Quantitative automated analysis of host-pathogen interactions

This work aims to broaden knowledge about neutrophil biology in their interaction with fungi species that most frequently cause invasive fungal diseases (IFD). The questions addressed include the alteration of neutrophil morphology after interaction with Candida albicans or C. glabrata, revealing factors that modulate the production and composition of neutrophil-derived extracellular vesicles (EVs) obtained in confrontation assay with conidia of Aspergillus fumigatus and analysing EVs activity against this fungus. Alongside fundamental interests, those questions have important applied aspects in the medicine of IFD. In particular, for diagnostic purposes and infection process monitoring. The results of this work include: 1 a novel segmentation and tracking algorithm which is capable of working with low-contrast cell images, producing accurate cell contours and providing data about positions of clusters, which would improve further analysis; 2 a novel workflow algorithm for analysis of neutrophil continuous morphological spectrum without consensus-based manual annotation; 3 quantitative evidence that morphodynamics of isolated neutrophils depends on the infectious agent (C. albicans or C. glabrata) used in whole blood infection assay; 4 quantitative evidence that neutrophil-derived extracellular vesicles, obtained in confrontation assays with conidia of A. fumigatus could inhibit hyphae development and damage hyphae cell wall; 5 quantitative evidence that EVs inhibition activity is strain-specific

Building vibration induced by sonic boom - field test in Russia

Infrasound and audible sound at very low frequency from sources such as military aircrafts, explosions and wind mills can induce building vibration involving both rattling and whole-body vibration strong enough to cause annoyance. Sonic boom is of special interest in this context due to its very low frequency content that coincides with the most important frequency range for both building vibration and human perception. This paper presents results from field tests with measurements of noise and building vibrations from sonic booms performed at the Tretyakovo airport in Russia. Transmission loss from outdoor to indoor noise, noise induced floor vibration and whole building vibration are determined. Furthermore, the measured acoustic vibration admittance is used to estimate vibration values in the same building from low boom flight passages using synthesized sound pressure time series. Boom induced floor vibration both from the measured flight passages in Russia and from synthesized low boom time series are estimated also for a lightweight wooden building, using previously measured acoustic vibration admittances. The results clearly show perceptible levels of vibrations from sonic boom along with a great influence of the building type which indicates that there can be a big difference between the European countries depending on the building tradition. Finally, it is shown that outdoor sound levels weighted with the C-curve correlates best with frequency weighted floor vibration values.Building vibration induced by sonic boom - field test in RussiapublishedVersio

Analysis of noisy signal restoration quality with Lanczos filter

The differential component of the feedback is used to enhance dynamics of controlled mechanisms in control systems. However, the use of the differential component increases the noise interference, resulting in the need to use different filtering methods. This paper describes the research of a differentiated signal input in a differential Lanczos filter. The influence of the filter order on the integrated square error of the filtered signal is defined. The impact of signal dispersion and a sampling interval on the integrated square error of the filtered signal is identified. Moreover, the most convenient parameter values for the Lanczos filter are determined. The paper also includes the comparison of the exponential moving average filter and the Lanczos filter with selected values of noise dispersion and a sampling interval. The research shows the possibility to apply the Lanczos filter for processing of the differentiated signals

Analysis of the diagnostic and economic impact of the combined artificial intelligence algorithm for analysis of 10 pathological findings on chest computed tomography

BACKGROUND: Artificial intelligence technology can help solve the significant problem of missed findings in radiology studies. An important issue is assessing the economic benefits of implementing artificial intelligence. AIM: To evaluate the frequency of missed pathologies detection and the economic potential of artificial intelligence technology for chest computed tomography compared and validated by experienced radiologists. MATERIALS AND METHODS: This was an observational, single-center retrospective study. The study included chest computed tomography without IV contrast from June 1 to July 31, 2022, in Clinical Hospital in Yauza, Moscow. The computed tomography was processed using a complex artificial intelligence algorithm for 10 pathologies: pulmonary infiltrates, typical for viral pneumonia (COVID-19 in pandemic conditions); lung nodules; pleural effusion; pulmonary emphysema; thoracic aortic dilatation; pulmonary trunk dilatation; coronary artery calcification; adrenal hyperplasia; and osteoporosis (vertebral body height and density changes). Two experts analyzed computed tomography and compared results with artificial intelligence. Further routing was determined according to clinical guidelines for all findings initially detected and missed by radiologists. The hospital price list determined the potential revenue loss for each patient. RESULTS: From the final 160 computed tomographies, the artificial intelligence identified 90 studies (56%) with pathologies, of which 81 (51%) were missing at least one pathology in the report. The second-stage lost potential revenue for all pathologies from 81 patients was RUB 2,847,760 ($37,251 or CNY 256,218). Lost potential revenue only for those pathologies missed by radiologists but detected by artificial intelligence was RUB 2,065,360 ($27,017 or CNY 185,824). CONCLUSION: Using artificial intelligence as an assistant to the radiologist for chest computed tomography can dramatically minimize the number of missed abnormalities. Compared with the normal model without artificial intelligence, using artificial intelligence can provide 3.6 times more benefits. Using advanced artificial intelligence for chest computed tomography can save money

Investigation of the Behavior of Hydrogen in the Aluminum Alloy in the Manufacture of Small Pigs at the Aluminum Plant UC RUSAL

В статье приведены результаты исследований динамики насыщения алюминия и его сплавов водородом в технологической схеме от алюминиевого электролизера до литейного конвейера в условиях Саяногорского алюминиевого завода ОК РУСАЛ. Показано, что одним из основных источников насыщения расплава алюминия водородом является его взаимодействие с влагой воздуха при открытых переливах металла в процессе его движения от электролизера до литейного конвейера. По результатам обследования предложены технические решения, направленные на снижение концентрации водорода в расплаве, которые составят предмет дальнейших исследованийThe results of studies of aluminum saturation dynamics and its alloys with hydrogen in the technological scheme of the electrolytic aluminum to the casting assembly line in a steel plant RUSAL. It was shown that one of the basic aluminum melt saturation hydrogen source is its interaction with moisture of air in open metal modulations during its movement from the electrolyzer molds. According to a survey of proposed technical solutions to reduce the hydrogen concentration in the melt during further investigation

Differences in the carcinogenic evaluation of glyphosate between the International Agency for Research on Cancer (IARC) and the European Food Safety Authority (EFSA)

The International Agency for Research on Cancer (IARC) Monographs Programme identifies chemicals, drugs, mixtures, occupational exposures, lifestyles and personal habits, and physical and biological agents that cause cancer in humans and has evaluated about 1000 agents since 1971. Monographs are written by ad hoc Working Groups (WGs) of international scientific experts over a period of about 12 months ending in an eight-day meeting. The WG evaluates all of the publicly available scientific information on each substance and, through a transparent and rigorous process,1 decides on the degree to which the scientific evidence supports that substance's potential to cause or not cause cancer in humans. For Monograph 112,2 17 expert scientists evaluated the carcinogenic hazard for four insecticides and the herbicide glyphosate.3 The WG concluded that the data for glyphosate meet the criteria for classification as a probable human carcinogen. The European Food Safety Authority (EFSA) is the primary agency of the European Union for risk assessments regarding food safety. In October 2015, EFSA reported4 on their evaluation of the Renewal Assessment Report5 (RAR) for glyphosate that was prepared by the Rapporteur Member State, the German Federal Institute for Risk Assessment (BfR). EFSA concluded that ?glyphosate is unlikely to pose a carcinogenic hazard to humans and the evidence does not support classification with regard to its carcinogenic potential?. Addendum 1 (the BfR Addendum) of the RAR5 discusses the scientific rationale for differing from the IARC WG conclusion. Serious flaws in the scientific evaluation in the RAR incorrectly characterise the potential for a carcinogenic hazard from exposure to glyphosate. Since the RAR is the basis for the European Food Safety Agency (EFSA) conclusion,4 it is critical that these shortcomings are corrected

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated $\mathrm{t}\overline{\mathrm{t}}$ events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The heavy-flavour jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV)