Adaptive Value of Phenological Traits in Stressful Environments: Predictions Based on Seed Production and Laboratory Natural Selection

Phenological traits often show variation within and among natural populations of annual plants. Nevertheless, the adaptive value of post-anthesis traits is seldom tested. In this study, we estimated the adaptive values of pre- and post-anthesis traits in two stressful environments (water stress and interspecific competition), using the selfing annual species Arabidopsis thaliana. By estimating seed production and by performing laboratory natural selection (LNS), we assessed the strength and nature (directional, disruptive and stabilizing) of selection acting on phenological traits in A. thaliana under the two tested stress conditions, each with four intensities. Both the type of stress and its intensity affected the strength and nature of selection, as did genetic constraints among phenological traits. Under water stress, both experimental approaches demonstrated directional selection for a shorter life cycle, although bolting time imposes a genetic constraint on the length of the interval between bolting and anthesis. Under interspecific competition, results from the two experimental approaches showed discrepancies. Estimation of seed production predicted directional selection toward early pre-anthesis traits and long post-anthesis periods. In contrast, the LNS approach suggested neutrality for all phenological traits. This study opens questions on adaptation in complex natural environment where many selective pressures act simultaneously

On the Application of Convolutional Neural Networks for 12-lead ECG Multi-label Classification Using Datasets from Multiple Centers

Cardiac arrhythmia is a group of conditions in which falls changes in the heartbeat. Electrocardiography (ECG) is the most common tool used to identify a pathology in the cardiac electrical conduction system. ECG analysis is usually manually performed by an expert physician. However, manual interpretation is time-consuming and challenging even for cardiologists. Many automatic algorithms relying on handcrafted features and traditional machine learning classifiers were developed to recognize cardiac diseases. However, a large a priori knowledge about ECG signals is exploited. To overcome this main limitation and provide higher performance, recently, deep neural networks were designed and applied for 12-lead ECG classification. In this study, we designed decoding workflows based on three state-of-the-art architectures for time series classification. These were InceptionTime, ResNet and XResNet. Experiments were conducted using the training datasets provided during the PhysioNet/Computing in Cardiology Challenge 2020. The best-performing algorithm was based on InceptionTime, scoring a training 5-fold cross-validation challenge metric of 0.5183±0.0016, while using a low number of parameters (510491 in total). Thus, this algorithm provided the best compromise between performance and complexity

Performance Comparison of Deep Learning Approaches for Left Atrium Segmentation from LGE-MRI Data

Quantification of viable left atrial (LA) tissue is a reliable information which should be used to support therapy selection in atrial fibrillation (AF) patients. Late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is employed for the non-invasive assessment of LA fibrotic tissue. Unfortunately, the analysis of LGE-MRI relies on manual tracing of LA boundaries. This task is time-consuming and prone to high inter-observer variability. Therefore, an automatic approach for LA wall detection would be very helpful. In this study, we compared the performance of different deep architectures - U-Net and attention U-Net (AttnU-Net) - and different loss functions - Dice loss (DL) and focal Tversky loss (FTL) to automatically detect LA boundaries from LGE-MRI data. In addition, AttnU-Net was trained without deep supervision (DS) and multi-scale inputs (MI), with DS and with DS+MI. No statistically significant differences were found training the networks with DL or FTL. U-Net was the best-performing algorithm overall, outperforming significantly AttnU-Net with a Dice Coefficient of 0.9015±0.0308 (mean ± standard deviation). However, no significant differences were found between U-Net and AttnU-Net DS/DS+MI. Based on these results, using a DL or FTL does not affect the performance and U-Net was the best-performing solution

A fully automated left atrium segmentation approach from late gadolinium enhanced magnetic resonance imaging based on a convolutional neural network

Background: Several studies suggest that the evaluation of left atrial (LA) fibrosis is a relevant information for the assessment of the appropriate strategy in catheter ablation in atrial fibrillation (AF). Late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) is a non-invasive technique, which might be employed for the non-invasive quantification of LA myocardial fibrotic tissue in patients with AF. Nowadays, the analysis of LGE MRI relies on manual tracing of LA boundaries and this procedure is time-consuming and prone to high inter-observer variability given the different degrees of observers\u2019 experience, LA wall thickness and data resolution. Therefore, an automated segmentation approach of the atrial cavity for the quantification of scar tissue would be highly desirable. Methods: This study focuses on the design of a fully automated LGE MRI segmentation pipeline which includes a convolutional neural network (CNN) based on the successful architecture U-Net. The CNN was trained, validated and tested end-to-end with the data available from the Statistical Atlases and Computational Modelling of the Heart 2018 Atrial Segmentation Challenge (100 cardiac data). Two different approaches were tested: using both stacks of 2-D axial slices and using 3-D data (with the appropriate changes in the baseline architecture). In the latter approach, thanks to the 3-D convolution operator, all the information underlying 3-D data can be exploited. Once the training was completed using 80 cardiac data, a post-processing step was applied on 20 predicted segmentations belonging to the test set. Results: By applying the 2-D and 3-D approaches, average Dice coefficient and mean Hausdorff distances were 0.896, 0.914, and 8.98 mm, 8.34 mm, respectively. Volumes of the anatomical LA meshes from the automated analysis were highly correlated with the volumes from ground truth [2-D: r=0.978, y=0.94x+0.07, bias=3.5 ml (5.6%), SD=5.3 mL (8.5%); 3-D: r=0.982, y=0.92x+2.9, bias=2.1 mL (3.5%), SD=5.2 mL (8.4%)]. Conclusions: These results suggest the proposed approach is feasible and provides accurate results. Despite the increase of the number of trainable parameters, the proposed 3-D CNN learns better features leading to higher performance, feasible for a real clinical application

An Automated Approach Based on a Convolutional Neural Network for Left Atrium Segmentation from Late Gadolinium Enhanced Magnetic Resonance Imaging

Late Gadolinium Enhanced (LGE) Magnetic Resonance Imaging (MRI) is a new emerging non-invasive technique which might be employed for the non-invasive quantification of left atrium (LA) myocardial fibrotic tissue in patients affected by atrial fibrillation. Nowadays, the analysis of LGE MRI relies on manual tracing of LA boundaries. An automated LA segmentation approach for the quantification of scar tissue would be highly desirable. This study focuses on the design of a fully automated LGE MRI segmentation pipeline which includes a convolutional neural network (CNN) based on the successful architecture U-Net. The CNN was trained with the data available from the Statistical Atlases and Computational Modelling of the Heart 2018 Atrial Segmentation Challenge (100 cardiac data) with two different approaches: using both stacks of 2-D axial slices and using 3-D data. Mean Dice coefficients on the test set were 0.896 and 0.914 by using the 2-D and 3-D approaches, respectively. Contour accuracy was highly variable along the LA longitudinal axis showing poorest results in correspondence of the pulmonary veins. These results suggest that, despite the increase of the number of trainable parameters, the proposed 3-D CNN learns better features leading to higher performance, feasible for a real clinical application

Ecology predicts parapatric distributions in two closely related Antirrhinum majus subspecies

International audienc

Post-pollination barriers do not explain the persistence of two distinct Antirrhinum subspecies with parapatric distribution

International audienc

Simulation of the Hemodynamic Effects of the Left Atrial Appendage Occlusion in Atrial Fibrillation: Preliminary Results

Atrial fibrillation (AF) is responsible for 15-18 % of all strokes. In AF patients, the left atrial appendage (LAA) represents the main thrombogenic spot, being the site of 90% of intracardiac thrombus formation. Therefore, the occlusion of the LAA (LAAO) is a novel strategy for cardioembolic stroke prophylaxis. The aim of this study was the simulation of the fluid dynamics effects of the LAAO in AF patients, by applying two different devices (Amulet™ and Watchman™), in order to predict patient-specific hemodynamic changes due to LAAO and to detect the most effective devices in reducing stroke risk as well