Carnosol Modulates Th17 Cell Differentiation and Microglial Switch in Experimental Autoimmune Encephalomyelitis

Medicinal plants as a rich pool for developing novel small molecule therapeutic medicine have been used for thousands of years. Carnosol as a bioactive diterpene compound originated from Rosmarinus officinalis (Rosemary) and Salvia officinalis, herbs extensively applied in traditional medicine for the treatment of multiple autoimmune diseases (1). In this study, we investigated the therapeutic effects and molecule mechanism of carnosol in experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis (MS). Carnosol treatment significantly alleviated clinical development in the myelin oligodendrocyte glycoprotein (MOG35–55) peptide-induced EAE model, markedly decreased inflammatory cell infiltration into the central nervous system and reduced demyelination. Further, carnosol inhibited Th17 cell differentiation and signal transducer and activator of transcription 3 phosphorylation, and blocked transcription factor NF-κB nuclear translocation. In the passive-EAE model, carnosol treatment also significantly prevented Th17 cell pathogenicity. Moreover, carnosol exerted its therapeutic effects in the chronic stage of EAE, and, remarkably, switched the phenotypes of infiltrated macrophage/microglia. Taken together, our results show that carnosol has enormous potential for development as a therapeutic agent for autoimmune diseases such as MS

Gene duplication and an accelerated evolutionary rate in 11S globulin genes are associated with higher protein synthesis in dicots as compared to monocots

Background: Seed storage proteins are a major source of dietary protein, and the content of such proteins determines both the quantity and quality of crop yield. Significantly, examination of the protein content in the seeds of crop plants shows a distinct difference between monocots and dicots. Thus, it is expected that there are different evolutionary patterns in the genes underlying protein synthesis in the seeds of these two groups of plants. Results: Gene duplication, evolutionary rate and positive selection of a major gene family of seed storage proteins (the 11S globulin genes), were compared in dicots and monocots. The results, obtained from five species in each group, show more gene duplications, a higher evolutionary rate and positive selections of this gene family in dicots, which are rich in 11S globulins, but not in the monocots. Conclusion: Our findings provide evidence to support the suggestion that gene duplication and an accelerated evolutionary rate may be associated with higher protein synthesis in dicots as compared to monocots

Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization

Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure for finding sparse solutions of underdetermined linear systems. This method has been shown to have strong theoretical guarantee and impressive numerical performance. In this paper, we generalize HTP from compressive sensing to a generic problem setup of sparsity-constrained convex optimization. The proposed algorithm iterates between a standard gradient descent step and a hard thresholding step with or without debiasing. We prove that our method enjoys the strong guarantees analogous to HTP in terms of rate of convergence and parameter estimation accuracy. Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods in sparse logistic regression and sparse precision matrix estimation tasks

Polarization Decomposition Algorithm for Detection Efficiency Enhancement

In the paper, a new polarization decomposition of the optimal detection algorithm in the partially homogeneous environment is presented. Firstly, the detectors Matched Subspace Detector (MSD) and Adaptive Subspace Detector (ASD) are adopted to deal with detection problems in the partially homogeneous environment. Secondly, the fitness function with polarization parameters is equivalently decomposed to enhance time detection efficiency in the algorithm. It makes the multiplication number of the fitness function from square to a linear increase along with the increase in parameters. Simulation results indicate that the proposed decomposition is much more efficient than direct use of the fitness function