trial structure. We show that such tracking can be achieved by canonical network architectures and dynamics, and that the resulting responses resemble observations from neurons in the insect olfactory system. Thus, our results provide hypotheses regarding the functional role of olfactory circuit activity at both single neuronal and population scales. Copyright © 2020 Mallik et al.BACKGROUND Clinic 'no shows' (NS) can be a burden on the healthcare system, and efforts to minimise them can reduce lost revenue and improve patient care. Leveraging a large data set via the electronic health record (EHR) has not been previously attempted to identify 'high risk' groups in paediatric orthopaedics. OBJECTIVE To use discrete data captured by the EHR system to identify predictors of non-attendance at paediatric orthopaedic outpatient appointments. METHODS Appointments from January 2014 to March 2016 were included. Variables included appointment status, age, gender, type of visit, payor type (government vs private insurance), distance of residence to clinic, region of residence, clinic location, clinic type, and appointment day of the week, hour and month. Classification and regression trees (CART) were constructed to identify predictors of NS. RESULTS 131 512 encounters were included, 15 543 of which were in the NS group (11.8%). CART identified three predictive covariates for NS days in between scheduling and appointment, insurance type, and specific orthopaedic clinic type. The combination of covariates provided predictability of NS if they had ≤38.5 days of waiting for appointment and had private insurance, the NS rate was 7.8% (the best result), compared with waiting >38.5 days for either a fracture or sports clinic, which had an NS rate of 29.3% (OR=4.9). CONCLUSION Payor type and duration between scheduling and appointment may predict non-attendance at outpatient paediatric orthopaedic appointments. Although these findings allow for predicting and interventions for at-risk groups, even the best performing NS group occurred 7.8% of the time, highlighting the complexity of the NS phenomenon. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.OBJECTIVE Indicators based on hospital administrative data have potential for misclassification error, especially if they rely on clinical detail that may not be well recorded in the data. We applied an approach using modified logistic regression models to assess the misclassification (false-positive and false-negative) rates of low-value care indicators. DESIGN AND SETTING We applied indicators involving 19 procedures to an extract from the New South Wales Admitted Patient Data Collection (1 January 2012 to 30 June 2015) to label episodes as low value. We fit four models (no misclassification, false-positive only, false-negative only, both false-positive and false-negative) for each indicator to estimate misclassification rates and used the posterior probabilities of the models to assess which model fit best.  https://www.selleckchem.com/products/colcemid.html  RESULTS False-positive rates were low for most indicators-if the indicator labels care as low value, the care is most likely truly low value according to the relevant recommendation. False-negative rates were much higher but were poorly estimated (wide credible intervals). For most indicators, the models allowing no misclassification or allowing false-negatives but no false-positives had the highest posterior probability. The overall low-value care rate from the indicators was 12%. After adjusting for the estimated misclassification rates from the highest probability models, this increased to 35%. CONCLUSION Binary performance indicators have a potential for misclassification error, especially if they depend on clinical information extracted from administrative data. Indicators should be validated by chart review, but this is resource-intensive and costly. The modelling approach presented here can be used as an initial validation step to identify and revise indicators that may have issues before continuing to a full chart review validation. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.Occipital alpha is a prominent rhythm (∼10 Hz) detected in electroencephalography (EEG) during wakeful relaxation with closed eyes. The rhythm is generated by a subclass of thalamic pacemaker cells that burst at the alpha frequency, orchestrated by the interplay of hyperpolarization-activated cyclic nucleotide-gated channels (HCN) and calcium channels in response to elevated levels of ambient acetylcholine. These oscillations are known to have a lower peak frequency and coherence in the early stages of Alzheimer's Disease (AD). Interestingly, calcium signaling, HCN channel expression and acetylcholine signaling, crucial for orchestrating the alpha rhythm, are also known to be aberrational in AD. In a biophysically detailed network model of the thalamic circuit, we investigate the changes in molecular signaling and the causal relationships between them that lead to a disrupted thalamic alpha in AD. Our simulations show that lowered HCN expression leads to a slower thalamic alpha, which can be rescued by increar effect on alpha rhythm. Our model demonstrates that pathology of HCN, crucial for alpha generation, alters calcium signaling, modifies excitation-inhibition balance in the thalamus makes the network more sensitive to noise. Our model, when seen in conjunction with diverse experimental data, posits a causal relationship between the formation of amyloid-beta plaques, the down-regulation of HCN channels and aberrations in the occipital alpha rhythm. Copyright © 2020 Sharma and Nadkarni.Axonal demyelination injury and neuronal degeneration are the primary causes of visual disability in multiple sclerosis-linked optic neuritis (MS/optic neuritis) patients. Immunomodulatory therapies targeting inflammation have failed to avert the disease progression and no therapies exist to prevent the neuronal deficits seen in MS to date. Neuroprotective strategies targeting oligodendrocytes and astroglia have shown limited success due to a lack of axonal regeneration from injured neurons. In this study, we used the chronic experimental autoimmune encephalomyelitis (EAE) mouse model of MS to investigate the axonal regenerative approach to improve the neuronal function. Our approach focused on targeted knockout of the developmentally regulated axon growth inhibitory Kruppel-like factor 4 (Klf4) gene in retinal ganglion cells (RGCs) of Klf4fl/fl mice by intravitreal delivery of AAV2-Cre-ires-EGFP recombinant virus 1) at the time of EAE sensitization, and 2) after the onset of optic neuritis-mediated visual defects in the mice.