Macronutrient intake and depressive symptoms among Japanese male workers: the Furukawa Nutrition and Health Study

This study was aimed to examine the cross-sectional association of protein, carbohydrate, and fat intake with depressive symptoms among 1794 Japanese male workers aged 18-69 years who participated in a health survey. Dietary intake was assessed with a validated self-administered diet history questionnaire. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale. Odds ratio of depressive symptoms (CES-D scale of ≥16) was estimated by using multiple logistic regression with adjustment for covariates including folate, vitamin B6, vitamin B12, polyunsaturated fatty acid, magnesium, and iron intake. Multivariable-adjusted odds ratio of depressive symptoms for the highest quartile of protein intake was 26%, albeit not statistically significant, lower compared with the lowest. The inverse association was more evident when a cutoff value of CES-D score ≥19 was used. The multivariable-adjusted odds ratios (95% confidence intervals) for the highest through lowest quartile of protein intake were 1.00 (reference), 0.69 (0.47-1.01), 0.69 (0.44-1.09), and 0.58 (0.31-1.06) (P for trend=0.096). Neither carbohydrate nor fat intake was associated with depressive symptoms. Our findings suggest that low protein intake may be associated with higher prevalence of depressive symptoms in Japanese male workers.

Association of serum leptin and ghrelin with depressive symptoms in a Japanese working population: a cross-sectional study

Leptin and ghrelin have been implicated in the pathogenesis of major depression. However, evidence is lacking among apparently healthy people. This study examined the relationship of these appetite hormones to depressive symptoms in a Japanese working population.

A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.