399 research outputs found
A combined confocal and scanning near-field optical microscope as an analysis tool in life sciences
No full textFreyland JM, Eckert R, Huser T, Rodrigues-Herzog R, Heinzelmann H. A combined confocal and scanning near-field optical microscope as an analysis tool in life sciences. Helv. Phys. Acta 71. 1998:19-20
Numerical recipes for landslide spatial prediction using R-INLA: A step-by-step tutorial
No full textChapitre 3International audienceThe geomorphological community typically assesses the landslide susceptibility at the catchment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates. Spatial dependence, which allows capturing similarities at neighboring sites that are not directly explained by covariate information, is typically not accounted for in the landslides literature, whilst such spatial models have become commonplace in the geostatistical literature. Here we explain step by step how to rigorously model and predict activations of debris flow based on an adequate statistical model by using the R-INLA library from the statistical software R in the context of a real multiple landslide event. This chapter follows the analysis of Lombardo, Opitz, and Huser with a few modifications; it is written in a tutorial style to provide the geomorphological community with a hands-on guide to replicate similar analyses in R. While our focus here is on implementation and computing, more details about the underlying statistical theory, modeling, and estimation can be found in the original work by Lombardo, Opitz, and Huser Our modeling approach deviates fundamentally from the commonly used regression models fitted to binary presence/absence data. Specifically, we use a Bayesian hierarchical Cox point process model to describe landslide counts at high resolution (i.e., at the pixel level), while capturing spatial dependence through a latent spatial effect defined at lower resolution over slope units. Our point process modeling approach allows us to derive the distribution of aggregated landslide counts for any areas of interest. Crucially, the latent spatial effect represents the unexplained but spatially structured component in the data when the linear or nonlinear effects of covariates are removed. Thus, in the case of sparse rain gauge or seismic networks, we suggest using the latent spatial effect to uncover the trigger distribution over space. In particular, for landslides triggered by extreme precipitation, the meteorological stress can play a dominant role with respect to the covariates that are typically introduced in predictive models; therefore, accounting for the trigger in modeling may dramatically improve the performance of landslide prediction
Measurement of stoichiometries of single biomolecular complexes using FRET photon statistics
No full textFore S, Yeh Y, Balhorn R, Huser T, Cosman M. Measurement of stoichiometries of single biomolecular complexes using FRET photon statistics. Biophysical Journal. 2005;88(1):363A-363A
Quantifying the number of fluorophores on a single hairpin DNA by photon antibunching spectroscopy.
No full textFore S, Huser T, Hollars C, Cosman M, Balhorn R, Yeh Y. Quantifying the number of fluorophores on a single hairpin DNA by photon antibunching spectroscopy. Biophysical Journal. 2003;84(2):302A-302A
Scanning Near-Field Optical Microscopes for High Resolution Imaging
No full textLacoste T, Huser T, Heinzelmann H, Güntherodt HJ. Scanning Near-Field Optical Microscopes for High Resolution Imaging. In: Marti O, Möller R, eds. NATO ASI Series E. Vol 300. Kluwer Academic Publishers, Dordrecht; 1995: 123-132
Modeling soil organic carbon with Quantile Regression : dissecting predictors' effects on carbon stocks
No full textSoil organic carbon (SOC) estimation is crucial to manage natural and anthropic ecosystems. Many modeling procedures have been tested in the literature, however, most of them do not provide information on predictors' behavior at specific sub-domains of the SOC stock. Here, we implement Quantile Regression (QR) to spatially predict the SOC stock and gain insight on the role of predictors (topographic and remotely sensed) at varying SOC stock (0–30cm depth) in the agricultural areas of an extremely variable semi-arid region (Sicily, Italy, around 25,000km2). QR produces robust performances (maximum quantile loss = 0.49) and allows to recognize dominant effects among the predictors at varying quantiles. In particular, clay mostly contributes to maintain SOC stock at lower quantiles whereas rainfall and temperature influences are constantly positive and negative, respectively. This information, currently lacking, confirms that QR can discern predictor influences on SOC stock at specific SOC sub-domains. The QR map generated at the median shows a Mean Absolute Error of 17 t SOC ha-1 with respect to the data collected at sampling locations. Such MAE is lower than those of the Joint Research Centre at Global (18 t SOC ha-1) and at European (24 t SOC ha-1) scales and of the International Soil Reference and Information Centre (23 t SOC ha-1) while higher than the MAE reached in Schillaci et al. (2017b) (Geoderma, 2017, issue 286, page 35–45) using the same dataset (15 t SOC ha-1). The results suggest the use of QR as a comprehensive method to map SOC stock using legacy data in agro-ecosystems and to investigate SOC and inherited uncertainty with respect to specific subdomains. The R code scripted in this study for QR is included
Nanoscopy of bacterial cells immobilized by holographic optical tweezers
No full textDiekmann R, Wolfson D, Spahn C, Heilemann M, Schüttpelz M, Huser T. Nanoscopy of bacterial cells immobilized by holographic optical tweezers. Nature Communications. 2016;7(1): 13711
Novel optical oxy/deoxy hemoglobin monitoring as a modality for non-invasive real-time monitoring of cognitive activity and beyond
No full textDavies-Shaw D, Huser T. Novel optical oxy/deoxy hemoglobin monitoring as a modality for non-invasive real-time monitoring of cognitive activity and beyond. In: Proc. SPIE int.Soc.Eng. 6863. 2008: 68630A-1-68630-10
Numerical Recipes for Landslide Spatial Prediction Using R-INLA: A Step-by-Step Tutorial
No full textThe geomorphological community typically assesses the landslide susceptibility at the catchment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates. Spatial dependence, which allows capturing similarities at neighboring sites that are not directly explained by covariate information, is typically not accounted for in the landslides literature, whilst such spatial models have become commonplace in the geostatistical literature. Here we explain step by step how to rigorously model and predict activations of debris flow based on an adequate statistical model by using the R-INLA library from the statistical software R in the context of a real multiple landslide event. This chapter follows the analysis of Lombardo, Opitz, and Huser with a few modifications; it is written in a tutorial style to provide the geomorphological community with a hands-on guide to replicate similar analyses in R. While our focus here is on implementation and computing, more details about the underlying statistical theory, modeling, and estimation can be found in the original work by Lombardo, Opitz, and Huser Our modeling approach deviates fundamentally from the commonly used regression models fitted to binary presence/absence data. Specifically, we use a Bayesian hierarchical Cox point process model to describe landslide counts at high resolution (i.e., at the pixel level), while capturing spatial dependence through a latent spatial effect defined at lower resolution over slope units. Our point process modeling approach allows us to derive the distribution of aggregated landslide counts for any areas of interest. Crucially, the latent spatial effect represents the unexplained but spatially structured component in the data when the linear or nonlinear effects of covariates are removed. Thus, in the case of sparse rain gauge or seismic networks, we suggest using the latent spatial effect to uncover the trigger distribution over space. In particular, for landslides triggered by extreme precipitation, the meteorological stress can play a dominant role with respect to the covariates that are typically introduced in predictive models; therefore, accounting for the trigger in modeling may dramatically improve the performance of landslide prediction
Creighton Law Review Board of Editors 2012/2013
No full textBack L-R: Antonio Fornasier, Michael Anderson, Michael Daly, John Matson, Yassin Patterson, Grant Mullin, Shane Strong, Eric Newhouse
Front L-R: Ronald Volkmer, Kelli Huser, Robert Henderso
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