Oxidative stress, protein glycation and nutrition – interactions relevant to health and disease throughout the lifecycle

Protein glycation has been studied for over a century now and plays an important role in disease pathogenesis throughout the lifecycle. Strongly related to diabetic complications, glycation of Hb has become the gold standard method for diabetes diagnosis and monitoring. It is however attracting attention in normoglycaemia as well lately. Longitudinal studies increasingly suggest a positive relationship between glycation and the risk of chronic diseases in normoglycaemic individuals, but the mechanisms behind this association remain unclear. The interaction between glycation and oxidative stress may be particularly relevant in the normoglycaemic context, as suggested by recent epidemiological and in vitro evidence. In that context nutritional and lifestyle factors with an influence on redox status, such as smoking, fruit and vegetable and antioxidants consumption, may have the capacity to promote or inhibit glycation. However, experimental data from controlled trials are lacking the quality and rigour needed to reach firm conclusions. In the present review, we discuss the importance of glycation for health through the lifecycle and focus on the importance of oxidative stress as a driver for glycation. The importance of nutrition to modulate glycation is discussed, based on the evidence available and recommendations towards higher quality future research are made

Sub-sampling for Efficient Non-Parametric Bandit Exploration

International audienceIn this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning. RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA [1] and SSMC [2] algorithms with different sub-sampling schemes. In particular, RB-SDA uses Random Block sampling. We perform an experimental study assessing the flexibility and robustness of this promising novel approach for exploration in bandit models

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits

International audienceWe introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, with an efficient, parameter-free, changepoint detector, the Bernoulli Generalized Likelihood Ratio Test, for which we provide new theoretical guarantees of independent interest. Unlike previous non-stationary bandit algorithms using a change-point detector, GLR-klUCB does not need to be calibrated based on prior knowledge on the arms' means. We prove that this algorithm can attain a $O(\sqrt{TA \Upsilon_T\log(T)})$ regret in $T$ rounds on some ``easy'' instances, where A is the number of arms and $\Upsilon_T$ the number of change-points, without prior knowledge of $\Upsilon_T$. In contrast with recently proposed algorithms that are agnostic to $\Upsilon_T$, we perform a numerical study showing that GLR-klUCB is also very efficient in practice, beyond easy instances

Humid Evolution of Haze in the Atmosphere of Super-Earths in the Habitable Zone

Photochemical hazes are expected to form and significantly contribute to the chemical and radiative balance of exoplanets with relatively moderate temperatures, possibly in the habitable zone of their host star. In the presence of humidity, haze particles might thus serve as cloud condensation nuclei and trigger the formation of water droplets. In the present work, we are interested in the chemical impact of such a close interaction between photochemical hazes and humidity on the organic content composing the hazes and on the capacity to generate organic molecules with high prebiotic potential. For this purpose, we explore experimentally the sweet spot by combining N-dominated super-Earth exoplanets in agreement with Titan's rich organic photochemistry and humid conditions expected for exoplanets in habitable zones. A logarithmic increase with time is observed for the relative abundance of oxygenated species, with O-containing molecules dominating after 1 month only. The rapidity of the process suggests that the humid evolution of N-rich organic haze provides an efficient source of molecules with high prebiotic potential

La construction du site pédagogique numérique CHIMACTIV : analyse d'une coopération réussie entre enseignants

International audienceUn collectif d'enseignants inter-établissements s'est organisé pour concevoir et co-construire un site pédagogique numérique. L'équipe de conception initiale a fortement coopéré (au sein de chaque établissement et entre établissements) et interagi étroitement avec différents acteurs (cellules TICE, étudiants, prestataires externes) pour aboutir à une version bilingue du site. La volonté d'élargir et de diversifier le champ des utilisateurs (enseignants et étudiants) a conduit à ouvrir ce collectif à de nouveaux enseignants, afin de faire évoluer le site et compléter son contenu. Après une analyse de l'organisation mise en place, nous discuterons des obstacles à surmonter, des facteurs de réussite et du ressenti des enseignants ayant vécu cette coopération, avant de conclure sur ce qu'apporte l'aspect « numérique » des ressources développées dans la coopération entre enseignants sur la base de notre expérience

Optimal Thompson Sampling strategies for support-aware CVaR bandits

Presented at the Thirty-eighth International Conference on Machine Learning (ICML 2021)International audienceIn this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce a new Thompson Sampling approach for CVaR bandits on bounded rewards that is flexible enough to solve a variety of problems grounded on physical resources. Building on a recent work by Riou & Honda (2020), we introduce B-CVTS for continuous bounded rewards and M-CVTS for multinomial distributions. On the theoretical side, we provide a non-trivial extension of their analysis that enables to theoretically bound their CVaR regret minimization performance. Strikingly, our results show that these strategies are the first to provably achieve asymptotic optimality in CVaR bandits, matching the corresponding asymptotic lower bounds for this setting. Further, we illustrate empirically the benefit of Thompson Sampling approaches both in a realistic environment simulating a use-case in agriculture and on various synthetic examples

Optimal Thompson Sampling strategies for support-aware CVaR bandits

Presented at the Thirty-eighth International Conference on Machine Learning (ICML 2021)International audienceIn this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce a new Thompson Sampling approach for CVaR bandits on bounded rewards that is flexible enough to solve a variety of problems grounded on physical resources. Building on a recent work by Riou & Honda (2020), we introduce B-CVTS for continuous bounded rewards and M-CVTS for multinomial distributions. On the theoretical side, we provide a non-trivial extension of their analysis that enables to theoretically bound their CVaR regret minimization performance. Strikingly, our results show that these strategies are the first to provably achieve asymptotic optimality in CVaR bandits, matching the corresponding asymptotic lower bounds for this setting. Further, we illustrate empirically the benefit of Thompson Sampling approaches both in a realistic environment simulating a use-case in agriculture and on various synthetic examples

Optimal Thompson Sampling strategies for support-aware CVaR bandits

Presented at the Thirty-eighth International Conference on Machine Learning (ICML 2021)International audienceIn this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce a new Thompson Sampling approach for CVaR bandits on bounded rewards that is flexible enough to solve a variety of problems grounded on physical resources. Building on a recent work by Riou & Honda (2020), we introduce B-CVTS for continuous bounded rewards and M-CVTS for multinomial distributions. On the theoretical side, we provide a non-trivial extension of their analysis that enables to theoretically bound their CVaR regret minimization performance. Strikingly, our results show that these strategies are the first to provably achieve asymptotic optimality in CVaR bandits, matching the corresponding asymptotic lower bounds for this setting. Further, we illustrate empirically the benefit of Thompson Sampling approaches both in a realistic environment simulating a use-case in agriculture and on various synthetic examples

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits

International audienceWe introduce GLR-klUCB, a novel algorithm for the piecewise iid non-stationary bandit problem with bounded rewards. This algorithm combines an efficient bandit algorithm, kl-UCB, with an efficient, parameter-free, changepoint detector, the Bernoulli Generalized Likelihood Ratio Test, for which we provide new theoretical guarantees of independent interest. Unlike previous non-stationary bandit algorithms using a change-point detector, GLR-klUCB does not need to be calibrated based on prior knowledge on the arms' means. We prove that this algorithm can attain a $O(\sqrt{TA \Upsilon_T\log(T)})$ regret in $T$ rounds on some ``easy'' instances, where A is the number of arms and $\Upsilon_T$ the number of change-points, without prior knowledge of $\Upsilon_T$. In contrast with recently proposed algorithms that are agnostic to $\Upsilon_T$, we perform a numerical study showing that GLR-klUCB is also very efficient in practice, beyond easy instances