Notes

Kefir along with Intestinal Microbiota Modulation: Significance within Individual Health.
Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction.Classroom communication involves teacher's behavior and student's responses. Extensive research has been done on the analysis of student's facial expressions, but the impact of instructor's facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher's emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor's facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor's facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn-Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.Nasopharyngeal carcinoma (NPC) is a malignant tumor in southern China, and nano Traditional Chinese Medicine (TCM) represents great potential to cancer therapy. To predict the potential targets and mechanism of polyphyllin II against NPC and explore its possibility for the future nano-pharmaceutics of Chinese medicine monomers, network pharmacology was included in the present study. Totally, ninety-four common potential targets for NPC and polyphyllin II were discovered. Gene Ontology (GO) function enrichment analysis showed that biological processes and functions mainly concentrated on apoptotic process, protein phosphorylation, cytosol, protein binding, and ATP binding. In addition, the anti-NPC effects of polyphyllin II mainly involved in the pathways related to cancer, especially in the PI3K-Akt signaling indicated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. RepSox datasheet The "drug-target-disease" network diagram indicated that the key genes were SRC, MAPK1, MAPK14, and AKT1. Taken together, this study revealed the potential drug targets and underlying mechanisms of polyphyllin II against NPC through modern network pharmacology, which provided a certain theoretical basis for the future nano TCM research.In this work, we develop and analyze a nonautonomous mathematical model for the spread of the new corona-virus disease (COVID-19) in Saudi Arabia. The model includes eight time-dependent compartments the dynamics of low-risk S L and high-risk S M susceptible individuals; the compartment of exposed individuals E; the compartment of infected individuals (divided into two compartments, namely those of infected undiagnosed individuals I U and the one consisting of infected diagnosed individuals I D ); the compartment of recovered undiagnosed individuals R U , that of recovered diagnosed R D individuals, and the compartment of extinct Ex individuals. We investigate the persistence and the local stability including the reproduction number of the model, taking into account the control measures imposed by the authorities. We perform a parameter estimation over a short period of the total duration of the pandemic based on the COVID-19 epidemiological data, including the number of infected, recovered, and extinct individuals, in different time episodes of the COVID-19 spread.
To describe the characteristics of fake news about COVID-19 disseminated in Brazil from January to June 2020.

The fake news recorded until 30 June 2020 in two websites (Globo Corporation website G1 and Ministry of Health) were collected and categorized according to their content. From each piece of fake news, the following information was extracted publication date, title, channel (e.g., WhatsApp), format (text, photo, video), and website in which it was recorded. Terms were selected from fake news titles for analysis in Google Trends to determine whether the number of searches using the selected terms had increased after the fake news appeared. The Brazilian regions with the highest percent increase in searches using the terms were also identified.

In the two websites, 329 fake news about COVID-19 were retrieved. Most fake news were spread through WhatsApp and Facebook. The most frequent thematic categories were politics (20.1%), epidemiology and statistics (e.g., proportion of cases and deaths) (19.5%), and prevention (16.
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