Serotyping and evaluation of the virulence in mice of Streptococcus suis strains isolated from diseased pigs

A total of 110 strains of Streptococcus suis, isolated from diseased pigs in Brazil were serotyped and analyzed for virulence. Serotyping of the strains resulted in the following classification: 42 strains of serotype 2 (38.2%), 10 strains of serotype 14 (9.1%), seven strains of serotype 9 (6.4%), three strains each of serotype 7 and 11 (2.7%), two strains each of serotype 1 and 8 (1.8%) and one strain each of serotypes ½, 3, 5, 6 and 10 (0.9%). Cross reactions among serotypes 1, 14 and 7 were observed in 21 strains (19.1%). Only 41.9% of the strains were lethal for mice using the pathogenicity test.Um total of 110 amostras de Streptococcus suis isoladas de suínos doentes, no Brasil foram sorotipificadas e analisadas para a virulência. Sorotipificação das amostras resultou na seguinte classificação: 42 amostras do sorotipo 2 (38,2%), 10 amostras do sorotipo 14 (9,1%), sete amostras do sorotipo 9 (6,4%), três amostras de cada sorotipo, 7 e 11 (2,7%), duas amostras de cada sorotipo, 1 e 8 (1,8%) e uma amostra de cada um dos sorotipos, ½, 3, 5, 6 e 10 (0,9%). Reações cruzadas entre os sorotipos 1, 14 e 7 foram observadas em 21 amostras (19,1%). Somente 41,9% das amostras foram patogênicas para camundongos

On Classification with Bags, Groups and Sets

Many classification problems can be difficult to formulate directly in terms of the traditional supervised setting, where both training and test samples are individual feature vectors. There are cases in which samples are better described by sets of feature vectors, that labels are only available for sets rather than individual samples, or, if individual labels are available, that these are not independent. To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors. However, having been proposed rather independently of each other, their mutual similarities and differences have hitherto not been mapped out. In this work, we provide an overview of such learning scenarios, propose a taxonomy to illustrate the relationships between them, and discuss directions for further research in these areas

On some aspects of second order response surface methodology : a thesis presented in partial fulfilment of the requirements for the degree of M. Sc. in Mathematics at Massey University

A unified development of the theoretical basis of response surface methodology, particularly as it applies to second order response surfaces, is presented. A rigorous justification of the various tests of hypothesis usually used is given, as well as a convenient means of making tests on whole factors, rather than on terms of a given degree, as is customary at present. Finally, the superimposition of some elementary classification designs on a response surface design is considered

Associations between health and different types of environmental incivility : a Scotland-wide study

Objectives: Concern about the impact of the environment on health and well being has tended to focuson the physical effects of exposure to toxic and infectious substances, and on the impact of large scale infrastructures. Less attention has been paid to the possible psychosocial consequences of people's subjective perceptions of their everyday, street level environment, such as the incidence of litter and graffiti. As little is known about the potential relative importance for health of perceptions of different types of environmental incivility, a module was developed for inclusion in the 2004 Scottish Social Attitudes survey in order to investigate this relationship. Study design: A random sample of 1637 adults living across a range of neighbourhoods throughout Scotland was interviewed. Methods: Respondents were asked to rate their local area on a range of possible environmental incivilities. These incivilities were subsequently grouped into three domains: (i) street level incivilities (e.g. litter, graffiti); (ii) large scale infrastructural incivilities (e.g. telephone masts); and (iii) the absence of environmental goods (e.g. safe play areas for children). For each of the three domains, the authors examined the degree to which they were thought to pose a problem locally, and how far these perceptions varied between those living in deprived areas and those living in less deprived areas. Subsequently, the relationships between these perceptions and self assessed health and health behaviours were explored, after controlling for gender, age and social class. Results: Respondents with the highest levels of perceived street level incivilities were almost twice aslikely as those who perceived the lowest levels of street level incivilities to report frequent feelings of anxiety and depression. Perceived absence of environmental goods was associated with increased anxiety (2.5 times more likely) and depression (90% more likely), and a 50% increased likelihood of being a smoker. Few associations with health were observed for perceptions of large scale infrastructural incivilities. Conclusions: Environmental policy needs to give more priority to reducing the incidence of street levelincivilities and the absence of environmental goods, both of which appear to be more important for health than perceptions of large scale infrastructural incivilities

Detecting modules in dense weighted networks with the Potts method

We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We discuss the dependence of the resolution of the method on its tuning parameter and network properties, using sparse and dense weighted networks with built-in modules as example cases. Finally, we apply the method to data on stock price correlations, and show that the resulting modules correspond well to known structural properties of this correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor changes. v3: 3 references added, minor change

Knowledge-based best of breed approach for automated detection of clinical events based on German free text digital hospital discharge letters

OBJECTIVES: The secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine. METHODS: We tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction "proton-pump inhibitor [PPI] use and osteoporosis". Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard. RESULTS: Precision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%. CONCLUSION: We could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period