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BackgroundGhrelin and peptide YY (PYY) are appetite regulating hormones secreted from the gastrointestinal tract (gut). Aside from their known effect on energy homeostasis, accumulating data indicates that these gut hormones also affect bone metabolism. However, data regarding the influence of ghrelin and PYY on bone density in humans is very limited, and the results are inconclusive. Therefore, this study was designed to investigate the potential association between circulating ghrelin and PYY with bone density indices in the general population.MethodsA total of 2257 adult subjects from the CODING (Complex Diseases in the Newfoundland Population: Environment and Genetics) study participated in this investigation. Acylated ghrelin and total PYY were measured in serum after a 12-hour fasting, with the Enzyme- Linked Immunosorbent Assay (ELISA) method. Bone mineral density was measured by dual-energy X-ray absorptiometry at the spine, femoral neck, and total hip. Multiple regression analyses adjusting for age, BMI, physical activity, smoking, and alcohol consumption were employed to analyze the association between serum ghrelin and PYY with bone mineral density parameters.ResultsSignificant positive associations of ghrelin concentration with L2-L4 BMD, L2-L4 Z-score, femoral neck BMD, femoral neck Z-score, total hip BMD, and total hip Z-score were found in women. No significant correlations between ghrelin and bone density indices were present in men. After dividing the female group into pre-menopausal and post-menopausal, ghrelin was positively correlated with femoral neck Z-score, and total hip Z-score in pre-menopausal women and L2-L4 BMD, and Z-score in post-menopausal group. Moreover, no significant association was discovered between serum PYY and bone density at any site.ConclusionOur results suggest a beneficial association of circulating ghrelin concentration with bone density in women at the population level. This association is independent of major confounding factors including BMI, physical activity, age, alcohol consumption, and smoking. Effect of menopause on this association seemed to be site specific. However, PYY does not seem to be associated with bone density parameters.

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Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy.

Our study suggests that increased CSF leptin, likely from blood-brain barrier breakdown, combined with elevated serum GH and IGF-1 after TBI, leads to accelerated fracture healing.