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Abstract The negative impact of refinery wastewater is of great concern to the aquatic, terrestrial, and aerial environment. In this study, N-hexadecylchitosan (NHDC) was successfully synthesized to deal with low mechanical strength, poor adsorption capacity, and limited selectivity of native chitosan. The NHDC was characterized by fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-Ray diffraction analysis (XRD) to study its composition, morphology, and structural attributes. The adsorption of hydrocarbon pollutants from refinery wastewater was studied in batch mode experiments. The results indicated that the removal of COD attained by chitosan and NHDC was 21 and 63%, respectively. COD removal was found to be maximal, i.e., 96% using 0.08 g of NHDC at 60 min in a solution of pH 6.5 maintained at 60 °C. Furthermore, kinetic data revealed that the adsorption system followed pseudo-second order kinetics, whereas equilibrium studies supported both monolayer and multilayer adsorption mechanisms. The designed adsorption platform was able to capture hydrocarbon pollutants under very mild optimized conditions. Furthermore, NHDC demonstrated long term stability when subjected to five successive cycles, which contributed to the sustainability of water treatment systems. On the basis of the outcome of this work, it is advocated that new biobased NHDC can be used as an efficient adsorbent for the remediation of organic contaminants laden wastewater streams generated from oil refineries.

Objective: This study was conducted to evaluate the prevalence of total aflatoxin (AF) and ochratoxin A (OTA) in poultry feed ingredients under different environmental conditions during the summer and winter seasons, while the hygiene quality of the feed ingredient was assessed through viable fungal count (VFC). Materials and Methods: A total of 288 poultry feed ingredients (n = 96 each) samples were collected from different poultry shops, which were initially analyzed for the presence of AF and OTA through thin layer chromatography (TLC) and then confirmed the contamination concentration through the enzyme-linked immunosorbent assay method. Results: The results of the current study confirmed the incidence of contamination with AF and OTA by TLC and ELISA methods. The contamination level of AF ranged from 26.09 to 50.56 (mean = 41.22 ± 9.45) μg/kg, whereas the contamination level of OTA ranged from 50.13 to 6.21 (mean 42.60 ± 6.21) μg/kg. The contamination level of AF was found to be above the permissible level set by the Food and Drug Administration (20 μg/kg), whereas the contamination level of OTA was below the permissible limits. Moreover, the VFC values were also below the recommended level. The results showed that the association between AF, OTA, and moisture content was significant (p < 0.05). Conclusion: Mycotoxin contamination was significantly (p < 0.05) highest in the winter season. These findings suggested that continuous monitoring regimes might prevent mycotoxin contamination in poultry feed ingredients. [J Adv Vet Anim Res 2024; 11(1.000): 107-113]

AbstractThe widespread use of information and communication technologies (ICT) is evidenced by various devices, systems, and applications that enable organizations and individuals to interact with one another and the digital world. In this study, a comparative analysis framework was employed to evaluate different panel data techniques for the period 1990–2020. The aim is to ensure the robustness of the results and effectively assess ICT modeling in an economic context. The panel techniques found to be relevant and utilized in this study include pooled ordinary least squares (OLS), the fixed effects model (FEM), the random effects model (REM), two-stage least squares (2SLS), fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and robust least squares (ROBUSTLS). The sample comprises 12 trading partners in Pakistan, including both developed and developing economies. The results were consistent and robust across all the techniques employed. Based on these findings, various policy implications can be derived, such as the need for Pakistan to form strategic partnerships with both developed and developing nations to increase the trade component of ICT for rapid growth, because enhancing the ICT-based domestic industry will provide more employment and production opportunities.

Background: Psoriasis Vulgaris (PsV) is one of the most severe chronic, immune mediated skin diseases. The Notch signaling pathway, a key regulator for epidermal renewal, contributes in differentiation, proliferation and survival of keratinocytes. Alterations in the NOTCH4 gene disrupts Notch pathway. The current study aimed to find out the association of NOTCH4 gene polymorphism(s) in Pakistani psoriatic patients. Methods: A case control study, 390 DNA samples (190 samples of Psoriasis Vulgaris and 200 healthy control individuals), from January-December 2019, were selected from Rawalpindi Leprosy Hospital and healthy population, respectively. For amplification of (rs387071) SNP of NOTCH4 gene, lab standard protocols for T-ARMS-PCR were followed. Frequencies of genotype and allele were calculated by using Hardy Weinberg theorem. Data were analyzed by using SPSS and p-value ≤0.05 was considered statistically significant. Results: The mean age of patients was 34.6±14 while among control subjects it was 32.8±10.0. In cases, genotype homozygous A/A119 (62.6%) was more prevalent, followed by heterozygous A/G171 (90%) while homozygous G/G19 (10%) was the least prevalent between cases and controls. Allele A frequency in diseased subjects was 0.76 while, for controls it was 0.78. In addition, allele G frequency in patients and controls was 0.24 and 0.22 respectively. Based on Hardy Weinberg equilibrium, no association of (rs387071) NOTCH4 gene with psoriasis cases was found. Conclusion: NOTCH4 gene (rs387071) polymorphism was not significantly associated with patients of psoriasis Vulgaris in Pakistan. Larger studies are required to establish ethnic-specific markers for psoriasis in Pakistani population. Keywords: Psoriasis Vulgaris; PCR; NOTCH4; Polymorphism.

Polyethylene (PE) mulches are widely used in strawberry (Fragaria×ananassa Duch) production for weed suppression and crop growth optimization. However, PE mulches are not biodegradable and contribute to plastic pollution. Our objective was to develop and test biodegradable liquid-applied ‘hydromulches’ (HMs) as a sustainable alternative to PE mulch. HM weed suppression efficacy, strawberry plant growth, and yield were evaluated. HM formulations consisted of shredded newsprint paper (NP), water, and a tackifier, either guar gum (GG) or psyllium husk (PH) added at 2 or 6%. Experiments were conducted at two environmentally distinct locations: northwest Washington (WA) and eastern North Dakota (ND). Five HM formulations were compared to black PE mulch within a randomized complete block design with four replications. PE mulch suppressed weeds completely at peak weed emergence and peak weed vegetative growth at both locations. Formulations of HM containing GG provided superior weed suppression compared to other HM formulations at peak weed emergence (4–6 vs. 18–22 plants m-2, respectively). At peak vegetative growth, HM formulations containing GG had the lowest weed density compared to other HMs in ND (1 vs. 9–12 plants m-2), whereas these differences were not observed in WA. Total weed biomass did not differ among HMs across both locations. GG HM formulations deteriorated similarly to PE mulch (3–5% vs. 2%, respectively) in ND, whereas other HMs deteriorated more substantially. In WA, all HMs deteriorated more than PE mulch (6–12% vs. 1%, respectively). Fruit yield did not differ among treatments in weedy and weed-free subplots (194–254 g plant-1) in WA. In ND, yield was greater in all HM treatments compared to PE mulch in both weedy and weed-free subplots. Across both locations, strawberry canopy cover was greater in PE mulch (56.1% canopy cover) compared to 2%GG and NP (42.4 and 39.8% canopy cover). Strawberry plant biomass was similar among mulch treatments. However, strawberry leaf and crown biomass were slightly lower in 2%PH compared to other mulch treatments. Results demonstrate HMs with GG tackifier are a promising alternative to PE mulch in organic strawberry systems based on ability to suppress weeds, enhance strawberry growth, and maintain yield.

Biohydrogen is a source of renewable and clean energy. Many countries are working to generate biohydrogen energy as a means of combating the present global warming trend. This review paper aims to highlight the available information on hydrogen production from municipal solid waste biomass and also highlight several factors influencing the rate of biohydrogen production and their challenges in the future. The study of hydrogen production processes was aimed at a complete understanding of modern hydrogen production technologies, both implemented in practice and under research or development. The review revealed some advantages of biological methods for producing hydrogen gas compared to chemical ones. Also, this paper identified different factors that affect the biohydrogen production process such as type of bioreactors, temperature, pH, light, nutrients. This study also brings to the surface the challenges that need attention from researchers

Schistosomiasis is a neglected tropical disease that affects developing countries worldwide and is caused by several species of parasites from the Schistosoma genus. Chronic infection is characterized by the formation of granulomas around the parasite eggs, the leading cause of pathology. The hepatosplenic clinical form is one of the most common, but urogenital schistosomiasis is another relevant clinical presentation responsible for infertility in men and women. Inflammatory response, anatomical deformations, and endocrine/biochemical changes are involved in the development of infertility. Schistosome parasites can synthesize catechol estrogen-like molecules and affect the sexual hormone balance in their host. Here, we review many aspects of the pathology of urogenital schistosomiasis, specifically infertility, and point to the biochemical and endocrinal elements that must be investigated in the future.

The mobile Internet business is rapidly developing, leading to a demand for higher data service rates. To address this, massive Multiple-Input Multiple-Output (MIMO) technology has gained widespread attention due to its ability to fully exploit spatial degree of freedom and improve spectral efficiency while maintaining high energy efficiency. However, there are challenges with channel estimation in multi-user massive MIMO systems, such as pilot overhead, accuracy, and complexity. To tackle these challenges, this paper proposes a highly demanding beam Domain Decomposition (DD) and Singular Value Decomposition (SVD) method-based channel estimation methodology. By dividing into various single-user MIMO systems, the multi-user massive MIMO system, the proposed approach models the beam domain channel. The channel autocorrelation matrix is optimized using the SVD method, which lowers pilot overhead and estimates error while increasing computing efficiency. The suggested approach also provides closed-form analytical formulations for the calculated error covariance matrix. The numerical outcomes show that the suggested algorithm outperforms conventional schemes in performance. Therefore, the proposed algorithm could provide an effective solution for channel estimation in multi-user massive MIMO systems, which is increasingly critical as the demand for high data rates in the mobile Internet business continues to grow.© 2017 Elsevier Inc. All rights reserved.

Unmanned aerial vehicles (UAV) rely on a variety of sensors to perceive and navigate their airborne environment with precision. The autopilot software interprets this sensory data, acting as the control mechanism for autonomous flights. As UAVs are exposed to physical environment, they are vulnerable to potential impairments in their sensory mechanism. Their real-time interactions with the actual atmosphere make them susceptible to cyber exploitations as well, where sensory data alterations through counterfeit wireless signals pose a significant threat. In this context, sensor failures can result into unsafe flight conditions, as the fault handling logic may fail to anticipate the context of the issue, allowing autopilot to execute operations without necessary adjustments. Untimely control of sensor failures can result in mid-air collisions or crashes. To address these challenges, we created Biomisa Arducopter Sensory Critique (BASiC) dataset, a state-of-the-art resource for UAV sensor failure analysis. The BASiC dataset comprises 70 autonomous flight data, spanning over 7 hours. It encompasses 3+ hours of (each) pre-failure and post-failure data, along with 1+ hour of no-failure data. We selected the ArduPilot platform as our demonstration aerial vehicle to conduct the experiments. By engineering Software in the Loop (SITL) parameters, we effectively executed sensor failure test simulations. Our dataset incorporates six representative sensors failures which are critical to UAV operations: global positioning system (GPS) for precise aerial positioning, remote control for communication with the ground control station (GCS), accelerometer for measuring linear acceleration, gyroscope for rotational acceleration measurement, compass providing heading information, and barometer for maintaining flight height based on atmospheric pressure data. The availability of the BASiC dataset will benefit the research community, empowering researchers to explore and experiment with state-of-the-art deep learning models by tailoring them for time series signal analysis. It may also contribute in enhancing the safety and reliability of mission-critical autonomous UAV flights.

The purpose of current research work was to investigate the effect of mutagenesis on endoglucanase B activity of indigenous strain of Aspergillus niger and its heterologous expression studies in the pET28a+ vector. The physical and chemical mutagens were employed to incorporate mutations in A. niger. For determination of mutations, mRNA was isolated followed by cDNA synthesis and cellulase gene was amplified, purified and sequenced both from native and mutant A. niger. On comparison of gene sequences, it was observed that 5 nucleotide base pairs have been replaced in the mutant cellulase. The mutant recombinant enzyme showed 4.5 times higher activity (428.5 µmol/mL/min) as compared to activity of native enzyme (94 µmol/mL/min). The mutant gene was further investigated using Phyre2 and I-Tesser tools which exhibited 71% structural homology with Endoglucanase B of Thermoascus aurantiacus. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg) and hydrogen bonds analysis were carried at 35°C and 50°C to explore the integrity of structure of recombinant mutant endoglucanase B which corresponded to its optimal temperature. Hydrogen bonds analysis showed more stability of recombinant mutant endoglucanase B as compared to native enzyme. Both native and mutant endoglucanase B genes were expressed in pET 28a+ and purified with nickel affinity chromatography. Theoretical masses determined through ExPaSy Protparam were found 38.7 and 38.5 kDa for native and mutant enzymes, respectively. The optimal pH and temperature values for the mutant were 5.0 and 50°C while for native these were found 4.0 and 35°C, respectively. On reacting with carboxy methyl cellulose (CMC) as substrate, the mutant enzyme exhibited less Km (0.452 mg/mL) and more Vmax (50.25 µmol/ml/min) as compared to native having 0.534 mg/mL as Km and 38.76 µmol/ml/min as Vmax. Among metal ions, Mg2+ showed maximum inducing effect (200%) on cellulase activity at 50 mM concentration followed by Ca2+ (140%) at 100 mM concentration. Hence, expression of a recombinant mutant cellulase from A. niger significantly enhanced its cellulytic potential which could be employed for further industrial applications at pilot scale.

Brucellosis is a bacterial zoonosis worldwide and a potential health risk in Pakistan due to socio-economic reasons and lack of awareness, veterinary health and modern dairy-processing facilities. The aim of this study was to determine the burden of brucellosis in hospitalized patients of selected districts of Punjab, Pakistan. To this end, a total of 280 serum samples were collected from suspected patients in districts Lahore, Okara and Khushab between 2016 and 2018. Anti-Brucella antibodies were detected in 89 (31.8%) sera by Rose Bengal plate test (RBPT), and Brucella abortus DNA was amplified by polymerase chain reaction (PCR) in 42 (15%) samples. In univariable analysis, the seroprevalence varied significantly (p < 0.05) between districts with Lahore indicating 41/101 (40.6%) Odds ratio (OR) = 53.98 (7.22–403.67; 95% CI), Okara 47/99 (47.5%) OR = 71.4 (9.55–533.65; 95% CI) and Khushab 1/80 (1.3%) as reference. Similarly, exposure to aborted materials and a history of blood transfusion did associate significantly (p < 0.05), posing OR = 2.91 (1.58–5.36; 95% CI) and OR = 4.48 (2.56–7.84; 95% CI) risk. However, consumption of raw milk and history of blood transfusion did associate significantly (p < 0.05) in multivariable analysis indicating OR = 1.91 (1.09–3.34; 95% CI) and OR = 4.92 (2.76–8.75; 95% CI), respectively. The association of history of blood transfusion as a brucellosis risk factor underscores the need to create brucellosis awareness programs both at public and institutional levels in the medical and veterinary fields, as well as facilities and capacity building to delve deeper into the epidemiology of brucellosis in the country. Particular attention should be paid to chronically infected patients. Therefore, laboratory modernization, standardization and validation of the diagnostic tests, and staff trainings are required. Finally, pasteurization of the milk is recommended before consumption.

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing.

The oxidation step in Oolong tea processing significantly influences its final flavor and aroma. In this study, a gas sensors detection system based on 13 metal oxide semiconductors with strong stability and sensitivity to the aroma during the Oolong tea oxidation production is proposed. The gas sensors detection system consists of a gas path, a signal acquisition module, and a signal processing module. The characteristic response signals of the sensor exhibit rapid release of volatile organic compounds (VOCs) such as aldehydes, alcohols, and olefins during oxidative production. Furthermore, principal component analysis (PCA) is used to extract the features of the collected signals. Then, three classical recognition models and two convolutional neural network (CNN) deep learning models were established, including linear discriminant analysis (LDA), k-nearest neighbors (KNN), back-propagation neural network (BP-ANN), LeNet5, and AlexNet. The results indicate that the BP-ANN model achieved optimal recognition performance with a 3–4–1 topology at pc = 3 with accuracy rates for the calibration and prediction of 94.16% and 94.11%, respectively. Therefore, the proposed gas sensors detection system can effectively differentiate between the distinct stages of the Oolong tea oxidation process. This work can improve the stability of Oolong tea products and facilitate the automation of the oxidation process. The detection system is capable of long-term online real-time monitoring of the processing process.

This study aims to develop various machine learning (ML) models to investigate the self-healing capacity of engineered cementitious composites (ECC) and to evaluate the effect of input parameters (raw materials and crack width before the healing process) on output parameters (crack width after the healing process) via Shapley additive explanations (SHAP) analysis. The combination of the individual and ensemble ML models has been introduced to check and compare the accuracy level towards the prediction of crack width after the healing process to access the most suitable model for this application. The support vector machine (SVM) from the individual, while XGBoost (XGB) and random forest (RF) from ensemble ML models have been investigated for prediction purposes. As per the obtained results, the RF model outperforms both the SVM and XGB algorithms in forecasting the fracture width after the healing process for the selected ECC in concrete material. The performance indicator, such as the coefficient of determination (R2), was reported as 0.97 for RF, 0.96 for XGB, and 0.93 for the SVM model. Statistical results and k-fold cross-validation for the employed ML models also confirm their legitimacy. It was also noted from the result of the SHAP analysis that the significant contribution was for the crack width before healing towards the prediction of crack width after the healing process. Furthermore, the ML models can also be utilized to anticipate the healing ability of the other ECC, such as soda glass powder, marble powder, and bagasse ash.

This study used machine learning methods to predict the water absorption (W-A) of cement-based material (CBM) containing eggshell and glass powder as sand and cement substitutes. A dataset from the laboratory experiments consisting of 234 points and seven input variables was used to develop models, including multilayer perceptron neural network (MLPNN), support vector machine (SVM), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost). Additionally, a SHapley Additive exPlanations (SHAP) analysis was performed to investigate the relevance and interaction of raw components. When evaluating the prediction models for the W-A of CBM, it was found that the MLPNN and SVM models were moderately accurate (R2 = 0.74 and 0.78, respectively), while the AdaBoost and XGBoost models showed good agreement with the lab test results (R2 = 0.86 and 0.91, respectively). The SHAP approach revealed that while the cement quantity had a higher negative association with W-A of CBM, the quantities of eggshell powder, sand, and glass powder showed both favourable and detrimental correlations. Therefore, eggshell and glass powder must be used in optimal proportions of around 60 kg/m3 and 80 kg/m3, respectively, for maximum resistance to W-A. The AdaBoost and XGBoost models can potentially compute the W-A of CBMs by utilising various input parameter values, which may help decrease unnecessary test trials in labs. Furthermore, the SHAP investigation revealed the impact and relationship of the inputs on the W-A of CBMs, which might potentially assist researchers and the industry in determining the appropriate amount of raw materials during CBM production.

Sunflower is sensitive to boron (B) and zinc (Zn) deficiency when grown on deficient soil, A field experiment was conducted to determine the main and interactive effects of soil applied Zn and B on total production of sunflower at Agronomic Research Area, University of Agriculture, Faisalabad. Experiment was laid out in randomized complete block design (RCBI) with factorial arrangement using three replications with net plot size of 6 m x 4.5 m. The soil application of variable levels of Zn (0, 10, 20 and 30 kg ha-1) and B (0, 1, 2 and 3 kg ha-1) in the form of zinc sulphate and boric acid, respectively were applied at time of sowing. All other agronomic and plant protection practices were kept uniform. The data regarding growth, yield and quality parameters were noted by using standard procedures. Results showed that Zn @ 20 kg ha-1 and B @ 3 kg ha-1 significantly increased the number of plants per plot at harvest, stem diameter, head diameter, number of achenes per head, 1000-achene weight, biological yield and days to maturity, achene yield kg per, harvest index, leaf concentrations or Zn at heading stage, leaf concentrations of B at heading stage (ppm), achene oil content (%), achene protein contents as compared to control. This study concluded that higher growth and yield of sunflower can be achieved by application of Zn at 20 kg ha-1 and B at 3 kg ha-1 under Faisalabad conditions.

Geranium (Pelargonium graveolens L’Hér.) is an important commercial horticultural plant extensively used in outdoor landscaping. Seed emergence has always remained a problem in geranium due to its hard seed nature. Hence its germination and other emergence-related attributes need to be adequately tested. The purpose of this study was to evaluate the germination and seedling growth of geraniums under different types of containers. In this regard, the seeds of two varieties of geranium viz. Large FID mixed and Star mixed were planted in different types of containers. The containers included black trays plastic pots, plastic bags, and nonwoven fabric bags. The seed emergence and other germination related parameters were significantly affected by the different types of containers. However, varieties exhibited similar responses for most germination and growth characteristics. Data were collected for seed germination, mean germination time, germination index and seedling vigor index. The type of containers showed a significant impact on seedling growth and development. The taller plants with more leaves and maximum biomass production were recorded from seeds sown in nonwoven fabric type bags. The results pertaining to varieties demonstrated that Star mixed showed better emergence and vigorous seedlings in comparison with large FID mixed. The taller seedlings with maximum leaves and shoot biomass were also recorded from Star mixed grown in nonwoven fabric type bags. Based on the results, it is concluded that germanium may be raised in nonwoven fabric bags for better seed emergence and seedling growth and development.

To investigate the effect of eggshell powder on the water-absorption capacity of cement mortar, this research employed experimental testing followed by machine learning (ML) modeling techniques. On cement mortar specimens, the eggshell powder was partially substituted for cement and sand in concentrations ranging from 0% to 15% with a 2.5% increment, and water absorption tests were conducted. Experimental tests were used to generate the dataset, which was then used to train ML algorithms and construct ML-based prediction models. The integration of eggshell powder lowered the water absorption capacity of cement mortar, as determined by the test findings. The highest reduction in water absorption was seen at 5% eggshell powder as a cement replacement, resulting in up to 41.7% decrease, and 7.5% eggshell powder as a sand replacement, resulting in up to 45.8% decrease in water absorption compared to the control sample. In addition, the constructed ML models exhibited good agreement with the actual results and may be utilized to determine the water absorption of cement mortar incorporating eggshell powder. Nevertheless, based on the R2 value, the disparity between experimental and predicted results, and error analysis, it was determined that the random forest model was more accurate than the decision tree model.

Staphylococcus aureus is one of the major pathogens responsible for causing food poisoning worldwide. The emergence of antibiotic resistance in this bacterium is influenced by various factors. Among them, bacterial acquired defense systems described as clustered regularly interspaced short palindromic repeats (CRISPR)-cas system might be involved in antibiotic resistance development in bacteria. The current study was designed to assess the prevalence of S. aureus and its antibiotic resistance profile and identify the relationship of the CRISPR-cas system with antimicrobial resistance, followed by phylogenetic analysis. Total samples (n = 188) of poultry meat were collected from the poultry bird market of Lahore, Punjab, Pakistan. We used both phenotypic (antibiotic disc diffusion) and genotypic methods (PCR) to identify multi-drug resistant (MDR) strains of S. aureus. Additionally, the role of the CRISPR-Cas system in the isolated MDR S. aureus was also assessed. In addition, real-time quantitative PCR (qRT-PCR) was used to evaluate the association of the CRISPR-cas system with antimicrobial resistance. All of the S. aureus isolates showed 100% resistance against erythromycin, 97.5% were resistant to tetracycline, and 75% were resistant to methicillin. Eleven isolates were MDR in the current study. The CRISPR system was found in all MDR isolates, and fifteen spacers were identified within the CRISPR locus. Furthermore, MDR S. aureus isolates and the standard strain showed higher expression levels of CRISPR-associated genes. The correlation of said system with MDR isolates points to foreign gene acquisition by horizontal transfer. Current knowledge could be utilized to tackle antibiotic-resistant bacteria, mainly S. aureus.

The relationship between compensation, performance, and risk in a sample of Pakistani banks, using panel data for 20 banks from 2011 to 2021 has been examined in this research. To the best of researcher’s knowledge, this is one of the first study examining the relationship between performance, risk and employee compensation using panel data approach in the context of Pakistan and using data for longer period. To examine the influence of employee compensation on the performance and risk of banks, we employed three performance measures: return on assets, return on equity, and operating profits. Risk assessment was conducted using non-performing loans and risk-weighted assets. Findings of study suggest that employee compensation has no significant impact on performance and risk of banks in Pakistan; this means in Pakistan higher employee’s compensation does not enable banks to improve their performance or enable banks to take more risk.

Traumatic brain injury (TBI) is the major and leading cause of mortality and an alarming public health challenge. TBI leads to permanent cognitive, motor, sensory and psychotic disabilities. Patients suffering from the various and long-term repercussions of TBI currently have limited therapy choices. The current research work was designed to evaluate the beneficial and neuroprotective role of Troxerutin (Trox) (a natural flavonoid) in a closed brain injury mouse model. The male BALB/c 8-weeks old mice (n꞊150) were randomly distributed in three experimental groups. Control group of mice (n꞊50), TBI group (n꞊50) and Trox pre-treated mice group (Trox + TBI, n꞊50). The mice in Trox + TBI were pre-treated with Trox (150 mg/kg, 7 days) before TBI. The weight-drop mechanism was used to induce mild-moderate injury in mice in both the groups. Our results showed that the mice pre-treated with troxerutin significantly improved neurological severity score, blood glucose level, food intake and brain edema as compared to the mice in the TBI group. Furthermore, compared to the TBI group, the mice treated with troxerutin improved cognitive behavior as evaluated by Open field test, Shallow Water Maze and Y-Maze, decreased brain-infarct volume and blood-brain barrier (BBB) permeability, significantly decreased Reactive Oxygen Species (ROS), improved neuronal morphology and survival in the brain regions such as cortex and hippocampus. In summary, our data provided evidence that pre-treatment with troxerutin improved neurological functions, decreased the BBB permeability, improved behavior, reduced ROS and increased neuronal survival in the weight-drop close head traumatic injury mouse model.

This research intended to increase the understanding of using modern machine intelligence techniques, including multi-expression programming (MEP) and gene expression programming (GEP), for the compressive strength (CS) prediction of rice husk ash (RHA) concrete. In addition, SHapley Additive exExplanations (SHAP) analysis was made to study the impact and interaction of raw materials on the CS of RHA concrete. A comprehensive database of 192 points with six inputs (cement, specimen age, RHA, superplasticizer, water, and fine aggregate) was used for developing prediction models. This research determined that both GEP and MEP models for the CS prediction of RHA concrete yielded reliable results, which were in close agreement with the real CS. Comparing the performance of both GEP and MEP models, it was noted that MEP, with an R2 of 0.89, outperformed the GEP model having an R2 of 0.83. Additionally, SHAP analysis indicated that specimen age was the most vital measure, followed by cement, which positively correlated with CS of RHA. The overall effect of RHA was found to be more positive, suggesting RHA utilization in the optimal range of 75–100 kg/m3 in the RHA concrete mix. The use of prediction models and SHAP analysis will help the building industry assess material properties and raw material effects faster and more economical.

Sustainable development might be promoted if waste eggshells are used in cement-based materials (CBMs) by decreasing waste disposal problems, CO2 emissions, and material costs. In this regard, experimental and machine learning (ML) strategies were used to assess the flexural strength (FS) of CBMs comprising waste eggshell powder (ESP). Initially, experimental methods were carried out to evaluate the FS of ESP-based CBMs. The acquired data was utilized to predict the FS by applying ML algorithms like a decision tree and an AdaBoost regressor. The efficacy of ML techniques was measured by comparing experimental and predicted FS and applying statistical checks and k-fold evaluations. The experiment outcomes demonstrated that the FS of CBMs was improved by the inclusion of ESP at lower replacement ratios. Additionally, based on the assessment of modeling results, the AdaBoost regressor model demonstrated a higher exactness in estimating the FS of CBMs containing ESP compared to the decision tree model. The assessment of error readings also indicated the higher precision of the AdaBoost regressor model than the decision tree. According to the feature importance analysis, the fine aggregate was the component with the lowest impact for FS, whereas cement had the prime impact. The findings showed that as opposed to using ESP as a cement replacement, using ESP as an FA replacement to keep FA in less quantity and cement in larger quantity would result in enhanced strength. The construction industry can gain from ML techniques since they allow for more time- and cost-effective approaches to assessing material attributes.

The intention of this study was to examine the use of the most prevalent industrial byproduct, silica fume, as a supplementary cementitious material (SCM). Along with the typical review, this study used the scientometric review technique to statistically examine the various features of the existing literature. A scientometric-based study can deal with the massive literature data using the applicable software tool. The bibliometric data on silica fume in concrete was gathered using the Scopus search engine between 2001 and July 2022. The top publication sources, co-occurrence of keywords, highly cited authors, and active contributing countries were all examined using scientometric analysis. Additionally, the properties of composites using silica fume as the SCM were discussed. The effect of silica fume addition on compressive, split-tensile, and flexural strength, as well as the durability and microstructure of cementitious composites, were described. It was concluded that the inclusion of silica fume as an SCM improved the material's properties while also providing environmental benefits. However, there is a limit beyond which silica fume addition showed an unfavorable effect on the performance of cementitious composites. The optimum proportion of silica fume in concrete was found to be in the range of 15–25 % by mass of cement. Moreover, literature data were utilized to develop prediction models for the strengths of cementitious composites containing silica fume, and the prediction models agreed with the actual results. These prediction models could be used to test the material strength, saving both time and cost.

Fluorinated compounds provide excellent oil and water repellency to cotton fabrics. But, they have various toxic effects on human health and the environment. To address this challenge, scientists are continuously struggling to develop the eco-friendly oil and water repellents. To support these efforts, this research focused on the synthesis of some bio-based and non-fluorinated oil and water repellent recipes for the cotton fabric through the polymerization of palmitic acid with an eco-friendly cross-linker “citric acid” considering the effects of catalyst, enhancer, time, temperature, and polymerization environment. After the numerous trials, an optimized water repellent finish has been developed. The performance of the cotton fabric treated with the newly developed water repellent was measured in terms of the oil and water repellency, crease recovery angle, contact angle, stain release, air permeability, shrinkage, washing durability, tensile strength, tear strength, and antimicrobial performance. Finally, the characterization of the optimized recipes and the treated fabric was performed through the various techniques such as H-NMR spectroscopy, scanning electron microscope (SEM), Fourier-transform infrared spectroscopy (FTIR), Energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). The results exhibited the successful synthesis and application of the newly developed recipes onto the cotton fabric.

Sulforaphane (SFN) is an isothiocyanate with multiple biomedical applications. Sulforaphane can be extracted from the plants of the genus Brassica. However, broccoli sprouts are the chief source of sulforaphane and are 20 to 50 times richer than mature broccoli as they contain 1,153 mg/100 g. SFN is a secondary metabolite that is produced as a result of the hydrolysis of glucoraphanin (a glucosinolate) by the enzyme myrosinase. This review paper aims to summarize and understand the mechanisms behind the anticancer potential of sulforaphane. The data was collected by searching PubMed/MedLine, Scopus, Web of Science, and Google Scholar. This paper concludes that sulforaphane provides cancer protection through the alteration of various epigenetic and non-epigenetic pathways. It is a potent anticancer phytochemical that is safe to consume with minimal side effects. However, there is still a need for further research regarding SFN and the development of a standard dose.

This study utilized both experimental testing and machine learning (ML) strategies to assess the effectiveness of waste glass powder (WGP) on the compressive strength (CS) of cement mortar. The cement-to-sand ratio was kept 1:1 with a water-to-cement ratio of 0.25. The superplasticizer content was 4% by cement mass, and the proportion of silica fume was 15%, 20%, and 25% by cement mass in three different mixes. WGP was added to cement mortar at replacement contents from 0 to 15% for sand and cement with a 2.5% increment. Initially, using an experimental method, the CS of WGP-based cement mortar at the age of 28 days was calculated. The obtained data were then used to forecast the CS using ML techniques. For CS estimation, two ML approaches, namely decision tree and AdaBoost, were applied. The ML model's performance was assessed by calculating the coefficient of determination (R2), performing statistical tests and k-fold validation, and assessing the variance between the experimental and model outcomes. The use of WGP enhanced the CS of cement mortar, as noted from the experimental results. Maximum CS was attained by substituting 10% WGP for cement and 15% WGP for sand. The findings of the modeling techniques demonstrated that the decision tree had a reasonable level of accuracy, while the AdaBoost predicted the CS of WGP-based cement mortar with a higher level of accuracy. Utilizing ML approaches will benefit the construction industry by providing efficient and economic approaches for assessing the properties of materials.

Alzheimer’s disease (AD), is a progressive neurodegenerative disorder that involves the deposition of β-amyloid plaques and the clinical symptoms of confusion, memory loss, and cognitive dysfunction. Despite enormous progress in the field, no curative treatment is available. Therefore, the current study was designed to determine the neuroprotective effects of N-methyl-(2S, 4R)-Trans-4-hydroxy-L-proline (NMP) obtained from Sideroxylon obtusifolium, a Brazilian folk medicine with anti-inflammatory and anti-oxidative properties. Here, for the first time, we explored the neuroprotective role of NMP in the Aβ1–42-injected mouse model of AD. After acclimatization, a single intracerebroventricular injection of Aβ1–42 (5 µL/5 min/mouse) in C57BL/6N mice induced significant amyloidogenesis, reactive gliosis, oxidative stress, neuroinflammation, and synaptic and memory deficits. However, an intraperitoneal injection of NMP at a dose of (50 mg/kg/day) for three consecutive weeks remarkably decreased beta secretase1 (BACE-1) and Aβ, activated the astrocyte and microglia expression level as well as downstream inflammatory mediators such as pNF-ĸB, TNF-α, and IL-1β. NPM also strongly attenuated oxidative stress, as evaluated by the expression level of NRF2/HO-1, and synaptic failure, by improving the level of both the presynaptic (SNAP-25 and SYN) and postsynaptic (PSD-95 and SNAP-23) regions of the synapses in the cortexes and hippocampi of the Aβ1–42-injected mice, contributing to cognitive improvement in AD and improving the behavioral deficits displayed in the Morris water maze and Y-maze. Overall, our data suggest that NMP provides potent multifactorial effects, including the inhibition of amyloid plaques, oxidative stress, neuroinflammation, and cognitive deficits.

Soybean is an important oilseed crop that is used as a feed for livestock and has several industrial uses. Lipid biosynthesis and accumulation primarily occur during seed development in plants. This process is regulated by several transcription factors and interconnected biochemical pathways. This study investigated the role of glycine max LEAFY COTYLEDON 1 (GmLEC1) in soybean seed development and the accumulation of storage reserves. The overexpression of GmLEC1 significantly increased the amount of triacylglycerol (TAG) in transgenic Arabidopsis seeds compared to the wild-type and an atlec1 mutant. Similarly, the high expression of GmLEC1 led to a 12% increase in TAG content in transgenic soybean hairy roots compared to the control. GmLEC1 also altered the fatty acid composition in transgenic Arabidopsis seeds and soybean hairy roots. Additionally, the overexpression of GmLEC1 resulted in a reduction in starch accumulation in seeds and vegetative tissues, as well as changes in cotyledon and seed morphology. The cotyledons of the atlec1 mutant displayed abnormal trichome development, and the seeds were smaller and less tolerant to desiccation. A complementation assay in Arabidopsis restored normal cotyledon phenotype and seed size. The main downstream targets of LEC1 are GL2 and WRI1, which were found to participate in fatty acid biosynthesis and trichome formation through the regulation of phytohormones and various transcription factors involved in seed development and maturation. The findings of this study suggest that GmLEC1 controls seed development and regulates the accumulation of seed storage compounds. Furthermore, these results demonstrate that GmLEC1 could be a reliable target for the genetic improvement of oil biosynthesis in soybean.