98 Matching Annotations
  1. Jul 2019
    1. The table 6.1 gives the mixing probabilities and the associated parametricvalues fork(number of components) = 2,3, and 4. It may be noted thatthe Log likelihood value is smaller fork= 4 (the results fork= 5 , 6 etc.are not better than that fork= 4 and hence are not given here). The fourcomponents Poisson Mixture model is given in table 6.2. It may be notedthat 58% of wards may have higher incidence/relative risk and the remainingwards have lesser/lower incidence for the Cancer disease. We computed theposterior probability for each component for each ward (see table 6.3). Eachward is assigned to a particular component so that the posterior probability islarger. These results are also given in table 6.3 Finally we present Choroplethmaps based on those results
    2. The Posterior Probability of Mixing Dis-tribution
    3. Algorithm
    4. EM Procedure
    5. Poisson Mixture
    6. Data Sources
    7. Poisson Mixtures Distribution
    1. Poisson Model
    2. We have analysed the Cancer data of patients in 155 wards of Chennai Cor-poration by the above described method. As preliminary analyses, we havecreated the Choropleth maps for Observed counts, Population of wards, ex-pected counts for patients and SMR's.The Choropleth map for the observed counts Figure 5.2 does not show anypattern. But the Choropleth map for the expected counts Figure: 5.4 indi-cate that the inner regions of the Chennai Corporations have lower expectedcounts and the regions along the border have larger counts of patients. As ameasure of spatial heterogeneity we have computed PSH= 0:7108:Hence ofthe total spatial random variation, nearly 71% is due to spatial heterogene-ity and the remaining 28:92% is due to Poisson variation. Thus the spatialvariation is present in the data.The Choropleth map for Empirical Bayes smoothed rates Figure 5.5 re-veals that only 13 sub regions have high risk values. The wards with numbers53, 64, 67, 70, 78, 93, 100, 103, 110, 117, 122, 147 and 151 have high riskvalues. Though this information could be used by the health managers toconcentrate their work on these regions, one can look for additional covariatesin these regions for further study
    3. Empirical Bayesian Smoothing
    4. Incidence Rate and SMR
    5. Spatial Analysis of Cancer PatientCount Data
    1. Haemocyte morphogenesis
    2. Spreading inhibitory behavior
    3. Inhibition of haemocyte aggregation
    4. Haemolymph protein profile
    5. Total haemocyte count assay
    6. Immunomodulatory
    7. Helicoverpa armiger
    8. Spodoptera litura
    9. Gut enzyme profile
    10. Insecticidal activity
    11. . Statistical analysis
    12. Inhibition of haemocytes spreading behavior
    13. . Inhibition of haemocytes aggregation behavior
    14. Haemolymph protein profiling
    15. Total haemocyte count (THC)
    16. Haemolymph collection
    17. Immunomodulatory
    18. Lactate dehydrogenase
    19. Asparate (AAT) and Alanine aminotransferase (ALT)
    20. Gut enzyme profile
    21. Oral toxicity bioassay
    22. Microinjection bioassay
    23. VS preparation
    24. Insect collection
    1. Statistical analysis
    2. Micronuclei test
    3. SDS -PAGE for serum protein analysis
    4. Estimation of serum proteins (SDS –PAGE) and Micronuclei in fish, Labeo rohita
    5. Estimation of bioaccumulation of trace metals inmuscle and gill tissue of fish, Labeo rohita
    6. Lactic dehydrogenase
    7. Malic dehydrogenase
    8. Succinic dehydrogenase
    9. Estimation of dehydrogenases activities (SDH, MDHand LDH) in muscle and gill tissue of fish, Labeo rohita
    10. Alkaline Phosphatase
    11. Acid Phosphatase
    12. Estimation of Phosphatases activities (Acid and Alkaline Phosphatase) in muscle and gill tissue of fish, Labeo rohita
    13. Cholesterol content
    14. Glycogen content
    15. Protein content
    16. Estimation of biomolecules (Protein, glycogen and cholesterol) inmuscle and gill tissue of fish, Labeo rohita
    17. Fish sampled from selected water bodies for further analysis
    18. Characteristic features ofLabeo rohita
    19. Animal model for the study-Labeo rohita
    20. Analysis of Physico-chemical parameters of the water sampled
    21. Survey of lakes to check their status of pollution