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Abstract Background Poliovirus is a highly infectious enterovirus (EV) that primarily affects children and can lead to lifelong paralysis or even death. Vaccine-derived polioviruses (VDPVs) are a great threat since they are derived from the attenuated virus in the Oral Poliovirus Vaccine (OPV) and can mutate to a more virulent form. The purpose of this study was to identify VDPV serotype 2 through the year 2020–2021 via surveillance of sewage samples collected from different localities and governorates in Egypt and stool specimens from Acute Flaccid Paralysis (AFP) cases. Both were collected through the national poliovirus surveillance system and according to the guidelines recommended by the WHO. Methods A total of 1266 sewage samples and 3241 stool samples from January 2020 to December 2021 were investigated in the lab according to World Health Organization (WHO) protocol for the presence of Polioviruses by cell culture, molecular identification of positive isolates on L20B cell line was carried out using real-time polymerase chain reactions (RT-PCR). Any positive isolates for Poliovirus type 2 and isolates suspected of Vaccine Derived Poliovirus Type 1 and type 3 screened by (VDPV1) or Vaccine Poliovirus Type 3 (VDPV3) assay in RT-PCR were referred for VP1 genetic sequencing. Results The outbreak was caused by circulating VDPV2 (cVDPV2) strains started in January 2021. By the end of February 2021, a total of 11 cVDPV2s were detected in sewage samples from six governorates confirming the outbreak situation. One additional cVDPV2 was detected later in the sewage sample from Qena (June 2021). The first and only re-emergence of VDPV2 in stool samples during the outbreak was in contact with Luxor in June 2021. By November 2021, a total of 80 VDPVs were detected. The Egyptian Ministry of Health and Population (MOHP), in collaboration with the WHO, responded quickly by launching two massive vaccination campaigns targeting children under the age of five. Additionally, surveillance systems were strengthened to detect new cases and prevent further spread of the virus. Conclusion The continued threat of poliovirus and VDPVs requires ongoing efforts to prevent their emergence and spread. Strategies such as improving immunization coverage, using genetically stable vaccines, and establishing surveillance systems are critical to achieving global eradication of poliovirus and efficient monitoring of VDPVs outbreaks.

Utilizing Wireless Sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100 % F1-score, 100 % recall, 100 % precision, and 100 % accuracy; ii) the Rice Seedling dataset with 99.5 % F1-score, 99.5 % recall, 99.5 % precision, and 99.5 % accuracy; and iii) the Rice Varieties dataset with 97.99 % F1-score, 97.99 % recall, 98.14 % precision, and 97.99 % accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.

Abstract Heavy metal accumulation is one of the major agronomic challenges that has seriously threatened food safety. As a result, metal-induced phytotoxicity concerns require quick and urgent action to retain and maintain the physiological activities of microorganisms, the nitrogen pool of soils, and the continuous yields of wheat in a constantly worsening environment. The current study was conducted to evaluate the plant growth-promoting endophytic Aspergillus flavus AUMC 16,068 and its EPS for improvement of plant growth, phytoremediation capacity, and physiological consequences on wheat plants (Triticum aestivum) under lead stress. After 60 days of planting, the heading stage of wheat plants, data on growth metrics, physiological properties, minerals content, and lead content in wheat root, shoot, and grains were recorded. Results evoked that lead pollution reduced wheat plants’ physiological traits as well as growth at all lead stress concentrations; however, inoculation with lead tolerant endophytic A. flavus AUMC 16,068 and its respective EPS alleviated the detrimental impact of lead on the plants and promoted the growth and physiological characteristics of wheat in lead-contaminated conditions and also lowering oxidative stress through decreasing (CAT, POD, and MDA), in contrast to plants growing in the un-inoculated lead polluted dealings. In conclusion, endophytic A. flavus AUMC 16,068 spores and its EPS are regarded as eco-friendly, safe, and powerful inducers of wheat plants versus contamination with heavy metals, with a view of protecting plant, soil, and human health.

Abstract Owing to the growing need for natural materials in different fields, studying melanin production from biological sources is imperative. In the current study, the extracellular melanin pigment was produced by the fungus Curvularia soli AS21 ON076460. The factors that affect the production of melanin were optimized by the Plackett-Burman design (P-BD). The effect of gamma irradiation on melanin productivity was investigated. The maximum melanin yield (3.376 mg/L) was elicited by a stimulus of gamma irradiation at 1.0 kGy. The results evoked that, Curvularia soli AS21 ON076460 melanin exhibited excellent antimicrobial activity against all tested bacteria and fungi. Klebsiella pneumoniae ATCC 13883 and P. digitatum were mostly affected by melanin registering the inhibition zone diameters of 37.51 ± 0.012 and 44.25 ± 0.214 mm, respectively. Moreover, Curvularia soli AS21 ON076460 melanin indicated a significant antiviral efficacy (77% inhibition) of Herpes simplex virus (HSV1). The melanin pigment showed antioxidant activities with IC50 of 42 ± 0.021 and 17 ± 0.02 µg/mL against DPPH and NO, respectively. Melanin had cytotoxic action against human breast cancer and skin cancer cell lines (Mcf7and A431) as well as exerting a low percentage of cell death against normal skin cell lines (Hfb4). Melanin was effective in wound management of human skin cells by 63.04 ± 1.83% compared with control (68.67 ± 1.10%). The novelty in the study is attributed to the possibility of using gamma rays as a safe method in small economic doses to stimulate melanin production from the fungi that have been isolated. In summary, melanin produced from fungi has significant biological activities that encourage its usage as a supportive medical route.

Abstract Around the world, a variety of crops, including tomatoes, suffer serious economic losses due to the Rhizoctonia root-rot disease. Herein, Bacillus velezensis, Bacillus megaterium, and Herpaspirillum huttiense isolated from strawberry (Fragaria chiloensis var. ananassa) plants were pragmatic as plant growth promotors for battling the Rhizoctonia root rot disease and bringing about defense mechanisms as well as growth promotional strategies in tomato plants. These endophytic bacteria demonstrated potent antifungal activity against R. solani in vitro along in vivo. Data explained that the isolated endophytic bacteria could produce Indole acetic acid, Gibberellic acid GA, and siderophore as well as solubilize phosphate in the soil. The consortium of (Bacillus velezensis, Bacillus megaterium, and Herpaspirillum huttiense) increased the protection % against Rhizoctonia infection by (79.4%), followed by B. velezensis by (73.52%), H. huttiense by (70.5%), and B. megaterium by (67.64%), respectively. There was an increase in soluble proteins and carbohydrates in infected plants treated with a consortium of endophytic bacteria by 30.7% and 100.2% over untreated infected plants, respectively. Applying endophytic bacteria either alone or in combination lowered the level of malondialdehyde MDA and hydrogen peroxide H2O2 and improved the activities of antioxidant enzymes in both infected and uninfected plants. Also, bacterial endophytes have distinctive reactions regarding the number and concentrations of isozymes in both infected and uninfected plants. It could be recommended the commercial usage of a mixture of targeted bacterial endophyte strains as therapeutic nutrients against Rhizoctonia root-rot disease as well as plant growth inducer.

Abstract Bardawil Lake is a unique aquatic ecosystem that provides a habitat for various fish and other marine organisms. This study aimed to analyze the quality of fish species to prove that this lake is free of pollution, not other Egyptian lakes, due to the accumulation of some heavy metals (Cd, Pb, Cu, and Zn) in various tissues of fish species that were caught from this lake. Thirty-five fish samples were caught during the Spring of 2018 from seven different species: Mugil cephalus, Liza auratus, Sparus aurata, Dicentrarchus labrax, Siganus rivulatus, Anguilla angilla, and Solae solea. The Association of Official Analytical Chemists methods using a spectrophotometer determined the biochemical composition. In contrast, atomic absorption spectrometry (AAS) was employed to determine the heavy metals expressed by µg/g wet weight. Results exposed that the accumulation of essential micronutrient (Cu, Zn) content was higher than toxic elements (Cd & Pb) in muscles in order to Zn > Cu > Pb > Cd. Muscles

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods.

Several phytopathogens attack eggplant, causing crop damage. One of the most destructive plant diseases, Root-Knot Nematode (RKN), causes significant damage to eggplant seedlings. Finding safe and effective biological alternatives to prevent eggplant root nematode disease, which significantly limits plant productivity, is the innovative aspect of this study. Six isolates of plant growth-promoting fungus (PGPF) were tested in the current work for improving biochemical defense and physio-biochemical performance in eggplant seedlings under the Meloidogyne incognita challenge. PGPF isolates were tested in vitro for some biochemical traits such as Siderophores and HCN production. Besides, the antagonistic efficacy of PGPF filtrates against M. incognita was tested in vitro. The best isolates capable of producing HCN were F5 and F3 respectively. Also, F5 followed by F3 exhibited the maximum mortality proportions of 74.20% and 60.35% mortality in nematode juveniles after 96 hours respectively. Moreover, F5 has the highest level of antioxidant activity, with IC50 145 µg/mL followed by F3 with IC50 350 µg/mL. thus, we identified F5 and F3 completely as Rhizopus microsporus (OQ291571.1 and Aspergillus oryzae OQ291572.1. Implementing R. microsporus and A. oryzae collectively in vivo study was the most successful therapy, limiting nematode recordings as 95.23%, 86.98%, 80.35%, 80%, and 68.78% reduction in females, galls, developmental stage, egg masses, second juveniles, respectively, in diseased seedlings. It could be suggested that the use of ethyl acetate extracts (EAE) of A. oryzae and R. microsporus might be commercially applied as a stimulator of eggplant and or anti-nematodes against M. incognita.

Backgrounds PON1 plays a main role in preventing oxidative stress, and also it is important in removal of carcinogenic lipid-soluble radicals caused by lipid peroxidation. Bronchoalveolar lavage (BAL) is a sampling technique used in flexible bronchoscopy that can detect several biochemical agents that could be used as possible lung cancer biomarkers. Objectives To investigate the diagnostic utility of serum and BAL levels of paraoxonase enzyme in patients with lung cancer. Patients and methods The study included two groups: group I consisted of 25 patients diagnosed as having lung cancer, and group II consisted of 15 healthy volunteers. Clinical history taking, physical examination, radiological investigations such as chest radiograph and computed tomography, and routine laboratory tests were done for all studied cases. Serum and BAL samples were collected from all studied cases to evaluate PON1 enzyme activity by ELISA test. Results Paraoxonase enzyme was significantly lower in serum and BAL fluid samples in lung cancer cases compared with healthy persons. A significant positive correlation between PON1 levels was found in BAL and serum, and also there were significant positive correlations comparing PON1 enzymatic activity and lipid parameters (high-density lipoprotein, low-density lipoprotein, and cholesterol). Conclusion Levels of PON1 and its related variables could be used as good biomarkers for assessing patients with lung cancer.

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient's overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows specialists to improve the accuracy and consistency of workflow. [ABSTRACT FROM AUTHOR]

This paper aims to improve the steering performance of the Ackermann personal mobility scooter based on a new meta-heuristic optimization algorithm named Differential Harris Hawks Optimization (DHHO) and the modeling of the steering encoder. The steering response in the Ackermann mechanism is crucial for automated driving systems (ADS), especially in localization and path-planning phases. Various methods presented in the literature are used to control the steering, and meta-heuristic optimization algorithms have achieved prominent results. Harris Hawks optimization (HHO) algorithm is a recent algorithm that outperforms state-of-the-art algorithms in various optimization applications. However, it has yet to be applied to the steering control application. The research in this paper was conducted in three stages. First, practical experiments were performed on the steering encoder sensor that measures the steering angle of the Landlex mobility scooter, and supervised learning was applied to model the results obtained for the steering control. Second, the DHHO algorithm is proposed by introducing mutation between hawks in the exploration phase instead of the Hawks perch technique, improving population diversity and reducing premature convergence. The simulation results on CEC2021 benchmark functions showed that the DHHO algorithm outperforms the HHO, PSO, BAS, and CMAES algorithms. The mean error of the DHHO is improved with a confidence level of 99.8047% and 91.6016% in the 10-dimension and 20-dimension problems, respectively, compared with the original HHO. Third, DHHO is implemented for interactive real-time PID tuning to control the steering of the Ackermann scooter. The practical transient response results showed that the settling time is improved by 89.31% compared to the original response with no overshoot and steady-state error, proving the superior performance of the DHHO algorithm compared to the traditional control methods. [ABSTRACT FROM AUTHOR]