Morphological Analysis Method Research Articles

Analysis on Ecological Network Pattern Changes in the Pearl River Delta Forest Urban Agglomeration from 2000 to 2020

The advancement of urbanization has led to a decline in the ecological function and environmental quality of cities, seriously reducing the services and sustainable development capacity of urban ecosystems. The construction of the National Forest Urban Agglomeration of China is conducive to alleviating the ecological and environmental problems brought about by rapid urbanization and promoting sustainable urban development. A time series analysis of ecological network changes can quickly and effectively explore the development and changes of ecological spatial patterns over time. Identifying ecological protection and restoration areas in urban agglomerations is an important way to promote ecosystem restoration and optimize ecological networks. This paper takes the Pearl River Delta forest urban agglomeration as the research area, uses multi-source remote sensing data from 2000 to 2020 (every 5 years), identifies ecological sources based on the morphological spatial pattern analysis (MSPA) method, generates ecological corridors based on the minimum cumulative resistance (MCR) model, constructs a time series ecological network pattern in the Pearl River Delta region, and analyzes the evolution process of the ecological network pattern over time. The results indicate that over time, the core green area in the ecological network pattern of the Pearl River Delta first decreased and then increased, and the complexity of ecological corridors first decreased and then increased. The main reason is that the urbanization process in the early 21st century led to severe ecological fragmentation. Under the promotion of the national forest urban agglomeration construction, the ecological network pattern of the Pearl River Delta was restored in 2015 and 2020. The time series analysis of the ecological network pattern in the Pearl River Delta region of this research confirms the effectiveness of the construction of forest urban agglomerations, providing a scientific reference for the identification of ecological networks and optimization of spatial patterns in forest urban agglomerations.

The Role of Spatial Morphology in Forest Landscape Fragmentation: Insights From Planted and Natural Forests of the Chinese Loess Plateau

ABSTRACTThis study aimed to emphasize the key role of spatial morphology of planted and natural forests on landscape fragmentation and to furnish a scientific foundation for the effective assessment of ecological restoration projects of vegetation on the Loess Plateau. The spatial morphological pattern and landscape fragmentation characteristics were analyzed using morphological spatial pattern analysis (MSPA) and forest area density methods. This is the inaugural study to reveal the linear and nonlinear relationships between forest landscape fragmentation and its driving factors using machine learning methods and introducing morphological indicators with two different strategies. The results showed significant differences in the spatial patterns and landscape fragmentation characteristics between planted and natural forests. The spatial patterns of planted and natural forests were found to be dominated by “Core” in terms of area, while “Branch” was more prevalent in terms of number. Compared to natural forests, planted forests were more fragmented. The introduction of the MSPA indicator significantly enhanced the explanatory power and predictive performance of the model despite the disparate contribution rates of the driving factors in planted and natural forests. This study highlights the importance of spatial morphology in understanding forest landscape fragmentation and provides a new combination of analytical techniques to better understand the complexity of forest ecosystems. These provide new insights into forest landscape restoration and sustainable management on the Loess Plateau.

Analysis Methods for Landscapes and Features of Traditional Villages Based on Digital Technology—The Example of Puping Village in Zhangzhou

Many traditional villages have been degraded to a certain extent due to urbanization and out-of-control management. In addition, due to the lack of recognition and continuation of spatial texture in some village conservation and planning that, in turn, resulting in the gradual disappearance of their distinctive landscape feature. Studying the spatial form of traditional villages helps preserve the authenticity of traditional villages as cultural landscape and inherits traditional historical characteristics. Using Puping Village in Zhangzhou City, Fujian Province as an example, this paper obtains the integrated information data of the village through UAV oblique photography, classifies and extracts the spatial constitution of the traditional village using digital technology, quantitatively analyses it from macroscopic to microscopic, and summarizes the spatial morphology analysis method of the traditional village. The results demonstrate that digital technology can effectively and accurately complete data collection and can provide an objective basis for zoning conservation of traditional villages based on the distinction between new and historic buildings. In addition, digital information collection on the traditional villages landscape features will prepare for the establishment of a database and comparative analysis in the future. We further suggest that digital technology analysis needs to be combined with traditional methods to have a deeper understanding of the formation process of village spatial morphology. The results of the practice in Puping Village show that the use of digital technology can provide a scientific basis for the protection and planning of traditional villages, and that this method is adaptable, which can help to efficiently collect and analyze data on landscape characteristics of other similar villages in China, and support innovative methodologies and technologies for China’s rural revitalization efforts.

Deep learning classification method for boar sperm morphology analysis.

Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics. To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide. Images of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy. Using the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification. We have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label-free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

Decellularized Umbilical Cord as a Scaffold to Support Healing of Full-Thickness Wounds.

The umbilical cord is a material that enhances regeneration and is devoid of age-related changes in the extracellular matrix (ECM). The aim of this work was to develop a biodegradable scaffold from a decellularized human umbilical cord (UC-scaffold) to heal full-thickness wounds. Decellularization was performed with 0.05% sodium dodecyl sulfate solution. The UC-scaffold was studied using morphological analysis methods. The composition of the UC-scaffold was studied using immunoblotting and Fourier transform infrared spectroscopy. The adhesion and proliferation of mesenchymal stromal cells were investigated using the LIVE/DEAD assay. The local reaction was determined by subcutaneous implantation in mice (n = 60). A model of a full-thickness skin wound in mice (n = 64) was used to assess the biological activity of the UC-scaffold. The proposed decellularization method showed its effectiveness in the umbilical cord, as it removed cells and retained a porous structure, type I and type IV collagen, TGF-β3, VEGF, and fibronectin in the ECM. The biodegradation of the UC-scaffold in the presence of collagenase, its stability during incubation in hyaluronidase solution, and its ability to swell by 1617 ± 120% were demonstrated. Subcutaneous scaffold implantation in mice showed gradual resorption of the product in vivo without the formation of a dense connective tissue capsule. Epithelialization of the wound occurred completely in contrast to the controls. All of these data suggest a potential for the use of the UC-scaffold.

Multimodal risk classifier for cardiorespiratory diseases taking into account concomitant diseases and synergy effect

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. A method for classifying the contours of the boundaries of segments of a complexly structured image is described. The method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. The method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. To classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. The method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Experimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. The purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". The training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. The quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Conclusion. The use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

Constructing Ecological Networks for Mountainous Urban Areas Based on Morphological Spatial Pattern Analysis and Minimum Cumulative Resistance Models: A Case Study of Yongtai County

In order to alleviate the increased habitat fragmentation caused by the accelerating urbanization and ecological deterioration, constructing ecological networks is an effective way to improve ecological connectivity, facilitate regional energy flow, and promote biodiversity enhancement. In this study, Yongtai County was taken as the research object, and the morphological spatial pattern analysis (MSPA) method was used to analyze the landscape pattern, identify the ecological source sites, classify the ecological source sites according to the importance degree by possible connectivity index (PC) and the Delta values for probability index of connectivity (dPC), and then construct the potential ecological corridors with the help of the minimum cumulative resistance (MCR) model to generate the ecological network, and then put forward the optimization strategy according to the current situation. The results show that (1) the core area of Yongtai County is 1071.06 km2, the largest among all landscape types, with a fragmented distribution, high degree of fragmentation, and poor connectivity, mainly in the east and southwest, and sparser in the middle. (2) The area of highest resistance value is mainly located in the built-up areas of towns and rural settlements in the central and northwestern parts of the country; the lowest value is distributed in the southwest and southeast, and the land use mode is mainly expressed as woodland. (3) The ecological network consists of 13 ecological sources and 78 potential ecological corridors. The ecological sources are mainly located in the east and southwest, with high connectivity; the potential ecological corridors are distributed in the form of a network, with fewer in the center, resulting in the phenomenon of ecological disconnection. (4) Lack of ecological sources and corridors, serious landscape fragmentation, and optimization of ecological network by adding and protecting ecological sources, repairing ecological breakpoints and building stepping stones. This study is of guiding significance for urban green space system planning, biodiversity protection, and ecosystem function enhancement in Yongtai County, and also provides reference for ecological protection and optimization in other mountainous cities.

Snail composition and its cercariae in rice field of Hoc Mon district, Ho Chi Minh City, Vietnam

The research on snail composition and their cercariae by morphological analysis method in Xuan Thoi Thuong rice field of Hoc Mon district, Ho Chi Minh City, Vietnam was carried out in the wet season of August 2022 and the dry season of February 2023. A total of 993 snails were collected and 11 snail species belonging to 9 genera and 5 families were classified. There were 9 snail species collected in the wet season and 7 snail species found in the dry season. Lymnaea viridis and Bithynia siamensis were infected with trematode (cercariae stage) with the combined prevalence of 14.3% and 4.1%, respectively. The other nine snail species had cercariae free. Two cercariae morphotypes were discovered from snails including Xiphidio cercariae and Echinostome cercariae. More research on snails and their cercariae in other waterbodies should be done in Hoc Mon district and other places to identify the snail diversity and sources of trematodes affecting fish culture and human health.

Morphological characteristics and cleaning effects of collapsing cavitation bubble in fractal cracks

When a cavitation bubble collapses in a crack, due to the lack of symmetry, spatial uniformity, and obvious correlations during the collapse process, it is difficult to effectively explore the interaction mechanism between the bubble and crack walls. In this paper, a combination of numerical simulation and experimental approaches are used to explore the collapse process of a cavitation bubble in a fractal crack. The numerical model is based on the pseudopotential multi-relaxation-time lattice Boltzmann method (LBM). The experiment platform including an underwater pulsed discharge device combined with a high-speed camera system. Moreover, the morphological analysis method based on Minkowski functionals is used to quantitatively depict the morphological features in this paper. The validity of the numerical model is qualitatively verified by the experimental platform, whereas the influence of the complexity and geometric features of the crack wall on the cavitation bubble collapse process is quantitatively studied by LBM simulation and Minkowski functionals. The research findings indicate that the complexity and geometric features of the crack wall markedly influence the collapse time and behavior of the cavitation bubble. Specifically, the collapse time of the cavitation bubble increases with the increasing complexity of the crack wall. Moreover, the maximum jet direction of cavitation bubble toward the wall is closest to the cavitation bubble. Furthermore, as the fractal dimension increases, the intensity of the pressure wave and jet acting upon the crack wall increase while the radiation range decreases. Cavitation cleaning can be applied to arbitrary complex solid surfaces in various environments.

Makna Dari Bentuk-Bentuk Fi’il Mazid Dalam Surat Maryam Dan Surat Al-Anbiyaa’ (Kajian Analisis Morfologi)

In this research, the researcher analyze about Fi’il mazid (verb with affix) which is found in Surah Maryam and Al-Anbiyaa’, because in the both Surah the researcher found some Fi’il mazid which is only found in another Surah in Al-Qur’an. The purpose of this study is to give the definition about Fi’il mazid that has been found in Surah Maryam and Al-Anbiyaa and analyze the meaning of every affix found in the verb. This research is a library research . The researcher used documentary research, and for analyzing the data the researcher used morphology analysis method. The result of this analysis shown that: the form of Fi’il mazid that has been found in Surah Maryam and Al-Anbiyaa is the comon Fi’il mazid that can be found in Arabic language. There are 41 Fi’il mazid found in Surah Maryam and 48 Fi’il mazid found in Surah Al-Anbiya, and the meaning of the affixes of the verb fopound in the both Surah is the common meaning that usually used and found in Arabic.

The evolution of traditional ancient towns in South China: Tangjiawan Ancient Town in Zhuhai city

ABSTRACT Based on the urban morphology theory of the Conzen School and the actual situation of the Tangjiawan Ancient Town, a morphological process analysis of the Tangjiawan Ancient Town was conducted. The end result is a visualization of the process of urban form evolution. The first objective is to describe the morphological framework of Tangjiawan Ancient Town and the morphological changes that occurred during its development. The current urban landscape is divided into units by superimposing morphological units, building types, and land use. The results of the division also reflect the morphological development, structure, and compositional characteristics of the ancient city of Tangjiawan. Using Tangjiawan Ancient Town in Zhuhai city as an example, on-site research, historical evidence, urban morphology analysis methods, and interviews were used to elaborate and analyze the spatial structure and evolution process of traditional ancient towns in South China. In particular, morphological units, building types, and land use are included. The morphological map of the Tangjiawan ancient town is inconsistent with the current economic-oriented development of ancient town planning. This shows that current planning is less concerned with the physical form of architectural heritage, which may lead to further damage to the landscape of the ancient town. This study provides a strong reference for the follow-up planning of ancient town protection of the same type from the perspective of morphology and area and provides ideas for the subsequent preparation of protection plans.

The self-healing and robust photostability of (PEA)2PbI4 perovskite via pressure-induced amorphization and recrystallization

The photostability of hybrid organic-inorganic perovskites (HOIPs) is an important criterion due to unavoidability of light in photoelectric elements of solar cells, light emitting diodes (LEDs) or photo detectors. This research aims to explore the photostability of a compressed two-dimensional (2D) (PEA)2PbI4 crystal. The structural and compositional responses of the sample to laser illumination under pressure were recorded via in-situ photoluminescence (PL) spectroscopy and morphological analysis methods. The enhancement in laser endurance limit from 3 to 11 mW and long-duration photostablity were achieved along with structural self-healing in the recrystallized (PEA)2PbI4 sample. The robust stability of the material was ascribed to pressure-induced suppression of the nonradiative decay and grain boundary minimization in the rebirthed crystal. A similar result was also obtained in compressed (BA)2PbI4 and MAPbI3 crystals. Therefore, high pressure offers an effective and green method to improve the photostability of HOIP crystals and has a profound significance in the widespread application of HOIP-based photoelectric devices.

Automated Classification of Abdominal Ultrasound lmages of the Pancreas Based on the Spectral Representation of the Border’s Contours

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. А method for classifying the contours of the boundaries of segments of a complexly structured image is described. Тhe method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. Тhe method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. Тo classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. Тhe method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Еxperimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. Тhe purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". Тhe training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. Тhe quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Тhe use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

A model-based hierarchical Bayesian approach to Sholl analysis.

Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.

Reframing Different Layers of Morphological Dimension - A Context of Urban Spaces w.r.t Net Zero Energy to Achieve Zero Carbon Emissions

Abstract: The contemporary city presents itself as an urban organism of great complexity, consisting of an increasingly number of elements, systems, dynamics and extracts, which makes their reading an equally complex task. the method of morphological analysis, it is assumed in this context, as an important reading tool of the city, giving order to what is naturally disordered through a classification process of the different elements that structure it –streets,squares, urban blocks,building , plots among others .Specifically, the paper discusses the morphological component of urban design, concentrating on the forms along with configurations of urban centers, as well as current preferences for urban blocks and interconnecting street patterns. Overall, it is concerned with the network of public spaces as well as actual public realm, which is the physical setting or stage for public activity. An important factor in this is one of the key tensions at the core of modern urban design: complexity, conflicts, andsynergies between the requirement for space and movement necessary for economic and social interaction and everyday living. Urban design goals, according to Jan Gehl, should be prioritized in the following order: first it islife, second it isspace, and third it isbuilding.

Rodent tests of depression and anxiety: Construct validity and translational relevance

Behavioral testing constitutes the primary method to measure the emotional states of nonhuman animals in preclinical research. Emerging as the characteristic tool of the behaviorist school of psychology, behavioral testing of animals, particularly rodents, is employed to understand the complex cognitive and affective symptoms of neuropsychiatric disorders. Following the symptom-based diagnosis model of the DSM, rodent models and tests of depression and anxiety focus on behavioral patterns that resemble the superficial symptoms of these disorders. While these practices provided researchers with a platform to screen novel antidepressant and anxiolytic drug candidates, their construct validity—involving relevant underlying mechanisms—has been questioned. In this review, we present the laboratory procedures used to assess depressive- and anxiety-like behaviors in rats and mice. These include constructs that rely on stress-triggered responses, such as behavioral despair, and those that emerge with nonaversive training, such as cognitive bias. We describe the specific behavioral tests that are used to assess these constructs and discuss the criticisms on their theoretical background. We review specific concerns about the construct validity and translational relevance of individual behavioral tests, outline the limitations of the traditional, symptom-based interpretation, and introduce novel, ethologically relevant frameworks that emphasize simple behavioral patterns. Finally, we explore behavioral monitoring and morphological analysis methods that can be integrated into behavioral testing and discuss how they can enhance the construct validity of these tests.

Differentiation of Cannabis seeds employing digital morphological screening and infrared spectroscopy coupled with multivariate modeling

Cultivation of Cannabis for medicinal purposes primarily relies on seed propagation with expected variations in yield, cannabinoid content, growth rate and seed material non-uniformity. This study aims to employ digital methods for morphological analysis and infrared spectroscopy, combining them with multivariate analysis to characterize and differentiate Cannabis seeds. Morphological traits of 100 seeds from both commercial Cannabis specimens and wild-growing local varieties were analyzed using the high-throughput phenotyping software in addition to their collected infrared spectra in the mid-IR region. Subsequently, a chemometrics approach by means of Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Partial Least Square-Discriminatory Analysis (PLS-DA) was applied. The statistical indicators of the PLS-DA model (R2X=0.99, R2Y=0.63, Q2=0.64) demonstrate strong predictive capabilities for the differentiation of Cannabis seed specimens based on morphological attributes. The score scatter plot clearly shows a distinct grouping pattern, primarily driven by seed size. Wild-type seeds predominantly cluster into group 1, characterized by smaller diameters, while commercial seeds cluster into group 2. By analysing spectral data, in contrast to the expected differentiation based on secondary metabolites (cannabinoids) in the seeds, differentiation was based on the macronutrient profile with characteristic bands at 3275 cm−1, 2921 cm−1, 2852 cm−1, 1743 cm−1, 1630 cm−1, 1532 cm−1, 1459 cm−1, 1239 cm−1, 1157 cm−1, 1094 cm−1, 1018 cm−1, identified as the most distinctive spectral features. The PCA model (R2X=0.88 and Q2=0.85) was composed of 5 principal components explaining 88% of the spectral variations. The loading plot of the PLS-DA model reveals the distinctive spectral features for both groups (lipid and carbohydrate bands - group 2 samples, protein and water content - group 1 samples). The developed models have the potential for application for rapid screening of quality parameters of Cannabis seeds and their differentiation.

Identification of Chromoblastomycosis and Phaeohyphomycosis Agents through ITS-RFLP.

Chromoblastomycosis (CBM) and phaeohyphomycosis (FEO) are infections caused by melanized filamentous fungal agents, primarily found in tropical and subtropical regions. Both infections pose significant challenges for the correct identification of the causative agent due to their morphological similarity, making conventional methods of morphological analysis highly subjective. Therefore, molecular techniques are necessary for the precise determination of these species. In this regard, this study aimed to contribute to a new methodology based on PCR-RFLP for the identification of agents causing CBM and FEO. Sequences from the Internal Transcribed Spacer (ITS) region were used to identify potential restriction enzyme sites in silico, followed by in vitro validation using the selected restriction enzymes. The obtained results were compared with species identification through morphological analyses and sequencing. The results demonstrated that the PCR-RFLP applied in this study accurately identified two major agents of chromoblastomycosis, Fonsecaea pedrosoi and Fonsecaea monophora, as well as Cladophialophora bantiana and Exophiala dermatitidis, both causative agents of phaeohyphomycosis. In this context, the proposed assay can complement current methods for identifying these species, aiding in diagnosis, and contributing to the proper management of these infections.

Адаптационные механизмы российского рынка труда в условиях неопределенности

The relevance of the study is due to the continuous changes in the labor market under the influence of external factors and the need to increase its adaptive abilities in conditions of uncertainty. The purpose of the study is to identify the features of the adaptation of the Russian labor market to the conditions of uncertainty caused by the pandemic, and the development of new forms of employment that ensure the structural transformation of the market and changes in labor relations. The objectives of the study are to analyze new forms of work arising on the basis of transformation of models of interaction between participants in a period of uncertainty; to determine the vector of development of labor relations that ensure the balance of the labor market. Methodology . The study was carried out using methods of theoretical analysis, systematization, economic analysis, graphical method, comparative analysis method, and morphological analysis method. Results . The analysis of the dynamics of the labor market development indicators during the pandemic allowed us to conclude that it was destabilized in the conditions of uncertainty, manifested in the form of a decrease in employment indicators, an increase in unemployment, an increase in tension in the market. By 2021, many indicators of the labor market development have returned to a doc-like level. The activity of the Russian population in the global online labor market is analyzed, as well as the demand of Russian companies for labor on digital platforms. The directions of ensuring greater adaptability of the Russian labor market through the use of various mechanisms of state policy are substantiated. Conclusions . The emergence of new forms of work based on the transformation of models of interaction between participants has been established. It is concluded that accelerated digitalization during the pandemic created opportunities for online employment of the population, which contributed to the partial removal of tension in the Russian labor market.

Morphological Analysis of Neurons and Glia Using Mosaic Analysis with Double Markers.

Mosaic Analysis with Double Markers (MADM) is a powerful genetic method typically used for lineage tracing and to disentangle cell autonomous and tissue-wide roles of candidate genes with single cell resolution. Given the relatively sparse labeling, depending on which of the 19 MADM chromosomes one chooses, the MADM approach represents the perfect opportunity for cell morphology analysis. Various MADM studies include reports of morphological anomalies and phenotypes in the central nervous system (CNS). MADM for any candidate gene can easily incorporate morphological analysis within the experimental workflow. Here, we describe the methods of morphological cell analysis which we developed in the course of diverse recent MADM studies. This chapter will specifically focus on methods to quantify aspects of the morphology of neurons and astrocytes within the CNS, but these methods can broadly be applied to any MADM-labeled cells throughout the entire organism. We will cover two analyses-soma volume and dendrite characterization-of physical characteristics of pyramidal neurons in the somatosensory cortex, and two analyses-volume and Sholl analysis-of astrocyte morphology.