Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Computer science & Population. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Assessment of preferences for treatment: validation of a measure.

Abstract: Systematic measurement of treatment preferences is needed to obtain well-informed preferences. Guided by a conceptualization of treatment preferences, a measure was developed to assess treatment acceptability and preference. The purpose of this study was to evaluate the psychometric properties of the treatment acceptability and preferences (TAP) measure. The TAP measure contains a description of each treatment under evaluation, items to rate its acceptability, and questions about participants' preferred treatment option. The items measuring treatment acceptability were internally consistent (alpha > .80) and demonstrated validity, evidenced by a one-factor structure and differences in the scores between participants with preferences for particular interventions. The TAP measure has the potential for the assessment of acceptability and preferences for various behavioral interventions.

Current Control for an Indirect Matrix Converter With Filter Resonance Mitigation

Abstract: A predictive control scheme for the indirect matrix converter including a method to mitigate the resonance effect of the input filter is presented. A discrete-time model of the converter, the input filter, and the load is used to predict the behavior of the instantaneous input reactive power and the output currents for each valid switching state. The control scheme selects the state that minimizes the value of a cost function in order to generate input currents with unity power factor and output currents with a low error with respect to a reference. The active damping method is based on a virtual harmonic resistor which damps the filter resonance. This paper shows experimental results to demonstrate that the proposed control method can generate good tracking of the output-current references, achieve unity input displacement power factor, and reduce the input-current distortion caused by the input filter resonance.

Identification of estrogen-responsive genes by complementary deoxyribonucleic acid microarray and characterization of a novel early estrogen-induced gene: EEIG1.

Abstract: Estrogen receptors (ERs) are nuclear transcription factors that regulate gene expression in response to estrogen and estrogen-like compounds. Identification of estrogen-regulated genes in target cells is an essential step toward understanding the molecular mechanisms of estrogen action. Using cDNA microarray examinations, 19 genes were identified as induced by 17β-estradiol in MCF-7 cells, 10 of which have been reported previously to be estrogen responsive or to be linked with ER status. Five known estrogen-regulated genes, E2IG4, IGFBP4, SLC2A1, XBP1 and B4GALT1, and AFG3L1, responded quickly to estrogen treatment. A novel estrogen-responsive gene was identified and named EEIG1for early estrogen-induced gene 1. EEIG1 was clearly induced by 17β-estradiol within 2 h of treatment, and was widely responsive to a group of estrogenic compounds including natural and synthetic estrogens and estrogenic environmental compounds. EEIG1 was expressed in ER-positive but not in ER-negative breast cancer cell lines. EEI...

Predictive models for diabetes mellitus using machine learning techniques

Abstract: Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body’s inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity – the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.