Humboldt University of Berlin

About: Humboldt University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Population & Medicine. The organization has 33671 authors who have published 61781 publications receiving 1908102 citations. The organization is also known as: Humboldt-Universität zu Berlin & Universitas Humboldtiana Berolinensis.

Signaling pathways in reactive oxygen species-induced cardiomyocyte apoptosis

Abstract: Background—The importance of free radical homeostasis and apoptosis in normal and diseased hearts and their interrelationships are poorly defined. We tested whether reactive oxygen species can trigger apoptosis in cardiomyocytes, and we explored the underlying pathways. Methods and Results—A cell culture model of isolated cardiac cells and different reactive oxygen species (ROS)–generating systems were used. Apoptosis became evident when cardiomyocytes were exposed to either H2O2 or superoxide anion (O2−). Both H2O2- and O2−-induced apoptosis of cardiomyocytes were associated with an increase in p53 protein content, whereas protein levels of Bax and Bcl-2 were unaltered. H2O2, but not O2−, induced an increase in the protein content of Bad. Furthermore, H2O2 elicited translocation of Bax and Bad from cytosol to mitochondria, where these factors formed heterodimers with Bcl-2, which was followed by the release of cytochrome c, activation of CPP32, and cleavage of poly(ADP-ribose) polymerase. Interestingly, ...

A survey of online failure prediction methods

Abstract: With the ever-growing complexity and dynamicity of computer systems, proactive fault management is an effective approach to enhancing availability. Online failure prediction is the key to such techniques. In contrast to classical reliability methods, online failure prediction is based on runtime monitoring and a variety of models and methods that use the current state of a system and, frequently, the past experience as well. This survey describes these methods. To capture the wide spectrum of approaches concerning this area, a taxonomy has been developed, whose different approaches are explained and major concepts are described in detail.

Can farmers’ adaptation to climate change be explained by socio-economic household-level variables?

Abstract: A better understanding of processes that shape farmers’ adaptation to climate change is critical to identify vulnerable entities and to develop well-targeted adaptation policies. However, it is currently poorly understood what determines farmers’ adaptation and how to measure it. In this study, we develop an activity-based adaptation index (AAI) and explore the relationship between socioeconomic variables and farmers’ adaptation behavior by means of an explanatory factor analysis and a multiple linear regression model using latent variables. The model was tested in six villages situated in two administrative wards in the Morogoro region of Tanzania. The Mlali ward represents a system of relatively high agricultural potential, whereas the Gairo ward represents a system of low agricultural potential. A household survey, a rapid rural appraisal and, a stakeholder workshop were used for data collection. The data were analyzed using factor analysis, multiple linear regression, descriptive statistical methods and qualitative content analysis. The empirical results are discussed in the context of theoretical concepts of adaptation and the sustainable livelihood approach. We found that public investment in rural infrastructure, in the availability and technically efficient use of inputs, in a good education system that provides equal chances for women, and in the strengthening of social capital, agricultural extension and, microcredit services are the best means of improving the adaptation of the farmers from the six villages in Gairo and Mlali. We conclude that the newly developed AAI is a simple but promising way to capture the complexity of adaptation processes that addresses a number of shortcomings of previous index studies.

Structural and Functional Characterization of Gene Clusters Directing Nonribosomal Synthesis of Bioactive Cyclic Lipopeptides in Bacillus amyloliquefaciens Strain FZB42

Abstract: The environmental strain Bacillus amyloliquefaciens FZB42 promotes plant growth and suppresses plant pathogenic organisms present in the rhizosphere. We sampled sequenced the genome of FZB42 and identified 2,947 genes with >50% identity on the amino acid level to the corresponding genes of Bacillus subtilis 168. Six large gene clusters encoding nonribosomal peptide synthetases (NRPS) and polyketide synthases (PKS) occupied 7.5% of the whole genome. Two of the PKS and one of the NRPS encoding gene clusters were unique insertions in the FZB42 genome and are not present in B. subtilis 168. Matrix-assisted laser desorption ionization-time of flight mass spectrometry analysis revealed expression of the antibiotic lipopeptide products surfactin, fengycin, and bacillomycin D. The fengycin (fen) and the surfactin (srf) operons were organized and located as in B. subtilis 168. A large 37.2-kb antibiotic DNA island containing the bmy gene cluster was attributed to the biosynthesis of bacillomycin D. The bmy island was found inserted close to the fen operon. The responsibility of the bmy, fen, and srf gene clusters for the production of the corresponding secondary metabolites was demonstrated by cassette mutagenesis, which led to the loss of the ability to produce these peptides. Although these single mutants still largely retained their ability to control fungal spread, a double mutant lacking both bacillomycin D and fengycin was heavily impaired in its ability to inhibit growth of phytopathogenic fungi, suggesting that both lipopeptides act in a synergistic manner.