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JianJiang Pan,1,* XiXuan Cai,1,* JieRu Chen,1 MingYing Xu,1 JingYu Hu,1 YueChun Mao,1 Tao Chen,2 LuSha Li,1 MengQi Jin,1 LiYing Chen1 1Department of General Practice, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310020, People’s Republic of China; 2Department of General Practice, Jianqiao Community Health Service Center, Hangzhou, Zhejiang, 310021, People’s Republic of China*These authors contributed equally to this workCorrespondence: LiYing Chen, Department of General Practice, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China, Email 3197020@zju.edu.cnPurpose: Understanding the role of systemic inflammation in the development of Metabolic Syndrome (MetS) is crucial for identifying individuals at a higher risk of this cluster of conditions that increase the risk of heart disease, stroke, and diabetes.Patients and Methods: A retrospective cohort study was conducted with 4,312 participants who were free from MetS at the study’s onset and had high-sensitivity C-reactive protein (hsCRP) levels measured. Latent class trajectory modeling was utilized to identify distinct hsCRP trajectory patterns. Multivariable regression and proportional hazards analyses were employed to evaluate the predictive value of hsCRP trajectories for the development of MetS.Results: During the 1.63-year follow-up period, 1,308 participants developed metabolic syndrome (MetS). Individuals with high hsCRP levels exhibited a significantly increased risk of developing MetS compared to those with low hsCRP levels (HR = 1.062, 95% CI 1.103– 1.113). The hsCRP trajectory analysis identified three distinct groups: low-stable, increasing, and decreasing. The decreasing and increasing hsCRP trajectory groups demonstrated a 1.408-fold (95% CI 1.115– 1.779) and a 1.618-fold (95% CI 1.288– 2.033) increased risk of MetS, respectively.Conclusion: This study suggests that participants with higher baseline hsCRP levels and increasing hsCRP trajectories are associated with a progression toward MetS. Long-term hsCRP trajectories may serve as useful tools for identifying individuals at higher risk of MetS who could benefit from targeted preventive and therapeutic interventions.Keywords: high-sensitivity C-reactive protein, metabolic syndrome, trajectory analysis, retrospective cohort study, risk prediction

Abstract Background Although it is well established that obesity is a risk factor for hypertension, the effect of distinct long-term patterns of body mass index (BMI) on blood pressure (BP) in later life is poorly understood. Methods Based on the China Health and Nutrition Survey, we analyzed 2920 participants aged 3–17 years with initial normal BP at baseline (1991–2011), who were followed up for the development of hypertension (1993–2015). The group-based trajectory model was applied to identify BMI trajectories, and Cox regression was used to assess their associations with hypertension risk. Stratified analyses were conducted to explore differences across subgroups. Results During an average follow-up time of 11.20 (7.69) years for males and 7.20 (5.21) years for females, 339 males and 212 females were identified with hypertension, respectively. Three BMI trajectories were identified: low-increasing (60.58% of males and 73.03% of females), moderate-increasing (33.08% of males and 24.22% of females), and high-increasing (6.34% of males and 2.76% of females). Our study found a significant positive association between a higher BMI trajectory and hypertension risk in males (all P for trend

Abstract Background The relationship between the dynamic changes in insulin resistance (IR) and the prognosis of septic patients remains unclear. This study aims to investigate the correlation between the clinical subphenotype of IR represented by the triglyceride-glucose (TyG) index trajectory and the mortality rate among patients with sepsis. Methods In this retrospective cohort study, we utilized data from septic patients within the Medical Information Mart for Intensive Care (MIMIC)-IV database version 2.0 to construct trajectories of the TyG index over 72 h. Subsequently, we computed the similarity among various TyG index trajectories with the dynamic time warping (DTW) algorithm and utilized the hierarchical clustering (HC) algorithm to demarcate distinct cluster and identified subphenotypes according to the trajectory trend. Subsequently, we assessed the mortality risk between different subphenotypes using analyses such as survival analysis and validated the robustness of the results through propensity score matching (PSM) and various models. Results A total of 2350 patients were included in the study. Two trajectory trends: TyG index decreasing (n = 926) and TyG index increasing (n = 1424) were identified, which indicated corresponding to the clinical subphenotype of increased and alleviative IR respectively. The 28-day and in-hospital mortality for the increased IR group was 28.51% and 25.49% respectively. In comparison, patients in the alleviative IR group with a 28-day mortality of 23.54% and an in-hospital mortality of 21.60%. These subphenotypes exhibited distinct prognosis, time dependent Cox model showed the increased IR group with a higher 28-day mortality [hazard ratio (HR): 1.07, 95% confidence interval (CI): 1.02–1.12, P = 0.01] and in-hospital mortality [HR: 1.05, 95% CI: 1.00–1.11, P = 0.045] compared to the alleviative IR group. Sensitivity analyses with various models further validated the robustness of our findings. Conclusion Dynamic increase in the TyG index trajectory is associated with elevated mortality risk among patients with sepsis, which suggests that dynamic increased IR exacerbates the risk of poor outcomes in patients.

Abstract Background The health effects of Life’s Essential 8 (LE8) on chronic diseases have been disclosed, but its association with hypertension remains unknown. The current study aimed to explore the potential link between 10-year LE8 trajectory and the incidence of hypertension. Methods LE8 was constructed from four behaviors and four metabolic factors, ranging from 0 to 100. Latent mixture models were used to identify trajectories of LE8 scores during 2006 to 2016. Incident hypertension was diagnosed based on self-reported clinical diagnoses and physical examinations from 2016 to 2020. Cox models were employed to assess the association of LE8 trajectories with hypertension. In addition to incorporating the mean hs-CRP levels from 2006 to 2016, age, sex, monthly income, educational level, and occupation at recruitment were adjusted for as confounding factors. Results 7500 participants aged 40.28 ± 10.35 years were included in the study, of whom 2907 (38.76%) were women. Five LE8 trajectory patterns were identified. After around four-year follow-up, 667 hypertension events were observed. Compared to the Low-Stable trajectory, the hazard ratios and 95% confidence intervals for the Moderate-Increasing, Moderate-Decreasing, Moderate-Stable, and High-Stable trajectories were 0.51 (0.40, 0.65), 0.81 (0.64, 1.02), 0.45 (0.36, 0.58), 0.23 (0.16, 0.33), respectively. The risk of incident hypertension decreased as participants improved their LE8 status. The robustness of the primary results was confirmed through several sensitivity analyses. Conclusions LE8 trajectories were associated with the incident hypertension. People who improved their LE8 scores over time experienced a decreased risk of hypertension, even if they started with lower LE8 scores initially.

Shichao Liang,1 Tengfei Yang2 1Department of Emergency Medicine, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China; 2Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of ChinaCorrespondence: Tengfei Yang, Department of Health Management, Shengjing Hospital of China Medical University, No. 36, Sanhao Street, Heping District, Shenyang, 110004, People’s Republic of China, Email yangtf@sj-hospital.orgObjective: Longitudinal cohort analysis was performed to identify the association between changes in the gamma-glutamyl transferase (GGT)/high-density lipoprotein cholesterol (HDL-C) ratio trajectory and the risk of developing diabetes mellitus.Methods: This was a retrospective cohort study. We analyzed the latent trajectory classes of changes in the GGT/HDL-C ratio by applying a latent class mixture model with healthy individuals who underwent medical checkups from January 2017 to December 2021 as the study subjects. To analyze the effect of the GGT/HDL-C ratio trajectory classes on new-onset diabetes mellitus, we then applied a multivariate Cox proportional risk regression model. Statistical analysis was performed using the R-software with the LCMM package.Results: The study cohort comprised 3410 participants. All participants were followed up for 5 years, and 95 developed diabetes (4-year incidence of 2.78%). By applying the latent class mixed model, we categorized participants into three trajectory groups: low-stability group (n = 2253), medium-increase group (n = 941), and high-increase group (n = 216). The Cox proportional risk regression model analysis showed that the hazard ratio (95% confidence interval) for the incidence of diabetes mellitus was 1.73 (1.04– 2.87) in the medium-increase group and 3.96 (2.11– 7.44) in the high-increase group. Moreover, we calculated the estimated model-based levels and linear slopes of the GGT/HDL-C ratios for each age group between 26 and 85 years at 10-year intervals, respectively. The results indicated the strongest correlation between the GGT/HDL-C ratio slope and diabetes in the 46– 55 year age group, with an odds ratio of 1.51 (1.25– 1.83).Conclusion: A large increase in the GGT/HDL-C ratio was highly associated with the risk of developing diabetes mellitus. This result suggests that vigilance for changes in the GGT/HDL-C ratio trajectory during community health screening can help identify potential patients with diabetes, enabling early intervention and treatment.Keywords: GGT/HDL-C ratio, trajectory analysis, diabetes mellitus, cohort study, health management

Abstract Background The binary diagnosis of Metabolic Syndrome(MetS) fails to accurately evaluate its severity, and the association between MetS severity and frailty progression remains inadequately elucidated. This study aims to clarify the relationship between the severity of MetS and the progression of frailty among the middle-aged and elderly population in China. Method Participants from the 2011–2018 China Health and Retirement Longitudinal Study(CHARLS) were included for a longitudinal analysis. The study employs a frailty index(FI) based on 32 health deficits to diagnose frailty and to assess FI trajectories. An age-sex-ethnicity-specific MetS scoring model (MetS score) was used to assess metabolic syndrome severity in Chinese adults. The Cumulative MetS score from 2012 to 2015 was calculated using the formula: (MetS score in wave 1 + MetS score in wave 3) / 2 × time(2015 - 2012). The association between MetS score, Cumulative MetS score, and the risk and trajectory of frailty were evaluated using Cox regression/logistic regression, and linear mixed models. Restricted Cubic Splines(RCS) models were utilized to detect potential non-linear associations. Results A higher MetS score was significantly associated with an increased risk of frailty(HR per 1 SD increase = 1.205; 95%CI: 1.14 to 1.273) and an accelerated FI trajectory(β per 1 SD increase = 0.113 per year; 95%CI: 0.075 to 0.15 per year). Evaluating changes in MetS score using a Cumulative MetS score indicated that each 1 SD increase in the Cumulative MetS score increased the risk of frailty by 22.2%(OR = 1.222; 95%CI: 1.133 to 1.319) and accelerated the rate of increase in FI(β = 0.098 per year; 95%CI: 0.058 to 0.138 per year). RCS model results demonstrated a dose-response curve relationship between MetS score and Cumulative MetS score with frailty risk. Stratified analysis showed consistency across subgroups. The interaction results indicate that in males and individuals under aged 60, MetS score may accelerate the increase in FI, a finding consistent across both models. Conclusions Our findings underscore the positive correlation between the severity of MetS and frailty progression in the middle-aged and elderly, highlighting the urgent need for early identification of MetS and targeted interventions to reduce the risk of frailty.

Aiming at the lack of effective analysis and identification of operational area in the traditional battlefield situation awareness process, a method of aerial battlefield target trajectory analysis method for operational area extraction is proposed. Firstly, based on the improved Douglas-Peucker algorithm, the featured trajectory is extracted based on the original target trajectory, thus reducing the trajectory redundant information. Secondly, the clustering by fast search and find of density peaks (CFSFDP) algorithm is used to cluster the featured trajectory, which obtains multiple featured points clusters. Finally, each featured points cluster is scanned based on Graham's convex hull algorithm to obtain closed polygons to characterize the combat areas. The feasibility and effectiveness of the algorithm model are verified by several case simulations.

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed ‘target estimation vector’ (TEV) closely approximated the mice’s route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map.

ObjectiveWe aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.MethodsBarcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression.ResultsThree trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58–133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33–113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0–46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] vs yes [95% CI], 3.70 [1.28–11.11]), no received radiotherapy (OR vs yes [95% CI], 8.33 [1.41–50.00]), diabetes (OR vs no [95% CI], 6.83 [1.57–29.73]), and extrahepatic metastasis (OR vs no [95% CI], 3.08 [1.07–8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m2 (OR vs no [95% CI], 4.49 [1.65–12.22]).ConclusionsThis latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Abstract Background No study has concentrated on the association of LE8 with cancer risk and death. We aim to examine the association of LE8 with death and cancer. Methods A total of 94733 adults aged 51.42 ± 12.46 years and 77551 participants aged 54.09±12.06 years were enrolled in longitudinal and trajectory analysis respectively. Baseline LE8 was divided into three groups based on the American Heart Association criteria and three trajectory patterns by latent mixture models. We reviewed medical records and clinical examinations to confirm incident cancer during the period from 2006 to 2020. Death information was collected from provincial vital statistics offices. Cox models were used. Results 12807 all-cause deaths and 5060 cancers were documented during a 14-year follow-up. Relative to participants with high LE8 at baseline, participants with lower levels of LE8 have a significantly increased risk of mortality and incident cancer. All these risks have an increasing trend with LE8 level decreasing. Meanwhile, the trajectory analysis recorded 7483 all-cause deaths and 3037 incident cancers after approximately 10 years. The associations of LE8 with death and cancer were identical to the longitudinal study. In the subtype cancer analysis, LE8 has a strong effect on colorectal cancer risk. Moreover, the cut point is 56.67 in the association between LE8 and death, while the cut point altered to 64.79 in the association between LE8 and incident cancers. These associations were enhanced among younger adults. Conclusions There was a significant association of LE8 with death and cancer risk, especially for the young population.

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Abstract To investigate the clinical characteristics of febrile infection‐related epilepsy syndrome (FIRES). We used trajectory analysis and logistic regression analysis to investigate the clinical characteristics and prognostic risk factors respectively. Twenty‐seven patients (16 males) were included. The median age of onset was 7 (IQR: 4–9) years. Routine cerebrospinal fluid (CSF) examination was normal. Electroencephalogram (EEG) showed frequent microseizures and electroseizures in all patients. Eight patients had claustrum signs in the acute phase. Anesthetics and anti‐seizure medications (ASM) were used in all patients. All patients received immunotherapy, including plasma exchange (n = 4), immunoglobulin (n = 26), and corticosteroids (n = 19). Trajectory diagrams of seizure showed 6 patients had bimodal disease course. Besides, we found there may be a linear relationship between body temperature and convulsion frequency (R2 = 0.25). The median Glasgow outcome scale (GOS) was 3 (IQR: 1–4). Nine deaths occurred, including abandonment of treatment (n = 3), hemodynamic instability (n = 3), brain hernia (n = 2), and brain hernia with hemodynamic instability (n = 1). Seizure onset combined with fever (p = 0.003), periodic discharge (p = 0.002), and non‐ketogenic diet (non‐KD) (p = 0.005) were independent risk factors for death. The KD group (n = 10) had lower mortality (p = 0.009), lower convulsion frequency at latest follow‐up (p

Cellular movement is essential for many vital biological functions where it plays a pivotal role both at the single cell level, such as during division or differentiation, and at the macroscopic level within tissues, where coordinated migration is crucial for proper morphogenesis. It also has an impact on various pathological processes, one for all, cancer spreading. Cell migration is a complex phenomenon and diverse experimental methods have been developed aimed at dissecting and analysing its distinct facets independently. In parallel, corresponding analytical procedures and tools have been devised to gain deep insight and interpret experimental results. Here we review established experimental techniques designed to investigate specific aspects of cell migration and present a broad collection of historical as well as cutting-edge computational tools used in quantitative analysis of cell motion.

Abstract Background Functional decline associated with dementia, including in Alzheimer’s disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Methods We retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE. Results The regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p

This research focuses on modifying the D $^{\ast }$ algorithm for path optimization of autonomous robots moving on sidewalks. The existing D $^{\ast }$ algorithm is designed to make the autonomous robots recognize and avoid obstacles. However, in real-world pedestrian settings, observations indicate that passersby on sidewalks tend to notice robots and avoid them themselves. By analyzing people’s trajectory data collected through lidar sensors, this study identified the average distance and angle of avoidance at which people start to avoid autonomous robots. Based on this, we proposed a modified D $^{\ast }$ algorithm that allows the robot to maintain the existing optimal path when people are willing to maneuver around while adopting an avoidance path only when they are not. Experimental results showed that the autonomous robot using the modified D $^{\ast }$ algorithm outperformed the conventional method regarding driving efficiency and time. This research is expected to contribute to optimizing autonomous robots’ walking paths by enabling efficient driving even under limited battery capacity.

Abstract Background Cigarette smoking and physical inactivity are two critical risk factors for noncommunicable diseases and all-cause mortality. However, few studies have compared the long-term trajectories of both behaviors, as well as multilevel factors associated with trajectory patterns. Using the National Longitudinal Study of Adolescent to Adult Health (Add Health) Wave I through V survey data, this study characterized distinct subgroups of the population sharing similar behavioral patterns from adolescence to adulthood, as well as predictors of subgroup membership for physical activity (PA) and cigarette smoking behavior respectively. Methods Using the Add Health Wave I through V survey data, we identified the optimal number of latent classes and class-specific trajectories of PA and cigarette smoking from early adolescence to adulthood, fitting latent growth mixture models with standardized PA score and past 30-day cigarette smoking intensity as outcome measures and age as a continuous time variable. Associations of baseline sociodemographic factors, neighborhood characteristics, and sociopsychological factors with trajectory class membership were assessed using multinomial logistic regression. Results We identified three distinct subgroups of non-linear PA trajectories in the study population: moderately active group (N = 1067, 5%), persistently inactive group (N = 14,257, 69%) and worsening activity group (N = 5410, 26%). Foror cigarette smoking, we identified three distinct non-linear trajectory subgroups: persistent non-smoker (N = 14,939, 72%), gradual quitter (N = 2357, 11%), and progressing smoker (N = 3393, 16%). Sex, race/ethnicity, neighborhood environment and perceived peer support during adolescence were significant predictors of both physical activity and cigarette smoking trajectory subgroup membership from early adolescence to adulthood. Conclusions There are three distinct subgroups of individuals sharing similar PA and cigarette smoking behavioral profile respectively from adolescence to adulthood in the Add Health study population. Behavioral interventions that focus on neighborhood environment (e.g. establish community-based activity center) and relationship to peers during adolescence (e.g. peer counseling) could be key to long-term PA promotion and cigarette smoking cessation.

Streets constitute a vital component of urban transportation systems and contribute significantly to enhancing the overall quality of urban life. Consequently, it becomes crucial to assess their performance in serving the needs of their users. Various methods exist for evaluating streets, one of which involves the direct observation of street users. This process involves employing different techniques such as video recording and analysis. In this project, a Python-based graphical user interface application called STUDIO was developed to facilitate the observation of street users using video footage. STUDIO offers a range of tools for reviewing recorded videos and analyzing the trajectories of street users to extract data on their movement, behavior, and activities. Further, STUDIO offers a set of analysis tools which can provide engineers and planners with valuable indicators to evaluate streets in terms of their three primary functions: mobility, access, and place.

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

Abstract The southwest vortices (SWVs) are a unique type of mesoscale vortex that frequently induce torrential rainfall in China. In this study, we focused a long‐lived quasi‐stationary SWV, which was the primary system for producing an extremely heavy rainstorm within/around Sichuan (the maximum hourly precipitation was ~103.8 mm) in Mid July 2021. After reproduced the SWV's formation by using Weather Research and Forecasting model, we conducted trajectory analyses and topography sensitivity simulations to understand the effects of complicated topography on the vortex's formation. It is found that, the regions south and southwest of the SWV acted as the most important source regions for the air clusters that formed the SWV (proportion ≥ 65%), and the air clusters originated from the upper layer contributed the most (≥60%). Of these, the air clusters sourced from the upper layer southwest and south of the SWV played the most important role in the SWV's formation, as their increase in cyclonic vorticity and their contributions to trajectory number and vorticity were all much larger than those of the others. Sensitivity simulations indicated that, detailed topography features around the Sichuan Basin were crucial in determining the structure, intensity and precipitation of the SWV, whereas, the topography features were not a decisive factor for the SWV's formation. In summary, our findings are useful to enrich the current understanding of the SWVs' formation, which would be helpful to improve the related forecasts.

IntroductionDrawing upon the framework of life course epidemiology, this study aligns with research on the mental health consequences of significant social transitions during early adulthood. The focus is on the variation in initial labour market attachment and the development of depressiveness, assuming that a firm attachment is associated with decreasing depressiveness.MethodsThe baseline investigation of the studied cohort (n = 1,001) took place during their final year of compulsory schooling at age 16. Follow-up surveys were conducted at ages 18, 21, 30, and 43. Depressiveness was measured with a five-item score. Multiple trajectory analysis, incorporating five labour market statuses observed over seven half-year periods from ages 18 to 21, was employed to categorize the cohort into six distinct groups. Among these, ‘All-time education,’ ‘From education to employment,’ ‘Education and employment,’ and ‘From employment to education’ were considered to demonstrate firm labour market attachment. Meanwhile, ‘Active labour market policy’ and ‘Unemployment’ represented less firm attachment.ResultsThe trajectory of depressive symptoms among the total cohort from age 16 to age 43 exhibited a ‘broken stick’ pattern, reaching its lowest point at age 21. This pattern was evident in all groups classified as having a firm attachment. A substantial decrease in depressiveness was also observed in the relatively weakly attached ‘Active labour market policy’ group, whereas no ‘broken stick’ pattern emerged in the ‘Unemployment’ group. The disparities in the levels of depressiveness observed at age 21 remained relatively stable across the measurements at ages 30 and 43.DiscussionThe results were as expected, except for the observed improvement in mental health within the ‘Active labour market policy’ group. Supported labour market attachment during emerging adulthood can enhance mental well-being similarly to regular mainstream attachment. In terms of policy recommendations, the consistently high levels of depressiveness within the ‘Unemployment’ group underscore the importance of reducing long-term and repeated unemployment in young age. The findings regarding the ‘Active labour market policy’ provide evidence of the intervention’s benefits. While the primary goal of these measures is to create jobs for the unemployed, they also include elements that contribute to participants’ mental health.

Helicoverpa armigera (Hübner) is a destructive agricultural pest. Facultative migration usually causes regional catastrophes; therefore, developing a simple and easy new technology for the monitoring and early warning of immigrant populations is urgent. Between 2021 and 2023, we conducted a population-monitoring study on H. armigera immigrants in Xundian County, Yunnan Province, where the migration pathway for pests from southeast Asia extends to southwest China. Based on the differences in the reproductive organ development parameters of H. armigera at different ages, we established an adult age discrimination model. The monitoring results of field populations with semiochemical attractants and sex pheromones between 2021 and 2023 showed that the daily average age of the adult population of H. armigera fluctuated above 6 days, and the trapping dynamics with semiochemical attractants and sex pheromones were the same. Both trapping methods yielded H. armigera males of different ages and had identical age structures, indicating that the H. armigera population primarily migrated from other regions. The trajectory analysis showed that the H. armigera population that migrated to Xundian between May and September was primarily from South Kunming, and after October, it was primarily the southward-migrating population north of Qujing City, Yunnan Province. Based on the relationship between the daily average fecundity and the age of H. armigera, a dynamic prediction model for the fecundity of the immigrant population was established. In this study, the prediction models and methods based on semiochemical attractants could potentially be used in the surveillance and population alerting of H. armigera.

In astrodynamics, orbit cranking is usually referred to as an interplanetary transfer strategy that exploits multiple gravity-assist maneuvers to change both the inclination and eccentricity of the spacecraft osculating orbit without changing the specific mechanical energy, that is, the semimajor axis. In the context of a solar sail-based mission, however, the concept of orbit cranking is typically referred to as a suitable guidance law that is able to (optimally) change the orbital inclination of a circular orbit of an assigned radius in a general heliocentric three-dimensional scenario. In fact, varying the orbital inclination is a challenging maneuver from the point of view of the velocity change, so orbit cranking is an interesting mission application for a propellantless propulsion system. The aim of this paper is to analyze the performance of a spacecraft equipped with an Electric Solar Wind Sail in a cranking maneuver of a heliocentric circular orbit. The maneuver performance is calculated in an optimal framework considering spacecraft dynamics described by modified equinoctial orbital elements. In this context, the paper presents an analytical version of the three-dimensional optimal guidance laws obtained by using the classical Pontryagin’s maximum principle. The set of (analytical) optimal control laws is a new contribution to the Electric Solar Wind Sail-related literature.

Abstract Background A resident vascular endothelial stem cell (VESC) population expressing CD157 and CD200 has been identified recently in the adult mouse. However, the origin of this population and how it develops has not been characterized, nor has it been determined whether VESC-like cells are present during the perinatal period. Here, we investigated the presence of perinatal VESC-like cells and their relationship with the adult VESC-like cell population. Methods We applied single-cell RNA sequencing of endothelial cells (ECs) from embryonic day (E) 14, E18, postnatal day (P) 7, P14, and week (W) 8 liver and investigated transcriptomic changes during liver EC development. We performed flow cytometry, immunofluorescence, colony formation assays, and transplantation assays to validate the presence of and to assess the function of CD157+ and CD200+ ECs in the perinatal period. Results We identified CD200− expressing VESC-like cells in the perinatal period. These cells formed colonies in vitro and had high proliferative ability. The RNA velocity tool and transplantation assay results indicated that the projected fate of this population was toward adult VESC-like cells expressing CD157 and CD200 1 week after birth. Conclusion Our study provides a comprehensive atlas of liver EC development and documents VESC-like cell lineage commitment at single-cell resolution.

Abstract Background Kinesiophobia is one of the most common and aversive psychological phenomena among patients after total knee arthroplasty (TKA). This study aimed to identify trajectories of kinesiophobia, examine factors distinguishing these trajectories, and clarify the association between trajectories of kinesiophobia and rehabilitation outcomes. Methods In this prospective cohort study, the patients who underwent TKA were recruited between December 2021 and April 2022 from three orthopedic wards of a tertiary hospital in China. Kinesiophobia was measured using the Tampa Scale for Kinesiophobia at baseline (T0), and then at 1 month (T1) and 3 months (T2) after TKA to perform latent class growth analysis. Meanwhile, rehabilitation outcomes were assessed at 3 months after TKA, using the Kessler Psychological Distress Scale, the Hospital for Special Surgery-Knee Scale, Barthel Index, and the Impact on Participation and Autonomy questionnaire. Results The four kinesiophobia trajectories identified were as follows: low stable group (n = 120), rapid recovering group (n = 31), slow recovering group (n = 48), and stable moderate group (n = 58). Body mass index, employment status, heart disease, and pain degree significantly predicted trajectory groups (all p

Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention.

BackgroundThis study aimed to assess the association of baseline insulin resistance (IR) surrogates and their longitudinal trajectories with cardiovascular diseases (CVD) to provide a useful reference for preventing CVD.MethodsThis study was a prospective cohort study conducted in the 51st Regiment of the Third Division of Xinjiang Corps. A total of 6362 participants were recruited in 2016 to conduct the baseline survey, and the follow-up surveys in 2019, 2020, 2021, and 2022. The Kaplan–Meier method was used to estimate the cumulative incidence of CVD according to the baseline IR surrogates of metabolic insulin resistance score (METS-IR) and triglyceride-glucose (TyG) index. Cox regression models were used to assess the association between the baseline IR surrogates and CVD. The impact of the longitudinal trajectories of the IR surrogates on CVD was analyzed after excluding those with IR surrogate data measured ≤2 times. Based on the group-based trajectory model (GBTM), the trajectory patterns of IR surrogates were determined. The Kaplan-Meier method was used to estimate the cumulative incidence of CVD in each trajectory group of METS-IR and TyG index. Cox regression models were used to analyze the association between different trajectory groups of each index and CVD. In addition, the Framingham model was utilized to evaluate whether the addition of the baseline IR surrogates increased the predictive potential of the model.ResultsBaseline data analysis included 4712 participants. During a median follow-up of 5.66 years, 572 CVD events were recorded (mean age, 39.42 ± 13.67 years; males, 42.9%). The cumulative CVD incidence increased with the ascending baseline METS-IR and TyG index quartiles (Q1–Q4). The hazard ratio and 95% confidence interval for CVD risk in Q4 of the METS-IR and TyG index were 1.79 (1.25, 2.58) and 1.66 (1.28, 2.17), respectively, when compared with Q1. 4343 participants were included in the trajectory analysis, based on the longitudinal change patterns of the METS-IR and TyG index, the following three trajectory groups were identified: low-increasing, moderate-stable, and elevated-increasing groups. Multivariate Cox regression revealed that the hazard ratio (95% confidence interval) for CVD risk in the elevated-increasing trajectory group of the METS-IR and TyG index was 2.13 (1.48, 3.06) and 2.63 (1.68, 4.13), respectively, when compared with the low-rising group. The C-index, integrated discrimination improvement value, and net reclassification improvement value were enhanced after adding the baseline METS-IR and TyG index values to the Framingham model (P

Abstract Background Hypoxic pulmonary hypertension (HPH) is a syndrome of abnormally elevated pulmonary artery pressure, and it is mostly caused by vasoconstriction and remodeling of the pulmonary artery induced by long-term chronic hypoxia. There is a high incidence of HPH, a short survival time of the patients, but currently no effective treatments. Methods In this study, HPH-related single cell sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data were downloaded from the public database of Gene Expression Omnibus (GEO) for bioinformatics analysis in order to find out genes with important regulatory roles in the development of HPH. 523 key genes were identified through cell subpopulation identification and trajectory analysis of the downloaded scRNA-seq data, and 41 key genes were identified through weighted correlation network analysis (WGCNA) of the bulk RNA-seq data. Three key genes: Hpgd, Npr3 and Fbln2 were identified by taking intersection of the key genes obtained above, and Hpgd was finally selected for subsequent verification. The human pulmonary artery endothelial cells (hPAECs) were treated with hypoxia for different periods of time, and it was found that the expression of Hpgd decreased in hypoxia-treated hPAECs in a time-dependent manner. In order to further confirm whether Hpgd affects the occurrence and development of HPH, Hpgd was overexpressed in hPAECs. Results Hpgd was confirmed to regulate the proliferation activity, apoptosis level, adhesiveness and angiogenesis ability of hypoxia-treated hPAECs through multiple experiments. Conclusions Downregulation of Hpgd can improve the proliferation activity, reduce apoptosis, and enhance adhesion and angiogenesis in endothelial cells (ECs), thus promoting the occurrence and development of HPH.

Background Single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed in recent years. The droplet-based single cell platforms enable the profiling of gene expression in tens of thousands of cells per sample. The goal of a typical scRNA-seq analysis is to identify different cell subpopulations and their respective marker genes. Additionally, trajectory analysis can be used to infer the developmental or differentiation trajectories of cells. Methods This article demonstrates a comprehensive workflow for performing trajectory inference and time course analysis on a multi-sample single-cell RNA-seq experiment of the mouse mammary gland. The workflow uses open-source R software packages and covers all steps of the analysis pipeline, including quality control, doublet prediction, normalization, integration, dimension reduction, cell clustering, trajectory inference, and pseudo-bulk time course analysis. Sample integration and cell clustering follows the Seurat pipeline while the trajectory inference is conducted using the monocle3 package. The pseudo-bulk time course analysis uses the quasi-likelihood framework of edgeR. Results Cells are ordered and positioned along a pseudotime trajectory that represented a biological process of cell differentiation and development. The study successfully identified genes that were significantly associated with pseudotime in the mouse mammary gland. Conclusions The demonstrated workflow provides a valuable resource for researchers conducting scRNA-seq analysis using open-source software packages. The study successfully demonstrated the usefulness of trajectory analysis for understanding the developmental or differentiation trajectories of cells. This analysis can be applied to various biological processes such as cell development or disease progression, and can help identify potential biomarkers or therapeutic targets.

Computational analysis of multi-agent trajectories is a fundamental issue in the study of real-world biological agents. For trajectory analysis, combining movement data with labels (e.g., whether a team scores in a ball game) can provide additional insights compared to relying only on trajectory data. However, existing deep-learning-based methods consider only single-agent animal trajectories, and cannot be directly applied to multi-agent trajectories in sports. In this paper, a comparative analysis method to analyze multi-agent trajectories in ball games is proposed. A neural network approach based on an attention mechanism using multi-agent motion characteristics (e.g., the distances between agents and objects) as the input is adopted, which is designed to detect distinct segments in trajectories of given classes. This enables us to understand differences between classes by highlighting segmented trajectories and which variables correlate with the given labels. The effectiveness of our approach was verified by comparing various baselines with effective/ineffective attack labels and goal/non-goal labels using different sizes of the dataset. The effectiveness of our method is also demonstrated by analyzing the attacking plays in an NBA dataset.

Non-active agricultural land (NAAL) mapping in West Africa is essential to accurately assess agricultural systems and its contribution to food security and agro-ecological sustainability of current practices, and yet the available mapping methodologies are not adapted to the environmental and cropping conditions encountered when addressing tropical smallholder agriculture. In this study we present a strategy that makes use of Sentinel-2 image time series, CHIRPS monthly rainfall data and multiple years of in-situ data obtained from the JECAM database to map NAAL in a Soudanian site in Burkina Faso (Koumbia) between the years 2016 and 2021. In a first step we generated annual land use maps in four broad classes (managed, unmanaged, evergreen and non-vegetated) to detect fields being actively cultivated in a given year, and in a second step, we used these annual land use maps to differentiate non-active agricultural land by identifying shifts from one year to another. For the validation part, we analyzed the sensitivity of classification accuracy to in-situ data pre-processing by building 5 experimental validation data sets. The unmanaged classes F1-scores of the land use maps ranged between 0.86 and 0.98, depending on the year, whereas NAAL classes F1-scores ranged from 0.75 to 0.92 when validated against the most restrictive data set (pixels with no missing reference data for the period considered). NAAL represents between 7% to 14% of the study site cropland depending on the year. The higher class probabilities are in areas where data was available, whereas the low probabilities are localized and linked to transition areas on the outskirts of the department. Our results indicate that a multi-annual approach can allow NAAL mapping under challenging environments, yet efforts are to be made to develop more cost-efficient unsupervised solutions.

Purpose: The present research aims to study the driver's analysis trajectories (commercial, logistical, and financial) of China's export Performance, comparing eastern countries (Japan) and south-eastern countries (Singapore, Hong Kong, South Korea, Philippines) between 1975 and 2022. For the research purpose, a growth trajectories model was used. This technique considers commercial, logistical, and financial drivers and their impact on export performance. The world bank database is used for this analysis. It contains information on the specified Asian countries and years. The growing trajectory analysis model is a data analysis technique that allows for identifying variations in the drivers in the time that have characterized the export dynamics of Asian Countries. The results show what drivers have been the ones that have had the most significant influence on countries' export performance.

Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d-1. For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm-3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

Abstract The growing trend of agricultural abandonment requires an understanding of the development of secondary forests on old fields in the context of restoration. However, few studies examine the regeneration trajectories of functional composition and functional diversity in afrotropical secondary forests. We tested how functional composition, diversity, and aboveground biomass (AGB) change with age and determined restoration success for a secondary forest restored through assisted natural regeneration in Uganda. We assessed the influence of distance to forests on regeneration. We sampled trees in 63 plots (2000 m2 each) in the secondary forest (16–22‐year old) and five plots in an old‐growth forest in 2011, 2014 and 2017. We computed functional composition (community‐weighted means—CWM) and diversity using categorical (habitat type, dispersal mode, fruit size, and successional group) and continuous traits (wood density and maximum height) of the species and calculated AGB. The secondary forest showed dissimilar trajectories of functional composition, diversity, and AGB. After 16–22 years, the secondary forest had not yet reached equivalent values of most attributes of functional composition, diversity and AGB in the old‐growth forest. The distance to forests had a negative effect on CWM of forest‐dependent species, nonpioneer light demanders, and functional divergence and a positive effect on CWM of pioneer species. We show that assisted natural regeneration can enhance the functional composition, functional diversity, and AGB of degraded forests and that continued monitoring is needed to attain full recovery. In planning passive restoration, sites closer to existing forests should be prioritized in order to achieve faster recovery.

BackgroundThe purpose of this study was to evaluate the role of differentiation-related genes (DRGs) in tumor-associated macrophages (TAMs) in non-small cell lung cancer (NSCLC).MethodsSingle cell RNA-seq (scRNA-seq) data from GEO and bulk RNA-seq data from TCGA were analyzed to identify DRGs using trajectory method. Functional gene analysis was carried out by GO/KEGG enrichment analysis. The mRNA and protein expression in human tissue were analyzed by HPA and GEPIA databases. To investigate the prognostic value of these genes, three risk score (RS) models in different pathological types of NSCLC were generated and predicted NSCLC prognosis in datasets from TCGA, UCSC and GEO databases.Results1,738 DRGs were identified through trajectory analysis. GO/KEGG analysis showed that these genes were predominantly related to myeloid leukocyte activation and leukocyte migration. 13 DRGs (C1QB, CCL4, CD14, CD84, FGL2, MS4A6A, NLRP3, PLEK, RNASE6, SAMSN1, SPN, TMEM176B, ZEB2) related to prognosis were obtained through univariate Cox analysis and Lasso regression. C1QB, CD84, FGL2, MS4A6A, NLRP3, PLEK, SAMSN1, SPN, and ZEB2 were downregulated in NSCLC compared to non-cancer tissue. The mRNA of 13 genes were significantly expressed in pulmonary macrophages with strong cell specificity. Meanwhile, immunohistochemical staining showed that C1QB, CCL4, SPN, CD14, NLRP3, SAMSN1, MS4A6A, TMEM176B were expressed in different degrees in lung cancer tissues. ZEB2 (HR=1.4, P

Abstract Background Single amino acid substitutions in the Iduronate-2-sulfatase enzyme result in destabilization of the protein and cause a genetic disorder called Hunter syndrome. To gain functional insight into the mutations causing Hunter syndrome, various bioinformatics tools were employed, and special significance is given to molecular docking. Results In-silico tools available online for preliminary analysis including SIFT, PolyPhen 2.0, etc., were primarily employed and have identified 51 Non-synonymous Single Nucleotide Polymorphisms (ns-SNPs) as possibly deleterious. Further, modelling and energy minimization followed by Root Mean Square Deviation (RMSD) calculation has labelled 42 mutations as probably deleterious ns-SNPs. Later, trajectory analysis was performed using online tools like PSIPRED, SRide, etc., and has predicted six ns-SNPs as potentially deleterious. Additionally, docking was performed, and three candidate ns-SNPs were identified. Finally, these three ns-SNPs were confirmed to play a significant role in causing syndrome through root mean square fluctuation (RMSF) calculations. Conclusion From the observed results, G134E, V503D, and E521D were predicted to be candidate ns-SNPs in comparison with other in-silico tools and confirmed by RMSF calculations. Thus, the identified candidate ns-SNPs can be employed as a potential genetic marker in the early diagnosis of Hunter syndrome after clinical validation.

Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat’s head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis.

This study was carried out in the central district of Kirklareli province in Turkey. It presents the chemical composition of three deposition types, namely wet, bulk and dry. In this study, 150 deposition samples (wet + bulk + dry) were collected for a year in the urban and suburban areas in the central district of Kirklareli. The concentrations of the main cations (Ca2+, K+, Na+, Mg2+, NH4 +) and main anions (SO4 2-, Cl-, NO3 -) in the collected deposition samples were determined. The seasonal variations of the concentrations of the major ions in the three deposition types were studied. The wet and dry deposition fluxes of major ions were calculated. Among the major ions, the ion with the highest annual mean flux values in the wet deposition is Ca2+ in the urban area, while it is Cl- in the suburban area. In the wet deposition, the annual mean flux values of Ca2+ in the urban area and Cl- in the suburban area were 65.40 and 38.09 mg m-2 h-1, respectively. The relationship between ion concentrations and acid deposition was also investigated and the sources of ions were determined. The arithmetic mean pH values of the samples in the urban and suburban areas were 6.39 and 6.14 in wet deposition, 6.75 and 6.41 in bulk deposition, and 7.36 and 6.56 in dry deposition, respectively. The backward trajectories arriving at the region during the study period were divided into two groups. In the first cyclone group, SO4 2- was to 97 % of anthropogenic origin in both the urban and suburban areas.

IntroductionThe COVID-19 pandemic resulted in a series of significant changes and adjustments within dentistry, as dental professionals dealt with temporary closures of dental practices, increased use of personal protective equipment, a reduction of clinical procedures, and extensions to training programmes. Recent research has illustrated the impact of the pandemic on the dental profession, indicating that many dental professionals felt emotionally exhausted and experienced significant uncertainty and anxiety. This qualitative study aimed to understand how these experiences and emotions changed over the course of six months, in dental trainees and primary dental care staff in Scotland.MethodsA longitudinal diary study was conducted (June—December 2020) with dental trainees and primary dental care staff. The diary asked respondents to answer three questions related to their emotional exhaustion, on a weekly basis. There was also an open question asking respondents to describe any significant issues or concerns they had experienced during the preceding week because of the impact of the COVID-19 pandemic on their work or training. This qualitative data was explored using a trajectory analysis approach to determine specifically changes over time.ResultsThe trajectory analysis revealed several key concerns prevalent amongst respondents, and how they fluctuated over the six months. Concerns included: the impact of the pandemic on respondents' future careers and on dentistry more generally; adapting to new working environments; the impact on their patients' dental treatment and oral health; the impact on their health and wellbeing; financial considerations and adjusting to new safety measures as part of the remobilization of dental services.DiscussionIn the second half of 2020, as the UK was adjusting to the introduction of new COVID-19 safety measures in everyday life, the dental profession were grappling with significant changes to their working environment, including PPE, redeployment, use of aerosol generating procedures (AGPs), and timelines for re-opening practices. This longitudinal diary study has shown some parts of the dental profession in Scotland expressed very varied and personal concerns and anxieties related to COVID-19. Respondents' candor in their diary entries revealed explicit, frequent and high levels of uncertainty and worry related to their training and career. Collectively, the data corpus highlighted the emotional toll these anxieties have taken on the dental professions in Scotland.ConclusionThese findings demonstrate the need for (a) increased provision of mental health and wellbeing support services for dental staff and (b) the study of the linkage between organization of pandemic management to the working practices of staff delivering services. Interventions, at various levels, should take into consideration the fluctuating nature of dental professionals' concerns and anxieties over time, to address both immediate and longer-term issues.

Air pollution remains the most serious environmental health issue in the United Kingdom while also carrying non-trivial economic costs. The COVID-19 lockdown periods reduced anthropogenic emissions and offered unique conditions for air pollution research. This study sources fine-granularity geo-spatial air quality and meteorological data for the capital cities of two UK countries (i.e. England's capital London and Scotland's capital Edinburgh) from the UK Automatic Urban and Rural Network (AURN) spanning 2016–2022 to assess long-term trends in several criteria pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) and the changes in ozone pollution during the pandemic period. Unlike other studies conducted thus far, this research integrates several tools in trend estimation, including the Mann-Kendall test, the Theil-Sen estimator with bootstrap resampling, and the generalized additive model (GAM). Moreover, several investigations, including cluster trajectory analysis, pollution rose plots, and potential source contribution function (PSCF), are also employed to identify potential origin sources for air masses carrying precursors and estimate their contributions to ozone concentrations at receptor sites and downwind areas. The main findings reveal that most of the criteria pollutants show a decreasing trend in both geographies over the seven-year period, except for O3, which presents a significant ascending trend in London and a milder ascending trend in Edinburgh. However, O3 concentrations have significantly decreased during the year 2020 in both urban areas, despite registering sharp increases during the first lockdown period. In turn, these findings indicate on one hand that the O3 generation process is in the VOC-limited regime in both UK urban areas and, on the other hand, confirm previous findings that, when stretching the analysis period, diminishing ozone levels can lead to NOx reduction even in VOC-controlled geographies. Trajectory analysis reveals that northern Europe, particularly Norway and Sweden, is a principal ozone pollution source for Edinburgh, whereas, for London, mainland Europe (i.e., the Benelux countries) is another significant source. The results have important policy implications, revealing that effective and efficient NOx abatement measures spur ozone pollution in the short-term, but the increase can be transient. Moreover, policymakers in London and Edinburgh should consider that both local and transboundary sources contribute to local ozone pollution.

This study aims to evaluate the effects of the spatiotemporal patterns of land-use and land-cover (LULC) changes on the dynamics of carbon storage in a tropical region of China by linking the trajectory analysis of LULC changes and the InVEST model. Based on remote sensing (RS), geographic information system (GIS) and change trajectories, the spatiotemporal evolution of LULC changes was explored. This evolution could be coupled with the spatiotemporal LULC change trajectories and the InVEST model for the quantitative study of the spatial distribution and temporal variation in regional carbon stocks. The results showed that during the 2000–2020 period, the built-up land continually increased to 206.05 km2 through urban expansion, and forestland became the dominant type of land, with an area of 357.39 km2. In addition to the change in land use, the carbon storage in the study region increased by 4.87 Tg C. The anaphasic trajectory had the largest area ratio at 7.05% in the total area, while the prophasic trajectory contributed to the largest increase in carbon storage, 5.87 Tg C. Moreover, the repetitive trajectories had no impact on carbon sinks and sources, whereas the anaphasic trajectory and the continual trajectory imposed passive impacts on carbon storage. These advances in research underpin scientific efforts to improve the understanding of the relationship between the optimization of land-use structure and patterns and the carbon storage service in the Nandu River Basin.

The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population’s mobility and routine locations after the pandemic’s peak. We use clustering algorithms to identify citizens’ stops and routine locations, at an antenna level, during the following months after the pandemic’s first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ.

Using the ERA5 reanalysis data and trajectory analysis provided by Hysplit4, a comparative analysis was conducted on the primary pathways of air particles and the dominant weather systems in two distinct cases of equatorward and poleward cyclonic Rossby wave-breaking (CWB) events. Subsequently, the characteristics of mass exchange between the stratosphere and troposphere in both CWBs were estimated and discussed. CWB events are frequently associated with the development of an upper front in subtropics and a ridge or blocking in mid-latitudes, leading to a tropopause anomaly characterized by a downward depression in the subtropics and an upward bulge in the mid-latitudes. High potential vorticity (PV) particles exhibit negligible vertical motion and are instead controlled by the circulation of the ridge or blocking, leading to a significant poleward transport. In contrast, low PV particles display noticeable vertical motion, with approximately one fourth of them ascending on the north side of the upper-level jet exit region. After CWB occurrence, approximately 25% of low PV particles moved southward and sank below 500 hPa with the downstream trough’s cold air. Most high PV particles remained in the stratosphere, and low PV particles predominantly remained in the troposphere. Only a small proportion (2% to 6%) of particles underwent stratosphere–troposphere exchange (STE). In equatorward CWB, STE manifested as transport from stratosphere to troposphere, occurring mainly in 24–48 h post breaking with a maximum mass transport of approximately 1.54 × 1013 kg. In poleward CWB, STE involved transport from troposphere to stratosphere, occurring mainly within 0–18 h post breaking with a maximum mass transport of approximately 1.48 × 1013 kg.

Abstract Background Understanding long-term patterns (trajectories) of cardiovascular diseases (CVD) risk and identifying different sub-groups with the same underlying risk patterns could help facilitate targeted cardiovascular prevention programs. Methods A total of 3699 participants of the Tehran Lipid and Glucose Study (TLGS) (43% men, mean age = 53.2 years), free of CVD at baseline in 1999–2001 and attending at least one re-examination cycle between the second (2002–2005) and fourth cycles (2009–2011) were included. We examined trajectories of CVD risk, based on the ACC/AHA pooled cohort equation, over ten years and subsequent risks of incident CVD during eight years later. We estimated trajectories of CVD risk using group-based trajectory modeling. The prospective association of identified trajectories with CVD was examined using Cox proportional hazard model. Results Three distinct trajectories were identified (low-low, medium-medium, and high-high risk). The high-high and medium-medium CVD risk trajectories had an increasing trend of risk during the time; still, this rising trend was disappeared after removing the effect of increasing age. Upon a median 8.4 years follow-up, 146 CVD events occurred. After adjusting for age, the medium-medium and high-high trajectories had a 2.4-fold (95% CI 1.46–3.97) and 3.46-fold (95% CI 1.56–7.70) risk of CVD compared with the low-low group, respectively. In all trajectory groups, unfavorable increasing in fasting glucose, but favorable raising in HDL and decreasing smoking and total cholesterol happened over time. Conclusions Although the risk trajectories were stable during the time, different risk factors varied differently in each trajectory. These findings emphasize the importance of attention to each risk factor separately and implementing preventive strategies that optimize CVD risk factors besides the CVD risk.

The study was conducted in the landscapes of south-eastern Ukraine during the nesting seasons 1988–2018. Within the landscape system associated with the Molochny Estuary, the ten most important ecosystem types were investigated, including the following: agricultural land, vegetated strips, meadows, islands and spits, reedbeds, urban areas, salt marshes, steppe, cliffs, artificial forests. Bird species responded to temperature and precipitation gradients. The patterns of responses were presented using Huisman, Olff and Fresco expanded by the Jansen-Oksanen hierarchical models. The nature of species response in the gradient of temperature or precipitation conditions depends on the type of particular ecosystem and is not uniform for all populations inhabiting the different landscape types. The bird communities were revealed to demonstrate an abrupt dynamic over time. The continuous changes in community structure initiated by the external environmental factors are combined with modifications of internal biotic interactions, which may lead to abrupt reorganization of the community.

Introduction First responders to disasters are at risk of developing post-traumatic stress disorder (PTSD). The trajectories of post-traumatic stress symptom severity differ among individuals, even if they are exposed to similar events. These trajectories have not yet been reported in non-Western first responders. Objectives We aimed to explore post-traumatic stress symptom severity trajectories and their risk factors in first responders to the 2011 Great East Japan Earthquake (GEJE)— a historically large earthquake that resulted in a tsunami and a nuclear disaster. Methods 56 388 Japan Ground Self-Defense Force (JGSDF) personnel dispatched to the GEJE were enrolled in this seven-year longitudinal cohort study. PTSD symptom severity was measured using the Impact of Event Scale-Revised (IES-R). Trajectories were identified using latent growth mixture models (LGMM). Nine potential risk factors for the symptom severity trajectories were analyzed using multinomial logistic regression. Results Five symptom severity trajectories were identified: “resilient” (54.7%), “recovery” (24.5%), “incomplete recovery” (10.7%), “late-onset” (5.7%), and “chronic” (4.3%). The main risk factors for the four non-resilient trajectories were older age, personal disaster experiences, and working conditions. These working conditions included duties involving body recovery or radiation exposure risk, longer deployment length, later or no post-deployment leave, and longer post-deployment overtime. Conclusions The majority of first responders to GEJE were resilient and developed few or no PTSD symptoms. A substantial minority experienced late-onset and chronic symptom severity trajectories. The identified risk factors can inform policies for prevention, early detection, and intervention in individuals at risk of developing symptomatic trajectories. Disclosure No significant relationships.

At present， parallelogram traction steering mechanism is mostly adopted in traction vehicles used in warehousing cargo loading and transfer. Because the steering mechanism does not conform to Ackermann's principle， the sliding friction force is large when steering， which will lead to traction and steering operation laborious. A trapezoidal traction steering mechanism based on Ackerman principle is designed， and uses the sequential quadratic programming genetic algorithm in multivariate nonlinear programming to optimize the specific dimensions of the trapezoidal traction steering mechanism， the size parameters which more satisfy the Ackerman principle are obtained. A prototype is made based on the optimized parameters. By using the Adams dynamics simulation software， the simulation and comparative experiment of the trapezoidal steering mechanism with the optimized parallelogram mechanism from three dimensions of steering trajectory， steering start torque， and energy conversion efficiency are carried out. The analysis results show that the optimized mechanism has a smoother steering trajectory， smaller steering starting torque and higher energy conversion efficiency.

The accurate prediction of human movement trajectory has a variety of benefits for many applications such as optimizing nurse’s trajectory in a hospital, optimizing movements of old or disabled people to minimize their routine efforts, etc. To perform human movement prediction, large amount of historical positioning data from sensors has to be collected and mined. We analyzed different human sequential movement prediction approaches and their limitations. In this work, we propose a new classifier named Apriori based Probability Tree Classifier (APTC) which predicts the human movement sequence patterns in indoor environment. The APTC classifier is integrated into Bagged J48 Machine learning algorithm which results in an ensemble model to predict the future human movement patterns. The patterns are mined based on spatial, temporal and social data which add more accuracy to our prediction. Our model also performs clustering mechanism to detect the abnormal patterns.

The fall armyworm (FAW), Spodoptera frugiperda, is a migratory noctuid pest that has recently invaded eastern Asia. From 2017 up till 2020, searchlight trapping was used to assess the extent of FAW migration across the South China Sea. Molecular and morphology-based identification confirmed that FAW made its first appearance on Yongxing Island on 11 April 2019, with most trapped individuals belonging to the S. frugiperda “corn-strain”. Carbon isotope analysis further showed that FAW moths originated from C4 host plants, while trajectory analyses revealed that migratory S. frugiperda adults are able to cross the South China Sea and enter mainland China. This long-distance migration process plausibly results in frequent genetic mixing between domestic FAW populations and those of neighboring Southeast Asian countries. Overall, this work provides unique insights into FAW migration across eastern Asia and ultimately can help advance pest forecasting, risk assessment and area-wide pest management.

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China’s railway network.

Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.