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Testing of Poisson Incidence Rate Restriction
Singh, Karan,Shanmugam, Ramalingam The Korean Reliability Society 2001 International Journal of Reliability and Applicati Vol.2 No.4
Shanmugam(1991) generalized the Poisson distribution to capture a restriction on the incidence rate $\theta$ (i.e. $\theta$ < $\beta$, an unknown upper limit), and named it incidence rate restricted Poisson (IRRP) distribution. Using Neyman's C($\alpha$) concept, Shanmugam then devised a hypothesis testing procedure for $\beta$ when $\theta$ remains unknown nuisance parameter. Shanmugam's C ($\alpha$) based .results involve inverse moments which are not easy tools, This article presents an alternate testing procedure based on likelihood ratio concept. It turns out that likelihood ratio test statistic offers more power than the C($\alpha$) test statistic. Numerical examples are included.
A Characterization of Negative Binomial Distribution Truncated at Zero
Shanmugam, R. The Korean Statistical Society 1982 Journal of the Korean Statistical Society Vol.11 No.2
Analogous to Singh's (1978) characterization of positive-Poisson distributioin and Shanmugam and Singh's (1992) characterization of logarithmic series distribution, a characterization and its statistical application of the negative binomial distribution truncated at zero are given in this paper. While it is known that under certain conditions the negative binomial distribution truncted at zero approaches the positive-Poisson and the logarithmic series distributions, we show here that the results of this paper approach in limit the results of Singh, and Shanmugam and Singh, respectively. Using the biologicla data from Sampford (1955), we illusrate our results. Also, expressions are explicitly given to test the hypothesis whether a random sample is indeed from a geometric distribution.
Shanmugam, Palanisamy,Ahn, Yu-Hwan,Sanjeevi , Shanmugam The Korean Society of Remote Sensing 2005 大韓遠隔探査學會誌 Vol.21 No.3
This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.
Shanmugam, Srinivasan,Park, Jae-Hyun,Chi, Sang-Cheol,Yong, Chul Soon,Choi, Han-Gon,Woo, Jong Soo Informa Healthcare 2011 Drug development and industrial pharmacy Vol.37 No.6
<P><I>Aim:</I> To investigate the physicochemical stability, pharmacokinetics (PK), and biodistribution of paclitaxel (PTX) from paclitaxel solid dispersion (PSD) prepared by supercritical antisolvent (SAS) process.</P><P><I>Methods:</I> Physicochemical stability was performed in accelerated (40°째C 70 ??±짹 ??5% RH) and stress (60°째C) storage conditions for a period of 6 months and 4 weeks, respectively. PK and biodistribution studies were performed in rats following i.v. administration of PTX equivalent to 6 and 12 ??mg/kg formulations.</P><P><I>Results:</I> Physical stability of PSD showed excellent stability with no recrystallization of the amorphous form. Chemical stability of PSD in terms of % PTX remaining was 98.2 ??±짹 ??0.6% at 6 months and 97.9 ??±짹 ??0.3% at 4 weeks of accelerated and stress conditions, respectively. The PK study showed a nonlinear increase in AUC with increasing dose, that is, 100% increase in dose (from 6 to 12 ??mg/kg) resulted in 405.90% increase in AUC. Unlike PK study, the organ distribution study of PTX from PSD showed linear relationship with dose escalation. The order of organ distribution of PTX from highest to lowest for both PSD and Taxol<SUP>®짰</SUP> was liver>kidney>lung>brain.</P><P><I>Conclusions:</I> This study demonstrated excellent physicochemical stability with insight information on the PK and biodistribution of PTX from PSD prepared by SAS process.</P>
Shanmugam Chandrasekar,Rajangam Sivakumar,Ramasamy Mathialagan,Jayachandran Subburaj,Muthusamy Thangaraj 국립중앙과학관 2019 Journal of Asia-Pacific Biodiversity Vol.12 No.3
Indian estuarine and coastal water habitats have reduced in recent decades because of anthropogenic activities such as coastal development. The pearlspot cichlid Etroplus suratensis is designated as Least Concern, given its wide distribution and presumably large overall population size in South India, despite the declining trend observed in wild populations. To assess the genetic diversity and connectivity among South Indian coastal populations, mitochondrial displacement loop sequence analysis was conducted to provide fundamental information for future conservation studies and an understanding of population dynamics by calculating the haplotype diversity of local populations. The haplotype (h) and nucleotide (π) diversity were very low at most localities, with values ranging from 0.56061 to 0.87879 and from 0.0014 to 0.0046, respectively, which may have resulted from recent population bottlenecks or founder events. The results also revealed a clear genetic differentiation between East and West coast populations, suggesting the existence of a gene flow barrier between them. As the maintenance of genetic connectivity is a prerequisite for local population stability, the preservation of extant habitats and the restoration of water bodies along the coast of India may be the most effective measures for the sustainable maintenance of this species.
Computer-Aided Drug Discovery in Plant Pathology
Shanmugam, Gnanendra,Jeon, Junhyun The Korean Society of Plant Pathology 2017 Plant Pathology Journal Vol.33 No.6
Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.