Measuring hidden phenotype: Quantifying the shape of barley seeds using the Euler Characteristic Transform

Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare, and analyze this information embedded in a robust and concise way, we turn to Topological Data Analysis (TDA), specifically the Euler Characteristic Transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray Computed Tomography (CT) technology at 127 micron resolution. The Euler Characteristic Transform measures shape by analyzing topological features of an object at thresholds across a number of directional axes. A Kruskal-Wallis analysis of the information encoded by the topological signature reveals that the Euler Characteristic Transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine (SVM) to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of “hidden” shape nuances which are otherwise not detected.

Efficacy of 3D Dynamic Image Analysis for Characterizing the Morphology of Natural Sands

Two-dimensional Dynamic Image Analysis (DIA) is gaining acceptance in geotechnical engineering research. Three-dimensional (3D) DIA extracts features from 8-12 projections of a particles thus it is believed to verge on the true particle morphology. DIA is fast, efficient, and convenient for characterizing thousands of particles quickly; nevertheless, it captures shapes that are fundamentally different than the 3D morphologies reconstructed using micro-computed tomography (μCT). In DIA particle features are interpreted using external images of a particle, which fail to account for differences in imaging perspectives. In addition, 2D and 3D shape descriptors are influenced by differences in dimensionality projection owing to variations in definition, dimensionality, and perspectives of the particle images employed which causes them to differ from their 3D counterparts. In this study we compared sand particle size and shape descriptors obtained using both DIA and μCT for three natural sands having wide granulometries. 3D DIA offers significant advantages in terms of efficiency, while providing adequate representation of Feret dimensions, Sphericity and Convexity. However, the study demonstrates that 3D Roundness is difficult to characterize using DIA and that shape measurements of complex irregular calcareous sands obtained from 3D DIA are not comparable to those obtained using μCT.

Right Ventricular Shape Feature Quantification for Evaluation of Pulmonary Hypertension: Feasibility and Preliminary Associations with Clinical Outcome Submitted for Publication

This study aimed to demonstrate feasibility of statistical shape analysis techniques to identify distinguishing features of right ventricle (RV) shape as related to hemodynamic variables and outcome data in pulmonary hypertension (PH).Cardiovascular magnetic resonance images were acquired from 50 patients (33 PH, 17 Non-PH). Contemporaneous right heart catheterization data was collected for all individuals. Outcome was defined by all-cause mortality and hospitalization for heart failure. RV endocardial borders were manually segmented, and 3D surfaces reconstructed at end-diastole and end-systole. Registration and harmonic mapping were then used to create a quantitative correspondence between all RV surfaces. Proper orthogonal decomposition was performed to generate modes describing RV shape features. The first 15 modes captured over 98 % of the total modal energy. Two shape modes, 8 (free wall expansion) and 13 (septal flattening), stood out as relating to PH state (Mode 13: r=0.424, p=0.002. Mode 8: r=0.429, p=0.002). Mode 13 was significantly correlated with outcome (r = 0.438, p = 0.001), more so than any hemodynamic variable. Shape analysis techniques can derive unique RV shape descriptors corresponding to specific, anatomically meaningful features. The modes quantify shape features that had been previously only qualitatively related to PH progression. Modes describing relevant RV features are shown to correlate with clinical measures of RV status, as well as outcomes. These new shape descriptors lay the groundwork for a non-invasive strategy for identification of failing RVs, beyond what is currently available to clinicians.

Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors

This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory.

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.

Vertical clinging and leaping induced evolutionary rate shifts in postcranial evolution of tamarins and marmosets (Primates, Callitrichidae)

Background Callitrichids comprise a diverse group of platyrrhine monkeys that are present across South and Central America. Their secondarily evolved small size and pointed claws allow them to cling to vertical trunks of a large diameter. Within callitrichids, lineages with a high affinity for vertical supports often engage in trunk-to-trunk leaping. This vertical clinging and leaping (VCL) differs from horizontal leaping (HL) in terms of the functional demands imposed on the musculoskeletal system, all the more so as HL often occurs on small compliant terminal branches. We used quantified shape descriptors (3D geometric morphometrics) and phylogenetically-informed analyses to investigate the evolution of the shape and size of the humerus and femur, and how this variation reflects locomotor behavior within Callitrichidae. Results The humerus of VCL-associated species has a narrower trochlea compared with HL species. It is hypothesized that this contributes to greater elbow mobility. The wider trochlea in HL species appears to correspondingly provide greater stability to the elbow joint. The femur in VCL species has a smaller head and laterally-oriented distal condyles, possibly to reduce stresses during clinging. Similarly, the expanded lesser trochanters visible in VCL species provide a greater lever for the leg retractors and are thus also interpreted as an adaptation to clinging. Evolutionary rate shifts to faster shape and size changes of humerus and femur occurred in the Leontocebus clade when a shift to slower rates occurred in the Saguinus clade. Conclusions Based on the study of evolutionary rate shifts, the transition to VCL behavior within callitrichids (specifically the Leontocebus clade) appears to have been an opportunity for radiation, rather than a specialization that imposed constraints on morphological diversity. The study of the evolution of callitrichids suffers from a lack of comparative analyses of limb mechanics during trunk-to-trunk leaping, and future work in this direction would be of great interest.

Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors

Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.