BackgroundBioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.ResultsA new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.ConclusionsA new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.

SARS-CoV-2’s population structure might have a substantial impact on public health management and diagnostics if it can be identified. It is critical to rapidly monitor and characterize their lineages circulating globally for a more accurate diagnosis, improved care, and faster treatment. For a clearer picture of the SARS-CoV-2 population structure, clustering the sequencing data is essential. Here, deep clustering techniques were used to automatically group 29,017 different strains of SARS-CoV-2 into clusters. We aim to identify the main clusters of SARS-CoV-2 population structure based on convolutional autoencoder (CAE) trained with numerical feature vectors mapped from coronavirus Spike peptide sequences. Our clustering findings revealed that there are six large SARS-CoV-2 population clusters (C1, C2, C3, C4, C5, C6). These clusters contained 43 unique lineages in which the 29,017 publicly accessible strains were dispersed. In all the resulting six clusters, the genetic distances within the same cluster (intra-cluster distances) are less than the distances between inter-clusters (P-value 0.0019, Wilcoxon rank-sum test). This indicates substantial evidence of a connection between the cluster’s lineages. Furthermore, comparisons of the K-means and hierarchical clustering methods have been examined against the proposed deep learning clustering method. The intra-cluster genetic distances of the proposed method were smaller than those of K-means alone and hierarchical clustering methods. We used T-distributed stochastic-neighbor embedding (t-SNE) to show the outcomes of the deep learning clustering. The strains were isolated correctly between clusters in the t-SNE plot. Our results showed that the (C5) cluster exclusively includes Gamma lineage (P.1) only, suggesting that strains of P.1 in C5 are more diversified than those in the other clusters. Our study indicates that the genetic similarity between strains in the same cluster enables a better understanding of the major features of the unknown population lineages when compared to some of the more prevalent viral isolates. This information helps researchers figure out how the virus changed over time and spread to people all over the world.

The COVID-19 pandemic has introduced to mild the risks of deadly epidemic-prone illnesses sweeping our globalized planet. The pandemic is still going strong, with additional viral variations popping up all the time. For the close to future, the international response will have to continue. The molecular tests for SARS-CoV-2 detection may lead to False-negative results due to their genetic similarity with other coronaviruses, as well as their ability to mutate and evolve. Furthermore, the clinical features caused by SARS-CoV-2 seem to be like the symptoms of other viral infections, making identification even harder. We constructed seven hidden Markov models for each coronavirus family (SARS-CoV2, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV), using their complete genome to accurate diagnose human infections. Besides, this study characterized and classified the SARS-CoV2 strains according to their different geographical regions. We built six SARS-CoV2 classifiers for each world's continent (Africa, Asia, Europe, North America, South America, and Australia). The dataset used was retrieved from the NCBI virus database. The classification accuracy of these models achieves 100% in differentiating any virus model among others in the Coronavirus family. However, the accuracy of the continent models showed a variable range of accuracies, sensitivity, and specificity due to heterogeneous evolutional paths among strains from 27 countries. South America model was the highest accurate model compared to the other geographical models. This finding has vital implications for the management of COVID-19 and the improvement of vaccines.

Many disorders can disrupt the neuromuscular channels used by the brain to communicate with and control its external environment. Patients with severe neural disorders lose most of voluntary muscle control. Some patients can control their eye movements and may be able to communicate. In the absence of methods for repairing the damage done by these disorders, the only option for restoring function to those with motor impairments is to provide the brain with a new, muscular/non-muscular and non invasive communication and control channel, a direct Brain Machine Interface (BMI) for conveying messages and commands to the external world. An attempt has been provided to collect the brain activities and extract specific estimations to control the wheelchair movement. The data has been collected though fourteen electrodes fixed on the scalp by modern strategy (without non-drying conductive past). This method has used a wireless communication head set based on the hydration sensors and Bluetooth technology. Four movements are detected (turn right-turn left-forward-stop) based on the eye blinks (right wing, left wing, single/double blinks). The wavelet has used to extract best signals and NN system to take the learning sessions and give the required action.

BackgroundOur study aimed to demonstrate the short-term impacts of right ventricular apical pacing (RVAP) and right ventricular septal pacing (RVSP) on left ventricular (LV) regional longitudinal strain (RLS) and global longitudinal strain (GLS) in patients with preserved ejection fraction (EF). LV strain and functions may be altered by RVAP. RVSP might be a better alternative. The detrimental effect of right ventricular (RV) pacing may be mediated by regional LV impairment.MethodsSixty-two patients indicated for permanent pacemaker implantation and preserved LV systolic function were included. Dual chamber pacemakers were implanted in all patients. Patients were divided into two groups according to RV lead position: group A (RVAP, n = 32) and group B (RVSP, n = 30). Patients were examined at baseline and after 6 months of implantation for LV systolic functions, global and regional strain by echocardiography and 2D speckle tracking echocardiography.ResultsPaced QRS duration was significantly shorter in group B compared to group A patients (P = 0.02). Regarding ventricular strain, there was no statistically significant difference between both groups at baseline measurements in comparisons of GLS, relative apical longitudinal strain (rALS) and RLS (P > 0.05). In contrast, there was statistically significant difference between both groups in results of GLS (P = 0.01) at 6 months. In addition, RLSs in septal, apical and rALS were affected after 6 months with P values of 0.02, 0.03 and 0.03, respectively.ConclusionRVAP appears to worsen GLS more than RVSP, and the resultant decrease in apical strain is most correlated region to decrease in GLS.