Enhanced error monitoring has been associated with higher levels of anxiety. This has been consistently demonstrated in its most reliable electrophysiological index, the error-related negativity (ERN), such that increased ERN is related with elevated anxiety symptomology. However, it is still unclear whether the structural properties of the brain are associated with individual differences in ERN amplitude. Moreover, the relationship between ERN and anxiety has recently been suggested to be moderated by sex, but the degree to which sex moderates the association between brain structure and ERN amplitude is unknown. The present study investigated the association between gray matter volume (GMV) and ERN amplitude in individuals with high trait anxiety (N = 98) as well as the role of sex in moderating this association. The ERN was elicited from a flanker task, whereas structural MRI images were obtained from whole brain structural T1-weighted MRI scans. The results of voxel-based morphometry analyses showed that the relationship between ERN difference scores and GMV was moderated by sex in the dorsal anterior cingulate cortex (dACC). This sex difference was derived from a negative correlation between ERN difference scores and dACC GMV in females and a positive correlation in males. Our findings are in accordance with the critical role of the dACC serving as a neural substrate of error monitoring. It also provides further evidence for sex-specific associations with brain structures related to error monitoring.
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http://dx.doi.org/10.1016/j.ijpsycho.2022.12.007 | DOI Listing |
Cureus
December 2024
Surgery, Norfolk and Norwich University Hospital, Norwich, GBR.
Surgeon fatigue significantly affects cognitive and motor functions, increasing the risk of errors and adverse patient outcomes. Traditional fatigue management methods, such as structured breaks and duty-hour limits, are insufficient for real-time fatigue detection in high-stakes surgeries. With advancements in artificial intelligence (AI), there is growing potential for AI-driven technologies to address this issue through continuous monitoring and adaptive interventions.
View Article and Find Full Text PDFEnviron Res
January 2025
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
The challenge of reconstructing air temperature for environmental applications is to accurately estimate past exposures even where monitoring is sparse. We present XGBoost-IDW Synthesis for air temperature (XIS-Temperature), a high-resolution machine-learning model for daily minimum, mean, and maximum air temperature, covering the contiguous US from 2003 through 2023. XIS uses remote sensing (land surface temperature and vegetation) along with a parsimonious set of additional predictors to make predictions at arbitrary points, allowing the estimation of address-level exposures.
View Article and Find Full Text PDFArtif Intell Med
January 2025
Department of Cardiovascular Ultrasound, The First Hospital of China Medical University, China; Clinical Medical Research Center of Imaging in Liaoning Province, Shenyang, China.
Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction.
View Article and Find Full Text PDFWaste Manag
January 2025
School of Environmental Science and Engineering, Zhejiang Provincial Key Laboratory of Solid Waste Treatment and Recycling, Zhejiang Engineering Research Center of Non-ferrous Metal Waste Recycling, Zhejiang Gongshang University, Hangzhou 310012, China. Electronic address:
Decentralized thermal treatment is a common method for municipal solid waste (MSW) disposal in rural areas. However, evaluating the effect of incineration has always been challenging owing to the difficult and time-consuming measurements involved. Herein, this study presented a rapid image recognition method for assessing the effects of thermal treatment on MSW using a neural network algorithm and a BAEVA 1.
View Article and Find Full Text PDFSci Total Environ
January 2025
State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR China; Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan 650093, PR China. Electronic address:
Solid waste is one of the primary contributors to environmental pollution currently, it is crucial to enhance the prevention and control of solid waste pollution in environmental management. The effectiveness of the second stage of purification in the industrial zinc hydrometallurgy is determined by the concentration of cobalt ion. Manual testing and monitoring of cobalt ion concentration are time consuming and costly, and prone to delays, which can result in discharge of cobalt ion concentration that does not meet the standards, leading to water pollution.
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