7,497 Matching Annotations
  1. Jul 2019
    1. DNA sequencing of the 18S rDNA fragment
    2. Purification of PCR product
    3. Analysis of internal transcribed spacer region
    4. RAPDand SSRscoring and data analysis
    5. PCR amplification
    6. Running of gel and visualization of DNA
    7. Determination of the yield
    8. Agarose gel electrophoresis
    9. Qualitative and quantitative estimation of DNA
    10. Determination of the yield
    11. Procedure for DNA isolation
    12. Reagents required for fungal DNA isolationand p
    13. DNA isolation of Trichodermaisolate
    14. Photography, evaluation and documentation
    15. Procedurefor SDS-PAGE
    16. Materialsrequired for SDS-PAGE
    17. Protein profiling of bioagent through SDS-PAGE
    18. Biochemical analysis (Protein estimation)
    19. Protein estimation through Kjeldahl method
    20. Dinitrosalicylate reagent (DNS)(per liter)
    21. Citrate phosphate buffer
    22. Reagents
    23. Materials for xylanase induction
    24. Evaluation of bioagents against the pathogen
    25. Effect of different media on growth of bioagentTrichoderma
    26. Identificationof bioagent
    27. Isolation and purification of pathogen, Fusarium udum
    28. Sterilization of glasswares
    29. Experimental site
    1. The recombinant Th1 stimulatory proteins (rLdADHT, and rLdTPR,) induced lymphoproliferative and NO responses in normal/infected/cured hamst
    2. Solutions used for cytokine assay
    3. Assessment of Lymphocyte proliferative responses (LTT) in cured/exposed patients and hamsters
    4. Parasites
    1. rLdADHT was cloned, overexpressed, purified and antibody raise
    2. Amplification, Cloning and Sequencing
    3. Transformation procedure
    4. Preparation of master plate and isolation of plasmid DNA from transformed E. coli (Mini Prep)
    5. Preparation of chemically competent Escherichia coli using calcium chloride method
    6. Genomic DNA isolatio
    7. Cloning, expression and purification ofADH
    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. Algorithm
    3. Data Sources
    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
  2. Jun 2019
    1. At each stage of developing the sample application, we will write small, bite-sized pieces of code—simple enough to understand, yet novel enough to be challenging. The cumulative effect will be a deeper, more flexible knowledge of Rails, giving you a good background for writing nearly any type of web application.
    2. Following the scaffolding approach risks turning you into a virtuoso script generator with little (and brittle) actual knowledge of Rails.
    1. Phenological traits and plant height
    2. Analysis of variance and differences among wheat varieties released in different years in India
    3. Estimation of total N% of wheat grainsand straw
    4. Chlorophyllcontent
    5. Root length (cm) and Root weight (mg)
    6. Coleoptile length(cm)
    7. Stomata / cm2
    8. Leaf area index (LAI)
    9. Physiological parameters
    10. Normalized difference vegetation index (NDVI)
    11. Spike length (cm)
    12. Last node to spike length(cm)
    13. Peduncle length(cm)
    14. HarvestIndex
    15. Grain yield per plot (g)
    16. Biological Yield(g)
    17. 1000 Kernel weight(mg)
    18. Number of grains per spike
    19. Number of productive tillers per meter
    20. Plant height (cm)
    21. Days to physiological maturity
    22. Days to heading
    23. Field observations
    1. T cell frequencies(Post vaccination response)
    2. Decreased CD3 ζ chain expression on CD8 T cells in HBsAgpositive newborns
    3. T cell phenotypic distribution in HBsAgPositive, HBsAgNegative from HBsAg positive mothers and healthy newborns.
    4. Clinical characteristics of the subjects