WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively

The Role of the Educational Supervisor and the Obstacles to Their Performance, According to the Supervisors’ Own Perspective, in Light of Some Variables in the South of Jordan

The study aimed to identify the role of the educational supervisor and the obstacles to performance from the supervisors' own perspective in light of some variables in the south of Jordan.  To reveal the status quo of supervisory practices, the study population was (212), a field study was conducted on (70) educational supervisors, representing (33.01%) of the total community. The questionnaire was used as a main tool for data collection and it consisted of five axes: supervision principles, characteristics of educational supervision, interests and problems facing educational supervision and obstacles of development. The validity and reliability of the study was investigated as the overall reliability coefficient was (0.884). The results of the study showed that the lowest arithmetic means in study sample responses were in the axes of problems facing educational supervision and the obstacles to its development, while the highest arithmetic means were in favor of the axes of characteristics and principles of educational supervision. The results also showed that there were statistically significant differences at the level of (α = 0.05) between the arithmetic means of the study sample responses  in the axis of educational supervision principles, in favor of educational supervisors with academic qualifications in (higher than BA). The results of the study also showed that there were statistical differences in the axe of educational supervisors ’interests in favor of years of experience in supervision (more than 5 years). As far as the axes of educational supervision problems and obstacles to its development are concerned, the results of the study showed that there were statistically significant differences in favor of (supervision with experience 5 years or less). According to the study results, many recommendations and suggestions were introduced. Keywords: supervision, educational supervisor, obstacles DOI: 10.7176/JEP/12-14-04 Publication date:May 31st 202

Repositioning of dipeptidyl peptidase-4 inhibitors and glucagon like peptide-1 agonists as potential neuroprotective agents

Repositioning of dipeptidyl peptidase-4 inhibitors and glucagon like peptide-1 receptor agonists is a breakthrough in the field of neural regeneration research increasing glucagon like peptide-1 bioavailability, hence its neuroprotective activities. In this article, the authors suggest not only crossing blood-brain barrier and neurodegenerative disease as off target for dipeptidyl peptidase-4 inhibitors and glucagon like peptide-1 receptor agonists, but also for ophthalmic preparations for diabetic retinopathy, which may be the latest breakthrough in the field if prepared and used in an appropriate nano-formulation to target the retinal nerves. The relation of neurodegenerative diseases’ different mechanisms to the dipeptidyl peptidase-4 inhibitors and glucagon like peptide-1 receptor agonists should be further examined in preclinical and clinical settings. The repositioning of already marketed antidiabetic drugs for neurodegenerative diseases should save the high cost of the time-consuming normal drug development process. Drug repositioning is a hot topic as an alternative to molecular target based drug discovery or therapeutic switching. It is a relatively inexpensive pathway due to availability of previous pharmacological and safety data. The glucagon like peptide-1 produced in brain has been linked to enhanced learning and memory functions as a physiologic regulator in central nervous system by restoring insulin signaling. Intranasal administration of all marketed gliptins (or glucagon like peptide-1 receptor agonists) may show enhanced blood-brain barrier crossing and increased glucagon like peptide-1 levels in the brain after direct crossing of the drug for the olfactory region, targeting the cerebrospinal fluid. Further blood-brain barrier crossing tests may extend dipeptidyl peptidase-4 inhibitors’ effects beyond the anti-hyperglycemic control to intranasal spray, intranasal powder, or drops targeting the blood-brain barrier and neurodegenerative diseases with the most suitable formula. Moreover, novel nano-formulation is encouraged either to obtain favorable pharmacokinetic parameters or to achieve promising blood-brain barrier penetration directly through the olfactory region. Many surfactants should be investigated either as a solubilizing agent for hydrophobic drugs or as penetration enhancers. Different formulae based on in vitro and in vivo characterizations, working on sister gliptins (or glucagon like peptide-1 receptor agonists), different routes of administration, pharmacokinetic studies, dose response relationship studies, monitoring of plasma/brain concentration ratio after single and multiple dose, and neurodegenerative disease animal models are required to prove the new method of use (utility) for dipeptidyl peptidase-4 inhibitors as potential neuroprotective agents. Furthermore, investigations of glucagon like peptide-1 receptor agonists’ neuroprotective effects on animal models will be considered carefully because they crossed the blood-brain barrier in previous studies, enabling their direct action on the central nervous system. Combination therapy of dipeptidyl peptidase-4 inhibitors or glucagon like peptide-1 receptor agonists with already marketed drugs for neurodegenerative disease should be considered, especially regarding the novel intranasal route of administration

Associations between genetic variants in the vitamin d metabolism pathway and severity of covid-19 among uae residents

Vitamin D has many effects on cells in the immune system. Many studies have linked low vitamin D status with severity of COVID-19. Genetic variants involved in vitamin D metabolism have been implicated as potential risk factors for severe COVID-19 outcomes. This study investigated how genetic variations in humans affected the clinical presentation of COVID-19. In total, 646 patients with SARS-CoV-2 infection were divided into two groups: noncritical COVID-19 (n = 453; 70.12%) and a critical group (n = 193; 29.87%). Genotype data on the GC, NADSYN1, VDR, and CYP2R1 genes along with data on serum 25-hydroxyvitamin D levels were compiled in patients admitted to a major hospital in the United Arab Emirates between April 2020 and January 2021. We identified 12 single-nucleotide polymorphisms associated with the critical COVID-19 condition: rs59241277, rs113574864, rs182901986, rs60349934, and rs113876500; rs4944076, rs4944997, rs4944998, rs4944979, and rs10898210; and rs11574018 and rs11574024. We report significant associations between genetic determinants of vitamin D metabolism and COVID-19 severity in the UAE population. Further research needed to clarify the mechanism of action against viral infection in vitamin D deficiency. These variants could be used with vaccination to manage the spread of SARS-CoV-2 and could be particularly valuable in populations in which vitamin D deficiency is common

Allelic variants within the ABO blood group phenotype confer protection against critical COVID-19 hospital presentation

Introduction: Coronavirus disease 2019 (COVID-19) disease severity differs widely due to numerous factors including ABO gene-derived susceptibility or resistance. The objective of this study was to investigate the association of the ABO blood group and genetic variations of the ABO gene with COVID-19 severity in a heterogeneous hospital population sample from the United Arab Emirates, with the use of an epidemiological and candidate gene approach from a genome-wide association study (GWAS). Methods: In this cross-sectional study, a total of 646 participants who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were recruited from multiple hospitals and population-based (quarantine camps) recruitment sites from March 2020 to February 2021. The participants were divided into two groups based on the severity of COVID-19: noncritical (n = 453) and critical [intensive care unit (ICU) patients] (n = 193), as per the COVID-19 Reporting and Data System (CO-RADS) classification. The multivariate logistic regression analysis demonstrated the association of ABO blood type as well as circulating anti-A antibodies and anti-B antibodies as well as A and B antigens, in association with critical COVID-19 hospital presentation. A candidate gene analysis approach was conducted from a GWAS where we examined 240 single nucleotide polymorphisms (SNPs) (position in chr9: 136125788-136150617) in the ABO gene, in association with critical COVID-19 hospital presentation. Results: Patients with blood group O [odds ratio (OR): 0.51 (0.33, 0.79); p = 0.003] were less likely to develop critical COVID-19 symptoms. Eight alleles have been identified to be associated with a protective effect of blood group O in ABO 3\u27untranslated region (UTR): rs199969472 (p = 0.0052), rs34266669 (p = 0.0052), rs76700116 (p = 0.0052), rs7849280 (p = 0.0052), rs34039247 (p = 0.0104), rs10901251 (p = 0.0165), rs9411475 (p = 0.0377), and rs13291798 (p = 0.0377). Conclusion: Our findings suggest that there are novel allelic variants that link genetic variants of the ABO gene and ABO blood groups contributing to the reduced risk of critical COVID-19 disease. This study is the first study to combine genetic and serological evidence of the involvement of the ABO blood groups and the ABO gene allelic associations with COVID-19 severity within the Middle Eastern population

Hepatobiliary manifestations following two-stages elective laparoscopic restorative proctocolectomy for patients with ulcerative colitis: A prospective observational study

BACKGROUNDHepatobiliary manifestations occur in ulcerative colitis (UC) patients. The effect of laparoscopic restorative proctocolectomy (LRP) with ileal pouch anal anastomosis (IPAA) on hepatobiliary manifestations is debated.AIMTo evaluate hepatobiliary changes after two-stages elective laparoscopic restorative proctocolectomy for patients with UC.METHODSBetween June 2013 and June 2018, 167 patients with hepatobiliary symptoms underwent two-stage elective LRP for UC in a prospective observational study. Patients with UC and having at least one hepatobiliary manifestation who underwent LRP with IPAA were included in the study. The patients were followed up for four years to assess the outcomes of hepatobiliary manifestations.RESULTSThe patients' mean age was 36 +/- 8 years, and males predominated (67.1%). The most common hepatobiliary diagnostic method was liver biopsy (85.6%), followed by Magnetic resonance cholangiopancreatography (63.5%), Antineutrophil cytoplasmic antibodies (62.5%), abdominal ultrasonography (35.9%), and Endoscopic retrograde cholangiopancreatography (6%). The most common hepatobiliary symptom was Primary sclerosing cholangitis (PSC) (62.3%), followed by fatty liver (16.8%) and gallbladder stone (10.2%). 66.4% of patients showed a stable course after surgery. Progressive or regressive courses occurred in 16.8% of each. Mortality was 6%, and recurrence or progression of symptoms required surgery for 15%. Most PSC patients (87.5%) had a stable course, and only 12.5% became worse. Two-thirds (64.3%) of fatty liver patients showed a regressive course, while one-third (35.7%) showed a stable course. Survival rates were 98.8%, 97%, 95.8%, and 94% at 12 mo, 24 mo, 36 mo, and at the end of the follow-up.CONCLUSIONIn patients with UC who had LRP, there is a positive impact on hepatobiliary disease. It caused an improvement in PSC and fatty liver disease. The most prevalent unchanged course was PSC, while the most common improvement was fatty liver disease

Phosphorus and sediment – two of the major pollutants of freshwater stream ecosystems – impact upon epilithon

Ecological degradation of rivers and streams resulting from multiple stressors is a big concern in the UK and other countries all over the world. The two largest stressors introduced by agriculture are phosphorus and fine sediment. The combined impacts of the multiple stressor and relative strength of each individual stressor needs to be understand. A Number of ecological response variables were tested through a field mesocosm experiment, including algal and ecosystem variables: (1) The subsidy-stress for phosphorus and sediment (where at first, an ecological variable increases positively with the increased level of phosphorus and sediment until very high levels are reached, when negative effects would be expected); (2) Whether the stressors work individually or as multiple stressors and whether they interact; (2.a) Three ecological guilds of algae (‘low profile’ growth form, ‘high profile’ growth form, ‘motile’ growth form) were used in order to test whether the high profile growth form decreases and motile growth form increases with increase of sediment deposition, or whether (2.b) Both high profile growth form and motile growth form increase with increase concentration of phosphorus. Most species showed subsidy stress responses for the gradient of phosphorus, but for the gradient of sediment the response was negative. Phosphorus and sediment together generally acted as multiple stressors and usually in a simple additive way, but complex interactions were also found. The algal community was impacted synergetically by phosphorus and sediment, as shown by the field study. The combined results from the field study and the mesocosm experiment indicate that phosphorus and sediment should be managed together in view of their acting most of the time as multiple stressors in their impacts on epilithic algae. Finally, in order to have a better evaluation for the possible reasons of a stream health decline, it is strongly recommended to measure routinely both fine sediment and phosphorus in the future

WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively

Accuracy and performance analysis for classification algorithms based on biomedical datasets

Diseases chronic, including heart disease, cancer, diabetes, and obesity, are the main causes of mortality in the United States and accounting for and consuming the majority of the country’s healthcare expenditure. As indicated by recent researches. The main reason for the emergence of these diseases prominently is their relationship to each other, where diabetes is one of the causes of cancer and heart disease, hepatitis also is associated with diabetes, and heart disease. This paper focuses on data mining and machine learning techniques in healthcare classification and prediction of diseases and rebuild disease detection systems (DDS). The study suggests finding a classifier among the most common kinds of classification algorithms within a combined approach represent in Bayesian, Trees, Rules, Function, and lazy algorithms to automate a better performance of early detection of diseases from the medical datasets. This paper presents and analyzes five different machine learning (ML) algorithms: Function-based Neural Network (MLP) algorithm. Trees based Decision Tree (ID3) algorithm, Bayesian Theorem based Hidden Naïve Bayes (HNB) algorithm. Lazy based k-nearest neighbors (IBK) algorithm, and Rules-based OneR algorithm. The analysis is based on four benchmark datasets in the healthcare sector, including the Pima Indian Diabetes PID, the Breast Cancer, Heart Cleveland, and Hepatitis Datasets, which were obtained from several ML repositories. The results show that the HNB predicts the best result with a relatively higher Precision, AUROC Statistic, highest accuracy, and performance when compared to MLP, IBK, OneR, ID3 algorithms