Cross-sectional survey of health management and prevalence of vector-borne diseases, endoparasites and ectoparasites in Samoan dogs.

OBJECTIVE: To determine the prevalence of selected canine vector-borne diseases (Leishmania infantum, Anaplasma spp., Ehrlichia canis, Borrelia burgdorferi and Dirofilaria immitis) and endo- and ectoparasites in Samoan dogs presenting for surgical sterilisation and to report on the general health management of the dogs. METHODS: This study was a prospective serological cross-sectional survey. Management data were obtained for 242 dogs by interview with their owners. Blood samples were collected from 237 dogs and screened for the canine vector-borne diseases using point-of-care qualitative ELISA assays. Anaplasma spp. positive samples were screened by PCR and sequenced for species identification. Rectal faecal samples were collected from 204 dogs for faecal flotation and immunofluorescent antibody tests were performed for Giardia and Cryptosporidium spp. on a subset of 93 faecal samples. The skin and coat of 221 dogs were examined for presence of ectoparasites. RESULTS: The D. immitis antigen was detected in 46.8% (111/237) of dogs. Seroprevalence of Anaplasma spp. was 8.4% (20/237); A. platys was confirmed by PCR. Prevalence of hookworm was 92.6% (185/205) and Giardia was 29.0% (27/93). Ectoparasites were detected on 210/221 (95.0%) of dogs examined and 228/242 dogs (94.2%) had previously never received any preventative medication. CONCLUSIONS: There was a very high prevalence of D. immitis, hookworm and external parasites in Samoan dogs, and prophylactic medication is rarely administered. This is the first report confirming A. platys in Samoa and the South Pacific islands. The public health implications of poor management of the dogs should be considered and investigated further.

Predicting protein structure using hidden Markov models.

We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over 1,000 structures from the Protein Data Bank (PDB). Each CASP2 target sequence was scored against this library of HMMs. In addition, an HMM was built for each of the target sequences and all of the sequences in PDB were scored against that target model, with a good score on both methods indicating a high probability that the target sequence is homologous to the structure. The method worked well in comparison to other methods used at CASP2 for targets of moderate difficulty, where the closest structure in PDB could be aligned to the target with at least 15% residue identity.

Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology.

We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to be combined with observed amino acid frequencies to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model or other statistical model. These estimates give a statistical model greater generalization capacity, so that remotely related family members can be more reliably recognized by the model. This paper corrects the previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation.

Hidden Markov models in computational biology. Applications to protein modeling.

Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionary preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both.

Using Dirichlet mixture priors to derive hidden Markov models for protein families.

A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Dirichlet mixture densities as priors over amino acid distributions. These mixture densities are determined from examination of previously constructed HMMs or multiple alignments. It is shown that this Bayesian method can improve the quality of HMMs produced from small training sets. Specific experiments on the EF-hand motif are reported, for which these priors are shown to produce HMMs with higher likelihood on unseen data, and fewer false positives and false negatives in a database search task.