The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is the cause of coronavirus disease 2019 (COVID-19); a severe respiratory distress that has emerged from the city of Wuhan, ...Hubei province, China during December 2019. COVID-19 is currently the major global health problem and the disease has now spread to most countries in the world. COVID-19 has profoundly impacted human health and activities worldwide. Genetic mutation is one of the essential characteristics of viruses. They do so to adapt to their host or to move to another one. Viral genetic mutations have a high potentiality to impact human health as these mutations grant viruses unique unpredicted characteristics. The difficulty in predicting viral genetic mutations is a significant obstacle in the field. Evidence indicates that SARS-CoV-2 has a variety of genetic mutations and genomic diversity with obvious clinical consequences and implications. In this review, we comprehensively summarized and discussed the currently available knowledge regarding SARS-CoV-2 outbreaks with a fundamental focus on the role of the viral proteins and their mutations in viral infection and COVID-19 progression. We also summarized the clinical implications of SARS-CoV-2 variants and how they affect the disease severity and hinder vaccine development. Finally, we provided a massive phylogenetic analysis of the spike gene of 214 SARS-CoV-2 isolates from different geographical regions all over the world and their associated clinical implications.
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The application of green nanotechnology in agriculture has been receiving substantial attention, especially in the development of new nano-fertilizers and nano-insecticides. Herein, the metabolites ...secreted by the fungal strain Penicillium chrysogenum are used as a reducing agent for selenium ions to form selenium nanoparticles (Se-NPs). The synthesized Se-NPs were characterized using color change, UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, transmission electron microscopy (TEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), and dynamic light scattering (DLS). The biomass filtrate of the fungal strain changed from colorless to a ruby red color after mixing with sodium selenite with a maximum surface plasmon resonance at 262 nm. Data exhibits the successful formation of spherical, amorphous Se-NPs with sizes ranging between 3–15 nm and a weight percentage of 38.52%. The efficacy of Se-NPs on the growth performance of sunflower (Helianthus annuus L.) and inhibition of cutworm Agrotis ipsilon was investigated. The field experiment revealed the potentiality of Se-NPs to enhance the growth parameters and carotenoid content in sunflower, especially at 20 ppm. The chlorophylls, carbohydrates, proteins, phenolic compounds, and free proline contents were markedly promoted in response to Se-NPs concentrations. The antioxidant enzymes (peroxidase, catalase, superoxide dismutase, and polyphenol oxidase) were significantly decreased compared with the control. Data analysis showed that the highest mortality for the 1st, 2nd, 3rd, 4th, and 5th instar larvae of Agrotis ipsilon was achieved at 25 ppm with percentages of 89.7 ± 0.3, 78.3 ± 0.3, 72.3 ± 0.6, 63.7 ± 0.3, and 68.7 ± 0.3 respectively after 72 h.
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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.
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The growing propagation of the microgrids and their remarkable effects on operating the smart grid is developing a sustained environment to drift away from the traditional framework. Tending the ...microgrid systems for uplifting their range of benefits can make an outstanding proponent for outlining an effective negotiation framework for the microgrids connected to the smart grid. This paper aims to provide a distributed energy management using a relaxed consensus + innovation approach in order to converge both the trading price and the transaction power among the microgrids and smart grid. The proposed smart grid consists of the generators, and lines coincided with an IEEE 24-bus test system, and the microgrid proposed in this paper includes renewable sources of wind turbines, photovoltaic units, tidal units and storage units with the aim of satisfying the microgrid's demand loads. In addition, this paper dedicates to analyzing the effect of the uncertainty parameters of the system on the performance of the proposed negotiated approach using the point estimate method. Comparing the proposed negotiated method with the centralized one can prove that this method is suitable as an effective negotiation approach for providing energy management in the system.
•Suggesting a property negotiation scheme to cooperate the energy among a microgrid and smart grid.•Bringing more benefits related to both the microgrid and smart grid.•Providing all-around description of influencing the stochastic problem on negotiation process.•Modeling the uncertainty parameters incorporating the microgrid and smart grid by PEM.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
As a central hub for cellular metabolism and intracellular signalling, the mitochondrion is a pivotal organelle, dysfunction of which has been linked to several human diseases including ...neurodegenerative disorders, and in particular Parkinson's disease. An inherent challenge that mitochondria face is the continuous exposure to diverse stresses which increase their likelihood of dysregulation. In response, eukaryotic cells have evolved sophisticated quality control mechanisms to monitor, identify, repair and/or eliminate abnormal or misfolded proteins within the mitochondrion and/or the dysfunctional mitochondrion itself. Chaperones identify unstable or otherwise abnormal conformations in mitochondrial proteins and can promote their refolding to recover their correct conformation and stability. However, if repair is not possible, the abnormal protein is selectively degraded to prevent potentially damaging interactions with other proteins or its oligomerization into toxic multimeric complexes. The autophagic-lysosomal system and the ubiquitin-proteasome system mediate the selective and targeted degradation of such abnormal or misfolded protein species. Mitophagy (a specific kind of autophagy) mediates the selective elimination of dysfunctional mitochondria, in order to prevent the deleterious effects the dysfunctional organelles within the cell. Despite our increasing understanding of the molecular responses toward dysfunctional mitochondria, many key aspects remain relatively poorly understood. Herein, we review the emerging mechanisms of mitochondrial quality control including quality control strategies coupled to mitochondrial import mechanisms. In addition, we review the molecular mechanisms regulating mitophagy with an emphasis on the regulation of PINK1/PARKIN-mediated mitophagy in cellular physiology and in the context of Parkinson's disease cell biology.
This work reports a thermo-kinetic study on unimolecular thermal decomposition of some ethoxyquinolines and ethoxyisoquinolines derivatives (1-ethoxyisoquinoline (1-EisoQ), 2-ethoxyquinoline (2-EQ), ...3-ethoxyquinoline (3-EQ), 3-ethoxyisoquinoline (3-EisoQ), 4-ethoxyquinoline (4-EQ), 4-ethoxyisoquinoline (4-EisoQ), 5-ethoxyquinoline (5-EQ), 5-ethoxyisoquinoline (5-EisoQ), 8-ethoxyquinoline (8-EQ) and 8-ethoxyisoquinoline (8-EisoQ)) using density functional theory DFT (BMK, MPW1B95, M06-2X) and ab initio complete basis set-quadratic Becke3 (CBS-QB3) calculations. In the course of the decomposition of the investigated systems, ethylene is eliminated with the production of either keto or enol tautomer. The six-membered transition state structure encountered in the path of keto formation is much lower in energy than the four-membered transition state required to give enol form. Rate constants and activation energies for the decomposition of 1-EisoQ, 2-EQ, 3-EQ, 3-EisoQ, 4-EQ, 4-EisoQ, 5-EQ, 5-EisoQ, 8-EQ, and 8-EisoQ have been estimated at different temperatures and pressures using conventional transition state theory combined with Eckart tunneling and the unimolecular statistical Rice-Ramsperger-Kassel-Marcus theories. The tunneling correction is significant at temperatures up to 1000 K. Rate constants results reveal that ethylene elimination and keto production are favored kinetically and thermodynamically over the whole temperature range of 400-1200 K and the rates of the processes under study increase with the rising of pressure up to 1 atm.
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•A chemically modified cellulose adsorbent bearing hydrazino-imidazoline groups was synthesized.•The adsorbent shows high selectivity and extraction performance towards precious metal ...ions.•Adsorption kinetics, isotherms, and thermodynamic parameters were investigated.•The adsorption process is achieved through chemical coordination mechanism.•The adsorbent selectively extract precious metal ions from geological samples.
A new hydrazono-imidazoline modified cellulose (HIMC) was synthesized for selective recovery of Pt(IV), Pd(II) and Au(III) from geological samples. Cellulose was oxidized by periodate and was further functionalized with hydrazono-imidazoline moieties to afford N-donor chelating fibers. Scanning electron microscopy (SEM), Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), N2 physisorption, elemental analysis, and energy-dispersive X-ray spectroscopy (EDX) were used for characterization. Introducing the hydrazono-imidazoline groups at the surface of cellulose fibers did not alert their ordered structure and crystallinity, as indicated by XRD and SEM results. Factors affecting the adsorption were systematically investigated. Under the optimized conditions, the HIMC sorbent exhibited high adsorption capacities of 105, 88 and 75 mg g−1 for Pt(IV), Pd(II) and Au(III), respectively. Besides, the metal ion adsorption process fitted by pseudo-second-order kinetic model and Langmuir adsorption isotherm. These results highlight the applicability of this carbohydrate-based sorbent for the selective recovery of precious metals from various matrices.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper basically concentrates on providing an appropriate distributed-based energy management framework in smart islands. Smart island is defined as insular territory with the ability to ...implement integrated solutions to the management of infrastructures and natural resources. The distributed optimization is accomplished by using primal-dual method of multipliers which has shown more promising performance in terms of execution time and convergence rate compared to the alternating-direction method of multipliers. The proposed energy management scheme is carried out between 5 different agents including an energy hub, a networked multi-microgrid with 3 agents and a transportation system. The transportation system comprises of the subway system and plug-in electric vehicles. The proposed networked multi-microgrid is made up of 3 microgrid systems, each one comprises of wind generation units, photovoltaic units and a tidal unit. Despite the assumption that the agents are both suppliers and consumers of energy and are completely operated and separated from the other sections, they share energy through a peer-to-peer energy trading. Such sharing scheme will be ended up by obtaining an equilibrium point through which a consensus is reached and all the parties are satisfied. Results prove the validity of the proposed approach in providing a proper energy negotiation framework in a smart island in terms of accuracy and applicability.
•Developing an effective transportation system model within the Smart Island.•Providing a structure for the smart island which comprises of EH, TS and NMMG.•Management of thermal, gas, electrical and water demands of Smart Island.•Investigating fully distributed energy trading scheme among Smart Island agents.
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Human life has been significantly impacted by the creation and spread of novel species of antibiotic-resistant bacteria and virus strains that are difficult to manage. Scientists and researchers have ...recently been motivated to seek out alternatives and other sources of safe and ecologically friendly active chemicals that have a powerful and effective effect against a wide variety of pathogenic bacteria as a result of all these hazards and problems. In this review, endophytic fungi and their bioactive compounds and biomedical applications were discussed. Endophytes, a new category of microbial source that can produce a variety of biological components, have major values for study and broad prospects for development. Recently, endophytic fungi have received much attention as a source for new bioactive compounds. In addition, the variety of natural active compounds generated by endophytes is due to the close biological relationship between endophytes and their host plants. The bioactive compounds separated from endophytes are usually classified as steroids, xanthones, terpenoids, isocoumarins, phenols, tetralones, benzopyranones and enniatines. Moreover, this review discusses enhancement methods of secondary metabolites production by fungal endophytes which include optimization methods, co-culture method, chemical epigenetic modification and molecular-based approaches. Furthermore, this review deals with different medical applications of bioactive compounds such as antimicrobial, antiviral, antioxidant and anticancer activities in the last 3 years.
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Skin cancer is one of most deadly diseases in humans. According to the high similarity between melanoma and nevus lesions, physicians take much more time to investigate these lesions. The automated ...classification of skin lesions will save effort, time and human life. The purpose of this paper is to present an automatic skin lesions classification system with higher classification rate using the theory of transfer learning and the pre-trained deep neural network. The transfer learning has been applied to the Alex-net in different ways, including fine-tuning the weights of the architecture, replacing the classification layer with a softmax layer that works with two or three kinds of skin lesions, and augmenting dataset by fixed and random rotation angles. The new softmax layer has the ability to classify the segmented color image lesions into melanoma and nevus or into melanoma, seborrheic keratosis, and nevus. The three well-known datasets, MED-NODE, Derm (IS & Quest) and ISIC, are used in testing and verifying the proposed method. The proposed DCNN weights have been fine-tuned using the training and testing dataset from ISIC in addition to 10-fold cross validation for MED-NODE and DermIS-DermQuest. The accuracy, sensitivity, specificity, and precision measures are used to evaluate the performance of the proposed method and the existing methods. For the datasets, MED-NODE, Derm (IS & Quest) and ISIC, the proposed method has achieved accuracy percentages of 96.86%, 97.70%, and 95.91% respectively. The performance of the proposed method has outperformed the performance of the existing classification methods of skin cancer.
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