Trigonoside II mitigates sepsis-induced myocardial injury via reduction in oxidative stress and regulation of TLR- 4/NF-kB inflammatory pathway

Purpose: To investigate the protective effect of trigonoside II against sepsis-induced myocardial injury in rats, and the mechanism involved. Methods: Adult male Sprague Dawley rats (n = 30) weighing 200 - 230 g (mean weight = 215 ± 15 g) were used for this study. The rats were randomly assigned to 3 groups (10 rats/group): sham, cecal ligation puncture (CLP), and trigonoside II. Rats in the treatment group received trigonoside II at a dose of 2 mg/kg intraperitoneally (i.p.) at 3, 12 and 24 h post-surgery. Sepsis was induced using CLP method. Lactate  dehydrogenase (LDH) and creatine kinase (CK-MB) activities, and hemodynamic functions were determined in the rats. The levels of interleukin (IL)-1β and IL-6, and tumor necrosis factor α (TNF-α) were assayed in rat serum. Oxidative stress and myocardial cell apoptosis were determined by measuring malondialdehyde (MDA) levels, while activities of glutathione peroxidase (GPx), superoxide dismutase (SOD) and myeloperoxidase (MPO), as well as levels of expression of bax, bcl-2 and caspase-3 were also assessed. Results: Treatment of myocardial injury rats with trigonoside II led to significant reductions in the activities of LDH, CK-MB and MPO, and decreases in levels of IL-1β, IL-6 and TNF-α (p < 0.05). It also significantly reversed the effects of sepsis on rat hemodynamic functions (p < 0.05). Trigonoside IItreatment significantly reduced MDA levels in rat myocardial tissues, but significantly increased SOD and GPx activities (p < 0.05). It significantly down-regulated protein expressions of NF-kB and TLR-4 in myocardial tissues (p < 0.05). The number of apoptotic cells and activity of caspase-3 were significant increased in myocardial tissues of rats in CLP group, when compared with sham group, but were reduced significantly in myocardial tissues of trigonoside II-treated rats (p < 0.05). Similarly, trigonoside II treatment down-regulated the protein expressions of caspase-3 and bax, but upregulated bcl-2 protein expression in the rat myocardial tissues (p < 0.05). Conclusion: The results of this study indicate that trigonoside II confers protection on sepsis-induced myocardial injury via reduction in oxidative stress and regulation of TLR-4/NF-kB inflammatory pathway. Keywords: Cecal ligation puncture, Myocardial injury, Oxidative stress, Sepsis, Trigonoside I

A Maximum Pseudo-Likelihood Approach for Estimating Species Trees under the Coalescent Model

Background: Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE) of the species tree (topology, branch lengths, and population sizes) from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE) of species trees, with branch lengths of the species tree in coalescent units. Results: We show that the MPE of the species tree is statistically consistent as the number $M$ of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate $O(M^{-1})$. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST) to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions: MP-EST can consistently estimate the topology and branch lengths (in coalescent units) of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT), the MP-EST method is robust to a small amount of HGT in the dataset. In addition, increasing the number of genes does not increase the computational time substantially. The MP-EST method is fast for analyzing datasets that involve a large number of genes but a moderate number of species.Organismic and Evolutionary Biolog

Research on urban distribution automation construction and renovation project

Urban Distribution Automation is a necessary requirement for power grid development . The article describes a network frame , Automation Master station ,Feeder automation "" Distribution Communication network construction and change making principles , features , Method , measures , to develop a construction planning goal 

A Maximum Pseudo-Likelihood Approach for Estimating Species Trees under the Coalescent Model

Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE) of the species tree (topology, branch lengths, and population sizes) from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE) of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1). The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST) to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units) of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT), the MP-EST method is robust to a small amount of HGT in the dataset. In addition, increasing the number of genes does not increase the computational time substantially. The MP-EST method is fast for analyzing datasets that involve a large number of genes but a moderate number of species

3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost

A Bayesian Model for Gene Family Evolution

Background A birth and death process is frequently used for modeling the size of a gene family that may vary along the branches of a phylogenetic tree. Under the birth and death model, maximum likelihood methods have been developed to estimate the birth and death rate and the sizes of ancient gene families (numbers of gene copies at the internodes of the phylogenetic tree). This paper aims to provide a Bayesian approach for estimating parameters in the birth and death model. Results We develop a Bayesian approach for estimating the birth and death rate and other parameters in the birth and death model. In addition, a Bayesian hypothesis test is developed to identify the gene families that are unlikely under the birth and death process. Simulation results suggest that the Bayesian estimate is more accurate than the maximum likelihood estimate of the birth and death rate. The Bayesian approach was applied to a real dataset of 3517 gene families across genomes of five yeast species. The results indicate that the Bayesian model assuming a constant birth and death rate among branches of the phylogenetic tree cannot adequately explain the observed pattern of the sizes of gene families across species. The yeast dataset was thus analyzed with a Bayesian heterogeneous rate model that allows the birth and death rate to vary among the branches of the tree. The unlikely gene families identified by the Bayesian heterogeneous rate model are different from those given by the maximum likelihood method. Conclusions Compared to the maximum likelihood method, the Bayesian approach can produce more accurate estimates of the parameters in the birth and death model. In addition, the Bayesian hypothesis test is able to identify unlikely gene families based on Bayesian posterior p-values. As a powerful statistical technique, the Bayesian approach can effectively extract information from gene family data and thereby provide useful information regarding the evolutionary process of gene families across genomes