Electron (Ukraine)

Outer membrane (OM) lipoproteins serve vital roles in Gram-negative bacteria, contributing to their pathogenicity and drug resistance. For these lipoproteins to function, they must be transported from the inner membrane (IM), where they are assembled, to the OM by the ABC transporter LolCDE. We have previously captured structural snapshots of LolCDE in multiple states, revealing its dynamic conformational changes. However, the exact mechanism by which LolCDE recognizes and transfers lipoprotein between domains remains unclear. Here, we characterized the E. coli LolCDE complex bound with endogenous lipoprotein or ATP to explore the molecular features governing its substrate binding and transport functions. We found that the N-terminal unstructured linker of lipoprotein is critical for efficient binding by LolCDE; it must be sufficiently long to keep the lipoprotein’s main body outside the complex while allowing the triacyl chains to bind within the central cavity. Mutagenic assays identified key residues that mediate allosteric communication between the cytoplasmic and transmembrane domains and in the periplasmic domain to form a lipoprotein transport pathway at the LolC–LolE interface. This study provides insights into the OM lipoprotein relocation process mediated by LolCDE, with significant implications for antimicrobial drug development.

This paper considers the use of phosphors with high effective atomic number Zeff in emergency dosimetry, in particular, to solve one of its unresolved problems - identification as fast as possible of an unknown radiation source in the event of a terrorist attack with a dirty bomb. Based on published studies of possible scenarios of such a radiation emergency, the identification problem can be reduced to the recognition of several radioisotopes. This identification can be done using tandem dosimeters containing two detectors with strongly different energy dependence of their dosimetric sensitivity. The experimental validation of such recognition method is fulfilled using available high-Z dosimetric material for OSL dosimetry - crystalline YAP:Mn<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>+ and tissue equivalent BeO ceramics as well as accessible monoenergetic radiation sources. It confirms generally the expectations as well as need for OSL phosphor possessing even higher Zeff. The new dosimetric phosphor YAP:Bi<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>+ is studied as an alternative to YAP:Mn<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>+. The new material has no disadvantages compared to YAP:Mn<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>+ and is very promising for use in OSL emergency dosimetry.

The main challenges of modern information and communication systems (ICS) in the fight against cyber threats were analyzed in the work. The necessity of using Recurrent neural networks (RNN) and Long short-term memory (LTSM) to improve the effectiveness of threat detection was determined. A Contextually-Sensitive Cybersecurity Monitoring Algorithm Based on Recurrent Neural Networks (CCM-RNN) was proposed, which allows taking into account the dynamics of systems changes in the established context, such as the type or volume of traffic from users, etc. The ability of the proposed algorithm to select the most effective number of features for cyber threat detection was described. The research results of the CCM-RNN algorithm that show the possibility to more quickly and accurately detect anomalies in the system's operation that may threaten its cyber security, were demonstrated.

Multifunctional nanocomposites from an equimolar As4S4/Fe3O4 cut section have been successfully fabricated from coarse-grained bulky counterparts, employing two-step mechanochemical processing in a high-energy mill operational in dry- and wet-milling modes (in an aqueous solution of Poloxamer 407 acting as a surfactant). As was inferred from the X-ray diffraction analysis, these surfactant-free and surfactant-capped nanocomposites are β-As4S4-bearing nanocrystalline-amorphous substances supplemented by an iso-compositional amorphous phase (a-AsS), both principal constituents (monoclinic β-As4S4 and cubic Fe3O4) being core-shell structured and enriched after wet milling by contamination products (such as nanocrystalline-amorphous zirconia), suppressing their nanocrystalline behavior. The fluorescence and magnetic properties of these nanocomposites are intricate, being tuned by the sizes of the nanoparticles and their interfaces, dependent on storage after nanocomposite fabrication. A specific core-shell arrangement consisted of inner and outer shell interfaces around quantum-confined nm-sized β-As4S4 crystallites hosting a-AsS, and the capping agent is responsible for the blue-cyan fluorescence in as-fabricated Poloxamer capped nanocomposites peaking at ~417 nm and ~442 nm, while fluorescence quenching in one-year-aged nanocomposites is explained in terms of their destroyed core-shell architectures. The magnetic co-functionalization of these nanocomposites is defined by size-extended heterogeneous shells around homogeneous nanocrystalline Fe3O4 cores, composed by an admixture of amorphous phase (a-AsS), nanocrystalline-amorphous zirconia as products of contamination in the wet-milling mode, and surfactant.

Today, new realities dictate entirely different working conditions for telecommunications systems. Telecommunications systems now generate large volumes of heterogeneous traffic, which is why classical methods of information management and processing face certain challenges when operating efficiently in real-time mode. For example, modern mobile networks exhibit complex behavior due to network heterogeneity and a large number of diverse mobile devices or systems operating on mobile networks. Proactive monitoring, based on machine learning, aims to address this issue through the analysis of big data and the discovery of new behavior patterns. This article describes the existing problem in monitoring systems and the interim results of using machine learning as an additional tool for predicting the behavior of telecommunications networks. Initially, we will discuss the key concepts related to machine learning. Then, we will present a conceptual framework for a machine learning-based prediction system. We will review interim results on test data and examine the challenges of implementing such systems in practice.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people&amp;#039;s daily schedules strongly influence the spread of SARS COV 2. For the more contagious &amp;quot;rapid&amp;quot; strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious &amp;quot;slow strains&amp;quot; (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – &amp;quot;fast&amp;quot; strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – &amp;quot;slow&amp;quot; strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

Vibration-driven locomotion systems are widely used in various industries, particularly, in the form of capsule-type robots, wheeled platforms, worm-like units, etc. Because of the changeable operating conditions, such systems require continuous control of their kinematic and dynamic characteristics. The main purpose of the present paper is to define the optimal excitation conditions (forced frequencies and phase shifts) of a wheeled two-module vibration-driven robot equipped with two unbalanced rotors. The research methodology contains four stages: developing the robot’s dynamic diagram and mathematical model describing its motion; designing the robot’s simulation model in the MapleSim software; numerical modeling of the system locomotion conditions in the Mathematica software; simulating the system dynamic behavior in the MapleSim software. The obtained results show the time dependencies of the system’s kinematic characteristics at different phase shift angles of the unbalanced rotors. The major scientific novelty of this paper consists in substantiating the possibilities of adjusting the system’s operational parameters in accordance with the changeable technological requirements by means of changing the phase shift angles of the unbalanced rotors. The proposed ideas and obtained results can be used while developing new designs of robots based on the two-module vibration-driven systems and while improving the control systems for adjusting their performance in accordance with the changeable operational conditions.

The article is devoted to characteristics of charge carrier transfer in silicon microcrystals modified with a transition metal admixture with an unfilled 3d+ local magnetic moment envelope and doped with boron to concentrations that correspond to the metal-dielectric transition. At cryogenic temperatures, the magnetoresistance of microcrystals was examined under the influence of magnetic fields up to 14 T. The outcomes of investigations into the magneto-transport characteristics of crystals were thoroughly examined. It was discovered that the transport of charge carriers for silicon microcrystals at low temperatures relies on hopping polarization conductivity. Based on the results of the Si < B, Ni > crystal magnetization study, the concentration of magnetic centers was determined, which is 4 × 1017см−3. It is suggested to employ silicon microcrystals in magnetic field sensors that operate on the magnetoresistive principle.