Quantifying the ideational context: political frames, meaning trajectories and punctuated equilibria in Spanish mainstream press during the Catalan nationalist challenge

This article presents a quantitative method for mapping semantic spaces and tracing political frames’ trajectories, that facilitate the analysis of the connections between changes in ideas and socio-political phenomena. We test our approach in Spain, where the Catalan conflict fostered a competition in terms of decontestation of meanings of key political concepts. Using unsupervised machine learning, we track the salience, level of semantic fragmentation and fluctuations in meanings of 216 frames in the two largest Spanish newspapers, El País and El Mundo, throughout 8 years. This is achieved via the extraction, vectorization, and comparison of over 70,000 words. We apply Latent Semantic Analysis, an innovative methodology for the alignment of semantic spaces, and new institutional theory. Our exploratory study suggests that the evolution of many nationalism-related frames resembles a punctuated equilibrium model, and that political events in Catalonia, acted as critical junctures, altering the meanings reflected in the Spanish press

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease : Validation Study Under Real Conditions of Use

A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%. The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting

Validación de la utilidad de los parámetros de deformación miocárdica para excluir el rechazo agudo tras el trasplante cardiaco: un estudio multicéntrico

Multicenter study[Abstract] Introduction and objectives: Two-dimensional speckle-tracking echocardiography has emerged as a promising alternative to endomyocardial biopsy to rule out acute cellular rejection after orthotopic heart transplantation (OHT) in single center studies. In an original cohort, 15.5% and 17% of cutoff points for left ventricular global longitudinal strain (LVGLS) and free-wall right ventricular longitudinal strain, respectively, achieved 100% negative predictive value to exclude moderate or severe acute cellular rejection (ACR ≥ 2R). Our objective was to demonstrate the usefulness of speckle-tracking and validate these cutoff points in an external cohort. Methods: A prospective, multicenter study that included patients who were monitored during their first year after OHT was conducted. Echocardiographic studies analyzed by local investigators were compared with simultaneous paired endomyocardial biopsies samples. Results: A total of 501 endomyocardial biopsy-echocardiographic studies were included in 99 patients. ACR≥2R was present in 7.4% of samples. LVGLS and free-wall right ventricular longitudinal strain were significantly reduced during ACR≥2R on univariate analysis. On multivariate analysis, LVGLS was independently associated with the presence of ACR≥2R. The original cutoff points demonstrated a negative predictive value of 94.3% to exclude ACR≥2R. Conclusions: This study maintained a strong negative predictive value to exclude ACR≥2R after OHT and LVGLS was independently associated with the presence of ACR≥2R. We propose the use of speckle-tracking, especially LVGLS, as part of the noninvasive diagnosis and management of ACR.[Resumen] Introducción y objetivos. Algunos estudios indican que los parámetros de strain por speckle-tracking pueden ser una alternativa no invasiva a la biopsia endomiocárdica para excluir el rechazo celular agudo (RCA) moderado o grave (≥ 2 R) tras el trasplante cardiaco (TxC). En una cohorte inicial, unos puntos de corte del 15,5% para el strain longitudinal global del ventrículo izquierdo (SLGVI) y el 17% para el strain de pared libre del ventrículo derecho mostraron un valor predictivo negativo del 100% para excluir RCA ≥ 2 R. Nuestro objetivo es analizar la utilidad del strain y validar estos puntos de corte en una cohorte multicéntrica prospectiva externa. Métodos. Estudio multicéntrico y prospectivo que incluyó a pacientes con seguimiento el primer año tras el TC. Se compararon los resultados de biopsias electivas con ecocardiogramas realizados el mismo día. Resultados. Se incluyó a 99 pacientes y 501 pares de biopsias-ecocardiogramas. El RCA ≥ 2 R en las biopsias fue del 7,4%. El SLGVI y el strain longitudinal de pared libre del ventrículo derecho fueron menores durante los RCA ≥ 2 R en el análisis univariante. En el análisis multivariante, el SLGVI se asoció de manera independiente con el RCA ≥ 2 R. Los puntos de corte originales mostraron un valor predictivo negativo del 94,3% el RCA ≥ 2 R. Conclusiones. Este estudio mantiene un alto valor predictivo negativo para excluir RCA ≥ 2 R tras el TxC y el SLGVI se asoció de manera independiente con el RCA ≥ 2 R. El strain y, principalmente, el SLGVI pueden ser de utilidad en el diagnóstico y el tratamiento no invasivo del RCA

Radiation and Dust Sensor for Mars Environmental Dynamic Analyzer Onboard M2020 Rover

32 pags., 26 figs., 3 tabs. -- This article belongs to the Section Remote SensorsThe Radiation and Dust Sensor is one of six sensors of the Mars Environmental Dynamics Analyzer onboard the Perseverance rover from the Mars 2020 NASA mission. Its primary goal is to characterize the airbone dust in the Mars atmosphere, inferring its concentration, shape and optical properties. Thanks to its geometry, the sensor will be capable of studying dust-lifting processes with a high temporal resolution and high spatial coverage. Thanks to its multiwavelength design, it will characterize the solar spectrum from Mars' surface. The present work describes the sensor design from the scientific and technical requirements, the qualification processes to demonstrate its endurance on Mars' surface, the calibration activities to demonstrate its performance, and its validation campaign in a representative Mars analog. As a result of this process, we obtained a very compact sensor, fully digital, with a mass below 1 kg and exceptional power consumption and data budget features.This work has been funded with the help of the Spanish National Research, Development and Innovation Program, through the grants RTI2018-099825-B-C31, ESP2016-80320-C2-1-R and ESP2014-54256-C4-3-R. DT acknowledges the financial support from the Comunidad de Madrid for an “Atracción de Talento Investigador” grant (2018-T2/TIC10500). ASL is supported by Grant PID2019-109467GB-I00 funded by MCIN/AEI/10.13039/501100011033/ and by Grupos Gobierno Vasco IT1366-19. The US co-authors performed their work under sponsorship from NASA’s Mars 2020 project, from the Game Changing Development program within the Space Technology Mission Directorate, and from the Human Exploration and Operations Directorate.Peer reviewe

A Failed Cross-Validation Study on the Relationship between LIWC Linguistic Indicators and Personality: Exemplifying the Lack of Generalizability of Exploratory Studies

(1) Background: Previous meta-analytic research found small to moderate relationships between the Big Five personality traits and different linguistic computational indicators. However, previous studies included multiple linguistic indicators to predict personality from an exploratory framework. The aim of this study was to conduct a cross-validation study analyzing the relationships between language indicators and personality traits to test the generalizability of previous results; (2) Methods: 643 Spanish undergraduate students were tasked to write a self-description in 500 words (which was evaluated with the LIWC) and to answer a standardized Big Five questionnaire. Two different analytical approaches using multiple linear regression were followed: first, using the complete data and, second, by conducting different cross-validation studies; (3) Results: The results showed medium effect sizes in the first analytical approach. On the contrary, it was found that language and personality relationships were not generalizable in the cross-validation studies; (4) Conclusions: We concluded that moderate effect sizes could be obtained when the language and personality relationships were analyzed in single samples, but it was not possible to generalize the model estimates to other samples. Thus, previous exploratory results found on this line of research appear to be incompatible with a nomothetic approach

A Failed Cross-Validation Study on the Relationship between LIWC Linguistic Indicators and Personality: Exemplifying the Lack of Generalizability of Exploratory Studies

(1) Background: Previous meta-analytic research found small to moderate relationships between the Big Five personality traits and different linguistic computational indicators. However, previous studies included multiple linguistic indicators to predict personality from an exploratory framework. The aim of this study was to conduct a cross-validation study analyzing the relationships between language indicators and personality traits to test the generalizability of previous results; (2) Methods: 643 Spanish undergraduate students were tasked to write a self-description in 500 words (which was evaluated with the LIWC) and to answer a standardized Big Five questionnaire. Two different analytical approaches using multiple linear regression were followed: first, using the complete data and, second, by conducting different cross-validation studies; (3) Results: The results showed medium effect sizes in the first analytical approach. On the contrary, it was found that language and personality relationships were not generalizable in the cross-validation studies; (4) Conclusions: We concluded that moderate effect sizes could be obtained when the language and personality relationships were analyzed in single samples, but it was not possible to generalize the model estimates to other samples. Thus, previous exploratory results found on this line of research appear to be incompatible with a nomothetic approach

A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease : Validation Study Under Real Conditions of Use

A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%. The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting

Validation of a portable device for mapping motor and gait disturbances in parkinson’s disease

Background: Patients with severe idiopathic Parkinson's disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients' treatment. Objective: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. Methods: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. Results: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). Conclusions: ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday life

Validation of a portable device for mapping motor and gait disturbances in parkinson’s disease

Background: Patients with severe idiopathic Parkinson\u27s disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients\u27 treatment. Objective: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. Methods: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. Results: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). Conclusions: ON/OFF motor fluctuations in Parkinson\u27s patients can be detected with a single sensor, which can be worn in everyday life

Validation of a portable device for mapping motor and gait disturbances in Parkinson’s disease

Background: Patients with severe idiopathic Parkinson’s disease experience motor fluctuations, which are often difficult to control. Accurate mapping of such motor fluctuations could help improve patients’ treatment. Objective: The objective of the study was to focus on developing and validating an automatic detector of motor fluctuations. The device is small, wearable, and detects the motor phase while the patients walk in their daily activities. Methods: Algorithms for detection of motor fluctuations were developed on the basis of experimental data from 20 patients who were asked to wear the detector while performing different daily life activities, both in controlled (laboratory) and noncontrolled environments. Patients with motor fluctuations completed the experimental protocol twice: (1) once in the ON, and (2) once in the OFF phase. The validity of the algorithms was tested on 15 different patients who were asked to wear the detector for several hours while performing daily activities in their habitual environments. In order to assess the validity of detector measurements, the results of the algorithms were compared with data collected by trained observers who were accompanying the patients all the time. Results: The motor fluctuation detector showed a mean sensitivity of 0.96 (median 1; interquartile range, IQR, 0.93-1) and specificity of 0.94 (median 0.96; IQR, 0.90-1). Conclusions: ON/OFF motor fluctuations in Parkinson's patients can be detected with a single sensor, which can be worn in everyday lifePeer Reviewe