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Abstract With the anticipated foliar application of nanoparticles (NPs) as a potential strategy to improve crop production and ameliorate heavy metal toxicity, it is crucial to evaluate the role of NPs in improving the nutrient content of plants under Lead (Pb) stress for achieving higher agriculture productivity to ensure food security. Herein, Brassica napus L. grown under Pb contaminated soil (300 mg/kg) was sprayed with different rates (0, 25, 50, and 100 mg/L) of TiO2 and ZnO-NPs. The plants were evaluated for growth attributes, photosynthetic pigments, leaf exchange attributes, oxidant and antioxidant enzyme activities. The results revealed that 100 mg/L NPs foliar application significantly augmented plant growth, photosynthetic pigments, and leaf gas exchange attributes. Furthermore, 100 mg/L TiO2 and ZnO-NPs application showed a maximum increase in SPAD values (79.1%, 68.9%). NPs foliar application (100 mg/L TiO2 and ZnO-NPs) also substantially reduced malondialdehyde (44.3%, 38.3%), hydrogen peroxide (59.9%, 53.1%), electrolyte leakage (74.8%, 68.3%), and increased peroxidase (93.8%, 89.1%), catalase (91.3%, 84.1%), superoxide dismutase (81.8%, 73.5%) and ascorbate peroxidase (78.5%, 73.7%) thereby reducing Pb accumulation. NPs foliar application (100 mg/L) significantly reduced root Pb (45.7%, 42.3%) and shoot Pb (84.1%, 76.7%) concentration in TiO2 and ZnO-NPs respectively, as compared to control. Importantly, macro and micronutrient analysis showed that foliar application 100 mg/L TiO2 and ZnO-NPs increased shoot zinc (58.4%, 78.7%) iron (79.3%, 89.9%), manganese (62.8%, 68.6%), magnesium (72.1%, 93.7%), calcium (58.2%, 69.9%) and potassium (81.5%, 68.6%) when compared to control without NPs. The same trend was observed for root nutrient concentration. In conclusion, we found that the TiO2 and ZnO-NPs have the greatest efficiency at 100 mg/L concentration to alleviate Pb induced toxicity on growth, photosynthesis, and nutrient content of Brassica napus L. NPs foliar application is a promising strategy to ensure sustainable agriculture and food safety under metal contamination.

Abstract Soil salinity is a major nutritional challenge with poor agriculture production characterized by high sodium (Na+) ions in the soil. Zinc oxide nanoparticles (ZnO NPs) and biochar have received attention as a sustainable strategy to reduce biotic and abiotic stress. However, there is a lack of information regarding the incorporation of ZnO NPs with biochar to ameliorate the salinity stress (0, 50,100 mM). Therefore, the current study aimed to investigate the potentials of ZnO NPs application (priming and foliar) alone and with a combination of biochar on the growth and nutrient availability of spinach plants under salinity stress. Results demonstrated that salinity stress at a higher rate (100 mM) showed maximum growth retardation by inducing oxidative stress, resulted in reduced photosynthetic rate and nutrient availability. ZnO NPs (priming and foliar) alone enhanced growth, chlorophyll contents and gas exchange parameters by improving the antioxidant enzymes activity of spinach under salinity stress. While, a significant and more pronounced effect was observed at combined treatments of ZnO NPs with biochar amendment. More importantly, ZnO NPs foliar application with biochar significantly reduced the Na+ contents in root 57.69%, and leaves 61.27% of spinach as compared to the respective control. Furthermore, higher nutrient contents were also found at the combined treatment of ZnO NPs foliar application with biochar. Overall, ZnO NPs combined application with biochar proved to be an efficient and sustainable strategy to alleviate salinity stress and improve crop nutritional quality under salinity stress. We inferred that ZnO NPs foliar application with a combination of biochar is more effectual in improving crop nutritional status and salinity mitigation than priming treatments with a combination of biochar.

Abstract This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p

ABSTRACTThe study examines the complex dynamics of changes in LULC over three decades, focused on the years 1992, 2002, 2012, and 2022. The research highlights the significance of comprehending these alterations within the framework of environmental and socio-economic consequences. The changes in land use and land cover (LULC) have significant and far-reaching effects on ecosystems, biodiversity, and human livelihoods. This study offers useful information for politicians, conservationists, and urban planners by examining historical patterns and forecasting future changes. The study utilized a Multilayer Perceptron Neural Network (MLP-NN), a well-known machine learning technique that excels at collecting intricate patterns. This model’s design had three layers: input, hidden, and output. The model underwent 10,000 iterations during its training process, and a thorough statistical analysis was conducted to assess the impact of each driving component. The MLP-NN model demonstrated impressive performance, with a skill measure of 0.8724 and an accuracy rate of 89.08%. The accuracy of the LULC estimates for 2022 was verified by comparing them with observed data, ensuring the model’s reliability. Moreover, the presence of evidence likely was found to be a significant factor that had a substantial impact on the accuracy of the model. The study highlights the effectiveness of the MLP-NN model in accurately predicting changes in LULC. The model’s exceptional accuracy and proficiency make it a powerful tool for future LULC forecasts. Identifying the primary causes of model performance and understanding their implications may help to enhance land management strategies, encourage spatial planning, guide accurate decision-making, and facilitate the development of policies that align with sustainable growth and development.

The increasing pressures of urban development and agricultural expansion have significant implications for land use and land cover (LULC) dynamics, particularly in ecologically sensitive regions like the Murree and Kotli Sattian tehsils of the Rawalpindi district in Pakistan. This study's primary objective is to assess spatial variations within each LULC category over three decades (1992–2023) using cross-tabulation in ArcGIS to identify changes in LULC and investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0) to classify forest into several classes such as patch, edge, perforated, small core, medium core, and large core. Utilizing remote sensing data from Landsat 5 and Landsat 9 satellites, the research focuses on the temporal dynamics in various land classes including Coniferous Forest (CF), Evergreen Forest (EF), Arable Land (AR), Buildup Area (BU), Barren Land (BA), Water (WA), and Grassland (GL). The Support Vector Machine (SVM) classifier and ArcGIS software were employed for image processing and classification, ensuring accuracy in categorizing different land types. Our results indicate a notable reduction in forested areas, with Coniferous Forest (CF) decreasing from 363.9 km2, constituting 45.0 % of the area in 1992, to 291.5 km2 (36.0 %) in 2023, representing a total decrease of 72.4 km2. Similarly, Evergreen Forests have also seen a significant reduction, from 177.9 km2 (22.0 %) in 1992 to 99.8 km2 (12.3 %) in 2023, a decrease of 78.1 km2. The study investigates into forest fragmentation analysis using the Landscape Fragmentation Tool (LFTv2.0), revealing an increase in fragmentation and a decrease in large core forests from 20.3 % of the total area in 1992 to 7.2 % in 2023. Additionally, the patch forest area increased from 2.4 % in 1992 to 5.9 % in 2023, indicating significant fragmentation. Transition matrices and a Sankey diagram illustrate the transitions between different LULC classes, providing a comprehensive view of the dynamics of land-use changes and their implications for ecosystem services. These findings highlight the critical need for robust conservation strategies and effective land management practices. The study contributes to the understanding of LULC dynamics and forest fragmentation in the Himalayan region of Pakistan, offering insights essential for future land management and policymaking in the face of rapid environmental changes.

A low-cost adsorbent developed from unmodified Azadirachta indica leaves was used for adsorptive removal of the Congo Red dye from an aqueous medium. The adsorbent was characterized by the Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), and scanning electron microscopy (SEM) techniques. For optimization of operational parameters such as dye concentration, solution pH, adsorbent dose, contact time, and temperature, batch adsorption experiments were performed. It was found that for neem leaves powder (NLP), the optimum conditions were as follows: adsorbent dose of 0.8 g, contact time of 100 min having a solution with pH value of 5, adsorbate initial concentration of 40 ppm at temperature 60 °C where maximum amount of dye, i.e., 84%, removal was observed. The process followed pseudo-first-order kinetics, which reveals physical adsorption. According to isothermal investigations, sorption data were best fit with the Freundlich isotherm model. Thermodynamically, the adsorption of the Congo Red dye by the neem leaf powder was exothermic. Furthermore, the mechanistic removal of the Congo Red dye by the NLP has been explored with the help of the surface complex formation (PHREEQC) mechanism. Overall, the results of the study explore the promising nature of NLP for Congo Red dye removal. HIGHLIGHTS Providing clean water to the population is one of humanity's main challenges.; Adsorption is one of the most cost-effective and promising techniques for dye removal from polluted water.; 84% of Congo Red dye removal takes place at optimized conditions.; Isotherm, kinetic, and thermodynamics were studied.; The promising nature of the developed system was checked with tap water.;

Elevation gradients significantly influence net primary productivity (NPP), but the relationship between elevation, climate variables, and vegetation productivity remains underexplored, particularly in diverse ecological zones. This study quantifies the impact of elevation and climatic variables on NPP in northern Pakistan, hypothesizing that elevation modulates NPP through its influence on temperature and precipitation patterns. Using remote sensing data (MODIS ERA5) and advanced ecological models like the Eddy Covariance-Light Use Efficiency (EC-LUE) model and the Thornthwaite Memorial Model (TMM), we analyzed Gross Primary Productivity (GPP) dynamics across various vegetation types and elevations from 2001 to 2023. Our findings show a mean annual NPP of 323.46 g C m-2 a-1, with an annual increase of 5.73 g C m-2 a-1. Significant elevation-dependent variations were observed, especially in mid-elevation zones (401 to 1600 meters), where NPP increased at a rate of 0.174 g C m-2 a-1 per meter (R² = 0.808, p < 0.01). In contrast, higher elevations (2800-5200 meters) exhibited a decline in NPP, decreasing by -0.171 g C m-2 a-1 per meter (R² = 0.905, p < 0.001). Temperature and precipitation were key drivers, with precipitation positively correlating with NPP across all vegetation types, particularly in Evergreen Needleleaf and Broadleaf Trees. The EC-LUE model's GPP estimates closely matched MODIS data (R² = 0.82), demonstrating the model's reliability. These findings highlight the critical role of elevation and climatic factors in vegetation productivity and underscore the need for targeted ecological management and conservation strategies. The insights from this research are vital for global climate adaptation policies and sustainable development goals, contributing to ecological resilience and carbon sequestration efforts worldwide.

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.

In this study, copper (Cu)- and manganese (Mn)-based layered double hydroxide (LDH) nanosheets were produced by modest and low-cost hydrothermal technique to display an improved photocatalytic performance toward the degradation of aqueous methylene blue (MB). The morphological and structural properties of the as-prepared photocatalysts were characterized through various techniques comprising XRD, FT-IR, SEM, EDS, and their MB degradation activity was evaluated under visible light irradiation. SEM results explore that the synthesized LDH materials have a sheet-like morphology and are stacked layer by layer. Various analysis parameters, such as the effect of the contact time, concentration and pH of MB solutions were performed to optimize the performance of fabricated LDH materials. The results revealed that the as-synthesized CuAl-LDH and MnAl-LDH exhibited a 74.95 and 70.93% removal of MB under solar light within 180 min. Moreover, synthesized photocatalysts showed an excellent performance of up to four regeneration cycles. We believe that this study provides novel mechanistic insights into the design and preparation of highly competent photocatalysts using low-cost materials, with applications in environmental remediation.

Agricultural soil contamination by heavy metal (cadmium) is a widespread issue and causes deleterious effects on crops. One of the most effective and ecofriendly methods for cadmium detoxification is microbe-assisted phytoremediation and soil amendment with biochar. The current study investigated the combined effect of biochar (Parthenium hysterophorus L.) and microbes (Bacillus cereus strain B2) in alleviating cadmium toxicity in rice grown in cadmium-contaminated soil (0, 10, 15, and 20 mg/kg). Results revealed that cadmium caused damage to plant health. Maximum reduction in plant biomass, gas exchange parameters, and chlorophyll content was observed on cadmium 20 mg/kg. As compared with the sole application of biochar (2 %) and microbes, the greatest plant height (33.68%), photosynthetic rate (23.52 %), stomatal conductance (48.93 %), water use efficiency (21.41 %), transpiration rate (58.87 %), chlorophyll a (74.81 %), chlorophyll b (31.28 %), total chlorophyll (60.53 %), and carotenoids (26.97 %) were recorded with combined application of biochar and microbes. Similarly, superoxidase (SOD), catalase (CAT), peroxidase (POD), and absorbate peroxidase (APX) activity was significantly boosted by combined application of microbes and biochar (2 %), which were 33.90 %, 24.18 %, 51.70 %, and 19.06 %, respectively, while significant reduction was found in the ROS (16.60 %), (20.06 %) and MDA (26.08 %) content in rice plants. However, cadmium concentrations in different parts of plant were decreased at higher concentrations of biochar (2 %) with microbes. These findings indicate that combined application of Bacillus cereus strain B2 and biochar could be a promising strategy for reducing rice crop cadmium toxicity and boosting its growth, physiology, and defense system efficiently as compared with sole application.

High-altitude mountain plants are at risk of extinction due to global warming. Our study examined the stoichiometric characteristics of high-altitude plants in response to a progressively warming climate, as well as exploring potential relationships between hydraulic conductivity efficiency, stoichiometric characteristics, and morphological traits. As the research object, it was decided to conduct this investigation using the Faxon fir, a dominant species in the Minjiang River Basin with better water conditions. The sampling time was four months. In July 2019, October 2019, January 2020, and April 2020, the stoichiometric characteristics (N and P) in needles, branches, stems, and roots of Faxon fir were measured along an altitude gradient at 200 m intervals from 2800 m to 3600 m. We also measured morphological traits and hydraulic traits. This study showed that the Faxon fir N P stoichiometric preferentially to needles, followed by branches and roots, and finally at the trunk, and the Faxon fir was in an N-restricted state. N and P elements exhibit a significant correlation with morphological traits, while the relationship between P elements and hydraulic characteristics is mediated indirectly through morphological traits. At high altitudes, climate warming will increase the N limitation of Faxon fir, but the effect on P limitation will be relatively limited. Climate environment and water status are closely related to the distribution of N P nutrients in the organs. Undoubtedly, climate change will directly affect the survival of high-altitude plants, and appropriate survival strategies can encourage plants to adapt to climate changes by balancing energy distribution among different organs and tissues.

ABSTRACTDiabetic peripheral neuropathy is one of the most prevalent complications of diabetes mellitus. It is the most common type of neuropathy characterized by decreased sensory functions in the lower extremities and substantial neuropathic pain. Based on clinical characterization, it is classified into symmetric and asymmetric neuropathy. The pathological changes and neuronal function impairment during diabetes are associated with various pathways, including polyol pathway activation, advanced glycation end-products formation, oxidative stress, protein kinase C activation, poly ADP-ribose polymerase, and hexosamine pathways. Demyelination, axonal atrophy, nerve fibre loss, reduced regeneration, and loss of neurovascular interactions are hallmarks. Although some symptomatic and supportive therapies, such as tricyclic agents, antiarrhythmics, opioid analgesics, incretin, aldose reductase, and protein kinase C inhibitors, are in practice, the outcome is not promising. To fill this gap, natural product-based therapy can prove prodigious as an effective alternative. This review aims to comprehend the available literature on the role of various biological molecules in ameliorating diabetic peripheral neuropathy. These molecules play a key role in reducing oxidative-nitrosative stress, aldose reductase activity, and neuronal apoptosis. They control glucose and HbA1c% levels and improve nerve conduction velocity, axonal regeneration, and antioxidant species (catalase, superoxide dismutase, malondialdehyde). They are known for their attenuating thermal and mechanical hyperalgesia and tactile allodynia. Therefore, there is a need to evaluate these molecules at the pre-clinical and clinical levels for efficacy. Hence, natural molecules may act as promising players against diabetic peripheral neuropathy and are a ray of hope for suffering individuals.