Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Transcriptional analysis of persistent Chlamydia pneumoniae infection in vitro

Chlamydia pneumoniae can cause acute respiratory infections including pneumonia. Repeated and persistent Chlamydia infections occur and persistent C. pneumoniae infection may have a role in the pathogenesis of atherosclerosis and coronary heart disease and may also contribute to the development of chronic inflammatory lung diseases like chronic obstructive pulmonary disease (COPD) and asthma. In this thesis in vitro models for persistent C. pneumonia infection were established in epithelial and monocyte/macrophage cell lines. Expression of host cell genes in the persistent C. pneumoniae infection model of epithelial cells was studied by microarray and RT-PCR. In the monocyte/macrophage infection model expression of selected C. pneumoniae genes were studied by RT-PCR and immunofluorescence microscopy. Chlamydia is able to modulate host cell gene expression and apoptosis of host cells, which may assist Chlamydia to evade the host cells' immune responses. This, in turn, may lead to extended survival of the organism inside epithelial cells and promote the development of persistent infection. To simulate persistent C. pneumoniae infection in vivo, we set up a persistent infection model exposing the HL cell cultures to IFN-gamma. When HL cell cultures were treated with moderate concentration of IFN-gamma, the replication of C. pneumoniae DNA was unaffected while differentiation into infectious elementary bodies (EB) was strongly inhibited. By transmission electron microscopy small atypical inclusions were identified in IFN-gamma treated cultures. No second cycle of infection was observed in cells exposed to IFN-gamma , whereas C. pneumoniae was able to undergo a second cycle of infection in unexposed HL cells. Although monocytic cells can naturally restrict chlamydial growth, IFN-gamma further reduced production of infectious C. pneumoniae in Mono Mac 6 cells. Under both studied conditions no second cycle of infection could be detected in monocytic cell line suggesting persistent infection in these cells. As a step toward understanding the role of host genes in the development and pathogenesis of persistent C. pneumoniae infection, modulation of host cell gene expression during IFN-gamma induced persistent infection was examined and compared to that seen during active C. pneumoniae infection or IFN-gamma treatment. Total RNA was collected at 6 to 150 h after infection of an epithelial cell line (HL) and analyzed by a cDNA array (available at that time) representing approximately 4000 human transcripts. In initial analysis 250 of the 4000 genes were identified as differentially expressed upon active and persistent chlamydial infection and IFN-gamma treatment. In persistent infection more potent up-regulation of many genes was observed in IFN-gamma induced persistent infection than in active infection or in IFN-gamma treated cell cultures. Also sustained up-regulation was observed for some genes. In addition, we could identify nine host cell genes whose transcription was specifically altered during the IFN-gamma induced persistent C. pneumoniae infection. Strongest up-regulation in persistent infection in relation to controls was identified for insulin like growth factor binding protein 6, interferon-stimulated protein 15 kDa, cyclin D1 and interleukin 7 receptor. These results suggest that during persistent infection, C. pneumoniae reprograms the host transcriptional machinery regulating a variety of cellular processes including adhesion, cell cycle regulation, growth and inflammatory response, all of which may play important roles in the pathogenesis of persistent C. pneumoniae infection. C. pneumoniae DNA can be detected in peripheral blood mononuclear cells indicating that the bacterium can also infect monocytic cells in vivo and thereby monocytes can assist the spread of infection from the lungs to other anatomical sites. Persistent infection established at these sites could promote inflammation and enhance pathology. Thus, the mononuclear cells are in a strategic position in the development of persistent infection. To investigate the intracellular replication and fate of C. pneumoniae in mononuclear cells we analyzed the transcription of 11 C. pneumoniae genes in Mono Mac 6 cells during infection by real time RT-PCR. Our results suggest that the transcriptional profile of the studied genes in monocytes is different from that seen in epithelial cells and that IFN-gamma has a less significant effect on C. pneumoniae transcription in monocytes. Furthermore, our study shows that type III secretion system (T3SS) related genes are transcribed and that Chlamydia possesses a functional T3SS during infection in monocytes. Since C. pneumoniae infection in monocytes has been implicated to have reduced antibiotic susceptibility, this creates opportunities for novel therapeutics targeting T3SS in the management of chlamydial infection in monocytes.