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This study is the first to demonstrate both the presence of circulating miRNAs in lymph and the alteration of specific miRNAs in AP. Furthermore, these miRNAs alter in rat and human AP plasma and have potential to be explored as novel biomarkers of pancreatitis.

Arabidopsis MYC2 bHLH transcription factor plays a negative regulatory role in blue light (BL)-mediated seedling development. HY5 bZIP protein works as a positive regulator of multiple wavelengths of light and promotes photomorphogenesis. Both MYC2 and HY5, belonging to two different classes of transcription factors, are the integrators of multiple signaling pathways. However, the functional interrelations of these two transcription factors in seedling development remain unknown. Additionally, whereas HY5-mediated regulation of gene expression has been investigated in detail, the transcriptional regulation of HY5 itself is yet to be understood. Here, we show that HY5 and MYC2 work in an antagonistic manner in Arabidopsis seedling development. Our results reveal that HY5 expression is negatively regulated by MYC2 predominantly in BL, and at various stages of development. On the other hand, HY5 negatively regulates the expression of MYC2 at various wavelengths of light. In vitro and in vivo DNA-protein interaction studies suggest that MYC2 binds to the E-box cis-acting element of HY5 promoter. Collectively, this study demonstrates a coordinated regulation of MYC2 and HY5 in blue-light-mediated Arabidopsis seedling development.

A Markov chain framework is developed for analyzing a wide variety of selection techniques used in genetic algorithms (GAs) and evolution strategies (ESs). Specifically, we consider linear ranking selection, probabilistic binary tournament selection, deterministic s-ary (s = 3,4, …) tournament selection, fitness-proportionate selection, selection in Whitley's GENITOR, selection in (μ, λ)-ES, selection in (μ + λ)-ES, (μ, λ)-linear ranking selection in GAs, (μ + λ)-linear ranking selection in GAs, and selection in Eshelman's CHC algorithm. The analysis enables us to compare and contrast the various selection algorithms with respect to several performance measures based on the probability of takeover. Our analysis is exact—we do not make any assumptions or approximations. Finite population sizes are considered. Our approach is perfectly general, and following the methods of this paper, it is possible to analyze any selection strategy in evolutionary algorithms.