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Soil salinity and drought are major environmental challenges that significantly affect soil functioning and soil organic matter (SOM) decomposition. Despite their importance, the combined effects of drought and salinity on residue decomposition are not well understood. This study addresses this gap by evaluating the decomposition of maize residue under salinity and drought stresses over a 75-day incubation period at 20 °C under controlled conditions. The experiment included two moisture levels: optimum moisture at 80% water-holding capacity (WHC) and drought conditions at 30% WHC, in both normal (ECe = 1.48 dS m−1) and saline (ECe = 8 dS m−1) soils, with 5 g DM kg−1 soil maize residues mixed in. A control treatment without maize residue addition was also included. The results indicated that salinity stress reduced maize residue decomposition, as evidenced by lower soil respiration, decay constant, metabolic quotient (qCO2), and soil extracellular enzyme activities. While drought did not affect total soil respiration in the presence of maize residue, it significantly decreased soil extracellular enzyme activities and decay constant rates. Combined drought and salinity stress further diminished maize residue decomposition, marked by reduced soil respiration, decay constant, microbial biomass carbon, and soil extracellular enzyme activities, while dissolved organic carbon (DOC) and qCO2 increased significantly. Similarly, extracellular enzyme activities were significantly reduced under abiotic stresses and further diminished under combined stress conditions. In conclusion, the simultaneous occurrence of drought and salinity can have compounded detrimental effects on microbial functioning, particularly in the presence of fresh plant residues. [ABSTRACT FROM AUTHOR]

An incubation study investigated the decomposition patterns, microbial activities, soil aggregates distribution, and soil organic carbon (SOC) contents, using four crop residues; wheat (Triticum aestivum L.), rice (Oryza sativa L.), cotton (Gossypium hirsutum L.), and maize (Zea mays L.), at low and high rates (10 and 20 g kg-1). An average increase of about 35% in cumulative C-CO2 emissions and 26% in soil microbial biomass carbon (MBC) was observed at high residue rates as compared to low rates. A positiv e and strong correlation (0.96) was found between C-CO2 emissions and MBC, and the effect of residue quality on decomposition was found to be less at high rates. The relative reduction in C-CO2 emissions by adding rice residues may be due to their high silica content. Differences in extracellular enzyme activities (EEA) with different residues indicated the effect of residue quality on microbial activities. Cotton residues increased β-glucosidase and chitinase activities by 41 and 38%, respectively, at high rates than at low rates. High maize residues also markedly increased chitinase activity by 41%, implying higher N cycling and fungal prevalence. Acid phosphatase activity was found to increase by about 25 to 32% with high rice and wheat residues, respecti vely. Higher EEA at high residues input likely reflected the microbial nutritional limitations. Crop residues raised SOC content from 0.7 under control to 1.0% at low residues input but not at high input. Increased macro-aggregates fraction at high residues input may be attributed to higher microbial activities. In summary, high residues input can minimize residue biochemical quality's effect on decomposition, with no further increase in SOC content. While a positive effect on the soil MBC, but not on the SOC content, was observed at high residue input, suggesting moderation in the additions of organic amendments is key for SOC buildup. Crop residues, added at 10 g kg-1, may help maintain a positive SOC balance in the arid agroecosystems by moderating higher microbial activities and soil respiration. [ABSTRACT FROM AUTHOR]

The fate of agriculture in Pakistan is predominantly concerned with excessive water mining threats to the subsurface water resources. The current study integrates the Visual MODFLOW-2000 application to estimate the water balance of an aquifer bounded by the Chenab River in the West and the Ravi River in the East, which covers an area of about 2.98 million hectares. An assimilated method of groundwater flow is employed to characterize the flow dynamics of the Rechna Doab aquifer. The Digital Elevation Model (DEM) produced by the Shuttle Radar Topography Mission (SRTM) and a mesh of discretized cell size (2500 m) were incorporated into the model design. The conceptual model of the alluvial aquifer involves trifold vertical boundaries (an initial fold thickness set up to 150 m). The model input parameters are precipitation, seepage through irrigation, return flow, recharge, hydraulic conductivity and evapotranspiration. Empirical relations are established (at the basin scale) for the discharge input of irrigation canals. Model results confirm that groundwater flow follows the topographic configuration of the study area (i.e., northeast to southwest), and the seepage from irrigating canals and rainfall appeared to be the main source of groundwater recharge among various resources. The zone budget study under steady state simulation showed that the total direct recharge to the aquifer is calculated as 522,910 acre foot. The simulated water balance of the studied aquifer reflects more fluctuations in river leakage. The predictive optimized model reflects an adaptation of canal lining and installation of additional tube wells that will minimize canal seepage by 70% and lead to the reclamation of 37,000 acres of water-logged land for normal cropping. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare the quadriceps isokinetic strength parameters of athletes and non-athletes. METHODOLOGY: The cross-sectional study was conducted from July to December 2019 at the Pakistan Sports Board, Islamabad. The sample size was 81, selected through a non-probability convenience sampling technique and calculated by the Rao Soft tool. The 41 participants were in the athletes' group, and 40 were in the non-athletes' group. Individuals with a history of sprain, strain, or fracture in the lower limbs within the last six months were excluded. The self-structured questionnaire includes demographic data, waist-to-hip ratio, thigh circumference, peak torque values and agility t-test score. The isokinetic strength of the right lower limb (RLL) was measured via Biodex system Pro 3. Data was analyzed through SPSS 22, and an independent t-test was applied for analysis between groups. Pearson test was used for the correlation coefficient. RESULTS: Athletes had significantly higher quadriceps muscle strength than non-athletes (p<0.001), indicating the positive impact of regular sports participation and physical training. Quadriceps peak torque positively correlated with thigh circumference (r=0.60, p<0.001), consistent with previous studies. Surprisingly, athletes exhibited a weak to moderate negative correlation between quadriceps peak torque and agility (r=-0.35, p<0.05), suggesting a potential trade-off between muscle mass and agility performance. Increased muscle bulk may compromise speed and flexibility. CONCLUSION: Athletes demonstrated superior quadriceps muscle strength, emphasizing the positive influence of sports involvement and physical training; thigh circumference positively correlated with Quadriceps peak torque, and Agility negatively correlated with quadriceps peak torque. [ABSTRACT FROM AUTHOR]

Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional molecular descriptors. The proposed in silico model achieved an outstanding accuracy performance of 98.56% on test data compared to other state-of-the-art approaches using standard quality measures. Furthermore, the model’s efficacy is validated via a statistical analysis ANOVA test. It is demonstrated that the developed model can be used effectively for early prediction of the substrate structure. The pipeline of this study is flexible and can be extended for in vitro assessment efficacy of anticancer drug response, identification of BCRP functions in transport experiments, and prediction of prostate or lung cancer cell lines. [ABSTRACT FROM AUTHOR]

Background: Potassium (K) substitution by sodium (Na) in sugar beet nutrition has been investigated during the last two decades and promising results have been revealed. However, substitution is still not used in agriculture although it has been recommended that Na can replace K to a large degree, proposing an interesting fertilization strategy for such plants. This study investigates the feasibility of sugar beet production on saline–sodic soils considering Na as a substituent for K. Aims: The aim of this study is to develop a fertilizer strategy considering Na concentration in saline–sodic soils as a partial substituent to K fertilizer and to determine the level of substitution. Methods: Two saline–sodic soils (SAR15 and SAR19 representing sodium adsorption ratios 15 and 19 [mmol L–1]1/2, respectively) with low plant‐available K were selected to investigate the substitution of K fertilizer requirements with indigenous soil Na. A pot experiment was conducted with 20 kg soil per pot having four treatments, that is, KA: recommended K; KB: recommended K–K equivalent to 2.5% of soil Na; KC: recommended K–K equivalent to 5% of soil Na; and control without K fertilizer application were established mixing potassium sulfate (K2SO4) in the soil at the time of sugar beet sowing. The sugar beet plants were harvested 90 days after sowing. Results: The results revealed that total plant biomass was significantly improved in KB as compared to KA. This indicates that K requirement of sugar beet was substituted by Na, and KB provided optimum results on SAR15 as compared SAR19 considering all other K combinations. Conclusion: In sugar beet nutrition, K can be replaced by Na to a large degree on saline–sodic soils under K‐deficient conditions. Therefore, sugar beet production on marginal saline–sodic soil can be a win–win strategy boosting sugar beet production and reducing input cost. [ABSTRACT FROM AUTHOR]

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learningbased automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It introduces a DRL algorithm, DQN, to select the most suitable augmentation method for each image. The proposed approach extracts geometric and pixel indicators to form states, and uses DeepLab-v3+ model to verify the augmented images and generate rewards. Image augmentation methods are treated as actions, and the DQN algorithm selects the best methods based on the images and segmentation model. The study demonstrates that the proposed framework outperforms any single image augmentation method and achieves better segmentation performance than other semantic segmentation models. The framework has practical implications for developing more accurate and robust automated optical inspection systems, critical for ensuring product quality in various industries. Future research can explore the generalizability and scalability of the proposed framework to other domains and applications. [ABSTRACT FROM AUTHOR]