Effect of exercise and heat-induced hypohydration on brain volume

Purpose: The aim of the present study was to quantify changes in brain volume following exercise/heat-induced hypohydration in man. Methods: Eight active men completed intermittent exercise in a warm environment, until 2.9 ± 0.1 % of body mass was lost. Subjects remained hypohydrated for two hours following the end of exercise. Brain volume was measured before, immediately following, and 1h and 2h after exercise using MRI (Philips 3T Achieva). Measures of subjective feelings and core body temperature were also monitored. Blood samples were drawn to determine serum electrolyte concentrations and osmolality and to allow calculation of changes in blood and plasma volumes. Results: Brain volume was not influenced by hypohydration (0.2 ± 0.4 %; ES 0.2; P = 0.310). Reductions in ventricular (4.0 ± 1.8 %; ES 4.6; P < 0.001) and CSF (3.1 ± 1.9%; ES 3.3; P = 0.003) volumes were observed following exercise. Compared with pre-exercise levels, serum osmolality was elevated throughout the 2h post-exercise period (+10 ± 2 mosmol/kg; P < 0.001). Core temperature increased from 37.1 ± 0.3oC at rest to 39.3 ± 0.5oC at the end of exercise (P = 0.001). Conclusions: These data demonstrate that brain volume remains unchanged in response to moderate hypohydration and the presence of serum hyperosmolality, suggesting that mechanisms are in place to defend brain volume

Fusing fine-tuned deep features for skin lesion classification

© 2018 Elsevier Ltd Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images