Exploring the recurrent states of football teams' tactical organization on the pitch during Brazilian official matches

Football teams' tactical organization on the pitch is usually represented by the surface area. Considering the different shapes adopted by the teams during the match, the role of the tactical variability for success is lacking. The aim of this study was to explore and to evaluate the association between recurrent states of tactical organization and technical performance during football matches. A total of 28 teams of Brazilian First Division Championships were analysed. Teams' surface area shapes were represented by the maximum value of the Multiscale Fractal Dimension in each timestamp, producing a time series. Recurrences of states of tactical organization were determined via recurrence plots and recurrence quantitative analysis during attacking and defending phases, and considering the whole match. The outcomes were correlated with nine traditional technical performance indicators. The main results showed that structural recurrence or variability on tactical organization is associated with performance success during the defending and attacking actions. Recurrence plot and measures based on the recurrence density proved to be valuable tools to represent teams' dynamics.

A word on habitat complexity

In their recent synopsis, Loke and Chisholm (Ecology Letters, 25, 2269-2288, 2022) present an overview of habitat complexity metrics for ecologists. They provide a review and some sound advice. However, we found several of their analyses and opinions misleading. This technical note provides a different perspective on the complexity metrics assessed.

Local R-Symmetry Co-Occurrence: Characterising Leaf Image Patterns for Identifying Cultivars

Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether analysing leaf patterns can provide sufficient information for further differentiating cultivars. This paper reports our attempt on cultivar recognition from leaves as a general very fine-grained pattern recognition problem, which is not only a challenging research problem but also important for cultivar evaluation, selection and production in agriculture. We propose a novel local R-symmetry co-occurrence method for characterising discriminative local symmetry patterns to distinguish subtle differences among cultivars. Through scalable and moving R-relation radius pairs, we generate a set of radius symmetry co-occurrence matrices (RsCoM)and their measures for describing the local symmetry properties of interior regions. By varying the size of the radius pair, the RsCoM measures local R-symmetry co-occurrence from global/coarse to fine scales. A new two-phase strategy of analysing the distribution of local RsCoM measures is designed to match the multiple scale appearance symmetry pattern distributions of similar cultivar leaf images. We constructed three leaf image databases, SoyCultivar, CottCultivar, and PeanCultivar, for an extensive experimental evaluation on recognition across soybean, cotton and peanut cultivars. Encouraging experimental results of the proposed method in comparison with the state-of-the-art leaf species recognition methods demonstrate the effectiveness of the proposed method for cultivar identification, which may advance the research in leaf recognition from species to cultivar.

Multiscale fractal dimension applied to tactical analysis in football: A novel approach to evaluate the shapes of team organization on the pitch

The aim of this study was to evaluate different shape descriptors applied to images of polygons that represent the organization of football teams on the pitch. The effectiveness of different shape descriptors (area/perimeter, fractal area, circularity, maximum fractal, rectangularity, multiscale fractal curve—MFC), and the concatenation of all shape descriptors (except MFC), denominated Alldescriptors (AllD)) was evaluated and applied to polygons corresponding to the shapes represented by the convex hull obtained from players’ 2D coordinates. A content-based image retrieval system (CBIR) was applied for 25 users (mean age of 31.9 ± 8.4 years) to evaluate the relevant images. Measures of effectiveness were used to evaluate the shape descriptors (P@n and R@n). The MFD (P@5, 0.46±0.37 and P@10, 0.40±0.31, p < 0.001; R@5, 0.14±0.13 and R@10, 0.24±0.19, p < 0.001) and AllD (P@5 = 0.43±0.36 and P@10 = 0.39±0.32, p < 0.001; R@5 = 0.13±0.11 and R@10 = 0.24±0.20, p < 0.001) descriptors presented higher values of effectiveness. As a practical demonstration, the best evaluated shape descriptor (MFC) was applied for tactical analysis of an official match. K-means clustering technique was applied, and different shapes of organization could be identified throughout the match. The MFC was the most effective shape descriptor in relation to all others, making it possible to apply this descriptor in the analysis of professional football matches.

Test-retest reliability of multiscale fractal dimension measurements of plantar pressure maps during dynamic tasks

The tools used to evaluate foot types are divergent since they adopt classic linear analyzes, based on anthropometric or image measurements, which do not dynamically contemplate the variability of foot shape. The use of newer techniques such as multiscale fractal dimension (MFD) may be a key to this type of problem. However, for these measures to be used safely and consistently, it is essential to evaluate their reliability. The aim of this study was to evaluate the test-retest reliability of MFD measurements of adult plantar pressure maps during gait, as well as the standard error of measurement (SEM), and minimal detectable change (MDC₉₀). Seventy-two subjects were included in the test-retest, with a one week interval. The plantar pressure maps were constructed using a pressure platform. The data were processed in a routine for extracting the MFD curve measurements (maximum and integral values). The Intraclass Correlation Coefficient results (ICC_3,k) were excellent for both measurements (maximum value 0.96, 95% confidence interval [0.93-0.97], and integral 0.95 [0.92-0.97]) with low SEM and MDC₉₀ values below 10% of the mean. The application of MFD to the plantar pressure data generated by the pressure platform is reliable and could allow exploration of the complexity of foot shapes, enabling their classification.

Advanced Microtexture Study of Anacardium occidentale L. Leaf Surface From the Amazon by Fractal Theory

This work applies stereometric parameters and fractal theory to characterize the structural complexity of the 3D surface roughness of Anacardium occidentale L. leaf using atomic force microscopy (AFM) measurements. Surface roughness was studied by AFM in tapping mode, in air, on square areas of 6,400 and 10,000 μm2. The stereometric analyses using MountainsMap Premium and WSXM software provided detailed information on the 3D surface topography of the samples. These data showed that the morphology of the abaxial and adaxial side of the cashew leaf is different, which was also observed in relation to their microtextures. Fractal analysis showed that the adaxial and abaxial sides have strong microtexture homogeneity, but the adaxial side presented higher surface entropy. These results show that image processing associated with fractal theory can be an indispensable tool for identifying plant species by their leaves because this species has singularities on each side of the leaf.

Complexity of Shapes Embedded in Zn with a Bias Towards Squares

Shape complexity is a hard-to-quantify quality, mainly due to its relative nature. Biased by Euclidean thinking, circles are commonly considered as the simplest. However, their constructions as digital images are only approximations to the ideal form. Consequently, complexity orders computed in reference to circle are unstable. Unlike circles which lose their circleness in digital images, squares retain their qualities. Hence, we consider squares (hypercubes in Zn) to be the simplest shapes relative to which complexity orders are constructed. Using the connection between L∞ norm and squares we effectively encode squareness-adapted simplification through which we obtain multi-scale complexity measure, where scale determines the level of interest to the boundary. The emergent scale above which the effect of a boundary feature (appendage) disappears is related to the ratio of the contacting width of the appendage to that of the main body. We discuss what zero complexity implies in terms of information repetition and constructibility and what kind of shapes in addition to squares have zero complexity.

Effects of Foliar Treatment with a Trichoderma Plant Biostimulant Consortium on Passiflora caerulea L. Yield and Quality

The influence of spore concentration on the ability of a Trichoderma consortium to colonize the Passiflora caerulea phyllosphere was evaluated by determining the effects of foliar treatments with two spore concentrations, in two repeated treatments, on the morphological, physiological, and ultrastructural characteristics, and on the yield and quality of P. caerulea. The studied crop quality features were related to its nutraceutical use: the accumulation of polyphenols and flavonoids, antioxidant activity, and effects on mouse fibroblast L929 cells. The Trichoderma consortium consisted of two strains, T. asperellum T36b and T. harzianum Td50b, and the concentrations used were 106 colony forming units (cfu)/mL and 108 cfu/mL. As a reference treatment, a commercial product that was based on herbs and algal extracts was used. As compared to the negative control, the treatment with the Trichoderma consortium at 108 cfu/mL concentration determines the accumulation of higher level of polyphenols and flavonoids and increased antioxidant activity. This enhancement of P. caerulea quality characteristics after treatment with the higher concentration of Trichoderma consortium was associated with larger leaves, increased number and size of chloroplasts, improved plant physiology characteristics, and an increased yield. The treatment with high concentration of Trichoderma consortium spores promotes phyllosphere colonization and benefits both crop yield and quality.

Leaf heteroblasty in Passiflora edulis as revealed by metabolic profiling and expression analyses of the microRNAs miR156 and miR172

BACKGROUND AND AIMS

Juvenile-to-adult phase transition is marked by changes in leaf morphology, mostly due to the temporal development of the shoot apical meristem, a phenomenon known as heteroblasty. Sugars and microRNA-controlled modules are components of the heteroblastic process in Arabidopsis thaliana leaves. However, our understanding about their roles during phase-changing in other species, such as Passiflora edulis, remains limited. Unlike Arabidopsis, P. edulis (a semi-woody perennial climbing vine) undergoes remarkable changes in leaf morphology throughout juvenile-to-adult transition. Nonetheless, the underlying molecular mechanisms are unknown.

METHODS

Here we evaluated the molecular mechanisms underlying the heteroblastic process by analysing the temporal expression of microRNAs and targets in leaves as well as the leaf metabolome during P. edulis development.

KEY RESULTS

Metabolic profiling revealed a unique composition of metabolites associated with leaf heteroblasty. Increasing levels of glucose and α-trehalose were observed during juvenile-to-adult phase transition. Accumulation of microRNA156 (miR156) correlated with juvenile leaf traits, whilst miR172 transcript accumulation was associated with leaf adult traits. Importantly, glucose may mediate adult leaf characteristics during de novo shoot organogenesis by modulating miR156-targeted PeSPL9 expression levels at early stages of shoot development.

CONCLUSIONS

Altogether, our results suggest that specific sugars may act as co-regulators, along with two microRNAs, leading to leaf morphological modifications throughout juvenile-to-adult phase transition in P. edulis.

Signature identification by Minkowski dimension

In this article, we propose and investigate the possibility of signature identification based on its fractal Minkowski dimension. We consider a signature as a trajectory of a pen tip that obeys the Langevin equations, for which we calculate the fractal Minkowski dimension. This parameter is different for original and intentionally falsified signatures, thus allowing one to reliably distinguish between the signatures left by different persons. The proposed approach together with machine learning techniques is a potentially powerful tool for identification and verification of signatures and any other kind of notations.

Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex (Veronica subsect. Pentasepalae, Plantaginaceae)

This study exhaustively explores leaf features seeking diagnostic characters to aid the classification (assigning cases to groups, i.e. populations to taxa) in a polyploid plant-species complex. A challenging case study was selected: Veronica subsection Pentasepalae, a taxonomically intricate group. The “divide and conquer” approach was implemented—that is, a difficult primary dataset was split into more manageable subsets. Three techniques were explored: two data-mining tools (artificial neural networks and decision trees) and one unsupervised discriminant analysis. However, only the decision trees and discriminant analysis were finally used to select diagnostic traits. A previously established classification hypothesis based on other data sources was used as a starting point. A guided discriminant analysis (i.e. involving manual character selection) was used to produce a grouping scheme fitting this hypothesis so that it could be taken as a reference. Sequential unsupervised multivariate analysis enabled the recognition of all species and infraspecific taxa; however, a suboptimal classification rate was achieved. Decision trees resulted in better classification rates than unsupervised multivariate analysis, but three complete taxa were misidentified (not present in terminal nodes). The variable selection led to a different grouping scheme in the case of decision trees. The resulting groups displayed low misclassification rates when analyzed using artificial neural networks. The decision trees as well as the discriminant analysis are recommended in the search of diagnostic characters. Due to the high sensitivity that artificial neural networks have to the combination of input/output layers, they are proposed as evaluation tools for morphometric studies. The “divide and conquer” principle is a promising strategy, providing success in the present case study.

Morphometric analysis of Passiflora leaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade

Background

Leaf shape among Passiflora species is spectacularly diverse. Underlying this diversity in leaf shape are profound changes in the patterning of the primary vasculature and laminar outgrowth. Each of these aspects of leaf morphology-vasculature and blade-provides different insights into leaf patterning.

Results

Here, we morphometrically analyze >3300 leaves from 40 different Passiflora species collected sequentially across the vine. Each leaf is measured in two different ways: using 1) 15 homologous Procrustes-adjusted landmarks of the vasculature, sinuses, and lobes; and 2) Elliptical Fourier Descriptors (EFDs), which quantify the outline of the leaf. The ability of landmarks, EFDs, and both datasets together are compared to determine their relative ability to predict species and node position within the vine. Pairwise correlation of x and y landmark coordinates and EFD harmonic coefficients reveals close associations between traits and insights into the relationship between vasculature and blade patterning.

Conclusions

Landmarks, more reflective of the vasculature, and EFDs, more reflective of the blade contour, describe both similar and distinct features of leaf morphology. Landmarks and EFDs vary in ability to predict species identity and node position in the vine and exhibit a correlational structure (both within landmark or EFD traits and between the two data types) revealing constraints between vascular and blade patterning underlying natural variation in leaf morphology among Passiflora species.

Microsatellite marker development by partial sequencing of the sour passion fruit genome (Passiflora edulis Sims)

Background

The Passiflora genus comprises hundreds of wild and cultivated species of passion fruit used for food, industrial, ornamental and medicinal purposes. Efforts to develop genomic tools for genetic analysis of P. edulis, the most important commercial Passiflora species, are still incipient. In spite of many recognized applications of microsatellite markers in genetics and breeding, their availability for passion fruit research remains restricted. Microsatellite markers in P. edulis are usually limited in number, show reduced polymorphism, and are mostly based on compound or imperfect repeats. Furthermore, they are confined to only a few Passiflora species. We describe the use of NGS technology to partially assemble the P. edulis genome in order to develop hundreds of new microsatellite markers.

Results

A total of 14.11 Gbp of Illumina paired-end sequence reads were analyzed to detect simple sequence repeat sites in the sour passion fruit genome. A sample of 1300 contigs containing perfect repeat microsatellite sequences was selected for PCR primer development. Panels of di- and tri-nucleotide repeat markers were then tested in P. edulis germplasm accessions for validation. DNA polymorphism was detected in 74% of the markers (PIC = 0.16 to 0.77; number of alleles/locus = 2 to 7). A core panel of highly polymorphic markers (PIC = 0.46 to 0.77) was used to cross-amplify PCR products in 79 species of Passiflora (including P. edulis), belonging to four subgenera (Astrophea, Decaloba, Distephana and Passiflora). Approximately 71% of the marker/species combinations resulted in positive amplicons in all species tested. DNA polymorphism was detected in germplasm accessions of six closely related Passiflora species (P. edulis, P. alata, P. maliformis, P. nitida, P. quadrangularis and P. setacea) and the data used for accession discrimination and species assignment.

Conclusion

A database of P. edulis DNA sequences obtained by NGS technology was examined to identify microsatellite repeats in the sour passion fruit genome. Markers were submitted to evaluation using accessions of cultivated and wild Passiflora species. The new microsatellite markers detected high levels of DNA polymorphism in sour passion fruit and can potentially be used in genetic analysis of P. edulis and other Passiflora species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3881-5) contains supplementary material, which is available to authorized users.

Plant Identification Based on Leaf Midrib Cross-Section Images Using Fractal Descriptors

The correct identification of plants is a common necessity not only to researchers but also to the lay public. Recently, computational methods have been employed to facilitate this task, however, there are few studies front of the wide diversity of plants occurring in the world. This study proposes to analyse images obtained from cross-sections of leaf midrib using fractal descriptors. These descriptors are obtained from the fractal dimension of the object computed at a range of scales. In this way, they provide rich information regarding the spatial distribution of the analysed structure and, as a consequence, they measure the multiscale morphology of the object of interest. In Biology, such morphology is of great importance because it is related to evolutionary aspects and is successfully employed to characterize and discriminate among different biological structures. Here, the fractal descriptors are used to identify the species of plants based on the image of their leaves. A large number of samples are examined, being 606 leaf samples of 50 species from Brazilian flora. The results are compared to other imaging methods in the literature and demonstrate that fractal descriptors are precise and reliable in the taxonomic process of plant species identification.

Fractal dimension of chromatin: potential molecular diagnostic applications for cancer prognosis

Fractal characteristics of chromatin, revealed by light or electron microscopy, have been reported during the last 20 years. Fractal features can easily be estimated in digitalized microscopic images and are helpful for diagnosis and prognosis of neoplasias. During carcinogenesis and tumor progression, an increase of the fractal dimension (FD) of stained nuclei has been shown in intraepithelial lesions of the uterine cervix and the anus, oral squamous cell carcinomas or adenocarcinomas of the pancreas. Furthermore, an increased FD of chromatin is an unfavorable prognostic factor in squamous cell carcinomas of the oral cavity and the larynx, melanomas and multiple myelomas. High goodness-of-fit of the regression line of the FD is a favorable prognostic factor in acute leukemias and multiple myelomas. The nucleus has fractal and power-law organization in several different levels, which might in part be interrelated. Some possible relations between modifications of the chromatin organization during carcinogenesis and tumor progression and an increase of the FD of stained chromatin are suggested. Furthermore, increased complexity of the chromatin structure, loss of heterochromatin and a less-perfect self-organization of the nucleus in aggressive neoplasias are discussed.

Phylogeny, biogeography and divergence times in Passiflora (Passifloraceae)

As part of a long-term investigation on the evolution of Passiflora L., we investigated the divergence ages of the genus and diversification of its subgenera, relating them with biogeographical and/or historical events, and other characteristics of this taxon. The main aim of the present work was to evaluate the biogeographic distribution of this genus to better understand its evolutionary history. This is the first time that representatives from South American and Old World Passifloraceae genera have been studied as a group comprising a total of 106 widely distributed species, with representative samples of the four suggested subgenera. Seven DNA regions were studied, comprising 7,431 nucleotides from plastidial, mitochondrial and nuclear genomes. Divergence time estimates were obtained by using a Bayesian Markov Chain Monte Carlo method and a random local clock model for each partition. Three major subgenera have been shown to be monophyletic and here we are proposing to include another subgenus in the Passiflora infrageneric classification. In general, divergence among the four subgenera in Passiflora is very ancient, ranging from ∼32 to ∼38 Mya, and Passifloraceae seems to follow a biogeographic scenario proposed for several plant groups, originating in Africa, crossing to Europe/Asia and arriving in the New World by way of land bridges. Our results indicated that Passiflora ancestors arrived in Central America and diversified quickly from there, with many long distance dispersion events.

Shape analysis using fractal dimension: a curvature based approach

The present work shows a novel fractal dimension method for shape analysis. The proposed technique extracts descriptors from a shape by applying a multi-scale approach to the calculus of the fractal dimension. The fractal dimension is estimated by applying the curvature scale-space technique to the original shape. By applying a multi-scale transform to the calculus, we obtain a set of descriptors which is capable of describing the shape under investigation with high precision. We validate the computed descriptors in a classification process. The results demonstrate that the novel technique provides highly reliable descriptors, confirming the efficiency of the proposed method.

Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

Fractal descriptors in the Fourier domain applied to color texture analysis

The present work proposes the development of a novel method to provide descriptors for colored texture images. The method consists of two steps. First, we apply a linear transform in the color space of the image aiming at highlighting spatial structuring relations among the color of pixels. Second, we apply a multiscale approach to the calculus of fractal dimension based on Fourier transform. From this multiscale operation, we extract the descriptors that are used to discriminate the texture represented in digital images. The accuracy of the method is verified in the classification of two color texture datasets, by comparing the performance of the proposed technique to other classical and state-of-the-art methods for color texture analysis. The results showed an advantage of almost 3% of the proposed technique over the second best approach.

AUTOMATIC LEAF STRUCTURE BIOMETRY: COMPUTER VISION TECHNIQUES AND THEIR APPLICATIONS IN PLANT TAXONOMY

This paper proposes a new methodology to extract biometric features of plant leaf structures. Combining computer vision techniques and plant taxonomy protocols, these methods are capable of identifying plant species. The biometric measurements are concentrated in leaf internal forms, specifically in the veination system. The methodology was validated with real cases of plant taxonomy, and eleven species of passion fruit of the genus Passiflora were used. The features extracted from the leaves were applied to the neural network system to perform the classification of species. The results showed to be very accurate in correctly differentiating among species with 97% of success. The computer vision methods developed can be used to assist taxonomists to perform biometric measurements in plant leaf structures.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

The use and limits of ITS data in the analysis of intraspecific variation in Passiflora L. (Passifloraceae)

The discovery and characterization of informative intraspecific genetic markers is fundamental for evolutionary and conservation genetics studies. Here, we used nuclear ribosomal ITS sequences to access intraspecific genetic diversity in 23 species of the genus Passiflora L. Some degree of variation was detected in 21 of these. The Passiflora and Decaloba (DC.) Rchb. subgenera showed significant differences in the sizes of the two ITS regions and in GC content, which can be related to reproductive characteristics of species in these subgenera. Furthermore, clear geographical patterns in the spatial distribution of sequence types were identified in six species. The results indicate that ITS may be a useful tool for the evaluation of intraspecific genetic variation in Passiflora.

LeafAnalyser: a computational method for rapid and large-scale analyses of leaf shape variation

A comprehensive understanding of leaf shape is important in many investigations in plant biology. Techniques to assess variation in leaf shape are often time-consuming, labour-intensive and prohibited by complex calculation of large data sets. We have developed LeafAnalyser, software that uses image-processing techniques to greatly simplify the measurement of leaf shape variation. LeafAnalyser places a large number of evenly distributed landmarks along leaf margins and records the position of each automatically. We used LeafAnalyser to analyse the variation in 3000 leaves from 400 plants of Antirrhinum majus. We were able to summarise the major trends in leaf shape variation using a principal components (PC) analysis and assess the changes in size, width and tip-to-base asymmetry within our leaf library. We demonstrate how this information can be used to develop a model that describes the range and variation of leaf shape within standard wild-type lines, and illustrate the shape transformations that occur between leaf nodes. We also show that information from LeafAnalyser can be used to identify novel trends in shape variation, as low-variance PCs that only affect a subset of position landmarks. These results provide a high-throughput method to calculate leaf shape variation that allows a large number of leaves to be visualised in higher-dimensional phenotypic space. To illustrate the applicability of LeafAnalyser we also calculated the leaf shape variation in 300 leaves from Arabidopsis thaliana.