Anti-inflammatory properties of Fu brick tea water extract contribute to the improvement of diarrhea in mice

Fu brick tea,a special kind of dark tea fermented dominantly by Eurotium cristatum,is traditionally used for diarrhea therapy in China.However,limited reports are available on the anti-diarrhea of Fu brick tea water extract(FTE)and its potential mechanisms.In the present study,the treatment effects of FTE on the senna-induced diarrhea in mice were investigated.We found that FTE effectively improved diarrhea index and inhibited gut peristalsis.Additionally,histopathological examination revealed that FTE protected the integrity and reduced inflammatory infiltration of the ileum mucosal barrier.Furthermore,FTE significantly decreased the levels of the pro-inflammatory factor 5-hydroxytryptamine(5-HT)and increased the expression of sodium–hydrogen exchanger 3(NHE-3).The association among both intestinal damage and electrolyte balance and inflammation has been reported by many studies.Collectively,our study showed that FTE had anti-diarrhea activity,which may be associated with anti-inflammatory properties.

Methods and applications of RNA contact prediction

The RNA tertiary structure is essential to understanding the function and biological processes. Unfortunately, it is still challenging to determine the large RNA structure from direct experimentation or computational modeling. One promising approach is first to predict the tertiary contacts and then use the contacts as constraints to model the structure. The RNA structure modeling depends on the contact prediction accuracy. Although many contact prediction methods have been developed in the protein field, there are only several contact prediction methods in the RNA field at present. Here, we first review the theoretical basis and test the performances of recent RNA contact prediction methods for tertiary structure and complex modeling problems. Then, we summarize the advantages and limitations of these RNA contact prediction methods. We suggest some future directions for this rapidly expanding field in the last.

LINEAR REGRESSION OF INTERVAL-VALUED DATA BASED ON COMPLETE INFORMATION IN HYPERCUBES

Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or more explanatory variables and a response variable by fitting a linear equation to the interval-valued observations. Despite of the well-known methods such as CM, CRM and CCRM proposed in the literature, further study is still needed to build a regression model that can capture the complete information in interval-valued observations. To this end, in this paper, we propose the novel Complete Information Method （CIM） for linear regression modeling. By dividing hypercubes into informative grid data, CIM defines the inner product of interval-valued variables, and transforms the regression modeling into the computation of some inner products. Experiments on both the synthetic and real-world data sets demonstrate the merits of CIM in modeling interval-valued data, and avoiding the mathematical incoherence introduced by CM and CRM.

Linear mixed-effects model for longitudinal complex data with diversified characteristics

The increasing richness of data encourages a comprehensive understanding of economic and financial activities,where variables of interest may include not only scalar(point-like)indicators,but also functional(curve-like)and compositional(pie-like)ones.In many research topics,the variables are also chronologically collected across individuals,which falls into the paradigm of longitudinal analysis.The complicated nature of data,however,increases the difficulty of modeling these variables under the classic longitudinal frame-work.In this study,we investigate the linear mixed-effects model(LMM)for such complex data.Different types of variables arefirst consistently represented using the corresponding basis expansions so that the classic LMM can then be conducted on them,which gener-alizes the theoretical framework of LMM to complex data analysis.A number of simulation studies indicate the feasibility and effectiveness of the proposed model.We further illustrate its practical utility in a real data study on Chinese stock market and show that the proposed method can enhance the performance and interpretability of the regression for complex data with diversified characteristics.

GCN2 deficiency protects mice from denervation-induced skeletal muscle atrophy via inhibiting FoxO3a nuclear translocation

Dear Editor Several recent clinical studies have indicated that dietary supplementation with branched-chain amino acids （BCAA）, particularly with leucine, is an effective anti-atrophic therapy （Bauer et al., 2015; Tsien et al., 2015; English et al., 2016）. In animal models, BCAA can prevent denervation （Ribeiro et al., 2015）, hindlimb suspension （Maki et al., 2012; Jang et al., 2015） or dexamethasone-induced （Yamamoto et al., 2010） muscle atrophy. General control nonderepressible 2 kinase （GCN2） is a well-known amino-acid sensor. Under conditions of amino-acid deprivation, the increased level of uncharged transfer RNA （tRNA） activates GCN2 through binding to the histadyl-tRNA synthetase-like domain （Wek et al., 1995）. Upon activation, GCN2 phosphorylates eukaryotic initiation factor 2 alpha at Ser51, which leads to translational arrest and restoration of amino acid home- ostasis （Wek et al., 1995; Sood et al., 2000）. As amino acids are potent modulators of protein turnover in skeletal muscle, we proposed that GCN2 may affect denervation-induced muscle atrol0hv, but the detail mechanism remains unclear.

A Review of Disentangled Representation Learning for Remote Sensing Data

representation that can identify and isolate different potential variables hidden in the highdimensional observations.Disentangled representation learning can capture information about a single change factor and control it by the corresponding potential subspace,providing a robust representation for complex changes in the data.In this paper,we first introduce and analyze the current status of research on disentangled representation and its causal mechanisms and summarize three crucial properties of disentangled representation.Then,disentangled representation learning algorithms are classified into four categories and outlined in terms of both mathematical description and applicability.Subsequently,the loss functions and objective evaluation metrics commonly used in existing work on disentangled representation are classified.Finally,the paper summarizes representative applications of disentangled representation learning in the field of remote sensing and discusses its future development.