Learning of networked spreading models from noisy and incomplete data.

Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, we introduce a universal learning method based on a scalable dynamic message-passing technique that addresses these challenges often encountered in real data.

Clinical metabolomics: current state and prospects in Russia.

Using analytical technologies it is possible now to measure the entire diversity of molecules even in a small amount of biological samples. Metabolomic technologies simultaneously analyze thousands of low-molecular substances in a single drop of blood. Such analytical performance opens new possibilities for clinical laboratory diagnostics, still relying on the measurement of only a limited number of clinically significant substances.

Mercury in the Barents region - River fluxes, sources, and environmental concentrations.

Arctic rivers are receiving increased attention for their contributing of mercury (Hg) to the Arctic Ocean. Despite this, the knowledge on both the terrestrial release sources and the levels of Hg in the rivers are limited. Within the Arctic, the Barents region has a high industrial development, including multiple potential Hg release sources.

New Constraint on the Local Relic Neutrino Background Overdensity with the First KATRIN Data Runs.

We report on the direct search for cosmic relic neutrinos using data acquired during the first two science campaigns of the KATRIN experiment in 2019. Beta-decay electrons from a high-purity molecular tritium gas source are analyzed by a high-resolution MAC-E filter around the end point at 18.57 keV.