EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl

Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic

The Playing games become a serious issue and may have adverse effects on the quality of life of children. The research aims at identify in the factors and degree of influence which lead to gaming addiction and its impact on the quality of life of world children employing a comprehensive. Our method collects 2,526 children and adults’ data for five significant regions globally contain schools and universities in municipal and non-municipal areas. The research also aims to investigate the effect that gaming addiction has on the quality of life of children. Structural equation test and the (NNM) were uutilized to analyze the data. The results indicate some differences between boys and girls as to what factors lead to gaming addiction. The average Root Means Square Error (RMSE) of the neural network model is relatively low (.0103 for male training data and .0113 for male examining data, while for females it was .0103 for exercising data and .0104 for examining data), But gaming addiction was found to harm the life for both genders. Discussions comprising both academic as well as practical perspectives are also presented

Implementing Blended Learning in Classrooms: Educators\u27 Perspectives

Blended learning is one of the modalities used to enhance students\u27 learning experiences in the 21st century. South African educators who migrate to countries overseas have mixed feelings about their teaching using technology in schools. This chapter presents findings on implementation of blended learning in mathematics classrooms from the perspectives of two South African educators working in an international school in Saudi Arabia. The study was done using a qualitative case study within an interpretivist paradigm and it was guided by the technology acceptance model. Two South African educators teaching at an international school in Saudi Arabia were purposively selected to participate in the study. Data was collected using semi-structured interviews and document analysis. A significant contribution of the study was the development of a model which shows that perspectives of the educators on blended learning hinges on two entities: technology acceptance and educators\u27 self-efficacy

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children <18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p<0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p<0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p<0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Ficus nitida L . THE EFFECT SUN LIGHT INTENSITY AND CUTTING TYPE IN ROOTING ABILITY

This study was carried out to investigate the effect of cutting type and sun light exposure. Terminal middle and basal cuttings were taken from upwar dowrward shoots. Based on results obtained , the medial cuttings and sun light exposure significantly exceeded by increasing rooting percentage(3.67 , 3.33)% roots length (12.77, 11.2) cm, chlorophyll percentage (22.7, 23.7)% ,leaves number (7.11, 6.92) leaf/plant and shoots number (2.77 , 2.47 )shot/plant , respectively Moreover , the interaction between medial cutting and sunlight exposure significantly increased rooting and vegetative growth characteristics

Augmenting nitric oxide (NO) bioavailability reduces ischaemic myocardial injury

Background: Psoriatic arthritis (PsA) is a complex inflammatory condition with both genetic and environmental risk factors contributing to disease. A potential environmental risk factor, known to modify the immune system, is the intestinal microbiota. In PsA there is evidence of intestinal inflammation 1,2 and recently dysbiosis of the gut microbiota has been reported in treatment na{\"\i}ve PsA patients 3. However, there is no information on the temporal stability of the microbiota over time in established PsA on treatment compared to matched PsC controls. Objectives: To explore the temporal stability of gut microbiota composition and reveal associations with PsA compared to PsC while on stable on treatment with methotrexate. Methods: Patients with PsA and PsC were recruited to the study if they had been on a stable dose of methotrexate for 6 months. Bacterial DNA was extracted and the V3-V4 hypervariable region of the 16S rRNA was amplified and sequenced on MiSeq. The resultant data was analysed using a bespoke bioinformatics pipeline and taxa were assigned using the Ribosomal Database Project classifier according to the SILVA119 database. The Wilcoxon rank sum test was used to assess alpha diversity indices, while permanova testing using Bray Curtis distance and DESeq2 values corrected for false-discovery rate (FDR) were used to compare beta diversity indices after removing low abundance (\<0.5\%) Operational Taxonomic Units (OTU). The ALDEx2 analysis package was used to assess effect size. Results: Stool samples were available 9 PsA (n=13) and 6 PsC (n=12) individuals. Second stool samples were also obtained from the PsA (n=5) and PsC (n=4) groups.View this table:Table 1. Baseline DemographicsNo significant difference in the alpha diversity indices was observed between PsA and PsC. The beta diversity index showed no significant difference between the two conditions using permanova test. However, using the DESeq2-FDR analysis, 8 OTUs were identified which had significantly (p\<0.01) different abundances in PsA compared to PsC. The taxa (Lachnospiraceae \& Ruminococcaeceae) predominantly belonged to the Firmicutes phylum, family Lachnospiraceae and Actinobacteria phylum, family Bifidobacteriaceae (Bifidobacteriaceae). The significant OTUs with DESEq2 had an effect size \>1 using ALDEx2 but the BH p-value was not significant (p\<0.01), which may be due to the small sample size. There were no significant differences in the diversity measures over time. Conclusions: These results suggest that a gut enterotype with predominant Firmicutes/Actinobacteria composition is associated with stable/well controlled disease and is stable over time. This requires replication in a larger cohort