Individual Trajectories Research Articles

Individual honey bee tracking in a beehive environment using deep learning and Kalman filter

The honey bee is the most essential pollinator and a key contributor to the natural ecosystem. There are numerous ways for thousands of bees in a hive to communicate with one another. Individual trajectories and social interactions are thus complex behavioral features that can provide valuable information for an ecological study. To study honey bee behavior, the key challenges that have resulted from unreliable studies include complexity (high density of similar objects, small objects, and occlusion), the variety of background scenes, the dynamism of individual bee movements, and the similarity between the bee body and the background in the beehive. This study investigated the tracking of individual bees in a beehive environment using a deep learning approach and a Kalman filter. Detection of multiple bees and individual object segmentation were performed using Mask R-CNN with a ResNet-101 backbone network. Subsequently, the Kalman filter was employed for tracking multiple bees by tracking the body of each bee across a sequence of image frames. Three metrics were used to assess the proposed framework: mean average precision (mAP) for multiple-object detection and segmentation tasks, CLEAR MOT for multiple object tracking tasks, and MOTS for multiple object tracking and segmentation tasks. For CLEAR MOT and MOTS metrics, accuracy (MOTA and MOTSA) and precision (MOTP and MOTSP) are considered. By employing videos from a custom-designed observation beehive, recorded at a frame rate of 30 frames per second (fps) and utilizing a continuous frame rate of 10 fps as input data, our system displayed impressive performance. It yielded satisfactory outcomes for tasks involving segmentation and tracking of multiple instances of bee behavior. For the multiple-object segmentation task based on Mask R-CNN, we achieved a 0.85 mAP. For the multiple-object-tracking task with the Kalman filter, we achieved 77.48% MOTA, 79.79% MOTSP, and 79.56% recall. For the overall system for multiple-object tracking and segmentation tasks, we achieved 77.00% MOTSA, 75.60% MOTSP, and 80.30% recall.

Genetic Factors and Environmental Influences during Innovation Explosion on Personality Development. A Review

The development of personality is an ongoing and lifelong process that commences at conception and endures throughout an individual's lifespan. Both genetic factors and environmental variables contribute to its influence. Genetic variables constitute the fundamental underpinnings of personality development, while the environment furthermore assumes a significant role. Environmental influences, such as parents, family, friends, employment, and various social groups, significantly influence an individual's developmental trajectory. Therefore, personality development is a multifaceted process influenced by the interplay of hereditary and environmental influences, with both playing a role in facilitating the construction of one's personality. The primary objective of this systematic review is to investigate the influence of genetic and environmental factors on the development of personality, encompassing both infancy and adulthood. This study addresses the abovementioned issue by analyzing the most up-to-date research findings from the past decade. This study's experimental procedures and techniques are described in the following section. This study presents a comprehensive overview of the existing literature investigating the impact of genetic and environmental factors on personality development. Fifteen scholarly research publications were chosen, focusing on individuals mostly in their youth and exploring the impact of genetic and environmental factors. The primary objective of this study is to address inquiries about the genetic impact, environmental role, and the combined contribution and interaction of these elements, as well as to elucidate the underlying causes of this connection and influence. The inclusion criteria for the papers encompassed several factors: language, relevance to the subject matter, age range of participants, publication time, journal of publication, study type, and methodological quality. In conclusion, while genetic factors and the environment play critical roles in shaping individuals, their complex interplay truly defines outcomes. Neither genes nor environment dictate destinies in isolation; their combined, intricate dance sculpts the vast diversity of human experiences. This interplay is fundamental to psychology, medicine, sociology, and even policymaking.

Medium-range forecasting of oceanic eddy trajectory

ABSTRACT The capability of continuous eddy tracking has been significantly advanced over the past decades, thanks to the availability of sea surface height measurements routinely collected by satellite altimeters. However, due to the complexity of eddy kinematic mechanisms and underlying oceanic processes, a medium-range forecast of individual eddy trajectories is a challenging task. This study develops an algorithm to effectively achieve a precise prediction of eddy propagation with a ten-day lead time by building a physics-embedded temporal evolution model. The theoretical eddy phase speed, current field, and eddy properties are incorporated as physical constraints. Compared to only considering eddy past/climatological velocities, the forecast accuracy of embedded physics constraints is improved by 21%. Forecasting of 2.3 million altimeter-derived eddy tracks globally shows that trajectory predictions are valid for eddies moving towards any direction, and the forecast error achieves 9.9 km on a ten-day average. After systematically evaluating the global forecast error, we conclude that the forecasting accuracy is geographically correlated and affected by external forcings (topography and wind) and eddy properties (geostrophy and life stage). Achieving accurate eddy trajectory prediction is expected to provide critical guidance for in-situ eddy observations, and bring substantial improvements for understanding oceanic substance transportations and biogeochemical processes.

Patterns of work and retirement in a pension system with a flexible old-age retirement age: a register study of Finnish employees and self-employed persons born in 1949

Abstract The aims of this study were to explore patterns of work and retirement of Finnish employees and self-employed persons in a pension system with a flexible old-age retirement age and how income develops in these patterns. We used individual-level register data from Statistics Finland of the total Finnish birth cohort born in 1949. The cohort was 62–70 years old over the study period 2011–2019. Sequence and cluster analyses were used to identify typical trajectories of individuals’ transitions in and out of salaried work and self-employment and work in old-age retirement. Our analysis yielded a ten-cluster solution: four clusters were found for employees who did not continue working in retirement (62%), four clusters for those employees who continued working in retirement (26%), and two clusters for the self-employed (12%). The clusters differed by the timing of retirement. The results showed that employees who retired earlier on an old-age pension and who were not working afterwards had lower incomes. Their incomes also decreased after old-age retirement. Employees who worked for several years in retirement had higher incomes which remained stable between the ages of 62 and 70. We found two types of self-employed persons: those who continued working in retirement with high incomes and those who stopped working earlier and had lower incomes. The results indicate that inequalities between lower and higher income groups might become exacerbated in a flexible retirement system.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs.

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Measurement-Induced Quantum Synchronization and Multiplexing.

Measurements are able to fundamentally affect quantum dynamics. We here show that a continuously measured quantum many-body system can undergo a spontaneous transition from asynchronous stochastic dynamics to noise-free stable synchronization at the level of single trajectories. We formulate general criteria for this quantum phenomenon to occur and demonstrate that the number of synchronized realizations can be controlled from none to all. We additionally find that ergodicity is typically broken, since time and ensemble averages may exhibit radically different synchronization behavior. We further introduce a quantum type of multiplexing that involves individual trajectories with distinct synchronization frequencies. Measurement-induced synchronization appears as a genuine nonclassical form of synchrony that exploits quantum superpositions.

Exploring factors associated with self-rated health in individuals with diabetes and its impact on quality of life: Evidence from the Survey of Health, Ageing, and Retirement in Europe.

Self-rated health (SRH), a measure of self-reported general health, is a robust predictor of morbidity and mortality in various populations, including people with diabetes. Diabetes is negatively associated with SRH and quality of life (QoL). Little is known about how people with diabetes rate their health and which aspects influence the rating. Also, the predictive value of SRH on future QoL has not yet been evaluated. We analyzed data from 46 592 participants of the Survey of Health, Ageing and Retirement in Europe (SHARE). Using linear regression, we aimed to determine which sociodemographic, socioeconomic, medical, social, mental, and health behavior factors determine SRH in people with diabetes. In addition, we analyzed the predictive value of SRH on future QoL using the generalized estimating equations procedure. We determined that country, current job situation, hospitalization, pain, polypharmacy, memory, eyesight, activities of daily living, number of chronic diseases, and depression are all linked to SRH. Together these variables explained 38% of the SRH's variance, whereas depression, pain, and memory had the greatest influence on SRH of people with diabetes. We also found that SRH independently predicted future QoL, supported by a regression coefficient of β = -1.261 (Wald chi-square test, χ2 = 22.097, df = 1, p < .05). As SRH is linked to future QoL, we conclude that incorporating SRH assessment into medical evaluations can help health care professionals gaining a more comprehensive understanding of an individual's health trajectory and supporting patients to enhance their QoL.

Fragility modeling practices and their implications on risk and resilience analysis: From the structure to the network scale

Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems.

Keeping the Accent? Voice, Alterity, and Memory in Oral History Interviews with Northern Ireland Migrants in England

ABSTRACT This article discusses a series of interviews with migrants from the North of Ireland to Britain during the Troubles (1969–1998) to explore how they narrate their experiences of movement and settlement by talking about voice and accent. Drawing specifically on two interviews—one with someone who felt her accent caused her to be seen as illiberal and uncivilized, and another who felt he could use his accent situationally and actively, sometimes to his advantage—we argue that accent is a site where interviewees can describe aspects of their memories of migration. Additionally, we argue that existing sociological work on voice, accent, and Irishness in Britain provides useful conceptual framing for this kind of research, but that an oral history methodology makes a more nuanced understanding of accent possible, both in terms of its relationship to individual life trajectories and in terms of the specific histories it relates to (in this instance, the history of the war in both Ireland and Britain).

BUILDING AN INDIVIDUAL LEARNING PATH FOR STUDENTS USING LMS MOODLE

In the modern environment of a higher education institution, an individual approach to learning is becoming increasingly important to maximize the efficiency of the educational process, improve the quality of education, and meet the needs of each student. The article investigates the capabilities of the Moodle learning management system for the implementation of individual student learning trajectories, which will consider both the level of initial and intermediate knowledge and the student's learning style: auditory, visual, digital, and kinesthetic. The functionalities of the LMS Moodle learning management system for personalizing training courses, and creating various types of educational content and assessment tools that will contribute to the effective learning of each student, taking into account their individual needs and capabilities by building an individual student trajectory are considered. The article demonstrates an example of successful implementation of an individual learning path using Moodle resources in a higher education institution, presents the procedure for forming an individual educational path of students based on the resources of the Moodle LMS, and discusses the advantages of this approach for the training and development of modern students. To determine the level of satisfaction of students, an online survey was conducted among 34 students majoring in 126 "Information Systems and Technologies" at the National University of Life and Environmental Sciences of Ukraine (NULES) who studied according to the implemented individual trajectory using the LMS Moodle within the Information Technology course. As a result, a conclusion was made about the positive impact of personalization of learning by selecting different types of resources considering the learning style (according to the VARK model) of the student and learning on an individual trajectory on their level of satisfaction.

Individual trajectory of professional improvement of a teacher: results of scientific research

The article presents the results of scientific research aimed at studying theoretical research and practical solutions focused on the design and implementation of the individual trajectory of professional improvement of teachers. The purpose of this article is to outline theoretical approaches and practical solutions for the professional improvement of teachers in the system of postgraduate pedagogical education. The task of the article is to clarify the meaning of the concepts of "professional improvement of the teacher" and "individual educational trajectory" based on the analysis of domestic and foreign researches; identify key contradictions in the practice of designing and implementing the individual educational trajectory of professional improvement of teachers in the system of postgraduate pedagogical education; determine the directions of further scientific research in theory and practice. As a result of the research, leading approaches to the professional development of teachers were clarified; clarified the content of the key concepts "professional improvement of the teacher" and "individual educational trajectory"; outlined the results of a number of experimental researches, including in the system of postgraduate pedagogical education in Kyiv.. Based on the analysis, the directions of further scientific research have been determined, which will focus on the theoretical justification and experimental verification of the model of designing the individual trajectory of the professional improvement of teachers, the development and testing of tools for individualization of professional improvement in the system of postgraduate education.

Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

The linear mixed-effects model is commonly utilized to interpret longitudinal data, characterizing both the global longitudinal trajectory across all observations and longitudinal trajectories within individuals. However, characterizing these trajectories in high-dimensional longitudinal data presents a challenge. To address this, our study proposes a novel approach, Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM), that leverages unsupervised learning to generate latent representations of both global and individual trajectories. We design an autoencoder with a latent space where an orthogonal constraint is imposed to separate the space of global trajectories from individual trajectories. We also devise a cross-reconstruction loss to ensure consistency of global trajectories and enhance the orthogonality between representation spaces. To evaluate UOMETM, we conducted simulation experiments on images to verify that every component functions as intended. Furthermore, we evaluated its performance and robustness using longitudinal brain cortical thickness from two Alzheimer's disease (AD) datasets. Comparative analyses with state-of-the-art methods revealed UOMETM's superiority in identifying global and individual longitudinal patterns, achieving a lower reconstruction error, superior orthogonality, and higher accuracy in AD classification and conversion forecasting. Remarkably, we found that the space of global trajectories did not significantly contribute to AD classification compared to the space of individual trajectories, emphasizing their clear separation. Moreover, our model exhibited satisfactory generalization and robustness across different datasets. The study shows the outstanding performance and potential clinical use of UOMETM in the context of longitudinal data analysis.

Bridging colloidal and electrochemical syntheses of metal nanocrystals with seeded electrodeposition for tracking single nanocrystal growth.

Metal nanocrystals (NCs) produced by colloidal synthesis have a variety of structural features, such as different planes, edges, and defects. Even from the best colloidal syntheses, these characteristics are distributed differently in each NC. This inherent heterogeneity can play a significant role in the properties displayed by NCs. This manuscript reports the use of electrochemistry to synthesize Au NCs in a system evaluated to track individual NC growth trajectories as a first step toward rapid identification of NCs with different structural features. Au nanocubes were prepared colloidally and deposited onto a glassy carbon electrode using either electrospray or an airbrush, resulting in well-spaced Au nanocubes. The Au nanocubes then served as seeds as gold salt was reduced to deposit metal at constant potential. Deposition at constant potential facilitates overgrowth on the Au nanocubes to achieve new NC shapes. The effects of applied potential, deposition time, precursor concentration, and capping agents on NC shape evolution were studied. The outcomes are correlated to results from traditional colloidal syntheses, providing a bridge between the two synthetic strategies. Moreover, scanning electron microscopy was used to image the same NCs before and after deposition, linking individual seed features to differences in deposition. This ability is anticipated to enable tracking of individual growth trajectories of NCs to elucidate sources of heterogeneity in NC syntheses.

Using within-person change in three large panel studies to estimate personality age trajectories.

How does personality change when people get older? Numerous studies have investigated this question, overall supporting the idea of so-called personality maturation. However, heterogeneous findings have left open questions, such as whether maturation continues in old age and how large the effects are. We suggest that the heterogeneity is partly rooted in methodological issues. First, studies may have failed to recover age effects as they did not stringently separate within-person changes from confounding between-person differences. Second, items supposedly belonging to the same trait may show different individual trajectories, thus rendering results sensitive to the specific set of items used. We analyzed panel data from Australia (N = 15,268; Study 1), Germany (N = 22,833; Study 2), and the Netherlands (N = 10,163; Study 3) to investigate age trends in the Big Five on the levels of both scores and items. We applied a fixed effects approach that incorporates only within-person changes over time. Developmental trends in the Big Five scores were generally moderate to large and broadly confirmed personality maturation at younger ages. At older ages, maturation consistently continued for Neuroticism, whereas we found mixed evidence for such changes in Conscientiousness and Agreeableness. Furthermore, in each study, individual items showed age trends that diverged from the rest of the corresponding trait, and these differential patterns could be partly replicated across the three studies. Our results highlight the importance of items in the study of personality development and provide an explanation for previously unaccounted for variability in age trends. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The history of teaching at the Fernandes Figueira Institute: an interview with Susana Wuillaume

This interview was conducted with Susana Maciel Wuillaume, a pediatrician with broad experience in education and in the management of the Fernandes Figueira Institute. Various topics in the institution's history and Wuillaume's individual trajectory were covered, such as the organization of the graduate course and the medical residency programs and the structure of the institution itself. This interview is part of a project to document and investigate the history of the Fernandes Figueira Institute, which celebrates its one hundredth anniversary in 2024.

Prescription Opioid Use before and after Diagnosis of Cancer Among Older Cancer Survivors With Non-Cancer Chronic Pain Conditions (NCPCs): An Application of Group-Based Trajectory Modeling (GBTM).

Prescription opioids are essential in managing pain among adults with chronic pain conditions. However, persistent use over time can lead to negative health consequences. Identifying individuals with persistent use over time and their characteristics can inform clinical decision-making and aid in reducing the risk of abuse and overdose deaths. This study aims to examine trajectories of prescription opioid use over time and factors associated with these trajectories among older cancer survivors with any non-cancer pain conditions (NCPC). We conducted a retrospective cohort study design with longitudinal data of older (age at cancer diagnosis ≥67years) cancer (incident breast, colorectal, and prostate cancers, or non-Hodgkin lymphoma) survivors with any NCPC. Data were derived from the 2007-2015 linked Surveillance, Epidemiology, and End Results (SEER)-Medicare dataset (N = 35,071). Group-Based Trajectory Modeling (GBTM) was used to identify homogeneous subgroups (distinct trajectories) of individuals based on every 90-day prescription opioid use during pre-cancer diagnosis (t1-t4), acute cancer treatment (t5-t8), and post-cancer treatment (t9-t12) periods. Biological factors, social determinants of health (SDoH), physical and mental health, medication use, health care use, and external factors associated with a trajectory membership were analyzed with multivariable multinomial logistic regressions. Four distinct trajectories of opioid use were identified: (1) increase-decrease use (6.1%); (2) short-term use after cancer diagnosis (40.6%); (3) low-use (41.0%); and (4) persistent use (12.3%). In the fully-adjusted multinomial logistic regression, the SDoH such as Non-Hispanic Black [adjusted odds ratios (AOR) = 1.69; 95%CI = 1.48, 1.93)] and rural residence (AOR = 1.49; 95%CI = 1.15, 1.94)], comorbid anxiety (AOR = 1.33; 95%CI = 1.18, 1.51), and medication use (NSAIDs - AOR = 1.20; 95%CI = 1.10, 1.30) were associated with membership in the persistent use group. Persistent use was less likely among those with higher fragmented care index (AOR = 0.95, 95%CI = 0.93, 0.97) and those living in counties with higher Medicare advantage penetration (AOR = 0.96; 95%CI = 0.95, 0.97). One in eight older adults had persistent opioid use over time. The profile characteristics of this group were different from the other trajectory groups. Policies and programs to reduce chronic opioid use need to consider the intra- and inter-individual variability to reduce opioid-related morbidity and mortality.

Integrating Bayesian Approaches and Expert Knowledge for Forecasting Continuous Glucose Monitoring Values in Type 2 Diabetes Mellitus.

Precise and timely forecasting of blood glucose levels is essential for effective diabetes management. While extensive research has been conducted on Type 1 diabetes mellitus, Type 2 diabetes mellitus (T2DM) presents unique challenges due to its heterogeneity, underscoring the need for specialized blood glucose forecasting systems. This study introduces a novel blood glucose forecasting system, applied to a dataset of 100 patients from the ShanghaiT2DM study. Our study uniquely integrates knowledge-driven and data-driven approaches, leveraging expert knowledge to validate and interpret the relationships among diabetes-related variables and deploying the data-driven approach to provide accurate forecast blood glucose levels. The Bayesian network approach facilitates the analysis of dependencies among various diabetes-related variables, thus enabling the inference of continuous glucose monitoring (CGM) trajectories in similar individuals with T2DM. By incorporating past CGM data including inference CGM trajectories, dietary records, and individual-specific information, the Bayesian structural time series (BSTS) model effectively forecasts glucose levels across time intervals ranging from 15 to 60 minutes. Forecast results show a mean absolute error of 6.41 ±0.60 mg/dL, a root mean square error of 8.29 ±0.95 mg/dL, and a mean absolute percentage error of 5.28 ±0.33%, for a 15-minute prediction horizon. This study makes the first application of the ShanghaiT2DM dataset for glucose level forecasting, considering the influences of diabetes-related variables. Its findings establish a foundational framework for developing personalized diabetes management strategies, potentially enhancing diabetes care through more accurate and timely interventions.

A study on the reasonable dietary trajectory of elderly people in the community and its correlation with body mass index.

To explore the reasonable dietary trajectory of elderly people in the community and to test the correlation between different dietary trajectories and body mass index (BMI) of the elderly people in the community to provide a reference for these individuals to formulate scientific interventions and cultivate healthy living habits. The data of The Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2011 to 2018 were used to evaluate the dietary status of elderly people in the community according to their diet, and body mass index was calculated according to height and weight. The latent variable growth mixed (LGMM) model was used to analyze the development trajectory of diet in elderly people, and the multivariate logistic regression model was used to test the relationship between different dietary development trajectories and BMI changes as well as to test the correlation between different dietary trajectories and BMI of the elderly people in the community. The LGMM fit four dietary trajectories of elderly individuals: the continuous reasonable diet group (37.81%), the dietary behavior decline group (28.84%), the continuous unreasonable diet group (20.16%), and the dietary behavior improvement group (13.19%). The results showed that factors including male sex, rural setting, being spouseless, nonformal education status, not being wealthy, living alone, and having tooth loss were more likely to be classified as the "persistently unreasonable diet group" (p < 0.05). The logistic regression results showed that the "continuous reasonable diet group" and the "dietary behavior improvement group" were significantly correlated with the development of obesity to a normal BMI. The dietary behavior of the elderly was significantly correlated with BMI value, and improving the reasonable dietary behavior of the elderly could reduce the high BMI to the normal range, but could not restore the low BMI to the normal range, indicating that reasonable dietary behavior is an important measure to prevent and improve overweight or obesity in the elderly. There is significant heterogeneity in the dietary behavior of the elderly, and community medical staff should identify the risk factors of various dietary behaviors of other groups as soon as possible, and provide corresponding intervention strategies to help them change their poor dietary behaviors and improve their nutritional status.

Может ли компонентная аллергодиагностика помочь в установлении траектории формирования «атопического марша»?

Recently, the atopic march (AM) is no longer considered a clear and sequential development of successive allergic diseases, such as atopic dermatitis (AD), asthma, and allergic rhinitis (AR). Recent studies have shown that associations between allergic disorders in children are much more complex, and there may be different variants of AM trajectories. Previously, eight classes of AM were described: (1) no allergic disorders, (2) AM, (3) AD and transient wheeze, (4) AD with later-onset rhinitis, (5) transient wheeze and later-onset rhinitis, (6) transient wheeze only, (7) eczema only, and (8) rhinitis only. 48.3% of children exhibit specific types of sequential development of allergic disorders, but only 7% exhibit profiles resembling ‘classic' AM. The aim of this article was to analyze AM trajectories in real clinical practice in children and to discuss the role of component resolved diagnosis (CRD) in the analysis of AM. The article presents anamnestic patient data and CRD results using a multiplex panel. The literature shows that AM classes do not always match the actual clinical presentation of a patient, indicating the presence of previously unreported AM trajectories. Molecular allergy diagnostics can identify true and cross-sensitization in complex clinical cases, allowing for consideration of the causes of established sensitization and conceptualization of clinical symptoms and individual AM trajectory. It is important to ensure that sensitization to true allergens is established and clinically verified. The analysis of anamnestic data can make it difficult to understand the sequence of allergic disease development. However, CRD significantly facilitates diagnostic search by allowing consideration of the order of sensitization and clinical manifestations. This can help predict the possible severity of allergic reactions to established allergens. KEYWORDS: atopic march, atopic dermatitis, allergic rhinitis, asthma, children, component resolved diagnosis, respiratory allergens. FOR CITATION: Lepeshkova T.S., Andronova E.V. Can component resolved diagnosis help establish "atopic march" trajectory? Russian Medical Inquiry. 2024;8(3):163–170 (in Russ.). DOI: 10.32364/2587-6821-2024-8-3-7.

The Fukuda stepping test is associated with various patterns of displacement

Abstract Objective: This study was to characterize individual path trajectories during the 50-step Fukuda stepping test (FST) and to determine the impact of step height on these trajectories. Methods: Kinematic data from the shoulders and feet were recorded in 12 young healthy adults while performing the FST at a comfortable step height (CoStep) and while stepping with lifting the knees high (HiStep). Path trajectories were depicted by step-by-step displacement of the right and left toes, and body rotation was obtained from the rotation of the shoulders. The effect of step height on the distance traveled and the final anteroposterior (AP) and mediolateral displacements and rotation were determined with paired t-tests. Results: Path trajectories were composed of various combinations of forward displacement, lateral deviation, and rotation to the left or right. In comparison with CoStep, the mean AP displacement and mean distance traveled were significantly shorter in HiStep (P &lt; 0.05) while the mean rotation was significantly larger in HiStep (P &lt; 0.05), but the displacement patterns were not modified. Conclusion: We found that the path trajectories vary greatly among participants, and that step height has a significant impact on the magnitude of displacements and rotation during the FST.