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BACKGROUND Cardiovascular health (CVH) in young adulthood (YA) has been associated with cardiovascular outcomes in older age. However, little is known about the relationship between YA CVH and mid-life blood pressure (BP) trajectories. METHODS Baseline CVH (defined by 7 of the American Heart Association's [AHA] Life's Essential 8 [LE8] metrics, excluding BP) was measured in YA with individual metrics scored and averaged as a composite LE8 score. Categorical CVH status was defined as high, moderate, and low. Latent class analysis was used to identify trajectories of mid-BP (mean of systolic blood pressure [SBP] and diastolic blood pressure [DBP]) from average ages 35 to 55 years. Multinomial logistic regression was used to estimate the association of YA CVH status (continuously and categorically) with mid-life BP trajectory group membership. RESULTS There were 3,688 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study in YA with follow-up data for mid-life BP trajectories. We observed 3 BP trajectory groups, labeled as Persistently-Low, Middle, and High-Increasing. On average, each 10-points higher baseline LE8 score (mean [SD] of 73.5 [13.1]) in YA was associated with adjusted odds ratios of 0.78 (95% CI, 0.72–0.84) for membership in the Middle and 0.65 (0.57–0.73) for membership in the High-Increasing trajectory groups. Compared with categorical low CVH status at baseline, those with high CVH were significantly less likely to be in the Middle and High-Increasing BP trajectory groups. CONCLUSIONS Moderate or low CVH status in YA is associated with elevated mid-life BP trajectory. These data suggest that young adult CVH promotion may be important for the primordial prevention of hypertension. [ABSTRACT FROM AUTHOR]

Objective: To characterize patterns of health care utilization before and after surgery and determine any association with preoperative frailty. Background: Frail patients experience worse postoperative outcomes and increased costs during the surgical encounter. Evidence is comparatively lacking for the longer-term effects of frailty on postoperative health care utilization. Methods: Retrospective, longitudinal cohort analysis of adult patients undergoing any elective surgical procedure after preoperative frailty assessment with the Risk Analysis Index from February 2016 to December 2020 at a large integrated health care delivery and financing system. Group-based trajectory modeling of claims data estimated distinct clusters of patients with discrete utilization trajectories. Multivariable regression predicted membership in trajectories of interest using preoperative characteristics, including frailty. Results: Among 29,067 surgical encounters, 4 distinct utilization trajectories emerged in longitudinal data from the 12 months before and after surgery. All cases exhibited a surge in utilization during the surgical month, after which most patients returned to “low” [25,473 (87.6%)], “medium” [1403 (4.8%)], or “high” [528 (1.8%)] baseline utilization states established before surgery. The fourth trajectory identified 1663 (5.7%) cases where surgery occasioned a transition from “low” utilization before surgery to “high” utilization afterward. Risk Analysis Index score alone did not effectively predict membership in this transition group, but a multivariable model with other preoperative variables was effective (c = 0.859, max rescaled R2 = 0.264). Conclusions: Surgery occasions the transition from low to high health care utilization for a substantial subgroup of surgical patients. Multivariable modeling may effectively discriminate this utilization trajectory, suggesting an opportunity to tailor care processes for these patients. [ABSTRACT FROM AUTHOR]

Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders' objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy. [ABSTRACT FROM AUTHOR]

AbstractMost longitudinal surveys construct weights and release wave-specific weights to adjust for attrition. However, there is no clear consensus in the literature on whether or how to apply weights in longitudinal trajectory modeling. We present a simulation study, motivated by a real-life longitudinal study of substance use, and consider different missing data mechanisms, weight construction processes, and specifications of substantive models of interest. Based on the results of the simulation study, we provide practical recommendations for analysts of longitudinal survey data with respect to weighting approaches that should be considered in alternative scenarios. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Almost all individuals with Down syndrome (DS) will develop neuropathological features of Alzheimer's disease (AD). Understanding AD biomarker trajectories is necessary for DS‐specific clinical interventions and interpretation of drug‐related changes in the disease trajectory. METHODS: A total of 177 adults with DS from the Alzheimer's Biomarker Consortium‐Down Syndrome (ABC‐DS) underwent positron emission tomography (PET) and MR imaging. Amyloid‐beta (Aβ) trajectories were modeled to provide individual‐level estimates of Aβ‐positive (A+) chronicity, which were compared against longitudinal tau change. RESULTS: Elevated tau was observed in all NFT regions following A+ and longitudinal tau increased with respect to A+ chronicity. Tau increases in NFT regions I‐III was observed 0–2.5 years following A+. Nearly all A+ individuals had tau increases in the medial temporal lobe. DISCUSSION: These findings highlight the rapid accumulation of amyloid and early onset of tau relative to amyloid in DS and provide a strategy for temporally characterizing AD neuropathology progression that is specific to the DS population and independent of chronological age. Highlights: Longitudinal amyloid trajectories reveal rapid Aβ accumulation in Down syndromeNFT stage tau was strongly associated with A+ chronicityEarly longitudinal tau increases were observed 2.5–5 years after reaching A+ [ABSTRACT FROM AUTHOR]

Abstract Background Although strengthening coverage has improved cancer care, there are concerns related to medical distortion. Previous studies have only examined whether patients visit a specific hospital, and not the continuum of patients with cancer, resulting in a lack of evidence in South Korea. This study aimed to investigate the patterns in hospital type for cancer care and analyze their association with outcomes. Methods The data for this study were obtained from the National Health Insurance Services Sampled Cohort database. This study included patients with four types of cancer (top four cancer incidence in 2020): gastric (3353), colorectal (2915), lung (1351), and thyroid (5158) cancer. The latent class mixed model was used to investigate cancer care patterns, and multiple regression or survival analysis was performed to examine medical cost, length of stay (LOS), and mortality. Results The patterns in each cancer type were classified into two to four classes, namely, mainly visited clinics or hospitals, mainly visited general hospitals, mainly visited tertiary hospitals (MT), and tertiary to general hospitals through trajectory modeling based on the utilization of cancer care. Compared to the MT pattern, other patterns were generally associated with higher cost, LOS, and mortality. Conclusion The patterns found in this study may be a more realistic way of defining patients with cancer in South Korea compared to previous studies, and its association‐related outcomes may be used as a basis to address problems in the healthcare system and prepare alternatives for patients with cancer. Future studies should review cancer care patterns related to other factors such as regional distribution.

The purpose of the study was to describe trajectories of relationship satisfaction across the transition to parenthood, and identify predictors of these trajectories. This study is based on the Norwegian Mother, Father, and Child Cohort Study (MoBa). Mothers (N = 43,517) reported on their relationship satisfaction at five timepoints from 17 weeks gestation to 5 years postpartum, as well as sociodemographic, psychological, and behavioral covariates. Latent Trajectory Modeling revealed 5 trajectories of relationship satisfaction: "stable very high" (18.05%), "stable high" (43.47%); "stable moderate" (17.21%); "high falling" (3.38%); and "low falling" (4.02%). Predictors of group membership were identified using multinomial logistic regression. Significant predictors included unplanned pregnancy, maternal social support, maternal history of depression, maternal history of abuse, postnatal depression, financial stress, sexual satisfaction, and child negative emotionality. These results may help identify families at risk of declining relationship satisfaction, and aid in targeting interventions aimed at improving satisfaction during this vulnerable transition. [ABSTRACT FROM AUTHOR]

Abstract This study focuses on the Gulf of Arzew in Oran, an important area of the Algerian economy and the first Algerian port in terms of hydrocarbon exports. Oil spills in this area represent a severe risk that can disrupt the marine and coastal ecosystem. Therefore, the aim is to estimate marine waters’ vulnerability to oil pollution, which has become a significant problem worldwide. Predictive simulations of oil slick drift, which may occur in the coastal area of Arzew in Oran, northwest Algeria, were carried out to study, prevent and map the spread of an oil spill in the context of a hypothetical oil spill. For this purpose, the National Oceanic and Atmospheric Administration’s general operational oil modeling environment and the Automated Data Inquiry for Oil Spills were also used. The results show that approximately 29.7 km of shoreline could potentially be impacted by oil during the winter season, with an evaporation rate of more than 74.6% of the spilled amount. In addition, the simulation results indicate that the area could be affected in distinct ways depending on the season, with completely different oil slick trajectories. It is important to note that no oil slick modeling work has been done in this area to date, although oil spills represent a serious risk that can disrupt coastal resources such as fisheries, tourism, aquatic life, and shoreline physical resources. The mapping results propose a new transposable approach to coastal risk and could serve as a decision-making tool.

Background: Although strengthening coverage has improved cancer care, there are concerns related to medical distortion. Previous studies have only examined whether patients visit a specific hospital, and not the continuum of patients with cancer, resulting in a lack of evidence in South Korea. This study aimed to investigate the patterns in hospital type for cancer care and analyze their association with outcomes. Methods: The data for this study were obtained from the National Health Insurance Services Sampled Cohort database. This study included patients with four types of cancer (top four cancer incidence in 2020): gastric (3353), colorectal (2915), lung (1351), and thyroid (5158) cancer. The latent class mixed model was used to investigate cancer care patterns, and multiple regression or survival analysis was performed to examine medical cost, length of stay (LOS), and mortality. Results: The patterns in each cancer type were classified into two to four classes, namely, mainly visited clinics or hospitals, mainly visited general hospitals, mainly visited tertiary hospitals (MT), and tertiary to general hospitals through trajectory modeling based on the utilization of cancer care. Compared to the MT pattern, other patterns were generally associated with higher cost, LOS, and mortality. Conclusion: The patterns found in this study may be a more realistic way of defining patients with cancer in South Korea compared to previous studies, and its association‐related outcomes may be used as a basis to address problems in the healthcare system and prepare alternatives for patients with cancer. Future studies should review cancer care patterns related to other factors such as regional distribution. [ABSTRACT FROM AUTHOR]

In order to solve the problems of poor surface quality and easy to produce burrs in the traditional forming process of 304 stainless steel, a new type of three-dimensional ultrasonic vibration cutting device was designed and tested. Firstly, through the analysis of the geometric deformation relationship of the device structure, the theoretical motion trajectory model of the tool tip is established. The trajectory model is fitted by Matlab and the actual output trajectory is simulated by ANSYS. The tool tip motion trajectory is a three-dimensional parabola in space. Secondly, the cutting experiment with or without ultrasonic vibration was carried out on the device， and the machined workpiece surface was measured and observed by the super depth of field system and the three-dimensional profiler. It was found that the arithmetic average height of the machined surface roughness under ultrasonic vibration cutting is significantly lower than that of the ordinary turning. The results show that ultrasonic vibration cutting has better machinability for 304 stainless steel than conventional turning. [ABSTRACT FROM AUTHOR]

Immigration is sometimes associated with crime and delinquency in the mind of the public, and it is often assumed that individuals not born in the U.S. engage in more crime and delinquency than do the native born. However, not enough research to date has looked at offending across the life-course and compared trajectories between U.S. born and non-U.S. born populations. Guided by life-course perspectives and utilizing group-based trajectory modeling (GBTM), this article uses four waves of National Longitudinal Study of Adolescent to Adult Health (Add Health) data to describe and contrast longitudinal variations in offending behavior between US born and non-US born individuals. The analyses show that there are fewer offending trajectory groups among the non-U.S. born, and that they generally offend at lower levels across the life-course than do the U.S. born. Further analysis also shows differences between the two groups in factors that shape trajectory group membership. [ABSTRACT FROM AUTHOR]

A three-armed drinking cessation trial in Vietnam found that both a brief and intensive version of an intervention effectively reduced hazardous drinking in people living with HIV. We used group-based trajectory modeling (GBTM) to assess the extent to which findings may vary by latent subgroups distinguished by their unique responses to the intervention. Using data on drinking patterns collected over the 12 months, GBTM identified five trajectory groups, three of which were suboptimal ["non-response" (17.2%); "non-sustained response" (15.7%), "slow response" (13.1%)] and two optimal ["abstinent" (36.4%); "fast response" (17.6%)]. Multinomial logistic regression was used to determine that those randomized to any intervention arm were less likely to be in a suboptimal trajectory group, even more so if randomized to the brief (vs. intensive) intervention. Older age and higher baseline coping skills protected against membership in suboptimal trajectory groups; higher scores for readiness to quit drinking were predictive of it. GBTM revealed substantial heterogeneity in participants' response to a cessation intervention and may help identify subgroups who may benefit from more specialized services within the context of the larger intervention. [ABSTRACT FROM AUTHOR]

Objectives: First, we test for differences in various cognitive abilities across trajectories of body mass index (BMI) over the later life course. Second, we examine whether genetic risk factors for unhealthy BMIs-assessed via polygenic risk scores (PRS)-predict cognitive abilities in late-life. Methods: The study used a longitudinal sample of Vietnam veteran males to explore the associations between BMI trajectories, measured across four time points, and later cognitive abilities. The sample of 977 individuals was drawn from the Vietnam Era Twin Study of Aging. Cognitive abilities evaluated included executive function, abstract reasoning, episodic memory, processing speed, verbal fluency, and visual spatial ability. Multilevel linear regression models were used to estimate the associations between BMI trajectories and cognitive abilities. Then, BMI PRS was added to the models to evaluate polygenic associations with cognitive abilities. Results: There were no significant differences in cognitive ability between any of the BMI trajectory groups. There was a significant inverse relationship between BMI-PRS and several cognitive ability measures. Discussion: While no associations emerged for BMI trajectories and cognitive abilities at the phenotypic levels, BMI PRS measures did correlate with key cognitive domains. Our results suggest possible polygenic linkages cutting across key components of the central and peripheral nervous system.

Purpose: This study aimed to identify physical activity patterns and examine their association with cardiometabolic biomarkers in a cross-sectional design. Methods: Overall 6072 participants (mean age, 60.2 yr; SD 8.6 yr, 50% women) from The Maastricht Study provided daily physical activity data collected with thigh-worn activPAL3 accelerometers. The patterns of daily physical activity over weekdays and weekend days were identified by using Group Based Trajectory Modeling. Cardiometabolic biomarkers included body mass index, waist circumference, office blood pressure, glucose, HbA1c, and cholesterol levels. Associations between the physical activity patterns and cardiometabolic outcomes were examined using the analyses of covariance adjusted for sex, age, education, smoking, and diet. Because of statistically significant interaction, the analyses were stratified by type 2 diabetes status. Results: Overall, seven physical activity patterns were identified: consistently inactive (21% of participants), consistently low active (41%), active on weekdays (15%), early birds (2%), consistently moderately active (7%), weekend warriors (8%), and consistently highly active (6%). The consistently inactive and low active patterns had higher body mass index, waist, and glucose levels compared with the consistently moderately and highly active patterns, and these associations were more pronounced for participants with type 2 diabetes. The more irregular patterns accumulated moderate daily total activity levels but had rather similar cardiometabolic profiles compared with the consistently active groups. Conclusions: The cardiometabolic profile was most favorable in the consistently highly active group. All patterns accumulating moderate to high levels of daily total physical activity had similar health profile suggesting that the amount of daily physical activity rather than the pattern is more important for cardiometabolic health. [ABSTRACT FROM AUTHOR]

Glaciers across the Kashmir Himalayan region are melting at an accelerated pace compared to other regions across the Himalayan arc. This study analyzed the recession patterns of nine glaciers in the Kashmir Himalaya region over 28 years between 1992 and 2020 using satellite images and field measurements. The recession patterns were correlated with debris cover, topographic factors, and ambient black carbon (BC) concentration at glacier sites. HYSPLIT model was used to track the air mass sources at a 7-day time-step from September 1, 2014, to September 28, 2014, over the selected region. All nine glaciers revealed high recession as indicated by changes in the area (average recession: 20.8%) and snout position (~ 14 m a−1). The relative percentage of debris on each glacier varied between ~ 0% (clean glacier) and 43%. Although the investigated glaciers lie in the same climatological regime, their topographical behavior is dissimilar with mean altitude ranging between 4000 and ~ 4700 m asl and the average slope varying from 17 to 24°. All the investigated glaciers are north-facing except G3 (southerly aspect). Our results indicate anomalously high ambient BC concentrations, ranging from 500 to 1364 ng m−3, at the glacier sites, higher than previously studied for glaciers in the Himalayas and neighboring Tibetan Plateau. The backward air-mass trajectory modeling indicated both local and global sources of particulate matter in the study area. A comparative analysis of BC measurements and glacier recession with the studies conducted across high Asia indicated the influence of BC in accelerating the melting of glaciers in the Kashmir region. [ABSTRACT FROM AUTHOR]

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With the increasing accumulation of spatial-temporal trajectory data, location-based data mining has recently been extensively studied. A fundamental research topic in this field is learning the embedding vectors of locations through self-supervised pre-training. Pre-trained embedding vectors can utilize the highly available unlabeled trajectory data, and benefit downstream tasks in multiple aspects. However, most existing methods ignore the temporal information hidden in the visited time of locations in trajectories. Considering that human activities are highly regulated by specific periods of a day, temporal information can reflect some intrinsic characteristics of locations, so it is necessary to fuse them into location embedding vectors. In this paper, we propose a Time-Aware Location Embedding (TALE) pre-training method based on the CBOW framework, which is able to incorporate temporal information into the learned embedding vectors of locations. A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. In order to verify the effectiveness of TALE, we apply the learned embedding vectors into three downstream location-based prediction tasks, i.e., location classification, location visitor flow prediction and user next location prediction. Experiments are conducted on four real-world user trajectory datasets, and the experimental results demonstrate that our TALE model can obviously help downstream tasks gain better performance. [ABSTRACT FROM AUTHOR]

This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback–Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples. [ABSTRACT FROM AUTHOR]

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words' feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features' space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction. [ABSTRACT FROM AUTHOR]

Resilience, the capacity to maintain or regain functionality in the face of adversity, is a dynamic process influenced by individual, familial, and community factors. Despite its variability, distinct resilience trajectories can be identified within populations, yet the predictors defining these distinct groups remains largely unclear. Here, using data from the Avon Longitudinal Study of Parents and Children (ages 0-18), we quantify resilience as the remaining variance in psychosocial functioning after taking into account the exposure to adversity. Growth mixture modeling identified seven distinct resilience trajectories, with over half of the study population maintaining resilience throughout early life. Factors increasing the likelihood of resilient trajectory membership included a less emotional temperament, high cognitive abilities, high self-esteem, low levels of autistic social traits, strong sibling relationships, high maternal care, and positive school experiences. Among the socioeconomic factors considered, maternal education - a significant indicator of socioeconomic status - and birth-order were associated with resilient trajectories. Our findings underscore the importance of fostering cognitive abilities, self-esteem, social relationships, positive school experiences, and extracurricular engagement to bolster resilience in adversity-exposed individuals and communities. This research informs resilience-focused interventions in mental health, education, and social policy sectors, and prompts further exploration of socioeconomic influences on resilience trajectories.

ObjectiveThis study aimed to identify trajectories of radiographic progression of the spine over time and use them, along with associated clinical factors, to develop a prediction model for patients with ankylosing spondylitis (AS).MethodsData from the medical records of patients diagnosed with AS in a single center were extracted between 2001 and 2018. Modified Stoke Ankylosing Spondylitis Spinal Scores (mSASSS) were estimated from cervical and lumbar radiographs. Group-based trajectory modeling classified patients into trajectory subgroups using longitudinal mSASSS data. In multivariate analysis, significant clinical factors associated with trajectories were selected and used to develop a decision tree for prediction of radiographic progression. The most appropriate group for each patient was then predicted using decision tree analysis.ResultsWe identified three trajectory classes: class 1 had a uniformly increasing slope of mSASSS, class 2 showed sustained low mSASSS, and class 3 showed little change in the slope of mSASSS but highest mSASSS from time of diagnosis to after progression. In multivariate analysis for predictive factors, female sex, younger age at diagnosis, lack of eye involvement, presence of peripheral joint involvement, and low baseline erythrocyte sedimentation rate (log) were significantly associated with class 2. Class 3 was significantly associated with male sex, older age at diagnosis, presence of ocular involvement, and lack of peripheral joint involvement when compared with class 1. Six clinical factors from multivariate analysis were used for the decision tree for classifying patients into three trajectories of radiographic progression.ConclusionWe identified three patterns of radiographic progression over time and developed a decision tree based on clinical factors to classify patients according to their trajectories of radiographic progression. Clinically, this model holds promise for predicting prognosis in patients with AS.

It is generally believed that higher dietary diversity is associated with better health status. The dietary diversity of individuals may change with age; however, evidence on the trajectory of change in the long-term and whether it is related to all-cause mortality is still scant. In this study, we used data from the China Health and Nutrition Survey (CHNS) collected in five follow-ups between 2004 and 2015 to explore the association between changes in dietary diversity scores (DDS) and all-cause mortality, as well as the dynamic change in DDS with age. In total, 6,737 subjects (aged between 30 and 60 at enrollment) were included in the analysis. Latent Class Trajectory Modeling (LCTM) was used to explore the different trajectories of DDS changes among participants. Four classes were identified: class 1 with the lowest average DDS (3.0) that showed a gradual decline during the follow-ups; class 2 with relatively low DDS (4.0) that experienced slight growth; class 3 with medium DDS (5.2) that also demonstrated similar growth rate to class 2; and class 4 with the highest DDS (6.7) maintained at a high level. Cox proportional hazards regression models were applied to investigate the association between the DDS trajectories and the risk of death. Only class 4, which was characterized by the highest and stable DDS, had significant reduced risk of all-cause mortality of 71.0% (hazard ratio [HR]: 0.29; 95% confidence interval [CI]: 0.10–0.83), 68% (HR: 0.32; 95% CI: 0.11–0.89), and 66.0% (HR: 0.34; 95% CI: 0.12–0.94), compared to classes 1, 2, and 3, respectively, while the first three classes showed no significant inter-class differences. When considering the average DDS during the study period, each point of increase in DDS corresponded to a 22% reduced risk of mortality (HR: 0.78; 95% CI: 0.69–0.89). In summary, reaching and maintaining a higher DDS was associated with a decreased risk of all-cause mortality. Therefore, promoting diversified eating and increasing the accessibility of varieties of foods should be paid more attention from policymakers and be more emphasized in dietary guidelines.

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans' responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features. [ABSTRACT FROM AUTHOR]

Offshore oil exploration and production in deepwater are associated with environmental risks to marine ecosystems. This research introduces DWOSM (Deep Water Oil Spill Model), a three-dimensional Lagrangian model, which is developed to simulate the transport and fate of oil spills resulting from subsea blowouts. DWOSM comprises three interconnected modules: DWOSM-DSD, which predicts the oil droplet size distribution from a blowout release; DWOSM-NearField, simulating plume dynamics and tracking oil droplets within the plume region; and DWOSM-FarField, modeling the evolution of dispersed oil beyond the near-field. Compared to other oil spill models, this integrated approach improves the transition between near and far fields using a near-field particle tracking algorithm. It also employs the thermodynamic models to enable the prediction of oil properties under varying deep water pressure and temperature. To gauge the reliability and efficacy of DWOSM, a hypothetical case situated within a North American context is employed for model testing. The DWOSM and its each module are juxtaposed with other established oil spill models. The outcomes indicate that DWOSM yields comparable results to these models by providing reasonable predictions of a deepwater blowout. The model's verification through case scenario testing and comparison underscores its potential as a decision tool for assessing and managing the potential environmental impacts of offshore petroleum activities. [Display omitted] • A new integrated deepwater oil spill model is developed in this study. • The model numerically captures the intricate dynamics of deep-sea oil blowouts. • Thermodynamic, physical, and chemical evolution of oil droplets are considered. • The coupling of near- and far-field models is tested through field scenarios. • New model excels other toolkits in systematically assessing Deepsea explorations. [ABSTRACT FROM AUTHOR]

Rutugandha Paranjpe,1 Michael L Johnson,1 Ekere J Essien,1 Jamie C Barner,2 Omar Serna,3 Esteban Gallardo,3 Zahra Majd,1 Marc L Fleming,4 Nancy Ordonez,1 Marcia M Holstad,5 Susan M Abughosh1 1Pharmaceutical Health Outcomes and Policy, University of Houston, Houston, TX, USA; 2Health Outcomes Division, The University of Texas at Austin, Austin, TX, USA; 3CareAllies, Houston, TX, USA; 4Department of Pharmacotherapy, University of North Texas Health Science Center, Fort Worth, TX, USA; 5Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, USACorrespondence: Susan M AbughoshDepartment of Pharmaceutical Health Outcomes and Policy, University of Houston, College of Pharmacy, 4849 Calhoun Road, Houston, TX 77204-5047, USATel +1 832-842-8395Fax +1 832-842-8383Email smabughosh@uh.eduPurpose: Commonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory.Patients and Methods: Patients with an ACEI/ARBs prescription were identified between July 2017 and December 2017 using a Medicare Advantage dataset. PDC was used to measure monthly patient adherence during the one-year follow-up period. The monthly PDC was added to a logistic group-based trajectory model to provide distinct patterns of adherence. Further, a multinomial logistic regression was conducted to determine predictors of each identified adherence trajectory. Predictors included various socio-demographic and clinical patient characteristics.Results: A total of 22,774 patients were included in the analysis and categorized into 4 distinct adherence trajectories: rapid decline (12.6%); adherent (58.5%); gaps in adherence (12.2%), and gradual decline (16.6%). Significant predictors associated with all lower adherence trajectories included 90 days refill, > 2 number of other medications, ≥ 1 hospitalizations, and prevalent users. Significant predictors associated with the rapid decline trajectory included male sex, comorbidities, and increased CMS risk score. Further, significant predictors associated with the gaps in adherence trajectory included increasing age, and comorbidities. Lastly, significant predictors associated with the gradual decline trajectory included increasing age, no health plan subsidy, comorbidities, and increasing CMS risk score.Conclusion: Identifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.Keywords: adherence, trajectory modeling, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, predictors

The Arctic stratosphere winter season of 2020–2021 was characterized by a weakened stratospheric polar vortex as a result of a major sudden stratospheric warming (SSW) in early January. After the SSW, which persisted for about 3 weeks, and until the end of the winter season, the lower stratosphere temperature inside the stratospheric polar vortex remained higher than required for the formation of polar stratospheric clouds. [ABSTRACT FROM AUTHOR]

Early onset of depression predicts unfavorable psychosocial and health outcomes, and depression often co-occurs for children and their parents, yet family profiles of depression trajectories are not fully examined. This population-based longitudinal prospective cohort study included 2,111 families drawn from the Chinese Family Panel Study (CFPS) with biannual assessments from 2010 to 2018. Group-based multitrajectory modeling was used to identify depression trajectories of children, fathers, and mothers. Six distinct profiles of depression symptoms were identified. Based upon multitrajectory findings of family depression profiles, when adolescents are at risk for depression, there is likely at least one parent concurrently at risk for depression, but not vice versa. Families with social disadvantages and children of delayed developmental milestones are at elevated risk for depression. Even when children are at low risk for depression, depression in parents can spill over to impact other psychosocial and health outcomes. These findings suggest examining depression and its associating psychosocial factors could help identify trajectories of varying onset and continuity, which can inform early prevention and intervention from a family system perspective. [ABSTRACT FROM AUTHOR]

This study characterizes the representation of convective transport by a Lagrangian trajectory model driven by kinematic (pressure tendency ω) vertical velocity. Four (re)analysis wind products are used in backward trajectory calculations with the TRAJ3D model, and their representations of convective transport are analyzed. Two observation‐based diagnostics are used for the evaluation: a database of observed convective cloud tops derived from satellite measurements and a set of transit time distributions (TTDs) derived from an airborne campaign during January–February 2014 in a domain over the Tropical Western Pacific (TWP). The analysis is designed to derive trajectory‐based TTDs that can be directly evaluated using the observation‐based TTDs. The results indicate a broad consistency between two independent TTD derivations characterizing vertical transport over the TWP, with a significant portion of convective transport processes represented in trajectory experiments driven by the selected (re)analysis wind products. The convective and boundary layer source regions of upper tropospheric air parcels are shown to be consistent with the climatological flow regime within the TWP. Furthermore, contributions of convection are identified in the ω fields of the (re)analysis wind data sets, as indicated by the spatiotemporal correlation of enhanced vertical velocity and observed convection. These results demonstrate the successful application of two observation‐based diagnostics and quantify the ability for kinematic trajectory models to represent convective transport processes. Key Points: Two observation‐based diagnostics are used to characterize the representation of convective transport in Lagrangian trajectory modelsTransit time distributions calculated by kinematic trajectory models capture a significant portion of convective transport processesSpatiotemporal colocation of enhanced kinematic vertical velocity with observed convection is found in chosen (re)analysis data sets [ABSTRACT FROM AUTHOR]

The estimates of chemical ozone depletion in winter-spring seasons are given for the Arctic stratosphere based on long-term observations of the vertical distribution of ozone. The features and possible causes for an unusually strong and stable stratospheric polar vortex in the Arctic in the winter 2019/2020, that led to a record ozone loss in recent years, and the dynamic processes associated with this polar vortex are analyzed. The TRACAO trajectory model and ERA5 reanalysis are used for the comparative analysis of ozone depletion in the polar vortex in the winter-spring seasons 2010/2011 and 2019/2020. [ABSTRACT FROM AUTHOR]

This paper considers trajectory a modeling problem for a multi-agent system by using the Gaussian processes. The Gaussian process, as the typical data-driven method, is well suited to characterize the model uncertainties and perturbations in a complex environment. To address model uncertainties and noises disturbances, a distributed Gaussian process is proposed to characterize the system model by using local information exchange among neighboring agents, in which a number of agents cooperate without central coordination to estimate a common Gaussian process function based on local measurements and datum received from neighbors. In addition, both the continuous-time system model and the discrete-time system model are considered, in which we design a control Lyapunov function to learn the continuous-time model, and a distributed model predictive control-based approach is used to learn the discrete-time model. Furthermore, we apply a Kullback–Leibler average consensus fusion algorithm to fuse the local prediction results (mean and variance) of the desired Gaussian process. The performance of the proposed distributed Gaussian process is analyzed and is verified by two trajectory tracking examples.

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features’ space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction.

On a grand scale, motion trajectories are usually defined by an origin, a sequence of waypoints, and a destination. A typical example is in air traffic management (ATM), where a flight from an origin passes several waypoints and arrives at a destination. The origin, the waypoints, and the destination contain useful information for trajectory modeling. On the other hand, due to trajectory design criteria and ATM restrictions and requirements, there are long-range dependencies in a flight trajectory. Such dependencies can be modeled by taking the origin, waypoints, and destination into account in trajectory modeling. In this article, we propose a class of conditionally Markov (CM) sequences to model such trajectories with long-range dependencies. First, we define a general CM sequence as a foundation. Then, we discuss its special cases for different scenarios. We derive dynamic models of these CM sequences in the Gaussian case. We show how parameters of the models can be learned from data or designed. We also justify the use of the proposed CM models for trajectory modeling. In addition, we obtain optimal filters and predictors for different models. Simulation demonstrations are given. [ABSTRACT FROM AUTHOR]

In this paper we present a new conceptual model of trajectories, which accounts for semantic and indoor space information and supports the design and implementation of context-aware mobility data mining and statistical analytics methods. Motivated by a compelling museum case study, and by what we perceive as a lack in indoor trajectory research, we combine aspects of state-of-the-art semantic outdoor trajectory models, with a semantically-enabled hierarchical symbolic representation of the indoor space, which abides by OGC's IndoorGML standard. We drive the discussion on modeling issues that have been overlooked so far and illustrate them with a real-world case study concerning the Louvre Museum, in an effort to provide a pragmatic view of what the proposed model represents and how. We also present experimental results based on Louvre's visiting data showcasing how state-of-the-art mining algorithms can be applied on trajectory data represented according to the proposed model, and outline their advantages and limitations. Finally, we provide a formal outline of a new sequential pattern mining algorithm and how it can be used for extracting interesting trajectory patterns. [ABSTRACT FROM AUTHOR]

Prior research has documented elevations in levels of internalizing and externalizing behaviors among children in lower income families in comparison to more advantaged peers. However, most studies focus on behavior problems at a single point in time or within a short developmental period. Associations between income dynamics and developmental trajectories of behavior problems over time are less understood. To address this, the current study uses data from the National Longitudinal Study of Youth (N = 7,476; 50.8% male) to examine how income dynamics (annual income and income volatility) across three distinct developmental periods from early childhood to early adolescence relate to trajectories of externalizing and internalizing problems. Group-based mixture modeling revealed a five-group trajectory model for externalizing behavior and a four-group trajectory model for internalizing behavior. Higher cumulative annual income predicted greater likelihood of belonging to the low-stable group compared to the other, more problematic groups for both externalizing and internalizing trajectories. In addition, income losses predicted higher risk of membership in any group other than the low-stable group for internalizing and externalizing behavior. Developmental period-specific income dynamics, though not as consistent as cumulative dynamics, also predicted trajectory group membership. [ABSTRACT FROM AUTHOR]

• The prevalence of hip migration requiring surgery is 24.3% in children with cerebral palsy. • The most advanced cases are not for the oldest children. • Hips of most children remain stable over time (up to 5 years). • Pelvic obliquity and hip migration are probably linked. • Three trajectories of hip migration may be distinguished. In children with cerebral palsy (CP), we have little information on when hip migration (HM) starts, what causes hip displacement, how HM changes over time, and how to halt this migration to avoid surgery. We aimed to estimate the prevalence of HM percentage (HMP) >4 0% in a homogeneous population of non-ambulant children with CP and model the changes in HMP over a 2.6-year mean follow-up. From September 2009 to September 2015, this observational, prospective, multicenter cohort study recruited 235 children from 51 centers who were 3 to 10 years old and had levels IV and V of the Gross Motor Function Classification System for CP. The outcomes were yearly HMP measurements by the Reimers index. Only children with at least one hip with HMP ≤ 40% at baseline were included in trajectory modeling. Comparisons of chidren's characteristics between trajectory groups were adjusted by the false discovery rate method. The prevalence of children with at least one hip with HMP > 40% was estimated at 24.3% (95% confidence interval 18.6–30.0). Pelvic obliquity was observed in 51.4% and 24.4% of children with asymmetric and symmetric HMP (P = 0.002). The trajectory modelling identified 3 types of MP changes over time. Many children (67.4% and 79.3% for the right and left hip) could be assigned to the "stable" trajectory group. In non-ambulant children with CP, the prevalence of HM requiring surgery is low and most hips remain practically stable over time. [ABSTRACT FROM AUTHOR]

The subseasonal scale dynamics of the Asian summer monsoon (ASM) upper troposphere (UT) anticyclone has been identified as a primary mechanism for convectively lofted Asian boundary layer air to leave the confinement of the anticyclone and impact the global UT and lower stratosphere (UTLS). This work quantifies eastward eddy shedding associated with the subseasonal scale oscillation of the anticyclone and associated chemical transport. Using reanalysis data together with satellite trace gas data, we examine the correlation between enhanced tropospheric trace gas species and the presence of a secondary anticyclone over the western Pacific Ocean. To diagnose the role of the western Pacific anticyclone (WPA) in the transport of ASM air into the global UTLS, transport pathways and transit times of air found within the WPA are analyzed using Lagrangian kinematic trajectories. Results show that about two thirds of the air within the WPA originates from, or is influenced by, the greater region of the Tibetan anticyclone. After leaving the WPA, air favors two pathways forward: eastward following the subtropical jet and southwestward following UTLS anticyclonic flow. Approximately 60% of the air parcels cross the tropopause into the lower stratosphere within 30 days, though most cross within a few days. These results highlight opportunities for investigating the impact of the ASM on global UTLS chemical and aerosol composition over the western Pacific via research aircraft. Plain Language Summary: The Asian summer monsoon system includes a large‐scale anticyclonic circulation in the upper troposphere spanning much of Asia and northern Africa. Within this anticyclone, high concentrations of surface pollutants and aerosols are reported from satellite observations. These chemical signatures highlight the role of monsoon convection in transporting regional emissions into the upper troposphere and lower stratosphere. In situ measurements of monsoon‐transported air masses are important for understanding the climate impacts of the system. In this work, we present a set of analyses to show that the subseasonal scale dynamics of the Asian monsoon anticyclone produces eastward shedding of monsoon‐lofted Asian boundary layer air over the western Pacific in a 10‐ to 20‐day time scale. The result of this transport analysis provides basic information for designing airborne situ measurement studies over the western Pacific Ocean. Key Points: The Asian summer monsoon upper tropospheric anticyclone centers form a statistical trimodal distributionThe western Pacific mode is a key pathway for the convectively lofted Asian boundary layer air to enter the global upper troposphereMore than half of the upper tropospheric air mass in the western Pacific mode move into the stratosphere within a week [ABSTRACT FROM AUTHOR]

This paper proposes a method to detect abnormal motions in real vehicle situations based on trajectory data. Our approach uses a Gaussian process (GP) regression that facilitates to approximate expected vehicle’s movements over a whole environment based on sparse observed data. The main contribution of this paper consists in decomposing the GP regression into spatial zones, where quasi-constant velocity models are valid. Such obtained models are employed to build a set of Kalman filters that encode observed vehicle’s dynamics. This paper shows how proposed filters enable the online identification of abnormal motions. Detected abnormalities can be modeled and learned incrementally, automatically by intelligent systems. The proposed methodology is tested on real data produced by a vehicle that interacts with pedestrians in a closed environment. Automatic detection of abnormal motions benefits the traffic scene understanding and facilitates to close the gap between human driving and autonomous vehicle awareness. [ABSTRACT FROM AUTHOR]

Introduction: Depression is associated with increased risk of incident and recurrent cardiovascular disease, while the association between depression and cardiovascular health (CVH) remains unknown. Because the natural course of depression varies widely, different patterns of depression, as well as co-occurring factors such as cigarette smoking, may influence this relationship. We examined potential interactions between longitudinal patterns of depression and smoking with CVH.Methods: Using data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, we modeled trajectories of depression (Center for Epidemiologic Studies Depression scale scores; Years 5, 10, 15, 20) and smoking (cigarettes/day; Years 0, 2, 5, 7, 10, 15, 20). We calculated a modified American Heart Association (AHA) CVH Score (weight, blood glucose, cholesterol, blood pressure, physical activity, and diet; Year 20); higher scores indicate better CVH. Generalized linear models evaluated associations between depression trajectories, smoking trajectories, and their interaction with CVH Score.Results: The depression trajectory x smoking trajectory interaction was not associated with CVH Score, but main effects of depression trajectory (p < .001) and smoking trajectory (p < .001) were observed. Participants with patterns of subthreshold depression (β = -0.26, SE=0.08), increasing depression (β = -0.51 SE = 0.14), and high depression (β = -0.65, SE = 0.32) had lower CVH Scores than those without depression. Compared to never smokers, participants who quit smoking had higher CVH Scores (β = 0.38, SE = 0.11), while participants with the greatest smoking exposure had lower CVH Scores (β = -0.49, SE = 0.22).Limitations: CVH Scores were adapted from the AHA guidelines based on the available CARDIA data.Conclusions: Deleterious depression and smoking trajectories are independently but not synergistically associated with worse CVH. [ABSTRACT FROM AUTHOR]

Introduction: Almost all individuals with Down syndrome (DS) will develop neuropathological features of Alzheimer's disease (AD). Understanding AD biomarker trajectories is necessary for DS-specific clinical interventions and interpretation of drug-related changes in the disease trajectory., Methods: A total of 177 adults with DS from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) underwent positron emission tomography (PET) and MR imaging. Amyloid-beta (Aβ) trajectories were modeled to provide individual-level estimates of Aβ-positive (A+) chronicity, which were compared against longitudinal tau change., Results: Elevated tau was observed in all NFT regions following A+ and longitudinal tau increased with respect to A+ chronicity. Tau increases in NFT regions I-III was observed 0-2.5 years following A+. Nearly all A+ individuals had tau increases in the medial temporal lobe., Discussion: These findings highlight the rapid accumulation of amyloid and early onset of tau relative to amyloid in DS and provide a strategy for temporally characterizing AD neuropathology progression that is specific to the DS population and independent of chronological age., Highlights: Longitudinal amyloid trajectories reveal rapid Aβ accumulation in Down syndrome NFT stage tau was strongly associated with A+ chronicity Early longitudinal tau increases were observed 2.5-5 years after reaching A., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)