An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

Background: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. Objectives: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). Patients and Methods: A population of 6647 individuals without diabetes, aged � 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. Results: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) � 30 kg/m2, family history of diabetes, wrist circumference > 16.5 cm and waist to height � 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio � 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. Conclusions: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone. © 2015, Research Institute For Endocrine Sciences and Iran Endocrine Society

Classification-based data mining for identification of risk patterns associated with hypertension in Middle Eastern population A 12-year longitudinal study

Hypertension is a critical public health concern worldwide. Identification of risk factors using traditional multivariable models has been a field of active research. The present study was undertaken to identify risk patterns associated with hypertension incidence using data mining methods in a cohort of Iranian adult population. Data on 6205 participants (44 men) age > 20 years, free from hypertension at baseline with no history of cardiovascular disease, were used to develop a series of prediction models by 3 types of decision tree (DT) algorithms. The performances of all classifiers were evaluated on the testing data set. The Quick Unbiased Efficient Statistical Tree algorithm among men and women and Classification and Regression Tree among the total population had the best performance. The C-statistic and sensitivity for the prediction models were (0.70 and 71) in men, (0.79 and 71) in women, and (0.78 and 72) in total population, respectively. In DT models, systolic blood pressure (SBP), diastolic blood pressure, age, and waist circumference significantly contributed to the risk of incident hypertension in both genders and total population, wrist circumference and 2-h postchallenge plasma glucose among women and fasting plasma glucose among men. In men, the highest hypertension risk was seen in those with SBP > 115mm Hg and age > 30 years. In women those with SBP > 114 mmHg and age>33 years had the highest risk for hypertension. For the total population, higher risk was observed in those with SBP > 114mm Hg and age > 38 years. Our study emphasizes the utility of DTs for prediction of hypertension and exploring interaction between predictors. DT models used the easily available variables to identify homogeneous subgroups with different risk pattern for the hypertension. Copyright © 2016 the Author(s). Published by Wolters Kluwer Health, Inc. All

Impact of the aging of a photovoltaic module on the performance of a grid-connected system

Photovoltaic systems belong to the green energy dynamics which is an ambitious program based on energy efficiency and sustainable development. In this study, the impact of the aging of a photovoltaic module is investigated on the electrical performance of a grid-connected system. A photovoltaic conversion chain with MPPT (Maximum Power Point Tracking) control and LC (Inductor-Capacitor) filter is modeled and dimensioned according to the grid constraints. A method of hybridation detection of the MPPT coupling long-time aging evolution and short-time determination is proposed. Aging laws for the electrical and optical degradations of the photovoltaic module are introduced for the long-time evolution. Results display the lowering of the maximal power point with a rate of 1%/year and a slight augmentation of the THD over time even though it remains inferior to the IEEE standard STD 19-1992 maximum value of 5% for a usage of 20 years. Moreover, an equivalent scheme for the additional electrical resistance engendered by the aging of the photovoltaic module regarding other resistances of the photovoltaic system is given. Finally, the elevation of this resistance by 12.8% in 20 years may have non-negligible consequences on the power production of a large-scale installation. © 201

Using nanomaterials as excellent immobilisation layer for biosensor design

The endless development in nanotechnology has introduced new vitality in device fabrication including biosensor design for biomedical applications. With outstanding features like suitable biocompatibility, good electrical and thermal conductivity, wide surface area and catalytic activity, nanomaterials have been considered excellent and promising immobilisation candidates for the development of high-impact biosensors after they emerged. Owing to these reasons, the present review deals with the efficient use of nanomaterials as immobilisation candidates for biosensor fabrication. These include the implementation of carbon nanomaterials—graphene and its derivatives, carbon nanotubes, carbon nanoparticles, carbon nanodots—and MXenes, likewise their synergistic impact when merged with metal oxide nanomaterials. Furthermore, we also discuss the origin of the synthesis of some nanomaterials, the challenges associated with the use of those nanomaterials and the chemistry behind their incorporation with other materials for biosensor design. The last section covers the prospects for the development and application of the highlighted nanomaterials

gDs∗DK∗(892)g_{D^{\ast}_{s}D K^{\ast}(892)} and gBs∗BK∗(892)g_{B^{\ast}_{s}B K^{\ast}(892)} coupling constants in QCD sum rules

The coupling constants $g_{D^{\ast}_{s}D K^{\ast}(892)}$ and $g_{B^{\ast}_{s}B K^{\ast}(892)}$ are calculated in the framework of three-point QCD sum rules. The correlation functions responsible for these coupling constants are evaluated considering contributions of both $D(B)$ and $K^*(892)$ mesons as off-shell states, but in the absence of radiative corrections. The results, $g_{D^{\ast}_{s}D K^{\ast}(892)}=(4.31\pm1.42) GeV^{-1}$ and $g_{B^{\ast}_{s}B K^{\ast}(892)}=(3.24\pm1.08) GeV^{-1}$ are obtained for the considered strong coupling constants.Comment: 13 Pages and 11 Figure

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: A decade follow-up in a Middle East prospective cohort study

Objective: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Design: Prospective cohort study. Setting: Tehran Lipid and Glucose Study (TLGS). Methods: A total of 6647 participants (43.4 men) aged >20 years, without T2D at baselines ((1999- 2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. Primary outcome measure: T2D was primary outcome which defined if fasting plasma glucose (FPG) was �7 mmol/L or if the 2h-PCPG was �11.1 mmol/L or if the participant was taking antidiabetic medication. Results: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78 and 0.75) and (78 and 0.78) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-toheight ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG�4.9 mmol/L and 2h-PCPG�7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG�5.2 mmol/L and WHtR�0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Conclusions: Our study emphasises the utility of DT for exploring interactions between predictor variables

Formation of ultracold dipolar molecules in the lowest vibrational levels by photoassociation

We recently reported the formation of ultracold LiCs molecules in the rovibrational ground state X1Sigma+,v''=0,J''=0 [J. Deiglmayr et al., PRL 101, 133004 (2008)]. Here we discuss details of the experimental setup and present a thorough analysis of the photoassociation step including the photoassociation line shape. We predict the distribution of produced ground state molecules using accurate potential nergy curves combined with an ab-initio dipole transition moment and compare this prediction with experimental ionization spectra. Additionally we improve the value of the dissociation energy for the X1Sigma+ state by high resolution spectroscopy of the vibrational ground state.Comment: Submitted to Faraday Discussions 142: Cold and Ultracold Molecules 18 pages, 8 figure