Athletes undergoing energy restriction for weight/fat reduction sometimes apply 'diet breaks' involving increased energy intake, but there is little empirical evidence of effects on outcomes. Twenty-six resistance-trained athletes (11/26 or 42% female) who had completed 12 weeks of intermittent energy restriction participated in this study. Participants had a mean (SD) age of 29.3 (6.4) years, a weight of 72.7 (15.9) kg, and a body fat percentage of 21.3 (7.5) %. During the 1-week diet break, energy intake was increased (by means of increased carbohydrate intake) to predicted weight maintenance requirements. While the 1-week diet break had no significant effect on fat mass, it led to small but significant increases in mean body weight (0.6 kg, P less then 0.001), fat-free mass (0.7 kg, P less then 0.001) and in resting energy expenditure, from a mean (and 95% confidence interval) of 7000 (6420 to 7580) kJ/day to 7200 (6620 to 7780) kJ/day (P = 0.026). Overall, muscle endurance in the legs (but not arms) improen increases in appetite might threaten dietary adherence. Trial registration Australian New Zealand Clinical Trials Registry Reference Number ACTRN12618000638235 anzctr.org.au. To evaluate the retinal nerve fiber layer (RNFL) and macular thicknesses and identify systemic risk factors for thinning of these layers in patients with metabolic syndrome (MetS). A cross-sectional observational study was performed on patients diagnosed with MetS and compared to normal controls. All patients underwent ophthalmic and anthropometric examination, serological and biochemical blood investigations; and ocular imaging using spectral-domain optical coherence tomography. Patients with ocular pathology were excluded. Unpaired t-test was used to compare mean thickness between the two groups. One-way ANOVA with Bonferroni correction for multiple comparisons was used to compare mean thickness between different tertiles of MetS parameters, and a generalized estimating equation was used to correct for inter-eye correlation and to assess association between mean thickness and covariates. Two hundred and forty-eight eyes from 124 participants (11 ratio of MetS patients to controls) were included. Age rL thinning was seen more than macula thinning in MetS patients, suggesting RNFL susceptibility to neurodegeneration than the macula. A higher number of metabolic components and elevated triglyceride levels were independent risk factors for retinal thinning in this group of patients.RNFL thinning was seen more than macula thinning in MetS patients, suggesting RNFL susceptibility to neurodegeneration than the macula. A higher number of metabolic components and elevated triglyceride levels were independent risk factors for retinal thinning in this group of patients.Cats are ubiquitous companion animals that have been keenly associated with humans for thousands of years and only recently have been intentionally bred for aesthetically appealing coat looks and body forms. The intense selection on single gene phenotypes and the various breeding histories of cat breeds have left different marks on the genomes. Using a previously published 63K Feline SNP array dataset of twenty-six cat breeds, this study utilized a genetic differentiation-based method (di) to empirically identify candidate regions under selection. Defined as three or more overlapping (500Kb) windows of high levels of population differentiation, we identified a total of 205 candidate regions under selection across cat breeds with an average of 6 candidate regions per breed and an average size of 1.5 Mb per candidate region. Using the combined size of candidate regions of each breed, we conservatively estimate that a minimum of ~ 0.1-0.7% of the autosomal genome is potentially under selection in cats. As positive controls and tests of our methodology, we explored the candidate regions of known breed-defining genes (e.g., FGF5 for longhaired breeds) and we were able to detect the genes within candidate regions, each in its corresponding breed. For breed specific exploration of candidate regions under selection, eleven representative candidate regions were found to encompass potential candidate genes for several phenotypes such as brachycephaly of Persian (DLX6, DLX5, DLX2), curled ears of American Curl (MCRIP2, PBX1), and body-form of Siamese and Oriental (ADGRD1), which encourages further molecular investigations. The current assessment of the candidate regions under selection is empiric and detailed analyses are needed to rigorously disentangle effects of demography and population structure from artificial selection.q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Selleckchem Milciclib Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment.

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