Chantal Guihenneuc-Jouyaux's research while affiliated with Paris Descartes, CPSC and other places

... Alteration to Mapt exon 10 splicing. Liu et al., 2008;Qian et al., 2013;Zhang et al., 2015;Dorard et al., 2016;Ahmed et al., 2017;García-Cerro et al., 2017;Yin et al., 2017;Chen et al., 2021 Aging dependant neuronal loss: Loss of basal forebrain cholinergic neurons in the MSN. Holtzman et al., 1996;Granholm et al., 2000;Hunter et al., 2003a,b;Seo and Isacson, 2005;Salehi et al., 2006Salehi et al., , 2009Lockrow et al., 2011b;Corrales et al., 2013 Neuroinflammation: Increased density of CD45+ microglia, decreased expression of homeostatic markers and increased levels of poinflammatory cytokines and ROS in aging dependant manner. ...

... is is because the atmospheric circulation processes influence the extent and direction of air mass transfer; hence, air pollutants are transported from one place to another [4]. Numerous studies have been conducted to identify air pollutant transport and trajectories over different regions of the world [23][24][25][26][27][28][29]. Yu et al. [30] used the HYSPLIT model to study the pollution characteristics of 29 cities in the Central Plains Economic Zone (CPEZ). ...

... Le modèle le plus récemment publié [BVDsim, Viet et al., 2004a] est un modèle stochastique de simulations dynamiques tenant compte des caractéristiques individuelles. Les auteurs ont mis en évidence l'influence de la structuration des troupeaux laitiers en lots sur la propagation du BVDV [Viet et al., 2004b]. Les animaux sont regroupés par statut d'infection vis-à-vis du BVDV (figure 2) : protégé par anticorps maternels (M), sensible (S), infecté transitoirement (T), guéri et immun (suivant le moment d'infection : hors gestation (R), en début (Ra), au milieu (Rb), ou en fin de gestation (Rc)), IPI (P). ...

... To view samples from the posterior and assess that the MCMC algorithm is behaving properly (Mengersen et al., 1999), we consider histograms and trace plots, respectively. Trace plots illustrate the entire sequence of samples from the posterior distribution, or chain, (after the first few thousand are discarded) with the value of the random variable plotted on the vertical axis vs. the sequence index. ...

... In neuronal cell lines, expression of SET results in abnormal hyperphosphorylation of tau at Thr231/Ser235, Ser262/356, and Ser396/404, which are sites known to inhibit binding of tau to microtubules (29). Interestingly, in the hippocampus of the temporal cortex of AD patients, SET was found accumulated in the cytosol and it co-localised with phosphorylated tau and cleaved amyloid precursor protein (APPcc)(6,124), suggesting that specific accumulation of SET in the cytosol could contribute to the pathogenesis of AD. This was proved by experiments performed by Qu et al. that, by using deletion mutants, showed that under genotoxic stress, targeted expression of SET to the cytoplasm induces cell death in cortical neurons(95). ...

... Identification of time-varying predictors of risk could provide insights into the dynamics of smoking-induced ALS. 24 Understanding these dynamics may inform hypotheses on the disease mechanism and will enhance insight into the aetiology of ALS. ...

... A number of articles discuss the computational difficulties associated with Bayesian cross-validation, e.g. Alqallaf and Gustafson (2001); Bhattacharya and Haslett (2007); Bornn et al. (2010); Lamnisos et al. (2012);McVinish et al. (2013); Vehtari et al. (2017). We first define the object of interest, before presenting our estimator. ...

... Illustrations of these techniques include state reduction methods, such as the lump process established by Kemeny and Snell [28] and the GTH elimination algorithm (proposed by Grassmann, Taksar, and Heyman) [29]. Methods for improving the performance of Markov chain approximations also include investigations into proper discretization of continuous Markov chain problems [30,31]. ...

... Still, achieving the latter criterion poses challenges on its own. Model parameters for which priors are needed might lack intuitive meaning for the domain expert, making it difficult to query the properties of these parameters directly (Albert et al., 2012). Moreover, the relationship between priors and the data may not be apparent from the model specification, especially for complex models, such as hierarchical models (da Silva et al., 2019). ...

... The hierarchical Bayes modeling has many applications in real life, such as medicine, biology, animal breeding, economics, and so on. In meta-analysis, several experiments about the same phenomenon undertaken at different places with different subjects and different protocols are pooled together (see [25,26]). ...