Analysis of the Creation of A. M. Smirnov’s Diptych “Storm on the Sea”

This article is devoted to the work of the honorary member of the Russian Academy of Arts Alexander Smirnov The purpose of this study is to identify the evolution of the artistic method and language of the specified author based on a comparative analysis of the works written by him on the gospel stories In particular the article draws attention to such works of his as Storm on the Sea and Walking on the Waters as well as some sketches written on the specified topic or close to it in different years and in different style

Elemental Composition of PM2.5 and PM10 and Health Risks Assessment in the Industrial Districts of Chelyabinsk, South Ural Region, Russia

Air pollution impacts all populations globally, indiscriminately and has site-specific variation and characteristics. Airborne particulate matter (PM) levels were monitored in a typical industrial Russian city, Chelyabinsk in three destinations, one characterized by high traffic volumes and two by industrial zone emissions. The mass concentration and trace metal content of PM2.5 and PM10 were obtained from samples collected during four distinct seasons of 2020. The mean 24-h PM10 ranged between 6 and 64 μg/m3. 24-h PM2.5 levels were reported from 5 to 56 μg/m3. About half of the 24-h PM10 and most of the PM2.5 values in Chelyabinsk were higher than the WHO recommendations. The mean PM2.5/PM10 ratio was measured at 0.85, indicative of anthropogenic input. To evaluate the Al, Fe, As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn concentration in PM2.5 and PM10, inductively coupled plasma mass spectrometry (ICP-MS) was used. Fe (337–732 ng/m3) was the most abundant component in PM2.5 and PM10 samples while Zn (77–206 ng/m3), Mn (10–96 ng/m3), and Pb (11–41 ng/m3) had the highest concentrations among trace elements. Total non-carcinogenic risks for children were found higher than 1, indicating possible health hazards. This study also presents that the carcinogenic risk for As, Cr, Co, Cd, Ni, and Pb were observed higher than the acceptable limit (1 × 10−6)

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice

Urban environment monitoring in industrial city using remote sensing of snow cover

The dynamic development of modern cities requires new solutions to urban planning and management by local regional authorities. The paper focuses on ecological indicators based on Earth Remote Sensing Data (ERSD) of the snow cover with the purpose to evaluate the city and to plan ecological environment protection strategy. The paper deals with the method of using space images to assess the snow cover pollution of Chelyabinsk, a large Russian industrial city. The assessment of the snow cover of Chelyabinsk was carried out by comparing the heavy metals concentrations with the Landsat 8 data. The spectral indices were calculated for fourteen sites evenly distributed over the urban area of four types: courtyards, car parks, industrial zones and roads. We found a statistically significant difference between the Swirl/Green index and the site type and a correlation with the concentrations of dissolved and suspended forms of heavy metals in snow cover. Snow cover indices can be used as ecological indicators of urban environment

Insights into Particle-Bound Metal(loid)s in Winter Snow Cover: Geochemical Monitoring of the Korkinsky Coal Mine Area, South Ural Region, Russia

Snow plays an important role in air quality and winter geochemical monitoring in the South Ural region. This study deals with the air pollution monitoring of particle-bound metal(loid) concentrations using snow cover around the deepest coal mine in Eurasia, the Korkinsky coal mine. We studied the concentrations and ratios of suspended and dissolved forms of metal(loid)s (Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, Sr, and Zn) in snow samples. We examined 56 snow cover samples, collected at 12 sites located north, south, east and west of the Korkinsky coal mine. All snow samples were taken in January 2020. The spectral reflectance curves, cluster analysis, and spatial distribution maps were used to evaluate the potential sources of PM-bound metal(loid)s and the potential relationship among them. The highest concentrations (μg/L) were reported for Fe, Al, and Zn. In addition to the mine influence, burning coal for residential heating was identified as the major anthropogenic metal(loid) source. It was shown that elevated concentrations of some trace metals in snow samples were associated with southerly winds and the location of spoil heaps

Positive Effect of Cognitive Training in Older Adults with Different APOE Genotypes and COVID-19 History: A 1-Year Follow-Up Cohort Study

(1) Background: Older people suffer from cognitive decline; several risk factors contribute to greater cognitive decline. We used acquired (COVID-19 infection) and non-modifiable (presence of APOE rs429358 and rs7412 polymorphisms) factors to study the progression of subjective cognitive impairment while observing patients for one year. Cognitive training was used as a protective factor. (2) Methods: Two groups of subjects over the age of 65 participated in the study: group with subjective cognitive decline receiving cognitive training and individuals who did not complain of cognitive decline without receiving cognitive training (comparison group). On the first visit, the concentration of antibodies to COVID-19 and APOE genotype was measured. At the first and last point (1 year later) the Mini-Mental State Examination scale and the Hospital Anxiety and Depression Scale were performed. (3) Results: COVID-19 infection did not affect cognitive function. A significant role of cognitive training in improving cognitive functions was revealed. Older adults with APOE-ε4 genotype showed no positive effect of cognitive training. (4) Conclusions: Future research should focus on cognitive dysfunction after COVID-19 in long-term follow-up. Attention to the factors discussed in our article, but not limited to them, are useful for a personalized approach to maintaining the cognitive health of older adults

Effects of Traumatic Brain Injury on the Gut Microbiota Composition and Serum Amino Acid Profile in Rats

Traumatic brain injury (TBI) heavily impacts the body: it damages the brain tissue and the peripheral nervous system and shifts homeostasis in many types of tissue. An acute brain injury compromises the “brain–gut-microbiome axis”, a well-balanced network formed by the brain, gastrointestinal tract, and gut microbiome, which has a complex effect: damage to the brain alters the composition of the microbiome; the altered microbiome affects TBI severity, neuroplasticity, and metabolic pathways through various bacterial metabolites. We modeled TBI in rats. Using a bioinformatics approach, we sought to identify correlations between the gut microbiome composition, TBI severity, the rate of neurological function recovery, and blood metabolome. We found that the TBI caused changes in the abundance of 26 bacterial genera. The most dramatic change was observed in the abundance of Agathobacter species. The TBI also altered concentrations of several metabolites, specifically citrulline and tryptophan. We found no significant correlations between TBI severity and the pre-existing gut microbiota composition or blood metabolites. However, we discovered some differences between the two groups of subjects that showed high and low rates of neurological function recovery, respectively. The present study highlights the role of the brain–gut-microbiome axis in TBI