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Optimum growing temperature is necessary for maximum yield-potential in any crop. The global atmospheric temperature is changing more rapidly and irregularly every year. High temperature at the flowering/reproductive stage in rice causes partial to complete pollen sterility, resulting in significant reduction in grain yield. Green Super Rice (GSR) is an effort to develop an elite rice type that can withstand multiple environmental stresses and maintain yield in different agro-ecological zones. The current study was performed to assess the effect of heat stress on agronomic and physiological attributes of GSR at flowering stage. Twenty-two GSR lines and four local checks were evaluated under normal and heat-stress conditions for different agro-physiological parameters, including plant height (PH), tillers per plant (TPP), grain yield per plant (GY), straw yield per plant (SY), harvest index (HI), 1000-grain weight (GW), grain length (GL), cell membrane stability (CMS), normalized difference vegetative index (NDVI), and pollen fertility percentage (PFP). Genotypes showed high significant variations for all the studied parameters except NDVI. Association and principal component analysis (PCA) explained the genetic diversity of the genotypes, and relationship between the particular parameters and grain yield. We found that GY, along with other agronomic traits, such as TPP, SY, HI, and CMS, were greatly affected by heat stress in most of the genotypes, while PH, GW, GL, PFP, and NDVI were affected only in a few genotypes. Outperforming NGSR-16 and NGSR-18 in heat stress could be utilized as a parent for the development of heat-tolerant rice. Moreover, these findings will be helpful in the prevention and management of heat stress in rice.

The Bacille Calmette-Guérin or BCG vaccine, the only vaccine available against Mycobacterium tuberculosis can induce a marked Th1 polarization of T-cells, characterized by the antigen-specific secretion of IFN-γ and enhanced antiviral response. A number of studies have supported the concept of protection by non-specific boosting of immunity by BCG and other microbes. BCG is a well-known example of a trained immunity inducer since it imparts ‘non-specific heterologous’ immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the recent pandemic. SARS-CoV-2 continues to inflict an unabated surge in morbidity and mortality around the world. There is an urgent need to devise and develop alternate strategies to bolster host immunity against the coronavirus disease of 2019 (COVID-19) and its continuously emerging variants. Several vaccines have been developed recently against COVID-19, but the data on their protective efficacy remains doubtful. Therefore, urgent strategies are required to enhance system immunity to adequately defend against newly emerging infections. The concept of trained immunity may play a cardinal role in protection against COVID-19. The ability of trained immunity-based vaccines is to promote heterologous immune responses beyond their specific antigens, which may notably help in defending against an emergency situation such as COVID-19 when the protective ability of vaccines is suspicious. A growing body of evidence points towards the beneficial non-specific boosting of immune responses by BCG or other microbes, which may protect against COVID-19. Clinical trials are underway to consider the efficacy of BCG vaccination against SARS-CoV-2 on healthcare workers and the elderly population. In this review, we will discuss the role of BCG in eliciting trained immunity and the possible limitations and challenges in controlling COVID-19 and future pandemics.

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (), the percentage of plasticizer (), the initial curing temperature (), the age of the specimen (), the curing duration (), the fine aggregate to total aggregate ratio (), the percentage of total aggregate by volume (), the percent SiO2 solids to water ratio () in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (), the activator or alkali to FA ratio (), the sodium oxide (Na2O) to water ratio () for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (). A GEP empirical equation is proposed to estimate the of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

Rice (Oryza sativa) is an important staple food crop worldwide, especially in east and southeast Asia. About one-third of rice cultivated area is under saline soil, either natural saline soils or irrigation with brackish water. Salinity stress is among the devastating abiotic stresses that not only affect rice growth and crop productivity but also limit its cultivation area globally. Plants adopt multiple tolerance mechanisms at the morphological, physiological, and biochemical levels to tackle salinity stress. To identify these tolerance mechanisms, this study was carried out under both a controlled glass house as well as natural saline field conditions using 22 green super rice (GSR) lines along with two local varieties (“IRRI 6 and Kissan Basmati”). Several morpho-physiological and biochemical parameters along with stress-responsive genes were used as evaluation criteria under normal and salinity stress conditions. Correlation and Principal Component Analysis (PCA) suggested that shoot-related parameters and the salt susceptible index (SSI) can be used for the identification of salt-tolerant genotypes. Based on Agglomerative Hierarchical Cluster (AHC) analysis, two saline-tolerant (“S19 and S20”) and saline-susceptible (“S3 and S24”) lines were selected for further molecular evaluation. Quantitative RT-PCR was performed, and results showed that expression of 1-5-phosphoribosyl -5-5-phosphoribosyl amino methylidene amino imidazole-4-carboxamide isomerase, DNA repair protein recA, and peptide transporter PTR2 related genes were upregulated in salt-tolerant genotypes, suggesting their potential role in salinity tolerance. However, additional validation using reverse genetics approaches will further confirm their specific role in salt tolerance. Identified saline-tolerant lines in this study will be useful genetic resources for future salinity breeding programs.

Direct seeding (DS) of rice gained much attention due to labor scarcity and unavailability of water. However, reduced emergence and poor seedling establishment are the main problems of DS which causes significant yield losses. Herein, DS-associated seedling traits of three major rice groups, i.e., Indica (Ind), Japonica (Jap), and aus-type (Aus), were evaluated by using an improved mesocotyl elongation assay. The associations among different traits at the seedling and maturity stage were also studied. Significant variation was observed among the cultivars of different rice groups. The Aus group cultivars showed higher mean values for coleoptile (C, 3.85 cm), mesocotyl (MC, 4.17 cm), shoot length (SL, 13.64 cm), panicle length (PL, 23.44 cm), tillers number (T, 15.95), culm length (CL, 105.29 cm), and plant height (PH, 128.73 cm), while the Indica and Japonica groups showed higher mean values of grain length (GL, 8.69 mm), grain length/width ratio (GL/WR, 3.07), and grain width (GW, 3.31 mm), with 1000 grain weight (TGWt, 25.53 g), respectively. Pairwise correlation analysis showed that MC, C, and SL were positively correlated among themselves and with PL, CL, and PH. Moreover, based on principal component analysis (PCA), C, MC, SL, CL, and PH were identified as the major discriminative factors in the rice cultivars. This study describes the development of desired DS rice variety with long MC and semidwarf in height and suggests that Aus group cultivars can be used as the donor parents of favorable DS-associated traits in rice breeding programs.

The object of this research is concrete-filled steel tubes (CFST). The article aimed to develop a prediction Multiphysics model for the circular CFST column by using the Artificial Neural Network (ANN), the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Gene Expression Program (GEP). The database for this study contains 1667 datapoints in which 702 are short CFST columns and 965 are long CFST columns. The input parameters are the geometric dimensions of the structural elements of the column and the mechanical properties of materials. The target parameters are the bearing capacity of columns, which determines their life cycle. A Multiphysics model was developed, and various statistical checks were applied using the three artificial intelligence techniques mentioned above. Parametric and sensitivity analyses were also performed on both short and long GEP models. The overall performance of the GEP model was better than the ANN and ANFIS models, and the prediction values of the GEP model were near actual values. The PI of the predicted Nst by GEP, ANN and ANFIS for training are 0.0416, 0.1423, and 0.1016, respectively, and for Nlg these values are 0.1169, 0.2990 and 0.1542, respectively. Corresponding OF values are 0.2300, 0.1200, and 0.090 for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The superiority of the GEP method to the other techniques can be seen from the fact that the GEP technique provides suitable connections based on practical experimental work and does not rely on prior solutions. It is concluded that the GEP model can be used to predict the bearing capacity of circular CFST columns to avoid any laborious and time-consuming experimental work. It is also recommended that further research should be performed on the data to develop a prediction equation using other techniques such as Random Forest Regression and Multi Expression Program.

Climate change can decrease the global maize productivity and grain quality. Maize crop requires an optimal temperature for better harvest productivity. A suboptimal temperature at any critical stage for a prolonged duration can negatively affect the growth and yield formation processes. This review discusses the negative impact of temperature extremes (high and low temperatures) on the morpho-physiological, biochemical, and nutritional traits of the maize crop. High temperature stress limits pollen viability and silks receptivity, leading to a significant reduction in seed setting and grain yield. Likewise, severe alterations in growth rate, photosynthesis, dry matter accumulation, cellular membranes, and antioxidant enzyme activities under low temperature collectively limit maize productivity. We also discussed various strategies with practical examples to cope with temperature stresses, including cultural practices, exogenous protectants, breeding climate-smart crops, and molecular genomics approaches. We reviewed that identified quantitative trait loci (QTLs) and genes controlling high- and low temperature stress tolerance in maize could be introgressed into otherwise elite cultivars to develop stress-tolerant cultivars. Genome editing has become a key tool for developing climate-resilient crops. Moreover, challenges to maize crop improvement such as lack of adequate resources for breeding in poor countries, poor communication among the scientists of developing and developed countries, problems in germplasm exchange, and high cost of advanced high-throughput phenotyping systems are discussed. In the end, future perspectives for maize improvement are discussed, which briefly include new breeding technologies such as transgene-free clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-mediated genome editing for thermo-stress tolerance in maize.