Binaural beat (BB) is a promising technique for memory improvement in elderly or people with neurological conditions. However, the related modulation of cortical networks followed by behavioral changes has not been investigated. The objective of this study is to establish a relationship between BB oscillatory brain activity evoked by stimulation and a behavioral response in a short term memory task. Three Groups A, B, and C of 20 participants each received alpha (10 Hz), beta (14 Hz), and gamma (30 Hz) BB, respectively, for 15 min. Their EEG was recorded in pre, during, and post BB states. Participants performed a digit span test before and after a BB session. A significant increase in the cognitive score was found only for Group A while a significant decrease in reaction time was noted for Groups A and C. Group A had a significant decrease of theta and increase of alpha power, and a significant increase of theta and decrease of gamma imaginary coherence (ICH) post BB. Group C had a significant increase in theta and gamma power accompanied by the increase of theta and gamma ICH post BB. The effectiveness of BB depends on the frequency of stimulation. A putative neural mechanism involves an increase in theta ICH in parieto-frontal and interhemispheric frontal networks.
With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer. The major goals of this study are to diagnose lung cancer and its seriousness and to identify malignant lung nodules from the provided input lung picture. This paper applies unique deep learning techniques to identify the exact location of the malignant lung nodules. Using a DenseNet model, mixed ground glass is analyzed in low-dose, low-resolution CT scan images of nodules (mGGNs) with a slice thickness of 5 mm in this study. This was done to categorize and identify many histological subtypes of lung cancer. Low-resolution CT scans are used to pathologically classify invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA). 105 low-resolution CT images with 5 mm thick slices from 105 patients at Lishui Central Hospital were selected. To detect and distinguish, IAC and MIA, extend and enhance deep learning two- and three-dimensional DenseNet models are used. The two-dimensional DenseNet model was shown to perform much better than the three-dimensional DenseNet model in terms of classification accuracy (76.67%), sensitivity (63.3%), specificity (100%), and area under the receiver operating characteristic curve (0.88). Finding the histological subtypes of persons with lung cancer should aid doctors in making a more precise diagnosis, even if the image quality is not outstanding.
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