Dataset Information

Peering into the Mediterranean black box: Lactifluus rugatus ectomycorrhizas on Cistus.

ABSTRACT: We describe the morpho-anatomical features of the ectomycorrhizas (ECMs) formed by Lactifluus rugatus on Cistus, a genus of about 20 species of woody shrubs typical of the Mediterranean maquis. The description of L. rugatus mycorrhizas on Cistus is the first ECM description of a species belonging to Lactifluus subgen. Pseudogymnocarpi. The ECM identity was verified through molecular tools. Anatomically, the characteristic of L. rugatus mycorrhiza is the presence of abundant, long "bottle-shaped" cystidia on mantle surface. Indeed, the overwhelming majority of milkcap mycorrhizas are acystidiate. This is the third Lactarius/Lactifluus mycorrhiza to have been described associated with Cistus, the others being Lactarius cistophilus and L. tesquorum. The phylogenetic distance between all these taxa is reflected by the diversity of the principal features of their ECMs, which share host-depending ECM features known for Cistus, but are otherwise distinguishable on the host roots. Comparison of Lactifluus rugatus ECM with those formed by L. vellereus and L. piperatus on Fagus reveals elevated intrageneric diversity of mycorrhizal structures. Such a diversity is supported by analysis of ITS sequences of relevant species within European Lactifluus species. Our study extends knowledge of Cistus mycorrhizal biology and confirms the informative value of mycorrhizal structures in understanding phylogenetic relationships in ECM fungi.

SUBMITTER: Leonardi M

PROVIDER: S-EPMC5159598 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Peering into the Mediterranean black box: Lactifluus rugatus ectomycorrhizas on Cistus.

Leonardi Marco M Comandini Ornella O Rinaldi Andrea C AC

IMA fungus 20161110 2

We describe the morpho-anatomical features of the ectomycorrhizas (ECMs) formed by Lactifluus rugatus on Cistus, a genus of about 20 species of woody shrubs typical of the Mediterranean maquis. The description of L. rugatus mycorrhizas on Cistus is the first ECM description of a species belonging to Lactifluus subgen. Pseudogymnocarpi. The ECM identity was verified through molecular tools. Anatomically, the characteristic of L. rugatus mycorrhiza ...[more]

PMID: 27990334

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Project description:BackgroundAdaptive radiation in Mediterranean plants is poorly understood. The white-flowered Cistus lineage consists of 12 species primarily distributed in Mediterranean habitats and is herein subject to analysis.Methodology/principal findingsWe conducted a "total evidence" analysis combining nuclear (ncpGS, ITS) and plastid (trnL-trnF, trnK-matK, trnS-trnG, rbcL) DNA sequences and using MP and BI to test the hypothesis of radiation as suggested by previous phylogenetic results. One of the five well-supported lineages of the Cistus-Halimium complex, the white-flowered Cistus lineage, comprises the higher number of species (12) and is monophyletic. Molecular dating estimates a Mid Pleistocene (1.04+/-0.25 Ma) diversification of the white-flowered lineage into two groups (C. clusii and C. salviifolius lineages), which display asymmetric characteristics: number of species (2 vs. 10), leaf morphologies (linear vs. linear to ovate), floral characteristics (small, three-sepalled vs. small to large, three- or five-sepalled flowers) and ecological attributes (low-land vs. low-land to mountain environments). A positive phenotype-environment correlation has been detected by historical reconstructions of morphological traits (leaf shape, leaf labdanum content and leaf pubescence). Ecological evidence indicates that modifications of leaf shape and size, coupled with differences in labdanum secretion and pubescence density, appear to be related to success of new species in different Mediterranean habitats.Conclusions/significanceThe observation that radiation in the Cistus salviifolius lineage has been accompanied by the emergence of divergent leaf traits (such as shape, pubescence and labdanum secretion) in different environments suggets that radiation in the group has been adaptive. Here we argued that the diverse ecological conditions of Mediterranean habitats played a key role in directing the evolution of alternative leaf strategies in this plant group. Key innovation of morphological characteristics is supported by our dated phylogeny, in which a Mediterranean climate establishment (2.8 Ma) predated the adaptive radiation of the white-flowered Cistus.

Project description:Software systems typically have an input domain that can be subdivided into sub-domains, each of which generates similar or related outputs. Testing it on the boundaries between these sub-domains is critical to ensure high-quality software. Therefore, boundary value analysis and testing have been a fundamental part of the software testing toolbox for a long time and are typically taught early to software engineering students. Despite its many argued benefits, boundary value analysis for a given software specification or application is typically described in abstract terms. This allows for variation in how testers apply it and in the benefits they see. Additionally, its adoption has been limited since it requires a specification or model to be analysed. We propose an automated black-box boundary value detection method to support software testers in performing systematic boundary value analysis. This dynamic method can be utilized even without a specification or model. The proposed method is based on a metric referred to as the program derivative, which quantifies the level of boundariness of test inputs. By combining this metric with search algorithms, we can identify and rank pairs of inputs as good boundary candidates, i.e., inputs that are in close proximity to each other but with outputs that are far apart. We have implemented the AutoBVA approach and evaluated it on a curated dataset of example programs. Furthermore, we have applied the approach broadly to a sample of 613 functions from the base library of the Julia programming language. The approach could identify boundary candidates that highlight diverse boundary behaviours in over 70% of investigated systems under test. The results demonstrate that even a simple variant of the program derivative, combined with broad sampling and search over the input space, can identify interesting boundary candidates for a significant portion of the functions under investigation. In conclusion, we also discuss the future extension of the approach to encompass more complex systems under test cases and datatypes.

Project description:We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest X-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.

Dataset Information

Peering into the Mediterranean black box: Lactifluus rugatus ectomycorrhizas on Cistus.

Publications

Peering into the Mediterranean black box: <i>Lactifluus rugatus</i> ectomycorrhizas on <i>Cistus</i>.

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