Factors associated with grazing behavior in candidates for bariatric surgery at a hospital in the Amazon

BlackgroundTo assess grazing behavior and associated factors in candidates for bariatric surgery monitored at a public hospital that is a reference in the care of people with severe obesity.MethodsCross-sectional analytical study, with candidates for bariatric surgery of both genders, treated in a public hospital in the Amazon. To assess grazing behavior, the Repetitive Eating Questionnaire was used, and to investigate patterns of eating behavior, the Three Factor Eating Questionnaire was used, which assesses: Emotional Eating, Cognitive Restriction and Uncontrolled Eating. Sociodemographic information was obtained through self-report and the description of medication use through the medical record. Body mass index (BMI) was also calculated by measuring weight and height. The SPSS program, v. 21.0 was used. The study was approved by the Research Ethics Committee.ResultsA total of 205 participants were evaluated, with a mean age of 37.5 ± 8.6 years, the majority (93.7%) being women and the majority (59.5%) was not also using medication to lose weight. About 66.3% of the participants had compulsive grazing. The factor with the highest score was cognitive restriction (p < 0.001). Individuals who used weight loss drugs had higher scores in the cognitive restriction factor (p = 0.015) and lower scores for uncontrolled eating (p = 0.008), compulsive grazing (p = 0.021) and non-compulsive grazing (p = 0.034).ConclusionLinear regression showed that emotional eating and uncontrolled eating were predictors of both compulsive grazing and non-compulsive grazing behavior. It was observed that grazing behavior, cognitive restriction, emotional eating and uncontrolled eating are present and correlated in the studied patients. In addition, the use of weight loss drugs seems to help reduce dysfunctional eating behaviors in patients with severe obesity

Systematization of reconstruction of the abdominal wall after reconstruction with TRAM

Introduction: Tram (transverse abdominal rectum flap) has remained the most used autologous breast reconstruction method over the last 30 years. First described by Holmström, the flap allows breast reconstruction with analogous tissue, providing natural appearance and consistency and lasting results. Reconstruction of the abdominal wall of the defect installed after flap transposition is a major challenge, and there is no consensus on the form for elevation or closure of the abdominal wall flap. The article aims to present a practical method for abdominal wall reconstructions to reduce morbidity in patients undergoing breast reconstruction with TRAM. Methods: This is a descriptive work of a systematic abdominal wall reconstruction technique using propylene mesh. Result: Once the technique is applied, we have an abdominal wall covered with polypropylene fabric, fully fixed and well adapted. The systematization of abdominal wall reconstruction after reconstruction with TRAM was performed, which is characterized by being easy to reproduce and applicable. Conclusion: The technique is a good alternative in abdominal wall reconstructions for the surgeon, systematizing polypropylene mesh adaptation

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Search for diboson resonances in hadronic final states in 139 fb −1 of pp collisions at s = 13 TeV with the ATLAS detector

Abstract: Narrow resonances decaying into W W, W Z or ZZ boson pairs are searched for in 139 fb−1 of proton-proton collision data at a centre-of-mass energy of s = 13 TeV recorded with the ATLAS detector at the Large Hadron Collider from 2015 to 2018. The diboson system is reconstructed using pairs of high transverse momentum, large-radius jets. These jets are built from a combination of calorimeter- and tracker-inputs compatible with the hadronic decay of a boosted W or Z boson, using jet mass and substructure properties. The search is performed for diboson resonances with masses greater than 1.3 TeV. No significant deviations from the background expectations are observed. Exclusion limits at the 95% confidence level are set on the production cross-section times branching ratio into dibosons for resonances in a range of theories beyond the Standard Model, with the highest excluded mass of a new gauge boson at 3.8 TeV in the context of mass-degenerate resonances that couple predominantly to gauge bosons

Measurement of the azimuthal anisotropy of charged-particle production in Xe+Xe collisions at sNN =5.44 TeV with the ATLAS detector

This paper describes the measurements of flow harmonics v2-v6 in 3μb-1 of Xe+Xe collisions at sNN=5.44 TeV performed using the ATLAS detector at the Large Hadron Collider (LHC). Measurements of the centrality, multiplicity, and pT dependence of the vn obtained using two-particle correlations and the scalar product technique are presented. The measurements are also performed using a template-fit procedure, which was developed to remove nonflow correlations in small collision systems. This nonflow removal is shown to have a significant influence on the measured vn at high pT, especially in peripheral events. Comparisons of the measured vn with measurements in Pb+Pb collisions and p+Pb collisions at sNN=5.02 TeV are also presented. The vn values in Xe+Xe collisions are observed to be larger than those in Pb+Pb collisions for n=2, 3, and 4 in the most central events. However, with decreasing centrality or increasing harmonic order n, the vn values in Xe+Xe collisions become smaller than those in Pb+Pb collisions. The vn in Xe+Xe and Pb+Pb collisions are also compared as a function of the mean number of participating nucleons, (Npart), and the measured charged-particle multiplicity in the detector. The v3 values in Xe+Xe and Pb+Pb collisions are observed to be similar at the same (Npart) or multiplicity, but the other harmonics are significantly different. The ratios of the measured vn in Xe+Xe and Pb+Pb collisions, as a function of centrality, are also compared to theoretical calculations

Measurement of hadronic event shapes in high-p T multijet final states at √s = 13 TeV with the ATLAS detector

A measurement of event-shape variables in proton-proton collisions at large momentum transfer is presented using data collected at s = 13 TeV with the ATLAS detector at the Large Hadron Collider. Six event-shape variables calculated using hadronic jets are studied in inclusive multijet events using data corresponding to an integrated luminosity of 139 fb−1. Measurements are performed in bins of jet multiplicity and in different ranges of the scalar sum of the transverse momenta of the two leading jets, reaching scales beyond 2 TeV. These measurements are compared with predictions from Monte Carlo event generators containing leading-order or next-to-leading order matrix elements matched to parton showers simulated to leading-logarithm accuracy. At low jet multiplicities, shape discrepancies between the measurements and the Monte Carlo predictions are observed. At high jet multiplicities, the shapes are better described but discrepancies in the normalisation are observed. [Figure not available: see fulltext.

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost