N-Myristoylethanolamine-cholesterol (1:1) complex: first evidence from differential scanning calorimetry,fast-atom-bombardment mass spectrometry and computational modelling

The interaction of N-myristoylethanolamine (NMEA) with cholesterol is investigated by differential scanning calorimetry (DSC), fast-atom-bombardment mass spectrometry (FAB-MS) and computational modelling. Addition of cholesterol to NMEA leads to a new phase transition at 55 degrees C besides the chain-melting transition of NMEA at 72.5 degrees C. The enthalpy of the new transition increases with cholesterol content up to 50 mol%, but decreases thereafter, vanishing at 80 mol%. The enthalpy of the chain-melting transition of NMEA decreases with an increase in cholesterol; the transition disappears at 50 mol%. FAB-MS spectra of mixtures of NMEA and cholesterol provide clear signatures of the formation of ([NMEA+cholesterol]+) ([NMEA+cholesterol+Na]+). These results are consistent with the formation of a 1:1 complex between NMEA and cholesterol. Molecular modelling studies support this experimental finding and provide a plausible structural model for the complex, which highlights multiple H-bond interactions between the hydroxy group of cholesterol and the hydroxy and carbonyl groups of NMEA besides appreciable dispersion interaction between the hydrocarbon domains of the two molecules.     

Gene expression analysis during liver stage development of Plasmodium

The complex life cycle of malaria parasites requires significant changes in gene expression as the parasites move from vector to host and back to the vector. Although recognised as an important vaccine and drug target, the liver stage parasite has remained difficult to study. One of the major impediments in identifying parasite gene expression at the liver stage has remained the large number of uninfected hepatocytes relative to the number of infected hepatocytes in the liver after sporozoite inoculation. This article describes several of the approaches that have been utilised to overcome this difficulty in rodent models of malaria. While significant progress has been made to identify genes that are expressed during liver stage parasite development, a great deal more work remains to be done.     

Use of quasi-least squares to adjust for two levels of correlation

This article considers data with two levels of association. Our motivating example is an international intervention trial with repeated observations on subjects who reside within geographically defined clusters. To account for the potential correlation within clusters and within the repeated measurements that pertain to each subject, we apply a method based on generalized estimating equations for a correlation structure proposed by Lefkopoulou, Moore, and Ryan (1989, Journal of the American Statistical Association 84, 810-815).     

Analysis of clustered and longitudinal binary data subject to response misclassification

Misclassified clustered and longitudinal data arise in studies where the response indicates a condition identified through an imperfect diagnostic procedure. Examples include longitudinal studies that use an imperfect diagnostic test to assess whether or not an individual has been infected with a specific virus. This article presents methods to implement both population-averaged and cluster-specific analyses of such data when the misclassification rates are known. The methods exploit the fact that the class of generalized linear models enjoys a closure property in the case of misclassified responses. Data from longitudinal studies of infectious disease will illustrate the findings.     

Differential expression of human 5S snoRNA genes

Four novel small nucleolar RNAs (snoRNAs), h5sn1, h5sn2, h5sn3, and h5sn4, were successfully amplified from human total RNAs using RT-PCR. They exhibited the structural hallmarks of box H/ACA snoRNAs and formed sequence complementarity to 5S rRNA. The nucleotide sequences of the snoRNAs from different donors were highly conserved as evidenced by single-stranded conformational polymorphism and direct nucleotide sequence analysis. Although their host genes had no protein-coding potential, the expression of the snoRNAs was differentially displayed in different tissues. Noticeably, h5sn2 was highly expressed in normal brain, but its expression drastically decreased in meningioma. This opens the fascinating possibility of the relationship between the processing of snoRNAs and carcinogenesis.     

Bifurcations in a white-blood-cell production model

We study the dynamics of a model of white-blood-cell (WBC) production. The model consists of two compartmental differential equations with two discrete delays. We show that from normal to pathological parameter values, the system undergoes supercritical Hopf bifurcations and saddle-node bifurcations of limit cycles. We characterize the steady states of the system and perform a bifurcation analysis. Our results indicate that an increase in apoptosis rate of either hematopoietic stem cells or WBC precursors induces a Hopf bifurcation and an oscillatory regime takes place. These oscillations are seen in some hematological diseases.     

Flowers for <Emphasis Type="Italic">Neotyphodium</Emphasis> endophytes detection: a new observation method using flowers of host grasses

Neotyphodium endophytes are vertically transmitted fungal symbionts of grasses. Being pest-repelling and growth-promoting agents for their hosts, and also potential mycotoxin producers, their detection in plants is important. Observation of chemically cleared flowers of infected grasses (Festuca arundinacea, F. pratensis, Lolium perenne, and L. multiflorum) using differential interference contrast microscopy revealed the existence of endophytes within immature ovaries of host plants. This observation method provides an accurate and easy way to detect and distinguish Neotyphodium endophytes in flowering host grasses and to investigate the seed transmission process, which is critical to their life cycle, and the practical use of infected plants.     

Multiple outputation: inference for complex clustered data by averaging analyses from independent data

This article applies a simple method for settings where one has clustered data, but statistical methods are only available for independent data. We assume the statistical method provides us with a normally distributed estimate, theta, and an estimate of its variance sigma. We randomly select a data point from each cluster and apply our statistical method to this independent data. We repeat this multiple times, and use the average of the associated theta's as our estimate. An estimate of the variance is given by the average of the sigma2's minus the sample variance of the theta's. We call this procedure multiple outputation, as all "excess" data within each cluster is thrown out multiple times. Hoffman, Sen, and Weinberg (2001, Biometrika 88, 1121-1134) introduced this approach for generalized linear models when the cluster size is related to outcome. In this article, we demonstrate the broad applicability of the approach. Applications to angular data, p-values, vector parameters, Bayesian inference, genetics data, and random cluster sizes are discussed. In addition, asymptotic normality of estimates based on all possible outputations, as well as a finite number of outputations, is proven given weak conditions. Multiple outputation provides a simple and broadly applicable method for analyzing clustered data. It is especially suited to settings where methods for clustered data are impractical, but can also be applied generally as a quick and simple tool.     

The intensity-score approach to adjusting for confounding

In a recent article on the efficacy of antihypertensive therapy, Berlowitz et al. (1998, New England Journal of Medicine 339, 1957-1963) introduced an ad hoc method of adjusting for serial confounding assessed via an intensity score, which records cumulative differences over time between therapy actually received and therapy predicted by prior medical history. Outcomes are subsequently regressed on the intensity score and baseline covariates to determine whether intense treatment or exposure predicts a favorable response. We use a structural nested mean model to derive conditions sufficient for interpreting the Berlowitz results causally. We also consider a modified approach that scales the intensity at each time by the inverse expected treatment given prior medical history. This leads to a simple, two-step implementation of G-estimation if we assume a nonstandard but useful structural nested mean model in which subjects less likely to receive treatment are more likely to benefit from it. These modeling assumptions apply, for example, to health services research contexts in which differential access to care is a primary concern. They are also plausible in our analysis of the causal effect of potent antiretroviral therapy on change in CD4 cell count, because men in the sample who are less likely to initiate treatment when baseline CD4 counts are high are more likely to experience large positive changes. We further extend the methods to accomodate repeated outcomes and time-varying effects of time-varying exposures.