Snidergustavsen2229

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories.

To examine the relationship between pre-corneal and pre-contact lens tear film stability (TFS), and to determine whether pre-corneal TFS is a reliable predictor of subsequent pre-lens TFS after a contact lens is placed on the eye.

667 records met inclusion criteria and were extracted from a soft contact lens multi-study database. Multivariable linear mixed effects models were fit to examine the association between pre-corneal and pre-lens TFS, adjusting for potential confounders and accounting for repeated measures. Receiver Operating Characteristic (ROC) analysis was employed to assess the predictive performance of pre-corneal TFS for subsequent pre-lens TFS. TFS was quantified for this analysis as the non-invasive tear breakup time (NITBUT).

Pre-corneal NITBUT was significantly related to the pre-lens NITBUT at both 10 min (p<0.001) and 2-6 hrs (p<0.001) post-lens insertion. However, the sensitivities of pre-corneal NITBUT for predicting symptom-associated thresholds of pre-lens NITBUT ranged from 50-65%, and specificities ranged from 57-72%, suggesting poor-to-moderate diagnostic performance.

Despite the association of pre-corneal and pre-lens TFS, the inherent lability and sensitivity to environmental exposures of the tear film introduce significant variability into NITBUT measurements. Using pre-corneal NITBUT to identify likely successful contact lens candidates prior to fitting is thus not sufficiently accurate to be relied upon in the clinical setting.

Despite the association of pre-corneal and pre-lens TFS, the inherent lability and sensitivity to environmental exposures of the tear film introduce significant variability into NITBUT measurements. Using pre-corneal NITBUT to identify likely successful contact lens candidates prior to fitting is thus not sufficiently accurate to be relied upon in the clinical setting.One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. PMSF concentration The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https//github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).Proteins of the major histocompatibility complex class I (MHC I), predominantly known for antigen presentation in the immune system, have recently been shown to be necessary for developmental neural refinement and adult synaptic plasticity. However, their roles in nonneuronal cell populations in the brain remain largely unexplored. Here, we identify classical MHC I molecule H2-Kb as a negative regulator of proliferation in neural stem and progenitor cells (NSPCs). Using genetic knockout mouse models and in vivo viral-mediated RNA interference (RNAi) and overexpression, we delineate a role for H2-Kb in negatively regulating NSPC proliferation and adult hippocampal neurogenesis. Transcriptomic analysis of H2-Kb knockout NSPCs, in combination with in vitro RNAi, overexpression, and pharmacological approaches, further revealed that H2-Kb inhibits cell proliferation by dampening signaling pathways downstream of fibroblast growth factor receptor 1 (Fgfr1). These findings identify H2-Kb as a critical regulator of cell proliferation through the modulation of growth factor signaling.Δ9-tetrahydrocannabinol (Δ9-THC), the main active ingredient of Cannabis sativa (marijuana), interacts with the human brain cannabinoid (CB1) receptor and mimics pharmacological effects of endocannabinoids (eCBs) like N-arachidonylethanolamide (AEA). Due to its flexible nature of AEA structure with more than 15 rotatable bonds, establishing its binding mode to the CB1 receptor is elusive. The aim of the present study was to explore possible binding conformations of AEA within the binding pocket of the CB1 receptor confirmed in the recently available X-ray crystal structures of the CB1 receptor and predict essential AEA binding domains. We performed long time molecular dynamics (MD) simulations of plausible AEA docking poses until its receptor binding interactions became optimally established. Our simulation results revealed that AEA favors to bind to the hydrophobic channel (HC) of the CB1 receptor, suggesting that HC holds essential significance in AEA binding to the CB1 receptor. Our results also suggest that the Helix 2 (H2)/H3 region of the CB1 receptor is an AEA binding subsite privileged over the H7 region.