Eurobio (France)

Abstract Capytaine is an open-source Python-based Boundary Element Method (BEM) program designed to simulate wave-structure interactions employing linear potential flow theory in frequency domain. A long term work is currently underway to improve its performance and accuracy, which is behind its commercial counterparts. The present paper focuses on two steps of the numerical resolution: the choice between the direct and indirect boundary integral equation and the accuracy of the tabulation used as surrogate model of the Green function. The results are illustrated on two test cases for which issues had been previously reported, namely the floating cylinder with a thin heave plate and the DeepCWind floating offshore wind turbine platform reference model. The inaccuracy of previous versions of Capytaine on these two test cases is explained and new results are presented in comparison with WAMIT and HAMS.

Abstract Background and Aims: Progastrin is a tumor promoting peptide which is detectable in the blood of patients with different cancers. Progastrin gene is a direct target of the WNT/ß-catenin oncogenic pathway involved in tumorigenesis and possibly tumor progression/ treatment efficacy. Since WNT/ß-catenin oncogenic pathway is dysregulated in advanced prostate cancer we evaluated plasma progastrin in metastatic prostate cancer as a predictive and prognostic biomarker. Methods: Metastatic hormone sensitive prostate cancer (mHSPC) and metastatic castration resistant prostate cancer (mCRPC) states were enrolled in a cohort study of blood sample collection and follow-up for outcomes between 9/2009 and 11/2013. Patients were enrolled in mHSPC unique sub-cohorts before initiating androgen deprivation therapy (ADT); during ADT; at the time of failure of ADT and before starting chemotherapy. Plasma progastrin was measured using the ELISA cancerREAD®. Progastrin concentrations in the cancer patients (test set) was assessed against 213 samples from asymptomatic volunteers from the French blood establishment (control set) and prograstin levels were also compared for each of the above four mHSPC and mCRPC cohorts as well as for association with time to failure on AA for the mHSPC cohort and overall survival for both mHSPC and mCRPC subcohorts. We also determined progastrin levels in patients with two serial samples to evaluate if changes were predictive for overall survival. Results: Of the 523 mHSPC+mCRPC patients 96 were in mHSPC state enrolled before starting ADT; 101 mHSPC patients were enrolled while receiving ADT; 143 mHSPC patients were enrolled at the time of ADT failure and 143 were clinically progressive mCRPC. All cohorts were unique and 246/523 patients had two serial samples collected and analyzed across all subcohorts. The median time of follow up of the whole cohort was 8.34 years (IQR: 4.53-12.97) and 371/523 had died at the time of the analysis. Plasma progastrin levels was detected in 87.6% of all the patients (cut-off value 1 pM, range 0 to 311 pM, median value of 4.7 pM) compared to the control set (median value=0.61 pM; IQR 0.00-1.58). The Receiver Operating Characteristic analysis indicated an area under the curve of 0.84 (p<0.0001; 95% CI 0.81 to 0.87). Of these, 106 patients had a decrease and 140 patients an increase of progastrin levels. Patients with a serial increase of progastrin had a worst overall survival compare to the other group (p=0.019). Conclusion: Progastrin is a blood based biomarker elevated in advanced prostate cancer patients serial increases in progastrin levels during treatment are predictive of poor survival. Progastrin assay might be useful for monitoring therapeutic interventions like androgen deprivation therapy effects as well for advanced prostate cancer patients. Citation Format: Manish Kohli, Winston Tan, Lea Payen, Carole Langlois-Jacques, Pierre Liaud, Delphine Maucort-Boulch, Dominique Joubert, Alexandre Prieur. Plasma progastrin level as a predictive and prognostic biomarker in advanced prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2294.

Abstract Background and Aims: Progastrin is a tumor promoting peptide which is detectable in the blood of patients with different cancers (Prieur et al. AACR 2017, Prieur et al. ASCO 2017). Progastrin gene is a direct target of the WNT/ß-catenin oncogenic pathway involved in tumorigenesis of many organs, but it is unknown if it has any prognostic significance in metastatic renal cell cancer (mRCC) patients. We evaluated progastrin as a prognosis marker along with known markers of prognosis in mRCCC. Methods: 145 patients with mRCCC were enrolled in this study and blood samples were drawn after consent. Progastrin was measured using the ELISA cancerREAD®. Progastrin concentrations in the 145 mRCCC patients (test set) was assessed against 213 samples from asymptomatic volunteers from the French blood establishment (control set). The prognostic impact of progastrin levels was determined with overall survival (OS) using Cox proportional hazards and also compared to MSKCC based clinical prognosis (good;intermediate; poor). Statistical significance was considered at P≤0.05. Results: The median follow-up of the cohort was 5.45 years (IQR: 1.87-9.95) and at the time of analysis 98/145 patients had died from disease progression. Plasma progastrin was detected in 95% of the patients (cut-off value 1 pM, range 0 to 272 pM, median value of 7.2 pM; IQR 3.20-19.71) compared to the control set (median value=0.61 pM; IQR 0.00-1.58). The Receiver Operating Characteristic analysis indicated an area under the curve of 0.92 (p<0.0001; 95% CI 0.89 to 0.94). At the univariate level, MSKCC scores (good; intermediate; poor categories) in this cohort was associated with OS and was prognostic (p<0.0001). We detected progastrin levels were higher with MSKCC score poor prognostic (p<0.0002) (median 30.39 pM; IQR 9.31-57.20). Elevated progastrin was also independently associated with poor survival (p<0.0001) and a multivariate model of MSKCC taken with progastrin levels remained significantly associated with poor survival (p<0.0001). Conclusion: Elevated progastrin levels in mRCCC is correlated with poor survival and further refines clinically used MSKCC prognostic scores. Progastrin assay is a simple and inexpensive blood test that might define subsets of mRCCC patients with poor survival who need to be identified for aggressive treatments. Citation Format: Manish Kohli, Winston Tan, Lea Payen, Carole Langlois-Jacques, Pierre Liaud, Delphine Maucort-Boulch, Dominique Joubert, Alexandre Prieur. Prognostic impact of progastrin levels in blood compared to MSKCC based clinical prognosis in metastatic renal cell cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2289.

Les travaux présentés dans cet article s’inscrivent dans un projet de R&D mis en place par RTE (Réseau de Transport d’Electricité) et visant à évaluer le vieillissement des conducteurs électriques de lignes aériennes par des outils de simulation numérique. Après avoir rappelé la géométrie de tels conducteurs et l’utilisation qui en est faite, le principal risque d’endommagement de ces derniers est présenté. Il s’agit du phénomène defretting-fatigue intervenant à proximité des systèmes de fixation des conducteurs au niveau des pylônes, pouvant produire la rupture de brins les constituant. Ainsi, une méthodologie originale est proposée de façon à évaluer, par l’intermédiaire d’un modèle éléments finis, la durée de vie des conducteurs vis-à-vis de ce risque. Cette méthodologie est ensuite appliquée sur un cas-test dont les données proviennent de la littérature. Enfin, des perspectives sont données concernant le modèle numérique employé et l’intégration de la démarche dans le projet de R&D concerné.

This paper considers space-time adaptive techniques for finite volume schemes with explicit time discretization. The purpose is to reduce memory and to speed-up computations by a multiresolution representation of the numerical solution on adaptive grids which are introduced by suitable thresholding of its wavelet coefficients. Further speed-up is obtained by the combination of the multiresolution scheme with an adaptive strategy for time integration, which is classical for ODE simulations. It considers variable time steps, controlled by a given precision, using embedded Runge–Kutta schemes. As an alternative to the celebrated CFL condition, the aim in the application of such an time-adaptive scheme for PDE simulations is to obtain accurate and safe integrations. The efficiency of this adaptive space-time method is analyzed in applications to typical Riemann–Lax test problems for the compressible Euler equations in one and two space dimensions. The results show that the accuracy properties of the reference finite volume scheme on the finest regular grid, where the time step is determined by the CFL condition, is preserved. Nevertheless, both CPU time and memory requirements are considerably reduced, thanks to the efficient self-adaptive grid refinement and controlled time-stepping.

This pioneering book describes the applications of agent-based modeling to financial markets. It presents a new paradigm for finance, where markets are treated as complex systems whose behavior emerges as a result of interactions of market participants, market institutions, and market rules. This includes both a presentation of the conceptual model and its software implementation. It also summarises the result of the profound research on the successful practical application of this new approach to answer questions regarding the Nasdaq Stock Market s decimalization that was implemented in 2001. The book presents conceptual foundations for modeling markets as complex systems. It describes the agent-based model of the Nasdaq stock market, including strategies used by market-makers and investors, market participants interactions, and impacts of rules and regulations. It includes analyses of simulation behavior, comparison with the behaviors observed in the real-world markets (existence of fat tails, spread clustering, etc.), and predictions about possible outcomes of decimalization. A framework for calibrating the market behavior and individual market-makers strategies to historical data is also presented.