Thinking territory historically.

BACKGROUND: While the randomised controlled trial (RCT) is generally regarded as the design of choice for assessing the effects of health care, within the social sciences there is considerable debate about the relative suitability of RCTs and non-randomised studies (NRSs) for evaluating public policy interventions. // OBJECTIVES: To determine whether RCTs lead to the same effect size and variance as NRSs of similar policy interventions; and whether these findings can be explained by other factors associated with the interventions or their evaluation. // METHODS: Analyses of methodological studies, empirical reviews, and individual health and social services studies investigated the relationship between randomisation and effect size of policy interventions by: 1) Comparing controlled trials that are identical in all respects other than the use of randomisation by 'breaking' the randomisation in a trial to create non-randomised trials (re-sampling studies). 2) Comparing randomised and non-randomised arms of controlled trials mounted simultaneously in the field (replication studies). 3) Comparing similar controlled trials drawn from systematic reviews that include both randomised and non-randomised studies (structured narrative reviews and sensitivity analyses within meta-analyses). 4) Investigating associations between randomisation and effect size using a pool of more diverse RCTs and NRSs within broadly similar areas (meta-epidemiology). // RESULTS: Prior methodological reviews and meta-analyses of existing reviews comparing effects from RCTs and nRCTs suggested that effect sizes from RCTs and nRCTs may indeed differ in some circumstances and that these differences may well be associated with factors confounded with design. Re-sampling studies offer no evidence that the absence of randomisation directly influences the effect size of policy interventions in a systematic way. No consistent explanations were found for randomisation being associated with changes in effect sizes of policy interventions in field trials

The effect of heating rates on low temperature hexane air combustion

Combustion of hydrocarbon fuels is traditionally separated into slow reaction, cool flame, and ignition regimes based on pressure and temperature. Standard tests, such as the ASTM E659, are used to determine the lowest temperature required to ignite a specific fuel mixed with air at atmospheric pressure. It is expected that the initial pressure and the rate at which the mixture is heated also influences the limiting temperature and the type of combustion. This study investigates the effect of heating rate, between 4 and 15 K/min, and initial pressure, in the range of 25–100 kPa, on ignition of n-hexane air mixtures. Mixtures with equivalence ratio ranging from Φ = 0.6 to Φ = 1.2 were investigated. The problem is also modeled computationally using an extension of Semenov’s classical autoignition theory with a detailed chemical mechanism. Experiments and simulations both show that in the same reactor either a slow reaction or an ignition event can take place depending on the heating rate. Analysis of the detailed chemistry demonstrates that a mixture which approaches the ignition region slowly undergoes a significant modification of its composition. This change in composition induces a progressive shift of the explosion limit until the mixture is no longer flammable. A mixture that approaches the ignition region sufficiently rapidly undergoes only a moderate amount of thermal decomposition and explodes quite violently

The effect of heating rates on low temperature hexane air combustion

Combustion of hydrocarbon fuels is traditionally separated into slow reaction, cool flame, and ignition regimes based on pressure and temperature. Standard tests, such as the ASTM E659, are used to determine the lowest temperature required to ignite a specific fuel mixed with air at atmospheric pressure. It is expected that the initial pressure and the rate at which the mixture is heated also influences the limiting temperature and the type of combustion. This study investigates the effect of heating rate, between 4 and 15 K/min, and initial pressure, in the range of 25–100 kPa, on ignition of n-hexane air mixtures. Mixtures with equivalence ratio ranging from Φ = 0.6 to Φ = 1.2 were investigated. The problem is also modeled computationally using an extension of Semenov’s classical autoignition theory with a detailed chemical mechanism. Experiments and simulations both show that in the same reactor either a slow reaction or an ignition event can take place depending on the heating rate. Analysis of the detailed chemistry demonstrates that a mixture which approaches the ignition region slowly undergoes a significant modification of its composition. This change in composition induces a progressive shift of the explosion limit until the mixture is no longer flammable. A mixture that approaches the ignition region sufficiently rapidly undergoes only a moderate amount of thermal decomposition and explodes quite violently

Expression of ski can act as a negative feedback mechanism on retinoic acid signaling

Background: Retinoic acid signaling is essential for many aspects of early development in vertebrates. To control the levels of signaling, several retinoic acid target genes have been identified that act to suppress retinoic acid signaling in a negative feedback loop. The nuclear protein Ski has been extensively studied for its ability to suppress transforming growth factor-beta (TGF-β) signaling but has also been implicated in the repression of retinoic acid signaling. Results: We demonstrate that ski expression is up-regulated in response to retinoic acid in both early Xenopus embryos and in human cell lines. Blocking retinoic acid signaling using a retinoic acid antagonist results in a corresponding decrease in the levels of ski mRNA. Finally, overexpression of SKI in human cells results in reduced levels of CYP26A1 mRNA, a known target of retinoic acid signaling. Conclusions: Our results, coupled with the known ability of Ski to repress retinoic acid signaling, demonstrate that Ski expression is a novel negative feedback mechanism acting on retinoic acid signaling. Developmental Dynamics 242:604-613, 2013. © 2013 Wiley Periodicals, Inc

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Merging enzymes with chemocatalysis for amide bond synthesis

Amides are one of the most fundamental chemical bonds in nature. In addition to proteins and other metabolites, many valuable synthetic products comprise amide bonds. Despite this, there is a need for more sustainable amide synthesis. Herein, we report an integrated next generation multi-catalytic system, merging nitrile hydratase enzymes with a Cu-catalysed N-arylation reaction in a single reaction vessel, for the construction of ubiquitous amide bonds. This synergistic one-pot combination of chemo- and biocatalysis provides an amide bond disconnection to precursors, that are orthogonal to those in classical amide synthesis, obviating the need for protecting groups and delivering amides in a manner unachievable using existing catalytic regimes. Our integrated approach also affords broad scope, very high (molar) substrate loading, and has excellent functional group tolerance, telescoping routes to natural product derivatives, drug molecules, and challenging chiral amides under environmentally friendly conditions at scale