Entwicklung eines schnellen Bio-Tests zur Untersuchung des Wirkungs-potentials von mikrobiellen Pflanzenstärkungsmitteln

Plant-growth-promoting soil microorganisms are increasingly distributed on the world market. Nutrient mobilization, stimulation of root growth, enhanced resistance to envi-ronmental stress factors are discussed as possible mechanisms. These assumptions are based only on scarce scientific evidence due to limited reproducibility of pot and field experiments, limited information concerning the conditions for successful applica-tion, limited standardization of inoculum preparation and quality. Thus, the develop-ment of rapid screening tests is to demonstrate the principle effectiveness of biofertil-izers prior to set-up of labourous pot or field experiments is urgently required. In this study, a rapid bio-test with cucumber (Cucumis sativa L.) as an indicator plant was developed to evaluate the effectiveness of five commercial biofertilizers based on Trichoderma spp. and Bacillus spp. (Biohealth-G, Biohealth-WSG, Biomex, Vitalin T50 and SP11) using germination rate, root and shoot biomass, maximum root length, and leaf area as test parameters. The experiment was repeated twice with 6 replicates in hydroponic culture under controlled conditions (pH 5.5, 22° C; Light: 230 mmol cm2 sec-1). Biofertilizers were applied at the rate of 3 g per 2.5 l mineral nutrient solution. Germination rate was increased by 20 - 25% in all biofertilizer treatments compared to the control. After 2 weeks culture period, root dry weight and leaf area of Biohealth-G, Vitalin T50, SP-11 and Biomex-treated cucumber seedlings were significantly in-creased. Biohealth-G and Vitalin T50 showed significantly higher main root length and Biohealth-G higher shoot dry weight than the remaining treatments, while Biohealth-WSG did not cause differences compared to untreated control plants. The pathogen-antagonistic potential of Trichoderma strains can be easily tested by co-inoculation with the pathogenic fungus Gaeumannomyces graminis on malt extract peptone agar plates. The results suggest that the activity potential of different Trichoderma-based biofertilizers could be easily screened by using the described bio-test with cucumber seedlings

Glyphosate-induced impairment of plant growth and micronutrient status in glyphosate-resistant soybean (Glycine max L.)

This investigation demonstrated potential detrimental side effects of glyphosate on plant growth and micronutrient (Mn, Zn) status of a glyphosate-resistant (GR) soybean variety (Glycine max cv. Valiosa), which were found to be highly dependent on the selected growth conditions. In hydroponic experiments with sufficient Mn supply [0.5 μM], the GR cv. Valiosa produced similar plant biomass, root length and number of lateral roots in the control treatment without glyphosate as compared to its non-GR parental line cv. Conquista. However, this was associated with 50% lower Mn shoot concentrations in cv. Conquista, suggesting a higher Mn demand of the transgenic cv. Valiosa under the selected growth conditions. Glyphosate application significantly inhibited root biomass production, root elongation, and lateral root formation of the GR line, associated with a 50% reduction of Mn shoot concentrations. Interestingly, no comparable effects were detectable at low Mn supply [0.1 μM]. This may indicate Mn-dependent differences in the intracellular transformation of glyphosate to the toxic metabolite aminomethylphosphonic acid (AMPA) in the two isolines. In soil culture experiments conducted on a calcareous loess sub-soil of a Luvisol (pH 7.6) and a highly weathered Arenosol (pH 4.5), shoot biomass production and Zn leaf concentrations of the GR-variety were affected by glyphosate applications on the Arenosol but not on the calcareous Loess sub-soil. Analysis of micronutrient levels in high and low molecular weight (LMW) fractions (80% ethanol extracts) of young leaves revealed no indications for internal immobilization of micronutrients (Mn, Zn, Fe) by excessive complexation with glyphosate in the LMW phase

Integrated knowledge utilization and evolution for the conservation of corporate know-how

Insufficient consideration of knowledge evolution is a frequent cause for the failure of knowledge-based systems (KBSs) in industrial practice. Corporate know-how about the design and manufacturing of a particular product is subject to rather rapid changes, and it is hard to specify in advance exactly what information will be requested by various users. Keeping a KBS for the conservation of corporate know-how up-to-date or even enhancing its utility, thus requires the continuous monitoring of its performance, noting deficiencies, and suggestions for improvements. In the current paper, we discuss different ways in which information collected during knowledge utilization can be exploited for system evolution. We present structure-based rule and concept editors which allow for an immediate integration and formalization of new information, even by rather inexperienced users. A prototypical knowledge conservation system for crankshaft design which was developed in cooperation between the DFKI and a German company is used to illustrate and evaluate our approach

Die Verbreitung von Lepidurus apus L. und Triops cancriformis Bosc in der DDR

.

Large-Scale Statistical Learning for Mass Transport Prediction in Porous Materials Using 90,000 Artificially Generated Microstructures

Effective properties of functional materials crucially depend on their 3D microstructure. In this paper, we investigate quantitative relationships between descriptors of two-phase microstructures, consisting of solid and pores and their mass transport properties. To that end, we generate a vast database comprising 90,000 microstructures drawn from nine different stochastic models, and compute their effective diffusivity and permeability as well as various microstructural descriptors. To the best of our knowledge, this is the largest and most diverse dataset created for studying the influence of 3D microstructure on mass transport. In particular, we establish microstructure-property relationships using analytical prediction formulas, artificial (fully-connected) neural networks, and convolutional neural networks. Again, to the best of our knowledge, this is the first time that these three statistical learning approaches are quantitatively compared on the same dataset. The diversity of the dataset increases the generality of the determined relationships, and its size is vital for robust training of convolutional neural networks. We make the 3D microstructures, their structural descriptors and effective properties, as well as the code used to study the relationships between them available open access