Amino acid sequence of the alpha subunit and computer modelling of the alpha and beta subunits of echicetin from the venom of Echis carinatus (saw-scaled viper)

Echicetin, a heterodimeric protein from the venom of Echis carinatus, binds to platelet glycoprotein Ib (GPIb) and so inhibits platelet aggregation or agglutination induced by various platelet agonists acting via GPIb. The amino acid sequence of the beta subunit of echicetin has been reported and found to belong to the recently identified snake venom subclass of the C-type lectin protein family. Echicetin alpha and beta subunits were purified. N-terminal sequence analysis provided direct evidence that the protein purified was echicetin. The paper presents the complete amino acid sequence of the alpha subunit and computer models of the alpha and beta subunits. The sequence of alpha echicetin is highly similar to the alpha and beta chains of various heterodimeric and homodimeric C-type lectins. Neither of the fully reduced and alkylated alpha or beta subunits of echicetin inhibited the platelet agglutination induced by von Willebrand factor-ristocetin or alpha-thrombin. Earlier reports about the inhibitory activity of reduced and alkylated echicetin beta subunit might have been due to partial reduction of the protein.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Stereological Modelling of Random Particles

Stochastic models for three-dimensional particles have many applications in applied sciences. Lévy–based particle models are a flexible approach to particle modelling. The structure of the random particles is given by a kernel smoothing of a Lévy basis. The models are easy to simulate but statistical inference procedures have not yet received much attention in the literature. The kernel is not always identifiable and we suggest one approach to remedy this problem. We propose a method to draw inference about the kernel from data often used in local stereology and study the performance of our approach in a simulation study.