Summarizing the motion of self-propelled cells: applications to sperm motility

Factor analytic models of clustered multivariate data with informative censoring

This article describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censoring process, and we account for dependency between these latent variables through a hierarchical model. A linear model is used to relate covariates and latent variables to the primary outcomes for each subunit. A generalized linear model accounts for covariate and latent variable effects on the probability of censoring for subunits within each cluster. The model accounts for correlation within clusters and within subunits through a flexible factor analytic framework that allows multiple latent variables and covariate effects on the latent variables. The structure of the model facilitates implementation of Markov chain Monte Carlo methods for posterior estimation. Data from a spermatotoxicity study are analyzed to illustrate the proposed approach.

Objective evaluation of hyperactivated motility in rat spermatozoa using computer-assisted sperm analysis

The aim of this study was to use computer-assisted sperm analysis (CASA) to examine changes in motion parameters of rat spermatozoa incubated under culture conditions that support IVF. Rat cauda epididymal spermatozoa were evaluated in six replicate experiments, at 0 and 4h of incubation. CASA was conducted at 60 Hz on digital 1s tracks ( approximately 100 spermatozoa/rat). Mean values of CASA parameters that describe the vigour of spermatozoa [curvilinear velocity (VCL), amplitude of lateral head displacement (ALH) and beat cross frequency (BCF)] increased, while those indicating progressiveness [straight line velocity (VSL), linearity (LIN) and straightness (STR)] decreased between 0 and 4 h. Visual inspection of sperm tracks after 4 h of incubation revealed classical hyperactivation patterns. Bivariate models were evaluated to objectively define the subpopulation of hyperactivated (HA) spermatozoa. Of all models considered, ALH and LIN, VCL and LIN, BCF and LIN, VCL and BCF, and VCL and ALH showed significant changes in the percentage of HA spermatozoa after the 4 h incubation period. The efficacy of detecting HA spermatozoa was evaluated using sperm tracks that were visually classified as HA or progressive. VCL and LIN provided the most accurate prediction of HA spermatozoa. It was concluded that analysis of CASA data using bivariate models could be used to detect and monitor hyperactivation in rat spermatozoa.