Chinese Organization Name Recognition Using Chunk Analysis

PACLIC 20 / Wuhan, China / 1-3 November, 200

Chinese Semantic Class Learning from Web Based on Concept-Level Characteristics

PACLIC 23 / City University of Hong Kong / 3-5 December 200

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

Systematic Review and Meta-Analysis on the Association Between Outpatient Statins Use and Infectious Disease-Related Mortality

Background: To update and refine systematic literature review on the association between outpatient statins use and mortality in patients with infectious disease. Materials and Methods: We searched articles published before September 31, 2012, on the association between statins and infectious disease-related mortality through electronic databases. Eligible articles were analyzed in Review Manager 5.1. We conducted stratification analysis by study design, infection types, clinical outcomes and study locations. Results: The pooled odds ratio (OR) for death (statins use vs. no use) across the 41 included studies was 0.71 (95% confidence interval: 0.64, 0.78). The corresponding pooled ORs were 0.58 (0.38, 0.90), 0.66 (0.57, 0.75), 0.71 (0.57, 0.89) and 0.83 (0.67, 1.04) for the case-control study, retrospective cohort studies, prospective cohort studies and RCTs; 0.40 (0.20, 0.78), 0.61 (0.41, 0.90), 0.69 (0.62, 0.78) and 0.86 (0.68, 1.09) for bacteremia, sepsis, pneumonia and other infections; 0.62 (0.534, 0.72), 0.68 (0.53, 0.89), 0.71 (0.61, 0.83) and 0.86 (0.70, 1.07) for 30-day, 90-day, in-hospital and long-term (>1 year) mortality, respectively. Conclusions: Outpatient statins use is associated with a lower risk of death in patients with infectious disease in observational studies, but in a less extent in clinical trials. This association also varies considerably by infection types and clinical outcomes

Biomedical Question Answering: A Survey of Approaches and Challenges

Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into 5 distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and discuss some potential future directions to explore.Comment: In submission to ACM Computing Survey

Synthesis and electrochemical performance of hierarchical Sb2S3 nanorod-bundles for lithium-ion batteries

Uniform hierarchical Sb2S3 nanorod-bundles were synthesised successfully by L-cysteine hydrochloride-assisted solvothermal treatment, and were then characterised by X-ray diffraction, field emission scanning electron microscopy, and high-resolution transmission electron microscopy, respectively. The electrochemical performance of the synthesised Sb2S3 nanorod-bundles was investigated by cyclic voltammetry and galvanostatic charge−discharge technique, respectively. This material was found to exhibit a high initial charge specific capacity of 803 mA h g-1 at a rate of 100 mA g-1, a good cyclability of 614 mA h g-1 at a rate of 100 mA g-1 after 30 cycles, and a good rate capability of 400 mA h g-1 at a rate of 500 mA g-1 when evaluated as an electrode candidate material for lithium-ion batteries

Internal/External information access and information diffusion in social media

As social media platform not only provide infrastructure but also actively perform algorithmic curation for profit and user experience, it leads to an information filter bubble phenomenon: users are trapped in their own personalized bubble and are exposed only to the opinions that conform their beliefs and interests, thus potentially creating social polarization and information islands. However, filter bubbles hardly restrict all the users in a large social network, some information explorers can break the bubble and bring external global knowledge back to the internal network. In this paper, we investigate this assumption via hashtag adoption prediction. First, we construct a heterogeneous graph and extract 17 features to describe the event of hashtag adoption. Then, we generate learning instances and train a lasso regression model to do prediction. Preliminary results show that information explorers are more likely to adopt new hashtags than others, thereby more internal and external information can be diffused via these special users

Absence of integrin-mediated TGFβ1 activation in vivo recapitulates the phenotype of TGFβ1-null mice

The multifunctional cytokine transforming growth factor (TGF) β1 is secreted in a latent complex with its processed propeptide (latency-associated peptide [LAP]). TGFβ1 must be functionally released from this complex before it can engage TGFβ receptors. One mechanism of latent TGFβ1 activation involves interaction of the integrins αvβ6 and αvβ8 with an RGD sequence in LAP; other putative latent TGFβ1 activators include thrombospondin-1, oxidants, and various proteases. To assess the contribution of RGD-binding integrins to TGFβ1 activation in vivo, we created a mutation in Tgfb1 encoding a nonfunctional variant of the RGD sequence (RGE). Mice with this mutation (Tgfb1RGE/RGE) display the major features of Tgfb1−/− mice (vasculogenesis defects, multiorgan inflammation, and lack of Langerhans cells) despite production of normal levels of latent TGFβ1. These findings indicate that RGD-binding integrins are requisite latent TGFβ1 activators during development and in the immune system

Evaluating the criteria for financial holding company operating ability based on the DEMATEL approach–the case of Taiwan

Evaluating criteria selection has significant impacts on data envelopment analysis (DEA) efficiency estimates. Selecting the proper evaluation criteria lead to successful and meaningful results of decision-making. This study aims to use the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate the most important constructs and criteria and also establish causality relationships among others for financial holding companies (FHCs) of banks’ operating ability in Taiwan. In this research 15 criteria were confirmed through reviewing various articles associated with this issue. Then, the information from the questionnaire was turned into the DEMATEL questionnaire and was distributed among nine experts and also members of the FHCs of Taiwan. The research results show that employees, total assets, total liabilities, non-interest income, income on investments, net profits before tax, net worth, and EPS are eight causal criteria. Furthermore, operating expenses, capital, interest expenses, interest income, operating income, return on assets (ROA), and return on equity (ROE) are seven effect criteria

Delivery Efficiency of miR-21i-CPP-SWCNT and Its Inhibitory Effect on Fibrosis of the Renal Mesangial Cells

MicroRNA 21 (miR-21) was proved to cause renal fibrosis and the inhibition of miR-21 would improve the poor prognosis in renal cell carcinoma diseases. The complementary oligonucleotide of mature miR-21 was considered to be an effective intracellular miR-21 inhibitor (miR-21i). The directly effective delivery of miR-21i into fibrotic cell is a facile method for treatment of renal fibrosis. Herein, the miR-21i-CPP-SWCNT delivery system, synthesized via single-walled carbon nanotube (SWCNT) and cell-penetrating peptide (CPP), was taken as a novel fibrosis-targeting therapeutic carrier. The miR-21i and CPP firstly bind together via electrostatic forces, and subsequently miR-21i-CPP binds to the surface of SWCNTs via hydrophobic forces. CPP could endow the delivery system with targeting property, while SWCNT would enhance its penetrating ability. The exogenous miR-21i released from the designed miR-21i-CPP-SWCNTs had successfully inhibited the expression of fibrosis-related proteins in renal mesangial cells (RMCs). We found that the expression of TGF-β1 proteins was more sensitive to miR-21i-CPP-SWCNT than the expression of α-SMA proteins