Diffraction-induced coherence levels.

Alexander Tavrov; Joanna Schmit; Norbert Kerwien; Wolfgang Osten; Hans Tiziani

doi:10.1364/ao.44.002202

Home/Archive/PubMed

PubMed · 15835365

Diffraction-induced coherence levels.

Alexander Tavrov·Joanna Schmit·Norbert Kerwien·Wolfgang Osten·Hans Tiziani

View original source ↗Export metadata ↓

Abstract

We examined the influence of complex diffraction effects on low-coherence fringes created for high-aspect depth-to-width ratio structures called trenches. The coherence function was analyzed for these micrometer-wide trenches and was registered with a white-light interference microscope. For some types of surface structure we observed that additional low-coherence fringes that do not correspond directly to the surface topology are formed near the sharp edges of the structures. These additional coherence fringes were studied by rigorous numerical evaluations of vector diffractions, and these simulated interference fields were then compared with experimental results that were obtained with a white-light interference microscope.

Explore related subjects

Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Microscopy, Interference Models, Biological Phantoms, Imaging Refractometry Reproducibility of Results Sensitivity and Specificity Tomography, Optical Coherence Evaluation Study Journal Article Validation Study

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Alexander Tavrov, Joanna Schmit, Norbert Kerwien, Wolfgang Osten, Hans Tiziani. 2005-04-10. Diffraction-induced coherence levels.. https://doi.org/10.1364/ao.44.002202

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed2006

Robust estimation in mixed linear models with non-monotone missingness.

We introduce a model to account for abrupt changes among repeated measures with non-monotone missingness. Development of likelihood inferences for such models is hard because it involves intractable integration to obtain the marginal likelihood. We use hierarchical likelihood to overcome such difficulty. Abrupt changes among repeated measures can be well described by introducing random effects in the dispersion. A simulation study shows that the resulting estimator is efficient, robust against misspecification of fatness of tails. For illustration we use a schizophrenic behaviour data presented by Rubin and Wu.

Computer Simulation↗