MicroRNAs (miRNAs), i.e. small non-coding RNA molecules (∼22 nt), can bind to one or more target sites on a gene transcript to negatively regulate protein expression, subsequently controlling many cellular mechanisms. A current and curated collection of miRNA–target interactions (MTIs) with experimental support is essential to thoroughly elucidating miRNA functions under different conditions and in different species. As a database, miRTarBase has accumulated more than 3500 MTIs by manually surveying pertinent literature after data mining of the text systematically to filter research articles related to functional studies of miRNAs. Generally, the collected MTIs are validated experimentally by reporter assays, western blot, or microarray experiments with overexpression or knockdown of miRNAs. miRTarBase curates 3576 experimentally verified MTIs between 657 miRNAs and 2297 target genes among 17 species. miRTarBase contains the largest amount of validated MTIs by comparing with other similar, previously developed databases. The MTIs collected in the miRTarBase can also provide a large amount of positive samples to develop computational methods capable of identifying miRNA–target interactions. miRTarBase is now available on http://miRTarBase.mbc.nctu.edu.tw/, and is updated frequently by continuously surveying research articles.

With the proliferation of mobile computing technology, mobile learning (m-learning) will play a vital role in the rapidly growing electronic learning market. M-learning is the delivery of learning to students anytime and anywhere through the use of wireless Internet and mobile devices. However, acceptance of m-learning by individuals is critical to the successful implementation of m-learning systems. Thus, there is a need to research the factors that affect user intention to use m-learning. Based on the unified theory of acceptance and use of technology (UTAUT), which integrates elements across eight models of information technology use, this study was to investigate the determinants of m-learning acceptance and to discover if there exist either age or gender differences in the acceptance of m-learning, or both. Data collected from 330 respondents in Taiwan were tested against the research model using the structural equation modelling approach. The results indicate that performance expectancy, effort expectancy, social influence, perceived playfulness, and self-management of learning were all significant determinants of behavioural intention to use m-learning. We also found that age differences moderate the effects of effort expectancy and social influence on m-learning use intention, and that gender differences moderate the effects of social influence and self-management of learning on m-learning use intention. These findings provide several important implications for m-learning acceptance, in terms of both research and practice.

Because of the increase in the electronic density of states in low-dimensional systems, semiconductor quantum wires constitute a most promising thermoelectric material. We report here the first experimental observation of a very large enhancement of the thermoelectric power of composites containing bismuth nanowires with diameters of 9 and 15 nm, embedded in porous alumina and porous silica. The temperature dependence of the electrical resistance shows that the samples are semiconductors with energy gaps between 0.17 and 0.4 eV, consistent with the theoretical predictions.

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The regulation of steroidogenic acute regulatory protein (StAR) gene expression and the synthesis of steroids from cholesterol in ectopic endometriosis tissues were investigated. Peritoneal fluid and endometrial tissues were collected from patients with endometriosis and otherwise healthy women. Peritoneal progesterone and 17 beta-E2 concentrations were highest in early stage endometriosis compared with those in advanced stage endometriosis and in normal women. In concordance with the profile of peritoneal steroids, StAR mRNA and protein were greatest in ectopic implants of early endometriosis. In the advanced stage, concentrations of StAR mRNA and protein were also greater compared with those in normal endometrium. In contrast, P450 side-chain cleavage enzyme and 3 beta-hydroxysteroid dehydrogenase transcripts were not different between normal endometrium and ectopic endometriotic implants. Expression of StAR mRNA was detected in purified stromal, but not epithelial, cells. Treatment with PGE(2), but not TNF alpha, or IL-1 beta significantly increased StAR expression and thus induced progesterone production in cultured endometriotic stromal cells. These results demonstrated that aberrant expression of StAR in ectopic endometriotic tissues leading to increased peritoneal progesterone is associated with the formation of endometriosis. Induction of StAR gene expression by peritoneal PGE(2) in endometriotic stromal cells may further contribute to the development of endometriosis.