Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Data on samara morphology and wind dispersal in the invasive tree Ailanthus altissima

The data presented belongs to the following research article: Planchuelo G, Catalán P, Delgado JA, & Murciano A. Estimating wind dispersal potential in Ailanthus altissima: The need to consider the three-dimensional structure of samaras. Plant Biosystems 151 (2017) 316-322, https://doi.org/10.1080/11263504.2016.117417

Estimating wind dispersal potential in Ailanthus altissima: the need to consider the three-dimensional structure of samaras

Plant dispersal is a very important ecological phenomenon, as it can enable species to move away from the parent plant. This contributes to shaping communities, determining patterns of distribution, landscape configuration, plant invasions and evolutionary processes. Measuring dispersal distance directly is difficult and thus, diaspore morphology can be used to make estimates. Previous research on the topic often resorts to analysing the diaspore's morphology as if it was a bi-dimensional structure; when in many cases, diaspores have three-dimensional qualities. In this study, we show how estimates of wind dispersal potential of Ailanthus altissima can be considerably improved using morphological variables that succeed in describing the three-dimensional nature of samaras. We suggest that this reasoning could be extensively applied to research involving not only other species, but also multi-specific scenarios with a wide range of diaspore morphologies.Publicad

Data on samara morphology and wind dispersal in the invasive tree Ailanthus altissima

The data presented belongs to the following research article: Planchuelo G, Catalán P, Delgado JA, & Murciano A. Estimating wind dispersal potential in Ailanthus altissima: The need to consider the three-dimensional structure of samaras. Plant Biosystems 151 (2017) 316-322, https://doi.org/10.1080/11263504.2016.117417

Data on samara morphology and wind dispersal in the invasive tree Ailanthus altissima

The data presented belongs to the following research article: Planchuelo G, Catalán P, Delgado JA, & Murciano A. Estimating wind dispersal potential in Ailanthus altissima: The need to consider the three-dimensional structure of samaras. Plant Biosystems 151 (2017) 316-322, https://doi.org/10.1080/11263504.2016.117417

Lupinus grisebachianus C.P. Sm.

Alrededores del Nevado del CastillopublishedVersio

Evaluación de aspectos agronómicos, químicos-nutricionales y tecnológicos de lupinos y quínoas. Agronomic, chemical-nutritional and technological evaluation of lupin and quinoa grain crops.

Este proyecto desarrolla estudios multidisciplinarios para contribuir con información avalada por métodos científicos, a propuestas de desarrollos regionales agronómicos y de tecnología de alimentos en la provincia de Córdoba. Se propone realizar evaluaciones de factibilidad de cultivo de variedades de lupinos y quínoas en parcelas experimentales y huertas, verificar las características químico-nutricionales de los granos y establecer mecanismos tecnológicos, para poder presentar productos alimenticios viables de comercialización. Estos granos, poco conocidos en los hábitos nutricionales actuales, fueron considerados desde la antigüedad por distintas civilizaciones, como importantes fuentes de alimento vegetal. En la actualidad la necesidad de contar con nuevas fuentes de alimentos provenientes de sistemas auto-sustentables y manufacturación artesanal, ha llevado investigar alternativas de nuevos cultivos y a revalorizar aquellos que han sucumbido a las tecnologías modernas. Tanto los granos de quínoa como la de los lupinos han cobrado interés a nivel internacional por su alto valor nutricional, farmacológico y por sus cualidades de plantas rústicas a los manejos de cultivo. Es por esas razones, que se propone desarrollar un proyecto de investigación con transferencia de tecnología, para contar con experiencias que permitan establecer los lineamientos agronómicos y de tecnología de alimentos necesarios para promover los granos de lupinos y quínoas dentro de los planes nutricionales de nuestra sociedad. La importancia de este proyecto se fundamenta en la necesidad de buscar nuevas alternativas de cultivos que se adapten a los recursos de clima y suelo en áreas rurales de las sierras de Córdoba y promover nuevos emprendimientos relacionados con los sectores agrícolas y de la alimentación. Para su mejor desarrollo el proyecto está diagramado en tres módulos que cubren las siguientes áreas: Módulo 1, ensayos de cultivo en parcelas experimentales y huertas comunales; Módulo 2, análisis químicos-nutricionales; Módulo 3, diseño y adaptación de equipamiento para la manufacturación de alimentos. La metodología de investigación está ampliamente respaldada por la experiencia que cuenta el equipo de trabajo en los módulos propuestos y que puede ser verificada en la producción científica plasmada en trabajos publicados en revistas con referato nacionales e internacionales, presentaciones a congresos y direcciones de tesis