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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