On finite-time ruin probabilities for classical risk models

info:eu-repo/semantics/publishe

Antimicrobial resistance of Escherichia coli and Enterococcus faecalis in housed laying-hen flocks in Europe

The aim of this study was to determine the potential association between housing type and multiple drug resistance (MDR) in Escherichia coli and Enterococcus faecalis isolates recovered from 283 laying-hen flocks. In each flock, a cloacal swab from four hens was collected and produced 1102 E. coli and 792 E. faecalis isolates. Broth microdilution was used to test susceptibility to antimicrobials. Country and housing type interacted differently with the MDR levels of both species. In the E. coli model, housing in a raised-floor system was associated with an increased risk of MDR compared to the conventional battery system [odds ratio (OR) 2·12, 95% confidence interval (CI) 1·13-3·97)]. In the E. faecalis model the MDR levels were lower in free-range systems than in conventional battery cages (OR 0·51, 95% CI 0·27-0·94). In Belgium, ceftiofur-resistant E. coli isolates were more numerous than in the other countrie

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain