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

Algorithm

Data Sources

Poisson Model

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

Empirical Bayesian Smoothing

Incidence Rate and SMR

Spatial Analysis of Cancer PatientCount Data

Total haemocyte count assay

Spodoptera litura

Insecticidal activity

Inhibition of haemocytes spreading behavior

. Inhibition of haemocytes aggregation behavior

Haemolymph protein profiling

Total haemocyte count (THC)

Haemolymph collection

Asparate (AAT) and Alanine aminotransferase (ALT)

Insect collection

Venomous saliva optimization

Starvation and collection method

Insects Collection

Salivary gland-Histology

Salivary gland-Morphology and Anatomy

Maxillary stylet

Mandibular stylet

Head

Gross morphology and histology of salivary gland

Morphometry of head and stylet

Head and stylet preparation

Insect collection and maintenance

GROSS MORPHOLOGY OF THE REDUVIID HEAD AND SALIVARY GLAND COMPLEX

Growth maximum values

Rates of growth (OD678/day

Growth

Experimental Set up

Materials

Material

Materials

Material

Total alkalinity

Dissolved oxygen and Biological oxygen demand

Total dissolved solids

Multi parameter analyzer

MATERIALS AND METHODS

PHYSICO-CHEMICAL PARAMETERS OF SEAWATER

Estimation of Stevioside

Extraction from the plant material

Extraction from the plant material

Extraction from the plant material

Qmmtification of stevioside

Estimation of Steviol

Extraction from plant material

Quantification of steviol

Estimation of free aminoacids

Estimation of soluble proteins

Estimation of sugars

Estimation of total phenols

Determination of moisture

Analytical methods

The experimental part of the study was categorized into five sections for the convenience of reference viz. analytical, toxicological, molecular, biochemical and genomic quantitation.

Plant material

e5 tevia-the natural smeetenei