Data representations and -analyses of binary diary data in pursuit of stratifying children based on common childhood illnesses

In this article we analyse diary reports concerning childhood symptoms of illness, these data are part of a larger study with other types of measurements on childhood asthma. The children are followed for three years and the diaries are updated, by the parents, on a daily basis. Here we focus on the methodological implications of analysing such data. We investigate two ways of representing the data and explore which tools are applicable given both representations. The first representation relies on proper alignment and point by point comparison of the signals. The second approach takes into account combinations of symptoms on a day by day basis and boils down to the analysis of counts. In the present case both methods are well applicable. However, more generally, when symptom episodes are occurring more at random locations in time, a point by point comparison becomes less applicable and shape based approaches will fail to come up with satisfactory results. In such cases, pattern based methods will be of much greater use. The pattern based representation focuses on reoccurring patterns and ignores ordering in time. With this representation we stratify the data on the level of years, so that possibly yearly differences can still be detected

Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology

Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control sample

Sparse Deconvolution in One and Two Dimensions: Applications in Endocrinology and Single-Molecule Fluorescence Imaging

Deconvolution of noisy signals is an important task in analytical chemistry, examples being spectral deconvolution or deconvolution in microscopy. When the number of spectral peaks or single emitters in imaging is limited, the solution of the deconvolution is required to be sparse, and desirable results are obtained using a penalized estimation techniques. We impose sparseness by using penalized regression with a penalty based on the <i>L</i>₀-norm, as discussed in earlier work. Several extensions to this approach are presented. Results are demonstrated on pulse identification in endocrine data where the aim is to model the secretion pattern as a sparse series of spikes. An application in single-molecule fluorescence imaging demonstrates the algorithm when applied to two-dimensional data

Intrinsic Gene Expression Profiles of Gliomas are a Better Predictor of Survival than Histology

Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control sample

Sparse deconvolution of high-density super-resolution images

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty - on the number of fluorophores rather than on their overall brightness - we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm(-2) and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.status: publishe

Sparse deconvolution of high-density super-resolution images

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty – on the number of fluorophores rather than on their overall brightness – we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm-2 and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling