Morphological Descriptors Research Articles

SVM-based fast 3D pore-scale rock-typing and permeability upscaling for complex rocks using Minkowski functionals

The rapid advancement of digital core analysis has greatly promoted the research progress of flow and transport in porous media. However, complex analytical process with exceeding computational load impedes the application on large data volume. Considering the strong heterogeneity of the underground porous media, the integration of pore-scale information into continuum scale is widely concerned for the future development of digital physical analysis. For hierarchical porous structures, pore-scale rock-typing and upscaling of petrophysical properties is a promising solution towards the issue, and morphological and topological descriptors associating data clustering methods are popularly utilized. However, the size of the regional support through which the parameter fields are generated heavily affects the descriptive capacities of the parameters and the following partitioning process. We propose in this work a robust integrated pore-scale rock-typing and upscaling technology for 3D porous structures which uses Minkowski functionals as the descriptive parameters. A fast-computational method utilising fast Fourier transform has been applied for efficient generation of the parameter fields. A comparative study between the two different classification methods of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) has been conducted on two complex artificial porous systems and a laminated sandstone through various regional support sizes. Throughout the test, SVM has illustrated obvious advantage of overcoming regional support size effect even with limited labelling information. The Upscaling of permeability on the natural sandstone sample based on the rock type distribution has demonstrated excellent accuracy comparing with full scale direct computation.

The integration of artificial intelligence with contrast-enhanced mammogram in the work up of suspicious breast lesions: what do you expect?

BackgroundThe enhancement overlaps at contrast-enhanced mammogram (CEM) between benign and malignant breast abnormalities presents a high probability of false-positive lesions and subjects females’ candidate for screening and diagnostic mammograms to unnecessary biopsy and anxiety. The current work aimed to evaluate the ability of mammograms scanned by artificial intelligence (AI) to enhance the specificity of CEM and support the probability of malignancy in suspicious and malignant looking breast lesions.MethodsThe study included 1524 breast lesions. The AI algorithm applied to the initial mammograms and generated location information for lesions. AI scoring suggested the probability of malignancy ranged from 100% (definite cancers) and < 10% (definite non-cancer) and correlated with recombinant contrast enhanced images.ResultsThe malignant proved abnormalities were 1165 (76.5%), and the benign ones were 359 (26.5%). BI-RADS 4 category was assigned in 704 lesions (46.2%) divided into 400 malignant (400/704, 56.8%) and 304 benign (304/704, 43.2%). BI-RADS 5 category presented by 820 lesions (53.8%), 765 of them were malignant (765/820, 93.3%) and 55 were benign (55/820, 6.7%). The sensitivity of digital mammogram whether supported by AI (93.9%) or contrast media (94.4%) was significantly increased to 97.2% (p < 0.001) when supported by both methods. Improvement of the negative predictive value (from 80.6% and 79.6% to 89.8%, p < 0.05) and the accuracy (from 91.1 and 88.8 to 94.0%, p < 0.01) was detected.ConclusionsContrast-enhanced mammogram helps in specification of different breast lesions in view of patterns of contrast uptake and morphology descriptors, yet with some overlap. The use of artificial intelligence applied on digital mammogram reduced the interpretational variability and limited attempts of re-biopsies of suspicious looking breast lesions assessed by contrast-enhanced mammograms.

Characterization of genetic variability among sorghum genotypes by morphological descriptors associated with high yield and shoot fly resistance

Characterization of genetic variability among sorghum genotypes by morphological descriptors associated with high yield and shoot fly resistance

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. This work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

The relationship between wing morphology and foraging guilds: exploring the evolution of wing ecomorphs in bats

Abstract Different aspects of foraging strategies in bats have been studied to understand the evolution of flight in mammals. General descriptors of wing morphology associated with flight performance, such as aspect ratio and wing loading, allowed us to describe ecomorphs determined by the dietary preferences of bat species. However, the role of wing shape divergence in the evolution of bat foraging strategies remains little explored. We adopted a two-dimensional geometric morphometric approach to quantify the wing shape and size variation explained by phylogenetic relatedness (families) and to evaluate the covariance between foraging guilds and flight descriptors based on phylogenetic comparative methods in 69 Neotropical bat species. We tested whether wing morphology represents a reliable marker of the foraging guild, and we explored the rate of shape evolution among foraging guilds to describe divergent trends that could explain the morphological and ecological diversification. Our results suggest that the earliest bat ancestor was an aerial forager occupying the edge space, which is congruent with the observed evolution of wing shape from an edge space wing morphology. The relationship between wing shape and foraging space defines wing ecomorphs, which probably evolved early in bat ancestors; a process other than convergence could explain this association.

The anisotropic grain size effect on the mechanical response of polycrystals: The role of columnar grain morphology in additively manufactured metals

Additively manufactured (AM) metals exhibit highly complex microstructures, particularly in terms of grain morphology which typically features heterogeneous grain size distribution, irregular and anisotropic grain shapes, and the so-called columnar grains. The conventional morphological descriptors based on grain shape idealization are generally inadequate for representing complex and anisotropic grain morphology of AM microstructures. The primary aspect of microstructural grain morphology is the state of grain boundary spacing or grain size whose effect on the mechanical response is known to be crucial. In this paper, we formally introduce the notion of axial grain size from which we derive mean axial grain size, effective grain size, and grain size anisotropy as robust morphological descriptors capable of effectively representing highly complex grain morphologies. We instantiated a discrete sample of polycrystalline aggregate as a representative volume element (RVE) featuring random crystallographic orientation and misorientation distributions. However, the instantiated RVE incorporates the typical morphological features of AM microstructures including distinctive grain size heterogeneity and anisotropic grain size owing to its pronounced columnar grain morphology. We ensured that any anisotropy observed in the macroscopic mechanical response of the instantiated sample primarily originates from its underlying anisotropic grain size. The RVE was then employed for mesoscale full-field crystal plasticity simulations corresponding to uniaxial tensile deformation along various axes via a spectral solver and a physics-based crystal plasticity constitutive model which was developed, calibrated, and validated in earlier studies. Through the numerical analyses, we isolated the contribution of anisotropic grain size to the anisotropy in the mechanical response of polycrystalline aggregates, particularly those with the characteristic complex grain morphology of AM metals. This contribution can be described by an inverse square relation.

NIMG-13. ROBUSTNESS OF PROGNOSTIC STRATIFICATION IN DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: INSIGHTS FROM THE RESPOND CONSORTIUM

Abstract PURPOSE Glioblastoma is the most prevalent primary malignant brain tumor in adults, with a median overall survival (OS) of approximately 15 months and only limited advancements in prognostication and survival prediction. This study aims to evaluate an AI-based prognostic stratification model for OS prediction trained on the ReSPOND consortium data and to validate its performance on an independent dataset. METHODS The AI model was trained on a cohort of 2,293 glioblastoma patients from 22 institutions across three continents. For validation, an independent cohort of 78 treatment-naïve patients was used from three institutions. Preoperative structural MRI scans were utilized for feature extraction. Automated segmentation defined three tumor sub-compartments: enhancing, necrotic, and peritumoral T2-FLAIR abnormality. The AI predictor incorporated variables such as patient age, normalized tumor sub-compartment volume, spatial distribution characteristics, and morphologic descriptors. The overall survival predictor index provided a continuous value between 0 and 1 for patient stratification that higher values indicating longer predicted OS. Generalizability was assessed using Leave-One-Cohort-Out-Cross-Validation (LOCOCV) for training data, and the model was subsequently applied to the validation cohort. RESULTS Survival analysis demonstrated a concordance index of 0.64 for LOCOCV training data and 0.59 for the independent validation data, indicating effective prognostic stratification of patients. CONCLUSION Multi-parametric AI assisted image analysis extracts prognostic biomarkers, which correlate with OS in glioblastoma patients. The generalizability of this method was validated using the extensive centralized glioblastoma imaging dataset registry from the ReSPOND consortium and an independent dataset, demonstrating its generalizability across diverse patient populations and acquisition settings. This model holds promise for robust prognostic stratification and prediction in de novo glioblastoma patients.

NIMG-66. RADIOGENOMICS-BASED HETEROGENEITY ANALYSIS OF GLIOBLASTOMA REVEALS MR IMAGING PHENOTYPICAL CHARACTERIZATION OF EGFR MUTATIONS

Abstract PURPOSE EGFR is one of the most frequently altered genes in glioblastoma, while also being an attractive therapeutic target for treatment. Having in vivo, imaging-based markers of EGFR mutations, and as well as characterizing molecular heterogeneity, are important for patient management and stratification into trials. Toward this end, we present deep learning models of imaging signatures of EGFR mutations built from MRI. METHODS Our cohort consists of 286 glioblastoma patients with multi-parametric MRI (mpMRI) scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI). Radiomics features, including histograms, morphologic and textural descriptors, were first derived. Genomic information was determined from a targeted next generation sequencing (NGS) panel. We jointly trained a variational auto-encoder and a multi-label classifier for predicting 4 key driver genes EGFR, NF1, PTEN, TP53 to learn a latent representation reflecting the molecular heterogeneity in the dataset. The latent representation was analyzed using principal component analysis (PCA). We calculated the Euclidean distance between imaging features of tumors with EGFR plus 1 of the other 3 mutations, from those of tumors having only EGFR mutations. We also analyzed the imaging signatures and variant allele frequencies (VAFs) of individuals with multiple (typically 3-4) resected tissue samples (n=49). RESULTS Co-occurrence of mutations in any of NF1, PTEN, TP53 genes with EGFR mutations resulted into dominance of imaging signatures of these other mutations over that of EGFR mutations. Interestingly, tumors with homogeneously high VAFs in EGFR mutations (n=3) exhibit strong phenotypical difference from the exclusive EGFR mutated group, while individuals with heterogeneous EGFR mutations (n=4) do not show such differences. CONCLUSION Our preliminary findings suggest EGFR imaging characteristics can be dominated by those of other co-occurring mutations. Tumors with homogeneously high VAFs, potentially indicating presence of germline EGFR mutations, lead to relatively distinct imaging phenotypes.

NIMG-39. BENCHMARKING MULTI-LABEL PREDICTION OF TARGET GENE MUTATION STATUS IN GLIOBLASTOMA USING RADIOGENOMICS AND MULTI-PARAMETRIC MR IMAGING

Abstract PURPOSE In vivo imaging-based prediction of gene mutation status can significantly contribute to the diagnosis and treatment of glioblastoma. Radiogenomics provides a non-invasive approach to capturing the phenotypical characteristics of molecular alterations. We build machine learning (ML) models on radiogenomics and MR imaging data for benchmarking the prediction of multiple target genes in a single institution dataset. METHODS Multi-parametric MRI scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of 286 individuals with glioblastoma were used to extract radiomics features, including histograms, morphological and textural descriptors. Genetic markers were obtained through a targeted next generation sequencing panel. We built ML models to predict mutation status in two settings: (a) 4 frequently-occurring mutations (EGFR, PTEN, NF1, TP53), (b) 13 mutations including some that are infrequent (PDGFRA, MET, FGFR2, PIK3CA, PIK3R1, BRAF, MDM4, CDKN2A). The models include k-nearest neighbor classifiers, fully-connected neural networks w/o dimensionality reduction trained on radiomics data, convolutional (CNN) and self-attention-based (ViT) networks trained on MR imaging data. All models were trained with 5-fold cross validation on discovery cohort (n=257) and tested on replication cohort (n=29). RESULTS Multi-label prediction models achieved highest accuracies of 75.9% / 88.6% for 4 / 13 mutations using radiomics features. Models trained with radiomics features have a marginally better average F1 score than CNNs and ViTs trained on MR imaging data (by 4.1% / 4.6% for 4 / 13 mutations). CONCLUSION We had modest success in finding highly distinct MR signatures of multiple target mutations in glioblastoma using multi-label ML methods, presumably because of the small sample size and the molecular, the spatial heterogeneity of the tumor, and the difficulty of simultaneously learning multiple imaging signatures. This highlights the importance of pooling and harmonizing radiogenomics data from multiple institutions to increase the sample size, and of incorporating biological domain knowledge into ML models.

NIMG-83. ELUCIDATING INTRA-TUMORAL HETEROGENEITY AND DISTINCT RADIOMIC FEATURES IN GLIOBLASTOMA METHYLATION SUBCLASSES

Abstract Glioblastoma, IDH-wildtype (GBM), is characterized by the inter- and intra-tumoral molecular heterogeneity. Genome-wide DNA methylation profiling has recently emerged as a promising tool offering complementary value to the classification of central nervous system tumors. In adult GBM patients, the most common methylation subclasses comprise RTK I, RTK II, and MES. We hypothesize that quantification of intricate and spatially complex radiomic features extracted from multi-parametric MRI (mpMRI) is informative to non-invasively determining characteristics of GBM methylation subclasses and their heterogeneity. In this study, mpMRI scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of newly diagnosed 23 GBM patients were retrospectively collected. We derived 6339 radiomic features, including histograms, morphologic and textural descriptors. For each case, 3-4 multiple samplings from different parts of the tumor were conducted. DNA was extracted from FFPE for each of sample and analyzed for genome-wide DNA methylation patterns using the Illumina EPIC array. Classification of methylation subclasses was performed using the MNP brain tumor classifier v12b6 of the DKFZ. Analysis of variance (ANOVA) with Bonferroni correction was employed to extract subclass-specific radiomic features. Of the 23 patients, 7 indicated intra-tumoral heterogeneity in methylation subclasses. We identified 68 radiomic features (p&amp;lt; 0.05) and 12 radiomic features (p&amp;lt; 0.01) that showed statistically significant differences among the methylation subclasses as determined by ANOVA with Bonferroni correction. A heatmap illustrating the relationships between subclasses and extracted radiomic features displayed distinct trends among subclasses and between heterogeneous and homogeneous cases within the same subclass. Our findings suggest the presence of heterogeneity within methylation subclasses and distinctive imaging patterns for each subclass. These findings potentially pave the way for a more personalized approach to glioblastoma management, highlighting the importance of further validation through the analysis of additional cases.

Copula-based modeling and simulation of 3D systems of curved fibers by isolating intrinsic fiber properties and external effects

In this paper we lay the foundation for data-driven 3D analysis of virtual fiber systems with respect to their microstructure and functionality. In particular, we develop a stochastic 3D model for systems of curved fibers similar to nonwovens, which is fitted to tomographic image data. By systematic variations of model parameters, efficient computer-based scenario analyses can be performed to get a deeper insight how effective properties of this type of functional materials depend on their 3D microstructure. In a first step, we consider single fibers as polygonal tracks which can be modeled by a third-order Markov chain. For constructing the transition function of the Markov chain, we formalize the intuitive notions of intrinsic fiber properties and external effects and build a copula-based transition function such that both aspects can be varied independently. Using this single-fiber model, in a second step we derive a model for the entire fiber system observed in a bounded sampling window and fit it to two different 3D datasets of nonwovens measured by CT imaging. Considering various geometric descriptors of the 3D microstructure related to effective properties of the pore space, we evaluate the goodness of model fit by comparing geometric descriptors of the 3D morphology of model realizations with those of tomographic image data.

Differential geometry-based thermodynamics derivation of isotropic and anisotropic eikonal non-local gradient (ENLG) damage models using a micromorphic media framework

The Eikonal non-local damage (ENL) approach models damage as a space-deforming phenomenon that affects the interaction distance between material points. As damage increases, interactions between points decrease, ultimately resulting in no interaction. In the integral version of such an approach, non-local interaction distances between material points are computed by solving a stationary Hamilton–Jacobi equation with a damage-dependent Riemannian metric. In the implicit gradient version of ENL models, the Riemannian metric figures in the Helmholtz equation to be solved for computing the non-local field controlling damage evolution. However, one of the main criticisms of such formulation is the lack of thermodynamics basis in its derivation. This paper presents a thermodynamics derivation of the Eikonal implicit gradient formulation based on differential geometry concepts to overcome this issue. A free-energy potential is defined considering the non-local strain as a morphological descriptor belonging to the abstract differentiable manifold (where the Riemannian metric is defined). Following a micromorphic media framework, the balance equations of the model are obtained. It is shown that the stress tensor and thermodynamic force associated with damage are the sum of standard and additional non-local contributions. It is also shown that the resulting energy dissipation is always positive, thus verifying the Clausius–Duhem inequality. After presenting all development considering second-order anisotropic continuum damage, the isotropic formulation is obtained as a particular case. Finally, a two-dimensional numerical implementation of an isotropic implicit Eikonal non-local gradient damage model is illustrated. Test cases are simulated to show the relocalization features of the considered formulation and its natural capability of naturally representing damage-to-fracture transition for high damage levels.

Interpretation of morphological descriptors on nanocellulose from oil palm frond fibers under weak acid, strong acid, and enzymatic treatments

Surface properties of the nanocellulose network from oil palm frond fibers have been modeled in this work by using a fractal model namely the generalized Cauchy process (GCP) to investigate the local fiber distribution and their geometrical effect on the loading capacity. Using the GCP model, the performance of the nanocellulose network under different treatment processes is quantified by interpreting the dual-fractal properties, namely power-law scaling in the local growth (variance) and correlation function, with independent scaling parameters. Our observations indicated a strong correlation between the fractal dimension of nanocellulose and the type and concentration of treatment chemicals, underlining its sensitivity to treatment variations. Additionally, we investigated the organization of cellulose nanocrystals within the morphological structure through simulated three-dimensional network topology, offering insights into pore distribution and aggregation formation. Furthermore, we delved into the influence of crosslinking on loading capacity. Our findings demonstrated a significant reduction in loading test result inconsistencies, emphasizing the enhanced stability and reliability of crosslinked nanocellulose. This research also highlighted the controlled release capabilities of crosslinked nanocellulose, positioning it as a promising candidate for tailored applications in sustained delivery systems.

Morphological descriptors of indigenous chickens: as a selection guide

ABSTRACT The visual morphologies in animals are associated positively or negatively with production, adaptation, and behavioral characteristics. For rural farmers with limited records, these features are considered reliable phenotypic markers. Thus, this study was intended to explore the unique morphological features of indigenous chickens as a farmer's selection guide. A total of 1,060 adult chickens from four rural districts were investigated for nine morphological traits. The single-comb type, supposed to be of good fertility, was the most prevalent. Yellow and white shanks were predominant, followed by gray, greenish-yellow, green, and black pigmentations. Crested and silky chickens were more frequent in highland areas, whereas naked-neck chickens were observed in lowlands. Chickens of the lowlands have brighter, more uniform plumage colors and yellow shank, beak, and earlobe colors. On the other hand, the highland and mid-altitude chickens are characterized by dark, colorful combinations of various feather colors: laced plumage, white shank, rose, and deformed rose combs. Moreover, multiple correspondence analyses revealed that morphological features have special associations with districts. Thus, the observed morphological heterogeneity of the indigenous chickens can be used as a good indicator of selective breeding. Therefore, the associations of these characteristics with productivity qualities could be confirmed by quantitative and molecular tools.

Morphological and Molecular Characterization of Pigeon Pea (Cajanus cajan (L.) Millsp.) For Improved Utilization

Background: Pigeon pea (Cajanus cajan (L.) Millsp.) is a multipurpose short-lived perennial food legume that provides fodder and wood for small holders. However, the crop remains underutilized with limited research on its diversification and improvement. Understanding the genetic diversity in germplasm of a crop is an efficient mean for unveiling unique traits that could be exploited for enhancing genetic improvement and utilization. This study characterized pigeon pea accessions using morphological descriptors and molecular markers. Methods: Twenty pigeon pea accessions were evaluated under field conditions in a randomized complete block replicated thrice. Data collected on agro-morphological descriptors, were used to characterize the accessions. Young leaf samples collected from each accession was used for DNA extraction following CTAB standard procedure. Genetic diversity was carried out using Diversity Arrays Technology Sequence Single Nucleotide Polymorphic (DArTseq SNP) markers. Data were analysed using PCA, cluster analysis, and ANOVA at α0.05. Results: The accessions differed significantly for all morphological parameters except days to 75% maturity and seed thickness. The accessions grouped into three clusters based on the morphological traits. Out of 2934 SNPs discovered all over the pigeon pea reference genome, only 2532 SNPs were retained after filtering. They were distributed all over the eleven chromosomes of pigeon pea. Conclusion: Morphological and molecular variability exists among the accessions. The variations in morphology of seed of pigeon pea accessions is a vital trait that could be selected for further improvement necessary for enhancing consumers’ preference and utilization

Patterns of change in coral reef communities of a remote Maldivian atoll revisited after eleven years

Coral reefs are exposed worldwide to several global and local human pressures including climate change and coastal development. Assessing the effects of such pressures on coral reef communities and the changes they undergo over time is mandatory to understand their possible future trends. Nonetheless, some coral reefs receive no or little scientific attention, as in the case of Huvadhoo Atoll that is an under-studied region in the southernmost area of the Maldives (Indian Ocean). This study analyzes the changes occurring over time in eight coral reefs (four inner reefs within the atoll lagoon and four outer reefs on the ocean side) at Huvadhoo Atoll, firstly surveyed in 2009 and revisited in 2020 using the same field methods. The cover of 23 morphological benthic descriptors (including different growth forms of Acropora) was taken into account and then grouped into three categories (i.e., hard coral, other benthic taxa and abiotic descriptors) to analyze the change in the composition of the coral reef community. Significant changes (e.g., increase in hard coral cover and decrease in abiotic descriptors) were observed in the inner reefs as compared to the outer reefs, which showed less variability. A significant decrease in tabular Acropora cover was observed in both inner and outer reefs, with possible negative effects on reef complexity and functioning. By comparing two time periods and two reef types, this study provides novel information on the change over time in the community composition of Maldivian coral reefs.

Diversidade genética e fenotípica de uma população de plântulas de moringa a partir de descritores morfológicos

Moringa (Moringa oleifera Lam. - Moringaceae) is a plant originating in India, which has been widely cultivated in different regions of the world, especially due to the nutritional properties of its leaves, which are of high economic, medicinal and industrial interest. However, there are no reports on the genotypic and phenotypic diversity of moringa seedlings cultivated in the Central-West region of Brazil. This study aimed to characterize a population of moringa seedlings using different qualitative morphological descriptors. A population consisting of 1,263 28-day-old moringa plants was initially classified and separated into four distinct phenotypic groups based on hypocotyl color [seedlings with hypocotyls colored green (100% green), ¾ green (75% green and 25% purple ), ¾ purple (25% green and 75% purple), and purple hypocotyl (100% purple)]. Initially, the seedling emergence rate and the frequency of occurrence of the different hypocotyl color classes in the total population were evaluated. Subsequently, 10 morphological descriptors were evaluated in each group of plants with different hypocotyl color. The plant emergence rate was 74%, indicating the adequate physiological quality of the seeds. The separation of the population into four phenotypic groups based on the color of the hypocotyl allowed us to separate 432 plants (34.2%) with a green hypocotyl, 480 plants (38.0%) with a ¾ green color, 336 plants (26.6% ) ¾ purple in color and 15 plants (1.2%) with purple hypocotyls. The results reported that of the ten morphological descriptors evaluated, only the type of insertion of the first pair of leaves (TI_1F) and the height of insertion of the first leaf (AI_1F) have significant differences (P &lt; 0.05) between the different phenotypic groups of seedlings. moringa. Seedlings from the phenotypic group with purple or ¾ purple hypocotyls have the entire insertion of the first pair of daughters of the opposite type, while seedlings with green and ¾ green hypocotyls have, respectively, 25% and 42% of the plants with the insertion of the first pair of alternating leaves. Seedlings with purple hypocotyls have a higher insertion height of the first leaf (AI_1F) when compared to green and ¾ green seedlings. Highly significant and negative correlations were obtained between TI_1F and DC (–0.99, P &lt; 0.05) and VR (–0.96; P &lt; 0.05). Highly significant and positive correlations were obtained between AI_1F and DC (0.96; P &lt; 0.05). It can be stated that there is genetic and phenotypic variability within a population of moringa seedlings, and the distinct phenotypic groups separated based on hypocotyl color have some characteristics or morphological descriptors typical of their group.

Genetic diversity and productive potential of starchy corn varieties evaluated in Peruvian highland environments

Corn cultivation is relevant to the province of Tayacaja, a region with the greatest biodiversity in the Huancavelica State, although to date there are no published studies. Phenotypic characterization pursues measuring the genetic diversity of a group of genotypes, using appropriate morphological descriptors. The main objective of this work was to study the agro-morphological characteristics and productive potential of 25 starchy corn varieties, evaluated in four contrasting environments of the province of Tayacaja, Perú, during the crop cycle 2019–2020. For this purpose, 25 plant and ear characteristics were chosen, using principal components (PC) and cluster analysis. The experiments were established in an alpha-lattice design, with three replications, in experimental units of two rows, with 0.20 m between plants and 0.80 m between furrows, 4 m long. The agro-morphological characterization grouped the 25 varieties within 6 well-defined clusters, giving an overview of the existing phenotypic variability among the evaluated starchy corn varieties. The varieties G6, G14, G20, GT3, and G18 were the genotypes with the most outstanding characteristics, which facilitated their separation from the rest of the materials studied. The multivariate analysis brought together representative varieties of certain races, with typical plant and ear characteristics. The study revealed the existence of a good productive and agronomic potential for the development of genetic materials for cancha (toasted kernel grains), choclo (fresh corn), and mote corn production; as well as the presence of anthocyanin in the grain, information that, together with the agro-morphological characterization, could be very useful to enhance gains in the selection process in future genetic improvement work.

Enhanced morphological assessment based on interocular asymmetry analysis for keratoconus detection.

To clarify the interocular asymmetry of corneal morphological descriptors and evaluate its discriminant ability of keratoconus (KC). This retrospective study recruited 344 normal participants and 290 KC patients, randomized to training and validation datasets. Interocular correlation and agreement were evaluated on 44 corneal morphological descriptors derived from Schiempflug tomography. Logistic regression models were constructed using binocular data and of which diagnostic performance was evaluated using the area under receiver operating characteristics curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Interocular agreement of corneal descriptors is better in the normal than in KC except for dimensions of cornea and anterior chamber. The interocular asymmetry increases along with the severity of KC. Interocular asymmetry in maximum anterior keratometry, mean anterior keratometry and higher-order aberrations of anterior surface show high AUC above 0.950. Binocular logistic regression index reaches an AUC of 0.963 with high specificity (95.2%) and brings gain to monocular parameters in distinguishing the normal eyes from KC (NRI = 0.080 (0.042 ~ 0.118), P < 0.001) and IDI = 0.071 (0.049 ~ 0.092), P < 0.001). Interocular asymmetry benefits even more in subclinical keratoconus (SKC) detection reflected by NRI (0.4784 (0.2703-0.6865), P < 0.001) and IDI (0.2680 (0.1495-0.3866), P < 0.001) measures. Interocular asymmetry is a well-characterized feature of KC and related to the severity. It is feasible to apply the interocular asymmetry in diagnosis of KC and SKC as a replenishment of monocular parameters and in progression tracking.

Seleção de acessos de rosa-do-deserto com alto potencial ornamental

Abstract Adenium obesum belongs to the Apocynaceae family and is characterized as a succulent shrub with a multitude of botanical and morphological features of ornamental interest. The aim of this study was to evaluate the genetic dissimilarity of 28 accessions of A. obesum using morphological descriptors and multivariate techniques with the aim of pre-selecting the genotypes with the greatest ornamental potential. The distribution of the number of flowers throughout the year showed two flowering peaks in January and September. Twenty-one petal pigmentation patterns were identified, and 50% of the accessions had double-petal arrangement. The Gower’s algorithm and the UPGMA-generated dissimilarity matrix indicated the formation of three groups. While Tocher’s clustering method separated the accessions into eight groups showing greater ability to distinguish the evaluated genotypes. In conclusion, the multivariate analyses applied were effective in accessing the genetic diversity among the 28 accessions evaluated. The accessions ICA001, ICA005, ICA006, ICA018, ICA019, and ICA027 were preselected to compose the germplasm collection due to their high ornamental potential.