The effects of morning preconditioning protocols on testosterone, cortisol and afternoon sprint cycling performance [conference presentation]

Opportunities exist for athletes to undertake morning exercise protocols in an attempt to potentate afternoon performance. Four sub elite track sprint cyclists completed a morning cycling (Cyc) or weights-based protocol (WP) prior to an afternoon cycling time trial (500m) in a repeated measures, counterbalance crossover design. Measured variables included heart rate, blood lactate, cycling peak power, salivary testosterone (T) and cortisol levels along with time trial performance. Standardised differences in means via magnitude-based inferences were calculated using paired samples T-tests in SPSS version 24 with statistical significance set at p < 0.05. The WP produced significantly faster times in the final 250m in comparison to CycP. The anticipated circadian decline of T was observed after the CycP but was however mitigated following the WP. While slight decreases in 500m times were experienced during the WP, they were not significant and were considered within the normal variations experienced between performances by elite athletes. The effect of the WP on the circadian rhythm of T could be linked to a greater recruitment of muscle fibres. Results suggest a morning resistance protocol can positively affect testosterone levels for afternoon performance. Possible gender and individual responses from conducting a W over Cyc protocol were observed and require further investigation

GPS analysis of a team competing at a national Under 18 field hockey tournament

The purpose of this study was to utilise global-positioning system (GPS) technology to quantify the running demands of national Under 18 field hockey players competing in a regional field hockey tournament. Ten male players (mean ± SD; age 17.2 ± 0.4 years; stature 178.1 ± 5.2 cm; body mass 78.8 ± 8.8 kg) wore GPS units while competing in six matches over seven days at the 2018 New Zealand national under 18 field hockey tournament. GPS enabled the measurement of total distance (TD), low-speed activity (LSA; 0 -14.9 km/hr), and high-speed running (HSR; ≥ 15 km/hr) distances. Differences in running demands (TD, LSA, HSR) between positions were assessed using effect size and percent difference ± 90% confidence intervals. Midfielders covered the most TD and LSA per game and strikers the most HSR during the 6 matches. There were “very large” differences between strikers and midfielders for TD and LSA, strikers and defenders for LSA and HSR, and defenders and midfielders for LSA. These results suggest that these playing positions are sufficiently different to warrant specialised position-specific conditioning training leading into a field hockey tournament

Feeding practices and growth among young children during two seasons in rural Ethiopia

BACKGROUND: The use of indices of infant and young child feeding practices to predict growth has generated inconsistent results, possibly through age and seasonal confounding. The aim of this study was to evaluate the association of a dietary diversity score (DDS) and infant and child feeding index (ICFI) with growth among young children in a repeated cross-sectional and a follow-up study in two distinct seasons in rural southwest Ethiopia. METHODS: We used a repeated cross-sectional design comparing child feeding practices to nutritional status in 6–12 month old children during harvest (HS; n = 320) and pre-harvest season (PHS; n = 312). In addition, 6–12 month old children from the HS were reassessed 6 months later during PHS. In addition to child anthropometry, child feeding practices were collected using 24-h and 7-day dietary recalls. RESULTS: The mean (±SD) length-for-age z-score (LAZ) of the 6–12 month old children was −0.77 (±1.4) and −1.0 (±1.3) in HS and PHS, respectively, while the mean (±SD) of the follow-up children in PHS was −1.0 (±1.3). The median DDS (IQR) was 2.0 (1.0, 3.0.), 2.0 (2.0, 3.0) and 3.0 (2.0, 4.0) for the children in HS, PHS and the follow-up children in PHS, respectively. The DDS in HS was positively associated with LAZ at follow-up (β = 0.16; 95% CI: 0.01, 0.30; P = 0.03) after controlling for confounding factors. ICFI and DDS were not associated with mean LAZ, weight-for-height z-score and weight-for-age z-score within season. However, the odds of being stunted when having a DDS ≤ 2 was 2.3 times (95% CI: 1.10, 4.78; P = 0.03) higher compared to a DDS &gt; 2 child in HS and 1.7 times (95% CI: 1.04, 2.71; P = 0.04) higher for the pooled sample of 6–12 months old children in HS and PHS. CONCLUSIONS: The DDS was found to be an indicator for child stunting during the Ethiopian harvest season. The DDS can be an appropriate tool to evaluate the association of child feeding practices with child growth irrespective of season. Inclusion of other dimensions in the construction of ICFI should be considered in future analysis as we found no association with growth

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Foot-pad dermatitis in broilers and turkeys

Foot-pad dermatitis is a condition characterised by lesions on the ventral foot-pads of poultry. It is a type of contact dermatitis, which in an early stage results in hyperkeratosis, erosions and discoloration of the skin. The erosions can develop into ulcers. In severe cases, the foot-pad lesions may cause pain which together with a deteriorated state of health constitutes a welfare issue. It has also been indicated that broilers with severe foot-pad dermatitis show slower weight gain. The aim of the present work was to improve the knowledge concerning the epidemiology of foot-pad dermatitis in meat-type poultry in Sweden. The studies were focused on surveying the occurrence of foot-pad dermatitis on Swedish commercial broiler and turkey farms, identifying endogenous and exogenous risk factors for foot-pad dermatitis in meat-type poultry and to evaluate the function of foot health as an indicator of management, hygiene and housing standards. The prevalence of foot-pad dermatitis in Swedish broilers at time of slaughter was estimated at 5-10 % for severe lesions and 10-35 % for mild lesions. The corresponding prevalence of foot-pad dermatitis in turkeys was estimated at approximately 20 % for severe lesions and 78 % for mild lesions. A significantly higher prevalence of foot-pad dermatitis was found in flocks reared on wet litter than on dry litter. In broilers, a significantly higher prevalence of lesions was found in flocks reared on thick layers of litter material than on thinner layers. There was an association between litter material and turkey foot-pad dermatitis. Type of drinker system, which is related to both water spillage and water consumption, was significantly associated with the prevalence of foot-pad dermatitis in both broilers and turkeys. There was a significant seasonal effect on the prevalence of broiler foot-pad dermatitis, with the highest prevalence found during October to January. The prevalence and severity of foot-pad dermatitis in broilers decreased over time when a surveillance programme was initiated and executed. In summary, foot-pad dermatitis in both broilers and turkeys was shown to be linked to a number of management, hygiene and housing factors, and can thus be used as an indicator of the standard of these factors. Surveillance and advisory programmes can be used successfully to decrease the incidence of foot-pad lesions in broiler and turkey populations and thus improve the health and welfare of the birds

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Origin and assessment of bruises in beef cattle at slaughter

Studies of bruises, as detected on carcasses at the slaughterhouse, may provide useful information about the traumatic situations the animals endure during the pre-slaughter period. In this paper, we review scientific data on the prevalence, risk factors and estimation of the age of bruises in beef cattle. Risk factors such as animal characteristics, transport conditions, stocking density, livestock auction and handling of the animals are discussed. Investigation of the age of bruises could provide information on when in the meat chain bruises occur and, could help to pinpoint where preventive measures should be taken, from the stage of collecting the animals on the farm until slaughter. We review the methods available to assess the age of the bruises; data on human forensic research are also included. The feasibility to identify traumatic episodes during the pre-slaughter period, in order to improve animal welfare is discusse