Interaction between Mycobacterium avium subsp. paratuberculosis and environmental protozoa

Background: Interactions between Mycobacterium avium subsp. paratuberculosis (Map) and free-living protozoa in water are likely to occur in nature. The potential impact of ingestion of Map by two naturally occurring Acanthamoeba spp. on this pathogen's survival and chlorine resistance was investigated. Results: Between 4.6 and 9.1% of spiked populations of three Map strains (NCTC 8578, B2 and ATCC 19698), which had been added at a multiplicity of infection of 10: 1, were ingested by Acanthamoeba castellanii CCAP 1501/1B and A. polyphaga CCAP 1501/3B during co-culture for 3 h at 25 C. Map cells were observed to be present within the vacuoles of the amoebae by acid-fast staining. During extended co-culture of Map NCTC 8578 at 25 degrees C for 24 d with both A. castellanii and A. polyphaga Map numbers did not change significantly during the first 7 days of incubation, however a 1-1.5 log(10) increase in Map numbers was observed between days 7 and 24 within both Acanthamoeba spp. Ingested Map cells were shown to be more resistant to chlorine inactivation than free Map. Exposure to 2 mu g/ml chlorine for 30 min resulted in a log(10) reduction of 0.94 in ingested Map but a log(10) reduction of 1.73 in free Map (p <0.001). Conclusion: This study demonstrated that ingestion of Map by and survival and multiplication of Map within Acanthamoeba spp. is possible, and that Map cells ingested by amoebae are more resistant to inactivation by chlorine than free Map cells. These findings have implications with respect to the efficacy of chlorination applied to Map infected surface waters.

Statistical Monitoring of Dynamic Multivariate Processes: Part I: Modeling Auto- and Cross-Correlation

The work in this paper is of particular significance since it considers the problem of modelling cross- and auto-correlation in statistical process monitoring. The presence of both types of correlation can lead to fault insensitivity or false alarms, although in published literature to date, only autocorrelation has been broadly considered. The proposed method, which uses a Kalman innovation model, effectively removes both correlations. The paper (and Part 2 [2]) has emerged from work supported by EPSRC grant GR/S84354/01 and is of direct relevance to problems in several application areas including chemical, electrical, and mechanical process monitoring.

Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature

This paper builds on work presented in the first paper, Part 1 [1] and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 [1]), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.

Numerical simulation of separation factor of NaCl in surface force pore flow model

This work deals with modelling and experimental verification of desalination theory (surface force pore flow) . The work has direct application in desalination of sea water.

Process monitoring approach using fast moving window PCA

This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.

Effect of acetonitrile on the solubility of carbon dioxide in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) amide

The effect of the addition of acetonitrile on the solubility of carbon dioxide in an ionic liquid, the 1-ethyl-3- methylimidazolium bis(trifluoromethanesulfonyl)amide, [C(2)mim][NTf2], was studied experimentally at pressures close to atmospheric and as a function of temperature between 290 and 335 K. It was observed that the solubility of carbon dioxide decreases linearly with the mole fraction of acetonitrile from a value of 2.6 x 10(-2) in the pure ionic liquid at 303 K to a mole fraction of 1.3 x 10(-2) in the mixture [C(2)mim][NTf2] + CH3CN with x(CH3CN) = 0.77 at the same temperature. The gas solubility decreases with temperature, and the thermodynamic properties of solvation could be calculated. The vapor pressures of the [ C2mim][ NTf2] + CH3CN mixtures were measured in the same temperature range, and strong negative deviations from Raoult's law were obtained: up to 36% for a mixture with x(CH3CN) = 0.46 at 334 K. Negative excess molar volumes of approximately -1 cm(3) mol(-1) at equimolar composition could also be calculated from density measurements of the pure components and of the mixtures. These observations are confirmed by neutron diffraction studies and are compatible with the existence of strong ion-dipole interactions in the mixed liquid solvent.