Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation.

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Adverse pregnancy, fetal and neonatal outcomes in women with sickle cell disease in a Middle Eastern country.

BACKGROUND: Sickle cell disease in pregnancy is associated with high maternal and fetal mortality. However, studies reporting pregnancy, fetal, and neonatal outcomes in women with sickle cell disease are extremely limited. OBJECTIVES: The objectives of the study are to determine whether women with sickle cell disease have a greater risk of adverse pregnancy, fetal, and neonatal outcomes than women without sickle cell disease and identify the predictors of adverse pregnancy, fetal, and neonatal outcomes in women with sickle cell disease. DESIGN: A retrospective pair-matched case-control study was conducted to compare 171 pregnant women with sickle cell disease to 171 pregnant women without sickle cell disease in Muscat, Sultanate of Oman. METHODS: All pregnant Omani women with sickle cell disease who delivered between January 2015 and August 2021 at Sultan Qaboos University Hospital and Royal Hospital, who were either primipara or multipara and who had a gestational age of 24-42 weeks, were included as patients, whereas women who had no sickle cell disease or any comorbidity during pregnancy, who delivered within the same timeframe and at the same hospitals, were recruited as controls. The data were retrieved from electronic medical records and delivery registry books between January 2015 and August 2021. RESULTS: Women with sickle cell disease who had severe anemia had increased odds of (χ2 = 58.56, p < 0.001) having adverse pregnancy outcomes. Women with sickle cell disease had 21.97% higher odds of delivering a baby with intrauterine growth retardation (χ2 = 17.80, unadjusted odds ratio = 2.91-166.13, p < 0.001). Newborns born to women with sickle cell disease had 3.93% greater odds of being admitted to the neonatal intensive care unit (χ2 = 16.80, unadjusted odds ratio = 1.97-7.84, p < 0.001). In addition, the children born to women with sickle cell disease had 10.90% higher odds of being born with low birth weight (χ2 = 56.92, unadjusted odds ratio = 5.36-22.16, p < 0.001). Hemoglobin level (odds ratio = 0.17, p < 0.001, 95% confidence interval = 0.10-3.0), past medical history (odds ratio = 7.95, p < 0.001, 95% confidence interval = 2.39-26.43), past surgical history (odds ratio = 17.69, p < 0.001, 95% confidence interval = 3.41-91.76), and preterm delivery (odds ratio = 9.48, p = 0.005, 95% confidence interval = 1.95-46.23) were identified as predictors of adverse pregnancy, fetal, and neonatal outcomes in women with sickle cell disease. CONCLUSION: As pregnant women with sickle cell disease are at increased risk for pregnancy, fetal, and neonatal adverse outcomes; improved antenatal surveillance and management may improve the outcomes.

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning.

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

Deep fake detection and classification using error-level analysis and deep learning.

Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The results prove the efficiency and robustness of the proposed technique; hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda.

A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.

Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method's classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

Capsaicin Is a Negative Allosteric Modulator of the 5-HT3 Receptor.

In this study, effects of capsaicin, an active ingredient of the capsicum plant, were investigated on human 5-hydroxytryptamine type 3 (5-HT3) receptors. Capsaicin reversibly inhibited serotonin (5-HT)-induced currents recorded by two-electrode voltage clamp method in Xenopus oocytes. The inhibition was time- and concentration-dependent with an IC50 = 62 µM. The effect of capsaicin was not altered in the presence of capsazepine, and by intracellular BAPTA injections or trans-membrane potential changes. In radio-ligand binding studies, capsaicin did not change the specific binding of the 5-HT3 antagonist [3H]GR65630, indicating that it is a noncompetitive inhibitor of 5-HT3 receptor. In HEK-293 cells, capsaicin inhibited 5-HT3 receptor induced aequorin luminescence with an IC50 of 54 µM and inhibition was not reversed by increasing concentrations of 5-HT. In conclusion, the results indicate that capsaicin acts as a negative allosteric modulator of human 5-HT3 receptors.

Development and validation of a new vision screening test algorithm for public use mobile application- A pilot study

@#algorithm for mobile application and perform a pilot study todetermine its validity and reliability as a tool for vision testin the community.Methods: A simple visual acuity test algorithm in the form ofa single letter E display was designed as the optotype fordevelopment of a mobile application. The standardisedoptotype is presented at random to test visual acuity forcorresponding level of 3/60, 6/60, 6/18, and 6/12. The finalresult is auto-generated based on the classification of theWHO for visual impairment and blindness. The Snellen chartwas used as the gold standard to determine its validity whilefive different users were involved to determine its inter-raterreliability. A pilot study was performed between April tillNovember 2019, in the Universiti Sultan Zainal AbidinMedical Centre (UMC) at Kuala Nerus and MoorisOptometrist Centre at Marang, Terengganu. A total of 279participants aged four years old and above were involved inthis study. Results: The highest sensitivity was found at the vision levelcut-off point of 6/12 with the percentage of 92.7% and 86.8%for the right and left eye, respectively. The specificity wasmore than 89% for all vision levels in both eyes. TheKrippendorff’s alpha value for the inter-rater reliability was0.87 and 0.83.Conclusion: The relatively high level of validity andreliability obtained indicate the feasibility of using thedesigned optotype to develop a valid and reliable mobile appfor vision test. The app can be used to screen vision by non-medical persons, at anytime and anywhere to help improvepublic awareness and capability to correctly determine theirvisual status.

Capsaicin inhibits the function of α7-nicotinic acetylcholine receptors expressed in Xenopus oocytes and rat hippocampal neurons.

Capsaicin is a naturally occurring alkaloid derived from Chili peppers fruits. Using the two-electrode voltage-clamp technique in Xenopus oocyte expression system, actions of capsaicin on the functional properties of α7 subunit of the human nicotinic acetylcholine (α7 nACh) receptor were investigated. Ion currents activated by ACh (100 µM) were reversibly inhibited with an IC50 value of 8.6 µM. Inhibitory actions of capsaicin was independent of membrane potential. Furthermore, Ca2+-dependent Cl- channels expressed endogenously in oocytes were not involved in inhibitory actions of capsaicin. In addition, increasing the ACh concentrations could not reverse the inhibitory effects of capsaicin. Importantly, specific binding of [125I] α-bungarotoxin remained unaltered by capsaicin suggesting that its effect is noncompetitive. Whole cell patch-clamp technique was performed in CA1 stratum radiatum interneurons of rat hippocampal slices. Ion currents induced by choline, a selective-agonist of α7-receptor, were reversibly inhibited by 10 min bath application of capsaicin (10 µM). Collectively, results of our investigation indicate that the function of the α7-nACh receptor expressed in Xenopus oocytes and in hippocampal interneurons are inhibited by capsaicin.

Primary care barriers to cataract surgery in the eastern zone of Peninsular Malaysia: an interpretative phenomenological analysis

Objective: Cataract is the leading cause of blindness inMalaysia. There is an alarming backlog of cataractextraction surgery as the majority believes they did notrequire surgery. This study aimed to explore the barriersat the primary care level to cataract surgery from theperspective of patients with severe cataract blindness.Methods: Eleven participants were involved in thisqualitative research which utilised the interpretativephenomenological analysis approach more renowned inhealth psychology research. All interviews conducted attheir home. The interviews were recorded, typedverbatim, and the transcripts were analysed using NVivosoftware version 8.0.Results: The main barriers identified at the primary carelevel were 1) nondisclosure of their visual problemsoriginated from their belated needs for better sight,delayed awareness of their visual status and socialstigma and 2) patient-provider-related issues namelymiscommunication and delayed referral. The first maintheme explains their belief for not requiring surgery. Thishas led to their delayed awareness and impededdisclosure of their visual problems to family members orprimary care providers. The second main theme reflectsthe provider-patient-related issues which retardedcataract detection and referral process required for earliercataract extraction surgery.Conclusion: Thus, the appropriate approach targeting thesespecific barriers at primary care level will be able to detect,motivate and assist patients for early uptake of cataractextraction surgery to improve their vision and prevent severeblindness.

A Combination of Trimethoprimsulfamethoxazole and Ceftazidime Showed Good In Vitro Activity against Stenotrophomonas maltophilia

Background: Stenotrophomonas maltophilia has emerged as an important nosocomial pathogen, capable of causing a wide spectrum of infections. Treatment is difficult because it is resistant to many antimicrobial agents, thus reducing the treatment options. The aims of this study were to describe the antimicrobial susceptibility patterns and synergistic effect of selected antimicrobial combinations against S. maltophilia isolates. Methods: This was a descriptive cross-sectional study undertaken in the Hospital Universiti Sains Malaysia from April 2011 to March 2012. S. maltophilia isolated from various clinical specimens were included in the study. Antimicrobial susceptibility testing was done using the epsilometer test (E-test) and interpreted according to the guidelines of the Clinical and Laboratory Standards Institute. In the synergy test, the isolates were tested against six different antimicrobial combinations. Results: In total, 84 S. maltophilia isolates were collected and analysed. According to the E-test, the antimicrobial susceptibility of trimethoprim-sulfamethoxazole (TMP-SMX), tigecycline, and ciprofloxacin was 100%, 91.1%, and 88.9% respectively. The antimicrobial combination of TMP-SMX and ceftazidime showed the highest synergistic effect. Conclusion: TMP-SMX remains the antimicrobial of choice to treat S. maltophilia infection. TMP-SMX and ceftazidime was the most effective combination in vitro.

Preliminary Study: Effects of Social Instability Stress on Depressive Behaviours in Ovariectomised Rats

Background: Depression is one of the common post-menopausal symptoms. In addition to estrogen deficiency, social instability stress may contribute as an additional underlying factor in the development of depressive behaviour in females. Therefore, this study was aimed at examining the influence of social instability stress on depressive behaviour in ovariectomized rats. Methods: The rats were divided into four groups (n = 5 per group); (i) sham-operated control without stress, (ii) sham-operated control with stress, (iii) ovariectomized without stress, and (iv) ovariectomized with stress. These rats were subjected to social instability stress procedures for 15 days prior to an enforced swimming test. Struggling, immobility, and swimming times were recorded promptly. Results: The results were analysed using the one-way analysis of variance (ANOVA) and a P value of < 0.05 was considered as significant. The mean durations of struggling, immobility, and swimming behaviour were significantly distinct among the four groups. Ovariectomized rats exhibited a substantial decrease in struggling and swimming behaviour, and an increase in immobility behaviour in comparison with the sham-operated controls (P < 0.05). Ovariectomized rats with stress displayed a supplementary decrease in struggling and swimming behaviour as well as an advanced increase in immobility behaviour, compared to sham-operated controls with or without stress (P < 0.05). Conclusion: In summary, these findings suggest that ovariectomized rats encountered an augmented amount of depressive behaviour following social instability stress.

Qatari women living with cardiovascular diseases-challenges and opportunities to engage in healthy lifestyles.

In Qatar, cardiovascular diseases are the leading causes of morbidity and mortality. Cardiovascular diseases can be prevented and controlled by modifying lifestyle risk behaviors. In this qualitative study, we investigate ways to increase participation in physical activity, and to promote a healthy diet, and nonsmoking behavior in Qatari women. Individual in-depth interviews were conducted with 50 Arabic women. Participation in physical activity, observing a healthy diet, and abstinence from smoking are desirable lifestyle practices among Qatari women. Social support networks, cultural values, religion, changing sociodemographic and economic conditions, heart disease, and a harsh climate affect the ability of these women to pursue a healthy lifestyle.

Prevalence of Clostridium difficile toxin in diarhoeal stool samples of patients from a tertiary hospital in North Eastern Penisular Malaysia

This study describes the prevalence of Clostridium difficile toxin (CDT) in loose stool samples from inpatients aged more than two years of a tertiary hospital. A total of 175 samples that had been examined were from stool samples that were sent to the Medical Microbiology & Parasitology Laboratory for various clinical indications. The toxin was detected by a commercial immunochromatograhic test, and the patients’ demography, clinical features, treatment and outcomes were analyzed from their medical records. Clostridium difficile toxin was positive in 24 (13.7%) of the stool samples. Male and female were 11 (45.8 %) and 13 (54.2 %) respectively, with the majority of them aged more than 50 years. Most were from medical wards (n=21, 87.5%), with the rest from surgical wards (n=2, 8.3%) and intensive care units (n=1, 3.4%). All the CDT positive patients had history of prior antibiotic usage within 6 weeks before the detection of the toxin. The mean duration of antibiotics usage was 17.75 (±13.75) days, while the mean duration of diarrhea was 5.21((± 5.85) days. Eighteen patients had underlying medical illnesses that were diabetes mellitus, chronic renal disease, hypertension, ischaemic heart disease, cerebrovascular disease and malignancy; with seven of them being CDT positive while on chemotherapy. Stool occult blood test was positive in 15 patients whereas presence of pus cells in the CD positive stool samples were detected in 21 patients. The duration of hospitalization among the patients was 27.96 (± 23.22) days.