Melanocortin-4 Receptor Gene Mutations in a Group of Turkish Obese Children and Adolescents.

OBJECTIVE: Melanocortin-4 receptor (MC4R) mutations are the most common known cause of monogenic obesity. Data regarding MC4R mutations in Turkish subjects are limited. To determine the prevalence of MC4R mutations in a group of Turkish morbid obese children and adolescents. METHODS: MC4R was sequenced in 47 consecutive morbidly obese children and adolescents (28 girls and 19 boys, aged 1-18 years) who presented during a one-year period. Inclusion criterion was a body mass index (BMI) ≥120% of the 95th percentile or ≥35 kg/m2. Patients with chronic diseases, Cushing syndrome, hypothyroidism, or suspected syndromes that could cause obesity were excluded. Onset of obesity was before age 10 years in all subjects. RESULTS: Mean age was 13.2±4.1 years, age at onset of obesity 5.1±2.1 years, height standard deviation (SD) score 1.21±0.93, BMI 40.0±8.8 kg/m2, and BMI SD score was 2.72±0.37. One novel (c.870delG) and two previously reported (c.496 G>A, c.346_347delAG) mutations were found in four (8.5%) obese children and adolescents. The novel mutation (c.870delG) was predicted to be a disease-causing frame-shift mutation using in silico analyses. Fasting glucose and lipid levels of the patients with MC4R mutation were normal, but insulin resistance was present in two of the subjects. Six more individuals with MC4R mutation (1 child, 5 adults) were detected following analyses of the family members of affected children. CONCLUSION: MC4R mutations are frequently found in morbid obese Turkish children and adolescents.

A low-computational approach on gaze estimation with eye touch system.

Among various approaches to eye tracking systems, light-reflection based systems with non-imaging sensors, e.g., photodiodes or phototransistors, are known to have relatively low complexity; yet, they provide moderately accurate estimation of the point of gaze. In this paper, a low-computational approach on gaze estimation is proposed using the Eye Touch system, which is a light-reflection based eye tracking system, previously introduced by the authors. Based on the physical implementation of Eye Touch, the sensor measurements are now utilized in low-computational least-squares algorithms to estimate arbitrary gaze directions, unlike the existing light reflection-based systems, including the initial Eye Touch implementation, where only limited predefined regions were distinguished. The system also utilizes an effective pattern classification algorithm to be able to perform left, right, and double clicks based on respective eye winks with significantly high accuracy. In order to avoid accuracy problems for sensitive sensor biasing hardware, a robust custom microcontroller-based data acquisition system is developed. Consequently, the physical size and cost of the overall Eye Touch system are considerably reduced while the power efficiency is improved. The results of the experimental analysis over numerous subjects clearly indicate that the proposed eye tracking system can classify eye winks with 98% accuracy, and attain an accurate gaze direction with an average angular error of about 0.93 °. Due to its lightweight structure, competitive accuracy and low-computational requirements relative to video-based eye tracking systems, the proposed system is a promising human-computer interface for both stationary and mobile eye tracking applications.