Centre selection for clinical trials and the generalisability of results: a mixed methods study.

BACKGROUND: The rationale for centre selection in randomised controlled trials (RCTs) is often unclear but may have important implications for the generalisability of trial results. The aims of this study were to evaluate the factors which currently influence centre selection in RCTs and consider how generalisability considerations inform current and optimal practice. METHODS AND FINDINGS: Mixed methods approach consisting of a systematic review and meta-summary of centre selection criteria reported in RCT protocols funded by the UK National Institute of Health Research (NIHR) initiated between January 2005-January 2012; and an online survey on the topic of current and optimal centre selection, distributed to professionals in the 48 UK Clinical Trials Units and 10 NIHR Research Design Services. The survey design was informed by the systematic review and by two focus groups conducted with trialists at the Birmingham Centre for Clinical Trials. 129 trial protocols were included in the systematic review, with a total target sample size in excess of 317,000 participants. The meta-summary identified 53 unique centre selection criteria. 78 protocols (60%) provided at least one criterion for centre selection, but only 31 (24%) protocols explicitly acknowledged generalisability. This is consistent with the survey findings (n = 70), where less than a third of participants reported generalisability as a key driver of centre selection in current practice. This contrasts with trialists' views on optimal practice, where generalisability in terms of clinical practice, population characteristics and economic results were prime considerations for 60% (n = 42), 57% (n = 40) and 46% (n = 32) of respondents, respectively. CONCLUSIONS: Centres are rarely enrolled in RCTs with an explicit view to external validity, although trialists acknowledge that incorporating generalisability in centre selection should ideally be more prominent. There is a need to operationalize 'generalisability' and incorporate it at the design stage of RCTs so that results are readily transferable to 'real world' practice

Dynamical mean-field theory of spiking neuron ensembles: response to a single spike with independent noises

Dynamics of an ensemble of $N$-unit FitzHugh-Nagumo (FN) neurons subject to white noises has been studied by using a semi-analytical dynamical mean-field (DMF) theory in which the original $2 N$-dimensional {\it stochastic} differential equations are replaced by 8-dimensional {\it deterministic} differential equations expressed in terms of moments of local and global variables. Our DMF theory, which assumes weak noises and the Gaussian distribution of state variables, goes beyond weak couplings among constituent neurons. By using the expression for the firing probability due to an applied single spike, we have discussed effects of noises, synaptic couplings and the size of the ensemble on the spike timing precision, which is shown to be improved by increasing the size of the neuron ensemble, even when there are no couplings among neurons. When the coupling is introduced, neurons in ensembles respond to an input spike with a partial synchronization. DMF theory is extended to a large cluster which can be divided into multiple sub-clusters according to their functions. A model calculation has shown that when the noise intensity is moderate, the spike propagation with a fairly precise timing is possible among noisy sub-clusters with feed-forward couplings, as in the synfire chain. Results calculated by our DMF theory are nicely compared to those obtained by direct simulations. A comparison of DMF theory with the conventional moment method is also discussed.Comment: 29 pages, 2 figures; augmented the text and added Appendice

Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis

By using the wavelet transformation (WT), we have analyzed the response of an ensemble of $N$ (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it transient} $M$-pulse spike trains ($M=1-3$) with independent Gaussian noises. The cross-correlation between the input and output signals is expressed in terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is evaluated by using the {\it denoising} method within the WT, by which the noise contribution is extracted from output signals. Although the response of a single (N=1) neuron to sub-threshold transient signals with noises is quite unreliable, the transmission fidelity assessed by the cross-correlation and SNR is shown to be much improved by increasing the value of $N$: a population of neurons play an indispensable role in the stochastic resonance (SR) for transient spike inputs. It is also shown that in a large-scale ensemble, the transmission fidelity for supra-threshold transient spikes is not significantly degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

Informed design of educational technology for teaching and learning? Towards an evidence-informed model of good practice

The aim of this paper is to model evidence-informed design based on a selective critical analysis of research articles. We draw upon findings from an investigation into practitioners’ use of educational technologies to synthesise and model what informs their designs. We found that practitioners’ designs were often driven by implicit assumptions about learning. These shaped both the design of interventions and the methods sought to derive evaluations and interpret the findings. We argue that interventions need to be grounded in better and explicit conceptualisations of what constitutes learning in order to have well-informed designs that focus on improving the quality of student learning

A discrete time neural network model with spiking neurons II. Dynamics with noise

We provide rigorous and exact results characterizing the statistics of spike trains in a network of leaky integrate and fire neurons, where time is discrete and where neurons are submitted to noise, without restriction on the synaptic weights. We show the existence and uniqueness of an invariant measure of Gibbs type and discuss its properties. We also discuss Markovian approximations and relate them to the approaches currently used in computational neuroscience to analyse experimental spike trains statistics.Comment: 43 pages - revised version - to appear il Journal of Mathematical Biolog

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Impact of sex on response to neoadjuvant chemotherapy in patients with bladder cancer

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Objective: To assess the effect of patient's sex on response to neoadjuvant chemotherapy (NAC) in patients with clinically nonmetastatic muscle-invasive bladder cancer (MIBC). Methods: Complete pathologic response, defined as ypT0N0 at radical cystectomy, and downstaging were evaluated using sex-adjusted univariable and multivariable logistic regression modeling. We used interaction terms to account for age of menopause and smoking status. The association of sex with overall survival and cancer-specific survival was evaluated using Cox regression analyses. Results: A total of 1,031 patients were included in the analysis, 227 (22%) of whom were female. Female patients had a higher rate of extravesical disease extension (P = 0.01). After the administration of NAC, ypT stage was equally distributed between sexes (P = 0.39). On multivariable logistic regression analyses, there was no difference between the sexes or age of menopause with regards to ypT0N0 rates or downstaging (all P > 0.5). On Cox regression analyses, sex was associated with neither overall survival (hazard ratio 1.04, 95% confidence interval 0.75–1.45, P = 0.81) nor cancer-specific survival (hazard ratio 1.06, 95% confidence interval 0.71–1.58, P = 0.77). Conclusion: Our study generates the hypothesis that NAC equalizes the preoperative disparity in pathologic stage between males and females suggesting a possible differential response between sexes. This might be the explanation underlying the comparable survival outcomes between sexes despite females presenting with more advanced tumor stage.Peer reviewedFinal Accepted Versio

Antagonistic Roles of SEPALLATA3, FT and FLC Genes as Targets of the Polycomb Group Gene CURLY LEAF

In Arabidopsis, mutations in the Pc-G gene CURLY LEAF (CLF) give early flowering plants with curled leaves. This phenotype is caused by mis-expression of the floral homeotic gene AGAMOUS (AG) in leaves, so that ag mutations largely suppress the clf phenotype. Here, we identify three mutations that suppress clf despite maintaining high AG expression. We show that the suppressors correspond to mutations in FPA and FT, two genes promoting flowering, and in SEPALLATA3 (SEP3) which encodes a co-factor for AG protein. The suppression of the clf phenotype is correlated with low SEP3 expression in all case and reveals that SEP3 has a role in promoting flowering in addition to its role in controlling floral organ identity. Genetic analysis of clf ft mutants indicates that CLF promotes flowering by reducing expression of FLC, a repressor of flowering. We conclude that SEP3 is the key target mediating the clf phenotype, and that the antagonistic effects of CLF target genes masks a role for CLF in promoting flowering