Faculty Publications
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Item Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease(Elsevier B.V., 2017) Singh, B.; Jana, S.K.; Ghosh, N.; Das, S.K.; Joshi, M.; Bhattacharyya, P.; Chaudhury, K.Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and L-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. © 2016 Elsevier B.V.Item Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality—a case study of Kanavi Halla Sub-Basin, Belagavi, India(Springer editorial@springerplus.com, 2020) B Patil, V.B.; Pinto, S.M.; Govindaraju, T.; Virupaksha, V.S.; Bhat, V.; Lokesh, K.N.Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser–Meyer–Olkin and Bartlett’s test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation. © 2020, Springer Nature B.V.Item Bivariate Modeling of Hydroclimatic Variables in Humid Tropical Coastal Region Using Archimedean Copulas(American Society of Civil Engineers (ASCE) onlinejls@asce.org 1801 Alexander Bell DriveGEO Reston VA 20191 Alabama, 2020) Uttarwar, S.B.; Deb Barma, S.; Mahesha, M.The present study focuses on the dependence modeling of hydroclimatic variables such as the El Niño-Southern Oscillation (ENSO) index, precipitation, tidal height, and groundwater level (GWL) in humid tropical coastal region of India. The rank-based correlation coefficient was used to determine the dependence between the pairs of cumulative monsoon precipitation of June-July-August-September (P_JJAS) and the postmonsoon groundwater level (PMGWL), ENSO-P_JJAS, ENSO-PMGWL, and GWL-tidal height. The results indicated that P_JJAS-PMGWL, ENSO-PMGWL, and GWL-tidal height had significant dependence, whereas P_JJAS-ENSO had no significant dependence. The best fit distributions for P_JJAS, PMGWL, and tidal height were found to be lognormal, extreme value, and generalized extreme value distributions, respectively, whereas for the ENSO index, it was the normal kernel-density function. The Archimedean families of copulas were used for dependence modeling, and it was observed that the ENSO-PMGWL was best modeled by the Frank copula, the P_JJAS-PMGWL by the Gumbel-Hougaard copula, and the GWL-tidal height by the Frank copula. The copula-based conditional probability for the Gumbel-Hougaard and Frank copulas for GWL were obtained to understand the risk associated with other hydroclimatic variables. Thus, copula-based dependence modeling could be useful for understanding the risk among hydroclimatic variables including groundwater. © 2020 American Society of Civil Engineers.Item Spatiotemporal Analysis of Compound Agrometeorological Drought and Hot Events in India Using a Standardized Index(American Society of Civil Engineers (ASCE), 2021) Muthuvel, D.; Mahesha, A.Meteorological droughts abetted by hot events could instigate an agricultural drought that eventually affects crop yield. Different types of droughts may coexist or occur in succession. A single index based on one particular variable may not be sufficient to quantify such compound drought events. Therefore, this study embedded drought indexes ofstandardized precipitation index (SPI), standardized soil-moisture index (SSI), and standardized temperature index (STI) with Gaussian copula functions to study compound agrometeorological drought and hot events in India from 1948 to 2014. By standardizing the joint probability of the SPI, SSI, and STI time series, the standardized compound drought and hot index (SCDHI) was developed. The SCDHI values in the monsoon months of different climatic zones have a strong correlation of about 0.95 with other well-established indexes such as the standardized compound event indicator (SCEI), which incorporates SPI and STI, and the multivariate standardized drought index (MSDI), which incorporates SPI and SSI. Based on the areal extent, 1965-1966, 1972, 1987, and 2002 were identified as significant compound drought years in India. The index also identified three successive compound events of the 2012-2014 northest monsoon in the southern peninsular region. A notable increase in the frequency of compound drought and hot events was found post-2000. The case studies of the major drought events and the dependent pattern of SCDHI on its constituent indexes indicate that SCDHI performs well as an indicator of compound agrometeorological and hot events across different climatic regions and in both southwest and northeast monsoons. © 2021 American Society of Civil Engineers.Item Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis(American Society of Civil Engineers (ASCE), 2021) Pathak, A.A.; Dodamani, B.M.Groundwater is a major resource of freshwater that provides additional resilience to agricultural drought during rainfall deficit and also helps in understanding the nature of the hydrological drought risk of an area. This study investigated the response of groundwater drought to meteorological drought and local aquifer properties by considering monthly groundwater levels of a tropical river basin in India. Further, bivariate frequency analysis was carried out for groundwater drought to develop severity-duration-frequency curves by considering the copula function. Long-term monthly groundwater levels were procured, and cluster analysis was performed on groundwater observations to classify the wells. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought for each cluster, and the same was compared with the meteorological drought of different association periods. The cluster analysis conveyed that wells can be grouped into three clusters optimally. Based on the comparison of groundwater drought with meteorological drought, it was inferred that SGI is well harmonized with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in humid and semiarid regions, respectively. Analysis of hydraulic diffusivity with the autocorrelation structure of SGI emphasizes the crucial role of aquifer characteristics in local groundwater droughts. The results of joint and conditional return periods obtained from bivariate frequency analysis conveyed that high severity and high-duration droughts were more frequent in the well of Clusters 1 as well as Cluster 3 and comparatively less for the well of Cluster 2. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies by considering conjunctive use of surface and groundwater. It also provides a framework to evaluate groundwater drought risk in other parts of the world. © 2021 American Society of Civil Engineers.Item Multivariate analysis of concurrent droughts and their effects on Kharif crops—A copula-based approach(John Wiley and Sons Ltd, 2022) Muthuvel, D.; Mahesha, M.Apart from creating an ecological imbalance, drought events could affect an agrarian country's economy and food security by reducing crop yields. The antecedent meteorological droughts could prolong into hydrological and (or) agricultural droughts and may co-exist as concurrent droughts. The current study aims to comprehensively study Indian concurrent droughts, their effects on crop yield, and possible teleconnection with ENSO (El Niño–Southern Oscillation), adopting a copula-based multivariate approach. The copula functions can replicate the correlation among the variables and keep the dependence structure intact. The concurrent drought characteristics are computed using a multivariate standardized drought index that incorporates the three primary drought indices using the Gaussian copula. Some of the severe concurrent drought years such as 2002, 1987, 1972, and 1965 caused considerable yield losses in Kharif season crops of groundnut, millet, and rice. This prompts to construct quad-variate models involving the crop yield and the three drought indices using the vine copulas that perform better than the elliptical and symmetric Archimedean copula. Though the isolated forms of droughts could cause mild yield losses, the probability of concurrent droughts causing high to exceptional losses is more. Further, the ENSO teleconnection with the concurrent monsoon droughts is analysed and mapped. The above-normal warming of the Nino 3.4 region over the tropical Pacific during the months leading up to the monsoon could signal concurrent monsoon droughts in the areas under the Ganga-Brahmaputra basin at a probability of around 45%. These results could be helpful in drought mitigation measures and policymaking. © 2021 Royal Meteorological Society.Item Future global concurrent droughts and their effects on maize yield(Elsevier B.V., 2023) Muthuvel, D.; Sivakumar, B.; Mahesha, A.Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950–2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950–2014), near future (2021–2060), and far future (2061–2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions. © 2022 Elsevier B.V.Item Perception on climate change, access to extension service and energy sources determining adoption of climate-smart practices: A multivariate approach(Academic Press, 2023) Tanti, P.C.; Jena, P.R.Climate change has an adverse impact on rural livelihoods by increasing vulnerability and reducing crop yields. Climate-smart agricultural (CSA) practices have been advanced as a possible solution to adopt and mitigate climate change issues. Administering a structured questionnaire survey among the 494 rural farming households of an eastern Indian state, namely Odisha, the study explores the key determinants of CSA adoption. Three districts, one from the state's coastal and two from the inland regions, are chosen for the study. The majority of the respondents (85%) perceive an increase in temperature and (76%) perceive a decrease in rainfall due to climate change in the region. The respondents have adopted a range of CSA practices such as rescheduling planting (74.5%), crop rotation (59.3%), crop diversification (31.2%), soil conservation (62.1%), drought-resistant seeds (36%) and agroforestry (10.3%) to adapt to these weather anomalies. The current paper employs a multivariate probit model in which the findings of econometric modelling have been triangulated to explore the key determinants of the adoption of CSA practices. The result shows that the key determinants are – perception of climate change, agricultural extension services, and access to energy for irrigation. © 2023 Elsevier LtdItem Regionalization of flow duration curves for catchments in southern India using a hierarchical cluster approach(IWA Publishing, 2023) Hiremath, C.G.; Nandagiri, L.The present study on the hydrologic regionalization was taken up to evaluate the utility of hierarchical cluster analysis for the delineation of hydrologically homogeneous regions and multiple linear regression (MLR) models for information transfer to derive flow duration curve (FDC) in ungauged basins. For this purpose, 50 catchments with largely unregulated flows located in South India were identified and a dataset of historical streamflow records and 16 catchment attributes was created. Using selected catchment attributes, three hydrologically homogenous regions were delineated using a hierarchical agglomerative cluster approach, and nine flow quantiles (10–90%) for each of the catchments in the respective clusters was derived. Regionalization approach was then adopted, whereby using step-wise regression, flow quantiles were related with readily derived basin-physical characteristics through MLR models. Cluster-wise performance analysis of the developed models indicated excellent performance with an average coefficient of determination (R2) values of 0.85, 0.97, and 0.8 for Cluster-1,-2, and-3, respectively, in comparison to poor performance when all 50 stations were considered to be in a single region. However, Jackknife cross-validation showed mixed performances with regard to the reliability of developed models with performance being good for high-flow quantiles and poor for low-flow quantiles. © 2023 The Authors.Item Study of Correlated Motions to Detect the Conformational Transitions of the Intrinsically Disordered Sheep Prion Peptide(American Chemical Society, 2024) Chakraborty, D.; Singh, O.; Parameswaran, D.Intrinsically disordered proteins (IDPs) are known for their random structural changes throughout their sequence based on the environment. The mechanism underlying these structural changes is difficult to explain. All biological processes are known to follow the direction through which they act. A study of the correlated motion can help to understand the direction of the change. Herein, we introduced the multivariate statistical analysis (MSA) technique to study the correlated motion of the peptide. The correlated motion of the sheep prion peptide was studied with the change in the temperature and solvent. These techniques helped to identify the contributing residual motions that helped to form the different secondary structures of the protein and also the triggering factors that drive these sorts of residual motions. The structural details match the experimentally reported data. It was found that the direction of the change of the secondary structure for this peptide shifted from the C-terminal to the N-terminal with an increase in the temperature. It was found that the involvement of the hydrophobic residues present at the C-terminal and the middle residues (residues 12-17) is responsible for forming a β-sheet at the normal temperature. Hydration water was found to play an important role in this change. Insights gained from this study can be used to design strategies for desirable structural changes in the IDPs. © 2024 American Chemical Society.