An Evolved 5 ' Untranslated Region of Alfalfa Mosaic Virus Allows the RNA Transport of Movement-Defective Variants

[EN] The results obtained in the present work could challenge the view of the role of the virus particle in the systemic transport of plant viruses. In this sense, we show that two different MPs are competent to systemically transport the AMV genome without the requirement of the virus particles, as reported for viruses lacking a CP (e.g., Umbravirus). Although the coat protein (CP) has a relevant role in the long-distance movement of alfalfa mosaic virus (AMV) and brome mosaic virus (BMV), its precise function is not fully understood. Previous results showed that a specific interaction between the C termini of the movement protein (MP) and the cognate CP is required for systemic transport. Thus, we have performed a compensatory evolution experiment using an AMV RNA3 derivative defective in long-distance transport that carries a BMV MP lacking the C-terminal 48 residues and unable to interact with the AMV CP. After several passages, five independent evolution lineages were able to move long distance. The analysis of the viral RNA of these lineages showed the presence of three different modifications located exclusively at the 5 ' untranslated region (5 ' UTR). The three evolved 5 ' UTR variants accumulated comparable levels of viral RNA and CP but reduced the accumulation of virus particles and the affinity between the 5 ' UTR and the AMV CP. In addition, the evolved 5 ' UTR increased cell-to-cell transport for both the AMV RNA3 carrying the BMV MP and that carrying the AMV MP. Finally, the evolved 5 ' UTRs allowed the systemic transport of an AMV RNA3 carrying a CP mutant defective in virus particles and increased the systemic transport of several AMV RNA3 derivatives carrying different viral MPs associated with the 30K superfamily. Altogether, our findings indicate that virus particles are not required for the systemic transport of AMV but also that BMV MP is competent for the short- and long-distance transport without the interaction with the CP. IMPORTANCE The results obtained in the present work could challenge the view of the role of the virus particle in the systemic transport of plant viruses. In this sense, we show that two different MPs are competent to systemically transport the AMV genome without the requirement of the virus particles, as reported for viruses lacking a CP (e.g., Umbravirus). The incapability of the viral MP to interact with the CP triggered virus variants that evolved to reduce the formation of virus particles, probably to increase the accessibility of the MP to the viral progeny. Our results point to the idea that virus particles would not be necessary for the viral systemic transport but would be necessary for vector virus transmission. This idea is reinforced by the observation that heterologous MPs also increased the systemic transport of the AMV constructs that have reduced encapsidation capabilities.We are grateful to Lorena Corachan for excellent technical support. This work was supported by grants PID2020-115571RB-I00 and PID2019-103998GB-I00 from the Spanish MCIN/AEI/10.13039/501100011033 granting agency-FEDER (V.P. and S.F.E., respectively) and PROMETEO/2015/010 and PROMETEO2019/012 from the Generalitat Valenciana (V.P. and S.F.E., respectively).Villar-Álvarez, D.; Pallás Benet, V.; Elena Fito, SF.; Sánchez-Navarro, JÁ. (2022). An Evolved 5 ' Untranslated Region of Alfalfa Mosaic Virus Allows the RNA Transport of Movement-Defective Variants. Journal of Virology. 96(22):1-17. https://doi.org/10.1128/jvi.00988-22117962

Influencia de un programa de supervisión reflexiva sobre la toma de decisiones y la ejecucución del pase en jóvenes jugadores de baloncesto

El propósito de esta investigación fue analizar la influencia de un programa de supervisión reflexiva sobre la toma de decisiones y la ejecución del pase en baloncesto en situación real de competición. En el estudio participaron untotal de 12 jugadores infantiles de baloncesto pertenecientes a un equipo de las categorías de formación de un club A.C.B., y estructurados en un grupo experimental (n = 6) y un grupo control (n = 6). La intervención llevada a cabo se orientó hacia la mejora de la selección de la respuesta, y consistió en el visionado y análisis de acciones de pase durante 11 sesiones individuales post-partido que mantenían el supervisor y cada uno de los jugadores. Los resultados han mostrado que los sujetos pertenecientes al grupo experimental mejoraron de forma significativa el porcentaje de acierto en la toma de decisiones y la ejecución del pase en situación real de juego

Spontaneous Mutation in the Movement Protein of Citrus Leprosis Virus C2, in a Heterologous Virus Infection Context, Increases Cell-to-Cell Transport and Generates Fitness Advantage

[EN] Previous results using a movement defective alfalfa mosaic virus (AMV) vector revealed that citrus leprosis virus C (CiLV-C) movement protein (MP) generates a more efficient local movement, but not more systemic transport, than citrus leprosis virus C2 (CiLV-C2) MP, MPs belonging to two important viruses for the citrus industry. Here, competition experiment assays in transgenic tobacco plants (P12) between transcripts of AMV constructs expressing the cilevirus MPs, followed by several biological passages, showed the prevalence of the AMV construct carrying the CiLV-C2 MP. The analysis of AMV RNA 3 progeny recovered from P12 plant at the second viral passage revealed the presence of a mix of progeny encompassing the CiLV-C2 MP wild type (MPWT) and two variants carrying serines instead phenylalanines at positions 72 (MPS72F) or 259 (MPS259F), respectively. We evaluated the effects of each modified residue in virus replication, and cell-to-cell and long-distance movements. Results indicated that phenylalanine at position 259 favors viral cell-to-cell transport with an improvement in viral fitness, but has no effect on viral replication, whereas mutation at position 72 (MPS72F) has a penalty in the viral fitness. Our findings indicate that the prevalence of a viral population may be correlated with its greater efficiency in cell-to-cell and systemic movements.This research was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant numbers 2014/0845-9, 2017/50222-0, 2015/10249-1, 2017/19898-8 and by the Spanish Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), grant number PID2020-115571RB-100.Oliveira Leastro, M.; Villar-Álvarez, D.; Freitas-Astúa, J.; Watanabe Kitajima, E.; Pallás Benet, V.; Sanchez Navarro, JA. (2021). Spontaneous Mutation in the Movement Protein of Citrus Leprosis Virus C2, in a Heterologous Virus Infection Context, Increases Cell-to-Cell Transport and Generates Fitness Advantage. Viruses. 13(12):1-16. https://doi.org/10.3390/v13122498S116131

Effort Oxygen Saturation and Effort Heart Rate to Detect Exacerbations of Chronic Obstructive Pulmonary Disease or Congestive Heart Failure

Background: current algorithms for the detection of heart failure (HF) and chronic obstructive pulmonary disease (COPD) exacerbations have poor performance. Methods: this study was designed as a prospective longitudinal trial. Physiological parameters were evaluated at rest and effort (walking) in patients who were in the exacerbation or stable phases of HF or COPD. Parameters with relevant discriminatory power (sensitivity (Sn) or specificity (Sp) 80%, and Youden index 0.2) were integrated into diagnostic algorithms. Results: the study included 127 patients (COPD: 56, HF: 54, both: 17). The best algorithm for COPD included: oxygen saturation (SaO(2)) decrease 2% in minutes 1 to 3 of effort, end-of-effort heart rate (HR) increase 10 beats/min and walking distance decrease 35 m (presence of one criterion showed Sn: 0.90 (95%, CI(confidence interval): 0.75-0.97), Sp: 0.89 (95%, CI: 0.72-0.96), and area under the curve (AUC): 0.92 (95%, CI: 0.85-0.995)); and for HF: SaO(2) decrease 2% in the mean-of-effort, HR increase 10 beats/min in the mean-of-effort, and walking distance decrease 40 m (presence of one criterion showed Sn: 0.85 (95%, CI: 0.69-0.93), Sp: 0.75 (95%, CI: 0.57-0.87) and AUC 0.84 (95%, CI: 0.74-0.94)). Conclusions: effort situations improve the validity of physiological parameters for detection of HF and COPD exacerbation episodes

Consolidación Gabinete de Fotografía de la Facultad de Bellas Artes

La creación de un Gabinete de Fotografía surgió de la necesidad de contar con una colección de imágenes fotográficas que reflejase la calidad de los trabajos realizados por los estudiantes durante su formación, así como de docentes vinculados a esta disciplina en la Facultad de Bellas Artes de nuestra Universidad. La voluntad de este proyecto es continuar y consolidar la labor iniciada en el año 2017 y plasmada en el Proyecto de Innovación “Creación de Gabinete de Fotografía de la Facultad de Bellas Artes", produciendo fotografías de alta calidad que enriquezcan los fondos del Gabinete de Fotografía, redefiniendo y consolidando los protocolos pertinentes para que cada año se nutra de los trabajos más representativos por su calidad artística o su finalidad educativa

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed

Wavelet analysis of overnight airflow to detect obstructive sleep apnea in children

Producción CientíficaThis study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación y Fondo Europeo de Desarrollo Regional (FEDER) - (Projects DPI2017-84280-R and RTC-2017-6516-1)Comisión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (POCTEP 0702_MIGRAINEE_2_E)Instituto de Salud Carlos III y Fondo Europeo de Desarrollo Regional (FEDER) - (CIBER-BBN)Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y Fondo Social Europeo - (grant RYC2019- 028566-I)Ministerio de Educación, Cultura y Deporte - (grant FPU16/02938)Institutes of Health - (grants HL130984, HL140548, and AG061824

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Producción CientíficaThe gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (project 10.13039/501100011033)Fondo Europeo de Desarrollo Regional - Unión Europea (projects PID2020-115468RB-I00 and PDC2021-120775-I00)Sociedad Española de Neumología y Cirugía Torácica (project 649/2018)Sociedad Española de Sueño (project Beca de Investigación SES 2019)Consorcio Centro de Investigación Biomédica en Red - Instituto de Salud Carlos III - Ministerio de Ciencia, Innovación y Universidades (project CB19/01/00012)National Institutes of Health (projects HL083075, HL083129, UL1-RR-024134 and UL1 RR024989)National Heart, Lung, and Blood Institute (projects R24 HL114473 and 75N92019R002)Ministerio de Educación, Cultura y Deporte (grant FPU16/02938)Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC2019-028566-I)National Institutes of Health (grants HL130984, HL140548, and AG061824

An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

Producción CientíficaDeep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.Ministerio de Ciencia e Innovación /AEI/10.13039/501100011033/ FEDER (grants PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012), Instituto de Salud Carlos IIINational Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989)National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I