CTIP2, une protéine multifonctionnelle : Implication en physiopathologie cellulaire et en thérapeutique

The transcription factor CTIP2 (BCL11B) is a multifunctional protein involved in numerous cell physiological processes. To date, many molecular mechanisms underlying this process have been discovered, which highlighted the importance of the epigenetic regulation of genes and the regulation of the elongation factor P-TEFb. Furthermore studies of the deregulation of CTIP2 showed the association of CTIP2 to numerous pathologies including cancer and cardiac hypertrophy. A better comprehension of the physiopathology of these diseases might lead to the design of therapeutical strategies intending to prevent CTIP2 deregulation. Moreover, CTIP2 and its associated proteins constitute potential targets in strategies aiming to reduce and/or purge HIV-1 cell reservoirs. English title : CTIP2, a multifunctional protein: cellular physiopathology and therapeutic implication

L'effet piston en milieu poreux

Ce travail vise à étudier les mécanismes de transfert de masse et de chaleur au sein d'un fluide pur, au voisinage de son point critique, lorsque celui-ci sature une matrice poreuse. Hors milieu poreux. L'étude hydrodynamique et thermique des fluides supercritiques en absence de gravité et maintenus à volume constant, a mis en évidence une « accélération critique » du transport de chaleur par effet piston. Cet effet a pour conséquence une thermalisation très rapide et homogène du volume de fluide. L'impact d'un milieu poreux homogène et indéformable sur le devenir de l'effet piston est l'objet de ce travail. Une partie simulation numérique porte sur la vérification d'un modèle théorique proposé pour décrire les régimes de propagation de la chaleur. Une partie expérimentale présente, quant à elle, la réalisation d'une cellule instrumentée correspondant à la situation d'étude et des mesures tests réalisées au sein de celle-ci

Predicting bikeshare system usage up to one day ahead

International audienceBike sharing systems are present in several modern cities. They provide citizens with an alternative and ecological mode of transportation, allowing them to avoid the use of personal car and all the problems associated with it in big cities (\emphi.e., traffic jam, roads reserved for public transport, \ldots). However, they also suffer from other problems due to their success: some stations can be full or empty (\emphi.e., impossibility to drop off or take a bike). Thus, to predict the use of such system can be interesting for the user in order to help him/her to plan his/her use of the system and to reduce the probability of suffering of the previously presented issues. This paper presents an analysis of various regressors from the state of the art on an existing public dataset acquired during two years in order to predict the global use of a bike sharing system. The prediction is done for the next twenty-four hours at a frequency of one hour. Results show that even if most regressors are sensitive to over-fitting, the best performing one clearly beats the baselines

Détection de vélos inutilisables grâce aux données ouvertes du système de vélos en libre service Citibike

National audienceThe number of bikes within a bikeshare system having reached more than a million in 2015, it seems necessary to detect in near realtime a broken bike rooted at a station. Indeed, it is not cost effective in terms of number of trips, and it frustrates users who were expecting to find a bike at that station without knowing that it is actually defective. We thus propose a methodology for feature extraction and anomaly detection on a distributed cloud infrastructure in order to detect bicycles requiring a repair. We also show why it is important for us to retrieve labeled data from mobile applications at our disposal.Le nombre de vélos partagés au sein d'un système de vélos en libre service ayant dépassé le million, il est nécessaire de détecter rapidement un vélo immobilisé en station à cause d'une panne. En effet, il n'est pas rentable en terme de nombre de trajets effectués et frustre les usagers qui s'attendent à trouver un vélo à cette station sans qu'ils ne sachent qu'il est en fait défectueux. Nous proposons une méthodologie d'extraction de caractéristiques et de détec-tion d'anomalies sur une infrastructure distribuée de type « cloud computing » afin de détecter les vélos nécessitant une réparation. Nous montrons également pourquoi il est important pour nous de récupérer des données étiquetées remontant des applications mobiles à notre disposition

Broken Bikes Detection Using CitiBike Bikeshare System Open Data

International audienceIt seems necessary to detect a broken bike rooted at a station in near realtime as the number of bikes within bikeshare systems has reached more than a million in 2015. Indeed, a bike that cannot be moved is not cost effective in terms of number of trips. This brings frustration to users who were expecting to find a bike at that station without knowing that it is actually defective. We thus propose a methodology from feature extraction to anomaly detection on a distributed cloud infrastructure in order to detect bicycles requiring a repair. Through a first step of K-means clustering, and a second step consisting of spotting samples that do not clearly belong to any cluster, we separate anomalies from normal behaviors. The proposal is validated on a publicly available dataset provided by Motivate, the operator of the New-York bikeshare system. The number of distinct bikes that have been classified by this algorithm as broken at least once during a month is close to the number of repairs given in monthly reports of Motivate

國立臺灣大學圖書館田中文庫植物圖譜

CTIP2 is a key transcriptional regulator involved in numerous physiological functions. Initial works have shown the importance of CTIP2 in the establishment and persistence of HIV latency in microglial cells, the main latent/quiescent viral reservoir in the brain. Recent studies have highlighted the importance of CTIP2 in several other pathologies, such as cardiac hypertrophy and various types of human malignancies. Targeting CTIP2 may therefore constitute a new approach in the treatment of these pathologies