An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

MicroRNA profiling of tomato leaf curl new delhi virus (tolcndv) infected tomato leaves indicates that deregulation of mir159/319 and mir172 might be linked with leaf curl disease

Background: Tomato leaf curl virus (ToLCV), a constituent of the genus Begomovirus, infects tomato and other plants with a hallmark disease symptom of upward leaf curling. Since microRNAs (miRs) are known to control plants developmental processes, we evaluated the roles of miRNAs in Tomato leaf curl New Delhi virus (ToLCNDV) induced leaf curling. Results: Microarray analyses of miRNAs, isolated from the leaves of both healthy and ToLCNDV agroinfected tomato cv Pusa Ruby, revealed that ToLCNDV infection significantly deregulated various miRNAs representing ~13 different conserved families (e.g., miR319, miR172, etc.). The precursors of these miRNAs showed similar deregulated patterns, indicating that the transcription regulation of respective miRNA genes was perhaps the cause of deregulation. The expression levels of the miRNA-targeted genes were antagonistic with respect to the amount of corresponding miRNA. Such deregulation was tissue-specific in nature as no analogous misexpression was found in flowers. The accumulation of miR159/319 and miR172 was observed to increase with the days post inoculation (dpi) of ToLCNDV agroinfection in tomato cv Pusa Ruby. Similarly, these miRs were also induced in ToLCNDV agroinfected tomato cv JK Asha and chilli plants, both exhibiting leaf curl symptoms. Our results indicate that miR159/319 and miR172 might be associated with leaf curl symptoms. This report raises the possibility of using miRNA(s) as potential signature molecules for ToLCNDV infection. Conclusions: The expression of several host miRNAs is affected in response to viral infection. The levels of the corresponding pre-miRs and the predicted targets were also deregulated. This change in miRNA expression levels was specific to leaf tissues and observed to be associated with disease progression. Thus, certain host miRs are likely indicator of viral infection and could be potentially employed to develop viral resistance strategies

An efficient network intrusion detection and classification system

Intrusion detection in computer networks is of great importance because of its effects on the different communication and security domains. The detection of network intrusion is a challenge. Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. Many approaches have already been proposed recently on network intrusion detection. However, they face critical challenges owing to the continuous increase in new threats that current systems do not understand. This paper compares multiple techniques to develop a network intrusion detection system. Optimum features are selected from the dataset based on the correlation between the features. Furthermore, we propose an AdaBoost-based approach for network intrusion detection based on these selected features and present its detailed functionality and performance. Unlike most previous studies, which employ the KDD99 dataset, we used a recent and comprehensive UNSW-NB 15 dataset for network anomaly detection. This dataset is a collection of network packets exchanged between hosts. It comprises 49 attributes, including nine types of threats such as DoS, Fuzzers, Exploit, Worm, shellcode, reconnaissance, generic, and analysis Backdoor. In this study, we employ SVM and MLP for comparison. Finally, we propose AdaBoost based on the decision tree classifier to classify normal activity and possible threats. We monitored the network traffic and classified it into either threats or non-threats. The experimental findings showed that our proposed method effectively detects different forms of network intrusions on computer networks and achieves an accuracy of 99.3% on the UNSW-NB15 dataset. The proposed system will be helpful in network security applications and research domains. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Coat Protein Gene based Characterization of Cucumber Mosaic Virus Isolates Infecting Banana in India

Banana plants showing typical yellow stripes on leaves as symptoms, in addition to leaf distortion and stunting of plant were collected from Karnataka (KAR), Maharashtra (MH) and Uttar Pradesh (UP) in India. The causal agent was identified as Cucumber mosaic virus (CMV) on the basis of transmission electron microscopy and reverse transcription polymerase chain reaction (RT-PCR). Complete coat protein (CP) gene of all isolates were amplified using gene specific primers for coat protein (CP), followed by cloning into desired cloning vector for sequencing. Sequenced region were found containing complete single open reading frame of 657 nucleotides, potentially coding 219 amino acids. Sequence analysis of CP gene showed 93%-98% (at nucleotide) and 94%-99% (at amino acid) sequence identity between all three Indian isolates. On comparing CP gene sequences of CMV KAR, CMV MH and CMV UP with CMV P isolate (Physalis minima); we got 94%, 99% and 96% identity respectively. High degree identity at nucleotide level between these isolates of banana and Physalis minima (a weed) suggest that Physalis minima could be an alternate host of CMV banana. Phylogenetic analysis of nucleotide along with amino acid sequence of coat protein gene revealed that all our isolates belong to IB subgroup.  In short, it appears that there occurs a high incidence of CMV infecting banana belonging to IB subgroup in most parts of Indian subcontinent.Key words: Banana, CMV, CP gene, RT-PC

Study of the Effect of Treatment of Helicobacter pylori Infection in Rheumatoid Arthritis Patients

Objective: To assess the disease activity after treatment of Helicobacter pylori infection in rheumatoid arthritis patients at a tertiary care hospital of Rawalpindi. Study Design: Comparative cross-sectional study. Place and Duration of Study: Department of Rheumatology, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Jun to Nov 2021. Methodology: A total of 197 adult patients diagnosed with rheumatoid arthritis and with symptoms of dyspepsia were inducted into this study. First, the disease activity was measured considering parameters such as clinical swollen and tender joints count, disease activity scores 28(DAS-28), visual analogue scale (VAS) and laboratory parameters like erythrocyte sedimentation rate at the start as the baseline. Then, these were repeated after six months, and the differences between the two groups were compared. Results: At the start of the study, patients who were positive for H. pylori had markedly more swollen and tender joint counts and raised ESR values than those in the negative group. In addition, the disease activity score of 28 and pain scores was markedly raised in the positive group. After H. pylori eradication, the H. pylori-positive patients differed significantly (p<0.001) from patients group negative for H. pylori infection in terms of improvement in DAS-28 score, visual analogue score and clinically. Laboratory indices like ESR showed significantly decreased values (p<0.001) in the H. pylori-treated group compared to those not infected with H. pylori. Conclusion: From our results, it is suggested that H. pylori infection has a role in the pathogenesis of rheumatoid arthritis