Proximal hyperspectral sensing and data analysis approaches for field-based plant phenomics

Field-based plant phenomics requires robust crop sensing platforms and data analysis tools to successfully identify cultivars that exhibit phenotypes with high agronomic and economic importance. Such efforts will lead to genetic improvements that maintain high crop yield with concomitant tolerance to environmental stresses. The objectives of this study were to investigate proximal hyperspectral sensing with a field spectroradiometer and to compare data analysis approaches for estimating four cotton phenotypes: leaf water content (Cw), specific leaf mass (Cm), leaf chlorophyll a+b content (Cab), and leaf area index (LAI). Field studies tested 25 Pima cotton cultivars grown under well-watered and water-limited conditions in central Arizona from 2010 to 2012. Several vegetation indices, including the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), and the physiological (or photochemical) reflectance index (PRI) were compared with partial least squares regression (PLSR) approaches to estimate the four phenotypes. Additionally, inversion of the PROSAIL plant canopy reflectance model was investigated to estimate phenotypes based on 3.68 billion PROSAIL simulations on a supercomputer. Phenotypic estimates from each approach were compared with field measurements, and hierarchical linear mixed modeling was used to identify differences in the estimates among the cultivars and water levels. The PLSR approach performed best and estimated Cw,Cm,Cab, and LAI with root mean squared errors (RMSEs) between measured and modeled values of 6.8%, 10.9%, 13.1%, and 18.5%, respectively. Using linear regression with the vegetation indices, no index estimated Cw,Cm,Cab, and LAI with RMSEs better than 9.6%, 16.9%, 14.2%, and 28.8%, respectively. PROSAIL model inversion could estimate Cab and LAI with RMSEs of about 16% and 29%, depending on the objective function. However, the RMSEs for Cw and Cm from PROSAIL model inversion were greater than 30%. Compared to PLSR, advantages to the physically-based PROSAIL model include its ability to simulate the canopy's bidirectional reflectance distribution function (BRDF) and to estimate phenotypes from canopy spectral reflectance without a training data set. All proximal hyperspectral approaches were able to identify differences in phenotypic estimates among the cultivars and irrigation regimes tested during the field studies. Improvements to these proximal hyperspectral sensing approaches could be realized with a high-throughput phenotyping platform able to rapidly collect canopy spectral reflectance data from multiple view angles

The C-terminal domain of TPX2 is made of alpha-helical tandem repeats

Background: TPX2 (Targeting Protein for Xklp2) is essential for spindle assembly, activation of the mitotic kinase Aurora A and for triggering microtubule nucleation. Homologs of TPX2 in Chordata and plants were previously identified. Currently, proteins of the TPX2 family have little structural information and only small parts are covered by defined protein domains. Methods: We have used computational sequence analyses and structural predictions of proteins of the TPX2 family, supported with Circular Dichroism (CD) measurements. Results: Here, we report our finding that the C-terminal domain of TPX2, which is responsible of its microtubule nucleation capacity and is conserved in all members of the family, is actually formed by tandem repeats, covering well above 2/3 of the protein. We propose that this region forms a flexible solenoid involved in protein-protein interactions. Structural prediction and molecular modeling, combined with Circular Dichroism (CD) measurements reveal a predominant alpha-helical content. Furthermore, we identify full length homologs in fungi and shorter homologs with a different domain organization in diptera (including a paralogous expansion in Drosophila). Conclusions: Our results, represent the first computational and biophysical analysis of the TPX2 proteins family and help understand the structure and evolution of this conserved protein family to direct future structural studies

Assessment of conjunctival, episcleral and scleral thickness in healthy individuals using anterior segment optical coherence tomography

Purpose: To determine the thickness of the conjunctiva, episclera and sclera in healthy individuals using anterior segment optical coherence tomography (AS-OCT).Methods: We prospectively included 107 healthy individuals of different age groups (18-39 years, 40-54 years, 55-69 years and >= 70 years). For each eye, AS-OCT scans of four quadrants (temporal, nasal, superior and inferior) were acquired. The thickness of the conjunctiva, episclera and sclera was measured for each scan. In addition, the axial length of both eyes was measured, and general characteristics, including smoking, allergies and contact lens use, were collected.Results: The mean conjunctival thickness was significantly different between the nasal and superior quadrants (87 +/- 30 mu m vs. 77 +/- 16 mu m; p < 0.001), as well as the superior and inferior quadrants (77 +/- 16 mu m vs. 86 +/- 19 mu m; p = 0.001). The mean episcleral thickness was larger in the superior (174 +/- 54 mu m) and inferior (141 +/- 43 mu m) quadrants, compared to the nasal (83 +/- 38 mu m) and temporal quadrants (90 +/- 44 mu m). The mean scleral thickness of the inferior quadrant was the largest (596 +/- 64 mu m), followed by the nasal (567 +/- 76 mu m), temporal (516 +/- 67 mu m) and superior (467 +/- 52 mu m) quadrants (all p < 0.001). The averaged scleral thickness increased 0.96 mu m per age year (0.41-1.47 mu m, p < 0.001).Conclusions: This study provides an assessment of the thickness of scleral and adjacent superficial layers in healthy individuals determined on AS-OCT, which could enable future research into the use of AS-OCT in diseases affecting the anterior eye wall

Assessment of conjunctival, episcleral and scleral thickness in healthy individuals using anterior segment optical coherence tomography

Purpose: To determine the thickness of the conjunctiva, episclera and sclera in healthy individuals using anterior segment optical coherence tomography (AS-OCT).Methods: We prospectively included 107 healthy individuals of different age groups (18-39 years, 40-54 years, 55-69 years and >= 70 years). For each eye, AS-OCT scans of four quadrants (temporal, nasal, superior and inferior) were acquired. The thickness of the conjunctiva, episclera and sclera was measured for each scan. In addition, the axial length of both eyes was measured, and general characteristics, including smoking, allergies and contact lens use, were collected.Results: The mean conjunctival thickness was significantly different between the nasal and superior quadrants (87 +/- 30 mu m vs. 77 +/- 16 mu m; p < 0.001), as well as the superior and inferior quadrants (77 +/- 16 mu m vs. 86 +/- 19 mu m; p = 0.001). The mean episcleral thickness was larger in the superior (174 +/- 54 mu m) and inferior (141 +/- 43 mu m) quadrants, compared to the nasal (83 +/- 38 mu m) and temporal quadrants (90 +/- 44 mu m). The mean scleral thickness of the inferior quadrant was the largest (596 +/- 64 mu m), followed by the nasal (567 +/- 76 mu m), temporal (516 +/- 67 mu m) and superior (467 +/- 52 mu m) quadrants (all p < 0.001). The averaged scleral thickness increased 0.96 mu m per age year (0.41-1.47 mu m, p < 0.001).Conclusions: This study provides an assessment of the thickness of scleral and adjacent superficial layers in healthy individuals determined on AS-OCT, which could enable future research into the use of AS-OCT in diseases affecting the anterior eye wall

Epithelial Ovarian Cancer Diagnosis of SecondHarmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol

A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. However, automated or semiautomated solutions are necessary to ensure objectivity and reproducibility of such analysis. This work describes automation and improvement methods for calculating the anisotropy (using fast Fourier transform analysis and the gray-level co-occurrence matrix). These were applied to analyze biopsy samples of human ovarian epithelial cancer at different stages of malignancy (mucinous, serous, mixed, and endometrial subtypes). The semiautomation procedure enabled us to design a diagnostic protocol that recognizes between healthy and pathologic tissues, as well as between different tumor types.Fil: Zeitoune, Angel Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; ArgentinaFil: Luna, Johana S. J.. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Sanchez Salas, Kynthia. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Erbes, Luciana Ariadna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; ArgentinaFil: Cesar, Carlos L.. Universidade Federal do Ceará; Brasil. National Institute of Science and Technology on Photonics Applied to Cell Biology; BrasilFil: Andrade, Liliana A. L. A.. Universidade Estadual de Campinas; BrasilFil: Carvahlo, Hernades F.. Universidade Estadual de Campinas; Brasil. National Institute of Science and Technology on Photonics Applied to Cell Biology; BrasilFil: Bottcher Luiz, Fátima. Universidade Estadual de Campinas; Brasil. National Institute of Science and Technology on Photonics Applied to Cell Biology; BrasilFil: Casco, Victor Hugo. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; ArgentinaFil: Adur, Javier Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Entre Ríos. Universidad Nacional de Entre Ríos. Centro de Investigaciones y Transferencia de Entre Ríos; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentin

Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques

Background and purpose: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. Methods: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. Results: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. Conclusions: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%. Keywords: Bagging; Multilayer Perceptron; Naive Bayes classifier; clinically isolated syndrome; diffusion tensor imaging; machine-learning; magnetic resonance imaging; multiple sclerosis; radiologically isolated syndrome.Comment: 24 pages, 2 table

Silver nanoparticles-composing alginate/gelatine hydrogel improves wound healing in vivo

Polymer hydrogels have been suggested as dressing materials for the treatment of cutaneous wounds and tissue revitalization. In this work, we report the development of a hydrogel composed of natural polymers (sodium alginate and gelatin) and silver nanoparticles (AgNPs) with recognized antimicrobial activity for healing cutaneous lesions. For the development of the hydrogel, different ratios of sodium alginate and gelatin have been tested, while different concentrations of AgNO3 precursor (1.0, 2.0, and 4.0 mM) were assayed for the production of AgNPs. The obtained AgNPs exhibited a characteristic peak between 430450 nm in the ultraviolet-visible (UVVis) spectrum suggesting a spheroidal form, which was confirmed by Transmission Electron Microscopy (TEM). Fourier Transform Infra-red (FTIR) analysis suggested the formation of strong intermolecular interactions as hydrogen bonds and electrostatic attractions between polymers, showing bands at 2920, 2852, 1500, and 1640 cm1. Significant bactericidal activity was observed for the hydrogel, with a Minimum Inhibitory Concentration (MIC) of 0.50 µg/mL against Pseudomonas aeruginosa and 53.0 µg/mL against Staphylococcus aureus. AgNPs were shown to be non-cytotoxic against fibroblast cells. The in vivo studies in female Wister rats confirmed the capacity of the AgNP-loaded hydrogels to reduce the wound size compared to uncoated injuries promoting histological changes in the healing tissue over the time course of wound healing, as in earlier development and maturation of granulation tissue. The developed hydrogel with AgNPs has healing potential for clinical applications.This research received funding from the Coordenação Aperfeiçoamento de Pessoal de Nivel Superior (CAPES), Fundação de Amparo à Pesquisa do Estado de Sergipe (FAPITEC), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, #443238/2014-6, #470388/2014-5), and from the Portuguese Science and Technology Foundation (FCT) projects M-ERA-NET/0004/2015 (PAIRED) and UIDB/04469/2020 (strategic fund).info:eu-repo/semantics/publishedVersio

Correction: Diniz et al. Silver Nanoparticles-Composing Alginate/Gelatine Hydrogel Improves Wound Healing In Vivo. Nanomaterials 2020, 10, 390

In the original publication, there was a mistake in Figure 6 as published [...]info:eu-repo/semantics/publishedVersio