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International audience; In this paper, we consider the problem of nonparametric hazard rate estimation in presence of right-censored observations. We provide a generalized risk bound for a regression type nonparametric estimator of the hazard function of interest. Under adequate integrability conditions, our bound is a generalization to non necessarily compactly supported bases, of strategies which were specific to compact support of estimation. We show that it encompasses those previous compact-support results. We discuss the model selection method which comes out from the new terms of the risk bounds, and compare the performance of the new estimator to previous ones, when using a non compact Laguerre basis. A real data example is also presented.

METTL3 is the RNA methyltransferase predominantly responsible for the addition of N-6-methyladenosine (m6A), the most abundant modification to mRNA. The prevalence of m6A and the activity and expression of METTL3 have been linked to the appearance and progression of acute myeloid leukemia (AML)1. EPICS has discovered and optimized a small molecule inhibitor of METTL3 (“M3i”). M3i was shown to inhibit the enzymatic activity of METTL3 (IC50 = 2 nM, SPA assay) and inhibit the presence of intracellular m6A in a cell-based assay (IC50 = 8 nM, Calu6). The anti-proliferative effects of METTL3 was demonstrated in a spheroid model (NCIH-560, IC50 = 100 nM) as well as cell viability assays in various AML (Kasumi-1, IC50 = 30 nM; LEXF 41283, IC50 = 46 nM; MV-4-11, IC50 = 248 nM) and solid tumor (Calu6, IC50= 6 nM; Caov3, IC50 = 27 nM) cell lines. In vivo efficacy was evaluated in a disseminated xenograft model using established systemic MV-4-11-Luc-mCh-Puro2 in female NSG mice where M3i (30 mg/kg, i.p., QDx31 days) significantly (p Citation Format: Graeme Fraser, Catherine Sorlet, Nicolas Parmentier, Elodie Brunel, Anne-France Hartiel, Killian Oukoloff, Guillaume Dutheuil. EP102: Pharmacological inhibition of METTL3 arrests tumor progression and prolongs survival in CDX and PDX models of AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6282.

International audience

International audience; In this paper, we propose a new strategy of estimation for the survival function S, associated to a survival time subject to interval censoring case 2. Our method is based on a least squares contrast of regression type with parameters corresponding to the coefficients of the development of S on an orthonormal basis. We obtain a collection of projection estimators where the dimension of the projection space has to be adequately chosen via a model selection procedure. For compactly supported bases, we obtain adaptive results leading to general non-parametric rates. However, our results can be used for non compactly supported bases, a true novelty in regression setting, and we use specifically the Laguerre basis which is R +-supported and thus well suited when nonnegative random variables are involved in the model. Simulation results comparing our proposal with previous strategies show that it works well in a very general context. A real data set is considered to illustrate the methodology. MSC 2010 subject classification: 62N02–62G05

Objective Pelvic fractures represent 5% of all traumatic fractures and 30% are isolated pelvic fractures. Pelvic fractures are found in 10 to 20% of severe trauma patients and their presence is highly correlated to increasing trauma severity scores. The high mortality of pelvic trauma, about 8 to 15%, is related to actively bleeding pelvic injuries and/or associated injuries to the head, abdomen or chest. Regardless of the severity of pelvic trauma, diagnosis and treatment must proceed according to a strategy that does not delay the management of the most severely injured patients. To date, in France, there are no guidelines issued by healthcare authorities or professional societies that address this subject. Design A consensus committee of 22 experts from the French Society of Anaesthesia and Intensive Care Medicine (Societe Francaise d’Anesthesie et de Reanimation; SFAR) and the French Society of Emergency Medicine (Societe Francaise de Medecine d’Urgence; SFMU) in collaboration with the French Society of Radiology (Societe Francaise de Radiologie; SFR), French Defence Health Service (Service de Sante des Armees; SSA), French Society of Urology (Association Francaise d’Urologie; AFU), the French Society of Orthopaedic and Trauma Surgery (Societe Francaise de Chirurgie Orthopedique et Traumatologique; SOCFCOT), and the French Society of Digestive Surgery (Societe Francaise de Chirurgie digestive; SFCD) was convened. A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independently from any industry funding. The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence. The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasised. Methods Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. The analysis of the literature and the recommendations were then conducted according to the GRADE® methodology. Results The SFAR Guideline panel provided 22 statements on prehospital and hospital management of the unstable patient with pelvic fracture. After three rounds of discussion and various amendments, a strong agreement was reached for 100% of recommendations. Of these recommendations, 11 have a high level of evidence (Grade 1 ± ), 11 have a low level of evidence (Grade 2 ± ). Conclusions Substantial agreement exists among experts regarding many strong recommendations for management of the unstable patient with pelvic fracture.

Functional linear regression has recently attracted considerable interest. Many works focus on asymptotic inference. In this paper we consider in a non asymptotic framework a simple estimation procedure based on functional Principal Regression. It revolves in the minimization of a least square contrast coupled with a classical projection on the space spanned by the m first empirical eigenvectors of the covariance operator of the functional sample. The novelty of our approach is to select automatically the crucial dimension m by minimization of a penalized least square contrast. Our method is based on model selection tools. Yet, since this kind of methods consists usually in projecting onto known non-random spaces, we need to adapt it to empirical eigenbasis made of data-dependent - hence random - vectors. The resulting estimator is fully adaptive and is shown to verify an oracle inequality for the risk associated to the prediction error and to attain optimal minimax rates of convergence over a certain class of ellipsoids. Our strategy of model selection is finally compared numerically with cross-validation.

International audience

BACKGROUND:It has been suggested that the complementary use of echocardiography could improve the diagnostic accuracy of lung ultrasonography (LUS) in patients with acute respiratory failure (ARF). Nevertheless, the additional diagnostic value of echocardiographic data when coupled with LUS is still debated in this setting. The aim of the current study was to compare the diagnostic accuracy of LUS and an integrative cardiopulmonary ultrasound approach (thoracic ultrasonography [TUS]) in patients with ARF. METHODS:We prospectively recruited patients consecutively admitted for ARF to the ICU of a university teaching hospital over a 12-month period. Inclusion criteria were age ≥ 18 years and the presence of criteria for severe ARF justifying ICU admission. We compared both LUS and TUS approaches and the final diagnosis determined by a panel of experts using machine learning methods to improve the accuracy of the final diagnostic classifiers. RESULTS:One hundred thirty-six patients were included (age, 68 ± 15 years; sex ratio, 1). A three-dimensional partial least squares and multinomial logistic regression model was developed and subsequently tested in an independent sample of patients. Overall, the diagnostic accuracy of TUS was significantly greater than LUS (P CONCLUSIONS:This study demonstrated for the first time to our knowledge a significantly better performance of TUS than LUS in the diagnosis of ARF. The value of the TUS approach was particularly important to disambiguate cases of hemodynamic pulmonary edema and pneumonia. We suggest that the bedside use of artificial intelligence methods in this setting could pave the way for the development of new clinically relevant integrative diagnostic models.

Clival osteomyelitis is a potentially life-threatening infection that can occur in healthy children. It can be related to congenital anomalies. We report the case of a 7-year-old boy with Streptococcus intermedius and Fusobacterium clival osteomyelitis arising from a Thornwaldt cyst situated in a fossa navicularis magna of the occipital bone. Multidisciplinary management is necessary to ensure rapid improvement and complete healing.

International audience; In this paper, we consider the multiplicative censoring model, given by Yi = XiUi where (Xi) are i.i.d. with unknown density f on R, (Ui) are i.i.d. with uniform distribution U([0, 1]) and (Ui) and (Xi) are independent sequences. Only the sample (Yi) 1≤i≤n is observed. Nonparametric estimators of both the density f and the corresponding survival function ¯ F are proposed and studied. First, classical kernels are used and the estimators are studied from several points of view: pointwise risk bounds for the quadratic risk are given, upper and lower bounds for the rates in this setting are provided. Then, an adaptive non asymptotic bandwidth selection procedure in a global setting is proved to realize the bias-variance compromise in an automatic way. When the Xi's are nonnegative, using kernels fitted for R +-supported functions, we propose new estimators of the survival function which are proved to be adaptive. Simulation experiments allow us to check the good performances of the estimators and compare the two strategies.

Consider an i.i.d. sample $(X_i,Y_i)$, $i=1, \dots, n$ of observations and denote by $F(x,y)$ the conditional cumulative distribution function of $Y_i$ given $X_i=x$. We provide a data driven nonparametric strategy to estimate $F$. We prove that, in term of the integrated mean square risk on a compact set, our estimator performs a squared-bias variance compromise. We deduce from this an upper bound for the rate of convergence of the estimator, in a context of anisotropic function classes. A lower bound for this rate is also proved, which implies the optimality of our estimator. Then our procedure can be adapted to positive censored random variables $Y_i$'s, i.e. when only $Z_i=\inf(Y_i, C_i)$ and $\delta_i=\1_{\{Y_i\leq C_i\}}$ are observed, for an i.i.d. censoring sequence $(C_i)_{1\leq i\leq n}$ independent of $(X_i,Y_i)_{1\leq i\leq n}$. Simulation experiments illustrate the method.

In addition to the right-censoring of the lifetimes, sampling bias pos- sibly occurs in many situations while collecting the data. In practical situations, the form of the bias function is always assumed to be known, but if no information is available on the selection bias this can turn out to be unbearable. We provide a model selection strategy for estimating the hazard and the density functions in presence of both censoring and unknown sampling bias. We use a new sampling scheme description based on the Lexis diagram including time-window or cohort studies. Adaptive projection estimators on trigonometric bases are developed by contrast penalization and optimal nonparametric rates of convergence are given. Both estimators are practically studied through simulation experiments in the case of the time-window study. observable r.v. has p.d.f. given by w(x)f(x)/ R w(u)f(u)du, for all x > 0. In this case, the r.v. is said to suffer from a "selection bias" and one may heuristically say that selection bias arises when the event {X = x} is observed with a probability proportional to w(x). Bias may occur in the following kind of model. Consider, in a population of individuals I, the r.v.'s of their birth dates (�i)i2I, and the non-negative r.v.'s of

The convergence of measure estimates in the sense of Kullback–Leibler divergence is required in many applications in decision and information theory. Recently, modified histograms have been shown to have good properties with respect to information divergences. For these estimates deterministic optimal bandwidths have been given, but no automatic smoothing procedure has been shown to be asymptotically optimal. In the present article, we consider the Kullback–Leibler cross-validation method for selecting the bin width of modified histograms. We analyze the behavior of the Kullback–Leibler divergence and of its expectation and prove that the cross-validated estimate is asymptotically optimal with respect to the Kullback–Leibler divergence.

It has been suggested that the complementary use of echocardiography could improve the diagnostic accuracy of lung ultrasonography (LUS) in patients with acute respiratory failure (ARF). Nevertheless, the additional diagnostic value of echocardiographic data when coupled with LUS is still debated in this setting. The aim of the current study was to compare the diagnostic accuracy of LUS and an integrative cardiopulmonary ultrasound approach (thoracic ultrasonography [TUS]) in patients with ARF.We prospectively recruited patients consecutively admitted for ARF to the ICU of a university teaching hospital over a 12-month period. Inclusion criteria were age ≥ 18 years and the presence of criteria for severe ARF justifying ICU admission. We compared both LUS and TUS approaches and the final diagnosis determined by a panel of experts using machine learning methods to improve the accuracy of the final diagnostic classifiers.One hundred thirty-six patients were included (age, 68 ± 15 years; sex ratio, 1). A three-dimensional partial least squares and multinomial logistic regression model was developed and subsequently tested in an independent sample of patients. Overall, the diagnostic accuracy of TUS was significantly greater than LUS (P.05, learning and test sample). Comparisons between receiver operating characteristic curves showed that TUS significantly improves the diagnosis of cardiogenic edema (P.001, learning and test samples), pneumonia (P.001, learning and test samples), and pulmonary embolism (P.001, learning sample).This study demonstrated for the first time to our knowledge a significantly better performance of TUS than LUS in the diagnosis of ARF. The value of the TUS approach was particularly important to disambiguate cases of hemodynamic pulmonary edema and pneumonia. We suggest that the bedside use of artificial intelligence methods in this setting could pave the way for the development of new clinically relevant integrative diagnostic models.

International audience; We consider the problem of estimation from right-censored data, when the censoring indicator is possibly missing. We compare different estimatio/imputation strategies for recovering nuisance functional parameters. More precisely, we propose either a parametric strategy following a logistic model standard or a pure nonparametric regression strategy. We provide theoretical properties and numerical comparisons for these procedures.

Resume Nous etablissons la vitesse de convergence en information de Kullback–Leibler de l'histogramme modifie de Barron et nous introduisons un critere de type validation croisee pour le choix automatique et optimal du nombre de classes de la partition.

International audience; In this paper, we consider the problem of hazard rate estimation in presence of covariates, forsurvival data with censoring indicators missing at random. We propose in the context usually denoted by MAR (missing at random, in opposition to MCAR, missing completely at random, which requires an additional independence assumption), nonparametric adaptive strategies based on model selection methods for estimators admitting finite dimensional developments in functional orthonormal bases. Theoretical risks bounds are provided, they prove that the estimators behave well in term of Mean Square Integrated Error (MISE). Simulation experiments illustrate the statistical procedure.

In this double blind, unicentre, randomized, placebo controlled study, we evaluated the changes in 25-hydroxyvitamin D (25(OH)D) serum levels in 150 young Belgian adults (18–30 years), monthly supplemented with 50,000 IU of vitamin D (VTD) or placebo for 6 months, from November 2010 to May 2011. At T0, 30% of the population presented 25(OH)D serum levels below 20 ng/mL. In the VTD-treated group, mean serum levels increased from 21.2 ± 8.2 to 30.6 ± 8.8 ng/mL (P<0.001) at T3mo and to 36.0 ± 9.2 ng/mL (P<0.001) at T6mo. Despite documented VTD intake, no changes in serum levels were, however, observed in 10% of the treated group. In the placebo group, mean 25(OH)D serum levels decreased from 22.8 ± 8.5 to 14.0 ± 6.9 ng/mL at T3mo (P<0.001) but returned to values not significantly different from those observed at T0 (23.5 ± 8.6 ng/mL) at T6mo. No difference between serum calcium levels was observed between the groups throughout the study. In conclusion, monthly supplementation with 50,000 UI of VTD in winter can warrant serum 25(OH)D levels above 20 ng/mL in 96.2% of those healthy young adults without inducing unacceptably high 25(OH)D concentration. This supplementation is safe and may be proposed without 25(OH)D testing.

In this paper, we consider the problem of nonparametric mean residual life (MRL) function estimation in presence of covariates. We propose a contrast that provides estimators of the bivariate conditional MRL function, when minimised over different collections of linear finite-dimensional function spaces. Then we describe a model selection device to select the best estimator among the collection, in the mean integrated squared error sense. A non-asymptotic oracle inequality is proved for the estimator, which both ensures the good finite sample performances of the estimator and allows us to compute asymptotic rates of convergence. Lastly, examples and simulation experiments illustrate the method, together with a short real data study.

We consider projection estimator methods for the nonparametric estimation of the density of i.i.d. biased observations with a general known bias function w and under right censoring. Adaptive procedures to catch the optimal estimator among a collection by contrast penalization are investigated and proved to give efficient estimators with optimal nonparametric rates of convergence. Monte-Carlo experiments complete the study and illustrate the method.

We consider projection methods for the estimation of the cumulative distribution function under interval censoring, case 1. Such censored data also known as current status data, arise when the only information available on the variable of interest is whether it is greater or less than an observed random time. Two types of adaptive estimators are investigated. The first one is a two-step estimator built as a quotient estimator. The second estimator results from a mean square regression contrast. Both estimators are proved to achieve automatically the standard optimal rate associated with the unknown regularity of the function, but with some restriction for the quotient estimator. Simulation experiments are presented to illustrate and compare the methods., Published in at http://dx.doi.org/10.1214/08-EJS209 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)

In this article, we study hazard projection estimators under right-censoring of the observations. We consider projection spaces generated by trigonometric or piecewise polynomials. First, we derive bounds on the MISE of the estimators. These bounds imply that the estimator reaches the standard optimal rate associated with the regularity of the hazard function, provided that the dimension of the projection space is relevantly chosen. Then we provide an adaptive procedure leading to an automatic choice of this dimension via a penalized minimum contrast estimator. Our procedure is based on a data-driven random penalty function. The resulting estimator automatically reaches the optimal rate.

Working paper; International audience; Consider an i.i.d. sample $(X_i,Y_i)$, $i=1, \dots, n$ of observations and denote by $\pi(x,y)$ the conditional density of $Y_i$ given $X_i=x$. We provide an adaptive nonparametric strategy to estimate $\pi$. We prove that our estimator realizes a global squared-bias/variance compromise in a context of anisotropic function classes. We prove that our procedure can be adapted to positive censored random variables $Y_i$'s, i.e. when only $Z_i=\inf(Y_i, C_i)$ and $\delta_i=\1_{\{Y_i\leq C_i\}}$ are observed, for an i.i.d. censoring sequence $(C_i)_{1\leq i\leq n}$ independent of $(X_i,Y_i)_{1\leq i\leq n}$. Simulation experiments illustrate the method.