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The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic algorithm to scrutinize whether regions of the spectrum from optical indices and SAR texture choose the optimum combinations of selected variables. The sub-setting variables resulting from each feature selection method were used to train the regression learners’ random forest (RF), linear regression (LR), backpropagation neural network (BPNN), and support vector regression (SVR). Combining the BPNN feature selection method with the RF regression learner better predicted soil salinity (RME 0.000246; sub-setting variables = 18). Integrating different remote sensing data and machine learning provides an opportunity to develop a robust prediction approach to predict soil salinity in drylands. This research evaluated the performances of various machine learning models, overcame the limitations of conventional techniques, and optimized the variable input combinations. This research can assist farmers in soil-salinization-affected areas in better managing planting procedures and enhancing the sustainability of their lands.

Over the past five years, a lot of research activities in polymer composites were done in order to improve environmental sustainability and to present advantages for commercial applications of water treatment and desalination. Polymers offered tunable properties, improved processability, remarkable stability, high surface area for fast decontamination, selectivity to eliminate different pollutants, and cost-cutting of water treatment. Hence, the development of polymeric materials is one of the future directions to meet the environmental water standards and to supply the water requirements of the growing populations. This review highlighted the very recent achievements in fabrication, characterization, and applications of polymeric composites used for water treatment and desalination. The polymeric modifications, the addition of functional groups, and the assemblies of nanomaterials were also discussed in detail. In particular, great attention was paid to the recent advances in polymer/polymer composites, polymer/carbon composites, and polymer/clay composites, presenting their usage in the removal of various types of contaminants, e.g., metal ions, dyes, and other toxic pollutants. The review also summarized the main advantages and disadvantages of the different adsorbent materials. Specific attention was paid to the mechanism of adsorption, including chemisorption and physisorption mechanisms. In addition, the challenges and the future perspectives were identified to reach the optimal performance of the different adsorbents.

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a life-threatening respiratory disease with a high case fatality rate; however, its risk factors remain unclear. We aimed to explore the influence of demographic factors, clinical manifestations and underlying comorbidities on mortality in MERS-CoV patients. Retrospective chart reviews were performed to identify all laboratory-confirmed cases of MERS-COV infection in Saudi Arabia that were reported to the Ministry of Health of Saudi Arabia between 23 April 2014 and 7 June 2016. Statistical analyses were conducted to assess the effect of sex, age, clinical presentation and comorbidities on mortality from MERS-CoV. A total of 281 confirmed MERS-CoV cases were identified: 167 (59.4%) patients were male and 55 (20%) died. Mortality predominantly occurred among Saudi nationals and older patients and was significantly associated with respiratory failure and shortness of breath. Of the 281 confirmed cases, 160 (56.9%) involved comorbidities, wherein diabetes mellitus, hypertension, ischemic heart disease, congestive heart failure, end-stage renal disease and chronic kidney disease were significantly associated with mortality from MERS-CoV and two or three comorbidities significantly affected the fatality rates from MERS-CoV. The findings of this study show that old age and the existence of underlying comorbidities significantly increase mortality from MERS-CoV.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual cognitive decline. Strong antioxidants that inhibit free radicals, such as polyphenols, reduce the likelihood of developing oxidative stress-related degenerative diseases such as AD. Naringin, a flavonoid found in citrus fruit shown to be neuroprotective, reduce oxidative damage and minimize histopathological changes caused by ischemic reperfusion, enhance the long-term memory in AD animal models. This work aimed to comprehend the role of naringin in the defense of the cerebellum against aluminum chloride (AlCl3)-induced AD in rats by investigating the behavioral, neurochemical, immunohistochemical, and molecular mechanisms that underpin its possible neuroprotective effects. Twenty-four adult albino rats were divided into four groups (n=6/group): i) Control (C) received saline per oral (p.o.), ii) Naringin(N)-received naringin (100mg/kg/d) p.o, iii) AlCl3-recived AlCl3 (100 mg/kg/d) p.o and iv) AlCl3 + Naringin (AlCl3 + N) received both AlCl3 and naringin p.o for 21 days. Behavioral tests showed an increase in the time to reach the platform in Morris water maze, indicating memory impairment in the AlCl3-treated group, but co-administration of naringin showed significant improvement. The Rotarod test demonstrated a decrease in muscle coordination in the AlCl3-treated group, while it was improved in the AlCl3 + N group. Neurochemical analysis of the hippocampus and cerebellum revealed that AlCl3 significantly increased lipid peroxidation and oxidative stress and decreased levels of reduced glutathione. Administration of naringin ameliorated these neurochemical changes via its antioxidant properties. Cerebellar immunohistochemical expression for microtubule assembly (tau protein) and oxidative stress (iNOS) increased in A1C13-treated group. On the other hand, the expression of the autophagic marker (LC3) in the cerebellum showed a marked decline in AlCl3-treated group. Western blot analysis confirmed the cerebellar immunohistochemical findings. Collectively, these findings suggested that naringin could contribute to the combat of oxidative and autophagic stress in the cerebellum of AlCl3-induced AD.

With a rapid increase in industrial growth around the world, the demand for an entirely novel category of nanoparticles and technologies for wastewater treatment has become a key concern for environmental protection. Recently, hybrids of layered double hydroxides (LDH), particularly those containing LDH, have gained attention as potential nanoscale adsorbents for water treatment. Recent research has shown that LDH-containing composites are interesting versatile materials with the ability to be used in energy storage, photocatalysis, nanocomposites, and water treatment. In the current work, LDH-containing composites were utilized as adsorbents for the purpose of purifying water. The adsorbents investigated are Zn–Co–Fe/LDH/Chitosan-in situ sample preparation (LDH/CS1) and Zn–Co–Fe/LDH/Chitosan-ex situ sample preparation (LDH/CS2). Furthermore, LDH/CS1 and LDH/CS2 were investigated for wastewater treatment from methyl orange dye (MO) with various adsorption conditions. When the initial MO concentration was 20 mg/L and the amount of adsorbent was 0.1 g, the removal efficiency reached 72.8 and 91.7% for LDH/CS1 and LDH/CS2, respectively. The MO’s maximum adsorption capabilities are 160.78 and 165.89 mg/g for LDH/CS1 and LDH/CS2, respectively, which is much greater than that of comparable commercial adsorbents. MO adsorption onto LDH/CS1 and LDH/CS2 was best characterized by the pseudo-second-order kinetic model. The equilibrium adsorption data was followed by the Freundlich and Langmuir models. The adsorption is favorable as evidenced by the equilibrium parameter R L values for MO adsorption onto LDH/CS1 and LDH/CS2, which were 0.227 and 0.144, respectively. Using the free volume distribution method and the positron annihilation lifetime technique, the nanostructure of the materials was examined.

A mathematical model of 2D-double diffusive layer flow model of boundary in MHD Maxwell fluid created by a sloping slope surface is constructed in this paper. The numerical findings of non-Newtonian fluid are important to the chemical processing industry, mining industry, plastics processing industry, as well as lubrication and biomedical flows. The diversity of regulatory parameters like buoyancy rate, magnetic field, mixed convection, absorption, Brownian motion, thermophoretic diffusion, Deborah number, Lewis number, Prandtl number, Soret number, as well as Dufour number contributes significant impact on the current model. The steps of research methodology are as followed: a) conversion from a separate matrix (PDE) to standard divisive calculations (ODEs), b) Final ODEs are solved in bvp4c program, which developed in MATLAB software, c) The stability analysis part also being developed in bvp4c program, to select the most effective solution in the real liquid state. Lastly, the numerical findings are built on a system of tables and diagrams. As a result, the profiles of velocity, temperature, and concentration are depicted due to the regulatory parameters, as mentioned above. In addition, the characteristics of the local Nusselt, coefficient of skin-friction as well as Sherwood numbers on the Maxwell fluid are described in detail.

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe.

(1) Background and Aim: All-trans retinoic acid (ATRA) induces differentiation and inhibits growth of many cancer cells. However, resistance develops rapidly prompting the urgent need for new synthetic and potent derivatives. EC19 and EC23 are two synthetic retinoids with potent stem cell neuro-differentiation activity. Here, these compounds were screened for their in vitro antiproliferative and cytotoxic activity using an array of different cancer cell lines. (2) Methods: MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, AV/PI (annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI)), cell cycle analysis, immunocytochemistry, gene expression analysis, Western blotting, measurement of glutamate and total antioxidant concentrations were recruited. (3) Results: HepG2, Caco-2, and MCF-7 were the most sensitive cell lines; HepG2 (ATRA; 36.2, EC19; 42.2 and EC23; 0.74 µM), Caco-2 (ATRA; 58.0, EC19; 10.8 and EC23; 14.7 µM) and MCF-7 (ATRA; 99.0, EC19; 9.4 and EC23; 5.56 µM). Caco-2 cells were selected for further biochemical investigations. Isobologram analysis revealed the combined synergistic effects with 5-fluorouracil with substantial reduction in IC50. All retinoids induced apoptosis but EC19 had higher potency, with significant cell cycle arrest at subG0-G1, -S and G2/M phases, than ATRA and EC23. Moreover, EC19 reduced cellular metastasis in a transwell invasion assay due to overexpression of E-cadherin, retinoic acid-induced 2 (RAI2) and Werner (WRN) genes. (4) Conclusion: The present study suggests that EC-synthetic retinoids, particularly EC19, can be effective, alone or in combinations, for potential anticancer activity to colorectal cancer. Further in vivo studies are recommended to pave the way for clinical applications.

Arterial stenosis is a common cardiovascular disease that restricts blood flow. A stenotic blood vessel creates tangent stress pressure, which lessens the arterial side and causes an aneurysm. The primary purpose of this study is to investigate blood flowing via an inclination pipe with stricture and expansion after stricture (widening) underneath the influence of a constant incompressible Casson liquid flowing with the magnetism field. The relations for surface shearing stress, pressure drop, flow resistance, and velocity are calculated analytically by applying a mild stenosis approximation. The effect of different physical characteristics on liquid impedance to flowing, velocity, and surface shearing stress are studied. With a non-Newtonian aspect of the Casson liquid, the surface shearing stress declines, and an impedance upturn. Side resistivity and shear-stress increase with the elevations of stricture, whilst together decreasing with a dilatation height.