Fabrice Denis, Ethan Basch, Anne-Lise Septans, Jaafar Bennouna, Thierry Urban, Amylou C. Dueck & Christophe Letellier
Two-year survival comparing web-based symptom monitoring vs routine surveillance following treatment for lung cancer,
JAMA. 321 (3), 306-307, 2019. Online
Symptom monitoring during chemotherapy via web-based patient-reported outcomes (PROs) was previously demonstrated to lengthen survival in a single-center study.1 A multicenter randomized clinical trial compared web-based monitoring vs standard scheduled imaging to detect symptomatic recurrence in patients with lung cancer following initial treatment. A planned interim analysis (9-month follow-up) found a significant survival benefit (19-month survival in the PRO group vs 12 months in the control group).2 We now present the final overall survival analysis.
I. Sendiña-Nadal & C. Letellier
Observability of dynamical networks from graphic and symbolic approaches
In Springer Proceedings in Complexity, X S. Cornelius, C. Granell Martorell, J. Gómez-Gardeñes & B. Gonçalves (eds) CompleNet 2019. Online or in ArXiv
A dynamical network, a graph whose nodes are dynamical systems, is usually characterized by a large dimensional space which is not always accessible due to the impossibility of measuring all the variables spanning the state space. Therefore, it is of the utmost importance to determine a reduced set of variables providing all the required information to non-ambiguously distinguish its different states. Inherited from control theory, one possible approach is based on the use of the observability matrix defined as the Jacobian matrix of the change of coordinates between the original state space and the space reconstructed from the measured variables. The observability of a given system can be accurately assessed by symbolically computing the complexity of the determinant of the observability matrix and quantified by symbolic observability coefficients. In this work, we extend the symbolic observability, previously developed for dynamical systems, to networks made of coupled d-dimensional node dynamics (d > 1). From the observability of the node dynamics, the coupling function between the nodes, and the adjacency matrix, it is indeed possible to construct the observability of a large network with an arbitrary topology.
G. D. Charó, D. Sciamarella, S. Mangiarotti, G. Artana & C. Letellier,
Observability of laminar bidimensional fluid flows seen as autonomous chaotic systems.
Chaos, 29 (12), 123126, 2019. Online
Lagrangian transport in the dynamical systems approach has so far been investigated disregarding the connection between the whole state space and the concept of observability. Key issues such as the definitions of Lagrangian and chaotic mixing are revisited under this light, establishing the importance of rewriting nonautonomous flow systems derived from a stream function in autonomous form, and of not restricting the characterization of their dynamics in subspaces. The observability of Lagrangian chaos from a reduced set of measurements is illustrated with two canonical examples : the Lorenz system derived as a low-dimensional truncation of the Rayleigh-Bénard convection equations and the driven double-gyre system introduced as a kinematic model of configurations observed in the ocean. A symmetrized version of the driven double-gyre model is proposed.
The motion of fluid is involved in many natural situations such as in the atmosphere, oceans, stars where the fluid is charged, many industrial processes such as in cooler and mixing tanks, and even in physiology with the blood circulation and ventilation. Understanding the motion of fluid particles is, therefore, of primary importance. A variety of mixing and transport problems involving incompressible bidimensional flows has been undertaken using a nonlinear dynamics approach, overlooking embedology and observability issues that are discussed for the first time in this work. When a stream function is written, an analogy between conservative flow and bidimensional incompressible flow can be exhibited. Interesting features such as chaotic mixing and nonmixing islands are commonly observed. It is shown here that such flows should be investigated in the state space and not only in the physical space, which is a plane projection of the higher-dimensional state space. The advantages of working in the whole state space are illustrated with two paradigmatic examples.