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Light-emitting diodes (LEDs) are an efficient alternative to traditional lamps for plant growth. To investigate the influence of LEDs on flowering and polyphenol biosynthesis in the leaves of chrysanthemum, the plants were grown under supplemental blue, green, red, and white LEDs. Flower budding was formed even after a longer photoperiod than a critical day length of 13.5 h per day under blue light illumination. The weights of leaves and stems were highest under the white light illumination growth condition, whereas the weight of roots appeared to be independent of light quality. Among nine polyphenols characterized by high-performance liquid chromatography-tandem mass spectroscopy, three polyphenols were identified for the first time in chrysanthemum. A quantitation and principal component analysis biplot demonstrated that luteolin-7-O-glucoside (2), luteolin-7-O-glucuronide (3), and quercetagetin-trimethyl ether (8) were the highest polyphenols yielded under green light, and dicaffeoylquinic acid isomer (4), dicaffeoylquinic acid isomer (5), naringenin (7), and apigenin-7-O-glucuronide (6) were greatest under red light. Chlorogenic acid (1) and 1,2,6-trihydroxy-7,8-dimethoxy-3-methylanthraquinone (9) were produced in similar concentrations under both light types. The white and blue light appeared inefficient for polyphenol production. Taken together, our results suggest that the chrysanthemum flowering and polyphenol production are influenced by light quality composition.

Snail is a key regulator of epithelial-mesenchymal transition (EMT), which is a major step in tumor metastasis. Although the induction of Snail transcription precedes EMT, posttranslational regulation, especially phosphorylation of Snail, is critical for determining Snail protein levels or stability, subcellular localization, and the ability to induce EMT. To date, several kinases are known that enhance the stability of Snail by preventing its ubiquitination; however, the molecular mechanism(s) underlying this are still unclear. Here, we identified p38 MAPK as a crucial posttranslational regulator that enhances the stability of Snail. p38 directly phosphorylated Snail at Ser107, and this effectively suppressed DYRK2-mediated Ser104 phosphorylation, which is critical for GSK3b-dependent Snail phosphorylation and bTrCP-mediated Snail ubiquitination and degradation. Importantly, functional studies and analysis of clinical samples established a crucial role for the p38-Snail axis in regulating ovarian cancer EMT and metastasis. These results indicate the potential therapeutic value of targeting the p38-Snail axis in ovarian cancer. Significance: These findings identify p38 MAPK as a novel regulator of Snail protein stability and potential therapeutic target in ovarian cancer.

This paper applies the algorithm of Hastie et al. [1] to the problem of learning the entire solution path of the one class support vector machine (OC-SVM) as its free parameter ν varies from 0 to 1.The OC-SVM with Gaussian kernel is a nonparametric estimator of a level set of the density governing the observed sample, with the parameter ν implicitly de ning the corresponding level. Thus, the path algorithm produces estimates of all level sets and can therefore be applied to a variety of problems requiring estimation of multiple level sets including clustering, outlier ranking, minimum volume set estimation, and density estimation. The algorithm's cost is comparable to the cost of computing the OC-SVM for a single point on the path. We introduce a heuristic for enforced nestedness of the sets in the path, and present a method for kernel bandwidth selection based in minimum integrated volume, a kind of AUC criterion.These methods are illustrated on three datasets.

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Flow cytometry is a technology that rapidly measures antigen-based markers associated to cells in a cell population. Although analysis of flow cytometry data has traditionally considered one or two markers at a time, there has been increasing interest in multidimensional analysis. However, flow cytometers are limited in the number of markers they can jointly observe, which is typically a fraction of the number of markers of interest. For this reason, practitioners often perform multiple assays based on different, overlapping combinations of markers. In this paper, we address the challenge of imputing the high-dimensional jointly distributed values of marker attributes based on overlapping marginal observations. We show that simple nearest neighbor based imputation can lead to spurious subpopulations in the imputed data and introduce an alternative approach based on nearest neighbor imputation restricted to a cell's subpopulation. This requires us to perform clustering with missing data, which we address with a mixture model approach and novel EM algorithm. Since mixture model fitting may be ill-posed in this context, we also develop techniques to initialize the EM algorithm using domain knowledge. We demonstrate our approach on real flow cytometry data.