A Neoclassic Growth Model of Leisure Time and China\u27s Economic Growth

The Purpose of this study was to assess the effect of leisure time on China’s long-run economic growth. Two compensation effects of leisure were introduced into the economic growth model to assess if leisure choice-set affect economic growth in the long-run. A time series data of 23 years (1981-2003) was used in the study. A neoclassic growth model was used to analyze the data. The result found a weak negative relationship between leisure time and China’s long-run economic growth

Heterospecific alarm-call recognition in two warbler hosts of common cuckoos

International audienceSpecies facing similar selection pressures should recognize heterospecific alarm signals. However, no study has so far examined heterospecific alarm-call recognition in response to parasitism by cuckoos. In this study, we tested whether two sympatric host species of the common cuckoo Cuculus canorus, Oriental reed warbler Acrocephalus orientalis (ORW, main host), and black-browed reed warbler Acrocephalus bistrigiceps (BRW, rare host), could recognize each other's alarm calls in response to cuckoos. Dummies of common cuckoo (parasite) and Eurasian sparrowhawk Accipiter nisus (predator) were used to induce and record alarm calls of the two warbler species, respectively. In the conspecific alarm-call playback experiments, ORW responded more strongly to cuckoo alarm calls than to sparrowhawk alarm calls, while BRW responded less strongly to cuckoo alarm calls than to sparrowhawk alarm calls. In the heterospecific alarm-call playback experiments, both ORW and BRW responded less strongly to cuckoo alarm calls than sparrowhawk alarm calls. BRW seemed to learn the association between parasite-related alarm calls of the ORW and the cuckoo by observing the process of ORW attacking cuckoos. In contrast, alarm calls of BRW to cuckoos were rarely recorded in most cases. BRW with low parasite pressure still developed recognition of heterospecific parasite-related alarm call. Unintended receivers in the same community should recognize heterospecific alarm calls precisely to extract valuable information

MiR-185 Targets the DNA Methyltransferases 1 and Regulates Global DNA Methylation in human glioma

<p>Abstract</p> <p>Background</p> <p>Perturbation of DNA methylation is frequent in cancers and has emerged as an important mechanism involved in tumorigenesis. To determine how DNA methylation is modified in the genome of primary glioma, we used Methyl-DNA immunoprecipitation (MeDIP) and Nimblegen CpG promoter microarrays to identify differentially DNA methylation sequences between primary glioma and normal brain tissue samples.</p> <p>Methods</p> <p>MeDIP-chip technology was used to investigate the whole-genome differential methylation patterns in glioma and normal brain tissues. Subsequently, the promoter methylation status of eight candidate genes was validated in 40 glioma samples and 4 cell lines by Sequenom's MassARRAY system. Then, the epigenetically regulated expression of these genes and the potential mechanisms were examined by chromatin immunoprecipitation and quantitative real-time PCR.</p> <p>Results</p> <p>A total of 524 hypermethylated and 104 hypomethylated regions were identified in glioma. Among them, 216 hypermethylated and 60 hypomethylated regions were mapped to the promoters of known genes related to a variety of important cellular processes. Eight promoter-hypermethylated genes (ANKDD1A, GAD1, HIST1H3E, PCDHA8, PCDHA13, PHOX2B, SIX3, and SST) were confirmed in primary glioma and cell lines. Aberrant promoter methylation and changed histone modifications were associated with their reduced expression in glioma. In addition, we found loss of heterozygosity (LOH) at the miR-185 locus located in the 22q11.2 in glioma and induction of miR-185 over-expression reduced global DNA methylation and induced the expression of the promoter-hypermethylated genes in glioma cells by directly targeting the DNA methyltransferases 1.</p> <p>Conclusion</p> <p>These comprehensive data may provide new insights into the epigenetic pathogenesis of human gliomas.</p

Data mining tools for Salmonella characterization: application to gel-based fingerprinting analysis

BACKGROUND: Pulsed field gel electrophoresis (PFGE) is currently the most widely and routinely used method by the Centers for Disease Control and Prevention (CDC) and state health labs in the United States for Salmonella surveillance and outbreak tracking. Major drawbacks of commercially available PFGE analysis programs have been their difficulty in dealing with large datasets and the limited availability of analysis tools. There exists a need to develop new analytical tools for PFGE data mining in order to make full use of valuable data in large surveillance databases. RESULTS: In this study, a software package was developed consisting of five types of bioinformatics approaches exploring and implementing for the analysis and visualization of PFGE fingerprinting. The approaches include PFGE band standardization, Salmonella serotype prediction, hierarchical cluster analysis, distance matrix analysis and two-way hierarchical cluster analysis. PFGE band standardization makes it possible for cross-group large dataset analysis. The Salmonella serotype prediction approach allows users to predict serotypes of Salmonella isolates based on their PFGE patterns. The hierarchical cluster analysis approach could be used to clarify subtypes and phylogenetic relationships among groups of PFGE patterns. The distance matrix and two-way hierarchical cluster analysis tools allow users to directly visualize the similarities/dissimilarities of any two individual patterns and the inter- and intra-serotype relationships of two or more serotypes, and provide a summary of the overall relationships between user-selected serotypes as well as the distinguishable band markers of these serotypes. The functionalities of these tools were illustrated on PFGE fingerprinting data from PulseNet of CDC. CONCLUSIONS: The bioinformatics approaches included in the software package developed in this study were integrated with the PFGE database to enhance the data mining of PFGE fingerprints. Fast and accurate prediction makes it possible to elucidate Salmonella serotype information before conventional serological methods are pursued. The development of bioinformatics tools to distinguish the PFGE markers and serotype specific patterns will enhance PFGE data retrieval, interpretation and serotype identification and will likely accelerate source tracking to identify the Salmonella isolates implicated in foodborne diseases