The causality and simultaneity between price and trading intensity in existing home markets

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1997.Includes bibliographical references (leaf 21).by Junyu Chen.M.S

Novel biomarkers in regulating human dense connective tissue repair

Painful and degenerative disorders of the musculoskeletal system pose a tremendous burden on the healthcare system. One main reason is the limited knowledge of the underlying key factors and pathways responsible for the suboptimal and often varying healing outcomes of dense connective tissues (CTs), such as tendons, ligaments and knee meniscus, which have vital supportive, protective and force transmitting roles in the human body. The overall aim of this thesis was to identify novel biomarkers of dense CT healing. The thesis was designed to explore for biomarkers during the inflammatory- and proliferative healing phases in tissue biopsies and micro-dialysate, respectively, after Achilles tendon rupture (ATR) as a model of dense CT healing. Using quantitative RT-PCR and immunohistochemistry of ATR tissue biopsies from the inflammatory healing phase the presence of potential biomarkers (Col I, Col III, FGF, FN, MMP-9) was identified. The gene expression of fibroblast growth factor (FGF) was positively associated with improved 1-year patient-reported- and functional outcomes. Higher Col III mRNA expression was associated with more perceived tendon strength at 1 year. Proteomic profiling using quantitative mass spectrometry of ATR biopsies from the inflammatory healing phase disclosed 769 proteins, including 51 differentially expressed proteins among patients with good- versus poor 1-year outcome. Among them a novel biomarker, elongation factor-2 (eEF2), was identified as being strongly prognostic of the 1-year clinical outcome. Experimental exploration revealed that eEF2 regulated autophagy, cell proliferation and migration, as well as reduced cell death and apoptosis in dense CT healing. Utilizing the proteomic profile from the inflammatory healing phase together with weighted co-expression network analysis a biomarker, inter-alpha-trypsin inhibitor heavy chain (ITIH4), associated with improved 1-year healing outcomes was discovered. Experimental explorations identified ITIH4 to stimulate collagen I production mediated by PPARγ signaling pathways. Further proteomic profiling of micro-dialysate from the proliferative healing phase discovered 1288 unique proteins, whereof 9 upregulated, and 23 downregulated proteins in patients with good- versus poor 1-year outcome. Upregulated proteins were related mainly to extracellular matrix organization, while downregulated pathways were associated with functions such as thrombosis formation. The most reliable predictive biomarker was the downregulated pro-inflammatory complement factor D (CFD). Expanded characterization of CFD demonstrated higher expression during inflammatory- and lower expression during proliferative healing phases in the good outcome patients. Further experimental explorations demonstrated that CFD improved repair by enhancing cell migration and collagen type I (Col1a1) production during inflammation, while exerting the opposite effects during proliferative healing. The results of this thesis have established biomarkers eEF2, FGF, ITIH4 during the inflammatory healing phase and CFD mainly during the proliferative healing phase, all prognostic of improved patient outcome in tendon repair, presumably reflective of dense CT healing in general. These findings may lead to improved individualized treatment decisions, as well as accelerate the development of improved therapies to promote good long-term clinical healing outcomes

Challenges of Employing VoiceThread to Enhance U.S. Collegiate Chinese Language Learners’ Oral Proficiency

The digital age in education inspires Chinese teachers to integrate Web 2.0 technologies into Chinese as a second language (CSL) curricula to enhance teaching effects. However, few studies examine the challenges that teachers face when integrating Web 2.0 technologies to improve student oral proficiency in Chinese. This study aims to investigate students’ perceptions of the effectiveness of the Web 2.0 technology VoiceThread (VT). It examines the challenges of integrating VT to enhance student oral proficiency. The authors conducted two studies with 31 students responding to a questionnaire in Study 1 and 48 students responding to the same questionnaire in Study 2. All students are beginner-level CSL learners. Three results were obtained. Result 1 shows that documents and articles report VT as popular and easy to use, however, few teachers employed VT in their teaching practices. Result 2 indicates another discrepancy. Although students highly evaluated the usefulness of the VT-facilitated activities, they were reluctant to perform these activities. By comparing Study 1 and Study 2, the authors obtained Result 3: Students in Study 2 evaluated VT-integrated teaching practices consistently higher than those in Study 1. Teachers’ using experiences primarily led these perception differences. This study provides CSL teachers with a vision of the opportunities and challenges that may occur when they integrate Web 2.0 technologies into curricular to improve CSL oral proficiency

An investigation of licensing of datasets for machine learning based on the GQM model

Dataset licensing is currently an issue in the development of machine learning systems. And in the development of machine learning systems, the most widely used are publicly available datasets. However, since the images in the publicly available dataset are mainly obtained from the Internet, some images are not commercially available. Furthermore, developers of machine learning systems do not often care about the license of the dataset when training machine learning models with it. In summary, the licensing of datasets for machine learning systems is in a state of incompleteness in all aspects at this stage. Our investigation of two collection datasets revealed that most of the current datasets lacked licenses, and the lack of licenses made it impossible to determine the commercial availability of the datasets. Therefore, we decided to take a more scientific and systematic approach to investigate the licensing of datasets and the licensing of machine learning systems that use the dataset to make it easier and more compliant for future developers of machine learning systems