Human research has shown interactions between rewards and cognitive control. In animal models of affective neuroscience, reward administration typically involves administering orosensory sugar signals (OSS) during caloric-deprived states. We adopted this procedure to investigate neurophysiological mechanisms of reward-cognitive control interactions in humans. We predicted that OSS would affect neurophysiological and behavioral indices of error processing oppositely, depending on the relative weight of the OSS-induced 'wanting' and 'liking' components of reward. We, therefore, conducted a double-blind, non-nutritive sweetener-controlled study with a within-subject design. Fasted (16 hr) participants (= 61) performed a modified Flanker task to assess neurophysiological (error-related negativity [N/ERN]) and behavioral (post-error adaptations) measures of error processing. Non-contingent to task performance, we repeatedly administered either a sugar (glucose) or non-nutritive sweetener (aspartame) solution, which had to be expulsed after short oral stimulation to prevent post-oral effects. Consistent with our hypothesis on how 'liking' would affect N/ERN amplitude, we found the latter to be decreased for sugar compared to aspartame. Unexpectedly, we found post-error accuracy, instead of post-error slowing, to be reduced by sugar relative to aspartame. Our findings suggest that OSS may interact with error processing through the 'liking' component of rewards. Adopting our reward-induction procedure (i.e. administering OSS in a state of high reward sensitivity [i.e. fasting], non-contingent to task performance) might help future research investigating the neural underpinnings of reward-cognitive control interactions in humans.
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http://dx.doi.org/10.1080/1028415X.2021.1993538 | DOI Listing |
BMJ Qual Saf
January 2025
National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, USA.
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classification system for categorising generative AI patient safety errors. Our classification system is organised around two AI system stages (input and output) with specific error types by stage.
View Article and Find Full Text PDFForensic Sci Int Genet
December 2024
Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, PR China. Electronic address:
DNA methylation at age-related CpG (AR-CpG) sites holds significant promise for forensic age estimation. However, somatic models perform poorly in semen due to unique methylation dynamics during spermatogenesis, and current studies are constrained by the limited coverage of methylation microarrays. This study aimed to identify novel semen-specific AR-CpG sites using double-enzyme reduced representation bisulfite sequencing (dRRBS) and validate these markers, alongside previously reported sites and neighboring CpGs, using bisulfite amplicon sequencing (BSAS) to develop robust age estimation models.
View Article and Find Full Text PDFSci Rep
January 2025
College of Intelligent systems Science and Engineering, Harbin Engineering University, Harbin, 150006, China.
Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem.
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January 2025
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, Republic of Korea.
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming.
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January 2025
Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, California, USA.
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-invasive alternative for estimating blood glucose levels. In this study, we propose an innovative 1-second signal segmentation method and evaluate the performance of three advanced deep learning models using a novel dataset to estimate blood glucose levels from PPG signals.
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