Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.Copyright © 2022 Doste, Lozano, Jimenez-Perez, Mont, Berruezo, Penela, Camara and Sebastian

Control of quantum transverse correlations on a four-photon system

Control of spatial quantum correlations in bi-photons is one of the fundamental principles of Quantum Imaging. Up to now, experiments have been restricted to controlling the state of a single bi-photon, by using linear optical elements. In this work we demonstrate experimental control of quantum correlations in a four-photon state comprised of two pairs of photons. Our scheme is based on a high-efficiency parametric downconversion source coupled to a double slit by a variable linear optical setup, in order to obtain spatially encoded qubits. Both entangled and separable pairs have been obtained, by altering experimental parameters. We show how the correlations influence both the interference and diffraction on the double slit.Comment: 14 pages, 6 figure

PDGFA-associated protein 1 protects mature B lymphocytes from stress-induced cell death and promotes antibody gene diversification

The establishment of protective humoral immunity is dependent on the ability of mature B cells to undergo antibody gene diversification while adjusting to the physiological stressors induced by activation with the antigen. Mature B cells diversify their antibody genes by class switch recombination (CSR) and somatic hypermutation (SHM), which are both dependent on efficient induction of activation-induced cytidine deaminase (AID). Here, we identified PDGFA-associated protein 1 (Pdap1) as an essential regulator of cellular homeostasis in mature B cells. Pdap1 deficiency leads to sustained expression of the integrated stress response (ISR) effector activating transcription factor 4 (Atf4) and induction of the ISR transcriptional program, increased cell death, and defective AID expression. As a consequence, loss of Pdap1 reduces germinal center B cell formation and impairs CSR and SHM. Thus, Pdap1 protects mature B cells against chronic ISR activation and ensures efficient antibody diversification by promoting their survival and optimal function

RIF1 acts as a gatekeeper of B cell identity during late differentiation

RIF1 is a multifunctional protein that promotes immunoglobulin (Ig) isotype diversification. Whether RIF1 plays additional roles in adaptive immunity is unknown. In this study, we showed that Rif1 expression is upregulated following mature B cell activation, while its deficiency skewed the transcriptional profile of activated B cells towards plasmablasts (PBs) and plasma cells (PCs). Additionally, RIF1 ablation resulted in increased PB formation ex vivo and enhanced terminal differentiation into PCs upon immunization. Therefore, RIF1 serves as a cell identity gatekeeper during late B cell differentiation, providing an additional layer of control in the establishment of humoral immunity

Pdap1 protects mature B lymphocytes from stress-induced cell death and promotes antibody gene diversification

The establishment of protective humoral immunity is dependent on the ability of mature B cells to undergo antibody gene diversification while adjusting to the physiological stressors induced by activation with the antigen. Mature B cells diversify their antibody genes by class switch recombination (CSR) and somatic hypermutation (SHM), which are both dependent on efficient induction of activation-induced cytidine deaminase (AID). Here, we identified PDGFA-associated protein 1 (Pdap1) as an essential regulator of cellular homeostasis in mature B cells. Pdap1 deficiency leads to sustained expression of the integrated stress response (ISR) effector activating transcription factor 4 (Atf4) and induction of the ISR transcriptional program, increased cell death, and defective AID expression. As a consequence, loss of Pdap1 reduces germinal center B cell formation and impairs CSR and SHM. Thus, Pdap1 protects mature B cells against chronic ISR activation and ensures efficient antibody diversification by promoting their survival and optimal function