Association between cognitive restraint, uncontrolled eating, emotional eating and BMI and the amount of food wasted in early adolescent girls.

Understanding of behavioral factors associated with obesity is of importance in addressing this issue. This study examined the association between cognitive restraint, uncontrolled eating, emotional eating and body mass index (BMI) and amount of food plated, consumed, leftovers, and leftover food thrown into the trash (food wasted) in early adolescent girls nine to 13 years in O'ahu, Hawai'i (n = 93). Food plated, consumed, leftovers, and food wasted were estimated using a three-day mobile food record™ (mFR). Weight and height were measured to compute BMI (kg/m 2). The three-factor eating questionnaire provided a score from 0 to 100 for cognitive restraint, uncontrolled eating, and emotional eating. Higher scores are indicative of greater cognitive restraint, uncontrolled eating, and emotional eating. Pearson's correlations were computed to examine the relationship between three factor eating scores and BMI. General linear models were conducted to examine the effect of each of three-factor eating scores on food plated, consumed, leftovers, and food wasted. Cognitive restraint was positively correlated with BMI (r = 0.36, p < 0.001) and with BMI z-score (r = 0.40, p < 0.001). There were no associations between three-factor eating scores and food plated, consumed, leftovers, and food wasted at lunch. However, at dinner, total energy plated, left over, and food wasted increased by 4.24 kcal/day (p = 0.030), 1.67 kcal/day (p = 0.002), and 0.93 kcal/day (p = 0.031), respectively, with a unit increase in uncontrolled eating score. Similarly, total energy plated and energy left over at dinner increased by 3.40 kcal/day (p = 0.045) and 1.51 kcal/day (p = 0.001), respectively, with a unit increase in emotional eating score. Additional research should examine the specific roles of cognitive restraint, uncontrolled eating, emotional eating and food waste in the development of obesity in adolescents

Manipulation and generation of synthetic satellite images using deep learning models

Generation and manipulation of digital images based on deep learning (DL) are receiving increasing attention for both benign and malevolent uses. As the importance of satellite imagery is increasing, DL has started being used also for the generation of synthetic satellite images. However, the direct use of techniques developed for computer vision applications is not possible, due to the different nature of satellite images. The goal of our work is to describe a number of methods to generate manipulated and synthetic satellite images. To be specific, we focus on two different types of manipulations: full image modification and local splicing. In the former case, we rely on generative adversarial networks commonly used for style transfer applications, adapting them to implement two different kinds of transfer: (i) land cover transfer, aiming at modifying the image content from vegetation to barren and vice versa and (ii) season transfer, aiming at modifying the image content from winter to summer and vice versa. With regard to local splicing, we present two different architectures. The first one uses image generative pretrained transformer and is trained on pixel sequences in order to predict pixels in semantically consistent regions identified using watershed segmentation. The second technique uses a vision transformer operating on image patches rather than on a pixel by pixel basis. We use the trained vision transformer to generate synthetic image segments and splice them into a selected region of the to-be-manipulated image. All the proposed methods generate highly realistic, synthetic, and satellite images. Among the possible applications of the proposed techniques, we mention the generation of proper datasets for the evaluation and training of tools for the analysis of satellite images. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI

Ovarian cancer molecular pathology.

Peer reviewe

Potential cellular and biochemical mechanisms of exercise and physical activity on the ageing process

Exercise in young adults has been consistently shown to improve various aspects of physiological and psychological health but we are now realising the potential benefits of exercise with advancing age. Specifically, exercise improves cardiovascular, musculoskeletal, and metabolic health through reductions in oxidative stress, chronic low-grade inflammation and modulating cellular processes within a variety of tissues. In this this chapter we will discuss the effects of acute and chronic exercise on these processes and conditions in an ageing population, and how physical activity affects our vasculature, skeletal muscle function, our immune system, and cardiometabolic risk in older adults

Benchmarking of image watermarking algorithms for digital rights management

We discuss in this paper the issues related to image watermarking benchmarking and scenarios based on digital rights management requirements. We show that improvements are needed in image quality evaluation, specially related to image geometrical deformation assessments, in risk evaluation related to specific delivery scenarios and in multidimensional criteria evaluation. Efficient benchmarking is still an open issue and we suggest the use of open-source Web-based evaluation systems for the collective progresses in this domain

Fooling PRNU-Based Detectors Through Convolutional Neural Networks

In the last few years, forensic researchers have developed a wide set of techniques to blindly attribute an image to the device used to shoot it. Among these techniques, those based on photo response non uniformity (PRNU) have shown incredibly accurate results, thus they are often considered as a reference baseline solution. The rationale behind these techniques is that each camera sensor leaves on acquired images a characteristic noise pattern. This pattern can be estimated and uniquely mapped to a specific acquisition device through a cross-correlation test. In this paper, we study the possibility of leveraging recent findings in the deep learning field to attack PRNU-based detectors. Specifically, we focus on the possibility of editing an image through convolutional neural networks in a visually imperceptible way, still hindering PRNU noise estimation. Results show that performing such an attack is possible, even though an informed forensic analyst can reduce its impact through a smart test