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.

Valuation of reduced eutrophication in the Gulf of Finland : Choice experiment with attention to heterogeneous and discontinuous preferences and respondent uncertainty

Eutrophication of the Baltic Sea is a serious problem. This thesis estimates the benefit to Finns from reduced eutrophication in the Gulf of Finland, the most eutrophied part of the Baltic Sea, by applying the choice experiment method, which belongs to the family of stated preference methods. Because stated preference methods have been subject to criticism, e.g., due to their hypothetical survey context, this thesis contributes to the discussion by studying two anomalies that may lead to biased welfare estimates: respondent uncertainty and preference discontinuity. The former refers to the difficulty of stating one s preferences for an environmental good in a hypothetical context. The latter implies a departure from the continuity assumption of conventional consumer theory, which forms the basis for the method and the analysis. In the three essays of the thesis, discrete choice data are analyzed with the multinomial logit and mixed logit models. On average, Finns are willing to contribute to the water quality improvement. The probability for willingness increases with residential or recreational contact with the gulf, higher than average income, younger than average age, and the absence of dependent children in the household. On average, for Finns the relatively most important characteristic of water quality is water clarity followed by the desire for fewer occurrences of blue-green algae. For future nutrient reduction scenarios, the annual mean household willingness to pay estimates range from 271 to 448 and the aggregate welfare estimates for Finns range from 28 billion to 54 billion euros, depending on the model and the intensity of the reduction. Out of the respondents (N=726), 72.1% state in a follow-up question that they are either Certain or Quite certain about their answer when choosing the preferred alternative in the experiment. Based on the analysis of other follow-up questions and another sample (N=307), 10.4% of the respondents are identified as potentially having discontinuous preferences. In relation to both anomalies, the respondent- and questionnaire-specific variables are found among the underlying causes and a departure from standard analysis may improve the model fit and the efficiency of estimates, depending on the chosen modeling approach. The introduction of uncertainty about the future state of the Gulf increases the acceptance of the valuation scenario which may indicate an increased credibility of a proposed scenario. In conclusion, modeling preference heterogeneity is an essential part of the analysis of discrete choice data. The results regarding uncertainty in stating one s preferences and non-standard choice behavior are promising: accounting for these anomalies in the analysis may improve the precision of the estimates of benefit from reduced eutrophication in the Gulf of Finland.

Production of F4 Fimbrial Adhesin in Plants: a Model for Oral Porcine Vaccine against Enterotoxigenic Escherichia coli

F4 fimbriae of enterotoxigenic Escherichia coli (ETEC) are highly stable multimeric structures with a capacity to evoke mucosal immune responses. With these characters F4 offer a unique model system to study oral vaccination against ETEC-induced porcine postweaning diarrhea. Postweaning diarrhea is a major problem in piggeries worldwide and results in significant economic losses. No vaccine is currently available to protect weaned piglets against ETEC infections. Transgenic plants provide an economically feasible platform for large-scale production of vaccine antigens for animal health. In this study, the capacity of transgenic plants to produce FaeG protein, the major structural subunit and adhesin of F4 fimbria, was evaluated. Using the model plant tobacco, the optimal subcellular location for FaeG accumulation was examined. Targeting of FaeG into chloroplasts offered a superior accumulation level of 1% of total soluble proteins (TSP) over the other investigated subcellular locations, namely, the endoplasmic reticulum and the apoplast. Moreover, we determined whether the FaeG protein, when isolated from its fimbrial background and produced in a plant cell, would retain the key properties of an oral vaccine, i.e. stability in gastrointestinal conditions, binding to porcine intestinal F4 receptors (F4R), and inhibition of the F4-possessing (F4+) ETEC attachment to F4R. The chloroplast-derived FaeG protein did show resistance against low pH and proteolysis in the simulated gastrointestinal conditions and was able to bind to the F4R, subsequently inhibiting the F4+ ETEC binding in a dose-dependent manner. To investigate the oral immunogenicity of FaeG protein, the edible crop plant alfalfa was transformed with the chloroplast-targeting construct and equally to tobacco plants, a high-yield FaeG accumulation of 1% of TSP was obtained. A similar yield was also obtained in the seeds of barley, a valuable crop plant, when the FaeG-encoding gene was expressed under an endosperm-specific promoter and subcellularly targeted into the endoplasmic reticulum. Furthermore, desiccated alfalfa plants and barley grains were shown to have a capacity to store FaeG protein in a stable form for years. When the transgenic alfalfa plants were administred orally to weaned piglets, slight F4-specific systemic and mucosal immune responses were induced. Co-administration of the transgenic alfalfa and the mucosal adjuvant cholera toxin enhanced the F4-specific immune response; the duration and number of F4+ E. coli excretion following F4+ ETEC challenge were significantly reduced as compared with pigs that had received nontransgenic plant material. In conclusion, the results suggest that transgenic plants producing the FaeG subunit protein could be used for production and delivery of oral vaccines against porcine F4+ ETEC infections. The findings here thus present new approaches to develop the vaccination strategy against porcine postweaning diarrhea.

United We Stand - Divided We Fall : A Standard Model of Unit Cohesion

Research on unit cohesion has shown positive correlations between cohesion and valued outcomes such as strong performance, reduced stress, less indiscipline, and high re-enlistment intentions. However, the correlations have varied in strength and significance. The purpose of this study is to show that taking into consideration the multi-component nature of cohesion and relating the most applicable components to specific outcomes could resolve much of the inconsistency. Unit cohesion is understood as a process of social integration among members of a primary group with its leaders, and with the larger secondary groups of which they are a part. Correspondingly, included in the framework are four bonding components: horizontal (peer) and vertical (subordinate and leader) and organizational and institutional, respectively. The data were collected as part of a larger research project on cohesion, leadership, and personal adjustment to the military. In all, 1,534 conscripts responded to four questionnaires during their service in 2001-2002. In addition, sociometric questionnaires were given to 537 group members in 47 squads toward the end of their service. The results showed that platoons with strong primary-group cohesion differed from other platoons in terms of performance, training quality, secondary-group experiences, and attitudes toward refresher training. On the sociometric level it was found that soldiers who were chosen as friends by others were more likely to have higher expected performance, better performance ratings, more positive attitudes toward military service, higher levels of well-being during conscript service, and fewer exemptions from duty during it. On the group level, the selection of the respondents own group leader rather than naming a leader from outside (i.e., leader bonding) had a bearing not only on cohesion and performance, but also on the social, attitudinal, and behavioral criteria. Overall, the aim of the study was to contribute to the research on cohesion by introducing a model that takes into account the primary foci of bonding and their impact. The results imply that primary-group and secondary-group bonding processes are equally influential in explaining individual and group performance, whereas the secondary-group bonding components are far superior in explaining career intentions, personal growth, avoidance of duty, and attitudes toward refresher training and national defense. This should be considered in the planning and conducting of training. The main conclusion is that the different types of cohesion components have a unique, positive, significant, but varying impact on a wide range of criteria, confirming the need to match the components with the specific criteria.

Identification of collagenolytic MMP networks as potential biomarkers in progressive chronic periodontitis subjects and MMP-8 null allele model

Chronic periodontitis results from a complex aetiology, including the formation of a subgingival biofilm and the elicitation of the host s immune and inflammatory response. The hallmark of chronic periodontitis is alveolar bone loss and soft periodontal tissue destruction. Evidence supports that periodontitis progresses in dynamic states of exacerbation and remission or quiescence. The major clinical approach to identify disease progression is the tolerance method, based on sequential probing. Collagen degradation is one of the key events in periodontal destructive lesions. Matrix metalloproteinase (MMP)-8 and MMP-13 are the primary collagenolytic MMPs that are associated with the severity of periodontal inflammation and disease, either by a direct breakdown of the collagenised matrix or by the processing of non-matrix bioactive substrates. Despite the numerous host mediators that have been proposed as potential biomarkers for chronic periodontitis, they reflect inflammation rather than the loss of periodontal attachment. The aim of the present study was to determine the key molecular MMP-8 and -13 interactions in gingival crevicular fluid (GCF) and gingival tissue from progressive periodontitis lesions and MMP-8 null allele mouse model. In study (I), GCF and gingival biopsies from active and inactive sites of chronic periodontitis patients, which were determined clinically by the tolerance method, and healthy GCF were analysed for MMP-13 and tissue inhibitor of matrix metalloproteinases (TIMP)-1. Chronic periodontitis was characterised by increased MMP-13 levels and the active sites showed a tendency of decreased TIMP-1 levels associated with increments of MMP-13 and total protein concentration compared to inactive sites. In study (II), we investigated whether MMP-13 activity was associated with TIMP-1, bone collagen breakdown through ICTP levels, as well as the activation rate of MMP-9 in destructive lesions. The active sites demonstrated increased GCF ICTP levels as well as lowered TIMP-1 detection along with elevated MMP-13 activity. MMP-9 activation rate was enhanced by MMP-13 in diseased gingival tissue. In study (III), we analysed the potential association between the levels, molecular forms, isoenzyme distribution and degree of activation of MMP-8, MMP-14, MPO and the inhibitor TIMP-1 in GCF from periodontitis progressive patients at baseline and after periodontal therapy. A positive correlation was found for MPO/MMP-8 and their levels associated with progression episodes and treatment response. Because MMP-8 is activated by hypochlorous acid in vitro, our results suggested an interaction between the MPO oxidative pathway and MMP-8 activation in GCF. Finally, in study (IV), on the basis of the previous finding that MMP-8-deficient mice showed impaired neutrophil responses and severe alveolar bone loss, we aimed to characterise the detection patterns of LIX/CXCL5, SDF-1/CXCL12 and RANKL in P. gingivalis-induced experimental periodontitis and in the MMP-8-/- murine model. The detection of neutrophil-chemoattractant LIX/CXCL5 was restricted to the oral-periodontal interface and its levels were reduced in infected MMP-8 null mice vs. wild type mice, whereas the detection of SDF-1/CXCL12 and RANKL in periodontal tissues increased in experimentally-induced periodontitis, irrespectively from the genotype. Accordingly, MMP-8 might regulate LIX/CXCL5 levels by undetermined mechanisms, and SDF-1/CXCL12 and RANKL might promote the development and/or progression of periodontitis.