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Compared with their younger counterparts, older adults are inclined to allocate more attentional resources to positive over negative materials. This age-related positivity effect has been reported in various experimental paradigms; however, studies have not investigated the attention stage at which it appears or its potential neural mechanism. Thus, we investigated the time and frequency domain dynamics of younger and older adults during emotional attention processes. We obtained electroencephalography oscillation and event-related potential data for 20 older and 20 younger participants while they performed an emotional dot-probe task. We focused our time and frequency domain dynamics analyses on the posterior regions as a key structure for facial emotion perception and the frontal regions as a crucial structure for cognitive control. In the time domain, older adults showed an initial attentional shift to happy-related stimuli, whereas their younger counterparts did not demonstrate emotional modulation, as reflected by the N2pc component. The time–frequency decomposition was analyzed for the N2pc time window. The results showed that compared with younger adults, older adults showed an increased alpha power for happy faces in the right-posterior regions. Moreover, a parallel pattern was seen in frontal theta activity. The current findings highlight how electrocortical activity of the brain might moderate the tendency to prioritize positive information among healthy older adults. The emergence of an age-related positivity effect may be related to frontal cognitive control processing. These findings provide insight into the prevention and treatment of unsuccessful aging, such as late-life depression and anxiety. [ABSTRACT FROM AUTHOR]

Background: Radiomic diagnosis models generally consider only a single dimension of information, leading to limitations in their diagnostic accuracy and reliability. The integration of multiple dimensions of information into the deep learning model have the potential to improve its diagnostic capabilities. The purpose of study was to evaluate the performance of deep learning model in distinguishing tuberculosis (TB) nodules and lung cancer (LC) based on deep learning features, radiomic features, and clinical information. Methods: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics‐DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1‐score metrics. Results: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82–0.88), sensitivity of 0.85 (0.80–0.88), and specificity of 0.84 (0.83–0.87), performing better than the other models. Conclusion: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC. [ABSTRACT FROM AUTHOR]