Lillelundmagnussen5630

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. 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%nd Northeast of Brazil.

Describe patterns in the dissemination of fake news in the context of COVID-19 mortality and infodemic management in six Latin American countries.

A descriptive ecological study explored the percentage of the population that is unable to recognize fake news, the percentage who trust social network content, and the percentage who use it as their sole news source in Argentina, Brazil, Chile, Colombia, Mexico, and Peru, up to 29 November 2020. Internet penetration rate, Facebook penetration rate, and COVID-19 mortality were calculated for each country. Information was obtained from literature searches and government and news portals in the selected countries, according to the World Health Organization's five proposed action areas identifying evidence, translating knowledge and science, amplifying action, quantifying impact, and coordination and governance.

Chile and Argentina were the countries with the greatest internet penetration rates (92.4% and 92.0%, respectively) and were also among the heaviest users of social media as their only means of obtaining news (32.0% and 28.0%, respectively). Brazil and Colombia showed intermediate behavior for both indicators. Mexico had the highest use of social networks, while Peru and Colombia had the highest indices of inability to recognize fake news.

It was observed that in countries with less use of social networks as the sole means for obtaining information and less trust in social network content, mortality was also lower.

It was observed that in countries with less use of social networks as the sole means for obtaining information and less trust in social network content, mortality was also lower.

The therapeutic armamentarium for patients with metastatic breast cancer is becoming more and more specific. Recommendations from clinical trials are not available for all treatment situations and patient subgroups, and it is therefore important to collect real-world data.

To develop recommendations for up-to-date treatments and participation in clinical trials for patients with metastatic breast cancer, the Prospective Academic Translational Research PRAEGNANT Network was established to optimize the quality of oncological care in the advanced therapeutic setting. The main aim of PRAEGNANT is to systematically record medical care for patients with metastatic breast cancer in the real-life setting, including the outcome and side effects of different treatment strategies, to monitor quality-of-life changes during therapy, to identify patients eligible for participation in clinical studies, and to allow targeted therapies based on the molecular structures of breast carcinomas.

This article describes the PRAEGNANT network and sheds light on the question of whether the various end points from clinical trials can be transferred to the real-world treatment situation.

This article describes the PRAEGNANT network and sheds light on the question of whether the various end points from clinical trials can be transferred to the real-world treatment situation.

Smoking inside the home affects the health of both the smoker and family members via secondhand exposure. This research examined the impact of a community participation program on creating smoke-free homes in a suburban community in Thanyaburi district, Pathumthani province in Thailand.

The study involved families, with a smoker in the home, that were randomly assigned to intervention and control groups each containing 27 families. The intervention group was administered with the community participation program for smoke-free homes for 5 sessions during the 6-month period of study. The program included providing information on secondhand smoking and harms, knowledge about quitting smoking and healthcare support, practice skills, campaigns in the community, visiting and encouraging, and reflecting and evaluation. The control group was normally treated by the community committee and health volunteers. Data collection was undertaken at baseline and at 6 months after implementation by an interview with questiraising awareness on the impact of secondhand smoke among family members and in working together to manage smoke-free home environments. this website The program may be applicable for further development within communities to achieve smoke-free homes.Network analysis is a topic in secondary mathematics education of growing importance because it offers students an opportunity to understand how to model and solve many authentic technology and engineering problems. However, very little is known about how students make sense of the algorithms typically used in network analysis. In this study, I used the Hungarian algorithm to explore how students make sense of a network algorithm and how it can be used to solve assignment problems. I report the results of a design-based research project in which eight Year 12 students participated in a teaching experiment that spanned four 60-min lessons. A hypothetical learning trajectory was developed in which students were introduced to the steps of the Hungarian algorithm incrementally. The results suggest that students made sense of the intermediate steps of the algorithm, the results of those steps, and how the algorithm works to solve assignment problems. The difficulties that students encountered are also discussed.

The online version contains supplementary material available at 10.