Effect of salt stress on growth and physiological parameters of sorghum genotypes at an early growth stage

404-411Physiological regulation affects plant salinity tolerance. The objective of this research was to investigate the effect of salt stress on the physiological regulation in sorghum at early growth stage. Two sorghum genotypes (GT), Bayeqi (salt-tolerant) and PL212 (salt-sensitive), were grown in an artificial climate chamber with a nutrition solution containing 0，80, 160, and 240 mM NaCl. Results showed that salt-tolerant sorghum had enhanced activities of antioxidant enzymes including catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD), and increased stress-related osmolytes including free amino acids, and reducing and soluble sugars. Furthermore, ion regulation plays an important role in the osmotic adjustment. Results also suggest that K+/Na+ and Ca2+/Na+ ratios are associated with tolerance under salt-stressed environments and higher Na+ and lower K+ and Ca2+ concentrations are deleterious to sorghum growth. As a result, under salt-stressed environments, the salt-tolerant sorghum GT had better growth performance than salt-sensitive sorghum GT, which was evidenced by a greater plant high, leaf area, leaf fresh weight, and root fresh weight. Overall, under salt-stressed environments, the salt-tolerant sorghum GT had better growth performance including yield than salt-sensitive sorghum GT, which was evidenced by a greater plant high, leaf area, leaf fresh weight, and root fresh weight

Effect of salt stress on growth and physiological parameters of sorghum genotypes at an early growth stage

Physiological regulation affects plant salinity tolerance. The objective of this research was to investigate the effect of salt stress on the physiological regulation in sorghum at early growth stage. Two sorghum genotypes (GT), Bayeqi (salt-tolerant) and PL212 (salt-sensitive), were grown in an artificial climate chamber with a nutrition solution containing 0，80, 160, and 240 mM NaCl. Results showed that salt-tolerant sorghumhad enhanced activities of antioxidant enzymes including catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD), and increased stress-related osmolytes including free amino acids, and reducing and soluble sugars. Furthermore, ion regulation plays an important role in the osmotic adjustment. Results also suggest that K+/Na+ and Ca2+/Na+ ratios are associated with tolerance under salt-stressed environments and higher Na+ and lower K+ and Ca2+ concentrations are deleterious to sorghum growth. As a result, under salt-stressed environments, the salt-tolerant sorghum GT had better growth performance than salt-sensitive sorghumGT, which was evidenced by a greater plant high, leaf area, leaf fresh weight, and root fresh weight. Overall, under salt-stressed environments, the salt-tolerant sorghum GT had better growth performance including yield than salt-sensitive sorghum GT, which was evidenced by a greater plant high, leaf area, leaf fresh weight, and root fresh weigh

Research progress and applications of epigenetic biomarkers in cancer

Epigenetic changes are heritable changes in gene expression without changes in the nucleotide sequence of genes. Epigenetic changes play an important role in the development of cancer and in the process of malignancy metastasis. Previous studies have shown that abnormal epigenetic changes can be used as biomarkers for disease status and disease prediction. The reversibility and controllability of epigenetic modification changes also provide new strategies for early disease prevention and treatment. In addition, corresponding drug development has also reached the clinical stage. In this paper, we will discuss the recent progress and application status of tumor epigenetic biomarkers from three perspectives: DNA methylation, non-coding RNA, and histone modification, in order to provide new opportunities for additional tumor research and applications

Liver fibrosis and MAFLD: the exploration of multi-drug combination therapy strategies

In recent years, the prevalence of metabolic-associated fatty liver disease (MAFLD) has reached pandemic proportions as a leading cause of liver fibrosis worldwide. However, the stage of liver fibrosis is associated with an increased risk of severe liver-related and cardiovascular events and is the strongest predictor of mortality in MAFLD patients. More and more people believe that MAFLD is a multifactorial disease with multiple pathways are involved in promoting the progression of liver fibrosis. Numerous drug targets and drugs have been explored for various anti-fibrosis pathways. The treatment of single medicines is brutal to obtain satisfactory results, so the strategies of multi-drug combination therapies have attracted increasing attention. In this review, we discuss the mechanism of MAFLD-related liver fibrosis and its regression, summarize the current intervention and treatment methods for this disease, and focus on the analysis of drug combination strategies for MAFLD and its subsequent liver fibrosis in recent years to explore safer and more effective multi-drug combination therapy strategies

Interferon-α Regulates Glutaminase 1 Promoter through STAT1 Phosphorylation: Relevance to HIV-1 Associated Neurocognitive Disorders

HIV-1 associated neurocognitive disorders (HAND) develop during progressive HIV-1 infection and affect up to 50% of infected individuals. Activated microglia and macrophages are critical cell populations that are involved in the pathogenesis of HAND, which is specifically related to the production and release of various soluble neurotoxic factors including glutamate. In the central nervous system (CNS), glutamate is typically derived from glutamine by mitochondrial enzyme glutaminase. Our previous study has shown that glutaminase is upregulated in HIV-1 infected monocyte-derived-macrophages (MDM) and microglia. However, how HIV-1 leads to glutaminase upregulation, or how glutaminase expression is regulated in general, remains unclear. In this study, using a dual-luciferase reporter assay system, we demonstrated that interferon (IFN) α specifically activated the glutaminase 1 (GLS1) promoter. Furthermore, IFN-α treatment increased signal transducer and activator of transcription 1 (STAT1) phosphorylation and glutaminase mRNA and protein levels. IFN-α stimulation of GLS1 promoter activity correlated to STAT1 phosphorylation and was reduced by fludarabine, a chemical that inhibits STAT1 phosphorylation. Interestingly, STAT1 was found to directly bind to the GLS1 promoter in MDM, an effect that was dependent on STAT1 phosphorylation and significantly enhanced by IFN-α treatment. More importantly, HIV-1 infection increased STAT1 phosphorylation and STAT1 binding to the GLS1 promoter, which was associated with increased glutamate levels. The clinical relevance of these findings was further corroborated with investigation of post-mortem brain tissues. The glutaminase C (GAC, one isoform of GLS1) mRNA levels in HIV associated-dementia (HAD) individuals correlate with STAT1 (p<0.01), IFN-α (p<0.05) and IFN-β (p<0.01). Together, these data indicate that both HIV-1 infection and IFN-α treatment increase glutaminase expression through STAT1 phosphorylation and by binding to the GLS1 promoter. Since glutaminase is a potential component of elevated glutamate production during the pathogenesis of HAND, our data will help to identify additional therapeutic targets for the treatment of HAND

Nonparametric Methods for Interpretable Copula Calibration and Sparse Functional Classification

Nonparametric estimation is a novelty statistical method which relaxes the distribution assumption about the relationship between response and covariate, in contrast to parametric estimation. This method has been applied in many field of interest, including density function, regression model and derivative function. One of the important application of nonparametric estimation is modelling dependence among random variables via copula approaches has attracted considerable research attention. With advances in data collection, the strength of dependence often varies according to some covariate, which motivates the dependence calibration using conditional copulas. We propose a penalized estimation framework for the copula parameter function that inherits the flexibility of a nonparametric method and, at the same time, yields a parsimonious and interpretable dependence structure. The theoretical analysis guarantees that the penalized estimators enjoy the oracle properties and behave asymptotically as well as their nonparametric counterparts, while numerical experiments demonstrate the improved empirical performance. We then apply the proposed method to a twin birth weights data. Another important application of nonparametric estimation is classifying the functional data. We consider the classification of sparse functional data that are often encountered in longitudinal studies and other scientific experiments. To utilize the information from not only the functional trajectories but also the observed class labels, we propose a probability enhanced method achieved by weighted support vector machine based on its Fisher consistency property to estimate the effective dimension reduction space. Since only a few measurements are available for some, even all, individuals, a cumulative slicing approach is suggested to borrow information across individuals. We provide justification for validity of the probability-based effective dimension reduction space, and a straightforward implementation that yields a low-dimensional projection space ready for applying standard classifiers. The empirical performance is illustrated through simulated and real examples, particularly in contrast to classication results based on the prominent functional principal component analysis.Ph.D

Geometric Evolvement, Simulation, and Test of a Bionic Lateral PDC Reamer Bit Inspired by Capra sibirica Horn

PDC (polycrystalline diamond compact) bit is the key equipment for drilling holes inside the rock in oil and mining industry. Inspired by the shape and structure of Capra sibirica horn, a bionic lateral PDC reamer bit with variable lateral reaming radius was developed. Side view of Capra sibirica horn was employed for fitting the horn shape curve based on picture processing method. PDC teeth were arranged on the horn shape blade imitating the transverse ridges on the horn to cut the rock material, found with only 30% utilization rate of the total teeth and load concentration of the last tooth. A bionic blade curve evolved from the Capra sibirica horn was defined with geometric method for the lateral reamer bit; the utilization rate of the teeth on the bionic blade curve was improved to 90% with uniformly distributed reaming load. Multigroup simulations were conducted with the finite element method; the effects of bit revolution speed and rotation feed speed of the bionic blade to reaming load were emphatically studied. Concrete sample was reamed indoors from 240 mm to 407 mm in diameter, and the bionic lateral PDC reamer bit was approved by the test result

Characterization of microbial community structure associated with pollution in Xiaoqing River Sediment

To understand the impacts of anthropogenic activities on structure and composition of microbial communities and evaluate how microbial communities respond environmental gradients at river sediments, the composition of microbial communities in sediments from Xiaoqing River (in spring, summer and autumn) were assessed using DGGE and real-time PCR approaches. Meanwhile, 16S rRNA clone library construction of three sites was constructed to represent the composition and structure of microbial communities in the three distinct site-groups. The gene copy number was ranged from 107-108 cells/g that was most influenced by sample sediment density and medium grain size. Through analysis of DGGE gel profile, there were no distinct variation on richness, evenness and Shannon-Weaver index with all samples, which ranged 3.69-5.21, 1.73-2.30, and 0.56-0.76 respectively. The clustering result on the DGGE patterns showed that the microbial diversity of all samples were more similar than 40%, while the distinction was formed with three groups at a level of 46% similarity. Redundancy analysis revealed that the distribution of microbial composition seemed to be determined by the variables of nitrite, medium grain size and total carbon content. The clone library of three sites revealed that the Proteobacteria was the dominant phylum, which consistent with DGGE bands sequencing result. In addition, members of Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes and Verrucomicrobia were recorded in all three libraries. The distribution of functional populations as denitrifier and anammox bacteria will be the focus of future research

Minimum Description Length Principle for Linear Mixed Effects Models

Abstracts The minimum description length (MDL) principle originated from data compression literature and has been considered for deriving statistical model selection procedures. Most of the existing methods that use the MDL principle focus on models with independent data, particularly in the context of linear regression. This paper considers data with repeated measurements and studies the selection of fixed effect covariates for linear mixed effect models. We propose a class of MDL procedures that incorporate the dependence structure within individual or cluster and use data-adaptive penalties that suit both finite and infinite dimensional data generating mechanisms. Theoretical justifications are provided from both data compression and statistical perspectives, where the covariance of random effects is treated as known or estimated by maximum likelihood. Numerical experiments are conducted to demonstrate the usefulness of the proposed MDL procedure and the influence of the estimated covariance, and an application to U.S. EPA data for air quality control is provided