Efficient Bayesian-based Multi-View Deconvolution

Light sheet fluorescence microscopy is able to image large specimen with high resolution by imaging the sam- ples from multiple angles. Multi-view deconvolution can significantly improve the resolution and contrast of the images, but its application has been limited due to the large size of the datasets. Here we present a Bayesian- based derivation of multi-view deconvolution that drastically improves the convergence time and provide a fast implementation utilizing graphics hardware.Comment: 48 pages, 20 figures, 1 table, under review at Nature Method

Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.PJN and SR were funded by the Medical Research Council. PJN also acknowledges partial support from the NIHR Cambridge Biomedical Research Centre.This is the accepted manuscript. The final version is available from SAGE at http://dx.doi.org/10.1177/096228021454874

A two-step method for variable selection in the analysis of a case-cohort study.

Background: Accurate detection and estimation of true exposure-outcome associations is important in aetiological analysis; when there are multiple potential exposure variables of interest, methods for detecting the subset of variables most likely to have true associations with the outcome of interest are required. Case-cohort studies often collect data on a large number of variables which have not been measured in the entire cohort (e.g. panels of biomarkers). There is a lack of guidance on methods for variable selection in case-cohort studies. Methods: We describe and explore the application of three variable selection methods to data from a case-cohort study. These are: (i) selecting variables based on their level of significance in univariable (i.e. one-at-a-time) Prentice-weighted Cox regression models; (ii) stepwise selection applied to Prentice-weighted Cox regression; and (iii) a two-step method which applies a Bayesian variable selection algorithm to obtain posterior probabilities of selection for each variable using multivariable logistic regression followed by effect estimation using Prentice-weighted Cox regression. Results: Across nine different simulation scenarios, the two-step method demonstrated higher sensitivity and lower false discovery rate than the one-at-a-time and stepwise methods. In an application of the methods to data from the EPIC-InterAct case-cohort study, the two-step method identified an additional two fatty acids as being associated with incident type 2 diabetes, compared with the one-at-a-time and stepwise methods. Conclusions: The two-step method enables more powerful and accurate detection of exposure-outcome associations in case-cohort studies. An R package is available to enable researchers to apply this method.P.J.N. and S.R. were supported by the Medical Research Council [www.mrc.ac.uk] (Unit Programme number MC_UP_0801/1). P.J.N. also acknowledges partial support from the NIHR Cambridge Biomedical Research Centre. S.S. and S.C. were supported by the Medical Research Council [www.mrc.ac.uk] (Unit Programme number MC_U105260558). S.J.S. was supported by the Medical Research Council [www.mrc.ac.uk] (Unit Programme number MC_UU_12015/1). Funding for the EPIC-InterAct project was provided by the EU FP6 programme (grant number LSHM_CT_2006_037197)

Looking at the same interaction and seeing something different: The role of information, judgment perspective and behavioral coding on judgment accuracy

Abstract. The role of information context, judgment perspective and cue type on the “accuracy” of first impressions of another’s Big5 personality was studied in three phases of data collection (n = 173). Accurate judgments were defined as the level of agreement between a target person’s aggregated personality score (i.e., average of self and informant ratings of personality) and a personality judgement about the target, indexed using item correlations. Results for Phase 1 found that completing a different task with the same partner improved accuracy for conscientiousness. Phase 2 investigated the relationship between a person’s role (judgment perspective) within an interaction (interactants, observers) and showed that Observers were better at judging the less interpersonal traits of conscientiousness and openness relative to Interactants. Finally, Phase 3 examined the types of cues that people used when rating another’s personality. Although Observers and Interactants had access to the same interaction, analyses revealed that they employed different types of cues when judging others. Findings are discussed in terms of Funder’s Realistic Accuracy Model (1995, 1999) along with practical implications, limitations and suggestions for future research

Fine sediment reduces vertical migrations of Gammarus pulex (Crustacea: Amphipoda) in response to surface water loss

Surface and subsurface sediments in river ecosystems are recognized as refuges that may promote invertebrate survival during disturbances such as floods and streambed drying. Refuge use is spatiotemporally variable, with environmental factors including substrate composition, in particular the proportion of fine sediment (FS), affecting the ability of organisms to move through interstitial spaces. We conducted a laboratory experiment to examine the effects of FS on the movement of Gammarus pulex Linnaeus (Crustacea: Amphipoda) into subsurface sediments in response to surface water loss. We hypothesized that increasing volumes of FS would impede and ultimately prevent individuals from migrating into the sediments. To test this hypothesis, the proportion of FS (1–2 mm diameter) present within an open gravel matrix (4–16 mm diameter) was varied from 10 to 20% by volume in 2.5% increments. Under control conditions (0% FS), 93% of individuals moved into subsurface sediments as the water level was reduced. The proportion of individuals moving into the subsurface decreased to 74% at 10% FS, and at 20% FS no individuals entered the sediments, supporting our hypothesis. These results demonstrate the importance of reducing FS inputs into river ecosystems and restoring FS-clogged riverbeds, to promote refuge use during increasingly common instream disturbances

A comparison of EGR correction factor models based on SI engine data

The article compares the accuracy of different exhaust gas recirculation (EGR) correction factor models under engine conditions. The effect of EGR on the laminar burning velocity of a EURO VI E10 specification gasoline (10% Ethanol content by volume) has been back calculated from engine pressure trace data, using the Leeds University Spark Ignition Engine Data Analysis (LUSIEDA) reverse thermodynamic code. The engine pressure data ranges from 5% to 25% EGR (by mass) with the running conditions, such as spark advance and pressure at intake valve closure, changed to maintain a constant engine load of 0.79 MPa gross mean effective pressure (GMEP). Based on the experimental data, a correlation is suggested on how the laminar burning velocity reduces with increasing EGR mass fraction. This correlation, together with existing models, was then implemented into the quasi-dimensional Leeds University Spark Ignition Engine (LUSIE) predictive engine code and resulting predictions are compared against measurements. It was found that the new correlation is in good agreement with experimental data for a diluent range of 5%-25%, providing the best fit for both engine loads investigated, whereas existing models tend to overpredict the reduction of burning velocity due to EGR

Clonal expansion of T memory stem cells determines early anti-leukemic responses and long-term CAR T cell persistence in patients

Low-affinity CD19 chimeric antigen receptor (CAR) T cells display enhanced expansion and persistence, enabling fate tracking through integration site analysis. Here we show that integration sites from early (1 month) and late (>3 yr) timepoints cluster separately, suggesting different clonal contribution to early responses and prolonged anti-leukemic surveillance. CAR T central and effector memory cells in patients with long-term persistence remained highly polyclonal, whereas diversity dropped rapidly in patients with limited CAR T persistence. Analysis of shared integrants between the CAR T cell product and post-infusion demonstrated that, despite their low frequency, T memory stem cell clones in the product contributed substantially to the circulating CAR T cell pools, during both early expansion and long-term persistence. Our data may help identify patients at risk of early loss of CAR T cells and highlight the critical role of T memory stem cells both in mediating early anti-leukemic responses and in long-term surveillance by CAR T cells

Discovering and Locating High-Energy Extra-Galactic Sources by Bayesian Mixture Modelling

Discovering and locating gamma-ray sources in the whole sky map is a declared target of the Fermi Gamma-ray Space Telescope collaboration. In this paper, we carry out an unsupervised analysis of the collection of high-energy photons accumulated by the Large Area Telescope, the principal instrument on board the Fermi spacecraft, over a period of around 7.5 years using a Bayesian mixture model. A fixed, though unknown, number of parametric components identify the extra-galactic emitting sources we are searching for, while a further component represents parametrically the dffuse gamma ray background due to both, extragalactic and galactic high energy photon emission. We determine the number of sources, their coordinates on the map and their intensities. The model parameters are estimated using a reversible jump MCMC algorithm which implements four different types of moves. These allow us to explore the dimension of the parameter space. The possible transitions remove from or add a source to the model, while leaving the background component unchanged. We furthermore present an heuristic procedure, based on the posterior distribution of the mixture weights, to qualify the nature of each detected source

Gene transfer: anything goes in plant mitochondria

Parasitic plants and their hosts have proven remarkably adept at exchanging fragments of mitochondrial DNA. Two recent studies provide important mechanistic insights into the pattern, process and consequences of horizontal gene transfer, demonstrating that genes can be transferred in large chunks and that gene conversion between foreign and native genes leads to intragenic mosaicism. A model involving duplicative horizontal gene transfer and differential gene conversion is proposed as a hitherto unrecognized source of genetic diversity

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer

A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study