Advances in the deposition of ceramics by soft chemistry process : example of rare- earth silicate coatings

The dip-coating process consists in immersing a sample to be coated in the liquid medium and then removing it at a controlled speed in order to obtain a film of regular thickness, as shown in Figure 1a). Dip-coating technique is now used in many industrial fields (biomedical, transportation, optics…). It is a very simple, and easy process to implement for the deposition and shaping of different natures of coatings (ceramic, metallic and polymer). In the case of ceramic coatings, after the dip-coating operation, the layers undergo a sintering post-treatment leading to the consolidation and/or the densification of the deposit. The corresponding mechanisms need a rigorous control of many parameters. The parameters involved in the dip-coating process are related to the medium and to the process. Concerning the medium, the dispersion medium nature, the particles concentration, viscosity, and stability are the main ones. The stability of the suspension is a first-order parameter and a preliminary formulation work has been carried out to cope with it. Moreover, parameters relative to the fabrication process such as the number of layers and the thermal profile (intermediary and final temperatures), will also be key factors to be taken into account in the formation of homogeneous and reproducible coatings by dip-coating.This work highlights the influence of these various parameters in the case of rare earth silicates based coatings. The various experiments were carried out in correlation to the coatings quality and microstructure. Homogeneous and conformal ceramic coatings of few tens of micrometers thick, as shown in Figure 1b), were obtained. A multi-layers deposit in a sol loaded at 40% mass generally allows to reach the desired thickness. With these experiments relationship between dip-coating parameters and coatings microstructure and morphology can be established. Please click Additional Files below to see the full abstract

Selection of amyloid positive pre-symptomatic subjects using automatic analysis of neuropsychological and MRI data for cost effective inclusion procedures in clinical trials

International audienc

Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic and Quantitative Review

Context. Automatically predicting if a subject with Mild Cognitive Impairment (MCI) is going to progress to Alzheimer's disease (AD) dementia in the coming years is a relevant question regarding clinical practice and trial inclusion alike. A large number of articles have been published, with a wide range of algorithms, input variables, data sets and experimental designs. It is unclear which of these factors are determinant for the prediction, and affect the predictive performance that can be expected in clinical practice. We performed a systematic review of studies focusing on the automatic prediction of the progression of MCI to AD dementia. We systematically and statistically studied the influence of different factors on predictive performance. Method. The review included 172 articles, 93 of which were published after 2014. 234 experiments were extracted from these articles. For each of them, we reported the used data set, the feature types (defining 10 categories), the algorithm type (defining 12 categories), performance and potential methodological issues. The impact of the features and algorithm on the performance was evaluated using t-tests on the coefficients of mixed effect linear regressions. Results. We found that using cognitive, fluorodeoxyglucose-positron emission tomog-raphy or potentially electroencephalography and magnetoencephalography variables significantly improves predictive performance compared to not including them (p=0.046, 0.009 and 0.003 respectively), whereas including T1 magnetic resonance imaging, amyloid positron emission tomography or cerebrospinal fluid AD biomarkers does not show a significant effect. On the other hand, the algorithm used in the method does not have a significant impact on performance. We identified several methodological issues. Major issues, found in 23.5% of studies, include the absence of a test set, or its use for feature selection or parameter tuning. Other issues, found in 15.0% of studies, pertain to the usability of the method in clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. Finally, we highlight possible biases in publications that tend not to publish methods with poor performance on large data sets, which may be censored as negative results. Conclusion. Using machine learning to predict MCI to AD dementia progression is a promising and dynamic field. Among the most predictive modalities, cognitive scores are the cheapest and less invasive, as compared to imaging. The good performance they offer question the wide use of imaging for predicting diagnosis evolution, and call for further exploring fine cognitive assessments. Issues identified in the studies highlight the importance of establishing good practices and guidelines for the use of machine learning as a decision support system in clinical practice

Advances in the control of electrophoretic process parameters to tune the ytterbium disilicate coatings microstructure

Suspensions of ytterbium disilicate in isopropanol were prepared using iodine dispersant. Their zeta potential, electrical conductivity, and pH dependence with iodine concentration is detailed. Electrophoretic deposition was performed on silicon substrates at various voltages (100‐200 V) and times (until 10 minutes) and the growth dynamic was investigated. It was observed that the deposited mass reaches a maximum value for [I2] = 0.2 g/L, and the coating microstructure becomes porous at higher iodine concentrations. Current density and voltage measurements allowed to correlate this behavior to the increase of free protons concentration in the suspension. In these conditions, it was proved that porosity increases with the increase in applied voltage, and a compaction occurs as the deposition time increases. This has been related to the coating resistance increase and subsequent decrease in effective voltage in the suspension. The denser coatings (20% of porosity) were obtained in the case of suspension without iodine, at the minimum applied voltage and for the longest deposition times

Création de systèmes d’aide à la décision pour la détection précoce de sujets à risque de développer la maladie d’Alzheimer

The goal of this thesis is to design data-driven methods to identify subjects at risk to develop Alzheimer's disease. As it is a progressive disease, subtle signs can appear several years before the first clinical symptoms. Identifying subjects who show these signs, and who are likely to develop the disease in the coming years, is a crucial point that could allow researchers to better study the disease mechanism, select patients for clinical trials and tailor patient care.In the first chapter, we conduct a review of methods predicting the future diagnosis of subjects suffering from mild cognitive impairment. We quantitatively and qualitatively study these methods, and take a critical view point by identifying several methodological issues.In the second chapter, we propose our own method to predict the future diagnosis by using a two-step approach: we first predict the future subject characteristics, and then use this result to predict the corresponding diagnosis.In the third chapter, we propose an automatic method to select subjects with a positive biomarker for clinical trials, so as to minimize the recruitment cost.In the last chapter, we analyze prescription patterns before and after diagnosis using a medical record database. We use them to predict if a patient will develop Alzheimer's disease in the next five or ten years.Across these works, we show the importance to take into account the adoption of these methods and the settings in which they can be used, especially regarding the test cohort, the data types and the interpretability of the method.Le but de cette thèse est de proposer des méthodes d’apprentissage automatique pour identifier des sujets à risque de développer la maladie d'Alzheimer. L'identification à un stade très précoce de sujets à risque de développer la maladie est une problématique clé, qui permettrait de mieux étudier la maladie, de sélectionner des patients pour des essais cliniques et de leur proposer un suivi adapté.Dans un premier chapitre, nous effectuons une revue des méthodes prédisant le diagnostic futur de sujets atteints de troubles cognitifs légers. Nous effectuons un travail de synthèse, à la fois qualitatif et quantitatif, des méthodes proposées pour effectuer cette prédiction et des problèmes méthodologiques qu'elles comportent. Dans un deuxième chapitre, nous proposons d’effectuer cette prédiction du futur diagnostic avec une approche en deux temps : nous prédisons d'abord l'évolution des caractéristiques des sujets, et utilisons ces résultats pour prédire le diagnostic correspondant à un stade ultérieur.Dans un troisième chapitre, nous proposons une méthode automatique permettant de repérer des sujets à biomarqueurs positifs pour les essais cliniques, de manière à minimiser le coût de recrutement. Dans un dernier chapitre, nous analysons l'évolution des prescriptions de médicaments avant et après le diagnostic grâce à des bases d'historiques médicaux. Nous les utilisons pour prédire si un patient va développer la maladie d'Alzheimer dans les 5 ou 10 années à venir.Nous mettons en avant l’importance de prendre en compte l’adoption des méthodes et leur cadre d’utilisation, notamment à travers la cohorte d’étude, les types de données, et l’interprétabilité de la méthode

Design of a decision support system for predicting the progression of Alzheimer’s disease

International audienceBackground: Knowing who will develop Alzheimer's disease (AD) and when is a crucial health issue, that could help treat subjects earlier, using new treatments targeting the earliest stages. We have participated in TADPOLE, a competition based on ADNI data aiming at developing an automatic prognosis system. We propose a method for predicting the time to conversion of MCI subjects to AD and a framework to evaluate such predictions. Method: We decompose the prediction into two steps: first, we monthly predict future values of the cognitive scores of each subject; second, we use them to automatically predict the corresponding disease status, using a Support Vector Machine. For the first step, we compare two approaches: the univariate and the multivariate approach. In the univariate approach, cognitive scores are predicted by linear regression with respect to time for each subject. In the multivariate approach, sociodemographic, cognitive and MRI features of the last visit are used to predict the future cognitive scores. This approach is automatically learned using all visits of training subjects. We evaluate our methods by splitting longitudinal data into training and test subjects. Each test subject is split into observed visits and a future visit, separated by different time periods (mostly 1 to 4 years in the challenge). We want to verify three hypotheses: the multivariate approach outperforms the univariate approach; separating the process into two parts perform better than directly using a classification algorithm; the time to prediction is an important parameter that has to be taken into account. Results: The univariate approach (AUC=82.6%±3.0) performs significantly worse (p=0.0001) than the multivariate approach (AUC=85.5%±2.5). Both perform significantly better (p<0.0001) than a simple classification (AUC=59.2%±6.2). Using a time to prediction of 1 to 8 years, as in the challenge leaderboard, lowers the performance (AUC=79.3% for the multivariate approach)

Prediction of future cognitive scores and dementia onset in Mild Cognitive Impairment patients

International audienc

Advances in the Electrophoretic Process to Control Oxide Coatings Microstructure for Sandrine Duluard

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Selection of amyloid positive pre-symptomatic subjects using automatic analysis of neuropsychological and MRI data for cost effective inclusion procedures in clinical trials

International audienc

Selection of amyloid positive pre-symptomatic subjects using automatic analysis of neuropsychological and MRI data for cost effective inclusion procedures in clinical trials

International audienc