Section I: Facultative and obligatory anaerobic bacteria were isolated from the ground water of a sanitary landfill characterized by sandy soil and a periodically high water table. Isolates were examined for 63 characteristics and subject to numerical analysis. Eight clusters were established and correlations with conventional taxonomy were made. The Bacteriodaceae were found to be the dominant group of organisms by the methods employed. The anaerobic population was observed to decrease as the period of seasonal rainfall ended. At the same time, gram positive anaerobes were largely replaced with gram negative ones. Leaching between sampling sites (wells) made correlations between metabolic end products (observed by gas-liquid chromatography) and metabolites produced by the organisms in vitro, impossible. Attempts were made to modify the original test battery to create a smaller battery which would yield approximately the same groupings as the original battery. Clusters became less discreet with these modifications and probably unacceptable for detailed taxonomic work. Section II: An index is described which measure the "goodness of fit" of an organism within a phenon as established by numerical taxonomy. A hypothetical mean organism was established for each phenon. Similarity and relevance coefficients were generated between this hypothetical organism and each member of the phenon. The product of these two coefficients has been termed the Index of Relevance and Similarity (IRS). This index ranges from zero to unity and can be generated with two-state and/or multistate data.

Symbiotic relationships of bacteria with higher organisms are commonly observed in nature; however, the functional role of these relationships is only rarely understood. This is particularly evident in epibiotic bacterial associations in the marine environment where the bacteria are often a diverse ensemble of microorganisms, thus complicating the identification of the functionally important members. Classical microbiological techniques, relying primarily on culturing these organisms, have provided an incomplete picture of these relationships. Molecular genetic techniques, focusing on the analyses of bacterial 16S rRNA sequences cloned directly from natural microbial populations, are now available which allow a more thorough examination of these associated bacterial populations. This study sought to characterize the epibiotic bacterial population associated with a very unique organism, Alvinella pompejana, using such a molecular approach. Alvinella pompejana is a polychaetous annelid that inhabits active deep-sea hydrothermal vent sites along the East Pacific Rise. This worm colonizes the walls of actively venting high temperature chimneys and is thought to be one of the most thermotolerant metazoans known. The chimney environment is characterized by high concentrations of sulfide and heavy metals in the vicinity of the worm colonies. A morphologically diverse epibiotic microflora is associated with the worm's dorsal integument, with a highly integrated filamentous morphotype clearly dominating the microbial biomass. It has been suggested that this bacterial population participates in either the nutrition of the worm or in detoxification of the worm's immediate environment; however, previous studies have been unable to confirm such a role. The primary goal of this study is to phylogenetically characterize the dominant epibionts through the analysis of 16S rRNA gene sequences. Nucleic acids were extracted from bacteria collected from the dorsal surface of Alvinella pompejana. 16S rRNA genes were amplified with universal bacterial primers by the polymerase chain reaction (PCR). These genes were subsequently cloned and the resulting clone library was screened by restriction fragment length polymorphism (RFLP) analysis to identify unique clone types. Thirty-two distinct clone families were found in the library. Four of these families were clearly dominant, representing over 65% of the library. The main assumption in this study is that the numerical dominance of the phylotypes in the starting population will be reflected in the clone library. Thus, representative clones from the four most abundant clone families were chosen for complete gene sequencing and phylogenetic analysis. These gene sequences were analyzed using a variety of phylogenetic inference methods and were found to be related to the newly established epsilon subdivision of the Proteobacteria. In future studies, these gene sequences will be used to construct specific oligodeoxynucleotide probes which can be used to confirm the morphology of the clone types in the epibiont population. / Graduation date: 1995

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

Though this current study was initiated independently and was not a test laboratory for the taxonomic sub-committee's evaluative program, the problem outlined in this treatise was also designed in an effort to test certain characteristics of the actinomycetes of both a biochemical and morphological nature. This problem employed methods that might absolve or establish certain criteria for taxonomic use in the group of actinomycetes.

The classification of the Azotobacteraceae to the level of genus and species has been uncertain since the studies of Beijerinck in 1901. This dissertation represents an effort to establish a system of classification more complete than the one now in use. In this study, both qualitative and quantitative taxonomy were used in order to establish a well founded classification scheme. Qualitative methods included certain important morphological and physiological characteristics, isoenzyme patterns, and immunological reactions. Quantitative methods included numerical taxonomy (based on total morphological and physiological characteristics) and numerical analysis of protein profiles. All the data from these experiments were subject to comparison with other genotypic and phenotypic data.

The aim of this work is proposal of new method for bacteria classification based on sequences of marker genes. For this purpose was chosen 10 marker genes. Resulting MultiGene classifier processes data set by dividing it in several groups and choosing gene for each group which can distinguish this group with best results. This work describes implementation of MultiGene classifier and its results in comparison with other bacteria classifiers and with classification based entirely on gene 16S rRNA.

Mastitis is a common disease among cows in dairy farms. Diagnosis of the infection is today done manually, by analyzing bacteria growth on agar plates. However, classifiers are being developed for automated diagnostics using images of agar plates. Input images need to be of reasonable quality and consistent in terms of scale, positioning, perspective, and rotation for accurate classification. Therefore, this thesis investigates if a combination of image processing techniques can be used to match each input image to a pre-defined reference model. A method was proposed to identify important key points needed to register the input image to the reference model. The key points were defined by identifying the agar plate, its compartments, and its rotation within the image. The results showed that image registration with the correct key points was sufficient enough to match images of agar plates to a reference model despite any varieties in scale, position, perspective, or rotation. However, the accuracy depended on the identification of the salient features of the agar plate. Ultimately, the work proposes an approach using image registration to transform images of agar plates based on a pre-defined reference model, rather than a reference image.

Tato práce se zabývá problematikou automatizované klasifikace a rozpoznávání bakterií po získání jejich DNA procesem sekvenování. V rámci této práce je navržena a popsána nová metoda klasifikace založená na základě segmentu 16S rRNA. Představený princip je vytvořen podle stromové struktury taxonomických kategorií a používá známé algoritmy strojového učení pro klasifikaci bakterií do jedné ze tříd na nižší taxonomické úrovni. Součástí práce je dále implementace popsaného algoritmu a vyhodnocení jeho přesnosti predikce. Přesnost klasifikace různých typů klasifikátorů a jejich nastavení je prozkoumána a je určeno nastavení, které dosahuje nejlepších výsledků. Přesnost implementovaného algoritmu je také porovnána s několika existujícími metodami. Během validace dosáhla implementovaná aplikace KTC více než 45% přesnosti při predikci rodu na datových sadách BLAST 16S i BLAST V4. Na závěr je zmíněno i několik možností vylepšení a rozšíření stávající implementace algoritmu.