Production of pectinases and cellulase by six Pythium species isolated from necrotic strawberry roots
Polygalacturonase and polymethylgalacturonase activity was detected in filtrates ofPythium irregulare, P. perniciosum, P. sylvaticum, andP. dissotocum but not in filtrates ofP. rostratum andP. acanthicum. OnlyP. irregulare, P. perniciosum andP. dissotocum elaborated pectin methyl esterase and in trace amounts. None of the species produced detectable pectintrans-eliminase and “macerating enzyme” activity.P. sylvaticum was the only species of four tested which produced cellulase.
A survey of the production of pectic enzymes by plant pathogenic and other bacteria.
SUMMARY: Sixty-seven cultures of plant pathogenic bacteria, of the genera Agrobacterium, Corynebacterium, Erwinia, Pseudomonas and Xanthomonas, and 56 cultures of non-pathogenic bacteria were tested for production of pectic enzymes in culture. γ-Pectinglycosidase was produced by 22 strains of pathogenic bacteria and by 15 non-pathogens. Pectin methyl esterase was produced by a few pathogens: the soft-rot erwinias, X. campestris and one of three strains of X. vasculorum.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.