Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a 'weakly-supervised' approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular 'rotor'. This deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data

Analysis of the Epidemiological Situation of Hemorrhagic Fever with Renal Syndrome in the Russian Federation in 2022 and Forecast of its Development for 2023

The paper provides the analysis of epidemiological process of hemorrhagic fever with renal syndrome (HFRS) in the Russian Federation in the context of federal districts in 2022 and a forecast of the HFRS incidence for 2023. According to the results of the analysis, there was a three-fold increase in the morbidity rates of HFRS in Russia in 2022 as compared to the indicators of 2021. The evidence of epizootiological survey and laboratory studies in certain federal districts of the Russian Federation indicate the continuing tense epidemiological situation on HFRS. In a number of regions of the country, high risk of infection with HFRS is predicted due to the favorable natural and climatic conditions of the winter period 2022/2023 for reservoir hosts of pathogenic for humans Hantaviruses. The findings of infected rodents attest to a high probability of complication of the epidemiological situation in the territories of increased epidemic hazard as regards HFRS

Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centered experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for the edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows and the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.Comment: Review Articl

EFFECT OF EXPOSURE TO UV RADIATION ON WEAKING PROPERTIES OF WOOD

The article discusses the feasibility of pre-treatment of wood by ultraviolet radiation. Samples of the two types of wood (pine and beech) were subjected to ultraviolet radiation treatment in air. After a period of exposure to the radiation source timber, wettability and surface free energy of both types of wood has increased significantly. It is assumed that the UV irradiation is the process of destruction of the surface layer of wood.Работа выполнялась при поддержке Гранта Президента Российской Федерации для государственной поддержки молодых российских ученых – докторов наук (МД-5596.2016.8)

Current State of Natural Foci of Dangerous Infectious Diseases in the Territory of the Russian Federation

Objective of the study is to evaluate the current state of epizootic activity of natural zoonotic foci, as the basis for the development of prophylactic measures aimed at prevention of natural-focal infections during mass events. Materials and methods. Utilized have been reports from the Center of Hygiene and Epidemiology in the Republic of Tatarstan, the data provided by Rospotrebnadzor Administration in the Republic of Tatarstan over the period of 2009-2014, and literature references. Results and conclusions. The most pressing natural-focal infectious diseases are hemorrhagic fever with renal syndrome, tick-borne borreliosis, and tick-borne viral encephalitis. Yersinioses, leptospiroses, and West Nile fever are rarely registered. Tularemia infections have not been reported within the past 20 years. The period of 2009-2013 is characterized by the decrease in the numbers of carriers and vectors of the diseases, as well as epizootic activity of natural foci, which came up to minimum values in 2013. Emerged since 2014 increment in the abundance rates of the carriers and later the vectors can lead to the increase in the incidence of natural-focal diseases. In the territory of the Republic, allocated are the spatial combination areas of natural foci of the diseases of various etiology with high risk of population exposure. Previous to conduction of mass events it is necessary to enhance the epizootiological surveillance in the natural foci, the results of which lay premises for the development of complex prophylactic activities

CHARACTERIZATION OF AVIAN INFLUENZA H5N8 VIRUS STRAINS THAT CAUSED THE OUTBREAKS IN THE RUSSIAN FEDERATION IN 2016–2017

Objective of the study is to investigate biological properties of avian influenza virus strains that caused the outbreaks in Russia in 2016–2017.Materials and methods. The study was performed using advanced virological and molecular-biological methods in state-of-the-art equipment.Results and conclusion. In 2016, the outbreaks among wild birds and poultry caused by highly pathogenic avian influenza H5N8 virus have occurred in the territory of the Russian Federation. In May, 2016 an outbreak of H5N8 among wild birds was registered in the territory of the Republic of Tyva. In October-November, 2016 influenza virus H5N8 was isolated in the territory of the Republics of Tatarstan and Kalmykia, Krasnodar and Astrakhan Regions of Russia. In 2017 avian influenza H5N8 has become widespread in European part of Russia and caused multiple outbreaks among wild birds and poultry. Results of the investigations of the isolated strains show that all of them are highly pathogenic and belong to the clade 2.3.4.4. Molecular-genetic and virological analysis has revealed the differences between the viruses isolated in 2016–2017 and the virus of the same clade 2.3.4.4 that was isolated in 2014