7 research outputs found
Energy-efficient construction materials in capital repair of apartment buildings (on the example of Rostov-on-Don)
This paper assesses the need to improve the energy efficiency of multi-apartment residential buildings in Rostov-on-Don, built in the period of industrial house building for the use of energy-efficient building materials. The paper analyses the existing housing stock, identifies the series of apartment buildings, determines the dynamics of construction by year, and the structural schemes of each series. Also, the heat transfer resistance of the building envelopes of all building series was calculated and high-performance insulation materials were selected according to the green materials assessment criteria
A Two-Step Soft Segmentation Procedure for MALDI Imaging Mass Spectrometry Data
We propose a new method for soft spatial segmentation of matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) data which is based on probabilistic clustering with subsequent smoothing. Clustering of spectra is done with the Latent Dirichlet Allocation (LDA) model. Then, clustering results are smoothed with a Markov random field (MRF) resulting in a soft probabilistic segmentation map. We show several extensions of the basic MRF model specifically tuned for MALDI-IMS data segmentation. We describe a highly parallel implementation of the smoothing algorithm based on GraphLab framework and show experimental results
Analysis and Interpretation of Imaging Mass Spectrometry Data by Clustering Mass-to-Charge Images According to Their Spatial Similarity
Imaging
mass spectrometry (imaging MS) has emerged in the past
decade as a label-free, spatially resolved, and multipurpose bioanalytical
technique for direct analysis of biological samples from animal tissue,
plant tissue, biofilms, and polymer films., Imaging
MS has been successfully incorporated into many biomedical pipelines
where it is usually applied in the so-called untargeted mode-capturing
spatial localization of a multitude of ions from a wide mass range. An imaging MS data set usually comprises thousands
of spectra and tens to hundreds of thousands of mass-to-charge (<i>m</i>/<i>z</i>) images and can be as large as several
gigabytes. Unsupervised analysis of an imaging MS data set aims at
finding hidden structures in the data with no a priori information
used and is often exploited as the first step of imaging MS data analysis.
We propose a novel, easy-to-use and easy-to-implement approach to
answer one of the key questions of unsupervised analysis of imaging
MS data: what do all <i>m</i>/<i>z</i> images
look like? The key idea of the approach is to cluster all <i>m</i>/<i>z</i> images according to their spatial similarity
so that each cluster contains spatially similar <i>m</i>/<i>z</i> images. We propose a visualization of both spatial
and spectral information obtained using clustering that provides an
easy way to understand what all <i>m</i>/<i>z</i> images look like. We evaluated the proposed approach on matrix-assisted
laser desorption ionization imaging MS data sets of a rat brain coronal
section and human larynx carcinoma and discussed several scenarios
of data analysis
FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry
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