Effects of Mountain Rivers Cascade Hydropower Stations on Water Ecosystems

China is rich in hydropower resources, and mountain rivers have abundant water resources and huge development potential, which have a profound impact on the pattern of water resources allocation in China. As the main way of water resources and hydropower development, the construction of cascade hydropower stations, while meeting the requirements of water resources utilization for social development, has also brought adverse effects on river ecosystems. Therefore, the impact of the construction of cascade hydropower stations on mountainous river ecosystems, where the minimum ecological flow of rivers must be ensured and reviewed. In addition, this paper proposed the deficiencies and outlooks for cascade hydropower stations based on previous research results

Discussion on the Construction of Ecological Water Network in Guangxi Province of China

The water network plays an important role in maintaining the stability of regional water resource and ecological environment. It is also affecting the harmonious development between environment and economy. Guangxi is one of the provinces with relatively rich water resources in China, while the ecological water network exists deficiencies and faces challenges. The current situation and defects of ecological water network in Guangxi province will be discussed. By studying the experience of the establishing and the preserve of ecological water network in various regions at home and abroad, some suggestions and targeted measures will be mentioned for a better ecological water network in Guangxi

Further validation of the Health Scale of Traditional Chinese Medicine (HSTCM)

<p>Abstract</p> <p>Background</p> <p>Few health measurement scales are based on Chinese medicine theory. The Health Scale of Traditional Chinese Medicine (HSTCM) was developed to fill this gap. The aim of this study is to validate the HSTCM.</p> <p>Methods</p> <p>A convenience sample of 630 participants was recruited in 11 settings. All participants were asked to complete the HSTCM and World Health Organization Quality of Life Measure-Abbreviated Version (WHOQOL-BREF).</p> <p>Results</p> <p>Properties of the HSTCM were tested. Intra-class correlation coefficient representing the inter-interviewer reliability was 0.99 (95%CI) for the overall instrument. Spearman-Brown correlation coefficient and Cronbach's coefficient alpha were 0.81 and 0.94 respectively, indicating satisfactory internal reliability and inter-interviewer reliability. Spearman's rho correlation coefficient between the HSTCM and WHOQOL-BREFF was -0.67. A receiver operating characteristic (ROC) curve analysis was performed to test the discriminate validation. Areas under the ROC curve analysis for the HSTCM and its domains ranged 0.71–0.87 and all the lower levels of 95%CI were greater than 0.50.</p> <p>Conclusion</p> <p>The HSTCM was validated as a generic health scale and may complement existing health measurement scales in Chinese medicine health care.</p

Genome-wide analysis of plant nat-siRNAs reveals insights into their distribution, biogenesis and function

Background: Many eukaryotic genomes encode cis-natural antisense transcripts (cis-NATs). Sense and antisense transcripts may form double-stranded RNAs that are processed by the RNA interference machinery into small interfering RNAs (siRNAs). A few so-called nat-siRNAs have been reported in plants, mammals, Drosophila, and yeasts. However, many questions remain regarding the features and biogenesis of nat-siRNAs. Results: Through deep sequencing, we identified more than 17,000 unique siRNAs corresponding to cis-NATs from biotic and abiotic stress-challenged Arabidopsis thaliana and 56,000 from abiotic stress-treated rice. These siRNAs were enriched in the overlapping regions of NATs and exhibited either site-specific or distributed patterns, often with strand bias. Out of 1,439 and 767 cis-NAT pairs identified in Arabidopsis and rice, respectively, 84 and 119 could generate at least 10 siRNAs per million reads from the overlapping regions. Among them, 16 cis-NAT pairs from Arabidopsis and 34 from rice gave rise to nat-siRNAs exclusively in the overlap regions. Genetic analysis showed that the overlapping double-stranded RNAs could be processed by Dicer-like 1 (DCL1) and/or DCL3. The DCL3-dependent nat-siRNAs were also dependent on RNA-dependent RNA polymerase 2 (RDR2) and plant-specific RNA polymerase IV (PoIIV), whereas only a fraction of DCL1-dependent nat-siRNAs was RDR- and PoIIV-dependent. Furthermore, the levels of some nat-siRNAs were regulated by specific biotic or abiotic stress conditions in Arabidopsis and rice. Conclusions: Our results suggest that nat-siRNAs display distinct distribution patterns and are generated by DCL1 and/or DCL3. Our analysis further supported the existence of nat-siRNAs in plants and advanced our understanding of their characteristics.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000308544200005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Biotechnology &amp; Applied MicrobiologyGenetics &amp; HereditySCI(E)48ARTICLE3null1

A risk signature based on endoplasmic reticulum stress-associated genes predicts prognosis and immunity in pancreatic cancer

Introduction: The involvement of endoplasmic reticulum (ER) stress in cancer biology is increasingly recognized, yet its role in pancreatic cancer (PC) remains unclear. This study aims to elucidate the impact of ER stress on prognosis and biological characteristics in PC patients.Methods: A bioinformatic analysis was conducted using RNA-seq data and clinicopathological information from PC patients in the TCGA and ICGC databases. The ER stress-associated gene sets were extracted from MSigDB. ER stress-associated genes closely linked with overall survival (OS) of PC patients were identified via log-rank test and univariate Cox analysis, and further narrowed by LASSO method. A risk signature associated with ER stress was formulated using multivariate Cox regression and assessed through Kaplan-Meier curves, receiver operating characteristic (ROC) analyses, and Harrell’s concordance index. External validation was performed with the ICGC cohort. The single-sample gene-set enrichment analysis (ssGSEA) algorithm appraised the immune cell infiltration landscape.Results: Worse OS in PC patients with high-risk signature score was observed. Multivariate analysis underscored our ER stress-associated signature as a valuable and independent predictor of prognosis. Importantly, these results based on TCGA were further validated in ICGC dataset. In addition, our risk signature was closely associated with homeostasis, protein secretion, and immune regulation in PC patients. In particular, PC microenvironment in the high-risk cluster exhibited a more immunosuppressive status. At last, we established a nomogram model by incorporating the risk signature and clinicopathological parameters, which behaves better in predicting prognosis of PC patients.Discussion: This comprehensive molecular analysis presents a new predictive model for the prognosis of PC patients, highlighting ER stress as a potential therapeutic target. Besides, the findings indicate that ER stress can have effect modulating PC immune responses

Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder involving variations in the transcriptome of many genes. AD does not affect all brain regions simultaneously. Identifying the differences among the affected regions may shed more light onto the disease progression. We developed a novel method involving the differential topology of gene coexpression networks to understand the association among affected regions and disease severity.</p> <p>Methods</p> <p>We analysed microarray data of four regions - entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC) and middle temporal gyrus (MTG) from AD affected and normal subjects. A coexpression network was built for each region and the topological overlap between them was examined. Genes with zero topological overlap between two region-specific networks were used to characterise the differences between the two regions.</p> <p>Results and conclusion</p> <p>Results indicate that MTG shows early AD pathology compared to the other regions. We postulate that if the MTG gets affected later in the disease, post-mortem analyses of individuals with end-stage AD will show signs of early AD in the MTG, while the EC, HIP and PCC will have severe pathology. Such knowledge is useful for data collection in clinical studies where sample selection is a limiting factor as well as highlighting the underlying biology of disease progression.</p

Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases

Investors' reaction to director trades : an empirical study of SGX listed firms.

Our study investigates if investors are able to earn abnormal returns by following insider trading activities of firms listed in the Singapore Stock Exchange. We also examine various factors such as corporate governance,level of uncertainty of the firms that might have an impact on the abnormalreturns generated by insider purchases

Semantic Community Identification in Large Attribute Networks

Identification of modular or community structures of a network is a key to understanding the semantics and functions of the network. While many network community detection methods have been developed, which primarily explore network topologies, they provide little semantic information of the communities discovered. Although structures and semantics are closely related, little effort has been made to discover and analyze these two essential network properties together. By integrating network topology and semantic information on nodes, e.g., node attributes, we study the problems of detection of communities and inference of their semantics simultaneously. We propose a novel nonnegative matrix factorization (NMF) model with two sets of parameters, the community membership matrix and community attribute matrix, and present efficient updating rules to evaluate the parameters with a convergence guarantee. The use of node attributes improves upon community detection and provides a semantic interpretation to the resultant network communities. Extensive experimental results on synthetic and real-world networks not only show the superior performance of the new method over the state-of-the-art approaches, but also demonstrate its ability to semantically annotate the communities