Evolutionary conservation of nested MIR159 structural microRNA genes and their promoter characterization in Arabidopsis thaliana

MicroRNAs (miRNAs) are endogenous small RNAs, that are vital for gene expression regulation in eukaryotes. Whenever a pri-miRNA precursor includes another miRNA precursor, and both of these precursors may generate independent, non-overlapping mature miRNAs, we named them nested miRNAs. However, the extent of nested miR159 structural evolutionary conservation and its promoter characterization remains unknown. In this study, the sequence alignment and phylogenetic analysis reveal that the MIR159 family is ancient, and its nested miR159 structures are evolutionary conserved in different plant species. The overexpression of ath-MIR159a, including the 1.2 kb downstream region, has no effect on rescuing the mir159ab phenotype. The promoter truncation results revealed that the 1.0 kb promoter of ath-MIR159a is sufficient for rescuing the mir159ab phenotype. The cis-regulatory elements in the ath-miR159a promoters indicated functions related to different phytohormones, abiotic stresses, and transcriptional activation. While the MybSt1 motif-containing region is not responsible for activating the regulation of the miR159a promoter. The qRT-PCR results showed that overexpression of ath-MIR159a led to high expression levels of miR159a.1–5 and miR159a.1–3 and complemented the growth defect of mir159ab via downregulation of MYB33 and MYB65. Furthermore, continuously higher expression of the miR159a.2 duplex in transgenic lines with the curly leaf phenotype indicates that miR159a.2 is functional in Arabidopsis and suggests that it is possible for a miRNA precursor to encode several regulatory small RNAs in plants. Taken together, our study demonstrates that the nested miR159 structure is evolutionary conserved and miRNA-mediated gene regulation is more complex than previously thought

BRAFV600E status and clinical characteristics in solitary and multiple papillary thyroid carcinoma: experience of 512 cases at a clinical center in China

Abstract Background Papillary thyroid carcinoma (PTC) is one of the most frequent endocrine malignancies. In most cases, it often presents as multifocal tumor. It has been reported that multifocal tumors are associated with elevated risk of lymph node and distant metastases. Multifocality is also one of the factors predicting prognosis. Recent studies show that BRAFV600E mutation occurs more frequently in aggressive PTC. The purpose of this study was to evaluate BRAFV600E status and clinicopathological features in multiple and solitary PTC. Methods We performed a retrospective study to analyze 512 PTC cases who received surgery, including 376 solitary PTCs and 136 multiple PTCs. Results Multiple PTC is more related to lymph node metastasis and vascular invasion than solitary PTC. However, the distant metastasis rate and 10-year survival rate showed no difference between these two groups. BRAFV600E mutation status was more frequent in multiple PTC patients with lymph node metastasis and late stage at diagnosis. Conclusion BRAFV600E mutation is most commonly associated with extra-thyroidal extension and lymph node metastasis in PTC. Multiple PTC patients with young age, large tumors and BRAFV600E mutation should be followed carefully. Our study provides useful information for PTC patients’ followup and treatment. </jats:sec

Iteratively reweighted least square for asymmetric L 2 -Loss support vector regression

In support vector regression (SVR) model, using the squared ϵ-insensitive loss function makes the objective function of the optimization problem strictly convex and yields a more concise solution. However, the formulation leads to a quadratic programing which is expensive to solve. This paper reformulates the optimization problem by absorbing the constraints in the objective function, and the new formulation shares similarity with weighted least square regression problem. Based on this formulation, we propose an iteratively reweighted least square approach to train the L 2 -loss SVR, for both linear and nonlinear models. The proposed approach is easy to implement, without requiring any additional computing package other than basic linear algebra operations. Numerical studies on real-world datasets show that, compared to the alternatives, the proposed approach can achieve similar prediction accuracy with substantially higher time efficiency

Smoothly approximated support vector domain description

Support vector domain description (SVDD) is a well-known tool for pattern analysis when only positive examples are reliable. The SVDD model is often fitted by solving a quadratic programming problem, which is time consuming. This paper attempts to fit SVDD in the primal form directly. However, the primal objective function of SVDD is not differentiable which prevents the well-behaved gradient based optimization methods from being applicable. As such, we propose to approximate the primal objective function of SVDD by a differentiable function, and a conjugate gradient method is applied to minimize the smoothly approximated objective function. Extensive experiments on pattern classification were conducted, and compared to the quadratic programming based SVDD, the proposed approach is much more computationally efficient and yields similar classification performance on these problems