Synergistic effect of energy drinks and overweight/obesity on cardiac autonomic testing using the Valsalva maneuver in university students

BACKGROUND: Obesity and caffeine consumption may lead to autonomic disturbances that can result in a wide range of cardiovascular disorders. OBJECTIVES: To determine autonomic disturbances produced by the synergistic effects of overweight or obesity (OW/OB) and energy drinks. DESIGN: Cross-sectional, analytical. SETTING: Physiology department at a university in Saudi Arabia. SUBJECTS AND METHODS: University students, 18–22 years of age, of normal weight (NW) and OW/OB were recruited by convenience sampling. Autonomic testing by the Valsalva ratio (VR) along with systolic and diastolic blood pressure, pulse pressure, and mean arterial blood pressure were measured at baseline (0 minute) and 60 minutes after energy drink consumption. MAIN OUTCOME MEASURE(S): Autonomic disturbance, hemodynamic changes. RESULTS: In 50 (27 males and 23 females) subjects, 21 NW and 29 OW/OB, a significant decrease in VR was observed in OW/OB subjects and in NW and OW/OB females at 60 minutes after energy drink consumption. Values of systolic and diastolic blood pressure, pulse pressure and mean arterial blood pressure were also significantly higher in OW/OB and in females as compared to NW and males. BMI was negatively correlated with VR and diastolic blood pressure at 60 minutes. CONCLUSION: Obesity and energy drinks alter autonomic functions. In some individuals, OW/OB may augment these effects. LIMITATIONS: Due to time and resource restraints, only the acute effects of energy drinks were examined

Texture image segmentation based on classification, skeletonization and crossing of contour elements

A method for texture image segmentation based on classification, skeletonization and crossing of contour elements is presented. The essence of the method consists in the contouring of the image, determining the position of contour elements in the image of different types (points, lines, and shapes) converting closely spaced similar contour elements into binary regions objects, binary coding mutual position obtained areal objects within the boundaries of the image segmentation are resulting in code matrix

Segmentation of texture-based image classification contour elements and logical addition classes

Предложен метод сегментации текстурных изображений на основе классификации контурных элементов и логического сложения классов. Сущность метода состоит в контурной обработке исходного изображения, определении положения на изображении контурных элементов различного типа (точек, линий и фигур), преобразовании близко расположенных друг к другу однотипных контурных элементов в бинарные площадные объекты, двоичном кодировании взаимного расположения полученных площадных объектов в границах исходного изображения, сегментации полученной кодовой матрицы.A method for texture segmentation of images based on classification of contour elements and logical addition of classes is offered. The essence of the method is concluded in the contouring of the original image, determining the position of the image of contour elements of different types (points, lines, and shapes), converting closely spaced similar contour elements into binary areal objects, binary coding mutual position obtained polygon objects within the boundaries of the original image segmentation resulting code matrix