La cooperación en la industria vitivinícola : Contribución para resolver la crisis vitivinícola nacional

En el trabajo de tesis, cuyo resumen se publica, el autor hace algunas referencias sobre la introducción de la vid en América del Sur y posteriormente en la Argentina. Analiza las crisis que ha soportado la industria vitivinícola y hace una comparación entre ellas. Llega en su estudio a determinar que, para lograr la estabilización definitiva del mercado de vinos, es necesario modificar la estructuración sobre la que está montadla actualmente, no solo en su faz industrial sino también comercial, recalcando que la solución no podrá lograrse sin suprimir la coexistencia de gremios con intereses distintos y opuestos, como son los que representan a los vinateros sin bodegas y bodegueros trasladistas por una parte y a las grandes bodegas por otra; reconociendo que, si bien los primeros están expuestos a la especulación de los grandes establecimientos, su permanencia como tales, ocasiona en contra de sus intereses, serios trastornos a la industria impidiendo la regulación definitiva de la misma.Facultad de Ciencias Agrarias y Forestale

Virtual cochlear electrode insertion via parallel transport frame

International audienceWe present an automatic, fast and parametrizable algorithm to perform the virtual insertion of a cochlear electrode array into a pre-existent mesh of the human cochlea. Our method reorients the electrode according to the parallel transport frame, a local parameterization of the cochlear centerline directions, robust to the centerline curvature changes. It allows to control the initial roll angle and the extension of insertion from full to partial. Such a virtual insertion, chained with finite element simulations on the electrical activity of the electrode and the cochlear nerves, will enable to test in silico the effects of implant design and positioning on a given patient, and optimize these parameters accordingly

Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration

Accurate segmentation of fetal brain magnetic resonance images is crucial for analyzing fetal brain development and detecting potential neurodevelopmental abnormalities. Traditional deep learning-based automatic segmentation, although effective, requires extensive training data with ground-truth labels, typically produced by clinicians through a time-consuming annotation process. To overcome this challenge, we propose a novel unsupervised segmentation method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image. This cascaded network can then be used to register multiple annotated images with the image to be segmented, and combine the propagated labels to form a refined segmentation. Our experiments demonstrate that the proposed cascaded architecture outperforms the state-of-the-art registration methods that were tested. Furthermore, the derived segmentation method achieves similar performance and inference time to nnU-Net while only using a small subset of annotated data for the multi-atlas segmentation task and none for training the network. Our pipeline for registration and multi-atlas segmentation is publicly available at https://github.com/ValBcn/CasReg.Comment: 17 pages, 8 figures, 5 tables, paper submitted to IEEE transaction on medical imagin

Validity of prognostic models of critical COVID-19 is variable. A systematic review with external validation

Objectives: To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties. Study design and setting: We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots). Results: We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors. Conclusion: The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use

Robust, Standardized Quantification of Pulmonary Emphysema in Low Dose CT Exams

RATIONALE AND OBJECTIVES: The aim of this study was to present and evaluate a fully automated system for emphysema quantification on low-dose computed tomographic images. The platform standardizes emphysema measurements against changes in the reconstruction algorithm and slice thickness. MATERIALS AND METHODS: Emphysema was quantified in 149 patients using a fully automatic, in-house developed software (the Robust Automatic On-Line Pulmonary Helper). The accuracy of the system was evaluated against commercial software, and its reproducibility was assessed using pairs of volume-corrected images taken 1 year apart. Furthermore, to standardize quantifications, the effect of changing the reconstruction parameters was modeled using a nonlinear fit, and the inverse of the model function was then applied to the data. The association between quantifications and pulmonary function testing was also evaluated. The accuracy of the in-house software compared to that of commercial software was measured using Spearman's rank correlation coefficient, the mean difference, and the intrasubject variability. Agreement between the methods was studied using Bland-Altman plots. To assess the reproducibility of the method, intraclass correlation coefficients and Bland-Altman plots were used. The statistical significance of the differences between the standardized data and the reference data (soft-tissue reconstruction algorithm B40f; slice thickness, 1 mm) was assessed using a paired two-sample t test. RESULTS: The accuracy of the method, measured as intrasubject variability, was 3.86 mL for pulmonary volume, 0.01% for emphysema index, and 0.39 Hounsfield units for mean lung density. Reproducibility, assessed using the intraclass correlation coefficient, was >0.95 for all measurements. The standardization method applied to compensate for variations in the reconstruction algorithm and slice thickness increased the intraclass correlation coefficients from 0.87 to 0.97 and from 0.99 to 1.00, respectively. The correlation of the standardized measurements with pulmonary function testing parameters was similar to that of the reference (for the emphysema index and the obstructive subgroup: forced expiratory volume in 1 second, -0.647% vs -0.615%; forced expiratory volume in 1 second/forced vital capacity, -0.672% vs -0.654%; and diffusing capacity for carbon monoxide adjusted for hemoglobin concentration, -0.438% vs -0.523%). CONCLUSIONS: The new emphysema quantification method presented in this report is accurate and reproducible and, thanks to its standardization method, robust to changes in the reconstruction parameters

Patient-specific estimation of detailed cochlear shape from clinical CT images

PURPOSE A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images. METHODS From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape. RESULTS We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores. CONCLUSIONS The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method