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Query: UMLS:C0220723 (PCA)
 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Air samples from lairage, hide/fleece pulling or dehairing/scraping, evisceration and chilling areas in commercial beef, sheep and pig plants were examined for Salmonella spp. and Listeria monocytogenes, by impaction or sedimentation onto selective (Brilliant Green Agar, BSA; Listeria Selective Agar, LSA) and non-selective (Plate Count Agar, PCA) media. Both pathogens were frequently detected in all three plants. Improved recoveries were achieved by combining sedimentation, and broth based resuscitation, suggesting cell injury. Salmonella were recovered from all three plants, with the highest counts on BGA in the pig plant. The most common serotypes were S. Typhimurium in the beef/sheep plants and S. Derby in the pig plant. Very low counts of L. monocytogenes (e.g. 2.6CFUm(2)) were detected at hide removal on LSA sedimentation plates in the beef plant. These included serogroup 1/2a-3a and 1/2b-3b-7. Pathogen counts in the three plants were generally very low, suggesting that air is unlikely to be a significant source of carcass or plant surface contamination.
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PMID:Airborne Salmonella and Listeria associated with Irish commercial beef, sheep and pig plants. 2459 73

Spatial EEG filters are widely used to isolate event-related potential (ERP) components. The most commonly used spatial filters (e.g. the Average Reference and the Surface Laplacian) are stationary. Stationary filters are conceptually simple, easy to use and fast to compute, but all assume that the EEG signal does not change across sensors and time. Given that ERPs are intrinsically non-stationary, applying stationary filters can lead to misinterpretations of the measured neural activity. In contrast, adaptive spatial filters (e.g. Independent Component Analysis, ICA; and Principal Component Analysis, PCA) infer their weights directly from the spatial properties of the data. They are thus not affected by the shortcomings of stationary filters. The issue with adaptive filters is that understanding how they work and how to interpret their output requires advanced statistical and physiological knowledge. Here we describe a novel, easy-to-use and conceptually-simple adaptive filter (Local Spatial Analysis, LSA) for highlighting local components masked by large widespread activity. This approach exploits the statistical information stored in the trial-by-trial variability of stimulus-evoked neural activity to estimate the spatial filter parameters adaptively at each time point. Using both simulated data and real ERPs elicited by stimuli of four different sensory modalities (audition, vision, touch, pain), we show that this method outperforms widely-used stationary filters and allows identifying novel ERP components masked by large widespread activity. Implementation of the LSA filter in Matlab is freely available to download.
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PMID:Local Spatial Analysis (LSA): An easy-to-use adaptive spatial EEG filter. 3317 97