UNLABELLED The anamorphic genus Phoma was subdivided into nine sections based on morphological characters, and included teleomorphs in Didymella, Leptosphaeria, Pleospora and Mycosphaerella, suggesting the polyphyly of the genus. Recent molecular, phylogenetic studies led to the conclusion that Phoma should be restricted to Didymellaceae. The present study focuses on the taxonomy of excluded Ph...

The genus Phoma Sacc. consists of important fungal plant pathogenic species occurring on economically important cultivated crops in spite of many other saprobic species. Thus, the aim of this study was to identify 35 isolates of Phoma species and related taxa collected from different hosts by means of morphological and molecular characters. Multi-locus phylogenetic analyses based on ITS1-5.8S-I...

Selected cultural and environmental variables were investigated for their influence on the efficacy of Phoma herbarum and Phoma exigua to cause disease on dandelion (Taraxacum officinale) under growth room conditions. In both species, mycelial fragments caused significantly greater disease severity on dandelion than spore suspensions. Mycelial age was not an important factor in disease severity...

INTRODUCTION Fungal infections in human skin, such as sporotrichosis, can occur after fish induced trauma. This work aimed to identify fungi in freshwater fish that are pathogenic to humans. METHODS Extraction of dental arches from Serrassalmus maculatus (piranha) and Hoplias malabaricus (wolf fish), stings from Pimelodus maculatus (mandis catfish), dorsal fin rays from Plagioscion spp. (corv...

Causality discovery without manipulation is considered a crucial problem to a variety of applications, such as genetic therapy. The state-of-the-art solutions, e.g. LiNGAM, return accurate results when the number of labeled samples is larger than the number of variables. These approaches are thus applicable only when large numbers of samples are available or the problem domain is sufficiently s...

In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for sparse models using slab and spike priors (two-component δ-function and conti...