Spatial-temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean.

In Syria, soil erosion (SoEr) by water is one of the major challenges for sustainability. Thus, the main goals of this research were to evaluate the spatial changes of SoEr between 2000 and 2018 in the whole coastal basin (CB) of Syria and to provide a soil water erosion risk map for the study area. For this purpose, monthly rainfall data, the SoilGrids dataset, satellite image derived NDVI layers, and Digital Elevation Model (DEM) were collected. Through the integration of these layers into the Revised Universal Soil Loss Equation (RUSLE), under a Geographic Information System (GIS), soil loss was assessed. Also, the contribution of land cover changes and R factor on SoEr were evaluated. The outcomes of this assessment illustrated that the R factor ranged from 800 to 2600 MJ mm ha-1 h-1 yr-1, while the soil erodibility factor (K factor) ranged from 0.048 to 0.035 ton ha MJ-1 mm-1. The C factor (vegetation coverage) values ranged between 0.07 and 1 with a spatial average value of 0.44 for the 2000-2009 period and 0.39 for the 2010-2018 interval. The output of RUSLE revealed that average annual SoEr was of 21.35 ton ha-1 y-1 (± 38) for 2000-2009 and 22.47 ton ha-1 y-1(± 41.8) for 2010-2018. Interestingly, the increased SoEr caused by the R factor was dominant (34.65%), followed by changes in both C factor and R factor (13.34%). However, decrease of SoEr rates is due to the increase of the C factor accounting for 36.82% of the CB. The outcome of this research can provide constructive spatial insights for rehabilitation plans for the post-war phase of Syria.

Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe.

The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi, Ptotal) (mm), daily maximum precipitation (Pd-max) (mm), monthly mean temperature (Tavg) (°C), daily maximum mean temperature (Td-max) (°C), and daily minimum mean temperature (Td-min) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal) and SC4 (Ptotal + Tavg + Td-max + Td-min) as the best scenarios for predicting MFI by using the ANN-MLP and ANN-RBF, respectively. However, the sensitivity analysis highlighted that Ptotal, pi, and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.

How to characterize a strain? Clonal heterogeneity in industrial Saccharomyces influences both phenotypes and heterogeneity in phenotypes.

Populations of microbes are constantly evolving heterogeneity that selection acts upon, yet heterogeneity is nontrivial to assess methodologically. The necessary practice of isolating single-cell colonies and thus subclone lineages for establishing, transferring, and using a strain results in single-cell bottlenecks with a generally neglected effect on the characteristics of the strain itself. Here, we present evidence that various subclone lineages for industrial yeasts sequenced for recent genomic studies show considerable differences, ranging from loss of heterozygosity to aneuploidies. Subsequently, we assessed whether phenotypic heterogeneity is also observable in industrial yeast, by individually testing subclone lineages obtained from products. Phenotyping of industrial yeast samples and their newly isolated subclones showed that single-cell bottlenecks during isolation can indeed considerably influence the observable phenotype. Next, we decoupled fitness distributions on the level of individual cells from clonal interference by plating single-cell colonies and quantifying colony area distributions. We describe and apply an approach using statistical modeling to compare the heterogeneity in phenotypes across samples and subclone lineages. One strain was further used to show how individual subclonal lineages are remarkably different not just in phenotype but also in the level of heterogeneity in phenotype. With these observations, we call attention to the fact that choosing an initial clonal lineage from an industrial yeast strain may vastly influence downstream performances and observations on karyotype, on phenotype, and also on heterogeneity.