Towards an integrated phylogenetic classification of the Tremellomycetes.

Families and genera assigned to Tremellomycetes have been mainly circumscribed by morphology and for the yeasts also by biochemical and physiological characteristics. This phenotype-based classification is largely in conflict with molecular phylogenetic analyses. Here a phylogenetic classification framework for the Tremellomycetes is proposed based on the results of phylogenetic analyses from a seven-genes dataset covering the majority of tremellomycetous yeasts and closely related filamentous taxa. Circumscriptions of the taxonomic units at the order, family and genus levels recognised were quantitatively assessed using the phylogenetic rank boundary optimisation (PRBO) and modified general mixed Yule coalescent (GMYC) tests. In addition, a comprehensive phylogenetic analysis on an expanded LSU rRNA (D1/D2 domains) gene sequence dataset covering as many as available teleomorphic and filamentous taxa within Tremellomycetes was performed to investigate the relationships between yeasts and filamentous taxa and to examine the stability of undersampled clades. Based on the results inferred from molecular data and morphological and physiochemical features, we propose an updated classification for the Tremellomycetes. We accept five orders, 17 families and 54 genera, including seven new families and 18 new genera. In addition, seven families and 17 genera are emended and one new species name and 185 new combinations are proposed. We propose to use the term pro tempore or pro tem. in abbreviation to indicate the species names that are temporarily maintained.

A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis.

Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.