General Adaptation Research Articles

Bacterial cell volume regulation and the importance of cyclic di-AMP.

SUMMARYNucleotide-derived second messengers are present in all domains of life. In prokaryotes, most of their functionality is associated with general lifestyle and metabolic adaptations, often in response to environmental fluctuations of physical parameters. In the last two decades, cyclic di-AMP has emerged as an important signaling nucleotide in many prokaryotic lineages, including Firmicutes, Actinobacteria, and Cyanobacteria. Its importance is highlighted by the fact that both the lack and overproduction of cyclic di-AMP affect viability of prokaryotes that utilize cyclic di-AMP, and that it generates a strong innate immune response in eukaryotes. In bacteria that produce the second messenger, most molecular targets of cyclic di-AMP are associated with cell volume control. Besides, other evidence links the second messenger to cell wall remodeling, DNA damage repair, sporulation, central metabolism, and the regulation of glycogen turnover. In this review, we take a biochemical, quantitative approach to address the main cellular processes that are directly regulated by cyclic di-AMP and show that these processes are very connected and require regulation of a similar set of proteins to which cyclic di-AMP binds. Altogether, we argue that cyclic di-AMP is a master regulator of cell volume and that other cellular processes can be connected with cyclic di-AMP through this core function. We further highlight important directions in which the cyclic di-AMP field has to develop to gain a full understanding of the cyclic di-AMP signaling network and why some processes are regulated, while others are not.

Improving few-shot named entity recognition via Semantics induced Optimal Transport

Named entity recognition (NER) is to identify and categorize entities in unstructured text, which serves as a fundamental task for a variety of natural language processing (NLP) applications. In particular, emerging few-shot NER methods aim to learn model parameters well with few samples and have received considerable attention. The dominant few-shot NER methods usually employ pre-trained language models (PLMs) as their basic architecture and fine-tune model parameters with few NER samples. Since the sample size is small and there are a large number of parameters in PLMs, fine-tuning may result in the parameters of PLMs being highly biased. To address this issue, this study introduces the semantic distribution distance constraints to optimize the fine-tuning process of few-shot NER models and develops a framework named Semantic Constraints on few-shot Named Entity Recognition (SCNER). Specifically, the framework formulates the general knowledge transfer of PLMs as an optimal transport procedure with a semantic prior. And, a Semantics-induced Optimal Transport (SOT) regularizer is developed to utilize the importance and similarities of tokens within sentences. SOT builds the semantic distribution of the sentence and defines the transport costs between tokens to achieve the token-level optimal transport procedures. Finally, SOT is employed as a regularization term of few-shot NER to introduce the semantic distribution distance constraint for effectively transferring general knowledge from PLMs. The experiments on four public datasets demonstrate that the proposed method significantly improves the performance of NER models in both few-shot and fully supervised scenarios. SCNER is a common framework that can be applied to a variety of models without adding additional learning parameters, and can be used to enhance the generalization ability and adaptability of various few-shot NER models.

Network Security Defense Strategy of Deep Reinforcement Learning Oriented to Game Battle

With the popularity of online games and the increasing frequency of game battles, game platforms are facing more and more network security threats and attacks. In order to effectively deal with these threats, this study proposes a network security defense strategy of Deep Reinforcement Learning (DRL) for game warfare. Through the modeling of game battle environment and the application of DRL algorithm, this strategy can monitor and identify all kinds of network attacks in game battle in real time, and take corresponding measures to defend and deal with them. In this study, the corresponding experimental environment is designed, and the network security defense strategy based on DRL is evaluated and analyzed. The experimental results show that the strategy has obvious advantages in network attack detection rate and false alarm rate, and has good stability and reliability. In addition, the strategy also shows strong generalization ability and adaptability, and can effectively deal with different types of network attack threats. The results of this study are of great significance for strengthening the network security defense of the game platform and improving the user experience and the stability of the game environment.

Genotype-by-environment interaction analysis for grain yield of barley genotypes in the warm climate of Iran

In the present study, multi-environmental trials were performed on 19 newly developed barley genotypes at five test regions across the warm climate in Iran for two consecutive years. The results of additive main effects and multiplicative interaction (AMMI), GGE biplot (GGE) model, and several BLUP-based stability parameters and integrated models (WAASB and WAASBY) were used to reveal GEI patterns and identify superior genotypes with stable grain yield across test environments. Statistical analyses revealed that grain yield was influenced by significant environments (E), genotypes (G), and GEI effects. Among AMMI-, BLUP-, and grain mean-based stability parameters, the harmonic means of genotypic values (HMGV), relative performance of genotypic value (RPGV), and harmonic mean of RPGV parameters indicated a dynamic concept of stability. Moreover, results of GGE biplot analysis identified three mega-environments with different winner genotypes in test environments. Regardless of the different years, the Ahvaz, Darab, and Moghan regions were identified as Type-II environments due to their highest discrimination power and representative ability. The grouping pattern considerably followed the ranking of genotypes in GGE biplot analysis and WAASB model. In conclusion, our results revealed that genotypes G9 and G10 had grain yield higher than the reference genotype and relative stability in specific environments with favorable environmental conditions. Genotype G5 had general adaptability and stability across all test environments. This genotype can be recommended for cultivation in a wide range of environments in the warm climate of Iran.

Can a “Pandemic Life Adaptation” Digital Technology Curriculum Reduce the Digital Disadvantage of Older Adults During COVID-19? An Intervention Study from Shanghai

ABSTRACT Older adults are at a digital disadvantage because of social stereotypes and a lack of social support; however, smartphones have become a necessary technology to cope with crises and daily life in China, especially during the pandemic. This study aimed to help marginalized older adults take on new tasks by developing digital technology education that used a framework of social cognitive theory in social work. The study followed a quasi-experimental design in which 153 elderly people were recruited from three community service centers; 90 of the participants received 6-weekly intervention. Intent-to-treat analysis, effect size calculations, and sensitivity analysis were conducted. The findings show that digital education significantly enhanced two domains of digital life adaptation abilities: general digital life adaptation abilities [g = .50, 95% CI (.70, 2.69)] and pandemic digital life adaptation abilities [g = .89, 95% CI (.96, 2.07)]. The intervention also improved three domains of digital self-efficacy: sharing and communication [g = .55, 95% CI (.04, .48)], verification [g = .34, 95% CI (.01, .59)], and influencing others [g = .53, 95% CI (.13, .77)]. The study showed that the new intervention approach reduced the harm to vulnerable older adults in the digital wave, especially during the pandemic.

Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection

Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems such as server clusters, spacecrafts and financial systems etc. However, upgrade or cross-platform deployment of these devices will introduce the issue of cross-domain distribution shift, which leads to the prototypical problem of Domain Adaptation for MTS-AD. Compared with general domain adaptation problems, MTS-AD domain adaptation presents two peculiar challenges: 1) The dimensions of data from the source domain and the target domain are usually different, so alignment without losing any information is necessary. 2) The association between different variates plays a vital role in the MTS-AD task, which is overlooked by traditional domain adaptation approaches. Aiming at addressing the above issues, we propose a V ariate A ssociated domai N a D aptation method combined with a Gr A ph Deviation Network (abbreviated as VANDA ) for MTS-AD, which includes two major contributions. First, we characterize the intra-domain variate associations of the source domain by a graph deviation network (GDN), which can share parameters across domains without dimension alignment. Second, we propose a sliding similarity to measure the inter-domain variate associations and perform joint training by minimizing the optimal transport distance between source and target data for transferring variate associations across domains. VANDA achieves domain adaptation by transferring both variate associations and GDN parameters from the source domain to the target domain. We construct two pairs of MTS-AD datasets from existing MTS-AD data and combine three domain adaptation strategies with six MTS-AD backbones as the benchmark methods for experimental evaluation and comparison. Extensive experiments demonstrate the effectiveness of our approach, which outperforms the benchmark methods, and significantly improves the AD performance of the target domain by effectively utilizing the source domain knowledge.

Adaptive features of winter triticale accessions from the VIR collection

Background. In the context of global climate change, the study and development of genetic material with environmental plasticity and stability in the manifestation of its traits becomes relevant. The scientific and practical results achieved in triticale breeding place this crop among the most popular cereals in terms of economic importance. However, the main problem for the use of triticale in breeding practice is its limited genetic resources.Materials and methods. The research materials were 161 winter triticale (× Triticosecale Wittm. ex A. Camus) accessions from the global collection of VIR. The study and breeding-oriented assessment of the material were based on conventional breeding techniques and carried out according to the guidelines approved by VIR with minor amendments. The obtained research data underwent statistical analysis using the method by A. V. Kilchevsky and L. V. Khotyleva.Results. Environmental and genetic parameters were analyzed under the conditions of the Middle Volga Region of Russia. Genotypes were identified for their high general adaptability, i. e., securing the highest average yield in the entire set of environments. Cultivars were also selected for their least variable high yields under the impact of weather conditions. The analysis of the studied set of accessions showed that both high-yielding and low-yielding genotypes were stable. The character’s mean value and its environmental sensitivity were relatively independent and could be incorporated in one genotype in various combinations. This finding can be used in the process of triticale breeding.

ROBUST QUANTIFICATION OF PERCENT EMPHYSEMA ON CT VIA DOMAIN ATTENTION: THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA) LUNG STUDY.

Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Although the domain shifts in different CT scanners are subtle compared to shifts existing in other modalities (e.g., MRI) or cross-modality, emphysema is highly sensitive to it. Such subtle difference limits the application of general domain adaptation methods, such as image translation-based methods, as the contrast difference is too subtle to be distinguished. Existing studies have explored several directions to tackle this challenge, including density correction, noise filtering, regression, hidden Markov measure field (HMMF) model-based segmentation, and volume-adjusted lung density. Despite some promising results, previous studies either required a tedious workflow or eliminated opportunities for downstream emphysema subtyping, limiting efficient adaptation on a large-scale study. To alleviate this dilemma, we developed an end-to-end deep learning framework based on an existing HMMF segmentation framework. We first demonstrate that a regular UNet cannot replicate the existing HMMF results because of the lack of scanner priors. We then design a novel domain attention block, a simple yet efficient cross-modal block to fuse image visual features with quantitative scanner priors (a sequence), which significantly improves the results.

KDPM: Knowledge‐driven dynamic perception model for evacuation scene simulation

AbstractEvacuation scene simulation has become one important approach for public safety decision‐making. Although existing research has considered various factors, including social forces, panic emotions, and so forth, there is a lack of consideration of how complex environmental factors affect human psychology and behavior. The main idea of this paper is to model complex evacuation environmental factors from the perspective of knowledge and explore pedestrians' emergency response mechanisms to this knowledge. Thus, a knowledge‐driven dynamic perception model (KDPM) for evacuation scene simulation is proposed in this paper. This model combines three modules: knowledge dissemination, dynamic scene perception, and stress response. Both scenario knowledge and hazard source knowledge are extracted and expressed. The improved intelligent agent perception model is designed by adopting position determination. Moreover, a general adaptation syndrome (GAS) model is first presented by introducing a modified stress system model. Experimental results show that the proposed model is closer to the reality of real data sets.

Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties.

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

CAMP in budding yeast: Also a messenger for sucrose metabolism?

S. cerevisiae (or budding yeast) is an important micro-organism for sucrose-based fermentation in biotechnology. Yet, it is largely unknown how budding yeast adapts to sucrose transitions. Sucrose can only be metabolized when the invertase or the maltose machinery are expressed and we propose that the Gpr1p receptor signals extracellular sucrose availability via the cAMP peak to adapt cells accordingly. A transition to sucrose or glucose gave a transient cAMP peak which was maximally induced for sucrose. When transitioned to sucrose, cAMP signalling mutants showed an impaired cAMP peak together with a lower growth rate, a longer lag phase and a higher final OD600 compared to a glucose transition. These effects were not caused by altered activity or expression of enzymes involved in sucrose metabolism and imply a more general metabolic adaptation defect. Basal cAMP levels were comparable among the mutant strains, suggesting that the transient cAMP peak is required to adapt cells correctly to sucrose. We propose that the short-term dynamics of the cAMP signalling cascade detects long-term extracellular sucrose availability and speculate that its function is to maintain a fermentative phenotype at continuously low glucose and fructose concentrations.

Multi Environment Analysis of Soybean Genotypes to Delineate Stability and Adaptability for Yield and Quality Parameters

The present investigation was carried out at three locations of Gujarat under the jurisdiction of Anand Agricultural University (AAU) during kharif-2022. Three diverse environments were selected for conducting an experiment. The experimental material comprised of 40 genotypes of soybean sown in a randomized block design with three replications at all environments. Based on the AMMI analysis for seed yield per plant, E3 was considered as the most suitable environment for seed yield per plant while, E2 was considered as the poor environment. The genotypes G4, G16 and G40 were considered as the most stable genotypes and had general adaptation for seed yield per plant among all the genotypes since they were least affected by the environment. The genotypes G15, G22, G24, G25, G27, G29, G32 and G39 performed well in the poor environment (E2). Genotypes G10, G20 and G33 had specific adaptability in potential environment (E3) Based on the AMMI stability value G24, G25 and G22 were considered as stable genotypes over environment while, G33, G20 and G35 were considered as unstable genotypes. According to the AMMI 2 biplot G15, G16, G22, G24, G25, G27, G32 and G39 were considered as high yielding with general adaptability. The vertex genotypes for E3 were G10, G12, G20 and G33. G31 was for E2 while, vertex genotype for E1 was G26.

Decomposition of an odorant in olfactory perception and neural representation.

Molecules-the elementary units of substances-are commonly considered the units of processing in olfactory perception, giving rise to undifferentiated odour objects invariant to environmental variations. By selectively perturbing the processing of chemical substructures with adaptation ('the psychologist's microelectrode') in a series of psychophysical and neuroimaging experiments (458 participants), we show that two perceptually distinct odorants sharing part of their structural features become significantly less discernible following adaptation to a third odorant containing their non-shared structural features, in manners independent of olfactory intensity, valence, quality or general olfactory adaptation. The effect is accompanied by reorganizations of ensemble activity patterns in the posterior piriform cortex that parallel subjective odour quality changes, in addition to substructure-based neural adaptations in the anterior piriform cortex and amygdala. Central representations of odour quality and the perceptual outcome thus embed submolecular structural information and are malleable by recent olfactory encounters.

The Impact of the Peer Support Programme on Interpersonal Relationship, Self-Esteem, General Health Questionnaire and Adaptation Scale for School Environments on Six Spheres among Japanese High School Pupils

This study aimed to explore how the peer support programme gave influences on technical high school pupils in Japan. The study invited a total 76 pupils (37 in an intervention group and 39 in a control group) to be involved into the peer support programme and assessments. The participants were annually assessed three times by adopting four scales, which examined their interpersonal relationship level, self-esteem, mental health, and school environmental adaptation states. The results showed that pupils in the intervention group (the peer supporters) tended to improve their own skills and abilities in terms of all the scales after joining the programme. Also, in terms of Adaptation Scale for School Environments on Six Spheres (ASSESS), which was a newly developed scale, there were no significant positive influences in both areas of “fulfillments in study” and “peer support activities against bullying”. As a conclusion, even in the technical high school where the majority of pupils were male, the peer support programme gave positive influences on the intervention group (the peer supporters) in terms of interpersonal relationship level, self-esteem, mental health, and school environmental adaptation states.

Soil Moisture Forecast Using Transfer Learning: An Application in the High Tropical Andes

Soil moisture is a critical variable in the hydrological cycle and the climate system, significantly impacting water resources, ecosystem functioning, and the occurrence of extreme events. However, soil moisture data are often scarce, and soil water dynamics are not fully understood in mountainous regions such as the tropical Andes of Ecuador. This study aims to model and predict soil moisture dynamics using in situ-collected hydrometeorological data for training and data-driven machine-learning techniques. Our results highlight the fundamental role of vegetation in controlling soil moisture dynamics and significant differences in soil water balance related to vegetation types and topography. A baseline model was developed to predict soil moisture dynamics using neural network techniques. Subsequently, by employing transfer-learning techniques, this model was effectively applied to different soil horizons and profiles, demonstrating its generalization capacity and adaptability. The use of neural network schemes and knowledge transfer techniques allowed us to develop predictive models for soil moisture trained on in situ-collected hydrometeorological data. The transfer-learning technique, which leveraged the knowledge from a pre-trained model to a model with a similar domain, yielded results with errors on the order of 1×10−6<ϵ<1×10−3. For the training data, the forecast of the base network demonstrated excellent results, with the lowest magnitude error metric RMSE equal to 4.77×10−6, and NSE and KGE both equal to 0.97. These models show promising potential to accurately predict short-term soil moisture dynamics with potential applications for natural hazard monitoring in mountainous regions.

GreenABR+: Generalized Energy-Aware Adaptive Bitrate Streaming

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This paper proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.

Deciphering genotype-by-environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods.

The multi-environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020-2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype-environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha-1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha-1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128-day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran.

Stability of grain zinc concentrations across lowland rice environments favors zinc biofortification breeding.

One-third of the human population consumes insufficient zinc (Zn) to sustain a healthy life. Zn deficiency can be relieved by increasing the Zn concentration ([Zn]) in staple food crops through biofortification breeding. Rice is a poor source of Zn, and in countries predominantly relying on rice without sufficient dietary diversification, such as Madagascar, Zn biofortification is a priority. Multi-environmental trials were performed in Madagascar over two years, 2019 and 2020, to screen a total of 28 genotypes including local and imported germplasm. The trials were conducted in the highlands of Ankazomiriotra, Anjiro, and Behenji and in Morovoay, a location representative of the coastal ecosystem. Contributions of genotype (G), environment (E), and G by E interactions (GEIs) were investigated. The grain [Zn] of local Malagasy rice varieties was similar to the internationally established grain [Zn] baseline of 18-20 μg/g for brown rice. While several imported breeding lines reached 50% of our breeding target set at +12 μg/g, only few met farmers' appreciation criteria. Levels of grain [Zn] were stable across E. The G effects accounted for a main fraction of the variation, 76% to 83% of the variation for year 1 and year 2 trials, respectively, while GEI effects were comparatively small, contributing 23% to 9%. This contrasted with dominant E and GEI effects for grain yield. Our results indicate that local varieties tested contained insufficient Zn to alleviate Zn malnutrition, and developing new Zn-biofortified varieties should therefore be a priority. GGE analysis did not distinguish mega-environments for grain [Zn], whereas at least three mega-environments existed for grain yield, differentiated by the presence of limiting environmental conditions and responsiveness to improved soil fertility. Our main conclusion reveals that grain [Zn] seems to be under strong genetic control in the agro-climatic conditions of Madagascar. We could identify several interesting genotypes as potential donors for the breeding program, among those BF156, with a relatively stable grain [Zn] (AMMI stability value (ASV) = 0.89) reaching our target (>26 μg/g). While selection for grain yield, general adaptation, and farmers' appreciation would have to rely on multi-environment testing, selection for grain [Zn] could be centralized in earlier generations.

General non-specific adaptation reactions and levels of organism reactivity in patients with pulmonary tuberculosis and associated with the human immunodeficiency virus at the stages of medication

The data of 165 patients were analyzed, of which 121 with pulmonary tuberculosis, new cases, who were treated according to the modified DOTS schemes with retrosternal instillation of the 10%-5ml isoniazid solution and 44 patients with pulmonary tuberculosis associated with human immunodeficiency virus, who received standard antituberculosis and antiretroviral treatment. The patients were assessed the reactions of general nonspecific adaptation, the reactivity levels of the organization and the sum of the score accumulated by the patient according to the method of L.H.Garcavi and coaut., 1979. It was found that in the quotas of patients from both groups up to treatment prevailed low levels of reactivity of the organism, which after the intensive phase DOTS (and corresponding – antiretroviral treatment) in patients only with pulmonary tuberculosis or more maintained at 50%, and in the group and with HIV Association low levels remained the same. Pathological adaptation reactions at that time in the first group persisted in every third patient, while in the other group they remained without changes. These data are useful in assessing the risk of activation of the process, the prognosis of further evolution of the disease and the correction of curative measures.

ОБЗОР ЗАРУБЕЖНЫХ ИССЛЕДОВАНИЙ СЕМЕЙНОЙ МОДЕЛИ ЗДОРОВЬЯ И БОЛЕЗНИ

The article presents an overview of foreign studies of the family model of health and disease. The sociocultural determination of the ideas of foreign scientists about health and disease is emphasized, confirmed by a number of studies related to the study of culturally determined cognitive aspects of physical and mental health, the role of culture in the perception of stress, culture-specific syndromes, etc. The main trends are identified: the features of the perception of health and disease in families, the quality of life of people with chronic somatic diseases, family health and prevention. One of the most important areas of foreign research is the study of the relationship between quality of life and the perception of the disease. The relationship between decision-making about any treatment and the perception of the disease is considered. It has been shown that the perception of the disease affects the attitude towards seeking help and health behavior and is associated with general adaptation and attitude towards seeking professional help. The roles of the family as a certain system of family medicine, of parents in the context of treatment of the child and his further rehabilitation are determined. Emphasis is placed on studying the influence of family dysfunction and the content characteristics of parenting strategies as factors provoking somatization of distress in adolescence. Despite a wide range of studies of the internal picture of the disease and the internal picture of health, there is a lack of works on the role of the family and parental education in the formation of the internal picture of the disease and the internal picture of health in families with children with chronic somatic diseases. It was also concluded that further research on the relationship between stigmatization and socialization of children with chronic somatic diseases is of particular importance.