Lettuce Leaves as Biosorbent Material to Remove Heavy Metal Ions from Industerial Wastewater

The current study was designed to remove Lead, Copper and Zinc from industrial wastewater using Lettuce leaves (Lactuca sativa) within three forms (fresh, dried and powdered) under some environmental factors such as pH, temperature and contact time. Current data show that Lettuce leaves are capable of removing Lead, Copper and Zinc ions at significant capacity. Furthermore, the powder of Lettuce leaves had highest capability in removing all metal ions. The highest capacity was for Lead then Copper and finally Zinc. However, some examined factors were found to have significant impacts upon bioremoval capacity of studied ions, where best biosorption capacity was found at pH 4, at temperature 50º C and contact time of 1 hour

Synergistic effect of arginine and Lactobacillus plantarum against potassium dichromate induced-acute liver and kidney injury in rats: Role of iNOS and TLR 4/ NF-κB signaling pathways

Objective(s): Our study was conducted to evaluate the synergistic effect of arginine (ARG) and Lactobacillus plantarum against potassium dichromate (K2Cr2O7) induced-acute hepatic and kidney injury.Materials and Methods: Fifty male Wistar rats were divided into five groups. The control group received distilled water. The potassium dichromate group (PDC) received a single dose of PDC (20 mg/kg; SC). The arginine group (ARG) and Lactobacillus plantarum group received either daily doses of ARG (100 mg/kg, PO) or L. plantarum (109 CFU/ml, PO) for 14 days. The combination group (ARG+L. plantarum) received daily doses of ARG (100 mg/kg) with L. plantarum (109 CFU/ml), orally for 14 days, before induction of acute liver and kidney injury. Forty eight hours after the last dose of PDC, serum biochemical indices, oxidative stress biomarkers, pro-inflammatory cytokines, histopathological and immunohistochemical analysis were evaluated.Results: Combining ARG with L. plantarum restored the levels of serum hepatic & kidney enzymes, hepatic & renal oxidative stress biomarkers, and TLR 4/ NF-κB signaling pathway. Furthermore, they succeeded in decreasing the expression of iNOS and ameliorate the hepatic and renal markers of apoptosis: Caspase-3, Bax, and Bcl2.Conclusion: This study depicts that combining ARG with L. plantarum exerted a new bacteriotherapy against hepatic and renal injury caused by PDC

Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images

Introduction: In humanity\u27s ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities

Protective and ameliorative effects of Curcumin and/or Quercetin against gentamicin induced testicular damage in rats

The aminoglycoside Gentamicin is a commonly used antibiotic counteracting the Gram-ve microorganisms. Rats administered with Gentamicin showing a reduced testicular weight and inhibited spermatogenesis, as gentamicin generates ROS, decreasing the antioxidant reserve and accelerate mitochondrial dysfunction which then leads to apoptosis and testicular tissue destruction. This study was designed to investigate the protective effects of curcumin and/or quercetin on the gentamicin induced testicular damage or toxicity in sexually mature adult rats. Pre-treatment with curcumin and/or quercetin, markedly inhibited and ameliorated the reduction in sperm count, viability, motility and sperm production in gentamicin treated rats. Moreover, curcumin and/or quercetin, significantly reduce teratospermia including head or tail abnormalities that observed in the gentamicin treated rats. These abnormalities were effectively normalized by curcumin and/or quercetin pretreatment improving the testicular tissue via counteracting of ROS, improvement of spermatogenesis and ameliorate the sperms quality and quantity. In conclusion supplementation of curcumin and/or quercetin improving the sperm count and morphology via testicular cell repair, counteracting the undesirable effect of gentamicin

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus

AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities

[[abstract]]Digital forgery has become one of the attractive research fields in today’s technology. There are several types of forgery in digital media transmission, especially digital image transmission. A common type of forgery is copy-move forgery (CMF). The CMF may be encountered in streets, railway stations, underground stations, or festivals. This type of forgery may lead to hugger-mugger in some cases. Therefore, there is a need to find a sufficient countermeasure mechanism to detect image forgeries. This paper presents a new CMFD approach that depends on deep learning for IoT based smart cities. Two well-known deep learning models, namely CNN and ConvLSTM, are adopted for CMFD. The proposed models are tested on MICC-220, MICC-600 and MICC 2000 datasets for validation. Several tests are performed to verify the effectiveness of the proposed models. The simulation results reveal that the testing accuracy reaches 95%, 73%, and 94% for MICC-F220, MICC-F600 and MICC-F2000 datasets. In addition, the proposed approach achieves an accuracy of 85% for a combined set of all datasets

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions

Prevalence and differences between type 1 and type 2 diabetes mellitus regarding female sexual dysfunction: a cross-sectional Egyptian study

Objective: To evaluate the female sexual dysfunction in both type 1 and type 2 diabetes mellitus (DM). Methods: This cross-sectional study was carried out at Suez Canal University Hospitals from the start of February 2015 to the end of May 2016 among 189 married premenopausal women attending endocrinology and diabetology outpatient clinic for regular follow-up; 25 of whom refused to participate and 18 more were excluded due to incomplete data sets resulting in a final sample of 146 diabetic females. Ninety healthy women were recruited from the administrative staff at the hospital as a control group. Sexual dysfunction was assessed using female sexual function index (FSFI), a validated 19-item, self-administered, screening questionnaire comprising the six major sexual domains: desire, arousal, lubrication, orgasm, satisfaction and pain. Responses to each question were reported and scored on 0–5 scale with 0 representing no sexual activity and 5 suggestive of normal sexual activity. Results: Prevalence of sexual dysfunction was significantly higher in both type 1 and 2 DM groups (44 and 25%, respectively) than in the control group (9%). FSFI mean total score was significantly lower in type 1 DM (21.1 ± 3.9) than type 2 DM (26.4 ± 4.2) and both were significantly lower than the control group (31.5 ± 5.8). With regard to FSFI domains, mean values for desire, arousal, lubrication, orgasm, satisfaction and pain were significantly lower in both type 1 and type 2 DM groups when compared with the controls. Conclusion: FSD is a significant health problem among premenopausal diabetic Egyptian women. Type 1 DM women were more affected than type 2 DM that in turn was more affected than healthy control females