China Three Gorges University

About: China Three Gorges University is a education organization based out in Yichang, China. It is known for research contribution in the topics: Catalysis & Landslide. The organization has 11161 authors who have published 8011 publications receiving 82224 citations. The organization is also known as: Sanxia Daxue.

Prognostic value of cancer stem cell marker CD133 in ovarian cancer: a meta-analysis

Abstract: Objective: To investigate the association between CD133 expression and prognosis and clinicopathological features of ovarian cancer. Methods: The electronic and manual searches were performed through the database of PubMed Chinese Wanfang databases (up to September 15, 2014) was performed using the following keywords ovarian cancer, CD133, AC133, prominin-1. Meta-analysis was performed by using Review Manager 5.2 and the outcomes included the overall survival and various clinicopathological features. Results: A total of 1051 ovarian cancer patients from 8 studies were included. Meta-analysis showed that overexpression of CD133 was highly correlated with reduced 2-year overall survival (OR = 1.67, 95% CI: 1.06-2.63, P = 0.03, fixed-effect). With respect to clinicopathological features, CD133 level was positively correlated with tumor stage (OR = 0.26, 95% CI: 0.12-0.58, P = 0.001 random-effect). But not correlated with patients’ age (OR = 1.12, 95% CI: 0.68-1.86, P = 0.65 fixed-effect), tumor grade (OR = 1.20, 95% CI: 0.06-1.62, P = 0.17 random-effect), histological type (OR = 1.10, 95% CI: 0.82-1.47, P = 0.54 fixed-effect) and response to treatment (OR = 0.84, 95% CI: 0.61-1.16, P = 0.29 fixed-effect). Conclusion: On the basis of current retrospective evidence, the present meta-analysis indicated that high level of CD133 expression trends to correlate with a worse prognosis in patients with ovarian cancer.

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

Abstract: This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies Twelve predisposing factors were then prepared using this available dataset To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0837) model Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation

Enhanced Electrochemical Performance of Fast Ionic Conductor LiTi2(PO4)3-Coated LiNi1/3Co1/3Mn1/3O2 Cathode Material.

Abstract: Layered LiNi1/3Co1/3Mn1/3O2 (NCM333) is successfully coated by fast ionic conductor LiTi2(PO4)3 (LTP) via a wet chemical method. The effects of LTP on the physicochemical properties and electrochemical performance are studied. The results reveal that a highly layered structure of NCM333 can be well maintained with less cation mixing after LTP coating. LTP of about 5 nm thickness is coated on the surface of NCM333. Such an LTP coating layer can effectively suppress the side reactions between NCM333 and electrolyte but will not hinder the lithium ion transmission. As a result, LTP-coated NCM333 owns an improved capability and cyclic performance, for example, NCM333/LTP delivers an initial capacity as high as 121.0 mA h g–1 with a capacity retention ratio of 82.3% after 200 cycles at 10 C, whereas NCM333 only has an initial capacity of 120.4 mA h g–1 with a very low capacity retention ratio of 66.4%. This method of using a fast ionic conductor like LTP as a coating material may provide a simple and effective...