Color lightness of velvet ants (Hymenoptera: Mutillidae) follows an environmental gradient.

Color traits are highly influenced by environmental conditions along the distributional range of many species. Studies on the variation of animal coloration across different geographic gradients are, therefore, fundamental for a better understanding of the ecological and evolutionary processes that shape color variation. Here, we address whether color lightness in velvet ants (Hymenoptera: Mutillidae) responds to latitudinal gradients and bioclimatic variations, testing three ecogeographic rules: The Thermal melanism hypothesis; the Photoprotection hypothesis; and Gloger's rule. We test these hypotheses across the New World. We used photographs of 482 specimens (n = 142 species) of female mutillid wasps and extracted data on color lightness (V). We analyzed whether variation in color is determined by bioclimatic factors, using Phylogenetic Generalized Least Square analysis. Our explanatory variables were temperature, ultraviolet radiation, humidity, and forest indicators. Our results were consistent with the Photoprotection hypothesis and Gloger's rule. Species with darker coloration occupied habitats with more vegetation, higher humidity, and UV-B radiation. However, our results refute one of the initial hypotheses suggesting that mutillids do not respond to the predictions of the Thermal melanism hypothesis. The results presented here provide the first evidence that abiotic components of the environment can act as ecological filters and as selective forces driving the body coloration of velvet ants. Finally, we suggest that studies using animals with melanin-based colors as a model for mimetic and aposematic coloration hypotheses consider that this coloration may also be under the influence of climatic factors and not only predators.

Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying H&E histological images of colorectal cancer.

In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance. The fuzzy strategy was a Gaussian function used to verify the pertinence of the distances between windows. The result was a method capable of computing similarities between pixels contained in windows of various sizes, as well as the information present in the colour channels. The power of quantification was tested in a public colorectal image dataset, which was composed of both benign and malignant classes. The results were given as inputs for classifiers of different categories and analysed by applying the k-fold cross-validation and holdout methods. The derived performances indicate that the proposed association was capable of distinguishing the benign and malignant groups, with values that surpassed those results obtained with important techniques available in the Literature. The best performance was an AUC value of 0.983, an important result, mainly when we consider the difficulties of clinical practice for the diagnosis of the colorectal cancer.

Lymphoma images analysis using morphological and non-morphological descriptors for classification.

Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.