Lack of the purinergic receptor P2X7 results in resistance to contact hypersensitivity

Engagement of P2X7 on mouse dendritic cells, presumably by ATP released in response to contact allergen, is needed for IL-1β production and the sensitization phase of contact hypersensitivity

Examining Committed Action in Chronic Pain:Further Validation and Clinical Utility of the Committed Action Questionnaire

Psychosocial treatments for chronic pain conditions, such as Acceptance and Commitment Therapy, have highlighted minimizing pain avoidance behaviors and increasing engagement in valued activities as key treatment targets. In terms of salient processes within Acceptance and Commitment Therapy, committed action is considered essential to the pursuit of a meaningful life, as it entails a flexible persistence over time in living consistently with one's values. To date, however, only 1 study has examined the association between measures of committed action and important aspects of pain-related functioning. The purpose of the present study was to analyze the reliability of the Committed Action Questionnaire (CAQ) in a sample of 149 chronic pain patients, perform a confirmatory analysis of its factor structure, and examine how CAQ scores uniquely account for variance in functioning. Confirmatory factor analyses provided support for a 2-factor model, and regression analyses, which examined the cross-sectional direct effects of the 2 subscales on health-related functioning, indicated that the CAQ accounted for significant variance in functioning after controlling for relevant covariates. Overall, these findings provide further support for the CAQ as a measure of adaptive functioning in those with longstanding pain. PERSPECTIVE: This article presents additional evidence for the reliability and validity of the CAQ with chronic pain patients. Confirmatory factor analyses provided support for the 2-factor model, with both subscales demonstrating significant associations with multiple facets of health- and pain-related functioning

Formation continue et parcours professionnels : entre aspirations des salariés et contexte de l’entreprise

« Levier déterminant de la compétitivité des entreprises », « élément structurant de la sécurisation des parcours des personnes », instrument de « liberté de choisir son avenir professionnel », la formation continue est érigée en solution privilégiée pour le marché du travail. Les enjeux de la crise ouverte par la pandémie du Covid 19 pourraient bien aussi se décliner en termes de formation continue, notamment autour des besoins de reconversion. Pourtant, concilier les besoins des entreprises avec les aspirations professionnelles des personnes ne va pas de soi. Les travaux présentés dans cet ouvrage collectif, réalisés dans le cadre du premier groupe d’exploitation du Dispositif d’enquêtes couplées sur les formations et les itinéraires des salariés (Defis), éclairent, chacun à sa façon et sous des angles variés, les multiples enjeux de la formation continue. Ils questionnent la formation des salariés en lien avec leurs aspirations et les parcours professionnels. Ils tentent également de mieux comprendre la manière dont les différentes configurations productives et le contexte des entreprises peuvent influencer leurs pratiques de formation, de recrutement et autres modes d’acquisition des compétences. Conçu comme un lieu d’échanges et de dialogue pluridisciplinaire autour des données Defis, le groupe d’exploitation a réuni des chercheurs issus du Céreq, des universités ou laboratoires CNRS et autres organismes publics. Leurs appartenances disciplinaires sont variées (économie, sociologie, gestion, sciences de l’éducation) et les méthodologies mobilisées associent parfois une approche qualitative à l’analyse des données quantitatives

Bedside breath tests in children with abdominal pain: a prospective pilot feasibility study

Background: There is no definitive method of accurately diagnosing appendicitis before surgery. We evaluated the feasibility of collecting breath samples in children with abdominal pain and gathered preliminary data on the accuracy of breath tests. Methods: We conducted a prospective pilot study at a large tertiary referral paediatric hospital in the UK. We recruited 50 participants with suspected appendicitis, aged between 5 and 15 years. Five had primary diagnosis of appendicitis. The primary outcome was the number of breath samples collected. We also measured the number of samples processed within 2 h and had CO2 ≥ 3.5%. Usability was assessed by patient-reported pain pre- and post-sampling and user-reported sampling difficulty. Logistic regression analysis was used to predict appendicitis and evaluated using the area under the receiver operator characteristic curve (AUROC). Results: Samples were collected from all participants. Of the 45 samples, 36 were processed within 2 h. Of the 49 samples, 19 had %CO2 ≥ 3.5%. No difference in patient-reported pain was observed (p = 0.24). Sampling difficulty was associated with patient age (p = 0.004). The logistic regression model had AUROC = 0.86. Conclusions: Breath tests are feasible and acceptable to patients presenting with abdominal pain in clinical settings. We demonstrated adequate data collection with no evidence of harm to patients. The AUROC was better than a random classifier; more specific sensors are likely to improve diagnostic performance. Trial registration: ClinicalTrials.gov, NCT03248102. Registered 14 Aug 2017

Nonconvex and Nonsmooth Inverse Problems

Thesis (Ph.D.)--University of Washington, 2021Optimization approaches to inverse problems and parameter estimation have wide-ranging applications, from classical physics and biology to recently developed topics in statistical computing. Here, we focus on solving nonsmooth and nonconvex inverse problems.These properties are increasingly prevalent in modern applications yet typically approximated in many settings to simplify analysis. Such difficulties preclude common algorithms, giving rise to approaches that are highly problem specific and in many cases intractable at scale. Nonsmooth, nonconvex inverse problems arise in a wide range of fields, from PDE-constrained optimization to machine learning applications. These objectives often have composite structure; an objective which minimizes data misfit, and a regularizer that controls model complexity. These regularizers are often nonsmooth or discontinuous, while expensive cost functions must be evaluated inexactly for numerical efficiency. We develop and analyze efficient relaxation algorithms that take advantage of this composite structure, and illustrate their performance on seismic interpolation, denoising, and data-fitting problems. We deploy algorithms that solve these problems in as general a manner as possible, while allowing us to leverage problem structure. The main route of study is the creation of fast, first order splitting algorithms for approximate subproblems.In particular, we develop a family of splitting methods that first relaxes key parts of the inverse problem, and then solves an augmented problem with improved numerical properties and easier analyzation. Our key application is seismic inversion, with more additional applications to data interpolation and denoising. We extend this framework to the trust-region setting for nonlinear objectives. This requires new results that align convergence analysis from splitting methods for nonconvex and nonsmooth models with classic trust region convergence analysis. The practical implementation allows us to use derivative information from smooth problem components, and atomic operators for nonsmooth and nonconvex components, all within the context of general trust region methods for unconstrained and constrained problems. Along with theoretical results, we illustrate the efficacy of the proposed method numerically. Finally, we address convergence for a large class of splitting methods. These work for a variety of nonconvex and even nonsmooth problems, but a-priori convergence knowledge is limited and in particular requires linear constraints. We attempt to solve both of these issues by guiding convergence with augmented Lagrangian filter methods, and solve a highly nonlinear nonnegative matrix factorizaton problem with applications to chemical spectra determination. We conclude by proposing new directions that enable large-scale implementation of these algorithms, such as leveraging inexact evaluations of gradients and operators, as well as mixed precision arithmetic in next generation hardware. We also propose extensions to nonlinear least squares algorithms, implicit sampling techniques, and a new way of looking at splitting methods for PDE inverse problems

A proximal quasi-Newton trust-region method for nonsmooth regularized optimization

We develop a trust-region method for minimizing the sum of a smooth term $f$ and a nonsmooth term $h$, both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of $f + h$ in a trust region. The model coincides with $f + h$ in value and subdifferential at the center. We establish global convergence to a first-order stationary point when $f$ satisfies a smoothness condition that holds, in particular, when it has Lipschitz-continuous gradient, and $h$ is proper and lower semi-continuous. The model of $h$ is required to be proper, lower-semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case $O(1/\epsilon^2)$ iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance in which we use a limited-memory quasi-Newton model of $f$ and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Our trust-region algorithm exhibits promising performance and compares favorably with linesearch proximal quasi-Newton methods based on convex models.Comment: 29 pages, 3 figures, 3 table