Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

Delineation of intracavitary electrograms for the automatic quantification of decrement-evoked potentials in the coronary sinus with deep-learning techniques.

Cardiac arrhythmias cause depolarization waves to conduct unevenly on the myocardial surface, potentially delaying local components with respect to a previous beat when stimulated at faster frequencies. Despite the diagnostic value of localizing the distinct local electrocardiogram (EGM) components for identifying regions with decrement-evoked potentials (DEEPs), current software solutions do not perform automatic signal quantification. Electrophysiologists must manually measure distances on the EGM signals to assess the existence of DEEPs during pacing or extra-stimuli protocols. In this work, we present a deep learning (DL)-based algorithm to identify decrement in atrial components (measured in the coronary sinus) with respect to their ventricular counterparts from EGM signals, for disambiguating between accessory pathways (APs) and atrioventricular re-entrant tachycardias (AVRTs). Several U-Net and W-Net neural networks with different configurations were trained on a private dataset of signals from the coronary sinus (312 EGM recordings from 77 patients who underwent AP or AVRT ablation). A second, separate dataset was annotated for clinical validation, with clinical labels associated to EGM fragments in which decremental conduction was elucidated. To alleviate data scarcity, a synthetic data augmentation method was developed for generating EGM recordings. Moreover, two novel loss functions were developed to minimize false negatives and delineation errors. Finally, the addition of self-attention mechanisms and their effect on model performance was explored. The best performing model was a W-Net model with 6 levels, optimized solely with the Dice loss. The model obtained precisions of 91.28%, 77.78% and of 100.0%, and recalls of 94.86%, 95.25% and 100.0% for localizing local field, far field activations, and extra-stimuli, respectively. The clinical validation model demonstrated good overall agreement with respect to the evaluation of decremental properties. When compared to the criteria of electrophysiologists, the automatic exclusion step reached a sensitivity of 87.06% and a specificity of 97.03%. Out of the non-excluded signals, a sensitivity of 96.77% and a specificity of 95.24% was obtained for classifying them into decremental and non-decremental potentials. Current results show great promise while being, to the best of our knowledge, the first tool in the literature allowing the delineation of all local components present in an EGM recording. This is of capital importance at advancing processing for cardiac electrophysiological procedures and reducing intervention times, as many diagnosis procedures are performed by comparing segments or late potentials in subsequent cardiac cycles.

Contributions to Ecuadorian butterworts (Lentibulariaceae, Pinguicula): two new species and a re-evaluation of Pinguiculacalyptrata.

Comparatively few species of the insectivorous genus Pinguicula L. have been recognized in South America so far. In recent years, a number of narrowly endemic taxa from the Andes have been described that simultaneously refined the broad taxonomic concepts of the "historical" species. Here, we describe two striking new species from Southern Ecuador that further condense the circumscription of Pinguiculacalyptrata Kunth. Pinguiculajimburensissp. nov. and P.ombrophilasp. nov. are clearly beyond the taxonomic scope of the known species and consequently described as new to science. The deviating morphological features of the two new taxa are described and illustrated and the remaining morphological spectrum of P.calyptrata in Ecuador is outlined. The two new species add to the exceptional biodiversity in the Amotape-Huancabamba Zone and underline its importance as a biodiversity hotspot in urgent need of protection.

ResumenActualmente pocas especies del género insectívoro Pinguicula L. se reconocen de forma comparativa en América del Sur. En los últimos años, se han descrito varios taxones micro-endémicos de los Andes que simultáneamente han refinado los amplios conceptos taxonómicos de las especies "históricas". Aquí, describimos dos nuevas especies sorprendentes del sur del Ecuador que condensan aún más la circunscripción de Pinguiculacalyptrata Kunth. Pinguiculajimburensissp. nov. and P.ombrophilasp. nov. están más allá del alcance taxonómico de las especies conocidas y, en consecuencia, descritas como nuevas para la ciencia. Se describen e ilustran los caracteres morfológicos divergentes de los dos nuevos taxones y se discute la variación morfológica restante de P.calyptrata en Ecuador. Las dos nuevas especies se suman a la excepcional biodiversidad de la zona Amotape-Huancabamba y se realza su importancia como un punto crítico de biodiversidad que necesita protección urgente.

The contribution of rare species to community phylogenetic diversity across a global network of forest plots.

Niche differentiation has been proposed as an explanation for rarity in species assemblages. To test this hypothesis requires quantifying the ecological similarity of species. This similarity can potentially be estimated by using phylogenetic relatedness. In this study, we predicted that if niche differentiation does explain the co-occurrence of rare and common species, then rare species should contribute greatly to the overall community phylogenetic diversity (PD), abundance will have phylogenetic signal, and common and rare species will be phylogenetically dissimilar. We tested these predictions by developing a novel method that integrates species rank abundance distributions with phylogenetic trees and trend analyses, to examine the relative contribution of individual species to the overall community PD. We then supplement this approach with analyses of phylogenetic signal in abundances and measures of phylogenetic similarity within and between rare and common species groups. We applied this analytical approach to 15 long-term temperate and tropical forest dynamics plots from around the world. We show that the niche differentiation hypothesis is supported in six of the nine gap-dominated forests but is rejected in the six disturbance-dominated and three gap-dominated forests. We also show that the three metrics utilized in this study each provide unique but corroborating information regarding the phylogenetic distribution of rarity in communities.

Distribution and Community Assembly of Trees Along an Andean Elevational Gradient.

Highlighting patterns of distribution and assembly of plants involves the use of community phylogenetic analyses and complementary traditional taxonomic metrics. However, these patterns are often unknown or in dispute, particularly along elevational gradients, with studies finding different patterns based on elevation. We investigated how patterns of tree diversity and structure change along an elevation gradient using taxonomic and phylogenetic diversity metrics. We sampled 595 individuals (36 families; 53 genera; 88 species) across 15 plots along an elevational gradient (2440-3330 m) in Ecuador. Seventy species were sequenced for the rbcL and matK gene regions to generate a phylogeny. Species richness, Shannon-Weaver diversity, Simpson's Dominance, Simpson's Evenness, phylogenetic diversity (PD), mean pairwise distance (MPD), and mean nearest taxon distance (MNTD) were evaluated for each plot. Values were correlated with elevation and standardized effect sizes (SES) of MPD and MNTD were generated, including and excluding tree fern species, for comparisons across elevation. Taxonomic and phylogenetic metrics found that species diversity decreases with elevation. We also found that overall the community has a non-random phylogenetic structure, dependent on the presence of tree ferns, with stronger phylogenetic clustering at high elevations. Combined, this evidence supports the ideas that tree ferns have converged with angiosperms to occupy the same habitat and that an increased filtering of clades has led to more closely related angiosperm species at higher elevations.