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

Liposome Mediated-CYP1A1 Gene Silencing Nanomedicine Prepared Using Lipid Film-Coated Proliposomes as a Potential Treatment Strategy of Lung Cancer

The occurrence of lung cancer is linked with tobacco smoking, mainly through the generation of polycyclic aromatic hydrocarbons (PAHs). Elevated activity of cytochrome P4501A1 (CYP1A1) plays an important role in the metabolic processing of PAHs and its carcinogenicity. The present work aimed to investigate the role of CYP1A1 gene in PAH-mediated growth and tumor development in vitro and using an in vivo animal model. RNAi strategy was utilized to inhibit the overexpression of CYP1A1 gene using cationic liposomes generated using a lipid film-coated proliposome microparticles. Treatment of PAH-induced human alveolar adenocarcinoma cell line with cationic liposomes carrying CYP1A1 siRNA resulted in down regulation of CYP1A1 mRNA, protein as well as its enzymatic activity, triggering apoptosis and inhibiting multicellular tumor spheroids formation in vitro. Furthermore, silencing of CYP1A1 gene in BALB/c nude xenografts inhibited tumor growth via down regulation of CYP1A1 expression. Altogether, our findings showed that liposome-based gene delivery technology is a viable and stable approach for targeting cancer causing genes such as CY1PA1. This technology facilitated by the use of sugar particles coated with lipid films has demonstrated ability to generate anticancer effects that might be used in the future for therapeutic intervention and treatment of lung cancer. [Abstract copyright: Copyright © 2019. Published by Elsevier B.V.

Global Geometric Affinity for Revealing High Fidelity Protein Interaction Network

Protein-protein interaction (PPI) network analysis presents an essential role in understanding the functional relationship among proteins in a living biological system. Despite the success of current approaches for understanding the PPI network, the large fraction of missing and spurious PPIs and a low coverage of complete PPI network are the sources of major concern. In this paper, based on the diffusion process, we propose a new concept of global geometric affinity and an accompanying computational scheme to filter the uncertain PPIs, namely, reduce the spurious PPIs and recover the missing PPIs in the network. The main concept defines a diffusion process in which all proteins simultaneously participate to define a similarity metric (global geometric affinity (GGA)) to robustly reflect the internal connectivity among proteins. The robustness of the GGA is attributed to propagating the local connectivity to a global representation of similarity among proteins in a diffusion process. The propagation process is extremely fast as only simple matrix products are required in this computation process and thus our method is geared toward applications in high-throughput PPI networks. Furthermore, we proposed two new approaches that determine the optimal geometric scale of the PPI network and the optimal threshold for assigning the PPI from the GGA matrix. Our approach is tested with three protein-protein interaction networks and performs well with significant random noises of deletions and insertions in true PPIs. Our approach has the potential to benefit biological experiments, to better characterize network data sets, and to drive new discoveries

Global SF in the twenty-first century: modernity and the other in Chinese and Anglophone SF

© 2019 Mengtian SunThe twenty-first century has witnessed the rapid rise of non-Anglo-American science fiction. In this comparative study, I examine the ways Chinese science fiction is transforming the global field. I read Chen Qiufan alongside Paolo Bacigalupi, Xia Jia alongside Dan Simmons, and Liu Cixin alongside Arthur C. Clarke. By focusing on global science fiction’s generic innovations and thematic concerns, I show that this new generation of writers captures the new developments and problems of contemporary modernity, such as the rise of transnational corporations, the forming of a centerless “Empire,” ecological devastation and the cycles of e-waste, Islamophobia and xenophobia, among others. In taking up these thematic concerns, these writers not only reconfigure science fiction’s relation to modernity, but they also emphasize a dimension of the rhetoric of modernity that had previously remained implicit: these texts stage, in different ways, the encounter with the Other. I argue that new generic transformations in contemporary global SF serve to reveal the hidden faces of modernity, to think about modernity in relation to tradition and the past, to dismantle old myths surrounding the discourse of modernity

Heat-passing framework for robust interpretation of data in networks.

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents

Temperature distribution descriptor for robust 3D shape retrieval

Recent developments in acquisition techniques are resulting in a very rapid growth of the number of available three dimensional (3D) models across areas as diverse as engineering, medicine and biology. It is therefore of great interest to develop the efficient shape retrieval engines that, given a query object, return similar 3D objects. The performance of a shape retrieval engine is ultimately determined by the quality and characteristics of the shape descriptor used for shape representation. In this paper, we develop a novel shape descriptor, called temperature distribution (TD) descriptor, which is capable of exploring the intrinsic geometric features on the shape. It intuitively interprets the shape in an isometrically-invariant, shape-aware, noise and small topological changes insensitive way. TD descriptor is driven by by heat kernel. The TD descriptor understands the shape by evaluating the surface temperature distribution evolution with time after applying unit heat at each vertex. The TD descriptor is represented in a concise form of a one dimensional (1D) histogram, and captures enough information to robustly handle the shape matching and retrieval process. Experimental results demonstrate the effectiveness of TD descriptor within applications of 3D shape matching and searching for the models at different poses and various noise levels

Heat-mapping: A robust approach toward perceptually consistent mesh segmentation

3D mesh segmentation is a fundamental low-level task with applications in areas as diverse as computer vision, computer-aided design, bio-informatics, and 3D medical imaging. A perceptually consistent mesh segmentation (PCMS), as defined in this paper is one that satisfies 1) invariance to isometric transformation of the underlying surface, 2) robust to the perturbations of the surface, 3) robustness to numerical noise on the surface, and 4) close conformation to human perception. We exploit the intelligence of the heat as a global structure-aware message on a meshed surface and develop a robust PCMS scheme, called Heat-Mapping based on the heat kernel. There are three main steps in Heat-Mapping. First, the number of the segments is estimated based on the analysis of the behavior of the Laplacian spectrum. Second, the heat center, which is defined as the most representative vertex on each segment, is discovered by a proposed heat center hunting algorithm. Third, a heat center driven segmentation scheme reveals the PCMS with a high consistency towards human perception. Extensive experimental results on various types of models verify the performance of Heat-Mapping with respect to the consistent segmentation of articulated bodies, the topological changes, and various levels of numerical noise