Search results for "Method"
showing 10 items of 13253 documents
Imaging through scattering media by microstructured illumination
2016
We describe a method to image objects through scattering media based on microstructured illumination. A spatial light modulator is used to project a set of microstructured light patterns onto the sample. The image is retrieved computationally from the photocurrent fluctuations provided by a detector with no spatial structure. We review several optical setups developed in the last years with different illumination strategies and applied to different turbid media. In particular we introduce a new non-invasive optical system based on a reflection configuration. Our technique does not require coherent light, raster scanning, time-gated detection or a-priori calibration processes. Furthermore it…
Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony
2016
In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…
CUDA-enabled hierarchical ward clustering of protein structures based on the nearest neighbour chain algorithm
2015
Clustering of molecular systems according to their three-dimensional structure is an important step in many bioinformatics workflows. In applications such as docking or structure prediction, many algorithms initially generate large numbers of candidate poses (or decoys), which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates can easily range from thousands to millions, performing the clustering on standard central processing units (CPUs) is highly time consuming. In this paper, we analyse and evaluate different approaches to parallelize the nearest neighbour chain algorithm to perform hierarc…
Fast UPLC/PDA determination of squalene in Sicilian P.D.O. pistachio from Bronte: Optimization of oil extraction method and analytical characterizati…
2017
Abstract A fast reversed-phase UPLC method was developed for squalene determination in Sicilian pistachio samples that entry in the European register of the products with P.D.O. In the present study the SPE procedure was optimized for the squalene extraction prior to the UPLC/PDA analysis. The precision of the full analytical procedure was satisfactory and the mean recoveries were 92.8 ± 0.3% and 96.6 ± 0.1% for 25 and 50 mg L−1 level of addition, respectively. Selected chromatographic conditions allowed a very fast squalene determination; in fact it was well separated in ∼0.54 min with good resolution. Squalene was detected in all the pistachio samples analyzed and the levels ranged from 5…
Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies
2016
Mieth, Bettina et al.
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…
Gene-based and semantic structure of the Gene Ontology as a complex network
2012
The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. This approach might be usefully complemented by a bottom-up approach based on the knowledge of relationships amongst genes. To this end, we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium and a gene-based …
L1-Penalized Censored Gaussian Graphical Model
2018
Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…
panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.
2018
Abstract Motivation The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic spec…
Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry.
2017
International audience; Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algori…