Marion Merrell Dow

About: Marion Merrell Dow is a based out in . It is known for research contribution in the topics: Receptor & Polyamine. The organization has 1445 authors who have published 1570 publications receiving 59342 citations. The organization is also known as: Marion Laboratories.

Gradient-tailored excitation for single-quantum NMR spectroscopy of aqueous solutions.

Abstract: A novel approach to tailored selective excitation for the measurement of NMR spectra in non-deuterated aqueous solutions (WATERGATE, WATER suppression by GraAdient-Tailored Excitation) is described. The gradient echo sequence, which effectively combines one selective 180° radiofrequency pulse and two field gradient pulses, achieves highly selective and effective water suppression. This technique is ideally suited for the rapid collection of multi-dimensional data since a single-scan acquisition produces a pure phase NMR spectrum with a perfectly flat baseline, at the highest possible sensitivity. Application to the fast measurement of 2D NOE data of a 2.2. mM solution of a double-stranded DNA fragment in 90% H2O at 5 °C is presented.

The Validity and Reproducibility of a Work Productivity and Activity Impairment Instrument

Abstract: The construct validity of a quantitative work productivity and activity impairment (WPAI) measure of health outcomes was tested for use in clinical trials, along with its reproducibility when administered by 2 different methods. 106 employed individuals affected by a health problem were randomised to receive either 2 self-administered questionnaires (self administration) or one self-administered questionnaire followed by a telephone interview (interviewer administration). Construct validity of the WPAI measures of time missed from work, impairment of work and regular activities due to overall health and symptoms, were assessed relative to measures of general health perceptions, role (physical), role (emotional), pain, symptom severity and global measures of work and interference with regular activity. Multivariate linear regression models were used to explain the variance in work productivity and regular activity by validation measures. Data generated by interviewer-administration of the WPAI had higher construct validity and fewer omissions than that obtained by self-administration of the instrument. All measures of work productivity and activity impairment were positively correlated with measures which had proven construct validity. These validation measures explained 54 to 64% of variance (p < 0.0001) in productivity and activity impairment variables of the WPAI. Overall work productivity (health and symptom) was significantly related to general health perceptions and the global measures of interference with regular activity. The self-administered questionnaire had adequate reproducibility but less construct validity than interviewer administration. Both administration methods of the WPAI warrant further evaluation as a measure of morbidity.

A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms

Abstract: The first part of this article presents the Monte Carlo implementation of the E step of the EM algorithm. Given the current guess to the maximizer of the posterior distribution, latent data patterns are generated from the conditional predictive distribution. The expected value of the augmented log-posterior is then updated as a mixture of augmented log-posteriors, mixed over the generated latent data patterns (multiple imputations). In the M step of the algorithm, this mixture is maximized to obtain the update to the maximizer of the observed posterior. The gradient and Hessian of the observed log posterior are also expressed as mixtures, mixed over the multiple imputations. The relation between the Monte Carlo EM (MCEM) algorithm and the data augmentation algorithm is noted. Two modifications to the MCEM algorithm (the poor man's data augmentation algorithms), which allow for the calculation of the entire posterior, are then presented. These approximations serve as diagnostics for the validity o...

Molecular modeling of protein-glycosaminoglycan interactions.

Abstract: Forty-nine regions in 21 proteins were identified as potential heparin-binding sites based on the sequence organizations of their basic and nonbasic residues. Twelve known heparin-binding sequences in vitronectin, apolipoproteins E and B-100, and platelet factor 4 were used to formulate two search strings for identifying potential heparin-binding regions in other proteins. Consensus sequences for glycosaminoglycan recognition were determined as [-X-B-B-X-B-X-] and [-X-B-B-B-X-X-B-X-] where B is the probability of a basic residue and X is a hydropathic residue. Predictions were then made as to the heparin-binding domains in endothelial cell growth factor, purpurin, and antithrombin-III. Many of the natural sequences conforming to these consensus motifs show prominent amphipathic periodicities having both alpha-helical and beta-strand conformations as determined by predictive algorithms and circular dichroism studies. The heparin-binding domain of vitronectin was modeled and formed a hydrophilic pocket that wrapped around and folded over a heparin octasaccharide, yielding a complementary structure. We suggest that these consensus sequence elements form potential nucleation sites for the recognition of polyanions in proteins and may provide a useful guide in identifying heparin-binding regions in other proteins. The possible relevance of protein-glycosaminoglycans interactions in atherosclerosis is discussed.