GROUNDWATER CHEMICAL STUDIES USING STATISTICAL ANALYSIS IN COIMBATORE CORPORATION, TAMIL NADU, INDIA

In place with comprehend the hydrochemistry and the probable contamination of groundwater for drinking and irrigation purposes, 60 groundwater samples have been collected from Coimbatore Corporation region in march 2014 and various physicochemical parameters (pH, EC, TDS, Alkalinity, TH, Ca2+, Mg2+, Na+, K+, Fe2+, NH3, NO2-, NO3-, Cl-, F-, SO42- and PO42-) were analysed. The concentrations of physiochemical parameters in the studied samples were compared with the WHO standards to study the suitability of water for drinking purpose. The statistical analysis  “Q-mode factor†and “cluster analyses†were carried out and found that geology and exchange between the river water and the groundwater plays a dominant role in the hydro chemical evolution of groundwater. Cluster tree diagram reveals that 41.67% of the study area comes under cluster I, II and III classification. Cluster tree clearly reveals that elevation high automatically geochemical concentration is low. The geochemical concentration is inversely proposed to elevation. A long term management strategy should be formulated for the protection of groundwater resources for drinking and agricultural activities

DMAP: a connectivity map database to enable identification of novel drug repositioning candidates

BACKGROUND: Drug repositioning is a cost-efficient and time-saving process to drug development compared to traditional techniques. A systematic method to drug repositioning is to identify candidate drug's gene expression profiles on target disease models and determine how similar these profiles are to approved drugs. Databases such as the CMAP have been developed recently to help with systematic drug repositioning. METHODS: To overcome the limitation of connectivity maps on data coverage, we constructed a comprehensive in silico drug-protein connectivity map called DMAP, which contains directed drug-to-protein effects and effect scores. The drug-to-protein effect scores are compiled from all database entries between the drug and protein have been previously observed and provide a confidence measure on the quality of such drug-to-protein effects. RESULTS: In DMAP, we have compiled the direct effects between 24,121 PubChem Compound ID (CID), which were mapped from 289,571 chemical entities recognized from public literature, and 5,196 reviewed Uniprot proteins. DMAP compiles a total of 438,004 chemical-to-protein effect relationships. Compared to CMAP, DMAP shows an increase of 221 folds in the number of chemicals and 1.92 fold in the number of ATC codes. Furthermore, by overlapping DMAP chemicals with the approved drugs with known indications from the TTD database and literature, we obtained 982 drugs and 622 diseases; meanwhile, we only obtained 394 drugs with known indication from CMAP. To validate the feasibility of applying new DMAP for systematic drug repositioning, we compared the performance of DMAP and the well-known CMAP database on two popular computational techniques: drug-drug-similarity-based method with leave-one-out validation and Kolmogorov-Smirnov scoring based method. In drug-drug-similarity-based method, the drug repositioning prediction using DMAP achieved an Area-Under-Curve (AUC) score of 0.82, compared with that using CMAP, AUC = 0.64. For Kolmogorov-Smirnov scoring based method, with DMAP, we were able to retrieve several drug indications which could not be retrieved using CMAP. DMAP data can be queried using the existing C2MAP server or downloaded freely at: http://bio.informatics.iupui.edu/cmaps CONCLUSIONS: Reliable measurements of how drug affect disease-related proteins are critical to ongoing drug development in the genome medicine era. We demonstrated that DMAP can help drug development professionals assess drug-to-protein relationship data and improve chances of success for systematic drug repositioning efforts

Groundwater potential zones delineation using geo-electrical resistivity method and GIS for Coimbatore, India

1088-1095Resistivity meter was used to conduct vertical electrical sounding (VES) using the Schlumberger electrical resistivity in the study region for a depth of 150 m. Geophysical study indicates four layers such as top soil, weathered first fractured zone and second fractured zones. 68.52% of study area is dominated by AA, AK and HA type curve-indicating low to moderate groundwater prospective zones. The survey result indicates that Calc-granulite and limestone rock types are better aquifers than the other rock types. GIS overlay indicate that good groundwater potential zone constitutes 93.37 sq.km (36.32%), in the study area located in Northeast, East and South edges of the study region. High groundwater potential zones fall in an area of 9.37 sq.km (3.65%)

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018