Influences of protein to energy ratios in breakfast on mood, alertness and attention in the healthy undergraduate students

Background: The high protein (HP) breakfast reduced gastric emptying and the most satiat-ing macronutrient appears to be dietary protein. Few studies have investigated the effects of protein to energy ratio in breakfast on mood, alertness and attention. Objective: This study was designed to investigate whether the HP breakfast is more beneficial to mood, alertness and attention of the healthy undergraduate student than adequate-protein (AP) breakfast through the rising body temperature and re-maining stable blood glucose or through other physiologic processes. Methods: Thirteen healthy male undergraduate students (18 - 23 y) were studied in a double-blind, randomized crossover design. Blood samples, body tem-perature, satiety, mood and Continuous Per-formance Test (CPT) were assessed after the consumption of two isocaloric breakfasts that differed in their protein and carbohydrate con-tent: an HP breakfast (50%, 30%, and 20% of energy from protein, carbohydrate, and fat, re-spectively) or an AP breakfast (10%, 70%, and 20% of energy from protein, carbohydrate, and fat, respectively). Results: Consumption of an HP breakfast resulted in more steady glucose and insulin than AP breakfast consumption (p < 0.05). Satiety scores and body temperature were higher after HP breakfast consumption (p < 0.05). And most important, the positive mood and CPT scores were higher after HP breakfast than after AP breakfast intake (p < 0.05). Conclusion: HP breakfast can effectively stabilize postprandial serum glucose concentration and elevate post-prandial temperature of healthy male under-graduate students. Our present findings dem-onstrate the relationship between HP breakfast and mood, alertness and attention. This study indicated that HP breakfast may enhance human performance probably by increasing the thermic effect of a food and elevating body temperature.

A comparative study on ApEn,SampEn and their fuzzy counterparts in a multiscale framework for feature extraction

Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis.Most existing methods,however,assume a linear model of the underlying dynamics.In this study,the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied.Firstly,fuzzy sample entropy (FSampEn) is formulated by defining a fuzzy membership function with clear physical meaning.Secondly,inspired by the multiscale sample entropy (multiscale SampEn) which is originally proposed to quantify the complexity of physiological time series,we placed approximate entropy (ApEn),fuzzy approximate entropy (FApEn) and the proposed FSampEn into the same multiscale framework.This led to the developments of multiscale ApEn,multiscale FApEn and multiscale FSampEn.Finally,all four multiscale entropies along with their single-scale counterparts were employed to extract discriminating features from bearing vibration signals,and their classification performance was evaluated using support vector machines (SVMs).Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones,whilst multiscale FSampEn was superior to other multiscale methods,especially when analyzed signals were contaminated by heavy noise.Comparisons with statistical features in time domain also support the use of multiscale FSampEn.