Prevalence, risk factors and genotype distribution of Toxoplasma gondii DNA in soil in China.

In the present study, we performed a cross-sectional survey to determine the occurrence and genotype distribution of T. gondii DNA in soil samples collected from different sources from six geographic regions in China. Between March 2015 and June 2017, 2100 soil samples were collected from schools, parks, farms and coastal beaches, and examined for T. gondii DNA using three PCR assays targeting 529-bp repeat element (RE) sequence, B1 gene and ITS-1 gene sequences. Also, we investigated whether geographic region, soil source and type, and sampling season can influence the prevalence of T. gondii DNA in the soil. Soil samples collected from farms and parks had the highest prevalence, whereas samples collected from school playgrounds and coastal beaches had the lowest prevalence. PCR assays targeting 529-bp RE and ITS-1 gene sequences were more sensitive than the B1 gene-based assay. Positive PCR products were genotyped using multi-locus PCR-RFLP, and ToxoDB #9 was the predominant genotype found in the contaminated soil samples. Multiple logistic regression identified factors correlated significantly with the presence of T. gondii DNA in the soil to be the source of the soil, including farms (odds ratio 3.10; 95% confidence interval [CI], 1.52 to 6.29; p = 0.002) and parks (2.59; 95% CI 1.28 to 5.27; p = 0.009). These results show that Chinese soil hosts T. gondii of the most prevalent genotype in China (ToxoDB#9) and that the soil type influences infection patterns.

BAYESIAN ALIGNMENT OF SIMILARITY SHAPES.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.