Spatio-temporal Trajectories Research Articles

Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET

PurposeAlzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE).MethodsA total of 1080 [18F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis.ResultsWe identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first.ConclusionThe spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.

Abstract P1-08-31: Simbiosys tumorscope: Biophysical modeling of patient-specific response to chemotherapy

Abstract Background: Breast Cancer (BC) patients exhibit a wide variety of responses to neoadjuvant chemotherapy (NACT). This is driven by factors both intrinsic (e.g., mutations, dysregulation, metabolic reprogramming) and extrinsic (e.g., nutrient/drug perfusion, interactions with surrounding healthy tissues and the tumor microenvironment (TME)) to the cells that make up each tumor. The SimBioSys TumorScope is a platform for making individualized predictions of the response of each patient's tumor to NACT. It employs 3D biophysical simulations that explicitly model the dynamics of cellular response to the ever-changing chemical milieu of drugs and nutrients that perfuse the TME during treatment, in order to predict when and where different regions of the tumor are growing, dying, and ultimately how a given patient will respond to treatment. Methods: The SimBioSys TumorScope constructs 3D in silico models of each patient's tumor directly from pretreatment DCE-MRIs. It combines this spatial model with personalized genome-scale models of tumor and tissue metabolism, pharmacokinetics, and pharmacodynamics, and vascular perfusion (based on DCE-MRI timeseries). The combined model is then simulated using a custom high-performance reaction-diffusion-material mechanics simulation engine which produces a spatio-temporal trajectory of tumor size, morphology, intra- and extracellular biochemistry. We evaluated the ability of the TumorScope software to predict volumetric response to NACT. A validation set comprising the pretreatment records (including MRIs) of 780 BC patients that underwent NACT was used. These patients spanned a wide range of tumor sizes, molecular subtypes, and NCCN-recommended treatment regimens. Simulations were initialized using each patient’s pretreatment MRI and pathology data and run from the start of therapy to the specified surgical date. Simulated tumor volumetric percent response (calculated as the ratio of change in tumor volume to initial volume) at the time of surgery was then compared with actual tumor volumetric percent response extracted from presurgical MRIs. Among patients for which event free survival data was available (n = 480), we performed a Cox proportional hazard analysis. Results: The SimBioSys TumorScope predicted pre-surgical tumor volumetric response with a median error of 0.03% and median absolute deviation of 8.2%. Among the patients for which EFS data was available, we found a hazard ratio of 1.8 associated with having a final simulated volume greater than 0.01 cc (p = 0.00048). Conclusions: The SimBioSys TumorScope produces accurate patient specific predictions of response to NACT using only standard-of-care pre-treatment data. Such predictions can aid in decision making, enabling physicians to select less-toxic regimens for patients in which a robust response is predicted, and more aggressive treatments and/or clinical trial enrollment when response is likely to be poor. Citation Format: John A Cole, Jr., Joseph R Peterson, Tyler M Earnest, Michael J Hallock, Daniel J Cook, Eduardo Braun, Anu Antony, John R Pfeiffer, Tushar Pandey. Simbiosys tumorscope: Biophysical modeling of patient-specific response to chemotherapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-31.

An analysis of twitter as a relevant human mobility proxy

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

BackgroundMaladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk. MethodsUsing an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines. ResultsWe identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, pperm < .001) and increases in heart rate (R2 = 0.075, pperm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR2 = 0.075, pperm = .030) but not the presence or absence of a mood and anxiety disorder. ConclusionsSpatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

Abdominal motion tracking with free-breathing XD-GRASP acquisitions using spatio-temporal geodesic trajectories.

Free-breathing external beam radiotherapy remains challenging due to the complex elastic or irregular motion of abdominal organs, as imaging moving organs leads to the creation of motion blurring artifacts. In this paper, we propose a radial-based MRI reconstruction method from 3D free-breathing abdominal data using spatio-temporal geodesic trajectories, to quantify motion during radiotherapy. The prospective study was approved by the institutional review board and consent was obtained from all participants. A total of 25 healthy volunteers, 12 women and 13 men (38years ± 12 [standard deviation]), and 11 liver cancer patients underwent imaging using a 3.0T clinical MRI system. The radial acquisition based on golden-angle sparse sampling was performed using a 3D stack-of-stars gradient-echo sequence and reconstructed using a discretized piecewise spatio-temporal trajectory defined in a low-dimensional embedding, which tracks the inhale and exhale phases, allowing the separation between distinct motion phases. Liver displacement between phases as measured with the proposed radial approach based on the deformation vector fields was compared to a navigator-based approach. Images reconstructed with the proposed technique with 20 motion states and registered with the multiscale B-spline approach received on average the highest Likert scores for the overall image quality and visual SNR score 3.2 ± 0.3 (mean ± standard deviation), with liver displacement errors varying between 0.1 and 2.0mm (mean 0.8 ± 0.6mm). When compared to navigator-based approaches, the proposed method yields similar deformation vector field magnitudes and angle distributions, and with improved reconstruction accuracy based on mean squared errors. Schematic illustration of the proposed 4D-MRI reconstruction method based on radial golden-angle acquisitions and a respiration motion model from a manifold embedding used for motion tracking. First, data is extracted from the center of k-space using golden-angle sampling, which is then mapped onto a low-dimensional embedding, describing the relationship between neighboring samples in the breathing cycle. The trained model is then used to extract the respiratory motion signal for slice re-ordering. The process then improves the image quality through deformable image registration. Using a reference volume, the deformation vector field (DVF) of sequential motion states are extracted, followed by deformable registrations. The output is a 4DMRI which allows to visualize and quantify motion during free-breathing.

Spatiotemporal trajectories of invasive tree species reveal the importance of collective action for successful invasion management

ABSTRACT Biological invasions are complex processes requiring coordinated and spatially targeted management. This study assessed spatiotemporal trajectories and determinants of Prosopis cover in Baringo County, Kenya. Land cover data for every seven years between 1988 and 2016 revealed the presence of Prosopis. We tested for trajectory clusters using spatial autocorrelation and overlaid the trajectory categories with landscape features. Generally, most plots were only temporarily managed or not managed at all, while continuous management of Prosopis occurred mainly near rivers and on plots suitable for cultivation. Parcels within 250 m from roads, which are dispersal pathways for Prosopis seeds, were rarely cleared of Prosopis. We conclude that successful management requires incentives for stakeholders’ engagement in collective management action at a landscape level. Trajectory mapping should be integrated into planning tools to foster the prioritization of timely and context-specific response mechanisms.

Short-Term 4D Trajectory Prediction for UAV Based on Spatio-Temporal Trajectory Clustering

To improve the trajectory prediction accuracy of unmanned aerial vehicles (UAVs) with random behavior intentions, this paper presents a short-term four-dimensional (4D) trajectory prediction method based on spatio-temporal trajectory clustering. A spatio-temporal trajectory clustering algorithm is first designed to cluster the UAV trajectory segments divided by a fixed time window. Each trajectory segment is given a category label that represents some certain type of behavior characteristics, such as climbing, turning, descending, etc. The convolutional neural network (CNN) is used to identify the category label of a given trajectory segment by learning the behavior characteristics of different trajectory segments. Based on the long-short-term memory network (LSTM), a short-term trajectory prediction model for different categories of label trajectory segments is established. The global trajectory prediction includes several steps adopting the corresponding prediction models. Historical trajectory data of UAVs are used to validate the proposed prediction method. Experiment results indicate that the method can obtain obviously better prediction accuracy in a short prediction time range (0-3s) with acceptable efficiency compared to LSTM, GRU and velocity trend extrapolation.

Scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data

The last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical

Offline segmentation of spatio-temporal order trajectories by mixed-integer linear programming for determining process times in production systems

Real-time indoor positioning systems in manufacturing systems are used to track production orders. This generates spatio-temporal trajectories which can be segmented to determine process times. We present formulations of the offline segmentation problem as mixed-integer linear programs (MILPs) that utilize the sequence of processing steps from ERP systems. The MILP formulations are compared with online heuristics in terms of their accuracy and computational effort on data generated with features from a real job shop. We show that in terms of accuracy our offline segmentation formulations outperform the online heuristics with increasing measurement errors, justifying their higher computational effort.

Real-Time Road Pothole Mapping Based on Vibration Analysis in Smart City

Vehicle-road collaboration is inseparable for smart cities, and the potholes detection is an essential direction in vehicle-road collaboration. With the development of artificial surface observation technology and surveying technology, road potholes detection accuracy has improved in recent years. However, the legacy detection methods do not have the ease of service and real-time observation capability, and thus the potholes in the road cannot be mapped in time. To solve this key issue, we proposed a reflectometry method to realize real-time potholes observation with vibration signals analysis and Spatio-temporal trajectory fusion. We further developed several prototype devices for validation. These prototype devices measure the acceleration signal mounted on the wheel steering lever and implement edge signal processing and Spatio-temporal information fusion on the prototype. Observation results and Spatio-temporal information are been rapidly transmitted to the sensing server via NB-IoT. Results and analyses demonstrated that this method successfully enabled the potholes observation in real-time through light and a rapidly deployable platform that relies on repeated trajectory data from the vehicle. Results also demonstrated that this method was not limited by vehicle type, speed, or engine operating condition. In the road experiment, the proposed method provided stable, efficient, and real-time potholes observation results. Compared with traditional methods, the method reduces costs and improves sensing efficiency based on Spatio-temporal trajectory fusion, and potholes information can be prompted in real-time. This innovation provides a strategic exploration and thinking to deal with road potholes in real-time sensing.

Application of GIS in the Analysis of Urban Economic Migration

As an intuitive embodiment of the city’s comprehensive strength, urban competitiveness, combined with the theory of regional gravity center, makes a comparative analysis of the urban economic migration of urban competitiveness in different years, which helps to analyze the spatial and temporal development differences of regional cities, thus providing a new perspective for the analysis and research of urban competitiveness. In the market economy, land price plays an important role in optimizing the allocation and intensive use of urban land resources. Therefore, it is of great practical significance for the government to reasonably allocate urban land resources and standardize the order of land market to master the spatiotemporal change law of urban land price and study the driving mechanism of land price change. This paper analyzes the urban economic migration with the help of GIS technology and carries out research at the same time, analyzes the spatio-temporal change law of the form, analyzes the urban form measurement, expansion intensity and expansion speed, and explores the driving forces and constraints affecting the urban spatial form. From this, we can obtain the spatio-temporal trajectory of GIS in urban economic migration and evolution, and the analysis and research of this trajectory can understand the direction of regional economic development and the balance of development.

The Spatio-Temporal Trajectory of Covid-19 in India: Insight into Past Pandemics and Future Recommendations

The Spatio-Temporal Trajectory of Covid-19 in India: Insight into Past Pandemics and Future Recommendations

PSOTSC: A Global-Oriented Trajectory Segmentation and Compression Algorithm Based on Swarm Intelligence

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.

Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation

The estimation of traffic conditions in cities is becoming essential to establish a sustainable transportation system and to help traffic management authorities plan the traffic of cities. Recently, taxi trajectory big datasets are being collected during taxi drivers are routing around the cities. Taxi trajectory datasets provide behavioral information about the city residents, urban flows of the taxi passengers, and infrastructure for traffic condition estimation. This study aims to estimate regional traffic velocity of New York City using New York taxi trajectory dataset. A new method is proposed that uses weighted spatio-temporal trajectory big data mining approach and scores each region of the cities in terms of traffic velocity. A new algorithm is proposed, namely Regional Traffic Velocity Estimation (RTVE) algorithm, which uses proposed regional spatio-temporal velocity estimation method and experimentally evaluated using New York taxi trajectory dataset. Experimental results show that each region in New York have different velocity and usage characteristics in terms of hourly and daily analyses. Also, borough-level analyses are performed that reveal knowledge about the boroughs of New York. The estimated regional traffic velocity of cities based on taxi trajectory datasets would provide a decision support system for decision-makers in terms of regional hourly and daily evaluation of cities with cost-free and widespread city traffic dataset.

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

Mining periodic patterns from spatio-temporal trajectories using FGO-based artificial neural network optimization model

Mining periodic patterns from spatio-temporal trajectories using FGO-based artificial neural network optimization model

Predicting flight delay with spatio-temporal trajectory convolutional network and airport situational awareness map

To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 min error) for predicting flight departure delay at Los Angeles International Airport.

Beyond the boundaries: Do spatio-temporal trajectories of land-use change and cross boundary effects shape the diversity of woody species in Uruguayan native forests?

Environmental management often lacks a harmonization across scales and beyond boundaries of the land with changed use. As an example, extensive areas of Uruguayan grassland have been transformed into crop and timber plantations in the past decades, but little is known about the possible impacts of these changes on neighboring native forests. We explore how the diversity of native forests differs in relation to land-use change trajectories, from grassland to monocultures, in the surrounding landscapes. We recorded woody species in 32 different native forest plots in Uruguay. Using Landsat images, we created land-use maps in a buffer of 3 km around each plot for the years 1986/1987, 1996/1997, 2006/2007 and 2016/2017. We calculated trajectories of changes regarding the percentage of cover per land use, the landscape shape and aggregation index for native forests, grassland, timber plantations and crops. Trajectories were explored based on the slope of the generalized linear model correlated with woody species composition. Landscape covered by native forests, timber plantations and crops increased whereas grassland cover decreased. Large and interconnected native forests in a landscape dominated by grasslands harbor a high diversity of woody species. Disaggregation of grassland resulted in a decrease in richness and diversity of woody species. The history of surrounding landscape determines diversity of native forests. We observed cross boundary effects of neighboring land uses on native forests. These effects have to be considered in all measures of environmental management to reduce the trade-offs between biodiversity conservation and economic profit. Nevertheless, other parameter such as climate and soil interplay, override and alter the effects of land use history and management.

Spatio-temporal trajectory anomaly detection based on common sub-sequence

Spatio-temporal trajectory anomaly detection based on common sub-sequence

Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder

Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.