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Bhat, M. A.
- In vitro Efficacy of Casing and Compost Isolated Bacterial Inoculants against Verticillium fungicola (Preuss) Hassebrauk and Agaricus bisporus (Lange) Imbach
Abstract Views :369 |
PDF Views:132
Authors
Affiliations
1 Department of Plant Protection, Allahabad Agricultural Institute-Deemed University, Allahabad 211007, Uttar Pradesh, IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Shalimar Campus, Srinagar 191121, J&K, IN
1 Department of Plant Protection, Allahabad Agricultural Institute-Deemed University, Allahabad 211007, Uttar Pradesh, IN
2 Sher-e-Kashmir University of Agricultural Sciences and Technology (K), Shalimar Campus, Srinagar 191121, J&K, IN
Source
Journal of Biological Control, Vol 24, No 2 (2010), Pagination: 137-141Abstract
The aim of this study is to determine some effective antagonistic bacterial inoculants for the biological control of Verticillium fungicola causing dry bubble disease of white button mushroom (Agaricus bisporus). Out of the 32 bacterial inoculants isolated from the casing and compost materials and screened against V. fungicola, only 5 isolates were selected for further investigation. The cell free culture filtrates of these bacterial isolates were further evaluated against the pathogen and host fungus at different concentrations. The bacterial culture filtrates inhibited mycelial growth and conidial germination of V. fungicola to varied extents. The isolate PC-I showed maximum inhibition 71.50 per cent of mycelial growth and similar trends of inhibition were recorded in case of conidial germination (84.13 per cent of the pathogen) followed by PS-I, PS-II, Bacillus thuringiensis, BC-IX, BC-V and BC-IV, respectively. Azotobacter sp. proved less effective in inhibiting the mycelial growth of the pathogen. The culture filtrates of PS-I and PS-II stimulated the growth of A. bisporus at all the test concentrations, whereas the remaining culture filtrates inhibited its growth.Keywords
Bacterial Inoculants, Casing, Compost, In vitro, Verticillium fungicola, White Button Mushroom.Full Text
- Synthesis and Evaluation of New 2-(Substituted Phenyl)- 3-[5'-(2'-Oxo-2H-chromen-3'-yl)-1,3,4-Oxadiazol-2-yl]-1, 3-Thiazolidin-4-Ones as Anticonvulsants
Abstract Views :143 |
PDF Views:68
Authors
Affiliations
1 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Jamia Hamdard (Hamdard University), Hamdard Nagar, New Delhi-110062, IN
1 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Jamia Hamdard (Hamdard University), Hamdard Nagar, New Delhi-110062, IN
Source
Journal of Pharmaceutical Research, Vol 8, No 1 (2009), Pagination: 12-14Abstract
A series of 2-(substituted phenyl)-3-[5'-(2"-oxo-2H-chromen-3'-yl)-1,3,4-oxadiazol-2-yl]-1,3-thiazolidin-4-ones 4-16 were synthesized in good yield and evaluated for their possible anticonvulsant and neurotoxic study. All the synthesized compounds were in good agreement with elemental and spectral data. Majority of the compounds were active in MES test. All the compounds were less neurotoxic than the standard drug phenytoin.Keywords
Chromene, Oxadiazole, Thiazolidinones, MES Test, Neurotoxicity.Full Text
- Variance Estimation Using Linear Combination of Tri-Mean and Quartiles
Abstract Views :178 |
PDF Views:0
Authors
Affiliations
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
2 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir(J&K), IN
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
2 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir(J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 143-145Abstract
In this paper, we have proposed a class of modified ratio type variance estimator for estimation of population variance of the study variable, when Tri-mean and Quartiles of the auxiliary variable are known. The bias and mean square error (MSE) of the proposed estimator are obtained. From the numerical study it is observed that the proposed estimator performs better than the existing estimators in the literature.Keywords
Simple Random Sampling, Bias, Mean Square Error, Tri-Mean, Quartiles, Auxiliary Variable.
References
- Cochran , W.G. (1977). Sampling Techniques. 3rd Ed., Wiley Eastern limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statistical Association,78 :117123.
- Kadilar, C. and Cingi, H. (2006). Improvement in Variance estimation using auxiliary information. Hacettepe J.Mathematics & Statistics, 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statistics,16 (1) : 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer- Verlag.
- Variance Estimation Using Linear Combination of Hodges-Lehmann and Quartiles
Abstract Views :179 |
PDF Views:0
Authors
Affiliations
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
1 Division of Agricultural Statistics, Sher-e-Kashmir University of Agricultural Science and Technology-Kashmir, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 1 (2017), Pagination: 183-185Abstract
In this paper, we have proposed a class of modified ratio type variance estimator for estimation of population variance of the study variable, when Hodges-Lehmann and Quartiles of the auxiliary variable are known. The bias and mean square error (MSE) of the proposed estimator are obtained. From the numerical study it is observed that the proposed estimator performs better than the existing estimators in the literature.Keywords
Simple Random Sampling, Bias, Mean Square Error, Hodges-Lehmann, Quartiles, Auxiliary Variable.References
- Cochran, W.G. (1977). Sampling Techniques. 3rd Ed., Wiley Eastern limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statist. Assoc.,78 :117-123.
- Kadilar, C. and Cingi, H. (2006). Improvement in Variance estimation using auxiliary information. Hacettepe J.Mathematics & Statist., 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statist.,16 (1) : 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer- Verlag.
- Variance Estimation Using Linear Combination of Non-Conventional Measures, Quartile Average and Deciles Mean
Abstract Views :159 |
PDF Views:0
Authors
Affiliations
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
1 Division of Agricultural Statistics, SKUAST, Kashmir (J&K), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 431-434Abstract
Use of auxiliary information in survey sampling plays important role in getting more precision for estimating population parameters. Thus it has now become indispensable to use auxiliary information, thus in this paper we propose new modified ratio estimators by using the auxiliary information of non conventional location measures, non conventional measures of dispersion, quartile average, decile mean and their linear combinations for estimating population variance. The properties associated with proposed estimators are assessed by mean square error, bias and compared with existing estimators. By this comparison we conclude that our proposed estimators are more efficient than the existing estimators. To support the theoretical results, numerical study is provided.Keywords
Simple Random Sampling, Bias, Mean Square Error, Downtown’s Method, Deciles, Efficiency.References
- Cochran , W. G. (1977). Sampling techniques. 3rd Ed., Wiley Eastern Limted.
- Isaki, C.T. (1983). Variance estimation using auxiliary information. J. American Statist. Assoc.,78 :117-123.
- Kadilar, C. and Cingi, H. (2006). Improvement in variance estimation using auxiliary information. Hacettepe J. Mathematics & Statist., 35(1) : 117-115.
- Murthy, M. N. (1967). Sampling theory and methods. Calcutta Statistical Publishing House, India.
- Sumramani, J. and Kumarapandiyan, G. (2015).Generalized modified ratio type estimator for estimation of population variance. Sri-Lankan J. Appl. Statist.,16 (1): 69-90.
- Wolter, K.M. (1985). Introduction to variance estimation. Springer-Verlag.
- An Application of Generalized Linear Model in Survival Analysis
Abstract Views :207 |
PDF Views:0
Authors
Affiliations
1 S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), IN
1 S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), IN
Source
Asian Journal of Home Science, Vol 13, No 1 (2018), Pagination: 68-74Abstract
Diabetes is a chronic, often debilitating and sometimes fatal disease, in which the body either cannot produce insulin or cannot properly use the insulin it produces. Type 1 diabetes occurs when the immune system mistakenly attacks and kills the beta cells of the pancreas. Type 2 diabetes occurs when the body can’t properly use the insulin that is released (called insulin insensitivity) or does not make enough insulin. Diabetic nephropathy, also known as Kimmelstiel Wilson syndrome or nodular diabetic glomerulosclerosis or intercapillary glomerulonephritis, is a clinical syndrome characterized by albuminuria (>300 mg/day or >200 mcg/min), permanent and irreversible decrease in glomerular filtration rate (GFR), the rate of rise in serum creatinine (SrCr). According to the WHO, 31.7 million people were affected by diabetes mellitus (DM) in India in the year 2000. This figure is estimated to rise to 79.4 million by 2030, the largest number in any nation in the world. In this paper, survival analysis will be done of the type 2 diabetic nephropathy patients through generalized linear model. Most appropriate distribution for duration of diabetes is selected through Bayesian information criterion value. Then two generalized linear models are fitted by taking the duration of diabetes as response variable and the predictors as SrCr, number of successes; GFR, number of successes, respectively. These covariates are linked with the response variable using different link functions. At the last, survival function under different links will be compared.Keywords
Generalized Linear Model, Link Function, Bayesian Information Criterion, Survival Function, Diabetic Nephropathy, GFR.References
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- Cox, C., Chu, H., Schneider, M.F. and Muñoz, A. (2007). Parametric survival analysis and taxonomy of hazard functions for the Generalized Gamma Distribution. Statistics Med., 26 : 4352-4374.
- Cox, J. and Mann, M. (2008).Maxquant enables high peptide identification rates, individualized Ppb-Range mass accuracies and proteome-wide protein quantification.Nature Biotechnol., 26 : 1367-1372.
- Grover, G., Sabharwal, A.S.A. and Mittal, J. (2013). An application of gamma generalized linear model for estimation of survival function of diabetic nephropathy patients. Internat. J. Statistics Med. Res., 2 : 209-219.
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- Karen, A. (2006).Application of the generalized linear model to the prediction of lung cancer survival. 2006; 1-18. http:// analytics.ncsu.edu/sesug/2006/ST09_06.PDF
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- Stroup, W.W. and Kachman, S.D. (1994). Generalized Linear Mixed Models-an Overview. Annual Conference on Applied Statistics in Agriculture
- US Renal Data System and USRDS (2003). Annual Data Report; Atlas of end stage renal diseases, in the united states. Bethesda MD. National Institute of Health. National Instuitute of Diabetes, Digestive and Kidney Disease.
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