Phonon mediated conversion of exciton-polaritons Rabi oscillation into THz radiation

Semiconductor microcavities in the strong-coupling regime exhibit an energy scale in the THz frequency range, which is fixed by the Rabi splitting between the upper and lower exciton-polariton states. While this range can be tuned by several orders of magnitude using different excitonic medium, the transition between both polaritonic states is dipole forbidden. In this work we show that in Cadmium Telluride microcavities, the Rabi-oscillation driven THz radiation is actually active without the need for any change in the microcavity design. This feature results from the unique resonance condition which is achieved between the Rabi splitting and the phonon-polariton states, and leads to a giant enhancement of the second order nonlinearity.Comment: 6 pages, 2 figure

National Accounts ESA. Aggregates 1970-1975. 1976

<p>Model 3 multiple logistic regression analysis for burnout diagnosis.</p

Invasive Plants and Enemy Release: Evolution of Trait Means and Trait Correlations in Ulex europaeus

Several hypotheses that attempt to explain invasive processes are based on the fact that plants have been introduced without their natural enemies. Among them, the EICA (Evolution of Increased Competitive Ability) hypothesis is the most influential. It states that, due to enemy release, exotic plants evolve a shift in resource allocation from defence to reproduction or growth. In the native range of the invasive species Ulex europaeus, traits involved in reproduction and growth have been shown to be highly variable and genetically correlated. Thus, in order to explore the joint evolution of life history traits and susceptibility to seed predation in this species, we investigated changes in both trait means and trait correlations. To do so, we compared plants from native and invaded regions grown in a common garden. According to the expectations of the EICA hypothesis, we observed an increase in seedling height. However, there was little change in other trait means. By contrast, correlations exhibited a clear pattern: the correlations between life history traits and infestation rate by seed predators were always weaker in the invaded range than in the native range. In U. europaeus, the role of enemy release in shaping life history traits thus appeared to imply trait correlations rather than trait means. In the invaded regions studied, the correlations involving infestation rates and key life history traits such as flowering phenology, growth and pod density were reduced, enabling more independent evolution of these key traits and potentially facilitating local adaptation to a wide range of environments. These results led us to hypothesise that a relaxation of genetic correlations may be implied in the expansion of invasive species

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Inférence causale sur données observationnelles : développement et application pour les soins critiques

The increasing amount of observational data, especially in critical care, leads to the consideration of causal inference statistical methods. This thesis presents two works that highlight the current challenges of these statistical methods used on observational health data. The first analysis evaluates the impact of barbiturates in a population of trauma brain injured patients prospectively included in the open critical care cohort AtlanRéa. The evaluation of the impact of barbiturates was possible by respecting the assumptions of causal inference and by using a method based on propensity scores: inverse probability weighting. Beyond the results of this analysis, which showed an increase in mortality in the group treated with barbiturates, we were faced with the problem of the violation of the positivity assumption. We then compared different statistical methods of causal inference in a context of violation of the positivity assumption, which can be associated with an extrapolation issue. The methods predicting the occurrence of the outcome are the most robust in these situations. In this context of accumulation of health data, a perspective of optimization of the use of statistical methods in the framework of causal inference will reside in the use of machine learning algorithms to avoid the problems of model specification.L’augmentation croissante des données observationnelles, notamment dans les services de soins critiques amènent à considérer l’utilisation des méthodes statistiques d’inférence causale. Cette thèse présente deux travaux permettant de mettre en lumière les défis actuels de ces méthodes statistiques appliquées sur des données observationnelles de santé. Le premier travail évalue l’impact des barbituriques dans une population de patients traumatisés crâniens, inclus de manière prospective dans la cohorte ouverte de soins critiques AtlanRéa. L’évaluation de l’impact des barbituriques a été permis par le respect des hypothèses de l’inférence causale et l’utilisation d’une méthode basée sur les scores de propension : la pondération. Au-delà du résultat de cette analyse, ayant notamment mis en évidence une augmentation de la mortalité dans le groupe traité par barbituriques, nous avons été confrontés à la problématique de l’infraction de l’hypothèse de positivité. Nous avons ensuite comparé différentes méthodes statistiques d’inférence causale dans un contexte d’infraction de l’hypothèse de positivité, pouvant être associée à la problématique d’extrapolation. Les méthodes prédisant la survenue de l’évènement sont les plus robustes dans ces situations. Dans ce contexte d’accumulation de données de santé, une perspective d’optimisation de l’utilisation des méthodes statistiques dans le cadre de l’inférence résidera dans le recours aux algorithmes d’apprentissage automatisé (machine learning) pour éviter les problèmes de spécification des modèles

Inférence causale sur données observationnelles : développement et application pour les soins critiques

The increasing amount of observational data, especially in critical care, leads to the consideration of causal inference statistical methods. This thesis presents two works that highlight the current challenges of these statistical methods used on observational health data. The first analysis evaluates the impact of barbiturates in a population of trauma brain injured patients prospectively included in the open critical care cohort AtlanRéa. The evaluation of the impact of barbiturates was possible by respecting the assumptions of causal inference and by using a method based on propensity scores: inverse probability weighting. Beyond the results of this analysis, which showed an increase in mortality in the group treated with barbiturates, we were faced with the problem of the violation of the positivity assumption. We then compared different statistical methods of causal inference in a context of violation of the positivity assumption, which can be associated with an extrapolation issue. The methods predicting the occurrence of the outcome are the most robust in these situations. In this context of accumulation of health data, a perspective of optimization of the use of statistical methods in the framework of causal inference will reside in the use of machine learning algorithms to avoid the problems of model specification.L’augmentation croissante des données observationnelles, notamment dans les services de soins critiques amènent à considérer l’utilisation des méthodes statistiques d’inférence causale. Cette thèse présente deux travaux permettant de mettre en lumière les défis actuels de ces méthodes statistiques appliquées sur des données observationnelles de santé. Le premier travail évalue l’impact des barbituriques dans une population de patients traumatisés crâniens, inclus de manière prospective dans la cohorte ouverte de soins critiques AtlanRéa. L’évaluation de l’impact des barbituriques a été permis par le respect des hypothèses de l’inférence causale et l’utilisation d’une méthode basée sur les scores de propension : la pondération. Au-delà du résultat de cette analyse, ayant notamment mis en évidence une augmentation de la mortalité dans le groupe traité par barbituriques, nous avons été confrontés à la problématique de l’infraction de l’hypothèse de positivité. Nous avons ensuite comparé différentes méthodes statistiques d’inférence causale dans un contexte d’infraction de l’hypothèse de positivité, pouvant être associée à la problématique d’extrapolation. Les méthodes prédisant la survenue de l’évènement sont les plus robustes dans ces situations. Dans ce contexte d’accumulation de données de santé, une perspective d’optimisation de l’utilisation des méthodes statistiques dans le cadre de l’inférence résidera dans le recours aux algorithmes d’apprentissage automatisé (machine learning) pour éviter les problèmes de spécification des modèles

Causal inference from observational data : development and applications for critical care

L’augmentation croissante des données observationnelles, notamment dans les services de soins critiques amènent à considérer l’utilisation des méthodes statistiques d’inférence causale. Cette thèse présente deux travaux permettant de mettre en lumière les défis actuels de ces méthodes statistiques appliquées sur des données observationnelles de santé. Le premier travail évalue l’impact des barbituriques dans une population de patients traumatisés crâniens, inclus de manière prospective dans la cohorte ouverte de soins critiques AtlanRéa. L’évaluation de l’impact des barbituriques a été permis par le respect des hypothèses de l’inférence causale et l’utilisation d’une méthode basée sur les scores de propension : la pondération. Au-delà du résultat de cette analyse, ayant notamment mis en évidence une augmentation de la mortalité dans le groupe traité par barbituriques, nous avons été confrontés à la problématique de l’infraction de l’hypothèse de positivité. Nous avons ensuite comparé différentes méthodes statistiques d’inférence causale dans un contexte d’infraction de l’hypothèse de positivité, pouvant être associée à la problématique d’extrapolation. Les méthodes prédisant la survenue de l’évènement sont les plus robustes dans ces situations. Dans ce contexte d’accumulation de données de santé, une perspective d’optimisation de l’utilisation des méthodes statistiques dans le cadre de l’inférence résidera dans le recours aux algorithmes d’apprentissage automatisé (machine learning) pour éviter les problèmes de spécification des modèles.The increasing amount of observational data, especially in critical care, leads to the consideration of causal inference statistical methods. This thesis presents two works that highlight the current challenges of these statistical methods used on observational health data. The first analysis evaluates the impact of barbiturates in a population of trauma brain injured patients prospectively included in the open critical care cohort AtlanRéa. The evaluation of the impact of barbiturates was possible by respecting the assumptions of causal inference and by using a method based on propensity scores: inverse probability weighting. Beyond the results of this analysis, which showed an increase in mortality in the group treated with barbiturates, we were faced with the problem of the violation of the positivity assumption. We then compared different statistical methods of causal inference in a context of violation of the positivity assumption, which can be associated with an extrapolation issue. The methods predicting the occurrence of the outcome are the most robust in these situations. In this context of accumulation of health data, a perspective of optimization of the use of statistical methods in the framework of causal inference will reside in the use of machine learning algorithms to avoid the problems of model specification

Sleep and COVID-19. A Case Report of a Mild COVID-19 Patient Monitored by Consumer-Targeted Sleep Wearables

Since its first description in Wuhan, China, the novel Coronavirus (SARS-CoV-2) has spread rapidly around the world. The management of this major pandemic requires a close coordination between clinicians, scientists, and public health services in order to detect and promptly treat patients needing intensive care. The development of consumer wearable monitoring devices offers physicians new opportunities for the continuous monitoring of patients at home. This clinical case presents an original description of 55 days of SARS-CoV-2-induced physiological changes in a patient who routinely uses sleep-monitoring devices. We observed that sleep was specifically affected during COVID-19 (Total Sleep time, TST, and Wake after sleep onset, WASO), within a seemingly bidirectional manner. Sleep status prior to infection (e.g., chronic sleep deprivation or sleep disorders) may affect disease progression, and sleep could be considered as a biomarker of interest for monitoring COVID-19 progression. The use of habitual data represents an opportunity to evaluate pathologic states and improve clinical care