GIS backed parametric surface and groundwater quality indexing in the vicinity of a multi-utility system tank

772-780Parametric water quality indexing (WQI) helps integrate the interwoven effects of independent parameters which may enhance or impair the desirable physico-chemical and biological characteristics of both surface waters and groundwater reserves. Monsoon and post-monsoon status of the distribution of such parameters will give an insight on improvising the qualities of water for multiple usages in the vicinity of a tank and its influential area on groundwater contamination. A typical system tank in Coimbatore urban reach of the Noyyal river was studied by using GIS applications. The test values of all parameters considered were subjected to indexing for water quality grades inflicted by the presence of contaminants. On a 1 to 5 gradation, the study area was found to have a weighted mean water quality index of 2.34 and 2.35 for tank surface water during the monsoon and post-monsoon periods, respectively. In case of ground water sampling, the same was found to be 2.17 and 2.19 for the respective situations. The overall hazard rating was characterized under medium to high, warranting quarantine measures to safeguard the water quality standards for multiple uses in the study locality, pinning the eyes on dissolved oxygen improvements, turbidity control mechanisms, alkalinity amendments and moderations of hardness prevailing as high hazard indicators. The pre-monsoon season ratings were nearing the critical conditions at overall ratings ranging from 3.03 to 2.65 (high to severe) for surface and groundwater, respectively

Modeling Volume Loss of Heat Treated Al 6061 Composites Using an Artificial Neural Network

AbstractIn the present study, artificial neural network (ANN) approach was used to predict the volume loss of heat treated Al 6061 metal matrix composites reinforced with 10% SiC particles and 2% graphite particles. Composite was produced using stir casting process. Volume loss of composite was measured during wear testing in a pin on disc apparatus. Microstructure examination at wear surface was investigated by Scanning Electron Microscope (SEM). In Artificial Neural Network (ANN), Multi Layer Perceptron (MLP) architecture with back-propagation neural network that uses gradient descent learning algorithm is utilized. The results clearly revealed that the developed ANN model is reliable and accurate

Activity-based protein profiling of the hepatitis C virus replication in Huh-7 hepatoma cells using a non-directed active site probe

<p>Abstract</p> <p>Background</p> <p>Hepatitis C virus (HCV) poses a growing threat to global health as it often leads to serious liver diseases and is one of the primary causes for liver transplantation. Currently, no vaccines are available to prevent HCV infection and clinical treatments have limited success. Since HCV has a small proteome, it relies on many host cell proteins to complete its life cycle. In this study, we used a non-directed phenyl sulfonate ester probe (PS4≡) to selectively target a broad range of enzyme families that show differential activity during HCV replication in Huh-7 cells.</p> <p>Results</p> <p>The PS4≡ probe successfully targeted 19 active proteins in nine distinct protein families, some that were predominantly labeled <it>in situ </it>compared to the <it>in vitro </it>labeled cell homogenate. Nine proteins revealed altered activity levels during HCV replication. Some candidates identified, such as heat shock 70 kDa protein 8 (or HSP70 cognate), have been shown to influence viral release and abundance of cellular lipid droplets. Other differentially active PS4≡ targets, such as electron transfer flavoprotein alpha, protein disulfide isomerase A5, and nuclear distribution gene C homolog, constitute novel proteins that potentially mediate HCV propagation.</p> <p>Conclusions</p> <p>These findings demonstrate the practicality and versatility of non-directed activity-based protein profiling (ABPP) to complement directed methods and accelerate the discovery of altered protein activities associated with pathological states such as HCV replication. Collectively, these results highlight the ability of <it>in situ </it>ABPP approaches to facilitate the identification of enzymes that are either predominantly or exclusively labeled in living cells. Several of these differentially active enzymes represent possible HCV-host interactions that could be targeted for diagnostic or therapeutic purposes.</p

Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices

Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.

Maize area mapping using multi-temporal Sentinel 1A SAR data in the Belagavi district of Karnataka, India

The study explores the integration of remote sensing technologies with ground truth data for precise estimation of maize cultivation areas in the Indian Belagavi district, Karnataka, during the rabi season of 2022-23. Leveraging Sentinel-1A satellite data and advanced processing techniques, the study provides insights into crop dynamics, phenology, and spatial distribution. Ground truth data collection involved 369 points covering diverse land use and land cover types. The multi-temporal Synthetic Aperture Radar (SAR) imagery underwent automated processing, extracting features crucial for maize classification. Classification accuracy assessment revealed robust performance, with 92.4% accuracy for maize and 91.1% for non-maize locations, supported by a Kappa index of 0.83. Taluk (sub- district) wise maize area estimation highlighted spatial variations, with Saudatti emerging as the leading taluk, contributing 25.74% of the total maize cultivation area. The study underscores the importance of localized agricultural planning strategies tailored to each region's agricultural landscape. Through comprehensive analysis and accurate area estimation, policymakers and stakeholders gain valuable insights for informed decision-making, ranging from optimizing input distribution to formulating targeted policies for rural development

Monitoring and mapping of seasonal vegetation trend in Tamil Nadu using NDVI and NDWI imagery

In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics

Effect of different herbicide spray volumes on weed control efficiency of a battery-operated Unmanned aerial vehicle sprayer in transplanted rice (Oryza sativa L.)

The effect of spray volume on weed control in transplanted rice ecosystems using the Unmanned aerial vehicle (UAV) needs to be better understood for management in the advancements of UAV-based spraying technology. The present study aimed to find out the influence of varied spray volumes of 15 L/ha, 20 L/ha and 25 L/ha using the UAV and 500 L/ha using a Knapsack sprayer (KS) to compare the weed density, weed dry matter and weed control efficiency and yield in transplanted rice (Oryza sativa L.). Pre-emergence (PE) application of Pyrazosulfuron-ethyl at 25 g a.i./ha at three days after transplanting (DAT) and post-emergence (PoE) application of Bis-pyribac sodium at 25 g a.i./ha at 25 DAT were used as herbicide treatments. The results revealed that varied spray volumes significantly influenced the weed density, dry matter, and weed control efficiency of the UAV and KS. Application of herbicides using KS (500 L/ha) and UAV (25 L/ha) had better control on the weeds by reducing weed density and dry matter at 20, 40, and 60 DAT, with no significant difference. Higher grain yield and straw yield were recorded in KS (500 L/ha) and UAV (25 L/ha), with no significant difference. However, applying 25 L/ha had better weed control efficiency and higher yield, possibly due to optimum deposition. Considering the low volume application of UAV (25 L/ha) as compared with KS (500 L/ha), it is better to go for the optimal application of 25 L/ha, which is an energy-efficient and cost-effective, labour-saving approach compared to KS

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period

Validation of the AASLD recommendations for Classification of Oesophageal Varices in Clinical Practice

Background & Aims The American Association for the Study of Liver Diseases recommends the use of a 2‐grade classification system (small and large) to describe the size of oesophageal varices (OV). Data on observer agreement (OA) on this system are currently lacking. We aimed to evaluate this classification and compare it to the widely used 3‐grade classification (grade 1 ‘small’, grade 2 ‘medium’, grade 3 ‘large’) among operators of variable experience. Methods High‐definition video recordings of 100 patients with cirrhosis were prospectively collected using standardised criteria. Nine observers of variable experience performed independent evaluations of the videos in random order. OV were scored using both systems. All assessments were repeated a year later by the same observers to assess intra‐observer agreement. Results Interobserver agreement (all observers) using the 2‐grade and the 3‐grade system was k = 0.71 (95% CI: 0.64‐0.78) and k = 0.73 (95% CI: 0.66‐0.79) respectively. When using the 2‐grade system, intra‐observer agreement between hepatologists (n = 3), luminal gastroenterologists (n = 3) and trainee gastroenterologists (n = 3) was k = 0.89 (95% CI: 0.86‐0.91), k = 0.72 (95% CI: 0.67‐0.77), and k = 0.74 (95% CI: 0.67‐0.8) respectively. With the 3‐grade system; intra‐observer agreement between the same three subgroups were k = 0.9 (95% CI: 0.87‐0.92), k = 0.73 (95% CI: 0.68‐0.78), k = 0.77 (95% CI: 0.71‐0.82) respectively. Conclusions There was no difference in OA between the 2‐grade and 3‐grade classification systems. Hepatologists had significantly higher levels of consistency in grading OV. This may have implications to create alternative training models for residents and fellows in the recognition and grading of OV

Utility and Cost-Effectiveness of a Nonendoscopic Approach to Barrett's Esophagus Surveillance After Endoscopic Therapy

BACKGROUND & AIMS: A non-endoscopic approach to Barrett's esophagus (BE) surveillance after radiofrequency ablation (RFA) would offer a less invasive method for monitoring. We assessed the test characteristics and cost-effectiveness of the Cytosponge® in post-RFA patients. METHODS: We performed a multicenter study of dysplastic BE patients after at least one round of RFA. A positive Cytosponge® before endoscopy was defined as intestinal metaplasia (IM) on cytological assessment and/or TFF3 immunohistochemistry. Sensitivity, specificity, and receiver operator characteristic (ROC) curves were calculated. Multivariable regression was used to estimate the odds of a positive Cytosponge® in BE. A microsimulation cost-effectiveness model was performed to assess outcomes of various surveillance strategies: endoscopy-only, Cytosponge®-only, and alternating endoscopy/Cytosponge®. RESULTS: Of 234 patients, Cytosponge® adequately sampled the distal esophagus in 175 (75%). Of the 142 with both endoscopic and histologic data, 19 (13%) had residual/recurrent BE. For detecting any residual Barrett's, Cytosponge® had a sensitivity of 74%, specificity of 85%, accuracy of 84%, and ROC curve showed an area under the curve of 0.74. The adjusted odds of a positive Cytosponge® in BE were 17.1 (95% CI: 5.2-55.9). Cytosponge®-only surveillance dominated all the surveillance strategies, being both less costly and more effective. Cytosponge®-only surveillance required <1/4th the endoscopies, resulting in only 0.69 additional EAC cases/1,000 patients, and no increase in EAC deaths when compared to currently-practiced endoscopy-only surveillance. CONCLUSIONS: A positive Cytosponge® test was strongly associated with residual BE after ablation. While the assay needs further refinement in this context, it could serve as a cost-effective surveillance examination