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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.

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.

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.

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.

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. 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.

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.

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.

International audience; Objective: The past decade has seen a rapid increase in the publication of studies using trajectory analysis to describe the course of osteoarthritis (OA) symptoms. The aim of this systematic review was to describe the distinct trajectories for OA outcomes and the predictors of these trajectories.Methods: MEDLINE and PsycInfo databases were searched for relevant studies. Selection criteria were 1) patients ≥ 18 years old, 2) patients at high risk of or diagnosed with knee or hip OA, 3) studies aiming to identify homogeneous subgroups with distinct trajectories of clinical outcomes, and 4) methodology and analysis designed to identify trajectories (longitudinal design and repeated measures). The search was limited to English or French-language publications.Results: Of the 5177 abstracts retrieved, 44 studies met the inclusion criteria; 21 described the disease progression before surgery. The most frequent outcomes were pain and physical function. Up to six trajectories of pain were found for hip and knee OA. For function, between one and five trajectories were identified for knee OA. Low educational level, high body mass index and high number of comorbidities were the most-reported predictors of bad evolution.Conclusion: Although studies were heterogeneous (outcome, subgroup number and composition), they revealed stable OA trajectories over time. This finding suggests that OA is a chronic disease that does not inevitably worsen in terms of patient-reported symptoms.

Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Results Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. Conclusion JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.

International audience; Objective: To identify temporal trajectories of anxiolytic benzodiazepine (A-BZD) use over 10 years among new A-BZD users aged 50 and older and describe treatment patterns and demographic and clinical characteristics associated with each trajectory. Method: A representative cohort of the French national health insurance fund users was tracked from 2006 through 2015. We used latent class mixed models to identify the trajectories. Results: We observed four trajectories among new users (no A-BZD dispensing in 2005) plus one non-use trajectory. The proportion of occasional use among users was 60%; early increasing use, 10%; late increasing use, 17%; and increasing/decreasing use, 13%. Prevalence of occasional use decreased with age in women, but not men. Duration of treatment episodes and doses differed between trajectories. Multiple regression analyses with occasional use as the reference showed that the other three trajectories shared characteristics (age, coprescriptions of other psychotropic drugs, and more general practitioner consultations) but differed by the presence at inclusion or occurrence during follow-up of psychiatric, neurodegenerative, and somatic conditions. Conclusion: We found four different long-term temporal trajectories in new A-BZD users (occasional, early increasing, late increasing, and increasing/decreasing use). Difficulties quitting or reducing consumption may be very different for each trajectory, requiring tailored care approaches.

BACKGROUND: The mental health of the UK population declined at the onset of the COVID-19 pandemic. Convenience sample surveys indicate that recovery began soon after. Using a probability sample, we tracked mental health during the pandemic to characterise mental health trajectories and identify predictors of deterioration.METHODS: This study was a secondary analysis of five waves of the UK Household Longitudinal Study (a large, national, probability-based survey that has been collecting data continuously since January, 2009) from late April to early October, 2020 and pre-pandemic data taken from 2018-19. Mental health was assessed using the 12-item General Health Questionnaire (GHQ-12). We used latent class mixed models to identify discrete mental health trajectories and fixed-effects regression to identify predictors of change in mental health.FINDINGS: Mental health was assessed in 19 763 adults (≥16 years; 11 477 [58·1%] women and 8287 [41·9%] men; 3453 [17·5%] participants from minority ethnic groups). Mean population mental health deteriorated with the onset of the pandemic and did not begin improving until July, 2020. Latent class analysis identified five distinct mental health trajectories up to October 2020. Most individuals in the population had either consistently good (7437 [39·3%] participants) or consistently very good (7623 [37·5%] participants) mental health across the first 6 months of the pandemic. A recovering group (1727 [12·0%] participants) showed worsened mental health during the initial shock of the pandemic and then returned to around pre-pandemic levels of mental health by October, 2020. The two remaining groups were characterised by poor mental health throughout the observation period; for one group, (523 [4·1%] participants) there was an initial worsening in mental health that was sustained with highly elevated scores. The other group (1011 [7·0%] participants) had little initial acute deterioration in their mental health, but reported a steady and sustained decline in mental health over time. These last two groups were more likely to have pre-existing mental or physical ill-health, to live in deprived neighbourhoods, and be of Asian, Black or mixed ethnicity. Infection with SARS-CoV-2, local lockdown, and financial difficulties all predicted a subsequent deterioration in mental health.INTERPRETATION: Between April and October 2020, the mental health of most UK adults remained resilient or returned to pre-pandemic levels. Around one in nine individuals had deteriorating or consistently poor mental health. People living in areas affected by lockdown, struggling financially, with pre-existing conditions, or infection with SARS-CoV-2 might benefit most from early intervention.FUNDING: None.

Accepted: 2018-10-08, 資料番号: SA1180410000

There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has great advantages in limited data situations, but only if background knowledge can be incorporated into the analysis via prior distributions. We highlight these advantages through a data set including patients with burn injuries and analyze trajectories of posttraumatic stress symptoms using the Bayesian framework following the steps of the WAMBS-checklist. In the included example, we illustrate how to obtain background information using previous literature based on a systematic literature search and by using expert knowledge. Finally, we show how to translate this knowledge into prior distributions and we illustrate the importance of conducting a prior sensitivity analysis. Although our example is from the trauma field, the techniques we illustrate can be applied to any field.

Objectives The aims of this study were to identify homogeneous subgroups of knee and/or hip OA patients with distinct trajectories of the combination of pain and physical function (PF) over time and to determine the baseline factors associated with these trajectories. Methods We used data from the Knee and Hip Osteoarthritis Long-term Assessment (KHOALA) cohort, a French population-based cohort of 878 patients with symptomatic knee and/or hip OA. Pain and PF were measured annually over 5 years with the Medical Outcomes Study Short Form 36 questionnaire. First, trajectory models were estimated with varying numbers of groups for each of the outcomes separately then fitted into a multi-trajectory model. We used multinomial logistic regression to determine the baseline characteristics associated with each trajectory. Results Univariate four-class models were identified as most appropriate for pain and PF. Comparison of separate trajectories showed that 41% of patients included in the severe functional limitations trajectory did not belong to the more severe pain trajectory (Cramér’s V statistic = 0.45). Group-based multi-trajectory modelling revealed four distinct trajectories of pain and PF. On multivariate analyses, female sex, older age, high Kellgren grade, low physical activity intensity, low psychosocial distress score (high distress) and low vitality score were associated with the more severe symptoms trajectory. Conclusion Over 5 years, we identified four distinct trajectories combining pain and PF. Management of weight, fatigue and psychosocial distress and the practice of physical activity seem important to maintain function and limit pain in patients with lower-limb OA.

Standard endoscopy is a screening method that allows the detection of GI tract pathologies and lesions. However, it was demonstrated that endoscopy suffers from a significant miss rate for cancer detection. Therefore, there is a need to establish a standard protocol that can quantify the quality of endoscopic procedures, assist physicians in analyzing their performance and train them on how to select navigation techniques that result in different levels of quality inspection. In this paper, we propose a method to evaluate the performance of endoscopists by analyzing the trajectory produced by the endoscopic camera. The proposed method consists of three main modules: i) the image analysis module for estimating camera positions; ii) Kalman filter for estimating velocity and acceleration profiles; and iii) analysis module to analyze the trajectory based on the density of observed images and the acceleration profile. The approach was preliminary tested with a dataset of images collected from an ex-vivo experiment, with porcine tissue.

International audience; Tracking technologies and location-acquisition have led to the increase of the availability of trajectory data. Many efforts are devoted to develop methods for mining and analysing trajectories due to its importance in lots of applications such as traffic control, urban planning etc. In this paper, we present a new trajectory analysis and visualisation framework for massive movement data. This framework leverages formal concepts, sequential patterns, emerging patterns, and analyses the evolution of mobility patterns through time. Tagged city maps are generated to display the resulting evolution analysis and directions at different spatio-temporal granularity values. Experiments on real-world dataset show the relevance of the proposition and the usefulness of the resulting tagged city maps.

Several studies have linked cytokine levels to severity of sepsis and outcome, but the relative timing of biomarker levels, the septic process and outcomes is often unclear.

Single-actuator mobile robots offer the benefits of low energy consumption, low weight and size, and low cost, but their motion is typically only one-dimensional. By using auxiliary binary mechanisms that redirect and channel the driving force of their only actuator in different ways, it is possible for these robots to perform higher-dimensional motions, such as walking straight, steering, or jumping, with only one motor. This paper presents the MASAR, a new Modular And Single-Actuator Robot that carries a single motor and several adhesion pads. By alternately releasing or attaching these adhesion pads to the environment, the proposed robot is able to pivot about different axes using only one motor, with the possibility of performing concave plane transitions or combining with other identical modules to build more complex reconfigurable robots. In this paper, we solve the planar trajectory tracking problem of this robot for polygonal paths made up of sequences of segments, which may include narrow corridors that are difficult to traverse. We propose a locomotion based on performing rotations of 180 ∘ , which we demonstrate to be the minimum-time solution for long trajectories, and a near-optimal solution for shorter ones.

Patients with cardiovascular disease (CVD) are a heterogeneous group regarding their body mass index (BMI) levels at the time of diagnosis. To address the heterogeneity of CVD, we examined the trajectories of change in body mass index (BMI) and in other cardio-metabolic risk factors before CVD diagnosis. The study included 6126 participants from the prospective population-based Rotterdam Study, followed over 22 years with clinical examinations every 4 years. Latent class trajectory analysis and mixed-effect models were used to develop trajectories of BMI and other cardio-metabolic risk factors respectively. During follow-up, 1748 participants developed CVD, among whom we identified 3 distinct BMI trajectories. The majority of participants (n = 1534, 87.8 %) had steady BMI levels during follow-up, comprising the “stable weight” group. This group showed decrease in mean high-density lipoprotein (HDL) cholesterol over time. The second group, the “progressive weight gain” group (n = 112, 6.4 %), showed a progressive increase in BMI levels. In this group, mean waist circumference increased, mean HDL cholesterol decreased and mean fasting glucose levels were fluctuating over follow-up. In the third group, the “progressive weight loss” group (n = 102, 5.8 %), BMI levels decreased during follow-up. This group showed a decrease in mean waist circumference and in fasting glucose. In conclusion, the majority of individuals who developed CVD had a stable weight during follow-up, suggesting that BMI alone is not a good indicator for identifying middle-aged and elderly individuals at high risk of CVD. Waist circumference, HDL cholesterol, and glucose trajectories differed between the identified BMI subgroups, further highlighting that CVD is a heterogeneous disease with different pathophysiological pathways. Electronic supplementary material The online version of this article (doi:10.1007/s10654-016-0131-0) contains supplementary material, which is available to authorized users.

In this paper we propose an innovative approach for behaviour recognition, from a multicamera environment, based on translating video activity into semantics. First, we fuse tracks from individual cameras through clustering employing soft computing techniques. Then, we introduce a higher-level module able to translate fused tracks into semantic information. With our proposed approach, we address the challenge set in PETS 2014 on recognising behaviours of interest around a parked vehicle, namely the abnormal behaviour of someone walking around the vehicle.

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Models of human habits in smart spaces can be expressed by using a multitude of formalisms whose readability influences the possibility of being validated by human experts. In this paper we present a visual analysis pipeline that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The intuition here is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The paper also presents some hints of how the proposed method can be employed to automatically extract models to be reused for ambient intelligence.

Focal epilepsy is a neurological disease that can be surgically treated by removing area of the brain generating the seizures. The stereotactic electroencephalography (SEEG) procedure allows patient brain activity to be recorded in order to localize the onset of seizures through the placement of intracranial electrodes. The planning phase can be cumbersome and very time consuming, and no quantitative information is provided to neurosurgeons regarding the safety and efficacy of their trajectories. In this work, we present a novel architecture specifically designed to ease the SEEG trajectory planning using the 3D Slicer platform as a basis. Trajectories are automatically optimized following criteria like vessel distance and insertion angle. Multi-trajectory optimization and conflict resolution are optimized through a selective brute force approach based on a conflict graph construction. Additionally, electrode-specific optimization constraints can be defined, and an advanced verification module allows neurosurgeons to evaluate the feasibility of the trajectory. A retrospective evaluation was performed using manually planned trajectories on 20 patients: the planning algorithm optimized and improved trajectories in 98% of cases. We were able to resolve and optimize the remaining 2% by applying electrode-specific constraints based on manual planning values. In addition, we found that the global parameters used discards 68% of the manual planned trajectories, even when they represent a safe clinical choice. Our approach improved manual planned trajectories in 98% of cases in terms of quantitative indexes, even when applying more conservative criteria with respect to actual clinical practice. The improved multi-trajectory strategy overcomes the previous work limitations and allows electrode optimization within a tolerable time span.

Nanotechnology finds in flagellar bacteria an uncomparable example of a very efficient and miniaturized motor. This and the complex behaviour of the bacteria colonies growth in a self-organized way make the study of flagellar bacteria very important and appealing for possible applications. This brief paper presents an innovative point of view: instead of designing nanoscale devices the control of flagellar bacteria is an alternative solution for nanoscale problems. For these reasons in this work single bacterium motion and colonies growth have been studied by applying non-linear methods in order to characterize their behaviour and control it. The characterization of the single bacterium motion leads to the conclusion that determinism (due to chemotaxis) is predominant with respect to random terms. This result is confirmed by the possibility of modelling the case study of colonies growth through an activation/inhibition dynamics. Copyright © 2004 John Wiley & Sons, Ltd.

Objective Several studies have analyzed a correlation between the apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI and the tumor cellularity of corresponding histopathological specimens in brain tumors with inconclusive findings. Here, we compared a large dataset of ADC and cellularity values of stereotactic biopsies of glioblastoma patients using a new postprocessing approach including trajectory analysis and automatic nuclei counting. Materials and Methods Thirty-seven patients with newly diagnosed glioblastomas were enrolled in this study. ADC maps were acquired preoperatively at 3T and coregistered to the intraoperative MRI that contained the coordinates of the biopsy trajectory. 561 biopsy specimens were obtained; corresponding cellularity was calculated by semi-automatic nuclei counting and correlated to the respective preoperative ADC values along the stereotactic biopsy trajectory which included areas of T1-contrast-enhancement and necrosis. Results There was a weak to moderate inverse correlation between ADC and cellularity in glioblastomas that varied depending on the approach towards statistical analysis: for mean values per patient, Spearman’s ρ = -0.48 (p = 0.002), for all trajectory values in one joint analysis Spearman’s ρ = -0.32 (p < 0.001). The inverse correlation was additionally verified by a linear mixed model. Conclusions Our data confirms a previously reported inverse correlation between ADC and tumor cellularity. However, the correlation in the current article is weaker than the pooled correlation of comparable previous studies. Hence, besides cell density, other factors, such as necrosis and edema might influence ADC values in glioblastomas.

Introduction While HIV-associated neurocognitive impairment remains common despite the widespread use of combined antiretroviral therapy (cART), there have been relatively few studies investigating the trajectories of neurocognitive change in longitudinal NeuroAIDS studies. Objective To estimate the magnitude and pattern of neurocognitive change over the first 3 years of follow-up using Group-Based Trajectory Analysis (GBTA) applied to participants in the longitudinal arm of the CHARTER cohort. Method The study population consisted of 701 CHARTER participants who underwent neuropsychological (NP) testing on at least 2 occasions. Raw test scores on 15 NP measures were modeled using GBTA. Each trajectory was categorized as stable, improved or declined, according to two different criteria for change (whether the magnitude of the estimated change at 36 months differed ≥ 0.5 standard deviations from baseline value or changed by > the standard error of measurement estimated at times 1 and 2). Individuals who declined on one or more NP measures were categorized as decliners. Results Overall, 111 individuals (15.8%) declined on at least one NP test over 36 months, with the vast majority showing decline on a single NP test (93/111-83.8%). The posterior probability of group assignment was high in most participants (71%) after only 2 sessions, and in the overwhelming majority of those with 3+ sessions. Heterogeneity of trajectories was the norm rather than the exception. Individuals who declined had, on average, worse baseline NP performance on every test, were older, had a longer duration of HIV infection and more follow-up sessions. Conclusion The present study identified heterogeneous trajectories over 3 years across 15 NP raw test scores using GBTA. Cognitive decline was observed in only a small subset of this study cohort. Decliners had demographics and HIV characteristics that have been previously associated with cognitive decline, suggesting clinical validity for the method.

This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pedestrian detection systems are indeed showing impressive results. Considerably less effort has been put into processing the detections further. We present a tracking system for pedestrians, which based on detection bounding boxes tracks pedestrians and is able to predict their positions in the near future.The tracking system is combined with a module which, based on the car's GPS position acquires a map and uses the road information in the map to know where the car can drive. Then the system warns the driver about pedestrians at risk, by combining the information about hazardous areas for pedestrians with a probabilistic position prediction for all observed pedestrians.

International audience; This paper focuses on the voltage dip diagnosis in a Wind Generator Turbine Farm. The proposed approach is based on the analysis of the grid currents vector trajectory in the stationary reference frame. Two features are used; the diameter variation for fault detection and the angle deviation for fault isolation. The approach is validated through simulation results of a two Doubly Fed Induction Generator and generalized for N Machines.

This paper proposes a non-parametric method to detect grid voltage dip in a Wind Turbine Generator. The method is based on the analysis of the grid currents vector trajectory in the stationary reference frame. Two features (amplitude variation and angle deviation) are extracted for Fault Detection and Isolation. The approach is validated through simulation results of a Doubly Fed Induction Generator. The results show that the method is robust to parameter and load variations. The fault detection time duration is less than one cycle.

Camera sensor networks have developed as a new technology for the wide-area video surveillance. In view of the limited power and computational capability of the camera nodes, the paper presents an abnormal behavior detection approach which is convenient and available for camera sensor networks. Trajectory analysis and anomaly modeling are carried out by single-node processing, whereas anomaly detection is performed by multinode voting. The main contributions of the proposed method are summarized as follows. First, target trajectories are reconstructed and represented as symbol sequences. Second, the sequences are taken into account using Markov model for building the transition probability matrix which can be used to automatically analyze abnormal behavior. Third, the final decision of anomaly detection is made through the majority voting of local results of individual camera nodes. Experimental results show that the proposed method can effectively estimate typical abnormal behaviors in real scenes.

International audience; The objective of the presented work is to present novel methods for big data exploration in the Air Traffic Control (ATC) domain. Data is formed by sets of airplane trajectories, or trails, which in turn records the positions of an aircraft in a given airspace at several time instants, and additional information such as flight height, speed, fuel consumption, and metadata (e.g. flight ID). Analyzing and understanding this time-dependent data poses several non-trivial challenges to information visualization. Materials and methods: To address this Big Data challenge, we present a set of novel methods to analyze aircraft trajectories with interactive image-based information visualization techniques.As a result, we address the scalability challenges in terms of data manipulation and open questions by presenting a set of related visual analysis methods that focus on decision-support in the ATC domain. All methods use image-based techniques, in order to outline the advantages of such techniques in our application context, and illustrated by means of use-cases from the ATC domain. Results: For each considered use-case, we outline the type of questions posed by domain experts, data involved in addressing these questions, and describe the specific image-based techniques we used to address these questions. Further, for each of the proposed techniques, we describe the visual representation and interaction mechanisms that have been used to address the above-mentioned goals. We illustrate these use-cases with real-life datasets from the ATC domain, and show how our techniques can help end-users in the ATC domain discover new insights, and solve problems, involving the presented datasets.

Inertial microfluidics has demonstrated the potential to provide a rich range of capabilities to manipulate biological fluids and particles to address various challenges in biomedical science and clinical medicine. Various microchannel geometries have been used to study the inertial focusing behavior of particles suspended in simple buffer solutions or in highly diluted blood. One aspect of inertial focusing that has not been studied is how particles suspended in whole or minimally diluted blood respond to inertial forces in microchannels. The utility of imaging techniques (i.e., high-speed bright-field imaging and long exposure fluorescence (streak) imaging) primarily used to observe particle focusing in microchannels is limited in complex fluids such as whole blood due to interference from the large numbers of red blood cells (RBCs). In this study, we used particle trajectory analysis (PTA) to observe the inertial focusing behavior of polystyrene beads, white blood cells, and PC-3 prostate cancer cells in physiological saline and blood. Identification of in-focus (fluorescently labeled) particles was achieved at mean particle velocities of up to 1.85 m s[superscript −1]. Quantitative measurements of in-focus particles were used to construct intensity maps of particle frequency in the channel cross-section and scatter plots of particle centroid coordinates vs. particle diameter. PC-3 cells spiked into whole blood (HCT = 45%) demonstrated a novel focusing mode not observed in physiological saline or diluted blood. PTA can be used as an experimental frame of reference for understanding the physical basis of inertial lift forces in whole blood and discover inertial focusing modes that can be used to enable particle separation in whole blood.

In this paper we present a Cloud-based framework for urban computing that can be tailored to be used in different scenarios of urban planning and management that can occur in smart cities. The focus in the paper is on the management of large-scale socio-geographic data obtained through the trajectories followed by mobile devices. Our goal is to mine human activities and routines from this socio-geographic data in order to catch user's behaviour. To this aim, we introduce a methodology for trajectory pattern mining consisting in (a) finding frequent regions, more densely passed through ones, and (b) extracting trajectory patterns from those regions. Experimental evaluation shows that due to complexity and large data involved in the application scenario, the trajectory pattern mining process can take advantage from a parallel execution environment offered by a Cloud architecture. © 2013 IEEE.

This study investigates the trajectory analysis of small solar sail demonstration spacecraft IKAROS considering the uncertainty of solar radiation pressure. Estimation of solar sail force model in space is the key factor for successful solar sail navigation because the solar sail have large uncertainty due to the flexible membrane. Since the sail wrinkles after the deployment and its surface will suffer from degradation, the solar sail force model is difficult to develop on the ground. In this paper, a practical analysis of estimating the solar sail force model from Doppler and range observable is investigated. This is demonstrated by orbit determination including parameter estimation of solar sail model. Some examples are described to investigate better parameters to estimate the solar sail force model.

Because of the low rates of killed people per km.passenger, and of the multiple causes of an accident, the accident statistics alone are of very little use to support research aiming to further reduce the mortality. Therefore, research works were undertaken on vehicle trajectory modelling and measurement to evaluate interactions between vehicle, drivers and road infrastructure, and to identify “quasi accidents” or “near miss”, as indicators of inappropriate driving behaviours or infrastructure risky zones. The trajectory means vehicle location, speed, acceleration and jerk, as time functions. Numerous technologies are available for trajectory measurement, used in roadside and on-board observatories. This paper shows the benefit of using trajectory observatories, their current limitations and perspectives of development, gives an overview of the available results on modelling and measurement techniques, from several projects supported by the French program PREDIT, and reports some case studies for bends, intersections, and low volume roads.