Linear Models for Multivariate Repeated Measures Data

We study the general linear model (GLM) with doubly exchangeable distributed error for m observed random variables. The doubly exchangeable linear model (DEGLM) arises when the m¡dimensional error vectors are \doubly exchangeable" (de¯ned later), jointly normally distributed, which is much weaker assumption than the independent and identically distributed error vectors as in the case of GLM or classical GLM (CGLM). We estimate the parameters in the model and also ¯nd their distributions.Multivariate repeated measures; Linear model; Replicated observations.

An Extension of the Traditional Classication Rules: the Case of Non-Random Samples

The paper deals with an heuristic generalization of the traditional classication rules by incorporating within sample dependencies. The main motivation behind this generalization is to develop a new classication rule when training samples are not random, but, jointly equicorrelated.Classication rules; Non-random samples; Jointly equicorrelated training vectors

Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data.

This paper considers the problem of estimating, and testing for, a Kronecker product covariance structure of three-level (multiple time points (p), multiple sites (u), and multiple response variables (q)) multivariate data. Testing of such covariance structures is potentially important when not enough samples are available to estimate the unstructures variance-covariance matrix. This hypothesis procedure not only can test the hypothesis on three-level multivariate data, but also can test the hypotheses on two-level multivariate data as special cases. We provide the maximum likelihood estimates of the unknown population parameters. The test is implemented with a real data set.Kronecker product covariance structure, maximum likelihood estimates, equicorrelated partitioned matrix, three-level multivariate data.

Classification Rules for Multivariate Repeated Measures Data with Equicorrelated Correlation Structure on both Time and Spatial Repeated Measurements

We study the problem of classi¯cation for multivariate repeated measures data with struc- tured correlations on both time and spatial repeated measurements. This is a very important problem in many biomedical as well as in engineering ¯eld. Classi¯cation rules as well as the algorithm to compute the maximum likelihood estimates of the required parameters are given.Kronecker product covariance structure; Repeated observations; Maximum Likeli- hood Estimates.

Early Probe and Drug Discovery in Academia: A Minireview

Drug discovery encompasses processes ranging from target selection and validation to the selection of a development candidate. While comprehensive drug discovery work flows are implemented predominantly in the big pharma domain, early discovery focus in academia serves to identify probe molecules that can serve as tools to study targets or pathways. Despite differences in the ultimate goals of the private and academic sectors, the same basic principles define the best practices in early discovery research. A successful early discovery program is built on strong target definition and validation using a diverse set of biochemical and cell-based assays with functional relevance to the biological system being studied. The chemicals identified as hits undergo extensive scaffold optimization and are characterized for their target specificity and off-target effects in in vitro and in animal models. While the active compounds from screening campaigns pass through highly stringent chemical and Absorption, Distribution, Metabolism, and Excretion (ADME) filters for lead identification, the probe discovery involves limited medicinal chemistry optimization. The goal of probe discovery is identification of a compound with sub-µM activity and reasonable selectivity in the context of the target being studied. The compounds identified from probe discovery can also serve as starting scaffolds for lead optimization studies

Development of High-Throughput Screening Assay for Antihantaviral Therapeutics

Humans acquire hantavirus infection by the inhalation of aerosolized excreta of infected rodent hosts. There is no treatment for hantavirus diseases at present. Therapeutic intervention during early stages of viral infection can improve the outcome of this zoonotic viral illness. The interaction between an evolutionary conserved sequence at the 5′ terminus of hantaviral genomic RNA and hantavirus nucleocapsid protein plays a critical role in the hantavirus replication cycle. This unique interaction is a novel target for therapeutic intervention of hantavirus disease. We developed a very sensitive, tractable, and cost-effective fluorescence-based assay to monitor the interaction between the nucleocapsid protein and the target RNA sequence. The assay was optimized for high-throughput screening of chemical libraries to identify molecules that interrupt this RNA–protein interaction. The assay was validated using a library of 6880 chemical compounds. This validation screen demonstrated the reproducibility and validity of required statistical criteria for high-throughput screening. The assay is ready to use for high-throughput screening of large chemical libraries to identify antihantaviral therapeutic molecules and can be amenable to similar targets in other viruses

Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure

In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.Fil: Leiva, Ricardo Anibal. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Roy, Anuradha. University of Texas; Estados Unido

Classification rules for multivariate repeated measures data with equicorrelated correlation structure on both time and spatial repeated measurements

We study the problem of classification for multivariate repeated measures data with structured correlations on both time and spatial repeated measurements. This is a very important problem in many biomedical as well as in engineering field. Classification rules as well as the algorithm to compute the maximum likelihood estimates of the required parameters are given.Fil: Roy, Anuradha. University of Texas; Estados UnidosFil: Leiva, Ricardo Anibal. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentin

Impact of pesticide tolerant soil bacteria on disease control, plant growth promotion and systemic resistance in cowpea

Cowpea, an annual legume, suffers from several disease symptoms caused by Macrophomina phaseolina. Rhizobacteria isolated from pesticide infested soil, identified by blast analysis as Bacillus cereus, Bacillus safensis, Pseudomonas donghuensis and Pseudomonas aeruginosa ascertained tolerant to at least 0.1% pesticides viz. methomyl, imidacloprid and carbendazim. In vitro antagonism against pathogen exhibited maximum by P. aeruginosa 63%. All rhizobacteria were bestowed with attributes responsible for pathogen control and plant growth promotion. Field evaluation resulted highest 75% disease control, enhancement of length, nodule counts, biomass or yield per plant by P. aeruginosa. All rhizobacteria induced systemic resistance in cowpea under challenged inoculation with pathogen by augmenting defensive enzyme production. Highest Phenylalanine Ammonia Lyase activity was expressed in P. aeruginosa treated plants 1.02 μMoles/ml/min, Polyphenol Oxidase by P. donghuensis 1.39 μMoles/ml/min, Chitinase by B. cereus 0.745 μMoles/ml/min and 400 percent relative activity of Peroxidase by P. aeruginosa. The rhizobacteria were prospective for plant disease control, growth promotion and as immunity boosters in pesticide and heavy metal infested toxic environment

Alternative Splicing: Associating Frequency with Isoforms

In the simplest model of protein production, a gene gives rise to a single protein; DNA is transcribed to form pre-mRNA, which is converted to mRNA by splicing or removing introns. The result is a chain of exons that is translated to form a protein. Alternative splicing of exons may result in the formation of multiple proteins from the same gene sequence. However, not all of these proteins may be functional. Thus, we ask whether we can predict and rank (in order of frequency of occurrence and functional importance) the set of possible proteins for a gene. Herein we describe a tool that predicts the relative frequencies of isoforms that can be produced from a given gene