Phenotypic and genetic characterization of unexplored, potential cattle population of Madhya Pradesh.

Bawri or Garri, a non-descript cattle population managed under an extensive system in Madhya Pradesh state of India, was identified and characterized both genetically and phenotypically to check whether or not it can be recognised as a breed. The cattle have white and gray colour and are medium sized with 122.5 ± 7.5 cm and 109.45 ± 0.39 cm height at withers in male and female, respectively. Double-digest restriction site associated DNA (ddRAD) sequencing was employed to identify ascertainment bias free SNPs representing the entire genome cost effectively; resulting in calling 1,156,650 high quality SNPs. Observed homozygosity was 0.76, indicating Bawri as a quite unique population. However, the inbreeding coefficient was 0.025, indicating lack of selection. SNPs found here can be used in GWAS and genetic evaluation programs. Considering the uniqueness of Bawri cattle, it can be registered as a breed for its better genetic management.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed.

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009-2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Use of machine learning-based classification algorithms in the monitoring of Land Use and Land Cover practices in a hilly terrain.

The current high rate of urbanization in developing countries and its consequences, like traffic congestion, slum development, scarcity of resources, and urban heat islands, raise a need for better Land Use Land Cover (LULC) classification mapping for improved planning. This study mainly deals with two objectives: 1) to explore the applicability of machine learning-based techniques, especially the Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm as the potential classifiers for LULC mapping under different scenarios, and 2) to prepare a better LULC classification model for mountain terrain by using different indices with combination of spectral bands. Due to differences in topography, shadows, spectral confusion from overlapping spectral signatures of different land cover types, and a lack of access for ground verification, classification in mountainous terrain is difficult task compared to plain terrain classification. An enhanced LULC classification model has been designed using two popular machine learning (ML) classifier algorithms, SVM and RF, explicitly for mountainous terrains by taking into consideration of a study area of Gopeshwer town in the Chamoli district of Uttarakhand state, India. Online-based cloud platform Google Earth Engine (GEE) was used for overall processing. Four classification models were built using Sentinel 2B satellite imagery with 20m and 10m resolutions. Two of these models (Model 'i' based on RF algorithm and Model 'ii' based on SVM algorithm) were designed using spectral bands of visible and infrared wavelengths, and the other two (Model 'iii' based on RF algorithm and Model 'iv' based on SVM algorithm) with the addition of indices with spectral bands. The accuracy assessment was done using the confusion matrix based on the output results. Obtained result highlights that the overall accuracy for model 'i' and model 'ii' were 82% and 86% respectively, whereas these were 87.17% and 87.2% for model 'iii' and model 'iv' respectively. Finally, the study compared the performance of each model based on different accuracy metrics for better LULC mapping. It proposes an improved LULC classification model for mountainous terrains, which can contribute to better land management and planning in the study area.

Fecal sludge characterization, treatment, and resource recovery options: a state-of-the-art review on fecal sludge management.

A rise in population and urbanization demanded that a robust fecal sludge management (FSM) value chain be used to restructure the sanitation system throughout the world securely. A significant global need exists to adopt efficient and sustainable FSM. On-site sanitation systems (OSS) produce fecal sludge (FS). FS is produced when excreta and blackwater are combined and stored or treated, either alone or in combination with greywater. FS can be semisolid or slurry and raw or partially digested. Critical examination of FS characteristics, i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD), total solids (TS), and pathogen count, varies from 600-56,836 mg/l, 6656 to 201,200 mg/l, 830-123,000 mg/l, and 105 to 109 E. coli/l of FS respectively. Helminth eggs range from 2500-25,000/l of FS. Public health and the environment are negatively impacted by septic tank overflows and the careless discharge of FS into open spaces affecting groundwater quality, water bodies, irrigation fields, open drains, places outside villages, etc. Thus, deciding on a proper treatment technology for FS before discharging it into open land or reusing FS is essential to create a pollution-free environment. This paper highlights the practices adopted for FSM under its different processes, such as collecting, characterization, treating, and reusing of on-site FS and bibliometric analysis on documents on fecal sludge. A thorough analysis has been carried out by reviewing all important literature available globally.

Lytic Polysaccharide Monooxygenase Activity of Tma12 Is Critical for Its Toxicity to Whitefly.

Lytic polysaccharide monooxygenases (LPMOs) are powerful redox enzymes that transform complex carbohydrates through oxidation and make them suitable for saccharification by canonical hydrolases. Due to this property, LPMOs are considered to be a valuable component of enzymatic consortia for industrial biorefineries. Tma12 is a fern entomotoxic protein that kills whitefly and has structural similarities with chitinolytic LPMO. However, its enzymatic activity is poorly understood. Studying the role of the LPMO-like activity in the insecticidal function of Tma12 can be of considerable importance. Our results show that Tma12 preferentially binds and digests ß-chitin. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis shows that the digestion of chitin produces chitin oligosaccharides of various lengths (DP2-DP7). The Michaelis constant (km) and catalytic constant (kcat) for hydrocoerulignone are 0.022 mM and 0.044 s-1, respectively. The attenuation of catalytic activity through diethylpyrocarbonate modification abolishes the insecticidal activity of the protein. Our findings reveal that (a) Tma12 is an active LPMO and (b) LPMO activity is indispensable for its function as a bioinsecticide.

Phase III Pivotal comparative clinical trial of intranasal (iNCOVACC) and intramuscular COVID 19 vaccine (Covaxin®).

One of the most preferable characteristics for a COVID-19 vaccine candidate is the ability to reduce transmission and infection of SARS-CoV-2, in addition to disease prevention. Unlike intramuscular vaccines, intranasal COVID-19 vaccines may offer this by generating mucosal immunity. In this open-label, randomised, multicentre, phase 3 clinical trial (CTRI/2022/02/40065; ClinicalTrials.gov: NCT05522335), healthy adults were randomised to receive two doses, 28 days apart, of either intranasal adenoviral vectored SARS-CoV-2 vaccine (BBV154) or licensed intramuscular vaccine, Covaxin®. Between April 16 and June 4, 2022, we enrolled 3160 subjects of whom, 2971 received 2 doses of BBV154 and 161 received Covaxin. On Day 42, 14 days after the second dose, BBV154 induced significant serum neutralization antibody titers against the ancestral (Wuhan) virus, which met the pre-defined superiority criterion for BBV154 over Covaxin®. Further, both vaccines showed cross protection against Omicron BA.5 variant. Salivary IgA titers were found to be higher in BBV154. In addition, extensive evaluation of T cell immunity revealed comparable responses in both cohorts due to prior infection. However, BBV154 showed significantly more ancestral specific IgA-secreting plasmablasts, post vaccination, whereas Covaxin recipients showed significant Omicron specific IgA-secreting plasmablasts only at day 42. Both vaccines were well tolerated. Overall reported solicited reactions were 6.9% and 25.5% and unsolicited reactions were 1.2% and 3.1% in BBV154 and Covaxin® participants respectively.

Molecular and genetic diversity in isolates of Trypanosoma evansi from naturally infected horse and dogs by using RoTat 1.2 VSG gene in Madhya Pradesh, India.

BACKGROUND: Trypanosoma evansi is a protozoan parasite that can infect a wide range of animals and is widespread around the world. In this study, we analyzed four fatal cases of T. evansi infection using clinical, parasitological, and molecular approaches. We also explored the genetic diversity, demographic history, and population-genetic structure of T. evansi using available Rode Trypanozoon antigenic type (RoTat) 1.2 gene sequences. METHODS AND RESULTS: Clinical findings of infected animals revealed high fever, anemia, weakness, and anorexia. The animals were treated with diminazene aceturate, which was moderately effective, and hematobiochemical parameters showed changes in hemoglobin and glucose levels. The molecular and genetic diversity of T. evansi was analyzed using the RoTat 1.2 VSG gene. Phylogenetic and haplotype analysis revealed two distinct clusters of T. evansi circulating in India. The genetic diversity indices, neutrality tests, gene flow, and genetic differentiation outcomes confirmed the genetic diversity of the T. evansi population, with a lack of uniformity. The identification of two distinct clusters, exhibiting differential demographic histories and evolutionary forces, implies that the clusters may have undergone independent evolutionary trajectories or experienced different environmental pressures. CONCLUSION: The present findings underlined the need of an early and precise diagnosis in order to treat and control T. evansi infections, and the RoTat 1.2 VSG gene is an important genetic marker for understanding the genetic diversity and evolutionary history of T. evansi. This knowledge can be used to create tailored strategies to control and manage the infection in an endemic region.

Flood risk assessment in the Karamana river basin, Kerala, using HEC-RAS.

The State of Kerala has frequently been facing a series of flooding phenomena that have adversely affected its multiple sectoral growths. The floods of 2018 have happened to be one of the most devastating floods that have occurred in the State of Kerala. It was seen that nearly thirteen out of fourteen districts in Kerala were tremendously affected during the 2018 August floods. The worst affected districts during the 2018 floods were Trivandrum, Pathanamthitta, Idukki, Thrissur, Ernakulam, and Kottayam. A sub-region near the Karamana basin located in the Trivandrum district is considered for the present study. The Karamana sub-region is a highly urbanized area that is also more or less prone to intense riverine flooding. The major rivers-Karamana and Killi-along with their respective tributaries, are the water bodies in the study region. Extensive urbanization, along with the overflowing of rivers during monsoon seasons, has paved the way for intense flooding in the region. This, in turn, necessitates developing a flood model for the sub-region. The development of an efficient flood model will aid in understanding the future challenges related to a flooding event in a region. In this study, the flood return probability water levels for the 5-year, 10-year, 25-year, 50-year, 100-year, 250-year, and 500-year were estimated for the Karamana sub-region. Besides, the flood risk zoning for the study area was conducted and elaborated as very high risk, high risk, moderate risk, and low risk for the different areas of the sub-region. Overall, the study can be helpful in identifying the most vulnerable areas to flooding in the Karamana region. By the proper identification of vulnerable areas in the region, proper planning and early warning measures can be devised and carried out by policymakers.

Wetland functional assessment and uncertainty analysis using fuzzy α-cut-based modified hydrogeomorphic approach.

Wetlands are significant ecosystems which perform several functions such as ground water recharge, flood control, carbon sequestration, and pollution reduction. Accurate evaluation of wetland functions is challenging, due to uncertainty associated with variables such as vegetation, soil, hydrology, land use, and landscape. Uncertainty is due to the factors such as the cost of evaluating quality parameters, measurement, and human errors. This study proposes an innovative framework based on modified hydrogeomorphic approach (HGMA) using fuzzy α-cut technique. HGMA has been used for wetland functional assessment and α-cut technique is used to characterize uncertainty corresponding to the input variables and wetland functions. The most uncertain variables were found to be the density of wetlands and basin count in the landscape assessment area with the scores of 4.38% and 3.614% respectively. Among the functions, the highest uncertainty is found in functional capacity index (FCI) corresponding to water storage (1.697%) and retain particulate (1.577%). The quantified uncertainty can help the practitioners to make informed decisions regarding planning best management practices for preserving and restoring the wetland functionality.

Removal of COD and color from textile industrial wastewater using wheat straw activated carbon: an application of response surface and artificial neural network modeling.

A novel approach has been undertaken wherein chemically modified wheat straw activated carbon (WSAC) as adsorbent is developed, characterized, and examined for the removal of COD and color from the cotton dyeing industry effluent. Thirty experimental runs are designed for batch reactor study using the central composite method (CCM) for optimizing process parameters, namely biochar dose, time of contact, pH, and temperature, for examining the effect on COD and color-removing efficiency of WSAC. The experimental data have been modeled using the machine learning approaches such as polynomial quadratic regression and artificial neural networks (ANN). The determined optimum conditions are pH: 7.18, time of contact: 85.229 min, adsorbent dose: 2.045 g/l, and temperature: 40.885 °C, at which the COD and color removal efficiency is 90.92 and 94.48%, respectively. The nonlinear pseudo-second order (PSO) kinetic model shows good coefficient of determination (R2 ~ 1) values. The maximum adsorption capacity for COD and color by WSAC is at the pH of 7, the temperature of 40 °C, adsorbent dose of 2 g/l is obtained at the contact time of 80 min is 434.78 mg/g and 331.55 PCU/g, respectively. The COD removal and decolorization is more than 70% in the first 20 min of the experiment. The primary adsorption mechanism involves hydrogen bonding, electrostatic attraction, n-π interactions, and cation exchange. Finally, the adsorbent is environmentally benign and cost-effective, costing 16.66% less than commercially available carbon. The result of the study indicates that WSAC is a prominent solution for treating textile effluent. The study is beneficial in reducing the pollutants from textile effluents and increasing the reuse of treated effluent in the textile industries.

Hydro-conditioning: Advanced approaches for cost-effective water quality management in agricultural watersheds.

Accurate simulation of landscape hydrological connectivity is pivotal for planning practices required for treating agricultural farm pollution. This study assesses the role of an advanced geospatial approach, namely, 'hydro-conditioning' employed for modifying Digital Elevation Models, termed hDEMs to replicate landscape hydrology by simulating continuous downslope flow through drainage structures such as bridges and culverts. The capabilities of manual and automated hDEMs in delineating optimal locations and water treatment potential of Best Management Practices (BMPs) in a typical agricultural watershed were evaluated. Parallel processing of both hDEMs revealed that 'ground truthing' plays a critical role in the accurate placement of breach lines for allowing water movement through digitally elevated surfaces. Outcomes guide the practitioners in selecting appropriate hDEM (manual or automated) depending on the complexity of modeled hydrological pathways, which is essential for planning BMPs in a cost-effective manner at different spatial scales. Modeling results show that hDEMs greatly influence hydrological connectivity, catchment boundaries, BMP locations, treatment capacities, and related costs. The accuracy of hDEMs was verified using a robust sub-basin scale validation approach. The study recommends a hybrid approach for utilizing the strengths of both, automated and manual hDEMs for efficient agricultural farm pollution in an economical manner.

Assessment of snout analysis of Himalayan glaciers: impact studies on Pindari, Kafni, Sundardhunga, and Baljuri base camp glaciers.

There are several causes for the increasing rate of deglaciation, such as global warming, increase in the concentration of black carbon, and extensive use of fossil fuels which causes the change in the overall climate system and shifting glacier ecosystem. This study was conducted on Pindari valley glaciers part of lesser Himalaya in Uttarakhand. This study investigates to (1) monitor and map change in the frontal length or the snout region of a glacier that can be studied with the help of remote sensing techniques and (2) evaluate the decadal and annual retreat rate of the glacier from 1972 to 2018. The study applies both the maximum likelihood classifier and NDSI spectral indices based classification for extracting the glacier region for different periods. This study reveals a significant amount of retreats taking place in the selected glaciers, Pindari, Sundardhunga, Kafni, and Baljuri base camp glaciers, from 1972 to 2018 as 1719.95 m, 1751.21 m, 1057.01 m, and 810.78 m, respectively. The highest amount of change is noticed in Pindari and Sundardhunga glaciers, higher than ~ 1700 m. The study analyses temporal variation of the annual and decadal retreat rate in the Pindari valley glaciers, which would be helpful for the further study of the other glaciers.

Impact of climate on vegetation in Pindari watershed of Western Himalayas, Kumaun, India, using spatiotemporal analysis: 1972-2018.

Vegetation dynamics is an important aspect for determining climate change trends. The present study delineates to examine spatiotemporal changes of vegetation cover in Pindari valley (Kumaun Himalaya) from the 1972 to 2018 timeline. The study includes the calculation of vegetation spectral indices of normalized vegetation index (NDVI), extraction of different vegetation classes, and statistical analysis of the Mann-Kendall (MK) test on historical metrological data (especially precipitation and temperature) of the study site. For the statistical analysis of metrological data, the power data access viewer datasets have been used. The central feature classes of the study are grassland, scrubland, and forest cover. The results revealed that the region's forest cover significantly decreased by 24.74 sq. km from 1972 to 2018, increased in grassland cover by 17.84 sq. km, respectively, and a slight increase in scrubland class by 3.13 sq. km for the study period. The calculated NDVI shows significant changes over the study location; it has been noticed that the maximum values of the NDVI decreased by 0.24, and the minimum values show growth of about 0.047. The analysis indicates that climatic parameters such as precipitation and temperature are the main limiting factors affecting vegetation growth. The annual mean maximum temperature showed a decreasing trend. The estimated results show an increase in annual rainfall and annual minimum temperature, while the decreasing trend is observed in the case of maximum annual temperature. Objectives of the study are (1) spatiotemporal analysis of the vegetation cover, (2) identification of the main causes of change in the vegetation cover, and (3) statistical trend analysis of long-term metrological data. The outcome of the presented research work would be beneficial for the proper management and monitoring of the forest ecosystem.

Effects of different concentration of organic and inorganic trace minerals (zinc, selenium, and chromium) supplementation on expression of chTLR4 gene and humoral immune response in broilers.

BACKGROUND AND AIM: Poultry production is the fastest-growing livestock sector in developing countries. In the poultry diet, trace minerals (zinc [Zn], selenium [Se], and chromium [Cr]) are normally administered in the inorganic form which has been traditionally considered as the most cost-effective and easily available but organic forms of these trace minerals have a higher bioavailability, lower dietary inclusion and cause less environmental pollution as compared to inorganic form. This study aimed to investigate the effect of different concentrations of organic and inorganic forms of trace minerals (Zn, Se, and Cr) supplementation (0-35 days) on expression of chTLR4gene and humoral immune response in broilers. MATERIALS AND METHODS: A total of 216 broilers were randomly divided into 12 groups and each group divided into three replicates consisting of six broilers each. T1 (R1, R2, and R3) group was kept as control. T2, T3, and T4 (R1, R2, and R3) groups were supplemented with inorganic form of Zn at 40 mg/kg of feed, organic form of Zn at 40 mg/kg of feed, and 50% organic form of Zn at 20 mg/kg of feed, respectively. T5, T6, and T7 (R1, R2, and R3) groups were supplemented with inorganic form of Se at 0.3 mg/kg of feed, organic form of Se at 0.3 mg/kg of feed, and 50% organic form of Se at 0.15 mg/kg of feed, respectively. T8, T9, and T10 (R1, R2, and R3) groups were supplemented with inorganic form of Cr at 2 mg/kg of feed, organic form of Cr at 2 mg/kg of feed, and 50% organic form of Cr at 1 mg/kg of feed, respectively. T11 and T12 (R1, R2, and R3) groups were supplemented with a combination of all three minerals from inorganic and organic forms, respectively. RESULTS: Reverse transcriptase-polymerase chain reaction gene expression analysis revealed that in bursa of Fabricius, maximum upregulation of chTLR4 (3.0214 fold) was observed in T6 group, and in spleen, maximum upregulation of chTLR4 (3.2140 fold) was observed in T3 group as compared to control group. On day 35, the maximum plasma immunoglobulin G concentration was observed in organic trace minerals (OTMs) supplemented, whereas the minimum concentration was recorded in control group. On day 28 and 35, the overall mean heterophil:lymphocyte ratio showed a significant difference (p<0.05) between control and OTM supplemented groups. A significantly higher immune organ weight (spleen and bursa of Fabricius) was recorded in OTM supplemented group as compared to control and other supplemented group. CONCLUSION: Supplementation of organic form of Zn, Se, and Cr either alone or in combination increase humoral immune response and upregulation of chTLR4 gene expression in bursa and spleen indicates a beneficial effect of OTM in augmentation of immune system in broilers.

Application of remote sensing in alpine grasslands cover mapping of western Himalaya, Uttarakhand, India.

Grasslands are the world's most extensive terrestrial ecosystem, which provides a variety of services for humans, such as carbon storage, food production, crop pollination, pest regulation, and are a major feed source for livestock. However, grasslands are today one of the most endangered ecosystems due to land-use change, agricultural intensification, land abandonment, as well as climate change. Grasslands are an integral part of human societies across the globe, which are broadly known as tropical savannah and temperate grasslands. In the Himalayan region, grasslands are found in more than 55% of the area and different climatic conditions lead to different varieties of grasslands like Danthonia grasslands, kobresia sedge meadow, etc. Grasslands deal with the spatial and temporal distribution of heterogeneous landscapes, which support a high diversity of various species. Owing to very rugged terrain and inaccessibility, the information on the extent of alpine grassland and percent grass cover (%) across the meadows is limited. Therefore, the present attempt was made to assess the current status of grassland in the alpine region of Uttarakhand above 3000 m asl. LANDSAT-8 (OLI and TIRS sensors) satellite data were used to delineate the grasslands using normalized difference vegetation indices (NDVIs) of the alpine region with the help of over 179 ground truth points out of which 50 points are testing points and 129 points are training points. Grass covers (%) were also assessed in the whole alpine region of Western Himalaya of Uttarakhand which nearly consists of over 75 meadows by using random plots (1 × 1 m, total 10 per site) in each meadow. Overall, 89.52% accuracy was achieved based on 50 randomly selected testing points. A total of 4949.25 sq. km area is under the different percentage of grass cover in the alpine region of Uttarakhand, Western Himalaya. Danthonia grasslands below 4000 m and Kobresia sedge meadows above 4000 m elevation are dominant in the state. In the alpine region, over 1056 sq. km grassland area have less than 10% grass cover indicating higher degraded and cold desert areas and only 565.69 sq. km area have more than 60% grass cover, which is highly favorable for rich biodiversity and grazing.

Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology.

The current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.

Effect of cumulus cells of cumulus-oocyte complexes on in vitro maturation, embryonic developmental and expression pattern of apoptotic genes after in vitro fertilization in water buffalo (Bubalus bubalis).

In the present study, the potential of different grades of cumulus-oocyte complexes (COCs) for in vitro maturation (IVM) and embryonic development was assessed. Further, the association of the expression pattern of anti-apoptotic Mcl-1 and pro-apoptotic Bax genes in embryonic development was analyzed. Abattoir derived oocytes were graded into grade A and B based on surrounding cumulus rings. Out of 1050 ovaries, a total number of 770 and 1360, were of grade A and B COCs, respectively, were aspirated. After IVM, grade A COCs had a significantly higher number of polar bodies (92.04 ± 0.60%) as compared to grade B (85.88 ± 0.46%). On IVF and embryo culture, grade A COCs produced the significantly higher rate of cleavage and blastocyst (90.44 ± 0.71% and 41.55 ± 0.96%) as compared to grade B COCs (79.77 ± 0.76% and 30.44 ± 0.96%). The transcriptional analysis of apoptotic genes expression by Real-time PCR revealed a significantly higher expression of Mcl-1 gene in embryos of grade A as compared to grade B, whereas, the relative expression of Bax gene was down-regulated in grade A than grade B embryos. Thus it was concluded that the pattern of apoptotic genes expression in early-stage embryos can be used as a marker gene to predict the developmental competence of COCs.

Utilization potential of fly ash and copper tailings in concrete as partial replacement of cement along with life cycle assessment.

Fly ash (FA) and copper tailings (CT) both are, anthropogenic wastes, spread all over the globe due to rapid growth in thermal power plants and progressive increase in the demand of copper. This study examines the feasibility of combined utilization of FA and CT in concrete as a partial replacement of cement by assessing compressive strength, cost, and environmental impact. Morphology and constituent minerals of FA and CT have been identified to understand the utilization potential. Subsequently, the concrete has been designed for 30 MPa target strength as per IS 10262:2009 for different mix proportions of FA and CT. Improvement (up to 8.27% compared to the control mix) in the compressive strength has been observed at combined replacement of 10% FA and 5% CT. The cost of concrete can also be reduced up to 16% without compromising its compressive strength. The environmental impact assessment of the modified concrete mix proportions has also been performed using life cycle assessment (LCA) as per ISO 14040:2006. Effect of all raw materials, electricity, and water consumption have been considered from their cradle to grave approach. One cubic meter concrete has been taken as a functional unit in LCA. Notable reduction has been observed in the chosen midpoint categories up to 38% in climate change, up to 32.6% in human toxicity, up to 33.6% in ozone depletion, up to 31.9% in agriculture land occupation, water depletion up to 34.3%, fossil depletion up to 34.8%, particulate matter up to 35.4%, and metal depletion up to 25.2%.

Managing water quality of a river using an integrated geographically weighted regression technique with fuzzy decision-making model.

In the recent times, water quality of most of the rivers in India has been steadily degrading due to increasing numbers of point and non-point sources of pollution. The tremendous increase in population, rapid urbanization, change in irrigation patterns, and unplanned growth of industries without proper enforcement of environmental standards are some of the major causes for poor quality of river water. In addition, unpredictable and scanty rainfall is resulting in uncertain natural stream flow which further leads to uncertainty in assessing and predicting the quality of river water. This paper deals with the assessment of the overall status of water quality of a river by developing a fuzzy-based water quality evaluation system. The quality of water needed for different beneficial uses is based on the value of various parameters. Since the quality attributes of the parameters are fuzzy in nature, they have been described by the linguistic variables. The water quality index of each specific site is then calculated by aggregating the attributes with respect to their degree of importance, which is also expressed in the form of linguistic terms. Finally, a case study of the river Yamuna has been carried out to evaluate the fuzzy comprehensive water quality index (FCWQI). In this study, the FCWQI has been determined only for the use of water for drinking purposes though this model can be applied for other uses as well. The FCWQI developed herein is based on an integrated approach, which clearly describes the overall state of the water quality by a single rational number. Spatial and parametric sensitivity of the FCWQI model of the river basin is also determined using GIS-based geographically weighted regression technique. The methodology suggests a novel way of introducing parametric sensitivity in defining water quality indices used for surface water quality assessment.