University of Hohenheim

About: University of Hohenheim is a education organization based out in Stuttgart, Germany. It is known for research contribution in the topics: Population & Soil water. The organization has 8585 authors who have published 16406 publications receiving 567377 citations.

Response of microbial activity and microbial community composition in soils to long-term arsenic and cadmium exposure

Abstract: Arsenic (As) and cadmium (Cd) in soils can affect soil microbial function and community composition and, therefore, may have effects on soil ecosystem functioning. The aim of our study was to assess the effects of long-term As and Cd contamination on soil microbial community composition and soil enzyme activities. We analyzed soils that have been contaminated 25 years ago and at present still show enhanced levels of either As, 18 and 39 mg kg−1, or Cd, 34 and 134 mg kg−1. Soil without heavy metal addition served as control. Polymerase chain reaction (PCR) followed by denaturing gradient gel electrophoresis (DGGE) showed that bacterial community composition in As and Cd contaminated soils differed from that in the control soil. The same was true for the microbial community composition assessed by analysis of respiratory quinones. Soil fungi and Proteobacteria appeared to be tolerant towards As and Cd, while other groups of bacteria were reduced. The decline in alkaline phosphatase, arylsulphatase, protease and urease activities in the As- and Cd-contaminated soils was correlated with a decrease of respiratory quinones occuring in Actinobacteria and Firmicutes. Xylanase activity was unaffected or elevated in the contaminated soils which was correlated with a higher abundance of fungal quinones, and quinones found in Proteobacteria.

Access to the odor world: olfactory receptors and their role for signal transduction in insects.

Abstract: The sense of smell enables insects to recognize and discriminate a broad range of volatile chemicals in their environment originating from prey, host plants and conspecifics. These olfactory cues are received by olfactory sensory neurons (OSNs) that relay information about food sources, oviposition sites and mates to the brain and thus elicit distinct odor-evoked behaviors. Research over the last decades has greatly advanced our knowledge concerning the molecular basis underlying the reception of odorous compounds and the mechanisms of signal transduction in OSNs. The emerging picture clearly indicates that OSNs of insects recognize odorants and pheromones by means of ligand-binding membrane proteins encoded by large and diverse families of receptor genes. In contrast, the mechanisms of the chemo-electrical transduction process are not fully understood; the present status suggests a contribution of ionotropic as well as metabotropic mechanisms. In this review, we will summarize current knowledge on the peripheral mechanisms of odor sensing in insects focusing on olfactory receptors and their specific role in the recognition and transduction of odorant and pheromone signals by OSNs.

Butyric acid stimulates rumen mucosa development in the calf mainly by a reduction of apoptosis.

Abstract: In ruminants the Stimulation of papillar growth by butyric acid is well described but effects on mitosis and apoptosis are not known. To clarify the effect of short chain fatty acids three groups of three calves received a basic ration of 100 g hay per day for 6 weeks and additionally milk replacer. From these, two groups were fed with increasing amounts of the salts of either propionic acid (53 to 390 g) or butyric acid up to (54 to 326 g). The control group instead received an additional isocaloric amount of milk replacer. Mitosis was characterized by Ki67 immunoreactivity, apoptosis by a modified TUNEL assay and by electron microscopy. The feeding regimes led to significant differences of papillar length, increasing from 1.0mm (controls) to 2.2 mm (propionic acid) and 4 mm (butyric acid). This enlargement was partly explained by an increased mitotic rate for the two fatty acid groups. The difference between the fatty acid groups was mainly explained by different apoptotic rates which were only one thir...

Molecular footprints of domestication and improvement in soybean revealed by whole genome re-sequencing

Abstract: Artificial selection played an important role in the origin of modern Glycine max cultivars from the wild soybean Glycine soja. To elucidate the consequences of artificial selection accompanying the domestication and modern improvement of soybean, 25 new and 30 published whole-genome re-sequencing accessions, which represent wild, domesticated landrace, and Chinese elite soybean populations were analyzed. A total of 5,102,244 single nucleotide polymorphisms (SNPs) and 707,969 insertion/deletions were identified. Among the SNPs detected, 25.5% were not described previously. We found that artificial selection during domestication led to more pronounced reduction in the genetic diversity of soybean than the switch from landraces to elite cultivars. Only a small proportion (2.99%) of the whole genomic regions appear to be affected by artificial selection for preferred agricultural traits. The selection regions were not distributed randomly or uniformly throughout the genome. Instead, clusters of selection hotspots in certain genomic regions were observed. Moreover, a set of candidate genes (4.38% of the total annotated genes) significantly affected by selection underlying soybean domestication and genetic improvement were identified. Given the uniqueness of the soybean germplasm sequenced, this study drew a clear picture of human-mediated evolution of the soybean genomes. The genomic resources and information provided by this study would also facilitate the discovery of genes/loci underlying agronomically important traits.

What's normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions

Abstract: We consider two questions important for applying analysis of variance (ANOVA): Should normality be checked on the raw data or on the residuals (or is it immaterial which of the two approaches we take)? Should normality and homogeneity of variance be checked using significance tests or diagnostic plots (or both)? Based on two examples, we show that residuals should be used for model checking and that residual plots are better for checking ANOVA assumptions than statistical tests. We also discuss why one should be very cautious when using statistical tests to check the assumptions.