MECHANISM FOR TRUE OPINION SHARING

A mechanism is proposed for sharing true opinions amongst a plurality of users in a cloud storage system. An opinion sharing service may generate a group public key and a plurality of member private keys. Responsive to receiving from a first member a request to open a discussion forum for the group, the opinion sharing service may send an invitation message to members of the group to join the discussion forum and, for those who joined, send the group public key to each member who joined the discussion forum. Once receiving a message encrypted using the group public key from a second member, the opinion sharing service may decrypt the encrypted message using a private key that corresponds to the second member. Next, the opinion sharing service may anonymously present the message content

BIBLIOMETRIC ANALYSIS OF SOCIAL PRESENCE BIBLIOMETRIC ANALYSIS OF SOCIAL PRESENCE

As a popular phenomenon today, social presence theory plays a signified issue in the information systems and education fields since this theory was introduced by Short et al. in 1976. Furthermore, social presence has been receiving a growing attention in the academic literature. This study aims to investigate the current state of the academic literature regarding social presence theory and to analyze its knowledge base by using bibliometric method. All articles collected from SSCI database were published from 1977 to 2013. The result indicated that the literature productivity on social presence theory is still growing. The result also found that the author productivity distribution data was related to social presence theory matched Lotka’s law

Factors Influencing the Usage of Mobile Value-Added Services

Mobile value-added services have drawn the attention of researchers and practitioners recently, due to the rapid development of the mobile telecommunications market. Various mobile-based services and applications have therefore been introduced in order to satisfy mobile phone subscribers’ needs. Facing intensive competition, service providers are eager to persuade mobile phone subscribers into using the mobile value-add services in the hope to expand market share and ultimately raise revenues. This study therefore intends to investigate the factors that influence mobile phone subscribers’ intention to use mobile value-added services in Taiwan by incorporating quality factors and perceived playfulness with the Technology Acceptance Model. A preliminary proposal is presented in this extended abstract, together with expected contributions for research and practice

Undiagnosed diabetes mellitus among residents in Taiwanese long-term care facilities: A comparison of fasting glucose, postprandial plasma glucose, and hemoglobin A1c

AbstractBackgroundThe prevalence of diabetes mellitus (DM) is escalating with an aging population, and the chances of diabetic older patients admitted to long-term care facilities (LTCFs) are increased because of DM-related complications. However, undiagnosed DM among LTCF residents is a recognized hidden problem in this setting and may result in adverse outcomes.MethodsIn May 2011, 10 private LTCFs in northern Taipei participated in this study. Trained research nurses reviewed the medical records and performed physical examinations and blood sampling for all participants. Diabetes mellitus was diagnosed, based on the levels of fasting glucose, 2-hour postprandial plasma glucose, and hemoglobin A1c (HbA1c). Patients were categorized as having DM if they met the diagnostic cut-offs of the aforementioned criteria.ResultsOne hundred and ninety-nine residents (mean age, 79.6 ± 10.5 years; 52.3% males) participated in this study. They were all moderately/severely disabled (Karnofsky Performance Scale mean score was 50 ± 13). Forty-six (23.1%) residents were diabetic, based on their medical records, or were current users of antidiabetic agents. The prevalence was 29.6% after testing with a mean HbA1c level of 6.9% ± 0.9%. The overall undiagnosed DM rate was 4%, 3.5%, and 4.5%, based on fasting glucose, 2-hour postprandial plasma glucose, and HbA1c criteria, respectively. Diabetic patients had significantly higher serum levels of prealbumin, compared to nondiabetic patients (220.8 ± 45.9 vs. 201.1 ± 62.2 mg/L; p = 0.03), but there were no differences in the levels of hemoglobin, serum albumin, or total cholesterol. Diabetic patients had a significantly higher serum triglyceride level, compared to the nondiabetic patients (1.6 ± 0.7 vs. 1.1 ± 0.5 mmol/L; p < 0.01) and a lower high-density lipoprotein level (1.0 ± 0.3 vs. 1.2 ± 0.3 mmol/L; p < 0.01). Among 43 pharmacologically treated diabetic patients, 65.1% (28/43) of patients were using oral antidiabetic agents and 41.9% (18/43) of patients had been prescribed insulin, whereas 32.6% of the patients were managed by combination therapy.ConclusionThe prevalence of DM among LTCF residents in Taipei was 29.6%, and the undiagnosed rate was no more than 5%, based on fasting glucose, 2-hour postprandial plasma glucose, or HbA1c. Further study is needed for the optimal treatment strategy of DM in LTCFs

Mining association language patterns using a distributional semantic model for negative life event classification

AbstractPurposeNegative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).MethodsThis study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.ResultsThe experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus

Inhibition of Serine Protease Activity Protects Against High Fat Diet-Induced Inflammation and Insulin Resistance.

Recent evidence suggests that enhanced protease-mediated inflammation may promote insulin resistance and result in diabetes. This study tested the hypothesis that serine protease plays a pivotal role in type 2 diabetes, and inhibition of serine protease activity prevents hyperglycemia in diabetic animals by modulating insulin signaling pathway. We conducted a single-center, cross-sectional study with 30 healthy controls and 57 patients with type 2 diabetes to compare plasma protease activities and inflammation marker between groups. Correlations of plasma total and serine protease activities with variables were calculated. In an in-vivo study, LDLR-/- mice were divided into normal chow diet, high-fat diet (HFD), and HFD with selective serine protease inhibition groups to examine the differences of obesity, blood glucose level, insulin resistance and serine protease activity among groups. Compared with controls, diabetic patients had significantly increased plasma total protease, serine protease activities, and also elevated inflammatory cytokines. Plasma serine protease activity was positively correlated with body mass index, hemoglobin A1c, homeostasis model assessment-insulin resistance index (HOMA-IR), tumor necrosis factor-α, and negatively with adiponectin concentration. In the animal study, administration of HFD progressively increased body weight, fasting glucose level, HOMA-IR, and upregulated serine protease activity. Furthermore, in-vivo serine protease inhibition significantly suppressed systemic inflammation, reduced fasting glucose level, and improved insulin resistance, and these effects probably mediated by modulating insulin receptor and cytokine expression in visceral adipose tissue. Our findings support the serine protease may play an important role in type 2 diabetes and suggest a rationale for a therapeutic strategy targeting serine protease for clinical prevention of type 2 diabetes

Liquid biopsy genotyping in lung cancer: ready for clinical utility?

Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy