Effects of Vitamin D levels on asthma control and severity in pre-school children

OBJECTIVE: Prevalence of asthma and Vitamin D deficiency has been increasing and leading to significant morbidities. This study aimed to compare the Vitamin D levels in the pre-school children with asthma and in healthy controls and to assess the relationship between Vitamin D levels and asthma clinical parameters and control. PATIENTS AND METHODS: Vi tamin D [25(OH)D3] levels were measured in 102 preschool children, aged 1-4 years with asthma and 102 healthy controls in winter. The patients with asthma were grouped according to serum Vitamin D levels as sufficient, insufficient and deficient. Asthma control was classified according to the Global Initiative for Asthma (GINA) guidelines and the Test for Respiratory and Asthma Control in Kids (TRACK) in 1-4 years-old children. RESULTS: Serum Vitamin D levels were 22.64 (9.96) ng/ml in the asthma group and 32.11 (14.74) ng/ml in the control group (p = 0.001). Total number of exacerbations during the previous year were significantly lower in the Vitamin D sufficient group, compared to the deficient and insufficient groups (p = 0.03). Frequency of patients with controlled asthma was higher in the sufficient group compared to the deficient and insufficient groups (p = 0.001 and p = 0.001, respectively). There was a positive correlation between serum Vitamin D levels and asthma control. CONCLUSIONS: The frequency of Vitamin D deficiency and insufficiency was higher in children with asthma, compared to the controls. Therefore, we suggest that lower levels of Vitamin D are associated with poor asthma control and increased asthma severity

Residents' views about family medicine specialty education in Turkey

<p>Abstract</p> <p>Background</p> <p>Residents are one of the key stakeholders of specialty training. The Turkish Board of Family Medicine wanted to pursue a realistic and structured approach in the design of the specialty training programme. This approach required the development of a needs-based core curriculum built on evidence obtained from residents about their needs for specialty training and their needs in the current infrastructure. The aim of this study was to obtain evidence on residents' opinions and views about Family Medicine specialty training.</p> <p>Methods</p> <p>This is a descriptive, cross-sectional study. The board prepared a questionnaire to investigate residents' views about some aspects of the education programme such as duration and content, to assess the residents' learning needs as well as their need for a training infrastructure. The questionnaire was distributed to the Family Medicine Departments (n = 27) and to the coordinators of Family Medicine residency programmes in state hospitals (n = 11) by e-mail and by personal contact.</p> <p>Results</p> <p>A total of 191 questionnaires were returned. The female/male ratio was 58.6%/41.4%. Nine state hospitals and 10 university departments participated in the study. The response rate was 29%. Forty-five percent of the participants proposed over three years for the residency duration with either extensions of the standard rotation periods in pediatrics and internal medicine or reductions in general surgery. Residents expressed the need for extra rotations (dermatology 61.8%; otolaryngology 58.6%; radiology 52.4%). Fifty-nine percent of the residents deemed a rotation in a private primary care centre necessary, 62.8% in a state primary care centre with a proposed median duration of three months. Forty-seven percent of the participants advocated subspecialties for Family Medicine, especially geriatrics. The residents were open to new educational methods such as debates, training with models, workshops and e-learning. Participation in courses and congresses was considered necessary. The presence of a department office and the clinical competency of the educators were more favored by state residents.</p> <p>Conclusions</p> <p>This study gave the Board the chance to determine the needs of the residents that had not been taken into consideration sufficiently before. The length and the content of the programme will be revised according to the needs of the residents.</p

Theoretically-Efficient and Practical Parallel DBSCAN

The DBSCAN method for spatial clustering has received significant attention due to its applicability in a variety of data analysis tasks. There are fast sequential algorithms for DBSCAN in Euclidean space that take $O(n\log n)$ work for two dimensions, sub-quadratic work for three or more dimensions, and can be computed approximately in linear work for any constant number of dimensions. However, existing parallel DBSCAN algorithms require quadratic work in the worst case, making them inefficient for large datasets. This paper bridges the gap between theory and practice of parallel DBSCAN by presenting new parallel algorithms for Euclidean exact DBSCAN and approximate DBSCAN that match the work bounds of their sequential counterparts, and are highly parallel (polylogarithmic depth). We present implementations of our algorithms along with optimizations that improve their practical performance. We perform a comprehensive experimental evaluation of our algorithms on a variety of datasets and parameter settings. Our experiments on a 36-core machine with hyper-threading show that we outperform existing parallel DBSCAN implementations by up to several orders of magnitude, and achieve speedups by up to 33x over the best sequential algorithms

Rule-by-rule input significance analysis in fuzzy system modeling

Input or feature selection is one the most important steps of system modeling. Elimination of irrelevant variables can save time, money and can improve the precision of model that we are trying to discover. In Fuzzy System Modeling (FSM) approaches, input selection plays an important role too. The input selection algorithms that are under our investigation did not consider one crucial fact. An input variable may of may not be significant in a specific rule, not in overall system. In this paper, an input selection algorithm that takes this observation into account is proposed as an extension of the input selection algorithms found in the literature. The proposed algorithm is applied on a nonlinear function and successful results are achieved

Two step feature selection: Approximate functional dependency approach using membership values

Feature selection is one of the most important issues in fields such as system modelling and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept and K-Nearest Neighbourhood method to implement the feature filter and feature wrapper, respectively. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data

A new fuzzy inference approach based on mamdani inference using discrete type 2 fuzzy sets

Fuzzy System Modeling (FSM) is one of the most prominent system modeling tools in analyzing the data in the presence of uncertainty. Linguistic Fuzzy Rulebase (LFR) structure, in which both the antecedent and consequent variables are represented by fuzzy sets, is the most well known fuzzy rulebase structure in the literature. The proposed FSM method identifies LFR system model by executing Fuzzy C-Means (FCM) clustering method. One of the sources of uncertainty in system modeling is the uncertainty in selecting learning parameters. In order to capture this uncertainty in a more realistic way, the antecedent and consequent variables are represented by using Type 2 fuzzy sets that are constructed by executing FCM method with different level of fuzziness, in, values. The proposed system modeling approach is applied on a well-known benchmark data set where the goal is to predict the price of a stock. After comparing the results with the ones obtained with other system modeling tools, it can be claimed successful results are achieved