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Query: UMLS:C0598853 (forgetting)
 3,232 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A plastic nervous system requires the ability not only to acquire and store but also to forget. Here, we report that musashi (msi-1) is necessary for time-dependent memory loss in C. elegans. Tissue-specific rescue demonstrates that MSI-1 function is necessary in the AVA interneuron. Using RNA-binding protein immunoprecipitation (IP), we found that MSI-1 binds to mRNAs of three subunits of the Arp2/3 actin branching regulator complex in vivo and downregulates ARX-1, ARX-2, and ARX-3 translation upon associative learning. The role of msi-1 in forgetting is also reflected by the persistence of learning-induced GLR-1 synaptic size increase in msi-1 mutants. We demonstrate that memory length is regulated cooperatively through the activation of adducin (add-1) and by the inhibitory effect of msi-1. Thus, a GLR-1/MSI-1/Arp2/3 pathway induces forgetting and represents a novel mechanism of memory decay by linking translational control to the structure of the actin cytoskeleton in neurons.
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PMID:Forgetting is regulated via Musashi-mediated translational control of the Arp2/3 complex. 2463 Jul 19

An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control.
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PMID:Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach. 2487 85