Application of Naïve Bayesian sequential analysis to primary care optometry

The objective of this study was to investigate the effects of circularity, comorbidity, prevalence and presentation variation on the accuracy of differential diagnoses made in optometric primary care using a modified form of naïve Bayesian sequential analysis. No such investigation has ever been reported before. Data were collected for 1422 cases seen over one year. Positive test outcomes were recorded for case history (ethnicity, age, symptoms and ocular and medical history) and clinical signs in relation to each diagnosis. For this reason only positive likelihood ratios were used for this modified form of Bayesian analysis that was carried out with Laplacian correction and Chi-square filtration. Accuracy was expressed as the percentage of cases for which the diagnoses made by the clinician appeared at the top of a list generated by Bayesian analysis. Preliminary analyses were carried out on 10 diagnoses and 15 test outcomes. Accuracy of 100% was achieved in the absence of presentation variation but dropped by 6% when variation existed. Circularity artificially elevated accuracy by 0.5%. Surprisingly, removal of Chi-square filtering increased accuracy by 0.4%. Decision tree analysis showed that accuracy was influenced primarily by prevalence followed by presentation variation and comorbidity. Analysis of 35 diagnoses and 105 test outcomes followed. This explored the use of positive likelihood ratios, derived from the case history, to recommend signs to look for. Accuracy of 72% was achieved when all clinical signs were entered. The drop in accuracy, compared to the preliminary analysis, was attributed to the fact that some diagnoses lacked strong diagnostic signs; the accuracy increased by 1% when only recommended signs were entered. Chi-square filtering improved recommended test selection. Decision tree analysis showed that accuracy again influenced primarily by prevalence, followed by comorbidity and presentation variation. Future work will explore the use of likelihood ratios based on positive and negative test findings prior to considering naïve Bayesian analysis as a form of artificial intelligence in optometric practice.

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

A spirochete isolated from a case of severe virulent ovine foot disease is closely related to a Treponeme isolated from human periodontitis and bovine digital dermatitis

The isolation of spirochetes from severe ovine foot disease has been reported recently by our research group. In this study we describe the preliminary classification of this spirochete based on nucleotide sequence analysis of the PCR-amplified 16S rRNA gene. Phylogenetic analysis of this sequence in comparison with other previously reported 16S rRNA gene sequences showed that the spirochete belonged to the treponemal phylotype Treponema vincentii which has been associated with bovine digital dermatitis and human periodontal disease. Further work is required to define the common virulence determinants of these closely related treponemes in the aetiology of these tissue destructive diseases.

Molecular identification of 26 syntaxin genes and their assignment to the different trafficking pathways in Paramecium

SNARE proteins have been classified as vesicular (v)- and target (t)-SNAREs and play a central role in the various membrane interactions in eukaryotic cells. Based on the Paramecium genome project, we have identified a multigene family of at least 26 members encoding the t-SNARE syntaxin (PtSyx) that can be grouped into 15 subfamilies. Paramecium syntaxins match the classical build-up of syntaxins, being 'tail-anchored' membrane proteins with an N-terminal cytoplasmic domain and a membrane-bound single C-terminal hydrophobic domain. The membrane anchor is preceded by a conserved SNARE domain of approximately 60 amino acids that is supposed to participate in SNARE complex assembly. In a phylogenetic analysis, most of the Paramecium syntaxin genes were found to cluster in groups together with those from other organisms in a pathway-specific manner, allowing an assignment to different compartments in a homology-dependent way. However, some of them seem to have no counterparts in metazoans. In another approach, we fused one representative member of each of the syntaxin isoforms to green fluorescent protein and assessed the in vivo localization, which was further supported by immunolocalization of some syntaxins. This allowed us to assign syntaxins to all important trafficking pathways in Paramecium.