Search Results (236)

Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping. Full article

The heart exhibits inherently nonlinear and chaotic electrical dynamics, making the early detection of myocardial ischemia (MI) challenging using traditional electrocardiography (ECG) or standard magnetocardiography (MCG). In this study, we propose an engineering-oriented framework that integrates classical nonlinear dynamics with machine-learning-based analysis, termed the Magnetocardiography Chaotic Dynamics Map (MCDM), to reconstruct nonlinear phase-space trajectories from 36-channel MCG recordings and capture differences in reconstructed nonlinear dynamics associated with ischemic conditions. Morphological and quantitative analyses of the MCDM patterns reveal marked differences between healthy and ischemic subjects. Using a machine-learning classifier trained on HOG and LBP descriptors, the proposed MCDM-based model achieved an accuracy of 92.19%, a sensitivity of 88.75%, a specificity of 95.63%, an F1-score of 91.91%, and an AUC of 89.80%, demonstrating effective discriminative capability for early ischemia screening. Owing to its computational simplicity and noninvasive nature, the proposed MCDM framework represents a promising tool for scalable screening of ischemic heart disease. Full article

Pod corn (Zea mays var. tunicata) bears leafy glumes that enclose kernels, resembling a partial reversion to wild-forms, yet remains poorly characterized in situ in Mexico. We evaluated Mexican accessions at two contrasting locations to quantify morphological/phenological diversity and to assess functional traits via proximate kernel composition. Standard descriptors captured variation in plant architecture, tassel/ear traits (including glume length), and reproductive timing. Accessions showed strong plasticity and significant accession × environment effects on ear morphology and maturation. Grain yield ranged from 6.32 to 10.78 t ha⁻¹, with peak values comparable to commercial hybrids and above-typical yields reported for native Mexican races (2.7–6.6 t ha⁻¹). Proximate analysis showed that milling with the tunic increased moisture/ash (up to 3.07% vs. 1.80% in dehulled grain), tended to lower fat and protein, and yielded lower crude fiber than dehulled samples (0.78–0.96% vs. 1.59–1.77%); protein varied widely (1.05–6.64%). Thus, the tunic modulates elemental composition, informing processing choices (with vs. without tunic). Our results document a spectrum of morphotypes and highlight developmental diversity and field adaptability. The observed accession × environment responses provide a practical baseline for comparisons with native and improved varieties, and help guide product development strategies. Collectively, these data underscore the high productive potential of pod corn (up to 10.78 t ha⁻¹ under optimal management) and show that including the tunic substantially alters proximate composition, establishing a quantitative foundation for genetic improvement and food applications. Overall, pod corn’s distinctive ear morphology and context-dependent composition reinforce its value for conservation, developmental genetics, and low-input systems. Full article

Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an integrated approach that simultaneously quantifies morphology and viscoelasticity in the human osteosarcoma cell line MG-63. Stress–relaxation experiments and optical imaging of the same cells were performed using a custom-built system that couples Atomic Force Microscopy (AFM) with an inverted optical microscope. Morphometric parameters were extracted from cell contours, while viscoelastic properties were obtained by fitting AFM data to the Fractional Kelvin (FK) and Fractional Zener (FZ) models. Among the morphological descriptors, the Shape Complexity (SC) was proposed. It is derived from the Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which captures fine-scale contour features overlooked by conventional metrics. Experimental results show that, in MG-63 cells, higher SC values are associated with greater stiffness, indicating a correlation between cell shape complexity and cell stiffness. Furthermore, loading-rate analysis shows that the FZ model captures strain-rate-dependent stiffening more effectively than the FK model. This methodology provides a first approach to jointly analyzing quantitative morphological parameters and mechanical properties, underlining the importance of combined studies to achieve a comprehensive understanding of cell behavior. Full article

This technical note presents a quantitative image-based framework for evaluating the packing and compressibility of granular soils, specifically applied to building foundation design in civil infrastructure projects. The Sedimaging system replicates hydraulic sedimentation in a controlled column, equipped with a high-resolution camera, to visualize particle orientation after deposition. Grayscale images of the settled bed are analyzed using Haar Wavelet Transform (HWT) decomposition to quantify directional intensity gradients. A new descriptor, termed the sediment index (B), is defined as the ratio of vertical to horizontal wavelet energy at the dominant scale, representing the preferential alignment and anisotropy of particles during sedimentation. Experimental investigations were conducted on fifteen granular materials that include natural sands, tailings, glass beads and rice grains with different shapes. The results demonstrate strong correlations between B and both microscopic shape ratios (d₁/d₂ and d₁/d₃) and macroscopic properties. Linear relationships predict the limiting void ratios (e_max, e_min) with mean absolute differences of 0.04 and 0.03, respectively. A power-law function relates B to the compression index (C_c) with an average deviation of 0.02. These findings confirm that the sediment index effectively captures the morphological influence of particle shape on soil packing and compressibility. Compared with conventional physical testing, the Sedimaging-based approach offers a rapid, non-destructive, and high-throughput solution for estimating soil packing and compressibility of cohesionless, sand-sized granular soils directly from post-settlement imagery, making it particularly valuable for preliminary site assessments, geotechnical screening, and intelligent monitoring of granular materials in building foundation design and other infrastructure applications, such as earth–rock dams. Full article

Sweetpotato genotypes, often known by regional names, are easily propagated via cuttings, which can lead to mixing and misidentification of cultivars. This complicates traceability and commercialization. Accurate characterization of common genotypes would support their formal registration and strengthen the sweetpotato value chain. Sweetpotato is a staple crop in Brazil, and in this study, four states, representing different geographic regions in Brazil, were selected. A total of 37 samples were collected in these states, and the samples were evaluated by SSR molecular markers and morphological traits. The samples were cleaned of virus and compared to the global sweetpotato collection held at the International Potato Center under the International Treaty on Plant Genetic Resources for Food and Agriculture. SSR markers effectively distinguished among accessions. The genotype locally known as “Canadense” matched closely both genetically and morphologically to the CIP accession ‘Blesbok’. This alignment paves the way for formalizing cuttings and root production of “Canadense”/‘Blesbok’ for commercial use. In contrast, several accessions marketed in Sergipe as “white skin sweetpotato” did not correspond to any known CIP accession, suggesting that they may be unique regional genotypes or acquired from other sources, since sweetpotato is an exotic crop in Brazil. Overall, the research identified key genotypes, supporting their official registration with Brazil’s Ministry of Agriculture, Livestock, and Supply, thereby enhancing the legal commercialization of cuttings and roots. Additionally, the clear molecular and trait-based classification will assist sweetpotato crop improvement programs in selecting appropriate parent lines for future crosses. Full article

Peripheral nerve injuries, particularly those involving the sciatic nerve, remain a major clinical challenge due to incomplete functional recovery and the limited translation of preclinical advances into effective therapies. This review synthesizes current evidence on the phase-specific evaluation of sciatic nerve regeneration in preclinical models, integrating behavioral, sensory, electrophysiological, and morphological approaches across the acute, subacute (Wallerian degeneration), early regenerative, and late regenerative phases. By mapping functional readouts onto the underlying biological events of each phase, we highlight how tools such as the Sciatic Functional Index, Beam Walk test, Rotarod test, nerve conduction studies, and nociceptive assays provide complementary and often non-interchangeable information about motor, sensory, and neuromuscular recovery. We further examine emerging therapeutic strategies, including intraoperative electrical stimulation, immunomodulation, platelet-rich plasma, bioengineered scaffolds, conductive and piezoelectric conduits, exosome-based hydrogels, tacrolimus delivery systems, and small molecules, emphasizing the importance of aligning their mechanisms of action with the dynamic microenvironment of peripheral nerve repair. Despite substantial advancements in experimental models, an analysis of publication trends and registries reveals a persistent translational gap, with remarkably few clinical trials relative to the high volume of preclinical studies. To illustrate how mechanistic insights can be complemented by molecular-level characterization, we also present a targeted computational analysis of alpha-lipoic acid (ALA,) including frontier orbital energies, physicochemical descriptors, and docking interactions with IL-6, TGF-β, and a growth-factor receptor—performed solely for this molecule due to its documented structural availability and relevance. By presenting an integrated, phase-specific framework for functional assessment and therapeutic evaluation, this review underscores the need for standardized, biologically aligned methodologies to improve the rigor, comparability, and clinical relevance of future studies in sciatic nerve regeneration. Full article

Quinoa, in addition to its nutritional benefits, is adaptable to, and tolerant of, high-altitude and Mediterranean environmental conditions. However, its largely cross-compatible free-living ancestor, pitseed goosefoot, possesses expansive adaptive variation as its ecotypes are found on arid or well-drained soils throughout temperate and subtropical North America. In this context, the objective of this study was to characterize F7:10 lines from quinoa × pitseed goosefoot hybrids to identify promising lines with desirable agronomic traits and adaptation to hyper-arid production environments. The agro-morphological characterization of 14 interspecific experimental lines plus wild parents (5), checks (3, including one derived from a much earlier wide cross), and an F2 population was performed for 25 quantitative and 26 qualitative descriptors, along with calculation of the selection index. Among the morphological variables, the average number of primary branches per plant (NPB) was six (CV = 78%), the average plant height (PH) was 143.5 cm (CV = 40%), and the average panicle diameter (PDI) was 17.9 cm (CV = 62%). With regard to the yield component variables, the average harvest index (HI) was 39% (CV = 36%), the average weight of 1000 grains (W1000G) was 2.59 g (CV = 42%), and the average yield per hectare (HYP) was 4.68 t ha⁻¹ (CV = 65%). Regarding the correlations between variables, it was observed that all phenological phases showed positive correlations with plant height (PH) and negative correlations with yield components, specifically with DG, DT, HI, and W1000G. The highest-yielding lines were GR10 (8.16 t ha⁻¹), GR07 (7.53 t ha⁻¹), GR11 (7.27 t ha⁻¹), and GR01 (7.02 t ha⁻¹). Multivariate and cluster analyses identified four groups of lines, with groups II and IV standing out for their desirable agronomic traits. However, based on the selection index, lines RL08, RL07, ER06, GR03, and GR11 were identified as the most promising. In terms of quality, 18 out of the 23 lines were classified as sweet (<0.11% saponin) and 5 as bitter (>0.11 saponin). In conclusion, the selection index identified pitseed goosefoot cross-derived quinoa lines having superior yield potential, short plant height, large grain size, early maturity, and low saponin content. Full article

Background/Objectives: Anatomical variation in the ethmoid roof and lateral lamella play an important role in anatomical vulnerability during endoscopic sinus and skull base surgery. However, widely used classifications, including the Keros system, primarily focus on vertical depth and may not fully reflect the complex geometric relationship between the ethmoid roof, lateral lamella, and the anterior ethmoidal artery (AEA). This study aimed to characterize ethmoid roof and lateral lamella anatomy using high-resolution CT and to propose a descriptive radiological framework—the Akcan Classification—that integrates AEA exit patterns with multiple morphometric parameters. Given the complexity of thin skull base structures, interobserver reproducibility of all morphometric parameters was additionally assessed to ensure measurement robustness. Methods: High-resolution paranasal sinus CT scans from 175 adults (350 sides) were retrospectively evaluated. Measurements included ethmoid roof width, lateral lamella depth, anterior–posterior length, lamellar angle, AEA–lamella distance, and sinonasal anatomical variations. Interobserver reliability was quantified using ICCs. AEA morphology was categorized as in-canal (Type 1), partially suspended (Type 2), or fully suspended (Type 3) based on radiological appearance of bony canalization. Appropriate statistical tests were used to compare morphometric features across groups. Results: Suspended AEA configurations demonstrated progressively wider ethmoid roofs, deeper lateral lamellae, steeper lamellar inclination, and shorter AEA–lamella distances (all p < 0.001). Supraorbital ethmoid cells were more frequently observed in Type 3 cases (p < 0.001). Other anatomical variations showed no significant association with ethmoid roof morphology. Interobserver reliability was excellent for all measurements (ICC range 0.87–0.94). Conclusions: The findings suggest that AEA configuration is associated with broader patterns of ethmoid roof and lateral lamella morphology. Rather than serving as a validated predictor of surgical outcomes, the Akcan Classification provides a structured anatomical and radiological descriptor that complements depth-based systems such as the Keros classification. The high reproducibility of measurements supports its potential utility for standardized anatomical assessment and preoperative radiological interpretation, while further studies incorporating surgical correlation are required. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

The species Caesalpinia spinosa, is a native forest tree of the Andes, which has multiple and valuable uses. In this study, a total of 39 guarango accessions from INIAP´s Gene Bank collection, were evaluated to determine their morphological and ecogeographical diversity. Seventeen quantitative and seven qualitative descriptors were used to characterize morphologically seeds and trees. Multivariate analyses revealed four morphological groups mainly differentiated by seed germination, viability rates, total tree height, and seed and leaflet dimensions, whereas descriptors such as seed color, shape and hilum position, presence of spines, and stem color were not discriminant. On the other hand, ecogeographical characterization, based on 21 bioclimatic, edaphic, and geophysical variables, identified six groups distributed latitudinally along the Ecuadorian Andes. A lack of significant correlation between morphological and ecogeographical variation (Mantel test) was found, suggesting that phenotypic expression is shaped by independent genetic and environmental drivers. This research is the first comprehensive morphological and ecogeographical characterization of the species in Ecuador. This new information will strengthen in situ and ex situ conservation efforts as well as promote the sustainable use of the species in the near future. Full article

Cherimoya is a fruit tree native to the Andean regions of South America, also in Central America, prized for its flavor, nutritional properties, and medicinal potential. Despite its economic relevance, in situ assessments of phenotypic diversity are limited, particularly in southern Ecuador, a key center of domestication. This study evaluated the morphological and ecogeographic diversity of 270 native trees across eight cantons in Loja province, Ecuador, using 34 qualitative and quantitative descriptors of leaves, flowers, fruits, and seeds. High phenotypic variability was observed, with coefficients of variation exceeding 40% for key traits, including mature fruit weight (48.15%), pulp weight (55.33%) and pulp-to-seed ratio (64.23%). Principal component analysis revealed three major axes of variation associated with productivity, floral morphology, and organoleptic quality. Cluster analysis identified four groups, with one distinguished by a favorable pulp-to-seed ratio and sugar–acid content. Species distribution modeling, which included bioclimatic and soil variables, showed that Gonzanamá, Quilanga and Espíndola possess the highest ecological suitability for cherimoya. These findings highlight priority areas for in situ conservation and phenotype selection, providing a foundation for sustainable use, genetic improvement, and the preservation of locally adapted germplasm to support climate-resilient agricultural systems. Full article

Honey authenticity, including its botanical origin, is traditionally assessed by melissopalynology, a labour-intensive and expert-dependent method. This study reports the final validation of a deep learning model for pollen grain classification in honey, developed within the NUTRITECH.I-004A/22 project, by comparing its performance with that of an independent palynology expert. A dataset of 5194 pollen images was acquired from five unifloral honeys, rapeseed (Brassica napus), sunflower (Helianthus annuus), buckwheat (Fagopyrum esculentum), phacelia (Phacelia tanacetifolia) and linden (Tilia cordata), under a standardized microscopy protocol and manually annotated using an extended set of morphological descriptors (shape, size, apertures, exine ornamentation and wall thickness). The evaluation involved training and assessing a deep learning model based solely on the ResNet152 architecture with pretrained ImageNet weights. This model was enhanced by adding additional layers: a global average pooling layer, a dense hidden layer with ReLU activation, and a final softmax output layer for multi-class classification. Model performance was assessed using multiclass metrics and agreement with the expert, including Cohen’s kappa. The AI classifier achieved almost perfect agreement with the expert (κ ≈ 0.94), with the highest accuracy for pollen grains exhibiting spiny ornamentation and clearly thin or thick walls, and lower performance for reticulate exine and intermediate wall thickness. Misclassifications were associated with suboptimal image quality and intermediate confidence scores. Compared with traditional melissopalynological assessment (approx. 1–2 h of microscopic analysis per sample), the AI system reduced the effective classification time to less than 2 min per prepared sample under routine laboratory conditions, demonstrating a clear gain in analytical throughput. The results demonstrate that, under routine laboratory conditions, AI-based digital palynology can reliably support expert assessment, provided that imaging is standardized and prediction confidence is incorporated into decision rules for ambiguous cases. Full article

Advancements in deep learning have revolutionized materials discovery by enabling predictive modeling of complex material properties. However, single-modal approaches often fail to capture the intricate interplay of compositional, structural, and morphological characteristics. This study introduces a novel multimodal deep learning framework for enhanced material property prediction, integrating textual (chemical compositions), tabular (structural descriptors), and image-based (2D crystal structure visualizations) modalities. Utilizing the Alexandriadatabase, we construct a comprehensive multimodal dataset of 10,000 materials with symmetry-resolved crystallographic data. Specialized neural architectures, such as FT-Transformer for tabular data, Hugging Face Electra-based model for text, and TIMM-based MetaFormer for images, generate modality-specific embeddings, fused through a hybrid strategy into a unified latent space. The framework predicts seven critical material properties, including electronic (band gap, density of states), thermodynamic (formation energy, energy above hull, total energy), magnetic (magnetic moment per volume), and volumetric (volume per atom) features, many governed by crystallographic symmetry. Experimental results demonstrated that multimodal fusion significantly outperforms unimodal baselines. Notably, the bimodal integration of image and text data showed significant gains, reducing the Mean Absolute Error for band gap by approximately 22.7% and for volume per atom by 22.4% compared to the average unimodal models. This combination also achieved a 28.4% reduction in Root Mean Squared Error for formation energy. The full trimodal model (tabular + images + text) yielded competitive, and in several cases the lowest, error metrics, particularly for band gap, magnetic moment per volume and density of states per atom, confirming the value of integrating all three modalities. This scalable, modular framework advances materials informatics, offering a powerful tool for data-driven materials discovery and design. Full article

Background/Objectives: Differentiating benign, premalignant, and early malignant vocal fold lesions is challenging. Perpendicular vascular changes (PVCs) per the European Laryngological Society (ELS) are key malignancy indicators. Enhanced contact endoscopy with narrow-band imaging (NBI-CE) visualizes intrapapillary capillary loops (IPCLs) at high magnification, independent of gross morphology. However, defining malignancy as any PVC increases sensitivity but lowers specificity—particularly in papillomas—whereas limiting malignancy to narrow-angle PVC improves specificity but risks false negatives and reduced reproducibility. Methods: We intraoperatively evaluated 146 histology-proven vocal fold lesions using NBI-CE. Six raters (three experienced otolaryngologists, three PhD students) classified vascular patterns. Two approaches were tested: (1) malignancy = narrow-angle PVC; (2) malignancy = any PVC. Outcomes were accuracy, sensitivity, specificity, and interrater agreement. Results: Approach (1) had higher specificity but lower sensitivity than (2) (~85% vs. ~70% specificity; ~50% vs. ~80% sensitivity). Accuracy did not differ significantly. Experienced raters showed higher interrater agreement and a more favorable sensitivity–specificity balance. Common errors were false positives in papillomas and false negatives in dysplasia/early carcinoma. Conclusions: PVC assessment with NBI-CE is feasible and informative. Choosing between “any PVC” and “narrow-angle only” entails a sensitivity–specificity trade-off and depends on lesion type and experience. Refined ELS descriptors and automated analysis may improve reproducibility and accuracy. Full article