Among abiotic stresses, salinity is the main abiotic stress limiting crop growth and yield worldwide. Improving agri-food production in salt-prone areas is the key to meet the increasing food demands in near future. A greenhouse experiment was conducted to investigate the effect of different soil conditioners, gypsum (GYP), citric acid (CA), ethylene diamine tetraacetic acid (EDTA) and polyvinyl alcohol (PVA), on growth and yield of wheat (Triticum aestivum L.) grown in salinesodic soil. Gypsum was applied at a rate of 100% soil gypsum requirement while other amendments were applied each at a rate of 5 g kg-1 of soil. The results showed that EDTA treatment increased pH and electrical conductivity (ECe) of soil while pH significantly decreased when treated with citric acid. Soil sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) decreased in all treatments following the order: CT > PVA > EDTA > CA > GYP. Addition of CA positively affected growth parameters as compared to other soil conditioners including plant height, number of tillers per plant, number of spikes per plant, plant dry weight and grain yield while EDTA negatively affected these parameters. Addition of CA also significantly increased photosynthetic rate, stomatal conductance, transpiration rate and chlorophyll contents while EDTA decreased these parameters. We conclude that increase in plant growth and yield with CA might be due to the effect of CA on soil properties which positively affected plant physiological parameters.
Advances in financial inclusions have contributed to economic growth and poverty alleviation, addressing environmental implications and implementing measures to mitigate climate change. Financial inclusions force advanced countries to progress their policies in a manner that does not hinder developing countries' current and future development. Consequently, this research examined the asymmetric effects of information and communication technology (ICT), financial inclusion, consumption of primary energy, employment to population ratio, and human development index on CO2 emissions in oil-producing countries (UAE, Nigeria, Russia, Saudi Arabia, Norway, Kazakhstan, Kuwait, Iraq, USA, and Canada). The study utilizes annual panel data spanning from 1990 to 2021. In addition, this study investigates the validity of the Environmental Kuznets Curve (EKC) trend on the entire sample, taking into account the effects of energy consumption and population to investigate the impact of financial inclusion on environmental degradation. The study used quantile regression, FMOLS, and FE-OLS techniques. Preliminary outcomes revealed that the data did not follow a normal distribution, emphasizing the need to use quantile regression (QR). This technique can effectively detect outliers, data non-normality, and structural changes. The outcomes from the quantile regression analysis indicate that ICT consistently reduces CO2 emissions in all quantiles (ranging from the 1st to the 9th quantile). In the same way, financial inclusion, and employment to population ratio constrains CO2 emissions across each quantile. On the other side, primary energy consumption and Human development index were found to increase CO2 emissions in each quantile (1st to 9th). The findings of this research have implications for both the academic and policy domains. By unraveling the intricate interplay between financial inclusion, ICT, and environmental degradation in oil-producing nations, the study contributes to a nuanced understanding of sustainable development challenges. Ultimately, the research aims to guide the formulation of targeted policies that leverage financial inclusion and technology to foster environmentally responsible economic growth in oil-dependent economies.
Extreme changes in weather including heat-wave and high-temperature fluctuations are predicted to increase in intensity and duration due to climate change. Wheat being a major staple crop is under severe threat of heat stress especially during the grain-filling stage. Widespread food insecurity underscores the critical need to comprehend crop responses to forthcoming climatic shifts, pivotal for devising adaptive strategies ensuring sustainable crop productivity. This review addresses insights concerning antioxidant, physiological, molecular impacts, tolerance mechanisms, and nanotechnology-based strategies and how wheat copes with heat stress at the reproductive stage. In this study stress resilience strategies were documented for sustainable grain production under heat stress at reproductive stage. Additionally, the mechanisms of heat resilience including gene expression, nanomaterials that trigger transcription factors, (HSPs) during stress, and physiological and antioxidant traits were explored. The most reliable method to improve plant resilience to heat stress must include nano-biotechnology-based strategies, such as the adoption of nano-fertilizers in climate-smart practices and the use of advanced molecular approaches. Notably, the novel resistance genes through advanced molecular approach and nanomaterials exhibit promise for incorporation into wheat cultivars, conferring resilience against imminent adverse environmental conditions. This review will help scientific communities in thermo-tolerance wheat cultivars and new emerging strategies to mitigate the deleterious impact of heat stress.
Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.
In this study, a polar extract of Aconitum lycoctonum L. was used for the synthesis of silver nanoparticles (AgNPs), followed by their characterization using different techniques and evaluation of their potential as antioxidants, amylase inhibitors, anti-inflammatory and antibacterial agents. The formation of AgNPs was detected by a color change, from transparent to dark brown, within 15 min and a surface resonance peak at 460 nm in the UV-visible spectrum. The FTIR spectra confirmed the involvement of various biomolecules in the synthesis of AgNPs. The average diameter of these spherical AgNPs was 67 nm, as shown by the scanning electron micrograph. The inhibition zones showed that the synthesized nanoparticles inhibited the growth of Gram-positive and negative bacteria. FRAP and DPPH assays were used to demonstrate the antioxidant potential of AgNPs. The highest value of FRAP (50.47% AAE/mL) was detected at a concentration of 90 ppm and a DPPH scavenging activity of 69.63% GAE was detected at a concentration of 20 µg/mL of the synthesized AgNPs. 500 µg/mL of the synthesized AgNPs were quite efficient in causing 91.78% denaturation of ovalbumin. The AgNPs mediated by A. lycoctonum also showed an inhibitory effect on α-amylase. Therefore, AgNPs synthesized from A. lycoctonum may serve as potential candidates for antibacterial, antioxidant, anti-inflammatory, and antidiabetic agents.