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Purpose: Family may play an important role in Hematopoietic stem cell transplantation (HSCT) recovery; however, little is known about the effect of family functioning on an individual’s health. The purpose of this study was to examine the effect of family cohesion (family functioning) on the trajectory of HSCT recipients’ symptom distress (symptom frequency and symptom bother) before, during, and after HSCT. Methods: Secondary analysis was conducted using data collected from178 individuals who underwent HSCT. Longitudinal parallel-process (LPP) modeling was used to examine how family cohesion and HSCT associated symptoms (symptom frequency and symptom bother) change over time; and how these longitudinal changes relate to each other. Results: The trajectory of family cohesion predicted the trajectories of HSCT associated symptom frequency and bother. HSCT recipients who experienced higher family cohesion at baseline (T1) showed lower symptom frequency (pConclusion: Findings indicate that higher family cohesion predicts decrease in symptom distress over the HSCT trajectory. Interventions aimed at enhancing family cohesion have the potential to lower HSCT recipients’ symptom distress. Further research is needed to understand the critical role of family cohesion and family functioning and their relationship with HSCT symptom distress prevention, early detection, and risk stratification.

Objectives We investigated the association between maternal depression trajectories from pregnancy until 2 years postpartum, and problematic internet use in children at nine years of age. Methods A total of 1132 mother-child pairs from the Panel Korea Study for Children were used. Maternal depression was assessed repeatedly using the Kessler scale (K-scale) (during pregnancy, and at 6, 12, and 24 months of age). Problematic internet use was assessed in children 9 years old using the K-scale. The children's behavioral problems were assessed using the Child Behavior Checklist at nine years old. Latent profile analysis was used to identify the group trajectories of maternal depression. We compared the severity of problematic internet use of the children according to the maternal depression trajectories. We also identified behavioral problems in children with problematic internet use in each maternal depression trajectory. Results Trajectory analysis identified three group trajectories of maternal depression: no symptoms (n = 478), mild symptoms (n = 534), and moderate symptoms (n = 120). The mild and moderate maternal depression trajectory groups were more associated with high-risk internet use in 9-year-old children compared to the non-depressed group. In children with high-risk internet use, more severe behavioral problems were reported when their mothers had depression. Conclusion Maternal depression up to two years after childbirth affected problematic internet use in middle childhood.

OBJECTIVE To visualize the postoperative clinical course using the comprehensive complication index (CCI) and to propose an early alarming sign for subsequent serious outcomes in perihilar cholangiocarcinoma. BACKGROUND Surgery for this disease carries a high risk of morbidity and mortality. The developmental course of the overall morbidity burden and its clinical utility are unknown. METHODS Patients who underwent major hepatectomy for perihilar cholangiocarcinoma between 2010 and 2019 were reviewed retrospectively. All postoperative complications were evaluated according to the Clavien-Dindo classification (CDC), and the CCI was calculated on a daily basis until postoperative day 14 to construct an accumulating graph as a trajectory. Group-based trajectory modeling was conducted to categorize the trajectory into clinically distinct patterns and the predictive power of early CCI for a subsequent serious course was assessed. RESULTS A total of 4230 complications occurred in the 484 study patients (CDC grade I, n=27; II, n=132; IIIa, n=290; IIIb, n=4; IVa, n=21; IVb, n=1; and V, n=9). The trajectory was categorized into 3 patterns: mild (n=209), moderate (n=235), and severe (n=40) morbidity courses. The 90-day mortality rate significantly differed among the courses: 0%, 0.9%, and 17.5%, respectively (P

Genetic background has been considered one of the important contributors to the rate of cognitive decline among patients with Alzheimer's disease (AD). We conducted a 4-year longitudinal follow-up study, recruited 255 AD and 44 mild cognitive impairment (MCI) patients, and used a data-driven trajectory analysis to examine the influence of selected AD risk genes on the age for and the rate of cognitive decline in Han Chinese population. Genotyping of selected single-nucleotide polymorphisms in the APOE, ABCA7, SORL1, BIN1, GAB2, and CD33 genes was conducted, and a Bayesian hierarchical model was fitted to analyze the trajectories of cognitive decline among different genotypes. After adjusting for sex and education years, the APOE e4 allele was associated with an earlier mean change of -2.39 years in the age at midpoint of cognitive decline, the G allele in ABCA7 rs3764650 was associated with an earlier mean change of -1.75 years, and the T allele in SORL1 rs3737529 was associated with a later mean change of 2.6 years. Additionally, the rate of cognitive decline was associated with the APOE e4 allele and SORL1 rs3737529. In summary, APOE and SORL1 might be the most important genetic factors related to cognitive decline in Han Chinese population.

The article deals with the issues of risk management of information projects. We propose not a deductive approach traditionally used in risk analysis, based on knowledge of the main provisions and their concretization in a real situation, but an inductive approach that requires data accumula- tion and the construction of training data sets as a result of a retrospective analysis of the progress and implementation of individual projects. It is proposed to use methods of trajectory analysis of sequences of events, each of which characterizes the state of risk of project implementation. To test this approach, random sequences of risk events are generated and data analysis methods are applied to these sequences.

Introduction Management of chronic kidney disease (CKD) entails high medical complexity and often results in high hospitalization burden. There are limited data on the associations of longitudinal hospital utilization patterns with adverse clinical outcomes in individuals with CKD. Methods We derived cumulative all-cause hospitalization trajectory groups using latent class trajectory analysis in 3012 participants of the Chronic Renal Insufficiency Cohort (CRIC) Study who were alive and did not reach end-stage kidney disease (ESKD) within 4 years of study entry. Cox proportional hazards models tested the associations between hospitalization trajectory groups and risks of ESKD and death prior to the onset of ESKD (ESKD-censored death). Results Within 4 years of study entry, there were 5658 hospitalizations among 3012 participants. We identified 3 distinct subgroups of individuals with CKD based on cumulative all-cause hospitalization trajectories over 4 years: low-utilizer (n = 1066), intermediate-utilizer (n = 1802), and high-utilizer (n = 144). High-utilizers represented a patient population of lower socioeconomic status who had a greater prevalence of comorbid conditions and lower kidney function compared with intermediate- and low-utilizers. After the 4-year ascertainment period to form the trajectory subgroups, there were 544 ESKD events and 437 ESKD-censored deaths during a median follow-up time of 5.1 years. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.49-fold (95% confidence interval [CI] 1.22–1.84) and 1.75-fold (95% CI 1.20–2.56) higher risk of ESKD in adjusted analyses, respectively. Compared with low-utilizers, intermediate-utilizers and high-utilizers were at 1.48-fold (95% CI 1.17–1.87) and 2.58-fold (95% CI 1.74–3.83) higher risk of ESKD-censored death in adjusted analyses, respectively. Conclusions Trajectories of cumulative all-cause hospitalization identify subgroups of individuals with CKD who are at high risk of ESKD and death., Graphical abstract

Evidence was limited on trajectory of body mass index (BMI) through adulthood and its association with hypertension. We aimed to evaluate their association by sex in large-scale study. Data were obtained from the China Health and Nutrition Survey (CHNS) from 1991 to 2015. Latent class trajectory analysis (LCTA) was used to capture BMI change trajectories. Hazard risks (HRs) were estimated from Cox proportion hazard regression. Among 14,262 participants (mean age, 38.8; 47.8% men), 5138 hypertension occurred (2687 men and 2451 women) occurred during a mean follow-up 9.6 years. Four body mass trajectory groups were identified as BMI loss, stable, moderate and substantial gain. Appropriately half of participants (48.0%) followed 1 of the 2 BMI gain trajectories, where BMI increased at least 3 kg/m2 overtime. Compared with participants with stable BMI, those gaining BMI substantially had higher risk of hypertension by 65% (HR 1.65, 95% CI 1.45–1.86) in male and 83% (HR 1.83, 95% CI 1.58–2.12) in female. The HRs in BMI loss patterns were 0.74 (0.62–0.89) in men and 0.87 (0.75–1.00) in women. Our findings imply that majority of Chinese adults transited up to a higher BMI level during follow-up. Avoiding excessive weight gain and maintaining stable weight might be important for hypertension prevention.

BackgroundThis study aimed to identify the trajectories of work ability over 16 years preceding the individual pensionable age and to examine the association with retirement timing.MethodsThe study population consisted of 2612 public sector employees from the Finnish Retirement and Aging study and the Finnish Public Sector study. Participants were grouped into ‘no-extension’ (retired at the individual pensionable date or worked no longer than 6 months after that date) and ‘extension’ (worked more than 6 months after individual pensionable age). Trajectories of self-reported work ability score (0–10) in maximum of eight measurement points over 16 years preceding retirement were examined using the group-based latent trajectory analysis. Log-binomial regression was used to analyse the association between trajectory groups and extended employment.ResultsFour stable (‘Stable excellent’, 7%; ‘Stable high’, 62%; ‘Stable medium’, 24%; ‘Low’, 4%) and one decreasing (‘Declining’, 3%) work ability trajectories were identified. After taking into account gender, age, occupational status, marital status and self-rated health, ‘Stable excellent’ trajectory was associated with a higher likelihood of extended employment compared with the ‘Low’ (risk ratio (RR) 2.38, 95% CI 1.21 to 4.68) and to the ‘Declining’ (RR 2.82, 95% CI 1.32 to 6.01) trajectories. There was no difference in retirement timing between ‘Declining’, ‘Low’ and ‘Stable medium’ trajectories.ConclusionWork ability remained relatively stable among majority of the participants over 16 years of follow-up. Stable excellent work ability from mid-life to late career was associated with higher likelihood of extending employment beyond individual pensionable age than those with low or declining work ability.

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.

OBJECTIVE: People with HIV continue to exhibit cognitive symptoms after suppressive antiretroviral therapy (ART). It remains unclear if initiating ART during acute HIV-1 infection (AHI) uniformly improves cognitive outcomes. METHODS: Sixty-seven individuals (96% men, median age 28 years) initiated ART immediately after AHI diagnosis and maintained viral suppression for 6 years. They underwent a four-test neuropsychological battery that measured fine motor speed and dexterity, psychomotor speed, and executive functioning at baseline (pre-ART AHI), weeks 12, 24 and 96, and annually thereafter through week 288. Performances were standardized to calculate an overall (NPZ-4) score and frequencies of impaired cognitive performance (≤-1 SD on at least two tests, or ≤-2 SD on at least one test). Group-based trajectory analysis (GBTA) was applied to identify distinct neuropsychological trajectories modelled from baseline to week 288. Posthoc analyses examined HIV-1 and demographic factors that differed between trajectory subgroups. RESULTS: NPZ-4 scores improved from baseline to week 96 (P

The aim of this study was to determine the predictability of clinical complications by analyzing the perfusion index historical behavior patterns with polynomial group-based trajectory model.

Movement datasets are often complex and require sophisticated processing and analysis. A thorough understanding of the dataset is needed to choose the right methods and to interpret their results. ...

ObjectiveIncreasing numbers of patients are developing rheumatoid arthritis (RA) at an older age, and optimal treatment of patients with elderly-onset RA (EORA) is attracting greater attention. This study aimed to analyze the efficacy and safety of biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in EORA and non-EORA elderly patients.MethodsA cohort of patients with RA treated with b/tsDMARDs were retrospectively analyzed. Only patients aged ≥ 60 years were included. Among them, patients who developed RA aged ≥ 60 years were categorized as EORA, whereas those aged < 60 years were categorized as non-EORA elderly. Disease activity was compared between the EORA and non-EORA elderly groups.ResultsIn total, 1040 patients were categorized as EORA and 710 as non-EORA elderly. There were no significant differences in characteristics at baseline between the 2 groups. The proportion of patients with low and high disease activity was comparable at Weeks 2, 22, and 54 between the EORA and the non-EORA elderly group. There were no significant differences in the reasons for the discontinuation of b/tsDMARDs between the 2 groups. Elderly RA onset did not affect changes in Clinical Disease Activity Index (CDAI) and Health Assessment Questionnaire–Disability Index, nor did it affect the reasons for b/tsDMARD discontinuation between the 2 groups. The trajectory analysis on CDAI responses to b/tsDMARDs for 54 weeks identified 3 response patterns. The proportion of patients categorized into each group and CDAI response trajectories to b/tsDMARDs were very similar between EORA and non-EORA elderly patients.ConclusionCDAI response patterns to b/tsDMARDs and HR of adverse events were similar between EORA and non-EORA elderly patients.

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN , k nearest neighbors (kNN) , and feature selection (FS) . DBSCAN-GTO first applies DBSCAN to derive the micro clusters , which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

INTRODUCTION The optimal ablation approach for persistent atrial fibrillation (AF) remains unclear. METHODS AND RESULTS Objective was to compare the long-term rates of freedom from AF/AT in patients that underwent STAR mapping guided ablation against outcomes of patients undergoing conventional ablation procedures. Patients undergoing ablation for persistent AF as part of the Stochastic Trajectory Analysis of Ranked signals (STAR) mapping study were included. Outcomes following 'pulmonary vein isolation (PVI) plus STAR mapping guided ablation (STAR mapping cohort) were compared to patients undergoing PVI alone ablation during the same time period and also a propensity-matched cohort undergoing PVI plus the addition of complex fractionated electrogram (CFAE) and/or linear ablation ("conventional ablation"). Rates of procedural AF termination and freedom from AF/AT during follow-up were compared. Sixty-five patients were included in both the STAR cohort and propensity matched conventional ablation cohort. AF termination rates were significantly higher in the STAR cohort (51/65, 78.5%) than conventional ablation cohort (10/65, 15.4%) and PVI alone ablation cohort (13/50, 26.0%; STAR cohort vs. other 2 cohorts both p

To identify low back pain (LBP) trajectories from early adolescence through to early adulthood and to investigate whether sustained levels of elevated subclinical C-reactive protein (CRP) are linked with these LBP trajectories. We analysed longitudinal data from 1513 participants who were enrolled in the Raine Study cohort. Data on LBP with impact on daily living and CRP were collected at the ages of 14, 17, 20, and 22. We constructed group-based trajectory models to identify discrete trajectories of LBP with impact. We then evaluated how the CRP trajectories and the LBP with impact trajectories evolved jointly over time using a multi-trajectory analysis. The model identified three LBP trajectories. One subgroup included almost half the participants (46.1%) who had a consistently low probability of LBP. Another subgroup comprising 43.5% of participants had an increasing probability of LBP, while one in ten participants (10.4%) had a decreasing probability of LBP. There were no associations between elevated CRP and LBP trajectory subgroup membership. Although young people follow distinct trajectories of LBP, CRP trajectories do not appear to be a distinguishing factor of the LBP trajectories. Previously reported associations between CRP and LBP may be explained by comorbidity or other factors. Future studies undertaking trajectory analysis should consider comorbidity clusters. Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding

Purpose – This research objective to investigate the relations between budgetary participation, organizational commitment and managerial performance. Design/methodology/approach – A descriptive, survey and quantitative study was carried out, based on trajectory analysis and with data from 150 managers with budgetary responsibilities of the largest Brazilian export companies in the business. Findings – The results reveal that budgetary participation influences managerial performance in two ways: directly, as a result of managers 'involvement and influence in the budgeting process, and indirectly, when managers' commitment to the organization increases due to their participation and involvement in budgetary definitions. Research limitations/implications – The limitation of the use of self-reported measures to measure the proposed relationships is highlighted. This research indicates that behavioral aspects related to the budgeting process, have significant implications for work-related results. Practical implications – The results suggest that organizations should allow the participation of managers in setting goals and budget objectives, as this action triggers a greater commitment of the individual to the organization and better performance in the work. Social implications – The findings can help organizations to understand factors that can be improved in the organization so that employees feel committed to the company in which they operate and consequently present better budgetary participation and performance. Originality/value – This research sought to expand the existing discussion in the accounting literature on the role of budgetary processes and their implications for organizational commitment and managerial performance in budgetary activities.

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA); however, few studies investigate beyond cross-sectional observations or comparisons across two timepoints. By characterizing the trajectory of cartilage thickness changes over 8 years in healthy individuals from the OA initiative data set, this study discovers associations between the dynamics of cartilage changes and OA incidence. A fully automated cartilage segmentation and thickness measurement method were developed and validated against manual measurements: mean absolute error = 0.11-0.14 mm (n = 4129 knees) and automatic reproducibility = 0.04-0.07 mm (n = 316 knees). The mean thickness for the medial and lateral tibia (MT, LT), central weight-bearing medial and lateral femur (cMF, cLF), and patella (P) cartilage compartments were quantified for 1453 knees at seven timepoints. Trajectory subgroups were defined per cartilage compartment such as stable, thinning to thickening, accelerated thickening, plateaued thickening, thickening to thinning, accelerated thinning, or plateaued thinning. For tibiofemoral compartments, the stable (22%-36%) and plateaued thinning (22%-37%) trajectories were the most common, with an average initial velocity (μm/month), acceleration (μm/month2 ) for the plateaued thinning trajectories LT: -2.66, 0.0326; MT: -2.49, 0.0365; cMF: -3.51, 0.0509; and cLF: -2.68, 0.041. In the patella compartment, the plateaued thinning (35%) and thickening to thinning (24%) trajectories were the most common, with an average initial velocity, acceleration for each -4.17, 0.0424; 1.95, -0.0835. Knees with nonstable trajectories had higher adjusted odds of OA incidence than stable trajectories: accelerated thickening, accelerated thinning, and plateaued thinning trajectories of the MT had adjusted odds ratio (OR) of 18.9, 5.48, and 1.47, respectively; in the cMF, adjusted OR of 8.55, 10.1, and 2.61, respectively.

Glioblastoma (GBM) is the most common and lethal primary brain tumor. In this study, we aimed to investigate the differentiation states of GBM cells and their clinical relevance. Integrated single-cell RNA-sequencing (scRNA-seq) data and bulk RNA-seq data from GBM samples were used for analysis. Two subsets of GBM cells in distinct differentiation states were characterized, and 498 GBM cell differentiation-related genes (GDRGs) were identified. GDRGs were significantly correlated with immune regulation and metabolic pathways. We classified the GBM patients into two groups based on the expression of GDRGs in tumors and found that the cell differentiation-based classification successfully predicted patient overall survival (OS), immune checkpoint expression and likelihood of immunotherapy response in GBMs. FN1, APOE, RPL7A and GSTM2 were the 4 most significant survival-predicting GDRGs, and patients with different expression levels of each of these genes had distinct survival outcomes. Finally, a nomogram composed of the GDRG signature, age, pharmacotherapy, radiotherapy, IDH mutations and MGMT promoter methylation was generated and validated in two large GBM cohorts to predict GBM prognosis. This study highlights the significant roles of cell differentiation in predicting the clinical outcomes of GBM patients and their potential response to immunotherapy, suggesting promising therapeutic targets for GBM.

Postnatal care is the aspect of maternity care with which women in England are least satisfied. Little is known about first-time mothers’ expectations of postnatal care, or how these expectations relate to their experiences and appraisal of care. Thirty-two first-time mothers took part in a longitudinal qualitative descriptive study, based on two semi-structured interviews—the first in pregnancy, and the second 2 to 3 months after birth. Trajectory analysis was used to identify the thematic patterns in the relationships between postnatal care expectations, needs, experiences, and confidence. Five trajectories were identified, showing that mothers’ satisfaction with postnatal care and confidence were primarily influenced not by the extent to which their expectations were met but the varied extent to which their individual postnatal needs were met. Rapid and responsive assessment of needs both antenatally and postnatally, and appropriate adjustment of care, is key in supporting women effectively at this time.

Purpose Study objectives were to examine: (a) biomarker trajectories (change from presurgical baseline values of Lymphedema index (L‐Dex) units and arm volume difference) and symptom cluster scores 24 months after breast cancer surgery and (b) associations of these objective biomarkers and symptom cluster scores. Patient/treatment characteristics influencing trajectories were also evaluated. Methods A secondary analysis of data from the published interim analysis of a randomized parent study was undertaken using trajectory analysis. Five hundred and eight participants included in the prior analysis with 24 months of postsurgical follow‐up were initially measured with bioelectric impedance spectroscopy (BIS) and tape measure (TM) and completed self‐report measures. Patients were reassessed postsurgery for continuing eligibility and then randomized to either BIS or TM groups and measured along with self‐report data at regular and optional* visits 3, 6,12,15*,18, 21*, and 24‐months. Results Three subclinical trajectories were identified for each biomarker (decreasing, stable, increasing) and symptom cluster scores (stable, slight increase/decrease, increasing). Subclinical lymphedema was identified throughout the 24‐month period by each biomarker. An L‐Dex increase at 15 months in the BIS group was noted. The self‐report sets demonstrated contingency coefficients of 0.20 (LSIDS‐A soft tissue, P = .031) and 0.19 (FACTB+4, P = .044) with the L‐Dex unit change trajectories. Conclusions These data support the need for long‐term (24 months) prospective surveillance with frequent assessments (every 3 months) at least 15 months after surgery. Statistically significant convergence of symptom cluster scores with L‐Dex unit change supports BIS as beneficial in the early identification of subclinical lymphedema., Long‐term (24 months) lymphedema prospective surveillance with frequent assessments (every 3 months) at least through 15‐months post‐surgery is indicated. Convergence of symptom cluster scores with L‐Dex unit change supports Bioelectric Impedance Spectroscopy as beneficial in the early identification of sub‐clinical lymphedema.

Vessel monitoring system (VMS) is an effective tool for the quantified study of fishing. As the fishing vessels equipped with VMS clients, a large amount of trajectory data has been collected, which brings a new opportunity for fishing research. According to fishery safety production regulations, fishing vessels should perform grouping operations based on actual conditions, but the group information cannot be collected by the VMS. In this paper, we propose the Fishing Vessels Relationships Discovery system (FVRD) by calculating the interaction time among fishing vessels and then using it as a weight to generate a relationship network. The experiment of the proposed FVRD on the vessel dataset of Zhejiang Province reveals that the generated fishing community is consistent with the type of operation of the fishing vessels, which means the proposed method is effective. The experiment also indicates that the fishing vessel relationship network has the characteristics of small-world and scale-free that is similar to the human social network, Moreover, FVRD shows that 86.78% of vessels share the collaboration relationships over one week, 10.72% of vessels are in the long-term cooperation, confirming the regulation that most fishing vessels are sailing together for fishing.

This paper presents a design and evaluation of a customized finger prosthesis that generates a natural finger motion. The design of the prosthesis followed two primary requirements: i) The size of the prosthesis should reflect that of an amputee’s finger; ii) the prosthesis should enable the natural finger motion of the amputee. To achieve these aims, two methods were employed: i) An incomplete fourbar mechanism by utilizing the remaining joint of a subject as the joint in the mechanism; ii) a fingertip trajectory analysis in polar coordinates to model the natural finger motion. In this paper, we focused on the design of the finger prosthesis so that the prototype was manufactured based on the finger information of a normal subject before applying it to the actual finger amputee. The performance of the proposed system was verified by intensive experiments for grasping and manipulation with the proposed system.

OBJECTIVE: To examine the hypothesis that PTSD with delayed expression in some cases occurs without subthreshold PTSD symptoms above background levels bridging the gap between the traumatic exposure(s) and the clinical diagnosis.METHODS: We performed systematic searches of peer-reviewed papers in English referenced in Pubmed, Embase, or PsycINFO and ascertained 34 prospective studies of PTSD symptom trajectories identified by latent class growth statistical modeling. Studies with delayed and low-stable trajectories provided appropriate data for this study. We computed the difference between the delayed trajectory PTSD symptom sumscore and the low-stable PTSD sumscore at the observed points in time after the traumatic event(s).RESULTS: In 29 study populations, the latent class growth analyses displayed delayed trajectories, and in these, we identified 110 data points (% PTSD sumscore difference/months since traumatic exposure). The median PTSD symptom sumscore was 25% higher during the initial 6 months among individuals in the delayed trajectory compared to those in low-stable trajectory. From this level, the difference widened and reached a plateau of 40-50% higher. The variation was large, and the baseline participation rate and loss to follow-up were exceeding 25% in the majority of the studies. Heterogeneity of populations, measures, and analyses precluded formal meta-analysis.CONCLUSION: Delayed PTSD is preceded by PTSD symptoms during the first year in most cases. Still, few individuals may experience an asymptomatic delay. The results underpin the rationale for monitoring PTSD symptoms and may inform forensic assessments in that delayed PTSD without symptoms bridging the traumatic event is rare.

One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand pedestrian patterns in the environment, such as train station. The rapid movement of pedestrians makes the robot more difficult to solve the collision problem. In this paper, we propose a navigation probability map to solve the pedestrians&rsquo, rapid movement problem based on the influencer recognition model (IRM). The influencer recognition model (IRM) is a data-driven model to infer a distribution over possible causes of pedestrian&rsquo, s turning. With this model, we can obtain a navigation probability map by analyzing the changes in the effective pedestrian trajectory. Finally, we combined navigation probability map and artificial potential field (APF) method to propose a robot navigation method and verified it on our data-set, which is an unobstructed, overlooked pedestrians&rsquo, data-set collected by us.

Objective In this study, we aimed to inform clinical practice by identifying distinct subgroups of U.S. veteran psychiatry inpatients on their alcohol and drug use severity, posttraumatic stress disorder (PTSD) symptoms, and suicidal ideation over time. Method Participants were 406 patients with co-occurring substance use and mental health disorders. A parallel latent growth trajectory model was used to characterize participants' symptom severity across 15 months posttreatment intake. Results Four distinct classes were identified: 47% "normative improvement," 32% "high PTSD," 11% "high drug use," and 9% "high alcohol use." Eighty percent of the sample had reduced their drinking and drug intake by half from baseline to 3 months, and those levels remained stable from 3 to 15 months. The High PTSD, High Drug Use, and High Alcohol Use classes all reported levels of PTSD symptomatology at baseline consistent with a clinical diagnosis, and symptom levels remained high and stable across all 15 months. The Normative Improvement class showed declining drug and alcohol intake and was the only class exhibiting reductions in PTSD symptomatology over time. High substance use classes showed initial declines in suicidal ideation, then an increase from 9 to 15 months. Conclusions The reduction in frequency of drinking and drug use for 80% of the sample was substantial and supports the potential efficacy of current treatment approaches. However, the high and stable levels of PTSD for more than 50% of the sample, as well as the reemergence of suicidal ideation in a sizable subgroup, underscore the difficulty in finding and linking patients to effective interventions to decrease symptomatology over time.

In order to deepen understanding of team processes in dynamic organizational contexts, we suggest that analyses employing techniques to identify and analyze team member interaction patterns and trajectories are necessary. After presenting a brief review of interaction data coding and reliability requirements, we first review examples of two approaches used in the identification and analysis of interaction patterns in teams: lag sequential analysis and T-pattern analysis. We then describe and discuss three statistical techniques used to analyze team interaction trajectories: random coefficient modeling, latent growth modeling, and discontinuous growth analysis. We close by suggesting several ways in which these techniques could be applied to data analysis in order to expand our knowledge of team interaction, processes, and outcomes in complex and dynamic settings.

With the rapid development of the Internet of Things (IoT), massive trajectories have been generated. Trajectory data is beneficial for many urban applications. This demo presents a holistic trajectory data management system based on distributed platforms, such as Spark and HBase, namely JUST-Traj. It provides a variety of indexes to efficiently support spatio-temporal queries and analyses on massive trajectories. Additionally, it provides a convenient SQL engine to execute all operations (storage, queries, analyses) through a SQL-like statement. Finally, we design a web portal for developers and demonstrate different operations in the portal.

BACKGROUND The current study aimed to investigate the patterns of postoperative pain trajectories over time and their associated risk factors in patients receiving video-assisted thoracoscopic surgery (VATS) and epidural analgesia (EA) for non-small cell lung cancer (NSCLC). METHODS This retrospective study was conducted at a tertiary medical center and included patients undergoing VATS for stage I NSCLC between 2011 and 2015. Maximal pain intensity was recorded daily during the first postoperative week. Group-based trajectory analysis was performed to categorize variations in pain scores over time. Associations between pain trajectory classification and amount of EA administered and length of hospital stay (LOS) after surgery were also evaluated. RESULTS A total of 635 patients with 4,647 pain scores were included in the analysis, and two postoperative pain trajectory groups were identified: group 1, mild pain trajectory (78%); and group 2, rebound pain trajectory (22%). Risk factors for rebound pain trajectory were a surgical time longer than 3 hours (odds ratio, OR: 1.97; 95% confidence interval, CI: 1.27 - 3.07), female sex (OR: 1.62; 95% CI: 1.04 - 2.53) and higher pain score on postoperative day 0 (OR: 1.21; 95% CI: 1.08 - 1.36, linear effect). Although group 2 had a longer LOS (p < 0.001), they did not receive more EA than group 1 (p = 0.805). CONCLUSION Surgical time, sex and pain intensity after surgery were major determinants of rebound pain trajectory, and more aggressive pain control strategies should be considered in high-risk patients.

Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.

As a kind of carcinoma with extremely high mortality and morbidity, there are immense demand for early detection and diagnosis of gastric carcinoma. At present, in medical research, the initial detection of gastric tumors mainly uses the laser detection methods. The Monte Carlo method has good adaptability for exploring the process of photon transportation, therefore, it has the value of extension and application in the research of photons’ transportation in biological tissues. Through exploring the influence of the tumor in gastric tissue on the photon transmission, it can help determine the existence of gastric tumor. After established the corresponding model, we using the program to perform the fitting process, analysising the result then we can draw a conclusion that: according to the trajectory analysis of photons, gastric tumors absorb more photons than the gastric tissues, and such basic features can be used to determine the existence of gastric tumors. Through the analysis of optical absorption density and fluence rate: when photons initially enter the first gastric tissue layer(the Z-axis 0-0.2cm region), because the water layer’s weak optical absorption and scattering effects, optical absorption decreases slowly: from 0.0805cm-1 drop to 0.073cm-1. After entering the second layer, the layer of gastric tissue, because the absorption and scattering effect of gastric tissue is higher than water, the optical absorption density rises sharply to 8.7028cm-1, then with the photon weight vdecreasing, the optical absorption density continues to drop to 0.7128cm-1. After entering the third layer, the layer of gastric tumor, the optical absorption density rises again. When z=0.5cm, the optical absorption density approaches to 1.8848cm-1 and then slowly drops to 0.1338cm-1. Finally, photons enter the second layer of gastric tissue and water layers, and continue to decrease to approach 0cm-1. These data demonstrate that there are effects of gastric tumor on photon transport in gastric tissues. This research will also provide reference and theoretical guidance for the optical imaging and diagnosis of gastric tumors.

Objectives:Caregiving and becoming widowed are risk factors for depression in older adults, but few studies have examined their combined effect on depressive symptom trajectories. In a cohort of ol...

Data prediction and imputation are important parts of marine animal movement trajectory analysis as they can help researchers understand animal movement patterns and address missing data issues. Compared with traditional methods, deep learning methods can usually provide enhanced pattern extraction capabilities, but their applications in marine data analysis are still limited. In this research, we propose a composite deep learning model to improve the accuracy of marine animal trajectory prediction and imputation. The model extracts patterns from the trajectories with an encoder network and reconstructs the trajectories using these patterns with a decoder network. We use attention mechanisms to highlight certain extracted patterns as well for the decoder. We also feed these patterns into a second decoder for prediction and imputation. Therefore, our approach is a coupling of unsupervised learning with the encoder and the first decoder and supervised learning with the encoder and the second decoder. Experimental results demonstrate that our approach can reduce errors by at least 10% on average comparing with other methods.

Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.

The combined role of objectively assessed moderate-vigorous intensity physical activity (MVPA) and sedentary behaviour (SB) is unclear in obesity prevention. This study aimed to identify latent groups for MVPA and SB trajectories from childhood to adolescence and examine their relationship with obesity risk at adolescence. From the Gateshead Millennium Study, accelerometer-based trajectories of time spent in MVPA and SB at ages 7, 9, 12, and 15 were derived as assigned as the predictor variable. Fat mass index (FMI), using bioelectrical impedance at age 15, was the outcome variable. From 672 children recruited, we identified three distinct multiple trajectory groups for time spent in MVPA and SB. The group with majority membership (54% of the cohort) had high MVPA and low SB at childhood, but MVPA declined and SB increased by age 15. One third of the cohort (31%) belonged to the trajectory with low MVPA and high time spent sedentary throughout. The third trajectory group (15% of the cohort) that had relatively high MVPA and relatively low SB throughout had lower FMI (−1.7, 95% CI (−3.4 to −1.0) kg/m2, p = 0.034) at age 15 compared to the inactive throughout group. High MVPA and low SB trajectories when combined are protective against obesity.

BACKGROUND: The current state of the science regarding the care and prognosis of patients with disorders of consciousness is limited. Scientific advances are needed to improve the accuracy, relevance, and approach to prognostication, thereby providing the foundation to develop meaningful and effective interventions. METHODS: To address this need, an interdisciplinary expert panel was created as part of the Coma Science Working Group of the Neurocritical Care Society Curing Coma Campaign. RESULTS: The panel performed a gap analysis which identified seven research needs for prognostic modeling and trajectory analysis (“recovery science”) in patients with disorders of consciousness: (1) to define the variables that predict outcomes; (2) to define meaningful intermediate outcomes at specific time points for different endotypes; (3) to describe recovery trajectories in the absence of limitations to care; (4) to harness big data and develop analytic methods to prognosticate more accurately; (5) to identify key elements and processes for communicating prognostic uncertainty over time; (6) to identify health care delivery models that facilitate recovery and recovery science; and (7) to advocate for changes in the health care delivery system needed to advance recovery science and implement already-known best practices. CONCLUSION: This report summarizes the current research available to inform the proposed research needs, articulates key elements within each area, and discusses the goals and advances in recovery science and care anticipated by successfully addressing these needs.

Background Young people living in remote Aboriginal communities experience some of the highest rates of chlamydia (CT) and gonorrhoea (NG) infection globally. A cluster-randomised controlled trial (TTANGO) in 11 remote primary health services demonstrated point-of-care (POC) testing for CT/NG was acceptable, accurate, improved the uptake and timeliness of treatment, and was cost-saving. Subsequently, POC testing was scaled-up and implemented in a further 20 remote health services (TTANGO2 program) across four jurisdictions (31 in total). We determine whether the uptake of POC testing observed in the trial was also achieved and sustained during the long-term program. Methods We conducted descriptive, interrupted time series and trajectory analyses to compare POC testing patterns over two time periods (trial: 2013–15 and program: 2016–19). For the trajectory analysis, we applied a Poisson model to identify and fit health services to testing groups. Results Among the 11 services who participated in both the trial and program, 7871 tests were conducted in total. The median number of tests per month in the trial was 241(IQR:178–305) and 408(IQR:294–538) in the program, with no significant trend in the trial (5.6 tests per month, p=0.190) but a significant increasing trend in the program (10.52 tests per month, p Conclusions Our findings suggest POC testing can be scaled-up and sustained as part of a routinely implemented program, achieving greater than expected testing numbers to support the clinical and public health benefits of more timely treatment. Further research is underway to investigate barriers among the ‘low’ testing sites. These findings support the proposed transition from syndromic management towards aetiological diagnosis and treatment in low- and middle-income countries.

Wandering is considered to be one of the most common behavioral symptoms of dementia. Designing robust models which are capable of detecting wandering episodes in people living with dementia would allow the prevention of the consequences of this behavior. To tackle this problem, this study proposes a framework where the skeletal trajectories are used to extract patterns from the movements of the participants. These patterns are utilized to classify a movement between wandering and non-wandering behavior using three machine learning methods. The proposed models were assessed based on two datasets collected in different environments consisting of trajectories that are associated with lapping, pacing, and random movements that represent wandering episodes. The predictive model based on the LSTM network achieved the best classification results in terms of macro F1-scores on both datasets with an overall accuracy higher than 70%. The findings of this study present the potential of LSTM-based predictive models in addressing the wandering recognition problem in a real-world scenario with patients suffering from dementia.

Introduction Type 2 diabetes mellitus (T2DM) frequently associates with increasing multi-morbidity/treatment complexity. Some headway has been made to identify genetic and non-genetic risk factors for T2DM. However longitudinal clinical histories of individuals both before and after diagnosis of T2DM are likely to provide additional insight into both diabetes aetiology/further complex trajectory of multi-morbidity. Methods This study utilised diabetes patients/controls enrolled in the DARE (Diabetes Alliance for Research in England) study where pre- and post-T2DM diagnosis longitudinal data was available for trajectory analysis. Longitudinal data of 281 individuals (T2DM n=237 vs matched non-T2DM controls n=44) were extracted, checked for errors and logical inconsistencies and then subjected to Trajectory Analysis over a period of up to 70 years based on calculations of the proportions of most prominent clinical conditions for each year. Results For individuals who eventually had a diagnosis of T2DM made, a number of clinical phenotypes were seen to increase consistently in the years leading up to diagnosis of T2DM. Of these documented phenotypes, the most striking were diagnosed hypertension (more than in the control group) and asthma. This trajectory over time was much less dramatic in the matched control group. Immediately prior to T2DM diagnosis a greater indication of ischaemic heart disease proportions was observed. Post-T2DM diagnosis, the proportions of T2DM patients exhibiting hypertension and infection continued to climb rapidly before plateauing. Ischaemic heart disease continued to increase in this group as well as retinopathy, impaired renal function and heart failure. Conclusion These observations provide an intriguing and novel insight into the onset and natural progression of T2DM. They suggest an early phase of potentially-related disease activity well before any clinical diagnosis of diabetes is made. Further studies on a larger cohort of DARE patients are underway to explore the utility of establishing predictive risk scores.

Blood-drop trajectory analysis can provide investigators with retrospective information regarding the spatial positioning of an injured person. To assist with bloodstain pattern analysis, various commercially available software have been developed and deployed. A systematic review was conducted to understand the extent of experimental validation and applications of blood-drop trajectory analysis software to case work. Ninety-two sources between 1987 and 2020 were identified including peer-reviewed studies and commercial websites. Thirty-four of these were validation studies, of which, only two involved impact patterns generated from greater than 1 m from the main target surface. Fifteen software were identified during this review with six documented to have been applied in casework. The reviewed software do not appear to fully satisfy relevant forensic validation criteria, based on publicly available literature. In some cases, software underwent limited experimental validation prior to real-world application with subsequent references to this in later literature. This review provides forensic investigators and bloodstain pattern analysts with a comprehensive overview of all available software options, knowledge of the extent of research into validating these techniques and highlights documented applications of these software in criminal cases.

International audience; Growth Hormone (GH) under its human recombinant homologue (rhGH), may be abused by athletes to take advantage of its well-known anabolic and lipolytic properties; hence it is prohibited in sports by the World Anti-Doping Agency. Due to the rapid turnover of rhGH, anti-doping screening tests have turned to monitor two endocrine biomarkers (IGF-I and P-III-NP), but unfortunately, they show population-wise variability, limiting the identification rate of rhGH users. Previous studies have evidenced the numerous effects of GH on human physiology, especially in hematopoiesis and steroidogenesis. In this work, aiming to discover novel physiological rhGH biomarkers, we analyzed the complete blood count and the steroidomics profile of healthy, physically active, young males treated either with EPO + rhGH or EPO + placebo. The time-trends of these two physiological routes have been analyzed through geometric trajectory analysis (GTA) and OPLS-DA. Individuals supplemented with micro-doses of rhGH exhibited different leukopoietic and steroidal profiles compared to the control population, suggesting a role of the rhGH in both pathways. In the article, hypotheses on the observed differences are discussed according to the most recent literature and compared to results in animal models. The use of leukopoietic and steroidal biomarkers together with endocrine biomarkers (IGF-1 and P-III-NP) allows to correctly classify over 98% of samples with no false positives, miss-classifying only one single sample (false negative) over a total of 56; a promising result, if compared to the current rhGH detection strategies.

Using a national sample, we explored the distribution of and longitudinal trajectory of handgrip strength (HGS) in Chinese non-institutionalized middle-aged and older adults. We included a total of 12292 participants in the cross-sectional distribution analysis and a total of 7193 in the longitudinal trajectory analysis. The mean HGS was significantly higher (P < 0.001) in males (38.88 kg) than in females (26.52 kg). The HGS centiles and mean HGS in both males and females decreased as age increased. HGS declined with time in a nonlinear manner. HGS declined faster in males than in females.

Empirical growth models have widespread application in the field of aquaculture. These models allow estimates of harvest size and waste outputs in addition to nutrient and feed requirements. In an effort to increase the ability to predict shrimp growth, the specific growth rate (SGR) and thermal‐unit growth coefficient (TGC) models were fitted to 15 datasets encompassing growth of Pacific whiteleg shrimp (Litopenaeus vannamei). Shrimp were reared under commercial conditions in Southeast Asia with weights ranging from 0.01 g to 34 g. Growth rates were regressed against body weights to identify changes in growth pattern across life stages. Analysis identified two distinct patterns of growth, with a break point between stanzas at 7.5 g. The body weight exponent of the TGC model, traditionally assumed to be (1 − b) = 1/3, was solved for iteratively in each identified growth stanza in an effort to improve the goodness of fit of the TGC model. Average body weight exponents in the first and second stanzas were 0.416 and 0.952 respectively. Projected growth trajectories using these exponents resulted in significantly better fits in comparison to the traditional TGC and the SGR on the basis of statistical measures of goodness of fit.

Introduction The optimal way to map localized drivers in persistent atrial fibrillation (AF) remains unclear. The objective of the study was to apply a novel vector mapping approach called Stochastic Trajectory Analysis of Ranked signals (STAR) in AF. Methods and Results Patients having persistent AF ablation were included. Early sites of activation (ESA) identified on global STAR maps created with basket catheters were used to guide AF ablation post‐pulmonary vein isolation (PVI). All patients also had sequential STAR maps created with ≥10 PentaRay recordings of 30 seconds. These were validated offline in their ability to identify the ESA targeted with a study‐defined ablation response (AF termination or cycle length [CL] slowing of ≥30 ms). Thirty‐two patients were included in whom 92 ESA were identified on the global STAR maps, with 73 of 83 targeted sites demonstrating an ablation response (24 AF termination and 49 CL slowing). Sixty‐one out of 73 (83.6%) ESA were also identified on the sequential STAR maps. These showed greater consistency (P