Comparative expression profiling of E. coli and S. aureus inoculated primary mammary gland cells sampled from cows with different genetic predispositions for somatic cell score

BACKGROUND: During the past ten years many quantitative trait loci (QTL) affecting mastitis incidence and mastitis related traits like somatic cell score (SCS) were identified in cattle. However, little is known about the molecular architecture of QTL affecting mastitis susceptibility and the underlying physiological mechanisms and genes causing mastitis susceptibility. Here, a genome-wide expression analysis was conducted to analyze molecular mechanisms of mastitis susceptibility that are affected by a specific QTL for SCS on Bos taurus autosome 18 (BTA18). Thereby, some first insights were sought into the genetically determined mechanisms of mammary gland epithelial cells influencing the course of infection. METHODS: Primary bovine mammary gland epithelial cells (pbMEC) were sampled from the udder parenchyma of cows selected for high and low mastitis susceptibility by applying a marker-assisted selection strategy considering QTL and molecular marker information of a confirmed QTL for SCS in the telomeric region of BTA18. The cells were cultured and subsequently inoculated with heat-inactivated mastitis pathogens Escherichia coli and Staphylococcus aureus, respectively. After 1, 6 and 24 h, the cells were harvested and analyzed using the microarray expression chip technology to identify differences in mRNA expression profiles attributed to genetic predisposition, inoculation and cell culture. RESULTS: Comparative analysis of co-expression profiles clearly showed a faster and stronger response after pathogen challenge in pbMEC from less susceptible animals that inherited the favorable QTL allele 'Q' than in pbMEC from more susceptible animals that inherited the unfavorable QTL allele 'q'. Furthermore, the results highlighted RELB as a functional and positional candidate gene and related non-canonical Nf-kappaB signaling as a functional mechanism affected by the QTL. However, in both groups, inoculation resulted in up-regulation of genes associated with the Ingenuity pathways 'dendritic cell maturation' and 'acute phase response signaling', whereas cell culture affected biological processes involved in 'cellular development'. CONCLUSIONS: The results indicate that the complex expression profiling of pathogen challenged pbMEC sampled from cows inheriting alternative QTL alleles is suitable to study genetically determined molecular mechanisms of mastitis susceptibility in mammary epithelial cells in vitro and to highlight the most likely functional pathways and candidate genes underlying the QTL effect.

Antibiotic resistance profile of Staphylococcus rostri, a new species isolated from healthy pigs

Staphylococcus rostri is a newly described Staphylococcus species that is present in the nasal cavity of healthy pigs. Out of the 225 pigs tested at slaughterhouse, 46.7% carried the new species alone and 22% in combination with Staphylococcus aureus. An antibiotic resistance profile was determined for S. rostri and compared to that of S. aureus isolated from the same pig. Resistance to tetracycline specified by tet(M), tet(K) and tet(L), streptomycin (str(pS194)), penicillin (blaZ), trimethoprim (dfr(G)), and erythromycin and clindamycin (erm genes), were found in both species; however, with the exception of streptomycin and trimethoprim, resistance was higher in S. aureus. S. rostri isolates display very low genetic diversity as demonstrated by pulsed-field gel electrophoresis, which generated two major clusters. Several clonal complexes (CC1, CC5, CC9, CC30 and CC398) were identified in S. aureus with CC 9 and CC 398 being the most frequent. Our study gives the first overview of the distribution, genetic relatedness, and resistance profile of one coagulase-negative Staphylococcus species that is commonly present in the nares of healthy pigs in Switzerland, and shows that S. rostri may harbor resistance genes associated with transferable elements like Tn916.

Prevention and control of surgical site infections: review of the Basel Cohort Study

INTRODUCTION: Surgical site infections (SSI) are the most common hospital-acquired infections among surgical patients, with significant impact on patient morbidity and health care costs. The Basel SSI Cohort Study was performed to evaluate risk factors and validate current preventive measures for SSI. The objective of the present article was to review the main results of this study and its implications for clinical practice and future research. SUMMARY OF METHODS OF THE BASEL SSI COHORT STUDY: The prospective observational cohort study included 6,283 consecutive general surgery procedures closely monitored for evidence of SSI up to 1 year after surgery. The dataset was analysed for the influence of various potential SSI risk factors, including timing of surgical antimicrobial prophylaxis (SAP), glove perforation, anaemia, transfusion and tutorial assistance, using multiple logistic regression analyses. In addition, post hoc analyses were performed to assess the economic burden of SSI, the efficiency of the clinical SSI surveillance system, and the spectrum of SSI-causing pathogens. REVIEW OF MAIN RESULTS OF THE BASEL SSI COHORT STUDY: The overall SSI rate was 4.7% (293/6,283). While SAP was administered in most patients between 44 and 0 minutes before surgical incision, the lowest risk of SSI was recorded when the antibiotics were administered between 74 and 30 minutes before surgery. Glove perforation in the absence of SAP increased the risk of SSI (OR 2.0; CI 1.4-2.8; p <0.001). No significant association was found for anaemia, transfusion and tutorial assistance with the risk of SSI. The mean additional hospital cost in the event of SSI was CHF 19,638 (95% CI, 8,492-30,784). The surgical staff documented only 49% of in-hospital SSI; the infection control team registered the remaining 51%. Staphylococcus aureus was the most common SSI-causing pathogen (29% of all SSI with documented microbiology). No case of an antimicrobial-resistant pathogen was identified in this series. CONCLUSIONS: The Basel SSI Cohort Study suggested that SAP should be administered between 74 and 30 minutes before surgery. Due to the observational nature of these data, corroboration is planned in a randomized controlled trial, which is supported by the Swiss National Science Foundation. Routine change of gloves or double gloving is recommended in the absence of SAP. Anaemia, transfusion and tutorial assistance do not increase the risk of SSI. The substantial economic burden of in-hospital SSI has been confirmed. SSI surveillance by the surgical staff detected only half of all in-hospital SSI, which prompted the introduction of an electronic SSI surveillance system at the University Hospital of Basel and the Cantonal Hospital of Aarau. Due to the absence of multiresistant SSI-causing pathogens, the continuous use of single-shot single-drug SAP with cefuroxime (plus metronidazole in colorectal surgery) has been validated.

Comparison of antibiotic resistance of udder pathogens in dairy cows kept on organic and on conventional farms

There has been a rapid rise in the emergence of multi-drug-resistant pathogens in the past 10 to 15 yr and some bacteria are now resistant to most antimicrobial agents. Antibiotic use is very restricted on Swiss organic dairy farms, and a purely prophylactic use, such as for dry cow mastitis prevention, is forbidden. A low prevalence of antibiotic resistance in organic farms can be expected compared with conventional farms because the bacteria are infrequently or not exposed to antibiotics. The occurrence of antibiotic resistance was compared between mastitis pathogens (Staphylococcus aureus, nonaureus staphylococci, Streptococcus dysgalactiae, Streptococcus uberis) from farms with organic and conventional dairy production. Clear differences in the percentage of antibiotic resistance were mainly species-related, but did not differ significantly between isolates from cows kept on organic and conventional farms, except for Streptococcus uberis, which exhibited significantly more single resistances (compared with no resistance) when isolated from cows kept on organic farms (6/10 isolates) than on conventional farms (0/5 isolates). Different percentages were found (albeit not statistically significant) in resistance to ceftiofur, erythromycin, clindamycin, enrofloxacin, chloramphenicol, penicillin, oxacillin, gentamicin, tetracycline, and quinupristin-dalfopristin, but, importantly, none of the strains was resistant to amoxicillin-clavulanic acid or vancomycin. Multidrug resistance was rarely encountered. The frequency of antibiotic resistance in organic farms, in which the use of antibiotics must be very restricted, was not different from conventional farms, and was contrary to expectation. The antibiotic resistance status needs to be monitored in organic farms as well as conventional farms and production factors related to the absence of reduced antibiotic resistance in organic farms need to be evaluated.

Analysis of key molecules of the innate immune system in mammary epithelial cells isolated from marker-assisted and conventionally selected cattle

Mastitis is the most prevalent infectious disease in dairy herds. Breeding programs considering mastitis susceptibility were adopted as approaches to improve udder health status. In recent decades, conventional selection criteria based on phenotypic characteristics such as somatic cell score in milk have been widely used to select animals. Recently, approaches to incorporate molecular information have become feasible because of the detection of quantitative trait loci (QTL) affecting mastitis resistance. The aims of the study were to explore molecular mechanisms underlying mastitis resistance and the genetic mechanisms underlying a QTL on Bos taurus chromosome 18 found to influence udder health. Primary cell cultures of mammary epithelial cells from heifers that were selected for high or low susceptibility to mastitis were established. Selection based on estimated pedigree breeding value or on the basis of marker-assisted selection using QTL information was implemented. The mRNA expression of 10 key molecules of the innate immune system was measured using quantitative real-time PCR after 1, 6, and 24 h of challenge with heat-inactivated mastitis pathogens (Escherichia coli and Staphylococcus aureus) and expression levels in the high and low susceptibility groups were compared according to selection criteria. In the marker-assisted selection groups, mRNA expression in cells isolated from less-susceptible animals was significantly elevated for toll-like receptor 2, tumor necrosis factor-alpha, IL-1beta, IL-6, IL-8, RANTES (regulated upon activation, normal t-cell expressed and secreted), complement factor C3, and lactoferrin. In the estimated pedigree breeding value groups, mRNA expression was significantly elevated only for V-rel reticuloendotheliosis viral oncogene homolog A, IL-1 beta, and RANTES. These observations provide first insights into genetically determined divergent reactions to pathogens in the bovine mammary gland and indicate that the application of QTL information could be a successful tool for the selection of animals resistant to mastitis.

A Staphylococcus xylosus isolate with a new mecC allotype

Recently, a novel variant of mecA known as mecC (mecA(LGA251)) was identified in Staphylococcus aureus isolates from both humans and animals. In this study, we identified a Staphylococcus xylosus isolate that harbors a new allotype of the mecC gene, mecC1. Whole-genome sequencing revealed that mecC1 forms part of a class E mec complex (mecI-mecR1-mecC1-blaZ) located at the orfX locus as part of a likely staphylococcal cassette chromosome mec element (SCCmec) remnant, which also contains a number of other genes present on the type XI SCCmec.

Evaluation of PCR electrospray-ionization mass spectrometry for rapid molecular diagnosis of bovine mastitis

Bovine mastitis, an inflammatory disease of the mammary gland, is one of the most costly diseases affecting the dairy industry. The treatment and prevention of this disease is linked heavily to the use of antibiotics in agriculture and early detection of the primary pathogen is essential to control the disease. Milk samples (n=67) from cows suffering from mastitis were analyzed for the presence of pathogens using PCR electrospray-ionization mass spectrometry (PCR/ESI-MS) and were compared with standard culture diagnostic methods. Concurrent identification of the primary mastitis pathogens was obtained for 64% of the tested milk samples, whereas divergent results were obtained for 27% of the samples. The PCR/ESI-MS failed to identify some of the primary pathogens in 18% of the samples, but identified other pathogens as well as microorganisms in samples that were negative by culture. The PCR/ESI-MS identified bacteria to the species level as well as yeasts and molds in samples that contained a mixed bacterial culture (9%). The sensitivity of the PCR/ESI-MS for the most common pathogens ranged from 57.1 to 100% and the specificity ranged from 69.8 to 100% using culture as gold standard. The PCR/ESI-MS also revealed the presence of the methicillin-resistant gene mecA in 16.2% of the milk samples, which correlated with the simultaneous detection of staphylococci including Staphylococcus aureus. We demonstrated that PCR/ESI-MS, a more rapid diagnostic platform compared with bacterial culture, has the significant potential to serve as an important screening method in the diagnosis of bovine clinical mastitis and has the capacity to be used in infection control programs for both subclinical and clinical disease.

Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study

BACKGROUND Empirical research has illustrated an association between study size and relative treatment effects, but conclusions have been inconsistent about the association of study size with the risk of bias items. Small studies give generally imprecisely estimated treatment effects, and study variance can serve as a surrogate for study size. METHODS We conducted a network meta-epidemiological study analyzing 32 networks including 613 randomized controlled trials, and used Bayesian network meta-analysis and meta-regression models to evaluate the impact of trial characteristics and study variance on the results of network meta-analysis. We examined changes in relative effects and between-studies variation in network meta-regression models as a function of the variance of the observed effect size and indicators for the adequacy of each risk of bias item. Adjustment was performed both within and across networks, allowing for between-networks variability. RESULTS Imprecise studies with large variances tended to exaggerate the effects of the active or new intervention in the majority of networks, with a ratio of odds ratios of 1.83 (95% CI: 1.09,3.32). Inappropriate or unclear conduct of random sequence generation and allocation concealment, as well as lack of blinding of patients and outcome assessors, did not materially impact on the summary results. Imprecise studies also appeared to be more prone to inadequate conduct. CONCLUSIONS Compared to more precise studies, studies with large variance may give substantially different answers that alter the results of network meta-analyses for dichotomous outcomes.

Conjugal Transfer of the Tn916-like Transposon TnFO1 from Enterococcus faecalis Isolated from Cheese to Other Gram-positive Bacteria

A Tn916-like transposon (TnFO1) was found in the multiple antibiotic resistant Enterococcus faecalis strain FO1 isolated from a raw milk cheese. In this strain, the tetracycline determinant was localized by DNA-DNA hybridization with a tetM nucleotide probe on the chromosome and on a 30-kb plasmid. The transposon TnFO1 was identified and characterized by DNA-DNA hybridization experiments with the five internal HincII fragments of Tn916. The tetracycline resistance determinant was identified by its complete nucleotide sequence as TetM. Transposon TnFO1 was also detected in its circular form by DNA-DNA hybridization and PCR amplification. Both ends including the joining region of the closed circular transposon TnFO1 were sequenced. TnFO1 could be transferred by conjugation from Enterococcus faecalis into Enterococcus faecalis, Lactococcus lactis subsp. lactis biovar. diacetylactis, Listeria innocua, Leuconostoc mesenteroides and Staphylococcus aureus, and from Lactococcus lactis subsp. lactis biovar. diacetylactis into Listeria innocua. Pulsed-field electrophoresis of genomic DNA from E. faecalis FO1 transconjugants showed that transposon TnFO1 integrated at different sites.

New MLSB resistance gene erm(43) in Staphylococcus lentus

The search for a specific rRNA methylase motif led to the identification of the new macrolide, lincosamide, and streptogramin B resistance gene erm(43) in Staphylococcus lentus. An inducible resistance phenotype was demonstrated by cloning and expressing erm(43) and its regulatory region in Staphylococcus aureus. The erm(43) gene was detected in two different DNA fragments, of 6,230 bp and 1,559 bp, that were each integrated at the same location in the chromosome in several S. lentus isolates of human, dog, and chicken origin.

Sequence of the 50-kb conjugative multiresistance plasmid pRE25 from Enterococcus faecalis RE25

The complete 50,237-bp DNA sequence of the conjugative and mobilizing multiresistance plasmid pRE25 from Enterococcus faecalis RE25 was determined. The plasmid had 58 putative open reading frames, 5 of which encode resistance to 12 antimicrobials. Chloramphenicol acetyltransferase and the 23S RNA methylase are identical to gene products of the broad-host-range plasmid pIP501 from Streptococcus agalactiae. In addition, a 30.5-kb segment is almost identical to pIP501. Genes encoding an aminoglycoside 6-adenylyltransferase, a streptothricin acetyltransferase, and an aminoglycoside phosphotransferase are arranged in tandem on a 7.4-kb fragment as previously reported in Tn5405 from Staphylococcus aureus and in pJH1 from E. faecalis. One interrupted and five complete IS elements as well as three replication genes were also identified. pRE25 was transferred by conjugation to E. faecalis, Listeria innocua, and Lactococcus lactis by means of a transfer region that appears similar to that of pIP501. It is concluded that pRE25 may contribute to the further spread of antibiotic-resistant microorganisms via food into the human community.

Revascularisation versus medical treatment in patients with stable coronary artery disease: network meta-analysis

OBJECTIVE To investigate whether revascularisation improves prognosis compared with medical treatment among patients with stable coronary artery disease. DESIGN Bayesian network meta-analyses to combine direct within trial comparisons between treatments with indirect evidence from other trials while maintaining randomisation. ELIGIBILITY CRITERIA FOR SELECTING STUDIES A strategy of initial medical treatment compared with revascularisation by coronary artery bypass grafting or Food and Drug Administration approved techniques for percutaneous revascularization: balloon angioplasty, bare metal stent, early generation paclitaxel eluting stent, sirolimus eluting stent, and zotarolimus eluting (Endeavor) stent, and new generation everolimus eluting stent, and zotarolimus eluting (Resolute) stent among patients with stable coronary artery disease. DATA SOURCES Medline and Embase from 1980 to 2013 for randomised trials comparing medical treatment with revascularisation. MAIN OUTCOME MEASURE All cause mortality. RESULTS 100 trials in 93 553 patients with 262 090 patient years of follow-up were included. Coronary artery bypass grafting was associated with a survival benefit (rate ratio 0.80, 95% credibility interval 0.70 to 0.91) compared with medical treatment. New generation drug eluting stents (everolimus: 0.75, 0.59 to 0.96; zotarolimus (Resolute): 0.65, 0.42 to 1.00) but not balloon angioplasty (0.85, 0.68 to 1.04), bare metal stents (0.92, 0.79 to 1.05), or early generation drug eluting stents (paclitaxel: 0.92, 0.75 to 1.12; sirolimus: 0.91, 0.75 to 1.10; zotarolimus (Endeavor): 0.88, 0.69 to 1.10) were associated with improved survival compared with medical treatment. Coronary artery bypass grafting reduced the risk of myocardial infarction compared with medical treatment (0.79, 0.63 to 0.99), and everolimus eluting stents showed a trend towards a reduced risk of myocardial infarction (0.75, 0.55 to 1.01). The risk of subsequent revascularisation was noticeably reduced by coronary artery bypass grafting (0.16, 0.13 to 0.20) followed by new generation drug eluting stents (zotarolimus (Resolute): 0.26, 0.17 to 0.40; everolimus: 0.27, 0.21 to 0.35), early generation drug eluting stents (zotarolimus (Endeavor): 0.37, 0.28 to 0.50; sirolimus: 0.29, 0.24 to 0.36; paclitaxel: 0.44, 0.35 to 0.54), and bare metal stents (0.69, 0.59 to 0.81) compared with medical treatment. CONCLUSION Among patients with stable coronary artery disease, coronary artery bypass grafting reduces the risk of death, myocardial infarction, and subsequent revascularisation compared with medical treatment. All stent based coronary revascularisation technologies reduce the need for revascularisation to a variable degree. Our results provide evidence for improved survival with new generation drug eluting stents but no other percutaneous revascularisation technology compared with medical treatment.

The novel macrolide-Lincosamide-Streptogramin B resistance gene erm(44) is associated with a prophage in Staphylococcus xylosus.

A novel erythromycin ribosome methylase gene, erm(44), that confers resistance to macrolide, lincosamide, and streptogramin B (MLSB) antibiotics was identified by whole-genome sequencing of the chromosome of Staphylococcus xylosus isolated from bovine mastitis milk. The erm(44) gene is preceded by a regulatory sequence that encodes two leader peptides responsible for the inducible expression of the methylase gene, as demonstrated by cloning in Staphylococcus aureus. The erm(44) gene is located on a 53-kb putative prophage designated ΦJW4341-pro. The 56 predicted open reading frames of ΦJW4341-pro are structurally organized into the five functional modules found in members of the family Siphoviridae. ΦJW4341-pro is site-specifically integrated into the S. xylosus chromosome, where it is flanked by two perfect 19-bp direct repeats, and exhibits the ability to circularize. The presence of erm(44) in three additional S. xylosus strains suggests that this putative prophage has the potential to disseminate MLSB resistance.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.