Direct detection and identification of Arcobacter species by multiplex PCR in chicken and wastewater samples from Spain

Twenty-two chicken livers, 10 chicken carcasses, and 15 wastewater samples were processed and analyzed for Arcobacter by PCR and traditional culture methods. Samples were enriched for 24 and 48 h, incubated at 30 degrees C under aerobic conditions, and streaked on blood selective media. To determine the best isolation conditions, 20 samples also were processed under microaerophilic conditions at 37 degrees C. Simple and multiplex PCR assays were used directly with enrichment broths and isolated strains. Seventeen Arcobacter strains were isolated from chicken samples, and A. butzleri was the only Arcobacter species identified. The direct PCR assay revealed that 29 of the 32 chicken samples were contaminated with Arcobacter. A. butzleri was the most frequently detected species, although Arcobacter cryaerophilus also was present in some of the samples and Arcobacter skirrowii occasionally was detected. All the wastewater samples were positive by PCR assay for Arcobacter after 24 h of enrichment. A. butzleri and A. cryaerophilus were detected with the multiplex PCR assay. Fourteen Arcobacter strains were isolated from 10 of the 15 water samples analyzed; 7 were identified as A. butzleri and the remaining 7 were A. cryaerophilus. Both for chicken and water samples, Arcobacter detection rate for PCR amplification was higher than for culture isolation. These results indicate the high prevalence of Arcobacter in chicken and wastewater and the inadequacy of available cultural methods for its detection. The species-specific multiplex PCR assay is a rapid method for assessing Arcobacter contamination in chicken and wastewater samples and is a viable alternative to biochemical identification of isolated strains.     

Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.