Connection Relationship Research Articles

Obstructive sleep apnoea, sleep bruxism and gastroesophageal reflux - mutually interacting conditions? A literature review.

The purpose of the present manuscript is to provide an overview for researchers and clinicians summarizing the knowledge concerning the relationship between some of the main sleep-related conditions of dental interest: sleep bruxism (SB), obstructive sleep apnoea (OSA) and gastroesophageal reflux disease (GERD). Starting with the discussion of the evolving current knowledge on SB, the interconnections are discussed. Most of the available literature focused on the possible relationship between OSA and SB, but a clear pathophysiological connection or temporal relationship has not been identified. Despite the paucity of data on the possible commonalities, SB, OSA and GERD constitute a complex network of conditions that may affect the clinical and research dental practice, and they are rarely found in isolation. In this scenario, the key role of dental practitioners as sentinel in the case of these sleep-related conditions is important, thanks to their ability and the knowledge to identify signs, symptoms and risk factors that are signs of ongoing sleep-related conditions. Thus, dental practitioners are recommended to view bruxism as a potential gateway to medicine.

Combine multi-order representation learning and frame optimization learning for skeleton-based action recognition

Skeleton-based action recognition has broad application prospects in many fields such as virtual reality. Currently, the most popular way is to employ Graph Convolutional Networks (GCNs) or Hypergraph Convolutional Networks (HGCNs) for this task. However, GCN-based methods may heavily rely on the physical connectivity relationship between joints while lack the capture of higher-order information about interactions among distant joints, and HGCN-based methods usually introduce unnecessary noise when capturing low-order information of skeleton structures with simple topology. Besides, the current methods do not deal well with redundant frames and confusing frames. These limitations hinder the improvement of recognition accuracy. In this paper, we propose a novel network, called Hyper-Net, which combines multi-order representation learning and frame optimization learning for skeleton-based action recognition. Specifically, the proposed Hyper-Net contains Temporal-Channel Aggregation Graph Convolution (TCA-GC), Spatial-Temporal Aggregation Hypergraph Convolution (STA-HC) and Frame Optimization Learning (F-OL) modules. The TCA-GC aggregates low-order and local information from simple joint and bone topologies across different temporal and channel dimensions. The STA-HC captures high-order and global information from complex motion streams as well as solving the problem of spatial-temporal weight imbalance. The F-OL can adaptively extract key frames and distinguish confusing frames, thus improving the ability of the network to recognize confusing actions. A large number of experiments are conducted on the NTU RGB+D, NTU RGB+D 120 and NW-UCLA datasets for action recognition task. Experimental results demonstrate the superiority and effectiveness of the proposed network.

Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--a case study of Bohai a oilfield

The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield. Conventional connectivity studies often use methods such as seismic attribute fusion, while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes. Aiming at the above problem in the Bohai A Oilfield, this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity. Firstly, by compiling the abundant exploration and development sandbodies data in the study area, typical sandbodies with reliable connectivity were selected. Then, sensitive seismic attribute were extracted to obtain training samples. Finally, based on the Deep Forest algorithm, mapping model between attribute combinations and sandbody connectivity was established through machine learning. This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield. Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis, this method can combine the sandbody characteristics of the study area in the process of machine learning, and jointly judge connectivity by combining multiple seismic attributes. The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies. Applied to the Bohai A Oilfield, it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization. This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions.

The Explanatory Effect of Cognitive Human Resources Skills in Supporting Quality Requirements: An Exploratory Study of the Board of Directors at Al-Nisour University College

This research discusses the explanatory impact of cognitive human resources skills in supporting quality requirements, by conducting an exploratory study of the opinions of the Board of Directors at University Al-Nisour College. As the research problem confirmed the existence of a significant deficiency in investing in cognitive skills and employing them in meeting quality requirements, which is considered one of the most important Indicators of success in the contemporary environment. Therefore, the importance of the research is to provide a set of recommendations that contribute to addressing this problem. The goal of the research is to identify the level of application of cognitive skills for human resources and total quality management, and then to identify the nature of the relationship of influence and connection that brings them together. The descriptive analytical method was used to complete the research. The research community was represented by employees at Al-Nisour University College, while the research sample consisted of members of the college council only, who numbered (23) individuals. The researchers also designed a questionnaire using a set of ready-made indicators and criteria, which was distributed to a sample of (23) individuals who were selected using a purposive sampling method. The scientific value of the research is to provide a modest addition to the Iraqi libraryو After processing the data using a set of central tendency measures available in the statistical program (SPSS-Ver.23), several results were produced that confirmed the importance of the role played by cognitive human resources skills in enhancing the organization’s ability to support quality requirements. These results confirm the validity of the hypothesis included in the research. Therefore, the researchers recommended the need to look at successful experiences in developed countries that have invested in cognitive human resources skills in meeting and supporting their quality requirements and programmers.

Analytical models for predicting the moment‒rotation relationships of steel hub joints with bolted steel side plates in single-layer wood reticulated domes

A very popular and efficient pattern of joints adopted for wood reticulated domes is steel hub joints featuring end-bearing and bolt-connected wood members (EBBC joints). This type of joint shows clear semi-rigidity, which affects the rigidity and overall stability of the structures. This study developed analytical models for predicting the moment‒rotation curves of EBBC joints, considering the effect of axial compression. Specifically, two models termed the WR and WEP models, were developed assuming rigid and elastic-plastic constitutive behaviour, respectively, of the wood in the compression zone of the joint. A tri-linear model for the lateral force-slip relationship of bolt connections was derived. A formula for calculating the deformation modulus and predicting the shear failure of wood members under local compression was proposed and verified. The moment–rotation relationship of the joint was established with equilibrium and compatibility conditions. The accuracy of the analytical models was verified by comparison with a series of experimental results as well as the parametric study results obtained using a refined finite element model. It was revealed that both the WR and the WEP models can predict the moment–rotation relationships accurately for joints without axial compression, whereas for the case of non-negligible axial compression loads, the WEP model offers more accurate predictions. The analytical WEP model proposed in the current paper provides a versatile tool for general design practice to predict moment–rotation curves for traditional or novel EBBC joints.

Research on the Cable-to-Terminal Connection Recognition Based on the YOLOv8-Pose Estimation Model

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2GRL) framework to model EEG signals as graph data. Specifically, MD 2GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Auto-Correlation and Channel Attention Enhanced Deep Graph Convolution Networks for Gait Phase Prediction Based on Multi-IMU System

Gait phase prediction is important in controlling assistive robotic devices such as exoskeletons, where the control unit must differentiate between gait phases to provide the necessary assistance when the user is wearing the exoskeleton. To achieve the objective of precisely identifying the gait phase of users for the accurate control of the exoskeleton, this study proposes Auto-Correlation and Channel Attention enhanced Deep Graph Convolutional Networks (ACCA-DGCN) for gait phase prediction, and a gait phase prediction model based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among joints, and enhance accuracy and reliability of gait phase prediction. First, a human lower limb gait data acquisition equipment was developed, and the gait data of human walking were collected. The skeleton graph of the human lower limb was constructed through the natural connection relationship of joints in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data of human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to effectively capture periodic features of gait data and focus on the channels with high contributions to gait phase prediction. Finally, the effect of the window size on the performance of the ACCA-DGCN model was explored, and the proposed algorithm was compared with the other five deep learning algorithms: CNN, RNN, TCN, LSTM, and DGCN. The experimental results show that the average accuracy of gait phase prediction model based on ACCA-DGCN reaches up to 92.26% and 97.21% in user-independent and user-dependent experiments, respectively, which is superior to the other five algorithms. This study provides a new method for gait phase prediction, which is useful for improving the control of exoskeleton robots.

Peri-urban dynamics: assessing expansion patterns and influencing factors

BackgroundPeri-urbanization, the expansion of large metropolitan centers into adjacent peri-urban regions, is a growing concern due to land scarcity and escalating housing costs. These zones, a blend of rural and urban features, blur the line between urban and rural areas, creating new landscapes. This study examines historical, present, and potential growth trends in the peri-urban area surrounding Durgapur Municipal Corporation (DMC). Analytical techniques and spatial metrics are used to track development intensity changes over time, including built-up density, Shannon’s entropy, Landscape expansion index, Average Weighted Mean Expansion Index, Annual Built-Up Expansion Rate, Built-Up Expansion Intensity Index, and Built-Up Expansion Difference Index. Landscape indices like Patch Density, Edge Density, Landscape Shape Index, Largest Patch Index, Ratio of Open Space, and Area Weighted Mean Patch Fractal are used to understand fragmentation, connectivity, and spatial relationships. The Logistic Regression Model (LRM) is used to identify influencing factors and CA-Markov modeling for future built-up areas.ResultsBetween 1991 and 2001, built-up area in the region increased significantly, primarily due to urban development near industrial zones, roadways, and mining areas. The growth was primarily concentrated in the western sector and near National Highway-2 (NH-2). Urban sprawl was a continuous trend, with the highest built-up density in the South-South-East (SSE) direction from 1991 to 2011. Additionally, a key determinant of built-up development was the distance to the city core. By 2031, the built-up area is expected to concentrate in the western and southeast regions, reaching 177.90 km2.ConclusionsThis expansion is attributed to urban development near industrial zones, roadways, mining areas, and other infrastructure. The study identifies distance to the city center as a significant influencing factor for built-up development. The results emphasize the need for inclusive urban planning methods prioritizing sustainable development principles and prudent resource management for future growth and efficient management in the DMC’s peri-urban area.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides.

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning's strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.

From disconnection to connection: the healing potential of household hospitality to the stranger

ABSTRACT This article finds its origin in a hermeneutic phenomenological research study which examined the practice of hospitality to strangers by lay persons within private Canadian Christian homes. Special attention is given to key findings from the study which reveal the disconnection experienced by strangers, and the potential within hospitable encounters to contribute to healing human disconnection. The healing power of hospitality is explored through the voices of those who practice hospitality to strangers within their homes, the contributions of biblical texts regarding hospitality, and key tenets of Relational-Cultural Theory (RCT) which emphasize the healing potential of relational connection. Hospitality was found to have the potential to heal disconnection through the provision of welcome, physical, emotional, and spiritual care, relational connection, communal living, and the appropriate use of power. Through proximity and mutual connection, both host and guest experience growth-fostering relationships that move them from disconnection to connection.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

Functional and Structural Cerebellar-Behavior Relationships in Aging.

Healthy aging is associated with deficits in cognitive performance and brain changes, including in the cerebellum. Yet, the precise link between cerebellar function/structure and cognition in aging remains poorly understood. We explored this relationship in 138 healthy adults (aged 35-86, 53% female) using resting-state functional connectivity MRI (fcMRI), cerebellar volume, and cognitive and motor assessments in an aging sample. We expected to find negative relationships between lobular volume for with age, and positive relationships between specific lobular volumes with motor and cognition respectively. We predicted lower cerebellar fcMRI to cortical networks and circuits with increased age. Behaviorally, we expected higher cerebello-frontal fcMRI cerebellar connectivity with association areas to correlate with better behavioral performance. Behavioral tasks broadly assessed attention, processing speed, working memory, episodic memory, and motor abilities. Correlations were conducted between cerebellar lobules I-IV, V, Crus I, Crus II, vermis VI and behavioral measures. We found lower volumes with increased age as well as bidirectional cerebellar connectivity relationships with increased age, consistent with literature on functional connectivity and network segregation in aging. Further, we revealed unique associations for both cerebellar structure and connectivity with comprehensive behavioral measures in a healthy aging population. Our findings underscore cerebellar involvement in behavior during aging.

Programmable thick-panel Miura-ori for ultra-large planar antennas with one-degree-of-freedom

Planar antennas are large spacecraft primarily used in various fields such as communication, Earth observation, astronomical observation, and scientific experiments. They require high surface accuracy, with the array needing to have uniform thickness and flat surfaces. An ultra-large planar antenna means higher receiving sensitivity and greater coverage area, which is the trend for the future. During launch, the planar antenna needs to be folded to fit into the payload capsule. In this study, a programmable thick-panel Miura-ori unit that has uniform thickness and satisfies the Bennett linkage condition is developed. After special design, this unit can be infinitely expanded in the row and column directions, allowing for the folding of planar antennas of any size (rows × columns). For each vertex inside the planar antenna, there are four panels around it, and their connection relationships all satisfy the Bennett linkage condition, resulting in the entire planar antenna having only one degree of freedom. The sector angle of each parallelogram can approach 90°, compacting the folding form of the planar antenna. Furthermore, a layout scheme for an ultra-large planar antenna is provided, the deployment ratio, envelope diameter and envelope height are derived, and the complete unfolding process of the ultra-large planar antenna is demonstrated. Finally, the effectiveness of the proposed method for satellite planar antennas is verified through an experiment.

Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

Geometric localized graph convolutional network for multi-view semi-supervised classification

Multi-view learning has received increasing attention in recent years due to its ability to leverage valuable patterns hidden in heterogeneous data sources. While existing studies have achieved encouraging results, especially those based on graph convolutional networks, they are still limited in their ability to fully exploit the connectivity relationships between samples and are susceptible to noise. To address the aforementioned limitations, we propose a framework called geometric localized graph convolutional network for multi-view semi-supervised classification. This framework utilizes a diffusion map to obtain the geometric structure of the feature space of multiple views and constructs a stable distance matrix that considers the local connectivity of nodes on the geometric structure. Additionally, we propose a truncated diffusion correlation function that maps the distance matrix of each view into correlations between samples to obtain a reliable sparse graph. To fuse the features, we use learnable weights to concatenate the coordinates of the geometric structure. Finally, we obtain a graph embedding of the fused feature and topology by using graph convolutional networks. Comprehensive experiments demonstrate the superiority of the proposed method over other state-of-the-art methods.

Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.

The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; it modulates cognitive, multisensory and autonomic circuits predominantly via the basal (BA), lateral (LA), and central (CeA) nuclei, respectively. Based on recent electrophysiological evidence suggesting mesoscale (millimeters-scale) nature of intra-amygdala functional organization, we have investigated the connectivity of these nuclei using Infrared Neural Stimulation of single mesoscale sites coupled with mapping in ultrahigh field 7T functional Magnetic Resonance Imaging (INS-fMRI). Stimulation of multiple sites within amygdala of single individuals evoked 'mesoscale functional connectivity maps', allowing comparison of BA, LA and CeA connected brainwide networks. This revealed a mesoscale nature of connected sites, complementary spatial patterns of functional connectivity, and topographic relationships of nucleus-specific connections. Our data reveal a functional architecture of systematically organized brainwide networks mediating sensory, cognitive, and autonomic influences from the amygdala.

FUNCTIONAL COMPONENT OF THE CARDIOVASCULAR SYSTEM IN INDIVIDUALS WITH CONNECTIVE TISSUE DYSPLASIA

The article discusses connective tissue dysplasia (CTD) and its genetic basis, classification issuesin the International Classification of Diseases (ICD), research on CTD in Uzbekistan, and the association of CTD with cardiovascular complications, particularly arrhythmias. Various studies and classifications related to CTD are mentioned, emphasizing thegenetic mutations underlying the disease and the prevalence of arrhythmias in syndromic forms such as Marfan syndrome and Ehlers-Danlos syndrome. The text underscores the importance of close monitoring, early detection, and appropriate management of arrhythmias in patients with CTD to optimize outcomes and reduce complications.Based on the foregoing, the purpose of this work is to establish in posttalontogenesis the laws of the formation and involution of bronchial vascular and lymphatic systems in a person, epithelial connective tissue relationships in the air and respiratory parts of the lung (1.4).

MORPHOLOGICAL FEATURES OF THE BRONCHUS OF THE LUNG IN CHILDREN FROM 8 TO 12 YEARS OF AGE

The pulmonary vessels and their endothelium plays the role of a physiological barrier for numerous biologically active substances, then the epithelium and connective tissue basis of the airways perform a barrier-protective function in relation to various environmental factors [1]. The bronchial secret produced by the mucous membrane is the result of the complex integration of the transudation of the plasma components of the blood, the secretory activity of the bronchial epithelium, glands, the cells of the connective tissue and blood cells (neutrophils, fat cells, eosinophils, macrophages, etc.). From the presented few literature presented, it becomes obvious that the structure of the lung function is closely integrated with the lymphatic system. Located in the immediate vicinity of both peribronchial and alveolar blood capillaries, the flow of lymph goes along the bronchial tree and along the subpleural tissue, in its most lungs and the lymph nodes located here, as well as tangential along the plane of the pleura [2.3] .Based on the foregoing, the purpose of this work is to establish in posttal ontogenesis the laws of the formation and involution of bronchial vascular and lymphatic systems in a person, epithelial connective tissue relationships in the air and respiratory parts of the lung (1.4).