metre temperature have been calibrated using ensemble model output statistics. The forecasts are produced with ECMWF's Integrated Forecast System and have horizontal resolutions between 18 km and 45 km. The ensemble sizes range from 8 to 254 members. The forecasts are verified with SYNOP station data. Results show that score differences between various single and dual-resolution configurations are strongly reduced by statistical post-processing. Therefore, the benefit of some dual-resolution configurations over single resolution configurations appears to be less pronounced than for raw forecasts. Moreover, the ranking of the ensemble configurations can be affected by the statistical post-processing. Comment: 25 pages, 12 figures, 2 tables