Double-shell CeO2:Yb, Er@SiO2@Ag upconversion composite nanofibers as an assistant layer enhanced near-infrared harvesting for dye-sensitized solar cells

Double-shell CeO2:Yb,Er@SiO2@ Ag upconversion composite nanofibers are synthesized by electro- spinning and subsequent process. CeO2:Yb,Er@SiO2@ Ag nanofibers show high upconversion luminescence property due to the coating of amorphous SiO2 and the surface plasmon resonance effect of Ag nanoparticles. CeO2:Yb,Er@SiO2@ Ag nanofibers act as an assistant layer in dye-sensitized solar cells (DSSCs) and enhance the photoelectric conversion efficiency (PCE) to 8.17%. The photocurrent-voltage characteristic is obtained under 980 nm laser as illumination light source. In addition, the absorption of the incident photon-to-current conversion efficiency curve in 900-1000 nm near-infrared light confirms that the introduction of the upconversion nanomaterial broadens the absorption range, improves the utilization rate of the sunlight and increases the PCE of DSSCs. (C) 2018 Elsevier B.V. All rights reserved

The Residual Lifetime of Surviving Components of Coherent System under Periodical Inspections

In this manuscript, we gain a mixture representation for reliability function of the residual lifetime of unfailed components in a coherent system under periodical inspections, given that the number of failed components before time t1 is r(≥0), but the system is still operating at time t1, and the system eventually failed at time t2(>t1). Some aging properties and stochastic orders of the residual lifetime on survival components are also established. Finally, some numerical examples and graphs are given in order to confirm the theoretical results

Identification of MiRNAs Affecting the Establishment of Brassica Alboglabra Seedling

MicroRNAs (miRNAs) are important for plant development including seed formation, dormancy, and germination, as well as seedling establishment. The Brassica vegetable seedling establishment stage influences the development of high quality seedlings, but also affects the nutrient content of sprouts. Chinese kale (Brassica alboglabra) seedlings at different growth stages were used to construct two small-RNA (sRNA) libraries. We comprehensively analyzed the miRNAs in 2- and 9-day-old seedlings. An average of 11,722,490 clean reads were generated after removing low-quality reads and adapter contaminants. The results revealed that 37.65% and 26.69% of the sRNAs in 2- and 9-day-old seedlings, respectively, were 24 nt long. In total, 254 known mature miRNA sequences from 228 miRNA families and 343 novel miRNAs were identified. Of these miRNAs, 224 were differentially expressed between the two analyzed libraries. The most abundant miRNAs identified by sequence homology were miR156, miR167, and miR157, each with more than 100,000 sequenced reads. Compared with the expression levels in 2-day-old seedlings, MiR8154 and miR390 were the most up- and down-regulated miRNAs respectively in 9-day-old seedlings. Gene ontology enrichment analysis of the differentially expressed-miRNA target genes affecting biological processes revealed that most genes were in the regulation of transcription category. Additionally, the expression patterns of some miRNAs and target genes were validated by quantitative real-time polymerase chain reaction. We determined that development-associated miRNAs (e.g., bal-miR156/157/159/166/167/172/396), were highly-expressed during seedling-establishment stage, as were stress-related (bal-miR408) and metabolism-related (bal-miR826) miRNAs. Combined with the low level of targets SPL9 and AP2, it was concluded that miR156-SPL9 and miR172-AP modules play key roles during the B. alboglabra seedling establishment stage

Comparative transcriptome analyses reveal a special glucosinolate metabolism mechanism in Brassica alboglabra sprouts

Brassica sprouts contain abundant phytochemicals, especially glucosinolates (GSs). Various methods have been used to enhance GS content in sprouts. However, the molecular basis of GS metabolism in sprouts remains an open question. Here we employed RNA-seq analysis to compare the transcriptomes of high-GS (JL-08) and low-GS (JL-09) Brassica alboglabra sprouts. Paired-end Illumina RNA-seq reads were generated and mapped to the B. oleracea reference genome. The differentially expressed genes were analyzed between JL-08 and JL-09. Among these, 1,477 genes were up-regulated and 1,239 down-regulated in JL-09 compared with JL-08. Enrichment analysis of these differentially expressed genes showed that the GS biosynthesis had the smallest enrichment factor and the highest Q value of all metabolic pathways in Kyoto Encyclopedia of Genes and Genomes database, indicating the main metabolic difference between JL-08 and JL-09 is the GS biosynthetic pathway. Thirty-seven genes of the sequenced data were annotated as putatively involved in GS biosynthesis, degradation and regulation, of which 11 were differentially expressed in JL-08 and JL-09. The expression level of GS degradation enzyme myrosinase in high-GS JL-08 was lower compared with low-GS JL-09. Surprisingly, in high-GS JL-08, the expression levels of GS biosynthesis genes were also lower than those in low-GS JL-09. As the GS contents in sprouts are determined by dynamic equilibrium of seed stored GS mobilization, de novo synthesis, degradation, and extra transport, the result of this study leads us to suggest that efforts to increase GS content should focus on either raising GS content in seeds or decreasing myrosinase activity, rather than improving the expression level of GS biosynthesis genes in sprouts

Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images

Toward Prevention of Parasite Chain Attack in IOTA Blockchain Networks by Using Evolutionary Game Model

IOTA is a new cryptocurrency system designed for the Internet of Things based on directed an acyclic graph structure. It has the advantages of supporting high concurrency, scalability, and zero transaction fees; however, due to the particularity of the directed acyclic graph structure, IOTA faces more complex security threats than the sequence blockchain, in which a parasite chain attack is a common double-spending attack. In this work, we propose a scheme that can effectively prevent parasite chain attacks to improve the security of the IOTA ledger. Our main idea is to analyze the behavior strategies of IOTA nodes based on evolutionary game theory and determine the key factors affecting the parasite chain attack and the restrictive relationship between them. Based on the above research, we provide a solution to resist the parasite chain attack and further prove the effectiveness of the scheme by numerical simulation. Finally, we propose the parasite chain attack prevention algorithms based on price splitting to effectively prevent the formation of the parasite chain

Comparison of Removal Behavior of Two Biotrickling Filters under Transient Condition and Effect of pH on the Bacterial Communities.

Although biotrickling filters (BTFs) applied under acidic condition to remove H2S from waste gases have been reported, the removal behavior of the acidic BTF under transient condition which was normal in most industry processes, and corresponding bacterial community have not been thoroughly studied. In the present study, two BTFs were run under neutral (BTFn) and acidic (BTFa) conditions, respectively. The results revealed that the removal performance of BTFa under transient condition was superior to that of BTFn; the maximum H2S eliminating capacities (ECs) achieved by BTFa and BTFn were 489.9 g/m3 h and 443.6 g/m3 h, respectively. High-throughput sequencing suggested that pH was the critical factor and several other factors including nutrient and the inlet loadings also had roles in shaping bacterial community structure. Acidithiobacillus was the most abundant bacterial group. The results indicated that BTF acclimation under acidic condition may facilitate generating microbial community with high H2S-degrading capability

Patterns of Expansion and Expression Divergence of the Polygalacturonase Gene Family in Brassica oleracea

Plant polygalacturonases (PGs) are closely related to cell-separation events during plant growth and development by degrading pectin. Identifying and investigating their diversification of evolution and expression could shed light on research on their function. We conducted sequence, molecular evolution, and gene expression analyses of PG genes in Brassica oleracea. Ninety-nine B. oleracea PGs (BoPGs) were identified and divided into seven clades through phylogenetic analysis. The exon/intron structures and motifs were conserved within, but divergent between, clades. The second conserved domain (GDDC) may be more closely related to the identification of PGs. There were at least 79 common ancestor PGs between Arabidopsis thaliana and B. oleracea. The event of whole genome triplication and tandem duplication played important roles in the rapid expansion of the BoPG gene family, and gene loss may be an important mechanism in the generation of the diversity of BoPGs. By evaluating the expression in five tissues, we found that most of the expressed BoPGs in clades A, B, and E showed ubiquitous expression characteristics, and the expressed BoPGs in clades C, D, and F were mainly responsible for reproduction development. Most of the paralogous gene pairs (76.2%) exhibited divergent expression patterns, indicating that they may have experienced neofunctionalization or subfunctionalization. The cis-elements analysis showed that up to 96 BoPGs contained the hormone response elements in their promoters. In conclusion, our comparative analysis may provide a valuable data foundation for the further functional analysis of BoPGs during the development of B. oleracea

BP Neural Networks with Harmony Search Method-based Training for Epileptic EEG Signal Classification

In this paper, the Harmony Search (HS)-based BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Two HS methods, the original version and a new variation recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training. ? 2012 IEEE.EI