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This article reflects the monitoring indicators from the research of living conditions in substitute families in the South of Russia. The authors present some study results of the living comfort in a substitute family, namely the relationships with foster parents; interaction with blood and other foster children in the family; the psycho-emotional state; physical, mental, and emotional violence in a foster family; the desire to live in a substitute family. The monitoring was carried out among 76 foster children being brought up in substitute families of the Azov city and the Azov district of the Rostov region and accompanied by the Azov Child Care Centre. The results obtained are of great importance for specialists of support services for substitute families, as well as researchers whose scientific interest is focused on studying substitute family care in Russia. The research has been carried out within the framework of the Ministry of General and Vocational Education order of the Rostov Region No. 747 of 28 July 2022 “On measures to assess the level of comfort of a child’s stay in a substitute family”.

The relevance of this study is due to the challenges of the global pandemic, which has led to the large-scale development of education digitalization. In this regard, this article is devoted to revealing the features of remote learning in the context of a global pandemic, identifying the risks and benefits of digital learning. The article reveals the discursive content of the counter-pandemic vector of distance learning for University students; defines the classification of risks and advantages of large-scale digitalization of students' remote learning. Based on the results of the study, the authors justified the consolidated model of students distance learning, the counter-pandemic vector of which is a large-scale digitalization of the University educational process. The effectiveness of the model is proved by the results of its use in the process of large-scale digitalization of students' remote learning in the context of a global pandemic. The materials of the article have practical application and can be useful in the development and implementation of digital educational complexes. They are recommended to teachers and students of the University, methodologists, curators, Tutors.

This article is aimed at identifying the peculiarities of students' attitude to distance education. As a leading method, the questionnaire method was used, which made it possible to identify the students' positive and negative attitudes regarding distance learning more effectively. The article considers students' ideas about distance learning, identifies the positive and negative aspects of the distance learning process. The authors of the article in the process of research revealed that many students of various fields of study are interested in participating in distance courses, but within the framework of mastering a second education. Some students note the usefulness of distance courses, the use of their materials at any time, and the convenience of going back to the materials they have completed at any time to fill in gaps or recall a topic. Some students reported a violation of the educational process in the distance format due to the repeated lack of Internet or its poor quality. Most of the students expressed dissatisfaction with the lack of contact communication in the distance learning process. It is proved that the majority of students believe that distance education will never replace traditional education. Mainly students of technical areas of study gave a negative assessment of the distance education system. It was revealed that students of various fields of study took distance courses that were held in other cities or countries. From the point of view of many students, educational websites have an inconvenient interface.

Although unprecedented efforts aiming to stop the COVID-19 pandemic have been made over the past two years, SARSCoV-2 virus still continues to cause intolerable health and economical losses. Vaccines are considered the most effective way to prevent infectious diseases, which has been reaffirmed for COVID-19. However, in the context of the continuing virus spread because of insufficient vaccination coverage and emergence of new variants of concern, there is a high demand for vaccination strategy amendment. The ability to elicit protective immunity at the entry gates of infection provided by mucosal vaccination is key to block virus infection and transmission. Therefore, these mucosal vaccines are believed to be a “silver bullet” that could bring the pandemic to an end. Here, we demonstrate that the intranasally delivered Gam-COVID-Vac (Sputnik V) vaccine induced a robust (no less than 180 days) systemic and local immune response in mice. High immunogenic properties of the vaccine were verified in non-human primates (common marmosets) by marked IgG and neutralizing antibody (NtAb) production in blood serum, antigen-specific Tcell proliferation and cytokine release of peripheral blood mononuclear cells accompanied by formation of IgA antibodies in the nasal mucosa. We also demonstrate that Sputnik V vaccine can provide sterilizing immunity in K18-hACE2 transgenic mice exposed to experimental lethal SARS-CoV-2 infection protecting them against severe lung immunopathology and mortality. We believe that intranasal Sputnik V vaccine is a promising novel needle-free mucosal vaccine candidate for primary immunization as well as for revaccination and is worth further clinical investigation.

Deep convolutional neural networks are highly efficient for computer vision tasks using plenty of training data. However, there remains a problem of small training datasets. For addressing this problem the training pipeline which handles rare object types and an overall lack of training data to build well-performing models that provide stable predictions is required. This article reports on the comprehensive framework XtremeAugment which provides an easy, reliable, and scalable way to collect image datasets and to efficiently label and augment collected data. The presented framework consists of two augmentation techniques that can be used independently and complement each other when applied together. These are Hardware Dataset Augmentation (HDA) and Object-Based Augmentation (OBA). HDA allows the users to collect more data and spend less time on manual data labeling. OBA significantly increases the training data variability and remains the distribution of the augmented images being close to the original dataset. We assess the proposed approach for the apple spoil segmentation scenario. Our results demonstrate a substantial increase in the model accuracy reaching 0.91 F1-score and outperforming the baseline model for up to 0.62 F1-score for a few-shot learning case in the wild data. The highest benefit of applying XtremeAugment is achieved for the cases where we collect images in the controlled indoor environment, but have to use the model in the wild.

The canopy height model (CHM) is a representation of the height of the top of vegetation from the surrounding ground level. It is crucial for the extraction of various forest characteristics, for instance, timber stock estimations and forest growth measurements. There are different ways of obtaining the vegetation height, such as through ground-based observations or the interpretation of remote sensing images. The severe downside of field measurement is its cost and acquisition difficulty. Therefore, utilizing remote sensing data is, in many cases, preferable. The enormous advances in computer vision during the previous decades have provided various methods of satellite imagery analysis. In this work, we developed the canopy height evaluation workflow using only RGB and NIR (near-infrared) bands of a very high spatial resolution (investigated on WorldView-2 satellite bands). Leveraging typical data from airplane-based LiDAR (Light Detection and Ranging), we trained a deep neural network to predict the vegetation height. The provided approach is less expensive than the commonly used drone measurements, and the predictions have a higher spatial resolution (less than 5 m) than the vast majority of studies using satellite data (usually more than 30 m). The experiments, which were conducted in Russian boreal forests, demonstrated a strong correlation between the prediction and LiDAR-derived measurements. Moreover, we tested the generated CHM as a supplementary feature in the species classification task. Among different input data combinations and training approaches, we achieved the mean absolute error equal to 2.4 m using U-Net with Inception-ResNet-v2 encoder, high-resolution RGB image, near-infrared band, and ArcticDEM. The obtained results show promising opportunities for advanced forestry analysis and management. We also developed the easy-to-use open-access solution for solving these tasks based on the approaches discussed in the study cloud-free composite orthophotomap provided by Mapbox via tile-based map service.

The search for possible ways to minimize dizziness in patients with vestibular migraine is especially relevant in the aspect of individualized complex vestibular rehabilitation. Aim. To study the possibilities of complex individualized vestibular rehabilitation in patients with vestibular migraine. Material and methods. The study included 122 patients (40 men and 82 women) aged 18 to 50 years, with significant vestibular migraine. We used the Dizziness Handicap Inventory scale for assessing dizziness and a 5-point Scale for subjective assessment of the severity of dizziness and a special complex method including a set of vestibular exercises, a method of the computer stabilometry with biofeedback. Objective control of vestibular rehabilitation was carried out with the help of stabilometric testing. Results and discussion. Positive dynamics were observed when analyzing the data of the physical, functional and emotional subscales of the Dizziness Handicap Inventory and the Scale of Subjective Assessment of the Dizziness Severity. In addition, the presented stabilometric diagnostics made it possible to fix the difference between the initial condition of patients with vestibular dysfunction and after 3 months of the special vestibular rehabilitation.Тhere was a statistically significant decrease in the basic stabilometric parameters: the area of the statokinesiogram (median area, mm² – 254 (95% CI 222-285)) and the rate of deviation of the center of pressure (median speed, mm/s – 21 (95% CI 17-24)) as well as a decrease in the shifts of the spectrum energy in the low-frequency and high-frequency spectrum that indicates an improvement in stability. The presented method for influencing the compensatorycapabilities of regulatory mechanisms in patients with vestibular migraine positively affects the efficiency of the equilibrium system as a whole, optimizing postural stability in this category of patients. Conclusion. An individually selected comprehensive program of vestibular gymnastics and hardware rehabilitation techniques on the stabilographic platform with biofeedback helps to improve postural stability and minimize dizziness in patients with vestibular migraine.

This paper represents a profound research of Kazakh and foreign scientific literature and tries to define the structure, the essence and meaningful characteristics of the art higher education institution students' competence as a set of professionally significant qualities of personality, which is determined by the nature and specifics of the forthcoming professional activities of those students. The results of the performed diagnostic study upon this subject are provided and allow to objectively evaluating the current development level of the components of social competence of students who are majoring in arts. The need for competence-oriented type of education for arts students will be justified. As a result of the conducted study, we concluded that the components of social competence, in their turn, are the components of the underlying method for forming this studied competence. The analysis of the criteria and a diagnostic research method, which comprises criteria, indicators and levels (reproductive, adaptively-transformative and creative) provided the possibility to perform an extensive revision and objectively evaluate the current maturity level of the components of the art higher education institution students' social competence.

Background: Until recently, about three‐quarters of all monogenic Parkinson's disease (PD) studies were performed in European/White ancestry, thereby severely limiting our insights into genotype–phenotype relationships at a global scale. Objective: To identify the multi‐ancestry spectrum of monogenic PD. Methods: The first systematic approach to embrace monogenic PD worldwide, The Michael J. Fox Foundation Global Monogenic PD Project, contacted authors of publications reporting individuals carrying pathogenic variants in known PD‐causing genes. In contrast, the Global Parkinson's Genetics Program's Monogenic Network took a different approach by targeting PD centers underrepresented or not yet represented in the medical literature. Results: In this article, we describe combining both efforts in a merger project resulting in a global monogenic PD cohort with the buildup of a sustainable infrastructure to identify the multi‐ancestry spectrum of monogenic PD and enable studies of factors modifying penetrance and expressivity of monogenic PD. Conclusions: This effort demonstrates the value of future research based on team science approaches to generate comprehensive and globally relevant results. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

In the context of global climate change and rising anthropogenic loads, outbreaks of both endemic and invasive pests, pathogens, and diseases pose an increasing threat to the health, resilience, and productivity of natural forests and forest plantations worldwide. The effective management of such threats depends on the opportunity for early-stage action helping to limit the damage expand, which is difficult to implement for large territories. Recognition technologies based on the analysis of Earth observation data are the basis for effective tools for monitoring the spread of degradation processes, supporting pest population control, forest management, and conservation strategies in general. In this study, we present a machine learning-based approach for recognizing damaged forests using open source remote sensing images of Sentinel-2 supported with Google Earth data on the example of bark beetle, Polygraphus proximus Blandford, polygraph. For the algorithm development, we first investigated and annotated images in channels corresponding to natural color perception--red, green, and blue--available at Google Earth. Deep neural networks were applied in two problem formulations: semantic segmentation and detection. As a result of conducted experiments, we developed a model that is effective for a quantitative assessment of the changes in target objects with high accuracy, achieving 84.56% of F1-score, determining the number of damaged trees and estimating the areas occupied by withered stands. The obtained damage masks were further integrated with medium-resolution Sentinel-2 images and achieved 81.26% of accuracy, which opened the opportunity for operational monitoring systems to recognize damaged forests in the region, making the solution both rapid and cost-effective. Additionally, a unique annotated dataset has been collected to recognize forest areas damaged by the polygraph in the region of study. [ABSTRACT FROM AUTHOR]

Chl-a concentration is one of the key characteristics of marine areas related to photosynthesis, along with oxygen levels and water salinity. Most studies focus on estimating chl-a concentration in closed water bodies, rivers, and coastal areas of the tropical and temperate Earth belts and are therefore limited to specific regions and also require direct measurements and chemical analysis to obtain precise information about marine environmental conditions. Remote sensing techniques and spatial modeling aim to offer tools for rapid and global analysis of climate and ecological changes. In this study, we aim to develop a machine learning (ML)-based approach to estimate chlorophyll-a concentration when satellite data are unavailable. To provide physical parameters that may influence the predicted variable (chl-a concentration), we combined satellite observations from MODIS with geophysical Weather Research & Forecasting (WRF) and Nucleus for European Modelling of the Ocean (NEMO) models. Classical ML and deep learning (DL) algorithms were compared and analyzed for their ability to extract key biogeochemical patterns in the Barents Sea. The proposed approach allows us to forecast chl-a concentration for the next 8 days based on spatial features and measurements from preceding days. The best R² metric achieved was 0.578 using a LightGBM algorithm, confirming the applicability of the developed solution to map the northern marine region even in cases where MODIS observations are unavailable for the preceding period due to insufficient illumination and dense cloud cover. [ABSTRACT FROM AUTHOR]

Introduction. Numerous molecular genetic studies have revealed a correlation between the polymorphism of milk protein genes and the technological properties of milk raw materials. DNA analysis, in particular, initiated research into the influence of allelic variants of κ-casein (CSN3) on thermal stability and cheese suitability of milk. This gives relevance to our study that compares the results of genotypic identification of lactating cows by the κ-casein gene, using raw and processed milk samples. Study objects and methods. Our study used raw and reconstituted milk samples from first-calf cows of the black motley breed with the AA and BB genotypes of the κ-casein gene. The samples were analyzed by standardized and generally accepted chemical engineering methods, as well as by capillary electrophoresis and PCR-RFLP analysis. Results and discussion. We compared the results of tests on thermal stability and cheese suitability of raw and reconstituted milk samples from cows with the AA and BB genotypes of the κ-casein gene. We tried out an integrated approach to monitoring milk raw materials based on the most relevant technological criteria and correlating the data with the associated CSN3 gene identification parameters. The PCR-RFLP analysis revealed reproducible results for both raw and dried milk samples in relation to the genotypical identification by the A- and B- allelic variants of the CSN3 gene. The tests showed higher thermal stability in the reconstituted milk from the BB genotype cow and better cheese suitability in the AA genotype sample. Conclusion. We developed a system for evaluating milk raw materials based on the most important technological parameters in combination with their genotypic characteristics. Our research procedure can unify the accumulation of experimental data and contribute to the formation of bioinformatics algorithms. This approach can be used in mathematical modeling of criteria to evaluate the compliance of the technological properties of milk with the recommended indicators.

Nowadays as part of the process of digitalization of the labor market there are structural changes: new forms and types of employment appear. A global trend in recent years is the increase in the number of employees working remotely, which has significant value in difficult epidemiological situation in the world. Today, women are far more at risk of being deprived of the benefits of digital innovations, job losses due to robotics and automatization, have to put much more effort to achieve wage levels of male workers. The article examines the typology of new non-standard forms of employment, their distinctive features. The author highlights the main advantages and risks associated with the wide spreading of new forms of employment in the context of the digitalization of the labor market. The study touches upon the gender aspect, special attention is paid to the analysis of innovative forms of employment and the involvement of women in innovative teams, ICT and STEM-specialties. The factors influencing the formation of innovative behavior in national innovation systems are described. The ways of coordination of state, business and research structures within the framework of "collective action" aimed at stimulating scientific and technological progress in the development of the knowledge economy, the digital economy are outlined. The author also highlighted a number of fundamental reasons for the socio-psychological insecurity of male and female workers associated with the use of non-standard employment, noted in general terms ways of solving problems in the field of precarious employment and leveling gender inequality in the context of the development of the digital economy.

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Among different forest inventory problems, one of the most basic is defining dominant species. These data are crucial in forest management to determine forest category, and a cheaper remote sensing-based approach would be a useful supplement to field surveys. We used WorldView multispectral satellite imagery to address this problem as an image segmentation task dividing the image into regions with particular dominant species. Neural networks have recently become one of the most useful tools for this kind of problem, including incomplete or erroneous training labels. However, it is still challenging to distinguish between such similar patterns as different forest compositions. To handle this, we represented the multiclass forest classification problem as a hierarchical set of binary classification tasks, which allowed us to reach better results with both high- and medium-resolution satellite imagery. We also examined supplementary data, such as tree height, to improve the species classification results for wider tree age diversity. We conducted experiments considering six neural network architectures to find the best one for each task in the hierarchical decomposition. The proposed approach was tested on sample territories in Leningrad Oblast of Russia, for which the field-based observations were acquired and made publicly available as a single dataset. The proposed approach showed significantly better results (average F1-score 0.84) than multiclass classification (average F1-score 0.7).

The unprecedented in recent history global COVID-19 pandemic urged the implementation of all existing vaccine platforms to ensure the availability of the vaccines against COVID-19 to every country in the world. Despite the multitude of high-quality papers describing clinical trials of different vaccine products, basic detailed data on general toxicity, reproductive toxicity, immunogenicity, protective efficacy and durability of immune response in animal models are scarce. Here, we developed a β-propiolactone-inactivated whole virion vaccine CoviVac and assessed its safety, protective efficacy, immunogenicity and stability of the immune response in rodents and non-human primates. The vaccine showed no signs of acute/chronic, reproductive, embryo- and fetotoxicity, or teratogenic effects, as well as no allergenic properties in studied animal species. The vaccine induced stable and robust humoral immune response both in form of specific anti-SARS-CoV-2 IgG and NAbs in mice, Syrian hamsters, and common marmosets. The NAb levels did not decrease significantly over the course of one year. The course of two immunizations protected Syrian hamsters from severe pneumonia upon intranasal challenge with the live virus. Robustness of the vaccine manufacturing process was demonstrated as well. These data encouraged further evaluation of CoviVac in clinical trials.

In this study the effect of k-casein gene polymorphism on the technological characteristics of milk powder obtained by spray (AA1:BB1) and freeze drying (AA2:BB2) was investigated. Standardized and generally accepted methods were used in the field of physical and chemical control of dairy products, as well as methods for assessing the heat stability and cheese making properties of milk. The most heat-resistant were the samples with a predominance of milk obtained from cows with the AA CSN3 genotype, in the pH range from 6.4 to 7.0 (36-91 minutes for AA1:BB1 and 37-101 minutes for AA2:BB2). In systems with a fraction of 25% to 100% of milk from cows with the AA CSN3 genotype obtained by freeze drying, higher (by 3-10%) stabilization qualities of protein were revealed when heated in the pH range from 6.4 to 7.0 compared to spray drying. The analysis of the results regarding the cheese making properties showed that with an increase in the proportion of milk from cows with the BB CSN3 genotype from 0% to 100% in model systems, the rennet coagulation time decreases for all samples, regardless of the drying method. It was also found that when using freeze drying, coagulum of all samples were assigned to the highest class of milk quality in terms of cheese making properties, while during spray drying only the samples consisting of min. 75% or completely of milk obtained from cows with genotype BB CSN3 corresponded to this category.

The sustainable development of the «knowledge-based economy» is the key factor in the growth of the country's national welfare and the evolution of social and political spheres. For a sociologist, it is important to analyze the processes of formation of social strata of innovators in the stratification structure of society, to highlight the distinctive features of the strata of innovators and the mechanisms of its development.

Developed database for optimal selection of agricultural machinery. An analytical review of today’s database management systems (DBMS) has shown that an easy-to-use, easy-to-implement DBMS for various software solutions is SBMS ite. Entities have been created in the relational database: land plots, agricultural machines, aggregates. Tables reflecting the traction and coupling properties of modern foreign and domestic tractors and units have been created using DBMSite. The essence of land plots is described by a set of geometric and attributive properties reflecting spatial (area, perimeter) and also technological properties of land plots. Such properties include: the length of the site gon, the soil energy intensity score, soil resistivity, soil rockiness, relief angle, relief inclination coefficient, internal distance, road group coefficient, relief inclination coefficient along the route. Practical implementation was carried out on the example of the economy of the Mirny Kochenevsky district of the Novosibirsk region using the geographic information system ArcGIS 10.6. The developed database allows the selection of agricultural machinery and can be used to compare the selection of different versions of machine-tractor units taking into account the technological properties of land plots. Methods: database design methods. Results: A database of agricultural machinery has been developed with the help of SOUNDite, containing tables of agricultural machinery (tractors and aggregates) and land plots. Information on land plots is structured in the spatial database of geodata ArcGIS 10 and includes information on the area, type of use, as well as technological properties associated with the traction and coupling properties of tractors. Application of the results: the developed database of agricultural machinery will allow further calculation of time costs in soil cultivation on a specific land plot.

Modern approaches to urban planning assume the dualistic nature of urban green infrastructure (GI). On the one hand, green infrastructure is as an integrated network of natural and semi-natural areas, featuring a delivery of various benefits to humans. On the other hand, GI is multifunctional and provides the residents by complex of ecosystem services to be user-oriented. Most official reports and programs use common indicators that do not characterize distribution, dynamics or state of GI. In our research, we assessed the quality of GI in 15 largest Russian cities by using an integrated assessment of 13 indicators that make up three groups: the ones 1) characterizing general GI availability; 2) supporting a comfortable urban environment («recreational indicators»); and 3) forming a stable ecosystem («integrity indicators»). The cities were ranked by values of every indicator from 1 to 15 and then the results were summed and normalized to get a total mark (max. 100). To assess the development of GI elements of each group, we also ranked cities separately by values of different groups indicators. Thus, our study revealed that satisfactory marks for both recreational and integrity indicators have Ufa, Nizhny Novgorod, Kazan, Ekaterinburg, Perm and Voronezh. In contrast, Saint Petersburg, being a densely built-up city in an auspicious natural zone, got the worst result. According to the final assessment, the quality of green infrastructure in Krasnoyarsk, a large industrial city, and four cities from the steppe zone (Rostov-on-Don, Samara, Omsk, Novosibirsk) is also unsatisfactory. Our method does not cover all GI aspects (like vegetation health) and since it is based solely on remote sensing data and statistics data, there is definitely a room for improvement. However, this method, while being relatively quick and simple to accomplish, allows to assess not only general availability of GI, but its quality and distribution as well, which are essential for urban spatial planning.

To optimize the technological process of manufacturing copper wire coated with solder of the POIN-52 grade, the optimal modes for the tinning temperature, linear velocity, diameter of the diamond die and flux were identified. It was found that the best wire tinning process is achieved when cleaning the surface of a copper wire by the method of electrochemical degreasing. The results of the tests showed that, in terms of mechanical and electrical parameters wire meets the require-ments for electrodes in the manufacture of solar modules. The results of climatic tests of solar modules showed a high degree of reliability with a power loss of 0.86% and thermal cycling tests with a power loss of 0.4%, which is within tolerance.