Inter-Variability Study of COVLIAS 1.0 : Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients

Mycorrhizal stress alleviation in Senecio bonariensis

Loss of biodiversity and accumulation of contaminants in urban soils and water bodies cause serious issues in metropolitan areas. The Matanza-Riachuelo river basin (metropolitan area of Buenos Aires, Argentina) is one of the most environmentally degraded regions in the world. Senecio bonariensis Hook & Arn (Asteraceae) grows in the periodically flooded soils of this wetland. This plant concentrates potentially toxic trace elements (PTEs) in its tissues and establishes symbiosis with arbuscular mycorrhizal (AM) fungi that collaborate with PTE phytostabilization in soils. The objective of this work was to evaluate tolerance and stress alleviation of AM-colonized S. bonariensis when transplanting and exposing to highly polluted environmental conditions of the river basin. Plants were initially inoculated with different AM strains and maintained in greenhouse conditions. After 6 mo, they were transplanted to the field. These plants showed a more equal distribution between shoot and root biomass production in comparison to field spontaneous S. bonaerensis plants. Plants in earlier contact with native soil inoculum showed positive correlation with phosphorus content and a significant increase of vesicle frequency. Plants belatedly contacted with native inoculum in the field (control) showed a higher catalase level that was positively correlated with the total colonization frequency and chlorophyll content. The ability to establish symbiosis with Rhizophagus intraradices (strain GC3), commonly used in the formulation of biofertilizers, was also analyzed. Plants inoculated with GC3 at the beginning of the assay showed lower colonization and were less efficient in the field. The preservation of spontaneous native plants with ornamental value and bioaugmentation of their associated microbiome can contribute to the stabilization of contaminants in soils.Fil: Bompadre, Maria Josefina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Micología Molecular; ArgentinaFil: Benavidez, Matias Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental. Laboratorio de Microbiología del Suelo; ArgentinaFil: Colombo, Roxana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental. Laboratorio de Microbiología del Suelo; ArgentinaFil: Silvani, Vanesa Analia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental. Laboratorio de Microbiología del Suelo; ArgentinaFil: Godeas, Alicia Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Biodiversidad y Biología Experimental y Aplicada. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biodiversidad y Biología Experimental y Aplicada; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Biodiversidad y Biología Experimental. Laboratorio de Microbiología del Suelo; ArgentinaFil: Scotti, Adalgisa. Comisión Nacional de Energía Atómica; ArgentinaFil: Pardo, Alejandro Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Micología Molecular; ArgentinaFil: Fernandez Bidondo, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Micología Molecular; Argentin

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients