In 2019, the world suffered from the emergence of COVID-19 infection, one of the most difficult pandemics in recent history. Millions of confirmed deaths from this pandemic have been reported worldwide. This disaster was caused by SARS-CoV-2, which is the last discovered member of the family of Coronaviridae. Various studies have shown that natural compounds have effective antiviral properties against coronaviruses by inhibiting multiple viral targets, including spike proteins and viral enzymes. This review presents the classification and a detailed explanation of the SARS-CoV-2 molecular characteristics and structure–function relationships. We present all currently available crystal structures of different SARS-CoV-2 proteins and emphasized on the crystal structure of different virus proteins and the binding modes of their ligands. This review also discusses the various therapeutic approaches for COVID-19 treatment and available vaccinations. In addition, we highlight and compare the existing data about natural compounds extracted from algae, fungi, plants, and scorpion venom that were used as antiviral agents against SARS-CoV-2 infection. Moreover, we discuss the repurposing of select approved therapeutic agents that have been used in the treatment of other viruses.

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.

Yield forecasting is a rational and scientific way of predicting future occurrences in agriculture—the level of production effects. Its main purpose is reducing the risk in the decision-making process affecting the yield in terms of quantity and quality. The aim of the following study was to generate a linear and non-linear model to forecast the tuber yield of three very early potato cultivars: Arielle, Riviera, and Viviana. In order to achieve the set goal of the study, data from the period 2010–2017 were collected, coming from official varietal experiments carried out in northern and northwestern Poland. The linear model has been created based on multiple linear regression analysis (MLR), while the non-linear model has been built using artificial neural networks (ANN). The created models can predict the yield of very early potato varieties on 20th June. Agronomic, phytophenological, and meteorological data were used to prepare the models, and the correctness of their operation was verified on the basis of separate sets of data not participating in the construction of the models. For the proper validation of the model, six forecast error metrics were used: i.e., global relative approximation error (RAE), root mean square error (RMS), mean absolute error (MAE), and mean absolute percentage error (MAPE). As a result of the conducted analyses, the forecast error results for most models did not exceed 15% of MAPE. The predictive neural model NY1 was characterized by better values of quality measures and ex post forecast errors than the regression model RY1.

Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

Cysteine (Cys) and α-lipoic acid (ALA) are naturally occurring antioxidants (sulfur-containing compounds) that can protect plants against a wide spectrum of environmental stresses. However, up to now, there are no conclusive data on their integrative roles in mitigation of drought stress in wheat plants. Here, we studied the influence of ALA at 0.02 mM (grain dipping pre-cultivation treatment) and Cys (25 and 50 ppm as a foliar application) under well watered and deficit irrigation (100% and 70% of recommended dose). The results showed that deficit irrigation markedly caused obvious cellular oxidative damage as indicated by elevating the malondialdehyde (MDA) and hydrogen peroxide content (H2O2). Moreover, water stressed plants exhibited multiple changes in physiological metabolism, which affected the quantitative and qualitative variables of grain yield. The enzymatic antioxidants, including superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT) and peroxidase (POX) were improved by Cys application. SOD and APX had the same response when treated with ALA, but CAT and POX did not. Moreover, both studied molecules stimulated chlorophyll (Chl) and osmolytes’ biosynthesis. In contrast, the Chl a/b ratio was decreased, while flavonoids were not affected by either of the examined molecules. Interestingly, all above-mentioned changes were associated with an improvement in the scavenging capacity of reactive oxygen species (ROS), leaf relative water content (RWC), grain number, total grain yield, weight of 1000 kernels, gluten index, falling number, and alveographic parameters (P, W, and P/L values). Furthermore, heatmap plot analysis revealed several significant correlations between different studied parameters, which may explore the importance of applied Cys and ALA as effective compounds in wheat cultivation under water deficit conditions.

In the present investigation, we study the effect of Bacillus thuringiensis MH161336 (106–8 CFU/cm3), silicon (25 mL L−1), and carrot extract (75 mL L−1) as seed primers, individually or in combination, on morphological, physio-biochemical and yield components of drought-stressed pea plants (Master B) during 2019/2020 and 2020/2021 seasons. Our results indicated that drought causes a remarkable reduction in plant height, leaf area, number of leaves per plant, and number of flowers per plant in stressed pea plants during two seasons. Likewise, number of pods, pod length, seeds weight of 10 dried plants, and dry weight of 100 seeds were decreased significantly in drought-stressed pea plants. Nevertheless, seed priming with the individual treatments or in combination boosted the morphological, physio-biochemical, and yield characters of pea plants. The best results were obtained with the Bacillus thuringiensis + carrot extract treatment, which led to a remarkable increase in the number of leaves per plant, leaf area, plant height, and number of flowers per plant in stressed pea plants in both seasons. Moreover, pod length, number of seeds per pod, seeds weight of 10 dried plants, and dry weight of 100 seeds were significantly increased as well. Bacillus thuringiensis + carrot extract treatment led to improved biochemical and physiological characters, such as relative water content, chlorophyll a, chlorophyll b, regulated the up-regulation of antioxidant enzymes, increased seed yield, and decreased lipid peroxidation and reactive oxygen species, mainly superoxide and hydrogen peroxide, in drought-stressed pea plants.

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

This investigation was carried out for genetic characterization and determination of drought tolerance of ten Egyptian cultivars of wheat (Triticum aestivum L.), namely Misr 1, Misr 2, Gemmiza 9, Gemmiza 10, Gemmiza 11, Gemmiza 12, Shandawel 1, Giza 168, Giza 171, and Sids 14. These cultivars were grown in two winter seasons: 2018/2019 and 2019/2020 at the experimental farm Fac. of Agric., Suez Canal Univ., Ismailia, Egypt, under two watering regimes: normal (100%) and stress (50% FC) conditions. Six agronomic traits and five tolerance indices, namely stress tolerance (TOL), mean productivity (MP), geometric mean productivity (GMP), yield stability index (YSI), and drought susceptibility index (DSI), were used to evaluate the impact of drought stress. The results reflected Giza 171, Misr 2, and Giza 168 as precious germplasm for breeding of high-yielding drought-tolerant wheat. A highly significant positive correlation was recorded between yield under normal and stress conditions on the one hand and each of MP and GMP on the other hand. In addition, YSI appeared engaged in a highly significant positive correlation with yield under drought conditions only. TOL and DSI appeared insignificantly correlated with yield. Therefore, MP and GMP were reflected as the first runners among indices suitable to distinguish the high-yielding cultivars under drought conditions. At the molecular level, five primers of Start Codon Targeted (SCoT) markers were able to resolve and characterize the studied cultivars, which reflected SCoT as a potent gene-targeting molecular marker, able to characterize and resolve genetic diversity in wheat at the cultivar level using few primers. Therefore, SCoT is a time-efficient molecular marker, and it can efficiently replace indices in characterization of drought-tolerant genotypes with a high confidence level and reasonable cost.