Concept and entity grounding using indirect supervision

Extracting and disambiguating entities and concepts is a crucial step toward understanding natural language text. In this thesis, we consider the problem of grounding concepts and entities mentioned in text to one or more knowledge bases (KBs). A well-studied scenario of this problem is the one in which documents are given in English and the goal is to identify concept and entity mentions, and find the corresponding entries the mentions refer to in Wikipedia. We extend this problem in two directions: First, we study identifying and grounding entities written in any language to the English Wikipedia. Second, we investigate using multiple KBs which do not contain rich textual and structural information Wikipedia does. These more involved settings pose a few additional challenges beyond those addressed in the standard English Wikification problem. Key among them is that no supervision is available to facilitate training machine learning models. The first extension, cross-lingual Wikification, introduces problems such as recognizing multilingual named entities mentioned in text, translating non-English names into English, and computing word similarity across languages. Since it is impossible to acquire manually annotated examples for all languages, building models for all languages in Wikipedia requires exploring indirect or incidental supervision signals which already exist in Wikipedia. For the second setting, we need to deal with the fact that most KBs do not contain the rich information Wikipedia has; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this thesis, we show that supervision signals can be obtained by carefully examining the redundancy and relations between multiple KBs. By developing algorithms and models which harvest these incidental signals, we can achieve better performance on these tasks

Investigating member commitment to virtual communities using an integrated perspective

[[abstract]]Purpose – Although the number of virtual communities has increased dramatically over the past few years, attracting and maintaining members remains the biggest challenge to establishing virtual social networks. This study seeks to integrate the roles of individual factors (issue involvement), social factors (social interaction), and system factors (system interactivity), and to explore how these factors contribute to member commitment in virtual communities. Design/methodology/approach – A total of 402 undergraduate students, who are all current members of virtual communities, participated in this study. Data were analyzed using structural equation modeling (SEM). Findings – The findings reveal that member commitment to communities was influenced more by their issue involvement compared to their perceived social interaction or perceived system interactivity. Originality/value – This research contributes to online community literature by integrating critical antecedent factors in the field of community commitment behavior. The findings indicate that issue involvement is more important than social interaction and system interactivity for influencing member commitment to communities. Additionally, the findings suggest that online community administrators should consider community positioning and topic selecting programs when attempting to influence users to commit to communities.[[notice]]補正完畢[[incitationindex]]SSCI[[booktype]]紙

Copper and Copper-Based Bimetallic Catalysts for Carbon Dioxide Electroreduction

Among many alternatives, CO2 electroreduction (CO2ER) is an emerging technology to alleviate its level in the atmosphere and simultaneously to produce essential products containing high energy density using various electrocatalysts. Cu-based mono- and bimetallics are electrocatalysts of concerns in this work due to the material's abundance and versatility. Intrinsic factors affecting the CO2ER are first analyzed, whereby understanding and characterizing the surface features of electrocatalysts are addressed. An X-ray absorption spectroscopy-based methodology is discussed to determine electronic and structural properties of electrocatalyst surface which allows the prediction of reaction mechanism and establishing the correlation with reduction products. The selectivity and faradaic efficiency of products highly depend on the quality of surface modification. Preparation and modification of electrocatalyst surfaces through various techniques are critical to increase the number of activity sites and the corresponding site activity. Mechanisms of CO2ER are complicate and thus are discussed in accordance with main products of interests. The authors try to concisely compile the most interesting, recent, and reasonable ideas that are agreeable to experimental results. Finally, this review provides an outlook for designing better Cu and Cu-based bimetallic catalysts to obtain selective products through CO2ER

Snowmass 2021 Cross Frontier Report: Dark Matter Complementarity (Extended Version)

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.Comment: v1 is first draft for community commen

Elevated BCRP/ABCG2 Expression Confers Acquired Resistance to Gefitinib in Wild-Type EGFR-Expressing Cells

The sensitivity of non-small cell lung cancer (NSCLC) patients to EGFR tyrosine kinase inhibitors (TKIs) is strongly associated with activating EGFR mutations. Although not as sensitive as patients harboring these mutations, some patients with wild-type EGFR (wtEGFR) remain responsive to EGFR TKIs, suggesting that the existence of unexplored mechanisms renders most of wtEGFR-expressing cancer cells insensitive.Here, we show that acquired resistance of wtEGFR-expressing cancer cells to an EGFR TKI, gefitinib, is associated with elevated expression of breast cancer resistance protein (BCRP/ABCG2), which in turn leads to gefitinib efflux from cells. In addition, BCRP/ABCG2 expression correlates with poor response to gefitinib in both cancer cell lines and lung cancer patients with wtEGFR. Co-treatment with BCRP/ABCG2 inhibitors enhanced the anti-tumor activity of gefitinib.Thus, BCRP/ABCG2 expression may be a predictor for poor efficacy of gefitinib treatment, and targeting BCRP/ABCG2 may broaden the use of gefitinib in patients with wtEGFR

Anti-Arthritic Effects of Magnolol in Human Interleukin 1β-Stimulated Fibroblast-Like Synoviocytes and in a Rat Arthritis Model

Fibroblast-like synoviocytes (FLS) play an important role in the pathologic processes of destructive arthritis by producing a number of catabolic cytokines and metalloproteinases (MMPs). The expression of these mediators is controlled at the transcriptional level. The purposes of this study were to evaluate the anti-arthritic effects of magnolol (5,5′-Diallyl-biphenyl-2,2′-diol), the major bioactive component of the bark of Magnolia officinalis, by examining its inhibitory effects on inflammatory mediator secretion and the NF-κB and AP-1 activation pathways and to investigate its therapeutic effects on the development of arthritis in a rat model. The in vitro anti-arthritic activity of magnolol was tested on interleukin (IL)-1β-stimulated FLS by measuring levels of IL-6, cyclooxygenase-2, prostaglandin E2, and matrix metalloproteinases (MMPs) by ELISA and RT-PCR. Further studies on how magnolol inhibits IL-1β-stimulated cytokine expression were performed using Western blots, reporter gene assay, electrophoretic mobility shift assay, and confocal microscope analysis. The in vivo anti-arthritic effects of magnolol were evaluated in a Mycobacterium butyricum-induced arthritis model in rats. Magnolol markedly inhibited IL-1β (10 ng/mL)-induced cytokine expression in a concentration-dependent manner (2.5–25 µg/mL). In clarifying the mechanisms involved, magnolol was found to inhibit the IL-1β-induced activation of the IKK/IκB/NF-κB and MAPKs pathways by suppressing the nuclear translocation and DNA binding activity of both transcription factors. In the animal model, magnolol (100 mg/kg) significantly inhibited paw swelling and reduced serum cytokine levels. Our results demonstrate that magnolol inhibits the development of arthritis, suggesting that it might provide a new therapeutic approach to inflammatory arthritis diseases