The present study was conducted to standardize a quadriplex PCR assay for rapid, sensitive, specific and simultaenous diagnosis of major haemoprotozoan diseases of bovines. Primers targeting cytochrome b gene, repetitive nucleotide sequences, gene encoding carbomyl phosphate synthetase II and small subunit rRNA were used for detection of Trypanosoma evansi, Theileria annulata, Babesia bovis and Babesia bigemina generating amplicon of 257bp, 312bp, 446bp and 689bp, respectively. Multiplex PCR assay was optimized in a final volume of 25 µl containing 12.5 µl dream taq green master mix (2X), 0.5 µl of each cytob1 primer, 0.4 µl of each TR3/TR4 primer, 0.4 µl of Bb1/Bb2 primer and 0.6 µl of Bg3/Bg4 primer, 4µl of template DNA and rest NFW. Twenty DNA samples extracted from field blood samples were screened by quadriplex PCR assay. Primers were found specific as did not produce amplicon with non-target DNA. Limit of detection was determined using tenfold serial dilution of parasitic DNA. Quadriplex PCR assay could able to amplify DNA of Theileria annulata and Babesia bigemina at copy number 1000 whereas of Babesia bovis and Trypanosoma evansi at copy number 100, respectively. Out of 20 samples, T. annulata was seen in 15% sample, B.bigemina and B.bovis in 5 % sample and B. bigemina + T. annulata in 10% samples, rest sample were negative. Assay was found to be highly specific and can be used for simultaneous diagnosis of major haemoprotozoan diseases.

In this study, a deep-water culture (DWC) hydroponic system integrating carbon dioxide nanobubble (CNB) water and biochar (BC) was explored as a potential substrate for carbon and nutrient management. Lettuce seedlings were cultivated under varying substrates, including tap water (TW) and deionized water (DW) with and without CNB and BC at concentrations of 0.1 or 0.5 g/L. CNB with BC treatments showed significant concentrations of Ca, Cu, Fe, K and Zn in lettuce tissues. BC treatments resulted in decreases of Cu, Zn and Fe in the solution by 24.82 %, 36.78 % and 50.45 %, respectively. CNB treatments appeared to increase Ca in roots by approximately 55 %. The highest Fe in edible parts was observed with CNB_DW_BC0.1 treatment, while for root highest was in TW_BC0.5. However, treatments with CNBs addition counteracted this effect by mobilizing nutrients for plant uptake. Chlorophyll content increased significantly by 125.59 % with CNB_TW_BC0.5, marking the most substantial improvement observed. Furthermore, initial higher concentrations of NH4+, NO2-, and NO3- in TW compared to DW were noted. BC displayed NH4+ availability reduction, particularly at high doses by 14.87 %, while influencing NO2- and NO3- inconsistently. CNBs impacted nitrogen dynamics, showing increased NO3- by 4.78 ppm when combined with BC in TW. The investigation into free radical generation revealed that the CNBs with BC exhibited the weakest signal for •OH, •O2- suggesting optimal growth conditions. Translocation factor (TF) indicated nutrient absorption efficiency, with BC notably decreasing TF for Cu, Fe, Zn while, CNBs increased TF for Fe and Zn. The order of target hazard quotient (THQ) for CNB_TW, was Zn > Fe > Cu. Moreover, the calculated daily intake rate, THQ and hazard quotient were all below 1, indicating negligible health risks associated with consuming the lettuce grown under the studied conditions.

Porcine enterovirus G (PEV-G) presents a considerable threat to the swine industry, causing a range of diseases that include diarrhea, encephalomyelitis, reproductive disorders, and respiratory infections. Conventional diagnostic approaches, such as virus isolation and RT-PCR, are frequently labor-intensive and reliant on specialized equipments. Therefore, there is an immediate need for isothermal nucleic acid amplification techniques-specifically, Recombinase Polymerase Amplification (RPA) and Polymerase Spiral Reaction (PSR) that offer rapid, sensitive, and field-deployable detection of PEV-G. In this study, we successfully developed and optimized two isothermal nucleic acid amplification assays namely RPA/RT-RPA and PSR/RT-PSR to detect PEV-G in swine populations in Haryana. Primers were specifically designed to target the polyprotein region of PEV-G for both assays. Optimal conditions regarding temperature, incubation time, primer concentration, and magnesium ion concentration were established. The RPA assay demonstrated a sensitivity of 1.417 × 10⁴ copies with a detection time of just 20 minutes. The PSR assay exhibited a lower sensitivity of 2.3 x 105 copies in comparison to RPA assay in gel based detection system and required 2.5 hours for detection. Both assays showed exceptional specificity for PEV-G, with no observable cross-reactivity with other related porcine viruses. Additionally, visual detection using Picogreen dye provided a practical solution for field use, with limits of detection of 14 copies for RPA and 2.3 copies for PSR. Validation on 100 archived field samples showed that isothermal assays have comparable sensitivity to conventional PCR. This study underscores the potential of RPA and PSR as effective and cost-efficient diagnostic tools, enabling timely and precise detection of PEV-G in both laboratory and field contexts. Such advancements are vital for improving disease management strategies and reducing economic losses within the swine industry.

This study investigates the synthesis of novel 2D porous Ti3C2Tx MXene/Biochar (MB) composites with varying ratios of 1:9, 3:7, and 5:5 for simultaneous removal of co-existing contaminants from wastewater. pH-dependent tests revealed that upon increasing Ti3C2Tx MXene content in the composite significantly improved metal cation removal efficiencies due to deprotonation and enhanced electrostatic attraction. Specifically, Cu removal increased from less than 20 % at pH 2 to nearly 100 % at pH 9-10 across all MB ratios. NH4+ removal was less influenced by pH variations, whereas PO43- removal peaked at 97.30 % at pH 4 with lower MXene content (1:9), decreasing at higher pH levels for all ratios. Interestingly, composites with decreased MXene content (1:9) exhibited superior performance in the removal of metallic contaminants. MXene incorporation proved to be crucial as it imparts unique properties such as high electrical conductivity, chemical stability, and the ability to remove specific contaminants at like NH4+ and PO43- through mechanisms that biochar alone cannot achieve effectively especially when diverse pollutant mixtures exist. The high surface area and tunable surface chemistry of Ti3C2Tx MXene contributed to strong electrostatic interactions and chemical bonding with contaminants, while biochar's porous structure and abundant functional groups enhanced sorption capacity and facilitated ion exchange processes. This synergy between MXene and biochar enhanced the versatility and overall efficiency of the synthesized composite. Adsorption isotherm studies revealed that the experimental data conformed well to the Freundlich adsorption model, indicating multilayer adsorption on heterogeneous surfaces. Kinetic analyses showed that the adsorption process followed the pseudo-second-order model, suggesting that chemisorption played a significant role in the removal mechanism. The study thereby underscores the potential of MB composites as sustainable and cost-effective solution for multi-contaminant wastewater treatment, advancing separation and purification technologies to improve water quality and environmental sustainability.

The present study aimed to genetically characterize Hepatozoon canis, identify associated risk factors, and evaluate the haematological and biochemical profiles of affected dogs to improve its management. One hundred twenty dogs were screened by PCR for various haemoprotozoan disease, of which 24 dogs confirmed positive for H. canis and negative for Ehrlichia canis, Babesia vogeli, and B. gibsoni were included in this study. Phylogenetic analysis of the 18 S rRNA region from four isolates revealed genetic relatedness to H. canis strains reported from Iran, Malaysia, Canada, Israel, Turkey, Iraq, China, and India. Common clinical signs included pyrexia (83.33

This study investigates the potential pathogens associated with joint swelling, yellow exudates, and scab formation near the joints and eyelids in adult white racing pigeons.Pooled samples of scab and swab were collected and subjected to molecular, microbiological, and histopathological analyses. Initial screening focused on the detection of poxvirus using polymerase chain reaction (PCR). Subsequently, next-generation sequencing (metagenomic sequencing) using the Illumina MiSeq platform was performed, followed by virus isolation in embryonated specific pathogen-free chicken eggs and Vero cells, along with histopathological examination.Polymerase chain reaction (PCR) analysis for pigeon pox virus did not produce the expected amplicons, indicating a negative result for this virus. However, metagenomic sequencing identified the complete genome of Pigeon Torque Teno Virus (PTTV), with a genome size of 1574 nucleotides. Comparative sequence analysis revealed a nucleotide sequence similarity of 96.47%-97.7%, with the highest identity to a Canadian PTTV genome previously detected in the Bursa of Fabricius of a dead pigeon. Genome annotation identified two open reading frames (ORFs): encoding replication-associated protein and viral capsid protein. The presence of PTTV was further confirmed through real-time PCR and virus isolation in embryonated SPF chicken eggs and Vero cell cultures.The present study marks the first identification of PTTV in white racing pigeons with joint, ocular, and pock-like lesions. Although pigeon pox virus (PPV) was not detected, the findings suggest that PTTV could be an emerging avian pathogen necessitating further investigation into its pathogenicity, transmission dynamics, and clinical significance in pigeons.

Pigs are a vital component of agricultural economies and a major source of livestock worldwide. The Porcine Stool-Associated virus (Posavirus), a newly identified member of the Picornavirales order, has been associated with enteric infections in swine. Recombinase Polymerase Amplification (RPA) and Polymerase Spiral Reaction (PSR), two isothermal amplification methods, were developed and optimized in this study to identify the posavirus in pig stool samples quickly and effectively. Primers that target the posavirus’s polyprotein region were designed for both RPA and PSR assays, and reaction parameters were optimized. Sensitivity assessments revealed that the RPA assay had a detection limit of 5.34 × 106copies, while the PSR assay has higher sensitivity at 6.5 × 103copies. Both assays showed high specificity for the posavirus, with no cross-reactivity. An evaluation of 132 field samples revealed that only three samples were positive for posavirus, highlighting the need for continued surveillance. This study reported the successfully development and optimisation of RPA and PSR assays as dependable and easily accessible diagnostic methods for posavirus detection. Their speed, sensitivity, and specificity make them adapted for use in a range of field and laboratory scenarios.

Bluetongue (BT), a significant economic disease affecting domestic and wild ruminants, requires rapid and precise diagnostic methods. The diversity of BTV serotypes, coupled with their high genetic and antigenic variability, poses substantial challenges for disease control and prevention. To address this, multiplex Magpix assays were developed for the simultaneous and accurate detection of BTV serotypes and topotypes. Primers and probes were designed to target segment 2 (Seg-2) of the BTV genome, a highly variable region that enables serotype-specific identification using probes conjugated to magnetic beads. The developed Magpix assays facilitate the identification of multiple BTV serotypes from a single sample. Five multiplex Magpix assays were created to detect eastern and western strains of 12 distinct currently circulating BTV serotypes (1, 2, 3, 4, 5, 9, 10, 12, 16, 21, 23, and 24) in India to align with the current epidemiological landscape. These assays were categorized as follows: eastern assays—E1 (1e, 2e, 4e), E2 (3e, 9e), and E3 (16e, 21e, 23e); and western assays—W1 (1w, 10w, 12w) and W2 (2w, 5w, and 24w). The detection limits varied across assays, with E1 showing a higher detection limit (500 pg) compared to E2 (50 pg), E3 (50 pg), W1 (5 pg), and W2 (5 pg). Importantly, the assays exhibited no cross-reactivity with other related viruses. These five multiplex Magpix assays provide an effective diagnostic tool for identifying circulating BTV strains in India. Additionally, the system offers flexibility for expansion to include more serotypes as needed, enhancing its utility for BT surveillance and control. In summary, the introduction of these advanced diagnostic methods presents a significant opportunity for more strategic and effective management of BTV, thereby ensuring better protection for both livestock and the livelihoods dependent on them.

In this study, the flexural capacity of reinforced concrete (RC) beams strengthened with fiber-reinforced cementitious matrices (FRCM) was computed using machine learning (ML) algorithms including: (i) adaptive neuro-fuzzy inference system (ANFIS), (ii) artificial neural network (ANN), and (iii) extreme gradient boosting (XGBoost). A total of 198 pertinent experimental datasets were compiled and included six types of FRCM composites (PBO, carbon, glass, basalt, coated carbon, and combined glass and carbon). The considered input parameters comprise the beam cross-sectional details, area of tensile and compressive steel reinforcement, mechanical properties of FRCM composite, and concrete compressive strength. To assess the reliability of ML models, four existing analytical models and one established standard guideline were used for comparison. Moreover, six statistical metrics were employed, along with an overfitting analysis, to determine the best-fitting model. Graphical fitting of the optimal model was depicted using the Taylor diagram, violin plot, as well as multi-panel histogram plot. Based on both graphical and statistical metrics, the XGBoost model attained the highest precision compared to all analytical and ML-based models. The correlation coefficient and MAPE of the XGBoost model were 0.9977% and 2.98%, respectively. To interpret the influence of individual parameters on the flexural strength of the FRCM-strengthened RC beams, a feature importance plot based on SHAP explanatory theory was deployed. Ultimately, a user-friendly graphical interface was developed and made accessible to aid practicing engineers in estimating the flexural strength of FRCM-strengthened RC beams, offering an effective alternative to complex design procedures.

Corrosion in reinforced concrete (RC) structures is one of the foremost and most severe causes of early degradation. This deterioration leads to structural failure, impacting human life as well as the environment. Furthermore, corrosion-related damage necessitates frequent and costly repair and maintenance work, imposing a financial burden on society. Therefore, it is vital to estimate the remaining capacity of corroded reinforced concrete (CRC) structures to perform necessary preventive maintenance work before structural collapse or required expansive rehabilitation techniques. This study employed artificial neural network (ANN), Gaussian process regressor, and linear regression-based machine learning (ML) models to develop a more dependable and precise model for estimating the residual flexural capacity (RFC) of CRC beams. Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization training techniques were used to train the ANN models. The performance and results of the developed ANN models were evaluated and compared with a design guideline (ACI-318), analytical model (one), and empirical models (eight). The results demonstrated that the developed ANN model with four neurons in the hidden layer (DANN-4) was trained using the Bayesian regularization algorithm with 80% of the dataset for training and the remaining 20% for testing, outperforming other developed ML models, design guidelines, analytical models, and empirical models. The comparative study indicated that the developed DANN-4 model had the highest correlation between the actual and predicted values with the lowest errors, demonstrating the efficacy of the developed ANN model in predicting the RFC of CRC beams compared to existing ML models, design guidelines, analytical models, and empirical models. The developed ANN-based model can be used by structural engineers, researchers, and rehabilitation industry experts to estimate the RFC of corrosion-damaged RC beams.

The deterioration of reinforced concrete (RC) structures is a significant global concern. Demolishing degraded structures is costly, time-consuming, and impacts the integrity of connected members. Implementing jackets, including fiber-reinforced polymer (FRP), fiber-reinforced cementitious matrix (FRCM), and ferro-cement, for the retrofitting and rehabilitation of existing structural members presents an advanced alternative to the deteriorated RC structures. As compared to FRP and FRCM, the ferro-cement is an economical solution that improves the mechanical strength of the structural members, service life, and offers fire resistance, durability, and versatility. This research evaluated the axial load-carrying capacity (ALCC) of ferro-cement confined RC columns using existing analytical models and machine learning (ML) algorithms. A database of 151 RC column specimens from literature was utilized. Four ML models (Artificial Neural Network, Decision Tree, Linear Regression, and Support Vector Machine) were developed to predict the axial capacity of ferro-cement confined RC columns. The ANN model performed the best out of all the ML models. The developed model was precise, user-friendly, and cost-effective. The ANN-based mathematical model can be easily used by structural engineers and ferro-cement jacketing designers to estimate the axial capacity of the ferro-cement strengthened RC columns.

The bond between steel and concrete enhances the structural integrity of the building and ensures a cohesive force-resistant system, facilitating composite action between the rebars and concrete. This composite action, in turn, ensures the longevity of the reinforced concrete (RC) structure. The current study focuses on evaluating the performance and comparing design guidelines and analytical models pertaining to hinge beams and pull-out test specimens. The bond strength between concrete and steel was determined by validating 124 experimental datasets collected from the literature. These datasets were based on hinge and pocket beam specimens with varying degrees of corrosion. To facilitate comparison, the international design guidelines and analytical models were designated as G-1 and G-2, respectively. The results indicate that Model M-6 from G-1 and Model M-10 from G-2 outperformed all other models within their respective groups. Upon comparing these two models, M-6 appears to be superior based on selected performance indices. Model M-6 demonstrated an R-value of 0.3275, MAE of 2.29 MPa, RMSE of 2.76 MPa, and MAPE of 39.12% sequentially. Additionally, sensitivity analysis based on the collected dataset was conducted to assess the impact of each parameter. This study offers a comprehensive analysis of bond strength between steel and concrete in RC structures, focusing on hinge beams and pull-out test specimens with varying degrees of corrosion. It introduces superior models for performance evaluation and comparative analysis, highlighting Model M-6 as a novel and robust approach with significant potential for enhancing structural design guidelines.

A wide variety of environmental or contagious microorganisms implicated in mastitis, impede the economic growth of dairy sector. Identification of polymorphism in candidate gene of host’s immune system and to rule out mastitis resistant allelic form of candidate gene usually remains prime focal point of research. Bovine peptidoglycan recognition protein-1 (PGLYRP-1), exclusively present in the granules of polymorphonuclear leukocytes has direct microbicidal properties. The present study was carried out to find the association between PGLYRP-1 polymorphic alleles with mastitis. Milk samples for somatic cell count and blood samples for PCR-RFLP analysis of PGLYRP-1 gene were collected from 20 mastitis negative and 20 mastitis positive Murrah buffaloes. There was significant difference in somatic cell count of mastitis and mastitis free animals. All amplified PCR products of ~862 bp size of partial region of PGLYRP-1 gene were subjected to each restriction enzyme (HincII or TaqαΙ or ApaI). Polymorphism in the partial region of PGLYRP-1 gene had not been established using PCR-RFLP as uniformity in pattern of digested fragments was seen. Target sequence PGLYRP-1 gene of Murrah buffalo was cloned and sequenced. BLAST analysis revealed sequence identity of PGLYRP-1 of Murrah buffalo with Bos taurus (JN085441.1) sequence at NCBI was 96%, 96% with Bos indicus (JN085440.1) and 96% with Bos indicus X Bos taurus (EU746454.1). In phylogenetic tree, the target sequence of PGLYRP-1 gene of Bubalus bubalis are found more closely related to Bos taurus than to Bos indicus.

Introduction A precise gestational age (GA) assessment is critical to monitoring fetal growth and planning delivery. Any disorder that affects the placenta will affect the fetus. Hence, the placenta serves as an indicator of fetal development. So, placental thickness (PT) measurement can be utilized as a parameter in the precise estimation of gestational age and prediction of the fetal outcome. Ultrasound (USG) remains the preferred method for detecting placental abnormalities due to its benefits. This study aimed to evaluate placental thickness by USG in various GA subgroups and to see the correlation of PT with GA and fetal outcome. Methods Cross-sectional observational study with short follow-up. A total of 296 antenatal women between 14 weeks and 40 weeks underwent USG to measure placental thickness and were followed up until delivery. The collected data was compiled systematically and analyzed using IBM SPSS Statistics for Windows, Version 25 (released 2017; IBM Corp., Armonk, New York, United States). The level of significance was taken as p<0.05. Results The mean placental thickness progressed from 1.8 cm to 3.5 cm as the gestational age advanced from 14 weeks to 35 weeks and six days. After that, it decreased until delivery (r-value = 0.531 (<0.8), p-value <0.001). PT was positively correlated only with birth weight (p-value 0.013) amongst all fetal outcome parameters. Conclusion GA can be determined using PT with the help of regression techniques. PT can be used as a replacement when a particular parameter of the composite growth formula is fallacious. The PT increase rate is a more reliable indicator than the actual PT to predict birth weight.

Deterioration of the concrete structure is a major problem in the construction industry. It reduces the strength, serviceability, and safety of the structures. Demolish these deteriorated structures is not the right solution, and it is uneconomical and produces a huge amount of waste. From a sustainability point of view, strengthening the deteriorated structures is an acceptable and possible solution. Strengthening with fiber-reinforced polymer (FRP), composites have been widely utilized for construction and rehabilitation purposes. The substrate bond between concrete and FRP composite is the crucial parameter deciding the influence of strengthening on the structural components. This bond is responsible to transfer the stresses from the concrete substrate to the FRP composite. There are various analytical models as well as standard guidelines available in the past studies to forecast the strength of the bond between the concrete and the FRP composite. The main disadvantage of these analytical models is that these models are valid only for limited datasets. In this chapter, a supervised machine learning (ML) model called artificial neural network (ANN) and optimized ANN with particle swarm optimization (PSO) was used to estimate the bond between the concrete and the FRP composite. The results of these ML models were compared with widely known standard guidelines such as fib, ACI, CNR-DT 200, and CS-TR-55-UK. It was found that the performance of PSO-ANN and ANN algorithms was good as compared to standard guidelines. The correlation coefficient of ANN and PSO-ANN models was 0.9777 and 0.9907, sequentially. The precision of the PSO-ANN algorithm was 1.33% higher as compared to the ANN model.

Introduction: The most crucial factor in improving animal reproduction efficiency is early pregnancy diagnosis. Early diagnosis not only reduces the time interval between two calvings but also aids farmers in identifying open animals, thereby preventing significant milk production losses. Therefore, the objective of this study was to discover circulatory miRNAs that would be useful for early pregnancy diagnosis in buffalo.Material and methods: Blood samples were taken on 0, 6th, 12th, and 18th day after artificial insemination from pregnant animals (n = 30) and non-pregnant animals (n = 20). During these stages of pregnancy, total RNA was extracted, and a small RNA library was subsequently generated and sequenced on the Illumina platform. Subsequently, Real-time PCR was used to validate the findings.Results and discussion: There were 4,022 miRNAs found during the pregnancy, with 15 of those lacking sequences and 4,007 having sequences already in the database. From the beginning of pregnancy until the 18th day, 25 of these miRNAs showed a substantial shift in expression levels in the maternal blood, with a change more than two logs. Furthermore, based on qPCR results, 19 miRNAs were found to be more abundant in pregnant animals than in non-pregnant animals. We used target prediction analysis to learn how maternally expressed miRNAs relate to fetal-maternal communication. In conclusion, miRNA based biomarkers that could be associated with the diagnosis of pregnancy were identified including miR-181a and miR-486 highly upregulated on the 18th day of pregnancy. This study also provides a comprehensive profile of the entire miRNA population in maternal buffalo blood during the early stages of pregnancy.

Gaseous nanobubbles (NBs) with dimensions ranging from 1 to 1000 nm in the liquid phase have garnered significant interest due to their unique physicochemical characteristics, including specific surface area, low internal gas pressure, long-term stability, efficient mass transfer, interface potential, and free radical production. These remarkable properties have sparked considerable attention in the scientific community and industries alike. These hold immense promise for environmental applications, especially for carbon-neutral water remediation. Their long-lasting stability in aqueous systems and efficient mass transfer properties make them highly suitable for delivering gases in the vicinity of pollutants. This potential has prompted research into the use of NBs for targeted delivery of gases in contaminated water bodies, facilitating the degradation of harmful substances and advancing sustainable remediation practices. However, despite significant progress in understanding NBs physicochemical properties and potential applications, several challenges and knowledge gaps persist. This review thereby aims to summarize the current state of research on NBs environmental applications and potential for remediation. By discussing the generation processes, mechanisms, principles, and characterization techniques, it sheds light on the promising future of NBs in advancing environmental sustainability. It explores their role in improving oxygenation, aeration, and pollutant degradation in water systems. Finally, the review addresses future research perspectives, emphasizing the need to bridge knowledge gaps and overcome challenges to unlock the full potential of this frontier technology for enhanced environmental sustainability.