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Characterizing the shape patterns of dimorphic yeast pseudohyphae.

ABSTRACT: Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.

SUBMITTER: Gontar A

PROVIDER: S-EPMC6227998 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Characterizing the shape patterns of dimorphic yeast pseudohyphae.

Gontar Amelia A Bottema Murk J MJ Binder Benjamin J BJ Tronnolone Hayden H

Royal Society open science 20181017 10

Pseudohyphal growth of the dimorphic yeast <i>Saccharomyces cerevisiae</i> is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or <i>a priori</i> knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classif ...[more]

PMID: 30473830

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Project description:Background and purposeAchieving a specific diagnosis in leukodystrophies is often difficult due to clinical and genetic heterogeneity. Mitochondrial defects cause 5%-10% of leukodystrophies. Our objective was to define MR imaging features commonly shared by mitochondrial leukodystrophies and to distinguish MR imaging patterns related to specific genetic defects.Materials and methodsOne hundred thirty-two patients with a mitochondrial leukodystrophy with known genetic defects were identified in the data base of the Amsterdam Leukodystrophy Center. Numerous anatomic structures were systematically assessed on brain MR imaging. Additionally, lesion characteristics were scored. Statistical group analysis was performed for 57 MR imaging features by hierarchic testing on clustered genetic subgroups.ResultsMR imaging features indicative of mitochondrial disease that were frequently found included white matter rarefaction (n = 50 patients), well-delineated cysts (n = 20 patients), T2 hyperintensity of the middle blade of the corpus callosum (n = 85 patients), and symmetric abnormalities in deep gray matter structures (n = 42 patients). Several disorders or clusters of disorders had characteristic features. The combination of T2 hyperintensity in the brain stem, middle cerebellar peduncles, and thalami was associated with complex 2 deficiency. Predominantly periventricular localization of T2 hyperintensities and cystic lesions with a distinct border was associated with defects in complexes 3 and 4. T2-hyperintense signal of the cerebellar cortex was specifically associated with variants in the gene NUBPL. T2 hyperintensities predominantly affecting the directly subcortical cerebral white matter, globus pallidus, and substantia nigra were associated with Kearns-Sayre syndrome.ConclusionsIn a large group of patients with a mitochondrial leukodystrophy, general MR imaging features suggestive of mitochondrial disease were found. Additionally, we identified several MR imaging patterns correlating with specific genotypes. Recognition of these patterns facilitates the diagnosis in future patients.

Dataset Information

Characterizing the shape patterns of dimorphic yeast pseudohyphae.

Publications

Characterizing the shape patterns of dimorphic yeast pseudohyphae.

Similar Datasets

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