For a Diversified Networked Culture: Bringing the Convention on the Protection and Promotion of the Diversity of Cultural Expressions in the Digital Age

Ministère des Affaires étrangères et Ministère de la Culture et de la Communication-Franc

Retention and Activation of Blood-Borne Proteases in the Arterial Wall Implications for Atherothrombosis

All forms of atheroma are characterized by a risk of arterial wall rupture leading to clinical complications. This involves medial and adventitial ruptures in abdominal aortic aneurysm (AAA) and intimal cap rupture in vulnerable atherothrombotic plaques. Extracellular proteases, including metalloproteinases, locally generated plasmin, and leukocyte elastase, are important molecular mediators of atheroma progression via their matrix degradation properties. The pathological evolution of AAA is linked to the biology of its associated mural thrombus. Indeed, in aneurysmal segments lined by a thrombus, the wall is thinner, the extracellular matrix more degraded, and the adventitial inflammatory response greater than in segments that are not. Several lines of evidence highlight the role of the thrombus, in AAA, as a reservoir of blood-borne proteases that conveys them from the lumen to the diseased wall. In stenosing atheroma, both previous and recent studies provide evidence that recurrent intraplaque hemorrhages play a dominant role in the evolution of the lesion toward vulnerability. In this review, we draw a parallel between the role of protease conveyance and activation of the mural thrombus in AAA and of intraplaque hemorrhages in stenosing atheroma. We hypothesize that intraplaque hemorrhages convey blood-borne proteases into lesions, where they are retained and activated upon thrombus/hematoma formation, thus contributing significantly to their deleterious action

Climate Change Impact on Water Column in Corsica

peer reviewedSTARECAPME

Decision-based interactive model to determine re-opening conditions of a large university campus in Belgium during the first COVID-19 wave

peer reviewedBackground The role played by large-scale repetitive SARS-CoV-2 screening programs within university populations interacting continuously with an urban environment, is unknown. Our objective was to develop a model capable of predicting the dispersion of viral contamination among university populations dividing their time between social and academic environments. Methods Data was collected through real, large-scale testing developed at the University of Liège, Belgium, during the period Sept. 28th-Oct. 29th 2020. The screening, offered to students and staff (n = 30,000), began 2 weeks after the re-opening of the campus but had to be halted after 5 weeks due to an imposed general lockdown. The data was then used to feed a two-population model (University + surrounding environment) implementing a generalized susceptible-exposed-infected-removed compartmental modeling framework. Results The considered two-population model was sufficiently versatile to capture the known dynamics of the pandemic. The reproduction number was estimated to be significantly larger on campus than in the urban population, with a net difference of 0.5 in the most severe conditions. The low adhesion rate for screening (22.6% on average) and the large reproduction number meant the pandemic could not be contained. However, the weekly screening could have prevented 1393 cases (i.e. 4.6% of the university population; 95% CI: 4.4–4.8%) compared to a modeled situation without testing. Conclusion In a real life setting in a University campus, periodic screening could contribute to limiting the SARS-CoV-2 pandemic cycle but is highly dependent on its environment

Leveraging Natural History Data in One- and Two-Arm Hierarchical Bayesian Studies of Rare Disease Progression

peer reviewedThe small sample sizes inherent in rare and pediatric disease settings offer significant challenges for clinical trial design. In such settings, Bayesian adaptive trial methods can often pay dividends, allowing the sensible incorporation of auxiliary data and other relevant information to bolster that collected by the trial itself. Previous work has also included the use of one-arm trials augmented by the participants’ own natural history data, from which the future course of the disease in the absence of intervention can be predicted. Patient response can then be defined by the degree to which post-intervention observations are inconsistent with the predicted “natural” trajectory. While such trials offer obvious advantages in efficiency and ethical hazard (since they expose no new patients to a placebo, anathema to patients or their parents and caregivers), they can offer no protection against bias arising from the presence of any “placebo effect,” the tendency of patients to improve merely by being in the trial. In this paper, we investigate the impact of both static and transient placebo effects on one-arm responder studies of this type, as well as two-arm versions that incorporate a small concurrent placebo group but still borrow strength from the natural history data. We also propose more traditional Bayesian changepoint models that specify a parametric functional form for the patient’s post-intervention trajectory, which in turn allow quantification of the treatment benefit in terms of the model parameters, rather than semi-parametrically in terms of a response relative to some “null” model. We compare the operating characteristics of our designs in the context of an ongoing investigation of centronuclear myopathies (CNMs), a group of congenital neuromuscular diseases whose most common and severe form is X-linked, affecting approximately 1 in 50,000 newborn boys. Our results indicate our two-arm responder and changepoint methods can offer protection against placebo effects, improving power while protecting the trial’s Type I error rate. However, further research into innovative trial designs as well as ongoing dialog with regulatory authorities remain critically important in rare disease research

Apport de la chimie calculatoire à la modélisation de diagrammes de phases

Résumé : Le comportement thermodynamique d'un système se décrit sur base d'une équation d'état. L'équation d'état de Sanchez-Lacombe est largement utilisée et constitue un bon compromis entre l'investissement calculatoire et la qualité des résultats fournis, surtout au niveau de l'équilibre liquide-vapeur. La paramétrisation de cette équation d'état, proposée par Heidemann, est basée sur la seule connaissance des paramètres critiques du composé. La modélisation des diagrammes de phases implique la description des forces intermoléculaires et les techniques de chimie quantique constituent un outil performant pour paramétrer les équations d'état. Ce travail repose sur l'évaluation prédictive des paramètres critiques de composés purs grâce à une approche de type QSPR (Quantitative Structure-Properties Relationships) associée à une méthode de régression linéaire multiple basée sur le calcul de descripteurs moléculaires (volume, multipoles, polarisabilités, surfaces chargées). Cette technique peut être étendue à la détermination des paramètres critiques de mélanges sur base de règles nécessitant la seule connaissance des paramètres critiques des constituants purs et les fractions molaires correspondant à chacun d'entre-eux. Abstract : The Sanchez-Lacombe equation of state is largely used due to the compromise it offers between the computing cost and quality of the results, especially for liquid-vapor equilibrium. Heidemann's parametrization of the Sanchez-Lacombe equation only requires the knowledge of critical values of the pressure, volume and temperature in order to solve the equation of state. Besides the development of equations of state, the modelling of phase behaviour implies reliable descriptors of intermolecular forces. Quantum chemistry methods are able to provide valid and consistent parameters for intermolecular forces, which, in turn, allow an efficient description of phase behaviour. In this work, predictive evaluation of pure component critical parameters is performed by using a QSPR approach in association with a multilinear regression method based on the calculation of judicious molecular descriptors (volume, multipoles, polarizabilities, surfaces). Together with the implementation of suitable mixing rules, QSPR allows the determination of critical parameters for mixtures by only taking into account molar fractions and pure component critical parameters(DOCSC02)--FUNDP, 200