Characterization of the alpha-haemolysin determinant from the human enteropathogenic Escherichia coli O26 plasmid pEO5

The 157-kb conjugative plasmid pEO5 encoding alpha-haemolysin in strains of human enteropathogenic Escherichia coli (EPEC) O26 was investigated for its relationship with EHEC-haemolysin-encoding plasmids of enterohaemorrhagic E. coli (EHEC) O26 and O157 strains. Plasmid pEO5 was found to be compatible with EHEC-virulence plasmids and did not hybridize in Southern blots with plasmid pO157 from the EHEC O157:H7 strain EDL933, indicating that both plasmids were unrelated. A 9227-bp stretch of pEO5 DNA encompassing the entire alpha-hlyCABD operon was sequenced and compared for similarity to plasmid and chromosomally inherited alpha-hly determinants. The alpha-hly determinant of pEO5 (7252 bp) and its upstream region was most similar to corresponding sequences of the murine E. coli alpha-hly plasmid pHly152, in particular, the structural alpha-hlyCABD genes (99.2% identity) and the regulatory hlyR regions (98.8% identity). pEO5 and alpha-hly plasmids of EPEC O26 strains from humans and cattle were very similar for the regions encompassing the structural alpha-hlyCABD genes. The major difference found between the hly regions of pHly152 and pEO5 is caused by the insertion of an IS2 element upstream of the hlyC gene in pHly152. The presence of transposon-like structures at both ends of the alpha-hly sequence indicates that this pEO5 virulence factor was probably acquired by horizontal gene transfer.

Fator necrosante citotóxico em Escherichia coli isolada de mastite clínica bovina

This report describes the production of cytotoxic necrotizing factor (CNF) by an Escherichia coli strain isolated from clinical bovine mastitis with clinical signs of toxemia The animal had hemorrhages and necrosis of the mammary glands, and died within 24 hours after the onset of clinical signs. In addition to CNF identification, alpha-haemolysin and siderophores production were also characterized in this strain. This report reinforce the association of CNF and alpha-haemolysin production in E. coli virulence associated with clinical cases of severe bovine mastitis.

Fatores de virulência em linhagens de Escherichia coli isoladas de mastite bovina

Avaliou-se a ocorrência de fatores de virulência e do sorotipo O157:H7 em 120 linhagens de Escherichia coli, isoladas de 80 casos de mastite clínica bovina e 40 de mastite subclínica. Verificou-se alfa-hemolisina em oito (6,7%) linhagens, isoladas de cinco casos de mastite clínica e três de mastite subclínica e em nenhuma das estirpes detectou-se enteroemolisina. A presença de sideróforos foi encontrada em 11 (9,2%) linhagens, sete de mastite clínica e quatro de subclínica. em duas (1,7%) estirpes isoladas de mastite subclínica, identificou-se enterotoxina STa. Observou-se efeito citopático em células vero compatível com a produção de verotoxina-VT em cinco (4,2%) linhagens, duas de mastite clínica e três subclínicas. em uma (0,8%) linhagem isolada de mastite clínica, detectou-se efeito citopático compatível com o fator necrosante citotóxico. Nenhuma estirpe apresentou-se sorbitol-negativa no MacConkey-sorbitol, tampouco aglutinou com o sorotipo O157:H7. Os antimicrobianos mais efetivos foram polimixina B (97,5%) e norfloxacina (95,8%). Observou-se multi-resistência a dois ou mais antimicrobianos em 24 (20%) estirpes, principalmente com o uso de ampicilina e ceftiofur.

Fatores de virulência em linhagens de Escherichia coli isoladas de infecção do trato urinário, piometra e fezes de cães

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.