A novel radial basis function neural network for fault section estimation in transmission network

In this paper, the application of Radial Basis Function Neural Network (RBF NN) to fault section estimation in power systems is addressed. The orthogonal least square algorithm has been extended to optimize the parameters of RBF NN. In order to assess the effectiveness of RBF NN, a classical Back-Propagation Neural Network (BP NN) has been developed to solve the same problem for comparison. Computer test is conducted on a 4-bus test system and the test results show that the RBF NN is quite effective and superior to BP NN in fault section estimation.published_or_final_versio

Amyloid-β Inhibits No-cGMP Signaling in a CD36- and CD47-Dependent Manner

Amyloid-β interacts with two cell surface receptors, CD36 and CD47, through which the matricellular protein thrombospondin-1 inhibits soluble guanylate cyclase activation. Here we examine whether amyloid-β shares this inhibitory activity. Amyloid-β inhibited both drug and nitric oxide-mediated activation of soluble guanylate cyclase in several cell types. Known cGMP-dependent functional responses to nitric oxide in platelets and vascular smooth muscle cells were correspondingly inhibited by amyloid-β. Functional interaction of amyloid-β with the scavenger receptor CD36 was indicated by inhibition of free fatty acid uptake via this receptor. Both soluble oligomer and fibrillar forms of amyloid-β were active. In contrast, amyloid-β did not compete with the known ligand SIRPα for binding to CD47. However, both receptors were necessary for amyloid-β to inhibit cGMP accumulation. These data suggest that amyloid-β interaction with CD36 induces a CD47-dependent signal that inhibits soluble guanylate cyclase activation. Combined with the pleiotropic effects of inhibiting free fatty acid transport via CD36, these data provides a molecular mechanism through which amyloid-β can contribute to the nitric oxide signaling deficiencies associated with Alzheimer's disease

An integrated bioinformatics analysis reveals divergent evolutionary pattern of oil biosynthesis in high- and low-oil plants

Seed oils provide a renewable source of food, biofuel and industrial raw materials that is important for humans. Although many genes and pathways for acyl-lipid metabolism have been identified, little is known about whether there is a specific mechanism for high-oil content in high-oil plants. Based on the distinct differences in seed oil content between four high-oil dicots (20~50%) and three low-oil grasses (<3%), comparative genome, transcriptome and differential expression analyses were used to investigate this mechanism. Among 4,051 dicot-specific soybean genes identified from 252,443 genes in the seven species, 54 genes were shown to directly participate in acyl-lipid metabolism, and 93 genes were found to be associated with acyl-lipid metabolism. Among the 93 dicot-specific genes, 42 and 27 genes, including CBM20-like SBDs and GPT2, participate in carbohydrate degradation and transport, respectively. 40 genes highly up-regulated during seed oil rapid accumulation period are mainly involved in initial fatty acid synthesis, triacylglyceride assembly and oil-body formation, for example, ACCase, PP, DGAT1, PDAT1, OLEs and STEROs, which were also found to be differentially expressed between high- and low-oil soybean accessions. Phylogenetic analysis revealed distinct differences of oleosin in patterns of gene duplication and loss between high-oil dicots and low-oil grasses. In addition, seed-specific GmGRF5, ABI5 and GmTZF4 were predicted to be candidate regulators in seed oil accumulation. This study facilitates future research on lipid biosynthesis and potential genetic improvement of seed oil content

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Hybrid fault section estimation system with radial basis function neural network and fuzzy system

In this paper, functional equivalence between a radial basis function neural networks (RBF NN) and a companion fuzzy system (CFS) is built up throughout the neural network training process, therefore the black-box-like knowledge in a RBF NN will be rule-based and transparent in its CFS. Through useful knowledge extraction from the old CFS and insertion back to the new CFS piece by piece, the RBF NN retraining issue under network expansion and topology change can be solved effectively and efficiently. The corresponding FSE system has been implemented and tested in the IEEE 118-bus power system. The simulation results show that the suggested approach for RBF NN retraining works successfully and efficiently in the case of power network expansion and topology change, which significantly improves the application potential of RBF NN in FSE of practical power systems.link_to_subscribed_fulltex

The Effect of Overconfidence on Persistent Behavior: The Mediation Effect of "I Think I Can Do It" Rather Than "I'm Attracted To It"

The present study explored the effects of state overconfidence on persistent behavior with consideration of feasibility and desirability as mediators. The persistent behaviors of 61 participants (35 women, 26 men; M age=20.9yr, SD=1.9), placed in overconfidence and under-confidence groups based on random positive or negative bogus feedback, were measured. Results showed that participants with overconfidence were more persistent in solving problems than were those with under-confidence. Further mediation analyses revealed that the rating of the feasibility rather than the desirability of the task mediated the influence of state overconfidence on persistent behaviors. These findings provided future support for the self-justification hypothesis, and addressed arguments regarding the origins of overconfidence.</p