Browsing by Author "Rashmi"

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Association of TCF7L2 gene variant with T2DM, T1DM and gestational diabetes in the population of Northeastern UP, India
(Springer India, 2016) Santosh K. Yadav; Rashmi; K.K. Tripathi; Royana Singh
The non-coding variant (rs7903146) for transcription factor 7-like 2 gene (TCF7L2) is known to be associated with increased risk of type 2 diabetes mellitus (T2DM), but this variant is also associated with type 1 diabetes mellitus (T1DM) and gestational diabetes mellitus (GDM). The association of TCF7L2 variant rs7903146 was confirmed in the Indian and European population for T2DM. We investigated whether TCF7L2 variant rs7903146 is associated with T1DM, T2DM and GDM in Uttar Pradesh population. Three hundred thirty-three patients were genotyped having T2DM, 175 patients with T1DM and 102 gestational GDM and 487 healthy controls. The rs7903146 polymorphism was genotyped using the PCR-based RFLP method. The heterozygous CT genotype of rs7903146 had a 0.0437-fold increased risk in T2DM [OR (95 % CI) 0.0437 (0.0059–0.3213), p < 0.00001] and a 0.0081-fold increased risk in T1DM [OR (95 % CI) 0.0131 (0.0011–0.0589), p < 0.00001] in comparison to control. The frequency of CT genotype was significantly higher in T2DM than in controls (10.51 vs. 0.62 %) with an OR of 0.0528 (95 % CI 0.0182–0.0189, p < 0.0001). The frequency of the CT genotype was significantly higher in T1DM than in controls (38.86 vs. 0.62 %) with OR of 0.0098 (95 % CI 0.0051–0.0497, p < 0.0001). The frequency of CT genotype in T1DM was more than in T2DM. No association was observed in GDM. The study proved that the rs7903146 variant of the TCF7L2 gene is associated with T2DM and T1DM but not GDM in the North Indian population of Uttar Pradesh. © 2016, Research Society for Study of Diabetes in India.
Comparative evaluation of simplified surface energy balance index-based actual ET against lysimeter data in a tropical river basin
(MDPI, 2021) Utkarsh Kumar; Rashmi; Chandranath Chatterjee; Narendra Singh Raghuwanshi
In the past decades, multispectral and multitemporal remote sensing has been popularly used for estimating actual evapotranspiration (ETc) across the globe. It has been proven to be a cost-effective tool for understanding agricultural practices in a region. Today, because of the availability of different onboard sensors on an increasing number of different satellites, land surface activity can be captured at fine spatial and time scales. In the present study, three multi-date satellite imageries were used for the evaluation of remote sensing-based estimation of actual evapotranspiration in paddy in the command area of the tropical Kangsabati river basin. A surface energy balance model, the Simplified-Surface Energy Balance Index (S-SEBI), was applied for all three dates of the Rabi season (2014–2015) for the estimation of actual evapotranspiration. The crop coefficient was calculated using the exhaustive survey data collected from the command area and adjusted to local conditions. The ETc estimated using the S-SEBI-based model was compared with the Food and Agriculture Organization Penman–Monteith (FAO-56 PM) method multiplied by the adjusted local crop coefficient and lysimeter data in the command area. The coefficient of determination (r2) was applied to examine the accuracy of the S-SEBI model with respect to lysimeter data and the FAO-56 PM-based ETc. The results showed that the S-SEBI model performed well with the lysimeter (r2 = 0.90) in comparison with FAO-56 PM, with an r2 of 0.65. In addition to this, the S-SEBI-based ET estimates correlated well with the FAO-56 PM, with r and RMSE values of 0.06 and 1.13 mm/day (initial stage), 0.85 and 0.48 mm/day (development stage), and 0.77 and 0.52 (maturity stage) for paddy, respectively. The S-SEBI-based ETc estimate varied with different stages of crop growth and successfully captured the spatial heterogeneity within the command area. In general, this study showed that the S-SEBI method has the potential to calculate spatial evapotranspiration and provide useful information for efficient water management. The results revealed the applicability and accuracy of remote sensing-based ET for managing water resources in a command area with scarce data. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
EIA of municipal solid waste disposal site in Varanasi using RIAM analysis
(2010) M.K. Mondal; Rashmi; B.V. Dasgupta
Rapid industrialization and population explosion in India has led to the migration of people from villages to cities which generate thousands of tones of municipal solid waste (MSW) daily. Waste generation and management of waste is one of the major problems these days. Environmental impact assessment (EIA) work is, therefore, becoming more and more extensive in the world. Rapid Impact Assessment Matrix (RIAM) comes under one of the options for the execution of EIA. This method is particularly advantageous over others as it provides a transparent and permanent record of analysis process while at the same time organizing the EIA procedure, which in turn considerably reduces the time taken in executing EIAs. Using this method of RIAM, EIA has been carried out on different municipal solid waste disposal options and it has been found that the sanitary landfill is the best recommended option under the existing circumstances. © 2009 Elsevier B.V. All rights reserved.
Evaluation of Spatio-Temporal Evapotranspiration Using Satellite-Based Approach and Lysimeter in the Agriculture Dominated Catchment
(Springer, 2021) Utkarsh Kumar; Ankur Srivastava; Nikul Kumari; Rashmi; Bhabagrahi Sahoo; Chandranath Chatterjee; Narendra Singh Raghuwanshi
Crop coefficient (Kc) represents the actual crop growth of the crop. It plays an important role in estimating water requirements at the different growth stages of the crop. However, FAO 56 Penman–Monteith Kc method does not account for spatial heterogeneity and uncertainty for regional climatic conditions significantly. Therefore, this study aims to develop the relation between Kc and normalized difference vegetation index (NDVI) using a linear regression and back calculations. These relationships were adjusted to local conditions using information from survey data obtained during Rabi season (2014–2015). The NDVI–Kc model (r2 = 0.86) has developed using NDVI–Kc from a fine resolution Landsat 8 remote sensing data. NDVI–Kc regression equation was utilized for generating crop coefficient for different month of season. The Vegetation Index-based AET estimated was evaluated with lysimeter data for different crop growth stage across the season. The results have shown that NDVI–Kc estimated AET has been better correlated with NDVI–Kc remote sensing model. Thus, the output of this research can help to calculate actual water demand in a command area and be helpful in allocating water from less demand area toward more demand area. © 2021, Indian Society of Remote Sensing.
Evaluation of Standardized MODIS-Terra Satellite-Derived Evapotranspiration Using Genetic Algorithm for Better Field Applicability in a Tropical River Basin
(Springer, 2023) Utkarsh Kumar; Rashmi; Ankur Srivastava; Nikul Kumari; Chandranath Chatterjee; Narendra Singh Raghuwanshi
Evapotranspiration (ET) estimation at different spatial and temporal scales with a paucity of climatic parameters in a river basin is becoming a challenging task. Accurate estimation of ET is necessary for efficient water resource management and improving water efficiency at the field scale. Therefore, this study attempts to indirectly estimate actual ET from version 006 of MODIS-Terra product (MOD16A2.006), Sentinel-2A and Variable infiltration capacity (VIC-3L) model using survey information collected from a traditional paddy field in Kangsabati river basin. Further, this study is undertaken to standardize raw MODIS-Terra ET product (MOD16A2.06) using a genetic-based algorithm for better field applicability at local condition. The MODIS-standardized ET and ET estimated using different methods along with raw MODIS-Terra ET product were evaluated against observed ET estimated using globally recommended FAO-56 Penman–Monteith (PM) equation coupled with a crop coefficient. MODIS-Terra ET estimates were standardized using a genetic-based algorithm to enhance the efficacy of MODIS-Terra ET (MODIS-raw ET) for better field applicability. The result revealed that the genetic-based algorithm (MODIS-standardized ET) improved significantly with the NSE and RMSE from approximately − 0.03 to 0.86 and 13.89 to 2.56 (mm/8 day). Of various ET models Sentinel-2A ET performed best followed by MODIS-standardized ET, VIC-3L ET and MODIS-raw ET with R2 = 0.92, NSE = 0.89, RMSE = 1.89 (mm/8 day), R2 = 0.88, NSE = 0.86, RMSE = 2.47 (mm/8 day), R2 = 0.77, NSE = 0.76, RMSE = 3.02 (mm/8 day) and R2 = 0.41, NSE = − 0.03, RMSE = 7.31 (mm/8 day), respectively. The result showed that Sentinel 2A and MODIS-standardized-based ET can be used under data scarce conditions for better field applicability and water management practices. © 2023, Indian Society of Remote Sensing.
Identification of functional SNPs in PAX3 gene and in silico analysis of damaging SNPs in relation to neural tube defect
(Reed Elsevier India Pvt. Ltd., 2015) Rashmi; Royana Singh; A.N. Gangopadhyay; Anjali Rani; Mayank Shah
Introduction: PAX3 gene belongs to the class of transcription factor and has a significant role in neural tube development. There are a number of SNP which are associated with neural tube defect. Hence, we must sort functional SNP for a population study. To fulfill this goal data from dbSNP and literature review can be used. Methods: In this study we analyzed the functional and structural impact of SNPs through computational prediction tool. A total of 8947 SNPs were observed from dbSNP in which SNP associated with neural tube defect having missense mutation is rs2234675. This nsSNP was found to be damaging by sequence homology based tool (Provean) and structural homology based tool (Polyphen). Modeling of wild and mutant protein structure were done using RMSD of wild and mutant protein structure were determined using Swiss PDB viewer and then the protein structure stability was determined using I-mutant 3. Results: The nsSNP present in dbSNP i.e., rs2234675 was identified as deleterious, which lead too decrease in stability of PAX3 protein. Discussion: A change of Thr315Lys, i.e. from polar neutral amino acid to polar basic amino acid showed a change in charge to positive and size of amino acid lead to change in structure. The modeled structure further, showed a decrease in stability. The result obtained from insilico study would open new prospect for association of PAX3 with neural tube defect. © 2015 Anatomical Society of India.
In-silico study of transcription factor binding elements of human PAX gene family members
(2013) Rashmi; V.K. Singh; A.N. Gangopadhyay; G.L. Shah; A. Khanna; T.M. Mohapatra; Om Shankar; Royana Singh
PAX gene family members, tissue specific transcription factors mainly involved, in the formation of tissues and. organs during embryonic development and has important role in transcriptional regulation. The presence of consensus paired domain play significant role in DNA-binding transcription regulation with PAX domain. Regulatory behavior of PAX family members were determined using cis-acting elements study and repeat identification. The study helped in investigating the potential conserved motifs in the paired domain. Further, investigation of cis-acting elements was done to elucidate the function for each PAX members and then repeat analyses and their correlation with functional elements were done. The study illustrates that the cis-acting elements are involved in tissue specific developmental expression and transcription al regulation of PAX family members. Further, based on physiochemical property study of these PAX gene family members it was found that they are mainly Ser, Pro, Gly and Ala rich amino acids. It was found that repeats containing functional DNA motifs interact with signature motifs of paired domain. The mam six signature motifs NQLGG, NGRPLP, RPC, SR, GCVSKIL and PGAIGGSKP are involved in interaction. Altogether, this study provides new insights into the regulatory behavior of upstream region of each PAX members and its effect in trans criptional regulation and developmental expression of these PAX members with involvement in disease management. © 2013 Asian Network for Scientific Information.
Modelling soil temperature at multiple depths in Saurashtra region (Junagadh) of Gujarat using machine learning and shapely approach
(Springer Nature, 2025) Utkarsh Kumar; Rashmi; Ankur Srivastava; H. V. Parmar; H. H. Mashru; Parthsarthi A. Pandya; G. V. Prajapati; H. D. Rank
Forecasting soil temperature (ST) at multiple depths is crucial for understanding meteorological processes, enhancing agricultural resilience, and assessing ecological and environmental risks. Data driven model represents an alternative tool to the conventional measurement of ST e.g. soil thermometer. To develop the ML model, weekly ST and relevant meteorological variables for the city of Junagadh (Saurashtra region) are collected for the period of 2010–2023. A thorough feature analysis was performed to select the most promising feature using Pearson correlation coefficient and shapely approach. The model was developed using different combinations of input parameters (M1–M7) and trained using different machine learning algorithms. This research aims to evaluate four different machine learning approaches namely, Random Forest (RF), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), to predict the soil temperature at 5 cm, 10 cm and 20 cm depth. The result of this study showed that by choosing the optimum input parameter, there is no significant impact on accuracy of model. The best performance was obtained for Model 7 f(TDB, TMax, TMin, Evapo) model at the 10-cm soil depth, as it provided the greatest correlation coefficient (r = 0.9967) and the lowest value for root mean square error (RMSE = 0.3410 °C) and percent bias (PBIAS = − 0.0115). The result showed that model performance differences are often statistically significant, especially at shallower depths (ST5, ST10), but less so at ST20. In the current study, besides evaluating the potential of four machine learning models, the interpretation of the machine learning algorithm for soil temperature prediction was explored using SHapley Additive exPlanations (SHAP). The study used an explainable artificial intelligence (XAI) approach to provide novel interpretation and insights to elucidate model formulation and relative predictor importance. © The Author(s) 2025.
The Morphological Features of Anencephaly in North Indian Population
(Wolters Kluwer Medknow Publications, 2023) Rashmi; Nitish Kumar Singh; Ashish; Abhay Kumar Yadav; Manpreet Kaur; Royana Singh
Background: Anencephaly occurs due to the complete absence of cranial vault and subsequent disruption of the cerebral cortex with a severely damaged brain. In anencephaly, the forebrain and brain stem are exposed. Forebrain either does not develop or is destroyed, leading to the absence of cerebrum and cerebellum. Methodology: Neural tube defects were taken in the study group. During the autopsy, clinical findings, external examination, internal examination, and photography were done along with the histopathology of the specimens to confirm the anomalies at microscopic level using hematoxylin and eosin staining. Results: In our study, we observed a simian crease in 4 out of 5 (80%) cases. Furthermore, there was presence of tooth which was not seen in previous studies. Central nervous system anomalies like spina bifida, gastro intestinal tract (GIT) anomalies like cleft palate, intestinal obstruction of megacolon, and malrotation of gut were some of the common anomalies which were observed in our study. Conclusion: It may be suggested that Anencephaly shows a female predisposition and the cases seems to be associated more in the primigravida females.The classical phenotypic presentation of anencephaly having absent cranial vault, low set ears, protruding eyes were present in all subjects studied. In our study, we observed a simian crease in 4 out of 5 (80%) cases. Furthermore, there was presence of tooth which was not seen in previous studies. Central nervous system anomalies like spina bifida, GIT anomalies like cleft palate, intestinal obstruction of megacolon, and malrotation of gut were some of the common anomalies which were observed in our study. © 2023 Journal of the Anatomical Society of India.
Variations of Wholesale Price of Wheat in Different States of India under COVID-19 Pandemic
(Society of Statistics, Computer and Applications, 2024) Rashmi; H.P. Singh; P.K. Singh
The present study investigates the impact of COVID-19 and restrictions imposed on wheat in different agricultural markets of India. The COVID-19 pandemic has had a significant impact on various sectors worldwide, including the food market. In India, the wheat crop harvest coincided with the lockdown imposed to control the spread of the virus. Monthly wholesale price data of seven states viz. Chhattisgarh, Uttar Pradesh, Madhya Pradesh, West Bengal, and Maharashtra were exercised from agricultural marketing portal of India. We compared monthly prices of April, May and June across 2019, 2020 and 2021. Linear piecewise regression was used to understand the impact COVID-19 on market whole sale price during different phases. The result revealed that wheat prices were at minimum support price in most of the states. Time series analysis showed the immediate impact of lockdown on decreased monthly wholesale price in all the states. Price risk was calculated using Cuddy Della Valle instability index (CDVI). Maharashtra showed the highest average monthly whole sale price and maximum price risk. The findings suggest that the agricultural markets have demonstrated a significant level of resilience in coping with the adverse effects of the COVID-19 pandemic. This is attributed to the provision of adequate policy support that has helped to mitigate the impact of the pandemic on the sector. © 2024, Society of Statistics, Computer and Applications. All rights reserved.