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The microstructure of the oxide scale and parent metal in ferritic 9Cr-1Mo steel was observed to explore the oxidation and carburisation mechanisms upon exposure to a CO2 gas environment at high temperature and high pressure. An experimental 9Cr-1Mo steel sample that had been oxidised at 580 °C for more than 165,000 hrs in coolant gas consisting primarily of CO2 was analysed. To elucidate the oxidation characterisation, scanning Raman spectrometry was used to analyse the oxide close to the metal/oxide interface. Carbon was found to be deposited in the spinel layer. The microstructure and the distribution of elemental chemical composition were examined and analysed using optical and scanning electron microscopy combined with energy dispersive X-ray analysis. The results are discussed with relation to understanding the mechanisms of oxidation and carburisation which aim to underpin extension of the service life of components fabricated from this steel. [ABSTRACT FROM AUTHOR]

Background: Polycystic ovary syndrome (PCOS), the most common endocrine disorder for women of reproductive age, is associated with increased risk for insulin resistance and type 2 diabetes. Current PCOS treatments insufficiently address the spectrum and severity of the disorder, and there is little evidence-based guidance available for lifestyle management of PCOS, especially through nutritional approaches. Some evidence shows that a very low-carbohydrate diet can improve glucose control compared to low-fat or moderate-carbohydrate diets, leading to improved glucose control and insulin levels that may help to treat symptoms of PCOS. This research investigates whether a very low-carbohydrate diet is more effective in improving glucose control and decreasing symptoms of PCOS in comparison to a DASH diet., Methods: The SUPER study aims to address the gap in knowledge about nutritional advice for people with PCOS through a randomized, comparative effectiveness trial comparing two approaches to glucose control: the dietary approaches to stopping hypertension (DASH) diet, and a very low-carbohydrate (VLC) diet. We will randomize 184 women with PCOS with body mass indexes (BMIs) between 25 and 50 kg/m 2 to a VLC or DASH diet. All participants will follow a 24-session, 12-month, online diet, and lifestyle intervention that teaches their assigned diet. Participants will receive nutritional education, support from diet coaches, and education about behavioral strategies to improve dietary adherence. The primary outcome measure is HbA1c, and secondary outcomes include glucose variance, lipid and hormone levels (including total and free testosterone), PCOS symptoms, inflammation (measured by high-sensitivity C-reactive protein), body composition and weight, psychological well-being, and intervention feasibility and acceptability., Discussion: The SUPER study is a randomized comparative effectiveness trial that compares two promising approaches to glucose control in people with PCOS. The study also aims to assess the effects of each diet on PCOS symptoms. The research addresses an important gap in knowledge regarding nutritional advice for people with PCOS., Trial Registration: ClinicalTrials.gov NCT05452642. Registered 6 July 2022., (© 2024. The Author(s).)

Objective: To compare endoscopic and histologic upper endoscopy (esophagogastroduodenoscopy [EGD]) findings in children with autism spectrum disorders (ASD) to age- and gender-matched controls with developmental delay (DD) or with typical development (TD)., Methods: Retrospective, cross-sectional study of children undergoing EGD, identifying those diagnosed with ASD, and matching on age and gender to children with DD or TD in ratio of 1:1:2. Rates of EGD findings were compared between the 3 groups using χ² or Fisher exact test. Multivariable linear regression was performed to identify predictors of abnormal histology., Results: A total of 2104 patients were included (526 ASD; 526 DD; 1052 TD). Children with ASD had higher rates of abnormal esophageal histology (ASD 38.4%; DD 33.4%; TD 30.4%, P = .008), particularly esophagitis. In multivariable modeling, ASD diagnosis was an independent predictor of abnormal esophageal histology (OR [95% CI] 1.38 [1.09, 1.76]) compared with TD. Stomach findings did not differ among the groups. In the duodenum, histologic abnormalities were observed with lower frequency in ASD (ASD 17.0%; DD 20.1%; TD 24.2%, P = .005). In multivariable analysis, ASD diagnosis was not a significant predictor (OR 0.78 [0.56, 1.09]) of abnormal duodenal histology., Conclusions: Children with ASD have higher rates of histologic esophagitis compared with age- and gender-matched DD and TD controls. ASD was a significant independent predictor of abnormal esophageal, but not, duodenal, histology. These results underscore the importance of EGD in children with ASD., Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest relevant to this article to disclose. Dr Hron is supported by National Institutes of Health (K23 DK133679) and NASPGHANAstra-Zeneca Research Award. Dr Silvester is supported by National Institutes of HealthK23 KD119584. The other authors received no external funding., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale. In previous studies we showed that time points corresponding to high-amplitude co-fluctuations disproportionately contributed to the time-averaged functional connectivity pattern and that these co-fluctuation patterns could be clustered into a low-dimensional set of recurring "states." Here, we assessed the relationship between these network states and quotidian variation in hormone concentrations. Specifically, we were interested in whether the frequency with which network states occurred was related to hormone concentration. We addressed this question using a dense-sampling dataset ( N = 1 brain). In this dataset, a single individual was sampled over the course of two endocrine states: a natural menstrual cycle and while the subject underwent selective progesterone suppression via oral hormonal contraceptives. During each cycle, the subject underwent 30 daily resting-state fMRI scans and blood draws. Our analysis of the imaging data revealed two repeating network states. We found that the frequency with which state 1 occurred in scan sessions was significantly correlated with follicle-stimulating and luteinizing hormone concentrations. We also constructed representative networks for each scan session using only "event frames"-those time points when an event was determined to have occurred. We found that the weights of specific subsets of functional connections were robustly correlated with fluctuations in the concentration of not only luteinizing and follicle-stimulating hormones, but also progesterone and estradiol., (© 2023 Massachusetts Institute of Technology.)

Resting-state functional connectivity is typically modeled as the correlation structure of whole-brain regional activity. It is studied widely, both to gain insight into the brain's intrinsic organization but also to develop markers sensitive to changes in an individual's cognitive, clinical, and developmental state. Despite this, the origins and drivers of functional connectivity, especially at the level of densely sampled individuals, remain elusive. Here, we leverage novel methodology to decompose functional connectivity into its precise framewise contributions. Using two dense sampling datasets, we investigate the origins of individualized functional connectivity, focusing specifically on the role of brain network "events" - short-lived and peaked patterns of high-amplitude cofluctuations. Here, we develop a statistical test to identify events in empirical recordings. We show that the patterns of cofluctuation expressed during events are repeated across multiple scans of the same individual and represent idiosyncratic variants of template patterns that are expressed at the group level. Lastly, we propose a simple model of functional connectivity based on event cofluctuations, demonstrating that group-averaged cofluctuations are suboptimal for explaining participant-specific connectivity. Our work complements recent studies implicating brief instants of high-amplitude cofluctuations as the primary drivers of static, whole-brain functional connectivity. Our work also extends those studies, demonstrating that cofluctuations during events are individualized, positing a dynamic basis for functional connectivity., (Copyright © 2022. Published by Elsevier Inc.)

The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the "out-of-the-box" version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting "space-independent" modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights multiple frontiers for future research and applications., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: To develop a prediction equation to estimate the body fat percentage of young men using simple measurements., Methods: Subjects were 150 males, age 18 to 26 years. Self-reported lifestyle variables and measured circumferences were used to predict body fat percentage, assessed using hydrostatic weighing., Results: The final model included 7 variables. The equation had a SEE of 2.66% and a PRESS SEE of 2.82%, and accounted for 80% of the variance in the criterion, body fat percentage., Conclusions: The regression equation developed in this study accurately estimates body fat percentage in young men by using simple, inexpensive measures.

The Multinational Force and Observers in an 11-nation peacekeeping force established to monitor the 1979 Egyptian-Israeli Treaty of Peace. Provision of medical support for this force was the first employment of the U.S. Army Medical Department in an effort of this kind. This article is an update to "Medical Support of the Sinai Multinational Force and Observers, 1982," published in Military Medicine in October 1983. We compare the execution of medical support in 1982 with that of 1990 and describe medical conditions and problems encountered during our tour.