The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema's theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.

• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

• Klíčová slova
• classification, computer-aided diagnosis, texture analysis, thyroid, ultrasound,
• MeSH
• algoritmy * MeSH
• diagnóza počítačová metody   MeSH
• diferenciální diagnóza MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• štítná žláza diagnostické zobrazování   patologie   MeSH
• support vector machine MeSH
• ultrasonografie metody   MeSH
• uzly štítné žlázy klasifikace   diagnostické zobrazování   patologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Silva invasion pattern can help predict lymph node metastasis risk in endocervical adenocarcinoma. We analysed Silva pattern of invasion and lymphovascular invasion to determine associations with clinical outcomes in stage IA and IB1 endocervical adenocarcinomas. International Federation of Gynecology and Obstetrics (FIGO; 2019 classification) stage IA-IB1 endocervical adenocarcinomas from 15 international institutions were examined for Silva pattern, presence of lymphovascular invasion, and other prognostic parameters. Lymph node metastasis status, local/distant recurrences, and survival data were compared using appropriate statistical tests. Of 399 tumours, 152 (38.1%) were stage IA [IA1, 77 (19.3%); IA2, 75 (18.8%)] and 247 (61.9%) were stage IB1. On multivariate analysis, lymphovascular invasion (p=0.008) and Silva pattern (p<0.001) were significant factors when comparing stage IA versus IB1 endocervical adenocarcinomas. Overall survival was significantly associated with lymph node metastasis (p=0.028); recurrence-free survival was significantly associated with lymphovascular invasion (p=0.002) and stage (1B1 versus 1A) (p=0.002). Five and 10 year overall survival and recurrence-free survival rates were similar among Silva pattern A cases and Silva pattern B cases without lymphovascular invasion (p=0.165 and p=0.171, respectively). Silva pattern and lymphovascular invasion are important prognostic factors in stage IA1-IB1 endocervical adenocarcinomas and can supplement 2019 International Federation of Gynecology and Obstetrics staging. Our binary Silva classification system groups patients into low risk (patterns A and B without lymphovascular invasion) and high risk (pattern B with lymphovascular invasion and pattern C) categories.

• Klíčová slova
• Silva pattern, Stage, endocervical adenocarcinoma, lymphovascular invasion, pattern of invasion,
• MeSH
• adenokarcinom * patologie   MeSH
• karcinom * patologie   MeSH
• lidé MeSH
• lymfatické metastázy MeSH
• nádory děložního čípku * MeSH
• prognóza MeSH
• retrospektivní studie MeSH
• staging nádorů MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.

• Klíčová slova
• Computer-aided diagnosis, Patch-based image analysis, Random forests, Texture analysis, Thyroid nodules, Ultrasound imaging,
• MeSH
• diagnóza počítačová metody   MeSH
• diferenciální diagnóza MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• senzitivita a specificita MeSH
• support vector machine * MeSH
• ultrasonografie metody   MeSH
• uzly štítné žlázy diagnostické zobrazování   patologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles-the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps-in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers-logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient's health.

• Klíčová slova
• ML-based binary classifiers, ROC analysis, SOTA clustering algorithm, classification, clustering, clustering quality criteria, fuzzy inference system, gene expression profiles, lung cancer,
• Publikační typ
• časopisecké články MeSH

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

• Klíčová slova
• COVID-19 diagnosis, Haralick, K-nearest neighbor, Local binary pattern, Machine learning, Support vector machine, X-ray image,
• Publikační typ
• časopisecké články MeSH

Objective. This paper introduces a winning solution (team ISIBrno-AIMT) to the official round of PhysioNet Challenge 2021. The main goal of the challenge was a classification of ECG recordings into 26 multi-label pathological classes with a variable number of leads (e.g. 12, 6, 4, 3, 2). The main objective of this study is to verify whether the multi-head-attention mechanism influences the model performance.Approach. We introduced an ECG classification method based on the ResNet architecture with a multi-head attention mechanism for the official round of the challenge. However, empirical findings collected during model development suggested that the multi-head attention layer might not significantly impact the final classification performance. For this reason, during the follow-up round, we removed a multi-head attention layer to test the influence on model performance. Like the official round, the model is optimized using a mixture of loss functions, i.e. binary cross-entropy, custom challenge score loss function, and custom sparsity loss function. Probability thresholds for each classification class are estimated using the evolutionary optimization method. The final architecture consists of three submodels forming a majority voting classification ensemble.Main results. The modified model without the multi-head attention layer increased the overall challenge score to 0.59 compared to the 0.58 from the official round.Significance. Our findings from the follow-up submission support the fact that the multi-head attention layer in the proposed architecture does not significantly affect the classification performance.

• Klíčová slova
• ECG, PhysioNet challenge 2021, attention mechanism, classification, deep learning,
• MeSH
• algoritmy * MeSH
• elektrokardiografie * metody   MeSH
• entropie MeSH
• pravděpodobnost MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The authors criticize the use of concepts of sensitivity and specificity calculated by means of a binary table in differential diagnostic reflections pertaining to more than one disease or several, i.e. more than one diagnostic test. On examples they present possibilities of different ways for calculating these concepts with different not correlating results. They indicate the justification of using multidimensional statistical methods in those instances and their better diagnostic yield.

• MeSH
• diagnóza * MeSH
• senzitivita a specificita MeSH
• statistika jako téma metody   MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH

The genus Viola (Violaceae) is among the 40-50 largest genera among angiosperms, yet its taxonomy has not been revised for nearly a century. In the most recent revision, by Wilhelm Becker in 1925, the then-known 400 species were distributed among 14 sections and numerous unranked groups. Here, we provide an updated, comprehensive classification of the genus, based on data from phylogeny, morphology, chromosome counts, and ploidy, and based on modern principles of monophyly. The revision is presented as an annotated global checklist of accepted species of Viola, an updated multigene phylogenetic network and an ITS phylogeny with denser taxon sampling, a brief summary of the taxonomic changes from Becker's classification and their justification, a morphological binary key to the accepted subgenera, sections and subsections, and an account of each infrageneric subdivision with justifications for delimitation and rank including a description, a list of apomorphies, molecular phylogenies where possible or relevant, a distribution map, and a list of included species. We distribute the 664 species accepted by us into 2 subgenera, 31 sections, and 20 subsections. We erect one new subgenus of Viola (subg. Neoandinium, a replacement name for the illegitimate subg. Andinium), six new sections (sect. Abyssinium, sect. Himalayum, sect. Melvio, sect. Nematocaulon, sect. Spathulidium, sect. Xanthidium), and seven new subsections (subsect. Australasiaticae, subsect. Bulbosae, subsect. Clausenianae, subsect. Cleistogamae, subsect. Dispares, subsect. Formosanae, subsect. Pseudorupestres). Evolution within the genus is discussed in light of biogeography, the fossil record, morphology, and particular traits. Viola is among very few temperate and widespread genera that originated in South America. The biggest identified knowledge gaps for Viola concern the South American taxa, for which basic knowledge from phylogeny, chromosome counts, and fossil data is virtually absent. Viola has also never been subject to comprehensive anatomical study. Studies into seed anatomy and morphology are required to understand the fossil record of the genus.

• Klíčová slova
• Viola, Violaceae, fossils, monophyletic, morphology, nomenclature, phylogeny, polyploidy, taxonomic revision,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

BACKGROUND: Histopathological classification of basal cell carcinoma (BCC) has important prognostic and therapeutic implications, but reproducibility of BCC subtyping among dermatopathologists is poor. OBJECTIVES: To obtain a consensus paper on BCC classification and subtype definitions. METHODS: A panel of 12 recognized dermatopathologists (G12) from nine European countries used a modified Delphi method and evaluated 100 BCC cases uploaded to a website. The strategy involved five steps: (I) agreement on definitions for WHO 2018 BCC subtypes; (II) classification of 100 BCCs using the agreed definitions; (III) discussion on the weak points of the WHO classification and proposal of a new classification with clinical insights; (IV) re-evaluation of the 100 BCCs using the new classification; and (V) external independent evaluation by 10 experienced dermatopathologists (G10). RESULTS: A simplified classification unifying infiltrating, sclerosing, and micronodular BCCs into a single "infiltrative BCC" subtype improved reproducibility and was practical from a clinical standpoint. Fleiss' κ values increased for all subtypes, and the level of agreement improved from fair to moderate for the nodular and the unified infiltrative BCC groups, respectively. The agreement for basosquamous cell carcinoma remained fair, but κ values increased from 0.276 to 0.342. The results were similar for the G10 group. Delphi consensus was not achieved for the concept of trichoblastic carcinoma. In histopathological reports of BCC displaying multiple subtypes, only the most aggressive subtype should be mentioned, except superficial BCC involving margins. CONCLUSIONS: The three BCC subtypes with infiltrative growth pattern, characteristically associated with higher risk of deep involvement (infiltrating, sclerosing, and micronodular), should be unified in a single group. The concise and encompassing term "infiltrative BCCs" can be used for these tumors. A binary classification of BCC into low-risk and high-risk subtypes on histopathological grounds alone is questionable; correlation with clinical factors is necessary to determine BCC risk and therapeutic approach.

• MeSH
• bazocelulární karcinom * patologie   MeSH
• konsensus MeSH
• lidé MeSH
• nádory kůže * patologie   MeSH
• reprodukovatelnost výsledků MeSH
• resekční okraje MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH