How Zinc Transporters in Escherichia coli Influence Ageing in the Nematode Caenorhabditis elegans

Gut microbes play an important role in mammalian physiology. Escherichia coli not only provide the Caenorhabditis elegans with vital nutrients but also influence worms lifespan. Studying such interactions could help us to understand how intestinal microbes influence mammalian ageing. A recent gene deletion study of 1041 E. coli in our lab identified 9 genes that are involved in the increase of worm’s lifespan. One gene identified was ZnuB, which forms part of the high affinity znuABC zinc ABC transporters, plays an important role in zinc homeostasis, and has been suggested to play a role in increased lifespan. To validate this hypothesis, levels of zinc were measured using ICP-MS in znuA, znuB, and znuC mutant bacteria and worms fed with the mutants, and compared with zinc levels in WT bacteria and C. elegans fed with WT bacteria. Zinc levels were also measured in LB and NGM media. It was found that although bacteria and worms could obtain zinc from LB media, the level of zinc was lower in worms and the three mutant bacteria than in WT bacteria alone. Lifespan of worms fed with those mutants was investigated. Worms fed with znuB and znuC bacteria showed extended lifespan, compered to worms fed with znuA bacteria. Reduced fecundity was observed in experimental worms fed with mutant as compared to worms fed with WT bacteria. Moreover, the worms fed with the znuB showed a delay in the reproductive cycle. These results suggest that reducing zinc concentration itself in the mutant bacteria does not make the worms live longer, but the mutation in the znuB could produce different effects. Results of zinc supplement experiments using mutants showed reversal effect on worm developmental delay when fed with znuB and zinc supplements. These results show that the znuB not only plays an important role in zinc uptake by bacteria, but also affects the lifespan of C. elegans

A Dynamic Clustering Algorithm for Object Tracking and Localization in WSN

A Wireless Sensor Network (WSN) is an assemblage of cooperative sensor nodes acting together into an environment to monitor an event of interest. However, one of the most limiting factors is the energy constrain for each node; therefore, it is a trade-off is required for that factor in designing of a network, while reporting, tracking or visualizing an event to be considered. In this paper, two object tracking techniques used in Wireless Sensor Networks based on cluster algorithms have been combined together to perform many functions in the proposed algorithm. The benefit of using clusters algorithms can be count as the detection node in a cluster reports an event to the Cluster Head (CH) node according to a query, and then the CH sends all the collected information to the sink or the base station. This way reduces energy consuming and required communication bandwidth. Furthermore, the algorithm is highly scalable while it prolongs the life time of the network

Machine Learning Approaches for Flow-Based Intrusion Detection Systems

In cybersecurity, machine/deep learning approaches can predict and detect threats before they result in major security incidents. The design and performance of an effective machine learning (ML) based Intrusion Detection System (IDS) depends upon the selected attributes and the classifier. This project considers multi-class classification for the Aegean Wi-Fi Intrusion Dataset (AWID) where classes represent 17 types of the IEEE 802.11 MAC Layer attacks. The proposed work extracts four attribute sets of 32, 10, 7 and 5 attributes, respectfully. The classifiers achieved high accuracy with minimum false positive rates, and the presented work outperforms previous related work in terms of number of classes, attributes and accuracy. The proposed work achieved maximum accuracy of 99.64% for Random Forest with supply test and 99.99% using the 10-fold cross validation approach for Random Forest and J48

Clinical and laboratory features of JAK2 v617f, CALR, and MPL mutations in Malaysian patients with classical myeloproliferative neoplasm (MPN)

Mutations of JAK2V617F, CALR, and MPL genes confirm the diagnosis of myeloproliferative neoplasm (MPN). This study aims to determine the genetic profile of JAK2V617F, CALR exon 9 Type 1 (52 bp deletion) and Type 2 (5 bp insertion), and MPL W515 L/K genes among Malaysian patients and correlate these mutations with clinical and hematologic parameters in MPN. Mutations of JAK2V617F, CALR, and MPL were analyzed in 159 Malaysian patients using allele-specific polymerase chain reaction, including 76 polycythemia vera (PV), 41 essential thrombocythemia (ET), and 42 primary myelofibrosis (PMF) mutations, and the demographics of the patients were retrieved. The result showed that 73.6% JAK2V617F, 5.66% CALR, and 27.7% were triple-negative mutations. No MPL W515L/K mutation was detected. In ET and PMF, the predominance type was the CALR Type 1 mutation. In JAK2V617F mutant patients, serum LDH was significantly higher in PMF compared to PV and ET. PV has a higher risk of evolving to post PV myelofibrosis compared to ET. A thrombotic event at initial diagnosis of 40.9% was high compared to global incidence. Only one PMF patient had a CALR mutation that transformed to acute myeloid leukemia. JAK2V617F and CALR mutations play an important role in diagnostics. Hence, every patient suspected of having a myeloproliferative neoplasm should be screened for these mutations

Towards Efficient Features Dimensionality Reduction for Network Intrusion Detection on Highly Imbalanced Traffic

The performance of an IDS is significantly improved when the features are more discriminative and representative. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS 2017 network intrusion dataset

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.http://dx.doi.org/10.3390/electronics803032

Machine Learning Based Feature Reduction for Network Intrusion Detection

The security of networked systems has become a critical universal issue. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6%. Furthermore, we propose a Multi-Class Combined performance metric CombinedMc with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset

Parental awareness regarding pediatric antibiotic use in Madinah, Saudi Arabia

Purpose: To determine the level of awareness of antibiotic use in children in Madinah, Saudi Arabia, and to identify factors associated with parental decisions regarding it. Methods: Using a multiple-choice-question-based questionnaire survey, 1256 forms were distributed to visitors of major shopping malls in Madinah City to obtain socio-demographic and antibiotics knowledgebased data from October 2017 to January 2018. Differences in scores between and within groups on knowledge of parents about antibiotics were determined. Results: Most participants (67 %) had good basic knowledge of antibiotics: 69 and 40 % of respondents were aware of their side effects and antibacterial resistance, respectively. Participants in high age groups (> 46 years old) have a significantly higher mean knowledge score (55.4 ± 20.1, p < 0.05) than those in younger groups. Educational status increased the mean knowledge score by approximately 60 %, with the most educated group having a mean score of 61.2 ± 16.4 (p < 0.05). Conclusion: These results reveal the importance of awareness campaigns on antibiotic use and the role of healthcare professionals in the education of patients and parents on correct use of antibiotics, as well as the significance of antibacterial resistance. Keywords: Antibiotics misuse, Pediatrics, Patient education, Antibacterial resistanc

Prevalence, Recognition, And Management Of Mental Disorders In Primary Care

According to latest figures, 50% of individuals will have a minimum of one mental health issue in their lifetime, with at least 25% experiencing a mental condition in the previous year. primary care doctors are overwhelmingly responsible for recognition, evaluation, therapy, and referral, with at least one-third of their consultations containing a direct and obvious mental aspect Primary care physicians are the foundation of the majority of medical care programs that involve recognizing, diagnosing, treating, and referring patients to specialists for every kind of diseases, whether physical, mental, or both. Over the last two decades, there has been a greater emphasis on this position, notably in the treatment of mental problems in primary care

Impak pandemik COVID-19 terhadap golongan B40

Kajian ini membincangkan impak pandemik COVID-19 ke atas golongan B40 di Malaysia. Kajian ini bertujuan untuk mengkaji impak pandemik ke atas aspek sosio-ekonomi golongan B40, jenis bantuan kerajaan yang diterima, serta impak bantuan tersebut terhadap golongan B40 dalam menghadapi saat getir sepanjang tempoh Perintah Kawalan Pergerakan (PKP) di Malaysia pada tahun 2020. Kajian ini melibatkan 141 responden yang terdiri daripada golongan B40 di daerah Johor Bahru. Data dikumpul menggunakan soal selidik dan dianalisis menggunakan statistik deskriptif. Hasil kajian menunjukkan keadaan sosio-ekonomi golongan B40 amat terkesan berikutan penularan pandemik COVID-19. Antara dapatan kajian yang penting untuk dikemukakan ialah bantuan kerajaan seperti Bantuan Prihatin Nasional (BPN) memberi impak positif demi kelangsungan hidup golongan B40 di era yang mencabar ini. Kajian ini dapat memberi implikasi yang bermanfaat buat pihak berwajib dalam menimbangtara keberkesanan bantuan yang disalurkan. Selain itu, penambahbaikan boleh dijana hasil daripada dapatan kajian agar kelestarian sumber manusia di Malaysia dapat dikekalkan