Biological Soil Crusts from Different Soil Substrates Harbor Distinct Bacterial Groups with the Potential to Produce Exopolysaccharides and Lipopolysaccharides.

Biological soil crusts (biocrusts) play an important role in improving soil stability and resistance to erosion by promoting aggregation of soil particles. During initial development, biocrusts are dominated by bacteria. Some bacterial members of the biocrusts can contribute to the formation of soil aggregates by producing exopolysaccharides and lipopolysaccharides that act as "glue" for soil particles. However, little is known about the dynamics of "soil glue" producers during the initial development of biocrusts. We hypothesized that different types of initial biocrusts harbor distinct producers of adhesive polysaccharides. To investigate this, we performed a microcosm experiment, cultivating biocrusts on two soil substrates. High-throughput shotgun sequencing was used to obtain metagenomic information on microbiomes of bulk soils from the beginning of the experiment, and biocrusts sampled after 4 and 10 months of incubation. We discovered that the relative abundance of genes involved in the biosynthesis of exopolysaccharides and lipopolysaccharides increased in biocrusts compared with bulk soils. At the same time, communities of potential "soil glue" producers that were highly similar in bulk soils underwent differentiation once biocrusts started to develop. In the bulk soils, the investigated genes were harbored mainly by Betaproteobacteria, whereas in the biocrusts, the major potential producers of adhesive polysaccharides were, aside from Alphaproteobacteria, either Cyanobacteria or Chloroflexi and Acidobacteria. Overall, our results indicate that the potential to form exopolysaccharides and lipopolysaccharides is an important bacterial trait for initial biocrusts and is maintained despite the shifts in bacterial community composition during biocrust development.

The modified glasgow prognostic score is an independent prognostic indicator in neoadjuvantly treated adenocarcinoma of the esophagogastric junction.

The modified Glasgow Prognostic Score (mGPS) combines the indicators of decreased plasma albumin and elevated CRP. In a number of malignancies, elevated mGPS is associated with poor survival. Aim of this study was to investigate the prognostic role of mGPS in patients with neoadjuvantly treated adenocarcinomas of the esophagogastric junction 256 patients from a prospective database undergoing surgical resection after neoadjuvant treatment between 2003 and 2014 were evaluated. mGPS was scored as 0, 1, or 2 based on CRP (>1.0 mg/dl) and albumin (<35 g/L) from blood samples taken prior (preNT-mGPS) and after (postNT-mGPS) neoadjuvant therapy. Scores were correlated with clinicopathological patients' characteristics. From 155 Patients, sufficient data was available. Median follow-up was 63.8 months (33.3-89.5 months). In univariate analysis, Cox proportional hazard model shows significant shorter patients OS (p = 0.04) and DFS (p = 0.02) for increased postNT-mGPS, preNT-hypoalbuminemia (OS: p = 0.003; DFS: p = 0.002) and post-NT-CRP (OS: p = 0.03; DFS: p = 0.04). Elevated postNT-mGPS and preNT-hypoalbuminemia remained significant prognostic factors in multivariate analysis for OS (p = 0.02; p = 0.005,) and DFS (p = 0.02, p = 0.004) with tumor differentiation and tumor staging as significant covariates. PostNT-mGPS and preNT-hypoalbuminemia are independent prognostic indicators in patients with neoadjuvantly treated adenocarcinomas of the esophagogastric junction and significantly associated with diminished OS and DFS.

A review of model applications for structured soils: b) Pesticide transport.

The past decade has seen considerable progress in the development of models simulating pesticide transport in structured soils subject to preferential flow (PF). Most PF pesticide transport models are based on the two-region concept and usually assume one (vertical) dimensional flow and transport. Stochastic parameter sets are sometimes used to account for the effects of spatial variability at the field scale. In the past decade, PF pesticide models were also coupled with Geographical Information Systems (GIS) and groundwater flow models for application at the catchment and larger regional scales. A review of PF pesticide model applications reveals that the principal difficulty of their application is still the appropriate parameterization of PF and pesticide processes. Experimental solution strategies involve improving measurement techniques and experimental designs. Model strategies aim at enhancing process descriptions, studying parameter sensitivity, uncertainty, inverse parameter identification, model calibration, and effects of spatial variability, as well as generating model emulators and databases. Model comparison studies demonstrated that, after calibration, PF pesticide models clearly outperform chromatographic models for structured soils. Considering nonlinear and kinetic sorption reactions further enhanced the pesticide transport description. However, inverse techniques combined with typically available experimental data are often limited in their ability to simultaneously identify parameters for describing PF, sorption, degradation and other processes. On the other hand, the predictive capacity of uncalibrated PF pesticide models currently allows at best an approximate (order-of-magnitude) estimation of concentrations. Moreover, models should target the entire soil-plant-atmosphere system, including often neglected above-ground processes such as pesticide volatilization, interception, sorption to plant residues, root uptake, and losses by runoff. The conclusions compile progress, problems, and future research choices for modelling pesticide displacement in structured soils.

A review of model applications for structured soils: a) Water flow and tracer transport.

Although it has many positive effects, soil structure may adversely affect the filtering function of the vadose zone that protects natural water resources from various sources of pollution. Physically based models have been developed to analyze the impacts of preferential water flow (PF) and physical non-equilibrium (PNE) solute transport on soil and water resources. This review compiles results published over the past decade on the application of such models for simulating PF and PNE non-reactive tracer transport for scales ranging from the soil column to the catchment area. Recent progress has been made in characterizing the hydraulically relevant soil structures, dynamic flow conditions, inverse parameter and uncertainty estimations, independent model parameterizations, stochastic descriptions of soil heterogeneity, and 2D or 3D extensions of PNE models. Two-region models are most widely used across all scales; as a stand-alone approach to be used up to the field scale, or as a component of distributed, larger scale models. Studies at all scales suggest that inverse identification of parameters related to PF is generally not possible based on a hydrograph alone. Information on flux-averaged and spatially distributed local resident concentrations is jointly required for quantifying PNE transport. At the column and soil profile scale, model predictions of PF are becoming increasingly realistic through the implementation of the 3D soil structure as derived from hydrogeophysical and tracer techniques. At the field scale, integrating effects of the soil structure and its spatial variability has been attempted by combining 1D PNE approaches with stochastic parameter sampling. At the catchment area scale, the scarcity of data makes validation of PF related model components a task yet to be accomplished. The quest for easily measurable proxy variables, as 'the missing link' between soil structure and model parameters, continues in order to improve the practical predictive capability of PF-PNE models. A follow-up paper complementing this manuscript reviews model applications involving non-equilibrium transport of pesticides, as representatives of reactive solutes.