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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

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.

Objectives Occipitocervical fusion (OCF) is an effective treatment for instability of occipitocervical junction (OCJ). The occipital condyle screw serves as a novel surgical technique for occipitocervical fixation. However, the intraoperative procedures for the occipital condyle screw technique have relied on surgeons' experience, so the pool of surgeons who are able to perform this surgery safely is limited. The present study aims to evaluate the feasibility and safety of the occipital condyle screw technique using human cadavers and to provide image anatomy for clinical application basis. Methods The scientific study comprised 10 fresh-frozen cadaveric specimens from the anatomy department of Qingdao University. Placement of the occipital condyle screws (3.5 mm diameter and 20.0 mm length) was performed in the 10 fresh-frozen cadaveric specimens with intact occipitocervical junctions, respectively. Occipitocervical CT was performed for all specimens and the DICOM data was obtained. Occipitocervical CT three-dimensional (3D) reconstruction was performed for the cadavers. Morphometric analysis was performed on the bilateral occipitocervical junction of 10 cadaveric specimens based on the 3D reconstruction CT images. Detailed morphometric measurements of the 20 occipital condyles screws were conducted including the average length of the screw trajectory, inside and upper tilting angles of screws, distance to the hypoglossal canal, and to the medial wall of occipital condyle. Results Placement of the occipital condyle screws into the 20 occipital condyles of the 10 cadaveric specimens was performed successfully and the trajectory of implantation was satisfactory according to 3D CT reconstruction images, respectively. There was no obvious injury to the spinal cord, nerve root, and vertebral artery. The length of the bilateral screw trajectory was, respectively, 20.96 ± 0.91 mm (left) and 20.59 ± 0.77 mm (right) (t = 1.306, P > 0.05). The upper tilting angle of bilateral screws was, respectively, 11.24° ± 0.74° (left) and 11.11° ± 0.64° (right) (t = 0.681, P > 0.05). The inside tilting angle of bilateral screws was, respectively, 31.00° ± 1.32° (left) and 30.85° ± 1.27° (right) (t = 0.307, P > 0.05). The screw's distance to the bilateral hypoglossal canal was, respectively, 4.84 ± 0.54 mm (left) and 4.70 ± 0.54 mm (right) (t = 0.685, P > 0.05). The screw's distance to the medial wall of the bilateral occipital condyle was, respectively, 5.13 ± 0.77 mm (left) and 5.04 ± 0.71 mm (right) (t = 0.384, P > 0.05). Conclusion The occipital condyle screw technique can serve as a feasible and safe treatment for instability of the occipitocervical junction with meticulous preoperative planning of the screw entry point and direction based on individual differences. Morphometric trajectory analysis is also an effective way to evaluate the surgical procedure.

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.

Summary Background Children exposed to poverty and family adversities including domestic violence, parental mental ill health and parental alcohol misuse may experience poor outcomes across the life course. However, the complex interrelationships between these exposures in childhood are unclear. We therefore assessed the clustering of trajectories of household poverty and family adversities and their impacts on adolescent health outcomes. Methods We used longitudinal data from the UK Millennium Cohort study on 11564 children followed to age 14 years. Family adversities included parent reported domestic violence and abuse, poor mental health and frequent alcohol use. We used a group-based multi-trajectory cluster model to identify trajectories of poverty and family adversity for children. We assessed associations of these trajectories with child physical, mental and behavioural outcomes at age 14 years using multivariable logistic regression, adjusting for confounders. Findings Six trajectories were identified: low poverty and family adversity (43·2%), persistent parental alcohol use (7·7%), persistent domestic violence and abuse (3·4%), persistent poor parental mental health (11·9%), persistent poverty (22·6%) and persistent poverty and poor parental mental health (11·1%). Compared with children exposed to low poverty and adversity, children in the persistent adversity trajectory groups experienced worse outcomes; those exposed to persistent poor parental mental health and poverty were particularly at increased risk of socioemotional behavioural problems (adjusted odds ratio 6·4; 95% CI 5·0 – 8·3), cognitive disability (aOR 2·1; CI 1·5 – 2·8), drug experimentation (aOR 2·8; CI 1·8 – 4·2) and obesity (aOR 1·8; CI 1·3 – 2·5). Interpretation In a contemporary UK cohort, persistent poverty and/or persistent poor parental mental health affects over four in ten children. The combination of both affects one in ten children and is strongly associated with adverse child outcomes, particularly poor child mental health. Funding The National Institute for Health Research (NIHR) Policy Research Programme, NIHR Applied Research Collaboration South London (ARC South London) at King's College Hospital NHS Foundation Trust and the Medical Research Council (MRC).

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.

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.

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.

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.

The aim of this study was to demonstrate that a stochastic vector-based mapping approach could guide ablation of atrial fibrillation (AF) drivers as evidenced by ablation response and long-term follow-up outcomes.The optimal method for mapping and ablation of AF drivers is yet to be defined.Patients undergoing persistent AF ablation were recruited. Patients underwent pulmonary vein isolation (PVI) with further ablation guided by the stochastic trajectory analysis of ranked signals (STAR) mapping method. The proportion of the time an electrode's activation was seen to precede its neighboring electrodes activation was used to determine early sites of activation (ESA). A positive ablation response at ESA was defined as AF termination or cycle length slowing of ≥30 ms. Clinical outcome was defined as recurrence of AF/atrial tachycardia (AT) during a follow-up of 12 months.Thirty-five patients were included (AF duration of 14.4 ± 5.3 months). After PVI, an average of 2.6 ± 0.8 ESA were ablated per patient with study-defined ablation response achieved in all patients. Of the 86 STAR maps created post-PVI, the same ESA was identified on 73.8 ± 26.1% of maps. ESA that resulted in AF termination were more likely to be identified on both pre- and post-PVI maps than those associated with cycle length slowing (23 of 24 vs. 16 of 49; p 0.001). During a follow-up of 18.5 ± 3.7 months, 28 (80%) patients were free from AF/AT.The ablation response at ESA suggests they may be drivers of AF. Ablation guided by STAR mapping produced a favorable clinical outcome and warrants testing through a randomized controlled trial. (Identification, Electro-mechanical Characterisation and Ablation of Driver Regions in Persistent Atrial Fibrillation [STAR MAPPING]; NCT02950844).

Introduction Stochastic trajectory analysis of ranked signals (STAR) is a novel method for mapping arrhythmia. The aim was to describe its development and validation as a mapping tool. Methods and Results The method ranks electrodes in terms of the proportion of the time they lead relative to neighboring electrodes and ascribes a predominant direction of activation between electrodes. This was conceived with the aim of mapping atrial fibrillation (AF) drivers. Validation of this approach was performed in stages. First, in vitro simultaneous multi‐electrode array and optical mapping were performed on spontaneously fibrillating HL1 cell cultures, to determine if such a method would be able to determine early sites of activation (ESA). A clinical study acquiring unipolar electrograms using a 64‐pole basket for the purposes of STAR mapping in patients undergoing atrial tachycardia (AT) ablation. STAR maps were analyzed by physicians to see if arrhythmia mechanisms could be correctly determined. Mapping was then repeated during atrial pacing. STAR mapping of in vitro activation sequences accurately correlated to the optical maps of planar and rotational activation. Thirty‐two ATs were mapped in 25 patients. The ESA accurately identified focal/micro‐reentrant ATs and the mechanism of macro‐reentrant ATs was effectively demonstrated. STAR method accurately identified four pacing sites in all patients. Conclusions This novel STAR method correlated well with the gold standard of optical mapping in vitro and was able to accurately identify AT mechanisms. Further analysis is needed to determine whether the method might be of use mapping AF.

We present an enhanced version of the input device evaluation application (IDEA) system as an objective method for evaluating upper limb motor skills performance. By introducing three new metrics for mouse cursor trajectory analysis, along with the application of the two-dimensional (2D) experiment in the case of multiple sclerosis (MS), we examine the sensitivity of the IDEA system for differentiating patients with early-stage MS and healthy participants. The IDEA system calculates multiple kinematic metrics for point-and-click tasks: movement time, index of difficulty, effective target width, effective index of difficulty, throughput, missed clicks, target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, movement offset, mean velocity, velocity peaks, and maximum/mean velocity ratio. The results reveal that the IDEA system sensitivity has been improved in comparison with previous studies, which is high enough to detect the presence of early-stage MS with a 70.9% success rate in the 2D experiment.

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.

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.

Study objective The objective of the study is to evaluate the acute pain intensity evolution in emergency department (ED) discharged patients, using group-based trajectory modeling. This method identifies patient groups with similar profiles of change over time without assuming the existence of a particular pattern or number of groups. Methods This was a prospective cohort study of ED patients aged 18 years or older, with an acute pain condition (≤2 weeks), and discharged with an opioid prescription. Patients completed a 14-day diary assessing daily pain intensity level (numeric rating scale of 0 to 10) and pain medication use. Results Among the 372 included patients, 6 distinct post-ED pain intensity trajectories were identified. Two started with severe levels of pain; one remained with severe pain intensity (12.6% of the sample) and the other ended with a moderate pain intensity level (26.3%). Two other trajectories had severe initial pain; one decreased to mild pain (21.7%) and the other to no pain (13.8%). Another trajectory had moderate initial pain that decreased to a mild level (15.9%) and the last one started with mild pain intensity and had no pain at the end of the 14-day period (9.7%). The pain trajectory patterns were significantly associated with age, type of painful conditions, pain intensity at ED discharge, and opioid consumption. Conclusion Acute pain resolution after an ED visit seems to progress through 6 different trajectory patterns that are more informative than simple linear models and could be useful to adapt acute pain management in future research.

Double Asteroid Redirection Test will be the first mission to demonstrate and characterize the concept of a kinetic impactor for planetary defense, by impacting the smaller member of a binary aster...

WOS:000614806200005; Ecological research focuses on the spatio-temporal patterns of ecosystems and communities. The recently proposed framework of Community Trajectory Analysis considers community dynamics as trajectories in a chosen space of community resemblance and utilizes geometrical properties of trajectories to compare and analyse temporal changes. Here, we extend the initial framework, which focused on consecutive trajectory segments, by considering additional metrics with respect to initial or baseline states. Addressing questions about community dynamics and more generally temporal and spatial ecological variability requires synthetic and efficient modes of representation. Hence, we propose a set of innovative maps, charts and trajectory roses to represent trajectory properties and complement the panel of traditional modes of representation used in community ecology. We use four case studies to highlight the complementarity and the ability of the new metrics and innovative figures to illustrate ecological trajectories and to facilitate their interpretation. Finally, we encourage ecologists skilled in multivariate analysis to integrate CTA into their toolbox in order to quantitatively evaluate spatio-temporal changes.

Background Although previous studies have shown connections between pain and worse cancer outcomes, few clinical studies have evaluated their direct association, and the current study aimed to investigate the potential association between acute pain trajectories and postoperative outcomes after liver cancer surgery. Methods This retrospective study was conducted in a single medical center and included patients who received liver cancer surgery between January 2010 and December 2016. Maximal pain intensity was recorded daily using a numerical rating scale during the first postoperative week. Group-based trajectory analysis was performed to classify the variations in pain scores over time. Cox and linear regression analyses were used to assess the effect of pain trajectories on recurrence-free survival, overall survival, and length of hospital stay (LOS) after surgery and to explore predictors of these outcomes. Results A total of 804 patients with 5396 pain score observations were analyzed within the present study. Group-based trajectory analysis categorized the changes in postoperative pain into three groups: group 1 had constantly mild pain (76.6%), group 2 had moderate/severe pain dropping to mild (10.1%), and group 3 had mild pain rebounding to moderate (13.3%). Multivariable analysis demonstrated that on average, group 3 had a 7% increase in LOS compared with the group 1 (p = 0.02) and no significant difference in the LOS was noted between pain trajectory groups 2 and 1 (p = 0.93). Pain trajectories were not associated with recurrence-free survival or overall survival after liver cancer surgery. Conclusion Acute pain trajectories were associated with LOS but not cancer recurrence and survival after liver cancer surgery. Group-based trajectory analysis provided a promising approach for investigating the complex relationships between variations in postoperative pain over time and clinical outcomes.

Objectives and design This study aimed to understand the longitudinal relationship between C-reactive protein (CRP) and body mass index (BMI) from adolescence to early adulthood. Methods CRP and BMI were collected from participants of the Raine Study Gen2 at 14-, 17-, 20- and 22-year follow-ups (n = 1312). A dual trajectory analysis was conducted to assess the association between CRP and BMI trajectories, providing conditional probabilities of membership of CRP trajectory membership given BMI trajectory membership. Best model fit was assessed by systematically fitting two to eight trajectory groups with linear and quadratic terms and comparing models according to the Bayesian Information Criterion statistic. Results The three CRP trajectories were; “stable-low” (71.0%), “low-to-high” (13.8%) and “stable-high” (15.2%). Participants in a “high-increasing” BMI trajectory had a higher probability of being in the “stable-high” CRP trajectory (60.4% of participants). In contrast, individuals in the “medium-increasing” BMI trajectory did not have a significantly increased probability of being in the “stable-high” CRP trajectory. Conclusions These findings support that chronic sub-clinical inflammation is present through adolescence into early adulthood in some individuals. Targeting chronic sub-clinical inflammation though obesity prevention strategies may be important for improving future health outcomes.

Background. Saudi Arabia is reported to have the highest number of children and adolescents with T1DM. However, data concerning glycemic control during adolescence are lacking. Objectives. To determine glycemic control at transition stage from pediatric to adult clinics, determine HBA1c patterns during follow-up, and identify any clinical or demographic variables that may predict a distinctive glycemic pattern. Design. Observational retrospective study. Setting. Dammam Medical Complex, secondary care hospital. Patients and Method. Adolescents aged ≥12 years, with HbA1c recorded at least once a year over 4 years of follow-up, were eligible for inclusion. A trajectory analysis from 2008 to 2019 was conducted, using latent class growth modelling (LCGM), and two-sample t-tests and Fisher’s exact tests were conducted to determine whether there was a statistically significant difference in demographic and clinical variables. Sample Size. 44 patients. Results. 61.36% were referred from pediatric clinics, and 84% were on multiple insulin daily injections. For the trajectory prediction, two groups were identified. Group 1 comprised 71.7%, had high HbA1c values at age 13 (HbA1c, 11.28%), and had a significant and stable decrease in HbA1c values with age (−0.32, p < 0.00 ). Group 2 comprised 28.2%, showed poor HbA1c values at age 13 (HbA1c, 13.28%), and showed increase in HbA1c values slightly by age 15, which then steadily decreased with age (−0.27). Results indicated that the initial HBA1c value was a significant predictor for group trajectory p = 0.01 , while the remaining variables did not have any significance. Conclusion. Our study identified two groups with poorly controlled diabetes; however, the first group performed relatively better than the second group. Both groups almost doubled their targets, with a trend towards HbA1c reduction by the age of 19 in both groups. Limitations. Retrospective study with convenient, small sample size.

Most of the existing single object trackers track the target in a unitary local search window, making them particularly vulnerable to challenging factors such as heavy occlusions and out-of-view movements. Despite the attempts to further incorporate global search, prevailing mechanisms that cooperate local and global search are relatively static, thus are still sub-optimal for improving tracking performance. By further studying the local and global search results, we raise a question: can we allow more dynamics for cooperating both results? In this paper, we propose to introduce more dynamics by devising a dynamic attention-guided multi-trajectory tracking strategy. In particular, we construct dynamic appearance model that contains multiple target templates, each of which provides its own attention for locating the target in the new frame. Guided by different attention, we maintain diversified tracking results for the target to build multi-trajectory tracking history, allowing more candidates to represent the true target trajectory. After spanning the whole sequence, we introduce a multi-trajectory selection network to find the best trajectory that delivers improved tracking performance. Extensive experimental results show that our proposed tracking strategy achieves compelling performance on various large-scale tracking benchmarks. The project page of this paper can be found at https://sites.google.com/view/mt-track/., Comment: Accepted by IEEE T-CSVT 2021

Background Exploring patterns of childhood wheeze may help to clarify the etiology and prognosis of respiratory diseases. The purpose of this study was to classify phenotypes of wheezing in children up to 9 years of age in Japan and to evaluate the individual and environmental risk factors for these phenotypes. Methods Wheeze was evaluated at approximately 1-year intervals based on the mothers' recollection of their child's wheezing or whistling in the chest during the preceding 12 months. The children were aged 1-9 years. In total, 1116 children who had at least five measures of wheezing at all nine time points were used for identifying trajectories. Trajectories were identified with group-based trajectory analysis. A multinomial logit model was built to evaluate the relationships between phenotypes and risk factors. Results Five typical trajectories were identified. The probability of group membership was 43.7%, 32.2%, 6.2%, 8.6%, and 9.2% for the never/infrequent wheeze, transient early wheeze, school-age-onset wheeze, early-childhood-onset remitting wheeze, and persistent wheeze trajectories, respectively. Infant tobacco exposure increased the odds of membership in the transient early wheeze trajectory compared to the never/infrequent wheeze trajectory. Conclusions Using the group-based trajectory modeling approach, we identified five trajectories of childhood wheeze development in a Japanese population. The trajectories shown here are based on formal statistical modeling rather than on subjective classification, and an assessment of its precision suggested that the model has high assignment accuracy.

With the continuous progress of science and technology, people's production and lifestyle which has lasted for thousands of years are also changing quietly. More and more things, people, data, and the Internet are connected together, which also makes the power of big data calculation and mining increase exponentially. The navigation data of Zhejiang coastal fishermen are recorded by satellite. Using big data calculation can combine the ocean climate information, fishing vessel location information, and the data formed by fishing grounds into a big fishery data platform. After many captains use the data provided by this platform for fishing guidance, they have achieved a very good catch. They use scientific and technological innovation to change fishing methods so that fishermen can no longer rely on heaven for food. The risk is difficult to predict. The research in this paper is to find the common characteristics of fishing grounds by analyzing the navigation data of fishermen in the Zhejiang coastal area recorded by satellite and at the same time, expand the search range to estimate the new fishing ground location. This work can be used to mine data by using convolutional neural network.

IntroductionEmerging evidence suggests working with men to prevent intimate partner violence (IPV) perpetration can be effective. However, it is unknown whether all men benefit equally, or whether different groups of men respond differentially to interventions.MethodsWe conducted trajectory modelling using longitudinal data from men enrolled in intervention arms of three IPV trials in South Africa and Rwanda to identify trajectories of IPV perpetration. We then use multinomial regression to describe baseline characteristics associated with group allocation.ResultsIn South Africa, the Stepping Stones and Creating Futures (SS-CF) trial had 289 men and the CHANGE trial had 803 men, and in Rwanda, Indashyikirwa had 821 men. We identified three trajectories of IPV perpetration: a low-flat (60%–67% of men), high with large reduction (19%–24%) and high with slight increase (10%–21%). Baseline factors associated men in high-start IPV trajectories, compared with low-flat trajectory, varied by study, but included higher poverty, poorer mental health, greater substance use, younger age and more childhood traumas. Attitudes supportive of IPV were consistently associated with high-start trajectories. In separate models comparing high-reducing to high-increasing trajectories, baseline factors associated with reduced IPV perpetration were depressive symptoms (relative risk ratio, RRR=3.06, p=0.01 SS-CF); living separately from their partner (RRR=2.14, p=0.01 CHANGE); recent employment (RRR=1.85, p=0.04 CHANGE) and lower acceptability of IPV (RRR=0.60, p=0.08 Indashyikirwa). Older aged men had a trend towards reducing IPV perpetration in CHANGE (p=0.06) and younger men in Indashyikirwa (p=0.07).ConclusionsThree distinct groups of men differed in their response to IPV prevention interventions. Baseline characteristics of past traumas and current poverty, mental health and gender beliefs predicted trajectory group allocation. The analysis may inform targeting of interventions towards those who have propensity to change or guide how contextual factors may alter intervention effects.Trial registration numbersNCT03022370; NCT02823288; NCT03477877.

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge&ndash, desire&ndash, intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users&rsquo, beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

Relative attributes play an important role in object recognition and image classification tasks. These attributes provide high-level semantic explanations for describing and relating objects to each other instead of using direct labels for each object. In the current study, we propose a new method utilizing relative attribute estimation for gait recognition. First, we propose a robust gait motion representation system based on extracted dense trajectories (DTs) from video footage of gait, which is more suitable for gait attribute estimation than existing heavily body shape-dependent appearance-based features, such as gait energy images (GEI). Specifically, we used a Fisher vector (FV) encoding framework and histograms of optical flows (HOFs) computed with individual DTs. We then compiled a novel gait dataset containing 1,200 videos of walking subjects and annotation of gait relative attributes based on subjective perception of gait pairs of subjects. To estimate relative attributes, we trained a set of ranking functions for the relative attributes using a Rank-SVM classifier method. These ranking functions estimated a score indicating the strength of the presence of each attribute for each walking subject. The experimental results revealed that the proposed method was able to represent gait attributes well, and that the proposed gait motion descriptor achieved better generalization performance than GEI for gait attribute estimation.

Background We investigated differing trajectories of childhood obesity prevalence amongst English local authorities (LAs). Methods Data on prevalence of childhood obesity (BMI ≥ 95th centile) for Reception year and Year 6 for 150 LAs in England from 2006/07 to 2015/16 were obtained from the National Child Measurement Programme (NCMP). Latent class mixture modelling (LCCM) was used to identify classes of change in obesity prevalence. Results In Reception, most LAs showed little change across the period (Class 1; stable, moderate obesity prevalence;84%), with a smaller group with a high prevalence that fell thereafter (Class 2; high but falling obesity prevalence; 16%). In Year 6 we identified three classes: moderate obesity prevalence (Class 3; 43%); high and rising obesity prevalence (Class 2; 36%); and stable low obesity prevalence (Class 1; 21%). Greater LA deprivation and higher LA proportion of non-white ethnicity increased risk of being in Class 2 (Reception) or Class 2 or 3 (Year 6) compared with Class 1. Conclusions The prevalence of childhood obesity in LAs in England follow a small number of differing trajectories that are influenced by LA deprivation and ethnic composition. LAs following a stable low obesity trajectory for Year 6 are targets for further investigation.

Background: The obesity epidemic is a global concern. Standard behavioral treatment including increased physical activity, reduced energy intake, and behavioral change counseling is an effective lifestyle intervention for weight loss. Purpose: To identify distinct step count patterns among weight loss intervention participants, examine weight loss differences by trajectory group, and examine baseline factors associated with trajectory group membership. Methods: Both groups received group-based standard behavioral treatment while the experimental group received up to 30 additional, one-on-one self-efficacy enhancement sessions. Data were analyzed using group-based trajectory modeling, analysis of variance, chi-square tests, and multinomial logistic regression. Results: Participants (N = 120) were mostly female (81.8%) and white (73.6%) with a mean (SD) body mass index of 33.2 (3.8) kg/m2. Four step count trajectory groups were identified: active (>10,000 steps/day; 11.7%), somewhat active (7500–10,000 steps/day; 28.3%), low active (5000–7500 steps/day; 27.5%), and sedentary (P = .001). At baseline, lower body mass index and higher perceived health predicted membership in the better performing trajectory groups. Conclusions: Within a larger group of adults in a weight loss intervention, 4 distinct trajectory groups were identified and group membership was associated with differential weight loss.

Rationale: A model for stratifying progression of respiratory muscle weakness in amyotrophic lateral sclerosis (ALS) would identify disease mechanisms and phenotypes suitable for future investigations. This study sought to categorize progression of FVC after presentation to an outpatient ALS clinic. Objectives: To identify clinical phenotypes of ALS respiratory progression based on FVC trajectories over time. Methods: We derived a group-based trajectory model from a single-center cohort of 837 patients with ALS who presented between 2006 and 2015. We applied our model to the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database with 7,461 patients with ALS. Baseline characteristics at first visit were used as predictors of trajectory group membership. The primary outcome was trajectory of FVC over time in months. Measurements and Main Results: We found three trajectories of FVC over time, termed “stable low,” “rapid progressor,” and “slow progressor.” Compared with the slow progressors, the rapid progressors had shorter diagnosis delay, more bulbar-onset disease, and a lower ALS Functional Rating Scale–Revised (ALSFRS-R) total score at baseline. The stable low group had a shorter diagnosis delay, lower body mass index, more bulbar-onset disease, lower ALSFRS-R total score, and were more likely to have an ALSFRS-R orthopnea score lower than 4 compared with the slow progressors. We found that projected group membership predicted respiratory insufficiency in the PRO-ACT cohort (concordance statistic = 0.78, 95% CI, 0.76–0.79). Conclusions: We derived a group-based trajectory model for FVC progression in ALS, which validated against the outcome of respiratory insufficiency in an external cohort. Future studies may focus on patients predicted to be rapid progressors.

Despite the advancement of Internet-of-Things (IoT) and social network, one of the main challenges in SIoT domain is intelligent service discovery and composition. This paper presents an SIoT architecture with personalized recommendation in order to deliver better service discovery. Our proposed approach has outperformed other methods during the experiments.

This study explores the use of forensic synthetic blood substitute (FBS) for impact pattern simulation and area of origin estimation. Ten impact patterns were created at a known origin using the FB...

In the present work, we have proposed an efficient approach for human action recognition (HAR) from silhouette image sequence in videos. The efficiency of the approach lies in feature extraction an...

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Classical Pre-Post Intervention Studies are often analyzed using traditional statistics. Nevertheless, the nutritional interventions have small effects on the metabolism and traditional statistics are not enough to detect these subtle nutrient effects. Generally, this kind of studies assumes that the participants are adhered to the assigned dietary intervention and directly analyzes its effects over the target parameters. Thus, the evaluation of adherence is generally omitted. Although, sometimes, participants do not effectively adhere to the assigned dietary guidelines. For this reason, the Trajectory Map is proposed as a visual tool where dietary patterns of individuals can be followed during the intervention and can also be related with nutritional prescriptions. The Trajectory Analysis is also proposed allowing both analysis: 1) adherence to the intervention and 2) intervention effects. The analysis is made by projecting the differences of the target parameters over the resulting trajectories between states of different time-stamps which might be considered either individually or by groups. The proposal has been applied over a real nutritional study showing that some individuals adhere better than others and some individuals of the control group modify their habits during the intervention. In addition, the intervention effects are different depending on the type of individuals, even some subgroups have opposite response to the same intervention.

In past years, the task of automatic vehicle trajectory analysis in video surveillance systems has gained increasing attention in the research community. Vehicle trajectory analysis can identify normal and abnormal vehicle motion patterns and is useful for traffic management. Although some analysis methods of vehicle trajectory have been developed, the application of these methods is still limited in practice. In this study, a novel adaptive vehicle trajectory classification method via sparse reconstruction and mutual information analysis based on video surveillance systems was proposed. The l0-norm minimization of sparse reconstruction in the method was relaxed to the lp-norm minimization (0 < p < 1). In addition, to consider the nonlinear correlation between the test trajectory and the dictionary, mutual information between the test trajectory and the reconstructed one was taken into account. A hybrid orthogonal matching pursuit–Newton method (HON) was developed to effectively find the sparse solutions for trajectory classification. Two real-world data sets (including the stop sign data set and straight data set) were used in the experiments to validate the performance and effectiveness of the proposed method. Experimental results show that the trajectory classification accuracy is significantly improved by the proposed method compared with most well-known classifiers, namely, NB, k–nearest neighbor, support vector machine, and typical extant sparse reconstruction methods.

The construction professionals attempt to enhance construction site visualization and modeling using unmanned aerial vehicle (UAV) technology. However, there is no research covering best flying and environmental variables for this purpose. This research aimed to determine the optimized flying and environmental variables for 2D dronography and 3D modeling for the construction progress monitoring. The research focused on building facade facing east and conducted an experimental study in the Eco-Home building at the University of Technology Malaysia. For 2D visualization, 900 photos were captured based on the three setpoints (20 m, 30 m, and 40 m) perpendicular to the building facade. Flying operation features of the employed drone (i.e., DJI Phantom4) enforced to set the first setpoint at 20 m perpendicular to the facade to get the whole facade in the required 80% of the photos. The 30 m and 40 m setpoints were defined based on the horizontal circles making the angle of 12° from the drone’s z-ax to x-ax perpendicular to the facade. The images were analyzed using image color summarizer (ICS) software. The research found that the best distance is 30 m, and noontime is the best time for dronography. The proper temperature, humidity, lux, and wind speed to quality 2D image are; 38.1°, 55.5% Rh, 38,107 lx, and 0.1 m/s. The ICS software results were validated by applying the Image Processing Toolbox of MATLAB, particularly the thresholding-based image RGB pixel analyzing technique. The regression analysis showed 93% accuracy and completeness of the results. A trajectory optimization study has also been conducted, which determined that the drone could considerably control trajectories at the target positions and referenced velocity. The findings can significantly help the construction professionals to calibrate their drone’s flying variables for quality 3D modeling the construction progress monitoring.

Study objective More than 17 million people have gained health insurance coverage through the Patient Protection and Affordable Care Act’s Medicaid expansion. Few studies have examined heterogeneity within the Medicaid expansion population. We do so based on time-varying patterns of emergency department (ED) and ambulatory care use, and characterize diagnoses associated with ED and ambulatory care visits to evaluate whether certain diagnoses predominate in individual trajectories. Method We used group-based multitrajectory modeling to jointly estimate trajectories of ambulatory care and ED utilization in the first 12 months of enrollment among Pennsylvania Medicaid expansion enrollees from 2015 to 2017. Results Among 601,877 expansion enrollees, we identified 6 distinct groups based on joint trajectories of ED and ambulatory care use. Mean ED use varied across groups from 3.4 to 48.7 visits per 100 enrollees in the first month and between 2.8 and 44.0 visits per 100 enrollees in month 12. Mean ambulatory visit rates varied from 0.0 to 179 visits per 100 enrollees in the first month and from 0.0 to 274 visits in month 12. Rates of ED visits did not change over time, but rates of ambulatory care visits increased by at least 50% among 4 groups during the study period. Groups varied on chronic condition diagnoses, including mental health and substance use disorders, as well as diagnoses associated with ambulatory care visits. Conclusion We found substantial variation in rates of ED and ambulatory care use across empirically defined subgroups of Medicaid expansion enrollees. We also identified heterogeneity among the diagnoses associated with these visits. This data-driven approach may be used to target resources to encourage efficient use of ED services and support engagement with ambulatory care clinicians.