Background: Previous studies have indicated that physical exercise enhances attentional function; however, the relationship between exercise mode and attentional networks has not been clarified for older adults (>60 years old). This study aimed to investigate the relationship between attentional networks and different exercise modes in older adults.
Methods: Two hundred and fifty-nine participants aged between 60 to 81 years were enrolled and classified into three groups (closed-skill group, open-skill group, or sedentary control group) using an exercise-related questionnaire. All participants completed an attention network test (ANT), which measured executive control, orienting, and alerting networks.
Results: The open-skill group had significantly higher executive network efficiency compared to the closed-skill ( < 0.01) and sedentary ( < 0.01) groups. The closed-skill group had significantly higher values compared to the sedentary control group ( < 0.05). Differences were not detected among groups for alerting and orienting networks ( > 0.05). The open-skill group had significantly higher values compared to the sedentary control group regarding proportion score of executive network ( < 0.01). In comparison, no significant differences were detected among groups for proportion scores of alerting and orienting networks.
Conclusion: This study extends current knowledge by demonstrating that open-skill exercises selectively enhance the executive control of attentional networks in older adults. Open-skill exercises combines physical exercise and cognitive training, potentially representing a more effective exercise mode to maintain or enhance attentional function in older adults.
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http://dx.doi.org/10.7717/peerj.8364 | DOI Listing |
Front Biosci (Landmark Ed)
January 2025
Department of Chemistry Education, Kongju National University, 32588 Gongju, Chungcheongnam-do, Republic of Korea.
In recent years, the role of coenzymes, particularly those from the vitamin B group in modulating the activity of metalloenzymes has garnered significant attention in cancer treatment strategies. Metalloenzymes play pivotal roles in various cellular processes, including DNA repair, cell signaling, and metabolism, making them promising targets for cancer therapy. This review explores the complex interplay between coenzymes, specifically vitamin Bs, and metalloenzymes in cancer pathogenesis and treatment.
View Article and Find Full Text PDFNutrients
January 2025
Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea.
Background: Food image recognition, a crucial step in computational gastronomy, has diverse applications across nutritional platforms. Convolutional neural networks (CNNs) are widely used for this task due to their ability to capture hierarchical features. However, they struggle with long-range dependencies and global feature extraction, which are vital in distinguishing visually similar foods or images where the context of the whole dish is crucial, thus necessitating transformer architecture.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, School of Chemical and Material Engineering, Jiangnan University, Wuxi 214122, China.
Atopic dermatitis (AD) is a chronic inflammatory skin disorder that has attracted global attention, and alkaloids from have been shown to have anti-inflammatory activity. Fermentation has been used for the structural modification of natural compounds to improve bioavailability and activity, but the AD therapeutic efficacy and mechanism of the fermented (FPN) are still unclear. The potential targets of FPN for AD were preliminarily screened using network pharmacology, and then PCR and WB were used to prove the therapeutic effect of FPN in AD.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
With the rapid development of AI algorithms and computational power, object recognition based on deep learning frameworks has become a major research direction in computer vision. UAVs equipped with object detection systems are increasingly used in fields like smart transportation, disaster warning, and emergency rescue. However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China.
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain.
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