For more than a century, blood agar plates have been the only test for beta-hemolysis. Although blood agar cultures are highly predictive for bacterial pathogens, they are too slow to yield actionable information. Here, we show that beta-hemolytic pathogens are able to lyse and release fluorophores encapsulated in sterically stabilized liposomes whereas alpha and gamma-hemolytic bacteria have no effect. By analyzing fluorescence kinetics, beta-hemolytic colonies cultured on agar could be distinguished in real time with 100% accuracy within 6 h. Additionally, end point analysis based on fluorescence intensity and machine-extracted textural features could discriminate between beta-hemolytic and cocultured control colonies with 99% accuracy. In broth cultures, beta-hemolytic bacteria were detectable in under an hour while control bacteria remained negative even the next day. This strategy, called beta-hemolysis triggered-release assay (BETA) has the potential to enable the same-day detection of beta-hemolysis with single-cell sensitivity and high accuracy.
Distance measures are part and parcel of many computer vision algorithms. The underlying assumption in all existing distance measures is that feature elements are independent and identically distributed. However, in real-world settings, data generally originate from heterogeneous sources even if they do possess a common data-generating mechanism. Since these sources are not identically distributed by necessity, the assumption of identical distribution is inappropriate. Here, we use statistical analysis to show that feature elements of local image descriptors are indeed non-identically distributed. To test the effect of omitting the unified distribution assumption, we created a new distance measure called the Poisson-Binomial Radius (PBR). PBR is a bin-to-bin distance which accounts for the dispersion of bin-to-bin information. PBR's performance was evaluated on twelve benchmark data sets covering six different classification and recognition applications: texture, material, leaf, scene, ear biometrics and category-level image classification. Results from these experiments demonstrate that PBR outperforms state-of-the-art distance measures for most of the data sets and achieves comparable performance on the rest, suggesting that accounting for different distributions in distance measures can improve performance in classification and recognition tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.