RESUMEN
Establishing effective charge transfer channels between two semiconductors is key to improving photocatalytic activity. However, controlling hetero-structures in situ and designing binding modes pose significant challenges. Herein, hydrolytic SnCl2·2H2O is selected as the metal source and loaded in situ onto a layered carbon nitriden supramolecular precursor. A composite photocatalyst, S4-Sn-N2, with electron pathways of SnS2 and tubular carbon nitriden (TCN) is prepared through pyrolysis and vulcanization processes. The contact interface of SnS2-TCN is increased significantly, promoting the formation of S4-Sn-N2 micro-structure in a Z-scheme charge transfer channel. This structure accelerates the separation and transport of photogenerated carriers, maintains the stronger redox ability, and improves the stability of SnS2 in this series of heterojunctions. Therefore, the catalyst demonstrated exceptional photocatalytic hydrogen production efficiency, achieving a reaction rate of 86.4 µmol h-1, which is 3.15 times greater than that of bare TCN.
RESUMEN
Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.
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The Amyloporia genus is an important Chinese medicinal fungus. Here, we present a draft genome sequence of Amyloporia xantha strain YAFMF0618. The genome resource will support subsequent research into the potential secondary metabolite diversity of A. xantha.
RESUMEN
Designing and developing photocatalysts with excellent performance in order to achieve efficient hydrogen production is an important strategy for addressing future energy and environmental challenges. Traditional single-phase photocatalytic materials either have a large bandgap and low visible light response or experience rapid recombination of the photogenerated carriers with low quantum efficiency, seriously hindering their photocatalytic applications. To solve these issues, an important solution is to construct well-matched heterojunctions with highly efficient charge separation capabilities. To this end, an in situ sulfurization reaction was adopted after the deposition of Bi3+ supramolecular complex on a layered supramolecular precursor of tubular carbon nitride (TCN). X-ray diffraction (XRD) patterns confirmed that the as-prepared sample has a good crystalline structure without any other impurities, while high-resolution transmission electron microscopy (HR-TEM) revealed that the heterojunction possesses a 2D structure with a layer of nano-array on its surface. Combined Fourier-transform infrared (FT-IR) spectra and energy-dispersive X-ray spectroscopy (EDX) revealed the interfacial interactions. Owing to the formation of the Z-scheme heterojunction, the visible light adsorption and the separation efficiency of the photo-generated carriers are both obviously enhanced, leaving the high energy electrons and high oxidative holes to participate in the photocatalytic reactions. As a result, the photocatalytic hydrogen evolution rate of Bi2S3-TCN achieves 65.2 µmol g-1·h-1. This proposed green and environmentally benign route can also be applied to construct other sulfides with 2D TCN, providing some important information for the design and optimization of novel carbon-nitride-based semiconductors.
RESUMEN
Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer; however, the specific genes and signaling pathways involved in this cancer remain largely unclear. The present study analyzed three profile datasets, GSE6004, GSE29265 and GSE60542, which were comprised of 47 PTC and 41 normal thyroid tissue samples, to identify key genes and pathways associated with PTC. Initially, differentially-expressed genes (DEGs) between PTC and normal thyroid tissue were screened using R 3.4.0 (2017-04-21, R Foundation, Vienna, Austria, http://www.R-project.org/). These DEGs were then clustered by gene ontology functional terms and representative signaling pathways. Additionally, specific key gene nodes were filtered out from a constructed protein-protein interaction (PPI) network. The results identified a total of 423 shared DEGs associated with PTC, including 211 upregulated and 212 downregulated genes. These 423 genes were primarily enriched in glycosaminoglycan binding, sulfur compound binding, heparin binding, enzyme activator activity, peptidase activator activity and hsa04512: Extracellular matrix (ECM)-receptor interaction. A total of 21 central node genes were identified as key genes in the PTC disease process including complement factor D (CFD), Collagen Type I α 1 Chain (COL1A1), Extracellular Matrix Protein 1 (ECM1) and Fibronectin 1 (FN1). These genes are involved in protease binding, G-protein coupled receptor binding, extracellular matrix structural constituent and peptidase regulator activity. To conclude, using bioinformatics analysis, the present study identified candidate DEGs and critical pathways in PTC that may improve the current understanding regarding the underlying mechanisms of PTC. These genes and pathways may be used as potential therapeutic targets of PTC in the future.