Weeds as hosts of Mycocentrospora acerina

Weeds were collected from carrot fields and some other sites at 27 localities in seven counties in Norway during August and September of 1986 and 1987. Leaf and petiole lesions were microscopically examined for Mycocentrospora acerina. Viola arvensis and Viola tricolor were commonly observed with the pathogen. The fungus was also registered on Fumaria officinalis, Galium aparine, Matricaria inodora, Matricaria matricarioides, Ranunculus repens, Senecio vulgaris and Stachys palustris. In inoculation experiments in the greenhouse, M. acerina infected carrot leaves and 13 of 16 species of weeds. The symptoms on the various susceptible plants showed some variation, but were essentially of the same pattern. The investigation indicates that weeds are common hosts of M. acerina. A list of 23 host weed species is given.     

Chisquare Tests From Clusters of Observations

A simple model for categorical observations that are correlated in clusters is developed. The implied correlation coefficient is used to define an effective number of observations, and a natural estimation procedure for the correlation coefficient is proposed. By grouping the clusters of observations, and by relating them to models of the probabilities for the implied categories, common chisquare tests are formulated and motivated asymptotically. As an example of applications we look at forage registrations for radio-collared moose.     

Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

Fourier‐transform infrared (FTIR) microspectroscopy is rounding the corner to become a label‐free routine method for cancer diagnosis. In order to build infrared‐spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global‐to‐local image registration for FTIR images and H&E stained histological images of parallel tissue sections.