The Use of Participatory Methods in Researching the Experiences of Children and Young People

Participatory research methods offer a very promising approach for gaining in-depth understanding of young people’s lives. However, when adopting such approaches, researchers need to be aware of methodological and theoretical issues. The aim of this article is to present a discussion of ways in which participatory methods may be used as a research strategy when investigating young people’s experiences and emotions. We explore the potential of these methods as well as some of their limitations

Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models

This is the final version. Available on open access from International Society for Bayesian Analysis (ISBA) via the DOI in this record. With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayesian inference scheme for a flexible class of latent Gaussian models (LGMs) where one or more of the likelihood parameters are modeled by latent additive Gaussian processes. Max-and-Smooth consists of two-steps. In the first step (Max), the likelihood function is approximated by a Gaussian density with mean and covariance equal to either (a) the maximum likelihood estimate and the inverse observed information, respectively, or (b) the mean and covariance of the normalized likelihood function. In the second step (Smooth), the latent parameters and hyperparameters are inferred and smoothed with the approximated likelihood function. The proposed method ensures that the uncertainty from the first step is correctly propagated to the second step. Since the approximated likelihood function is Gaussian, the approximate posterior density of the latent parameters of the LGM (conditional on the hyperparameters) is also Gaussian, thus facilitating efficient posterior inference in high dimensions. Furthermore, the approximate marginal posterior distribution of the hyperparameters is tractable, and as a result, the hyperparameters can be sampled independently of the latent parameters. In the case of a large number of independent data replicates, sparse precision matrices, and high-dimensional latent vectors, the speedup is substantial in comparison to an MCMC scheme that infers the posterior density from the exact likelihood function. The proposed inference scheme is demonstrated on one spatially referenced real dataset and on simulated data mimicking spatial, temporal, and spatio-temporal inference problems. Our results show that Max-and-Smooth is accurate and fast.NER

Approximate Bayesian inference for analysis of spatiotemporal flood frequency data

This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this recordExtreme floods cause casualties and widespread damage to property and vital civil infrastructure. Predictions of extreme floods, within gauged and ungauged catchments, is crucial to mitigate these disasters. In this paper a Bayesian framework is proposed for predicting extreme floods, using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are involved and a time trend is present. Other challenges involve balancing model complexity and parsimony, using an appropriate model selection procedure and making inference based on a reliable and computationally efficient approach. We here propose a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models, which include catchment descriptors as covariates and spatially correlated model components, are proposed for the four parameters at the latent level. To achieve computational efficiency with large datasets and richly parametrized models, we exploit a highly accurate and fast approximate Bayesian inference approach which can also be used to efficiently select models separately for each of the four regression models at the latent level. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom. The framework performed well in terms of flood predictions for both ungauged catchments and future observations at gauged catchments. The results show that the spatial model components for the transformed location and scale parameters as well as the time trend are all important, and none of these should be ignored. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5% per decade with range 0.1% to 2.8% and reveal a spatial structure across the United Kingdom. When the interest lies in estimating return levels for spatial aggregates, we further develop a novel copula-based postprocessing approach of posterior predictive samples in order to mitigate the effect of the conditional independence assumption at the data level, and we demonstrate that our approach indeed provides accurate results.University of Iceland Research Fun

Approximate Bayesian inference for analysis of spatio-temporal flood frequency data

This is the final version. Available from the Institute of Mathematical Statistics via the DOI in this record. Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the generalized extreme-value (GEV) distribution within gauged and ungauged catchments. A major methodological challenge is to find a suitable parametrization for the GEV distribution when covariates or latent spatial effects are involved. Other challenges involve balancing model complexity and parsimony using an appropriate model selection procedure, and making inference using a reliable and computationally efficient approach. Our approach relies on a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models are proposed for the four parameters at the latent level. For computational efficiency with large datasets and richly parametrized models, we exploit an accurate and fast approximate Bayesian inference approach. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom (UK). Our model performed well in terms of flood predictions for both gauged and ungauged catchments. The results show that the spatial model components for the transformed location and scale parameters, and the time trend, are all important. Posterior estimates of the time trend parameters correspond to an average increase of about $1.5\%$ per decade and reveal a spatial structure across the UK. To estimate return levels for spatial aggregates, we further develop a novel copula-based post-processing approach of posterior predictive samples, in order to mitigate the effect of the conditional independence assumption at the data level, and we show that our approach provides accurate results.University of Iceland Research Fun

High-contrast, fast chemical imaging by coherent Raman scattering using a self-synchronized two-colour fibre laser

Kong C, Pilger C, Hachmeister H, et al. High-contrast, fast chemical imaging by coherent Raman scattering using a self-synchronized two-colour fibre laser. Light: Science &amp; Applications. 2020;9(1): 25.Coherent Raman scattering (CRS) microscopy is widely recognized as a powerful tool for tackling biomedical problems based on its chemically specific label-free contrast, high spatial and spectral resolution, and high sensitivity. However, the clinical translation of CRS imaging technologies has long been hindered by traditional solid-state lasers with environmentally sensitive operations and large footprints. Ultrafast fibre lasers can potentially overcome these shortcomings but have not yet been fully exploited for CRS imaging, as previous implementations have suffered from high intensity noise, a narrow tuning range and low power, resulting in low image qualities and slow imaging speeds. Here, we present a novel high-power self-synchronized two-colour pulsed fibre laser that achieves excellent performance in terms of intensity stability (improved by 50 dB), timing jitter (24.3 fs), average power fluctuation (20 dB) and pulse width variation (<1.8%) over an extended wavenumber range (2700–3550 cm−1). The versatility of the laser source enables, for the first time, high-contrast, fast CRS imaging without complicated noise reduction via balanced detection schemes. These capabilities are demonstrated in this work by imaging a wide range of species such as living human cells and mouse arterial tissues and performing multimodal nonlinear imaging of mouse tail, kidney and brain tissue sections by utilizing second-harmonic generation and two-photon excited fluorescence, which provides multiple optical contrast mechanisms simultaneously and maximizes the gathered information content for biological visualization and medical diagnosis. This work also establishes a general scenario for remodelling existing lasers into synchronized two-colour lasers and thus promotes a wider popularization and application of CRS imaging technologies

Monitoring Dynamic Protein Expression in Single Living E. Coli. Bacterial Cells by Laser Tweezers Raman Spectroscopy

Laser tweezers Raman spectroscopy (LTRS) is a novel, nondestructive, and label-free method that can be used to quantitatively measure changes in cellular activity in single living cells. Here, we demonstrate its use to monitor changes in a population of E. coli cells that occur during overexpression of a protein, the extracellular domain of myelin oligodendrocyte glycoprotein (MOG(1-120)) Raman spectra were acquired of individual E. coli cells suspended in solution and trapped by a single tightly focused laser beam. Overexpression of MOG(1-120) in transformed E. coli Rosetta-Gami (DE3)pLysS cells was induced by addition of isopropyl thiogalactoside (IPTG). Changes in the peak intensities of the Raman spectra from a population of cells were monitored and analyzed over a total duration of three hours. Data was also collected for concentrated purified MOG(1-120) protein in solution, and the spectra compared with that obtained for the MOG(1-120) expressing cells. Raman spectra of individual, living E. coli cells exhibit signatures due to DNA and protein molecular vibrations. Characteristic Raman markers associated with protein vibrations, such as 1257 cm{sup -1}, 1340 cm{sup -1}, 1453 cm{sup -1} and 1660 cm{sup -1}, are shown to increase as a function of time following the addition of IPTG. Comparison of these spectra and the spectra of purified MOG protein indicates that the changes are predominantly due to the induction of MOG protein expression. Protein expression was found to occur mostly within the second hour, with a 470% increase relative to the protein expressed in the first hour. A 230% relative increase between the second and third hour indicates that protein expression begins to level off within the third hour. It is demonstrated that LTRS has sufficient sensitivity for real-time, nondestructive, and quantitative monitoring of biological processes, such as protein expression, in single living cells. Such capabilities, which are not currently available in flow cytometry, open up new possibilities for analyzing cellular processes occurring in single microbial and eukaryotic cells

Subcellular heterogeneity of ryanodine receptor properties in ventricular myocytes with low T-tubule density

Rationale: In ventricular myocytes of large mammals, not all ryanodine receptor (RyR) clusters are associated with T-tubules (TTs); this fraction increases with cellular remodeling after myocardial infarction (MI). Objective: To characterize RyR functional properties in relation to TT proximity, at baseline and after MI. Methods: Myocytes were isolated from left ventricle of healthy pigs (CTRL) or from the area adjacent to a myocardial infarction (MI). Ca2+ transients were measured under whole-cell voltage clamp during confocal linescan imaging (fluo-3) and segmented according to proximity of TTs (sites of early Ca2+ release, F&gt;F50 within 20 ms) or their absence (delayed areas). Spontaneous Ca2+ release events during diastole, Ca2+ sparks, reflecting RyR activity and properties, were subsequently assigned to either category. Results: In CTRL, spark frequency was higher in proximity of TTs, but spark duration was significantly shorter. Block of Na+/Ca2+ exchanger (NCX) prolonged spark duration selectively near TTs, while block of Ca2+ influx via Ca2+ channels did not affect sparks properties. In MI, total spark mass was increased in line with higher SR Ca2+ content. Extremely long sparks (&gt;47.6 ms) occurred more frequently. The fraction of near-TT sparks was reduced; frequency increased mainly in delayed sites. Increased duration was seen in near-TT sparks only; Ca2+ removal by NCX at the membrane was significantly lower in MI. Conclusion: TT proximity modulates RyR cluster properties resulting in intracellular heterogeneity of diastolic spark activity. Remodeling in the area adjacent to MI differentially affects these RyR subpopulations. Reduction of the number of sparks near TTs and reduced local NCX removal limit cellular Ca2+ loss and raise SR Ca2+ content, but may promote Ca2+ waves