Aberrated dark-field imaging systems

We study generalized dark-field imaging systems. These are a subset of linear shift-invariant optical imaging systems, that exhibit arbitrary aberrations, and for which normally-incident plane-wave input yields zero output. We write down the theory for the forward problem of imaging coherent scalar optical fields using such arbitrarily-aberrated dark-field systems, and give numerical examples. The associated images may be viewed as a form of dark-field Gabor holography, utilizing arbitrary outgoing Green functions as generalized Huygens-type wavelets, and with the Young-type boundary wave forming the holographic reference

Phase-and-amplitude recovery from a single phase contrast image using partially spatially coherent X-ray radiation

A simple method of phase-and-amplitude extraction is derived that corrects for image blurring induced by partially spatially coherent incident illumination using only a single intensity image as input. The method is based on Fresnel diffraction theory for the case of high Fresnel number, merged with the space-frequency description formalism used to quantify partially coherent fields and assumes the object under study is composed of a single material. A priori knowledge of the object's complex refractive index and information obtained by characterizing the spatial coherence of the source is required. The algorithm was applied to propagation-based phase contrast data measured with a laboratory-based micro-focus X-ray source. The blurring due to the finite spatial extent of the source is embedded within the algorithm as a simple correction term to the so-called Paganin algorithm and is also numerically stable in the presence of noise

Situations in traffic - how quickly they change

Spatio-temporal correlations of intensity of traffic are analysed for one week data collected in the motorway M-30 around Madrid in January 2009. We found that the lifetime of these correlations is the shortest in the evening, between 6 and 8 p.m. This lifetime is a new indicator how much attention of drivers is demanded in given traffic conditions.Comment: 9 pages, 6 figure

Monte-Carlo method for identifying aberrantly expressed genes in cancer

Gene expression provides insight into the functional variations on the cellular level that shape biological phenomena. Several recent sequencing technologies have produced an abundance of expression profiles spurring entirely new disciplines of biological study. With this myriad of data, the new task is deciding how to assess and extract meaningful insights. Identifying genes with expression changes in disease conditions is often the first step in finding potential biomarkers for diagnosis, and targets for pharmaceutical treatments. Parametric statistical tests at the individual gene level have been the conventional approach for finding differentially expressed genes. These tests exhibit high statistical power but rely on distributional assumptions that are difficult to validate. Which has led to a vast number of selected genes, with very few being effective in clinical applications. Alternatively, machine learning algorithms have been developed to identify patterns in high-dimensional data that can be easily applied to gene expression analysis. Here we present a novel algorithm for identifying aberrantly expressed genes in cancer. By comparing the expression pattern of individual genes to the cumulative pattern of the whole profile, we have developed a robust classification tool. We provide evidence that aberrant expression is effective in reporting biologically relevant gene signatures that may be overlooked by traditional methods. Due to the general assumptions used in our approach, we demonstrate its ability to assess gene expression from multiple technologies (microarray, RNA-Seq, scRNA-Seq) and for multiple insights (disease associations, treatment associations, cell/tissue variability). Lastly, we apply our method to single-cell RNA profiles, where robust identification of AEGs is possible with fewer samples than the conventional approaches. We hope these results inspire further research into developing a generalized framework for assessing gene expression patterns that can lead to the improvement of clinical outcomes and the development of personalized medicine