A TOPIC SENSITIVE SIMRANK (TSSR) MODEL FOR EXPERTS FINDING ON ONLINE RESEARCH SOCIAL PLATFORMS

As an efficient online academic information repository and information channel with crowds’ contribution, online research social platforms have become an efficient tool for various kinds of research & management applications. Social network platforms have also become a major source to seek for field experts. They have advantages of crowd contributions, easy to access without geographic restrictions and avoiding conflict of interests over traditional database and search engine based approaches. However, current research attempts to find experts based on features such as published research work, social relationships, and online behaviours (e.g. reads and downloads of publications) on social platforms, they ignore to verify the reliability of identified experts. To bridge this gap, this research proposes an innovative Topic Sensitive SimRank (TSSR) model to identify “real” experts on social network platforms. TSSR model includes three components: LDA for Expertise Extension, Topic Sensitive Network for Reputation Measurement, and Topic Sensitive SimRank for unsuitable experts detection. We also design a parallel computing strategy to improve the efficiency of the proposed methods. Last, to verify the effectiveness of the proposed model, we design an experiment on one of the research social platforms-ScholarMate to seek for experts for companies that need academic-industry collaboration

Gene rearrangement analysis and ancestral order inference from chloroplast genomes with inverted repeat

Background Genome evolution is shaped not only by nucleotide substitutions, but also by structural changes including gene and genome duplications, insertions, deletions and gene order rearrangements. The most popular methods for reconstructing phylogeny from genome rearrangements include GRAPPA and MGR. However these methods are limited to cases where equal gene content or few deletions can be assumed. Since conserved duplicated regions are present in many chloroplast genomes, the inference of inverted repeats is needed in chloroplast phylogeny analysis and ancestral genome reconstruction. Results We extend GRAPPA and develop a new method GRAPPA-IR to handle chloroplast genomes. A test of GRAPPA-IR using divergent chloroplast genomes from land plants and green algae recovers the phylogeny congruent with prior studies, while analysis that do not consider IR structure fail to obtain the accepted topology. Our extensive simulation study also confirms that GRAPPA has better accuracy then the existing methods. Conclusions Tests on a biological and simulated dataset show GRAPPA-IR can accurately recover the genome phylogeny as well as ancestral gene orders. Close analysis of the ancestral genome structure suggests that genome rearrangement in chloroplasts is probably limited by inverted repeats with a conserved core region. In addition, the boundaries of inverted repeats are hot spots for gene duplications or deletions. The new GRAPPA-IR is available from http://phylo.cse.sc.ed

An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia

In recent years, metabolomics analyses have been widely applied to cerebral ischemia research. This paper introduces the latest proceedings of metabolomics research on cerebral ischemia. The main techniques, models, animals, and biomarkers of cerebral ischemia will be discussed. With analysis help from the MBRole website and the KEGG database, the altered metabolites in rat cerebral ischemia were used for metabolic pathway enrichment analyses. Our results identify the main metabolic pathways that are related to cerebral ischemia and further construct a metabolic network. These results will provide useful information for elucidating the pathogenesis of cerebral ischemia, as well as the discovery of cerebral ischemia biomarkers

Evaluation of conventional or hydrolyzed stickwater from food-grade skipjack tuna by-product in diet for hybrid grouper (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂)

acceptedVersio

ATGL promotes the proliferation of hepatocellular carcinoma cells via the p‐AKT signaling pathway

Abnormal metabolism, including abnormal lipid metabolism, is a hallmark of cancer cells. Some studies have demonstrated that the lipogenic pathway might promote the development of hepatocellular carcinoma (HCC). However, the role of adipose triglyceride lipase (ATGL) in hepatocellular carcinoma cells has not been elucidated. We evaluated the function of ATGL in hepatocellular carcinoma using methyl azazolyl blue and migration assay through overexpression of ATGL in HepG2 cells. Quantitative reverse‐transcription polymerase chain reaction and Western blot analyses were used to assess the mechanisms of ATGL in hepatocellular carcinoma. In the current study, we first constructed and transiently transfected ATGL into hepatocellular carcinoma cells. Secondly, we found that ATGL promoted the proliferation of hepatoma cell lines via upregulating the phosphorylation of AKT, but did not affect the metastatic ability of HCC cells. Moreover, the p‐AKT inhibitor significantly eliminated the effect of ATGL on the proliferation of hepatoma carcinoma cells. Taken together, our results indicated that ATGL promotes hepatocellular carcinoma cells proliferation through upregulation of the AKT signaling pathway

Adaptive evolution of chloroplast genome structure inferred using a parametric bootstrap approach

BACKGROUND: Genome rearrangements influence gene order and configuration of gene clusters in all genomes. Most land plant chloroplast DNAs (cpDNAs) share a highly conserved gene content and with notable exceptions, a largely co-linear gene order. Conserved gene orders may reflect a slow intrinsic rate of neutral chromosomal rearrangements, or selective constraint. It is unknown to what extent observed changes in gene order are random or adaptive. We investigate the influence of natural selection on gene order in association with increased rate of chromosomal rearrangement. We use a novel parametric bootstrap approach to test if directional selection is responsible for the clustering of functionally related genes observed in the highly rearranged chloroplast genome of the unicellular green alga Chlamydomonas reinhardtii, relative to ancestral chloroplast genomes. RESULTS: Ancestral gene orders were inferred and then subjected to simulated rearrangement events under the random breakage model with varying ratios of inversions and transpositions. We found that adjacent chloroplast genes in C. reinhardtii were located on the same strand much more frequently than in simulated genomes that were generated under a random rearrangement processes (increased sidedness; p < 0.0001). In addition, functionally related genes were found to be more clustered than those evolved under random rearrangements (p < 0.0001). We report evidence of co-transcription of neighboring genes, which may be responsible for the observed gene clusters in C. reinhardtii cpDNA. CONCLUSION: Simulations and experimental evidence suggest that both selective maintenance and directional selection for gene clusters are determinants of chloroplast gene order

An equilibrium analysis on the tripartite evolutionary game of garbage classification recycling

The garbage classification recycling policy is proposed to curb the waste of recyclable and land resources to reduce the environmental pollution caused by garbage. This paper establishes a tripartite evolutionary game model with governments, recycling companies, and citizens as stakeholders to discuss their corresponding strategic behaviors. Through the stability analysis, we draw a conclusion that only when governments choose to regulate the classification, while both recycling companies and citizens take an active part in the classification, can the environmental benefit be maximized. In addition, the government and recycling companies are advised to increase the rate at which the evolutionary game model converges to a steady state by reducing their own operating costs during the implementation of garbage classification. On this basis, we also recommend an appropriate increase in the benefits given to citizens, which will have a significantly positive impact on citizens and even also on the government and the recycling companies themselves

Establishment and application of 72 alkaloids database with high resolution mass spectrometry

Objective The high resolution mass spectra database of common alkaloids were established and applied to the automatic screening of poisoning samples. Methods The accurate molecular mass and appropriate collision energy of each alkaloid was obtained by injecting alkaloid standard solutions into the Q exactive mass spectrometry directly or separated by high performance liquid chromatography and mass spectrometry analysis. The chromatographic analysis was performed on a HSS T3 column (2.1 mm×100 mm, 1.8 μm) by gradient elution using methanol and 5 mmol/L ammonium acetate containing 0.1% formic acid solution as mobile phase. The secondary mass information of alkaloid was collected in positive electrospray ionization and targeted-selected ion monitor-ddMS2 mode. The mass spectrometry data were applied to the automatic screening of actual samples. Results The mass spectrometry database of 72 alkaloids were established containing the theoretical accurate molecular mass, retention time collision energy and fragment ions. The multiple alkaloids in actual samples were locked rapidly by automatic screening with the mass spectra database. Conclusion This method is simple, fast, accurate and suitable for the rapid automatic screening of alkaloids in poisoned food sample

An Evidence-Based Review of Related Metabolites and Metabolic Network Research on Cerebral Ischemia

In recent years, metabolomics analyses have been widely applied to cerebral ischemia research. This paper introduces the latest proceedings of metabolomics research on cerebral ischemia. The main techniques, models, animals, and biomarkers of cerebral ischemia will be discussed. With analysis help from the MBRole website and the KEGG database, the altered metabolites in rat cerebral ischemia were used for metabolic pathway enrichment analyses. Our results identify the main metabolic pathways that are related to cerebral ischemia and further construct a metabolic network. These results will provide useful information for elucidating the pathogenesis of cerebral ischemia, as well as the discovery of cerebral ischemia biomarkers