20 research outputs found
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Sozialstaatliche Kürzungspolitik in Deutschland: Nur eine Mär? Eine quantitative Gesetzgebungsanalyse 1974–2014
Quantitative analyses on welfare state dynamics have to cope with the “dependent variable problem”, as studies on social spending reach different conclusions than analyses of replacement rate data. This article suggests a way around this problem by presenting results from a fine-grained analysis of welfare state legislation in Germany between 1974 and 2014.
We show that the German welfare state has seen both cuts and expansions occur in all decades. Moreover, we show by means of a regression analysis that partisan politics play a role. Supporting the “Nixon-in-China”-thesis, social democratic governments are associated with a higher probability of cutbacks – especially in times of budgetary pressure – whereas expansions are more likely under Christian democratic governments
Inferring microRNA and transcription factor regulatory networks in heterogeneous data
Background: Transcription factors (TFs) and microRNAs (miRNAs) are primary metazoan gene regulators. Regulatory mechanisms of the two main regulators are of great interest to biologists and may provide insights into the causes of diseases. However, the interplay between miRNAs and TFs in a regulatory network still remains unearthed. Currently, it is very difficult to study the regulatory mechanisms that involve both miRNAs and TFs in a biological lab. Even at data level, a network involving miRNAs, TFs and genes will be too complicated to achieve. Previous research has been mostly directed at inferring either miRNA or TF regulatory networks from data. However, networks involving a single type of regulator may not fully reveal the complex gene regulatory mechanisms, for instance, the way in which a TF indirectly regulates a gene via a miRNA. Results: We propose a framework to learn from heterogeneous data the three-component regulatory networks, with the presence of miRNAs, TFs, and mRNAs. This method firstly utilises Bayesian network structure learning to construct a regulatory network from multiple sources of data: gene expression profiles of miRNAs, TFs and mRNAs, target information based on sequence data, and sample categories. Then, in order to produce more meaningful results for further biological experimentation and research, the method searches the learnt network to identify the interplay between miRNAs and TFs and applies a network motif finding algorithm to further infer the network. We apply the proposed framework to the data sets of epithelial-to-mesenchymal transition (EMT). The results elucidate the complex gene regulatory mechanism for EMT which involves both TFs and miRNAs. Several discovered interactions and molecular functions have been confirmed by literature. In addition, many other discovered interactions and bio-markers are of high statistical significance and thus can be good candidates for validation by experiments. Moreover, the results generated by our method are compact, involving a small number of interactions which have been proved highly relevant to EMT. Conclusions: We have designed a framework to infer gene regulatory networks involving both TFs and miRNAs from multiple sources of data, including gene expression data, target information, and sample categories. Results on the EMT data sets have shown that the proposed approach is able to produce compact and meaningful gene regulatory networks that are highly relevant to the biological conditions of the data sets. This framework has the potential for application to other heterogeneous datasets to reveal the complex gene regulatory relationships.Thuc D Le, Lin Liu, Bing Liu, Anna Tsykin, Gregory J Goodall, Kenji Satou and Jiuyong L
Einleitung: Demokratisierung, Konsolidierung und Wohlfahrtsstaat — Überlegungen zu einem ungeklärten Verhältnis
A novel series of isoreticular metal organic frameworks: realizing metastable structures by liquid phase epitaxy
Liu J, Lukose B, Shekhah O, et al. A novel series of isoreticular metal organic frameworks: realizing metastable structures by liquid phase epitaxy. Scientific Reports. 2012;2(1): 921.A novel class of metal organic frameworks (MOFs) has been synthesized from Cu-acetate and dicarboxylic acids using liquid phase epitaxy. The SURMOF-2 isoreticular series exhibits P4 symmetry, for the longest linker a channel-size of 3 x 3 nm(2) is obtained, one of the largest values reported for any MOF so far. High quality, ab-initio electronic structure calculations confirm the stability of a regular packing of (Cu++)(2)-carboxylate paddle-wheel planes with P4 symmetry and reveal, that the SURMOF-2 structures are in fact metastable, with a fairly large activation barrier for the transition to the bulk MOF-2 structures exhibiting a lower, twofold (P2 or C2) symmetry. The theoretical calculations also allow identifying the mechanism for the low-temperature epitaxial growth process and to explain, why a synthesis of this highly interesting, new class of high-symmetry, metastable MOFs is not possible using the conventional solvothermal process