Spatio-temporal Events Research Articles

RhoA Inhibitor Treatment At Acute Phase of Spinal Cord Injury May Induce Neurite Outgrowth and Synaptogenesis

The therapeutic use of RhoA inhibitors (RhoAi) has been experimentally tested in spinal cord injury (SCI). In order to decipher the underlying molecular mechanisms involved in such a process, an in vitro neuroproteomic-systems biology platform was developed in which the pan-proteomic profile of the dorsal root ganglia (DRG) cell line ND7/23 DRG was assessed in a large array of culture conditions using RhoAi and/or conditioned media obtained from SCI ex vivo derived spinal cord slices. A fine mapping of the spatio-temporal molecular events of the RhoAi treatment in SCI was performed. The data obtained allow a better understanding of regeneration/degeneration induced above and below the lesion site. Results notably showed a time-dependent alteration of the transcription factors profile along with the synthesis of growth cone-related factors (receptors, ligands, and signaling pathways) in RhoAi treated DRG cells. Furthermore, we assessed in a rat SCI model the in vivo impact of RhoAi treatment administered in situ via alginate scaffold that was combined with FK506 delivery. The improved recovery of locomotion was detected only at the early postinjury time points, whereas after overall survival a dramatic increase of synaptic contacts on outgrowing neurites in affected segments was observed. We validate these results by in vivo proteomic studies along the spinal cord segments from tissue and secreted media analyses, confirming the increase of the synaptogenesis expression factors under RhoAi treatment. Taken together, we demonstrate that RhoAi treatment seems to be useful to stimulate neurite outgrowth in both in vitro as well in vivo environments. However, for in vivo experiments there is a need for sustained delivery regiment to facilitate axon regeneration and promote synaptic reconnections with appropriate target neurons also at chronic phase, which in turn may lead to higher assumption for functional improvement.

Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells.

Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle cells and Ca2+ puffs and syntillas in neurons.

Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871

Abstract. The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140-year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period. This work then introduces tools for defining spatio-temporal extreme low-flow events. Extreme low-flow events are first locally defined through the sequent peak algorithm using a novel combination of a fixed threshold and a daily variable threshold. A dedicated spatial matching procedure is then established to identify spatio-temporal events across France. This procedure is furthermore adapted to the SCOPE Hydro 25-member ensemble to characterize in a probabilistic way unrecorded historical events at the national scale. Extreme low-flow events are described and compared in a spatially and temporally homogeneous way over 140 years on a large set of catchments. Results highlight well-known recent events like 1976 or 1989–1990, but also older and relatively forgotten ones like the 1878 and 1893 events. These results contribute to improving our knowledge of historical events and provide a selection of benchmark events for climate change adaptation purposes. Moreover, this study allows for further detailed analyses of the effect of climate variability and anthropogenic climate change on low-flow hydrology at the scale of France.

An agent-based model of leukocyte transendothelial migration during atherogenesis.

A vast amount of work has been dedicated to the effects of hemodynamics and cytokines on leukocyte adhesion and trans-endothelial migration (TEM) and subsequent accumulation of leukocyte-derived foam cells in the artery wall. However, a comprehensive mechanobiological model to capture these spatiotemporal events and predict the growth and remodeling of an atherosclerotic artery is still lacking. Here, we present a multiscale model of leukocyte TEM and plaque evolution in the left anterior descending (LAD) coronary artery. The approach integrates cellular behaviors via agent-based modeling (ABM) and hemodynamic effects via computational fluid dynamics (CFD). In this computational framework, the ABM implements the diffusion kinetics of key biological proteins, namely Low Density Lipoprotein (LDL), Tissue Necrosis Factor alpha (TNF-α), Interlukin-10 (IL-10) and Interlukin-1 beta (IL-1β), to predict chemotactic driven leukocyte migration into and within the artery wall. The ABM also considers wall shear stress (WSS) dependent leukocyte TEM and compensatory arterial remodeling obeying Glagov’s phenomenon. Interestingly, using fully developed steady blood flow does not result in a representative number of leukocyte TEM as compared to pulsatile flow, whereas passing WSS at peak systole of the pulsatile flow waveform does. Moreover, using the model, we have found leukocyte TEM increases monotonically with decreases in luminal volume. At critical plaque shapes the WSS changes rapidly resulting in sudden increases in leukocyte TEM suggesting lumen volumes that will give rise to rapid plaque growth rates if left untreated. Overall this multi-scale and multi-physics approach appropriately captures and integrates the spatiotemporal events occurring at the cellular level in order to predict leukocyte transmigration and plaque evolution.

Fast Variable Structure Stochastic Automaton for Discovering and Tracking Spatiotemporal Event Patterns.

Discovering and tracking spatiotemporal event patterns have many applications. For example, in a smart-home project, a set of spatiotemporal pattern learning automata are used to monitor a user's repetitive activities, by which the home's automaticity can be promoted while some of his/her burdens can be reduced. Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise. However, such design is conservative because it needs continuous and identical feedbacks to converge, thus leading to its very low convergence rate. In many real-life applications, such as ambient assisted living, consecutive nonoccurrences of an elder resident's routine activities should be treated with an alert as quickly as possible. On the other hand, no alert should be output even for some occurrences in order to diminish the effects caused by noise. Clearly, confronting a pattern's change, slow speed and low accuracy may degrade a user's life security. This paper proposes a fast and accurate leaning automaton based on variable structure stochastic automata to satisfy the realistic requirements for both speed and accuracy. Bias toward alert is necessary for elder residents while the existing method can only support the bias toward "no alert." This paper introduces a method to allow bias toward alert or no alert to meet a user's specific bias requirement. Experimental results show its better performance than the state-of-the-art methods.

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%.

An Evaluation of a Visual Analytics Prototype for Calendar-Related Spatiotemporal Periodicity Detection and Analysis

Whether it is sunrise, the weekend, or Christmas, some form of temporal structure or periodic pattern governs our daily activities. Understanding them is essential to making sense of human activity, because they frame normality and allow us to identify abnormalities. However, cultural heterogeneity and scale greatly complicate our ability to uncover and understand human activity at a given time within a region. Current research in the field of visual analytics and geography provide methods of addressing spatiotemporal periodicity, but they fall short in providing access to multiple spatial and temporal scales via a relevant calendar. In response to these shortcomings, we developed PerSE (periodicity in spatiotemporal events), a coordinated-view Web application designed to aid users in the detection and analysis of calendar-related periodicity in spatiotemporal event data sets. Given the complexity of such a visualization tool, this paper focuses on the usability and learnability of PerSE. We evaluated the tool through a 20-participant study that consisted of training, a multiple-choice test, and the System Usability Scale. Our analysis of the results shows that the complex combination of visual tools and multi-scale, multi-calendar capability used within PerSE is effective, but still in need of usability improvements.

Spatiotemporal extreme events in a laser with a saturable absorber

We study extreme events occurring in the transverse $(x,y)$ section of the field emitted by a broad-area semiconductor laser with a saturable absorber. The spatio-temporal events on which we perform the statistical analysis are identified as maxima of the field intensity in the 3D space $(x,y,t)$. We identify regions in the parameter space where extreme events are more likely to occur and we study the connection of those extreme events with the cavity solitons that are known to exist in the same system, both stationary and self-pulsing.

Predicting Soccer Highlights from Spatio-Temporal Match Event Streams

Sports broadcasters are continuously seeking to make their live coverages of soccer matches more attractive. A recent innovation is the “highlight channel,” which shows the most interesting events from multiple matches played at the same time. However, switching between matches at the right time is challenging in fast-paced sports like soccer, where interesting situations often evolve as quickly as they disappear again. This paper presents the POGBA algorithm for automatically predicting highlights in soccer matches, which is an important task that has not yet been addressed. POGBA leverages spatio-temporal event streams collected during matches to predict the probability that a particular game state will lead to a goal. An empirical evaluation on a real-world dataset shows that POGBA outperforms the baseline algorithms in terms of both precision and recall.

Grade-based Spatio-Temporal Sequential Pattern Mining using Support and Event Index Measures

Objectives: The knowledge on cause-effect relationships between instances of real-world entities can be gathered by extracting sequential patterns from spatio-temporal databases. The discovery of the patterns in the context of space and time is a challenging issue. The sequential pattern mining algorithms designed for traditional databases may result in the loss of spatio-temporal correlations due to the improper estimations of properties related to the time and space. The proposed work approaches the problem of designing sequential pattern mining algorithm specifically for spatio-temporal event datasets. Methods/Statistical Analysis: An algorithm is proposed which is based on frequency-based measures for mining frequent spatio-temporal sequential patterns. The spatio-temporal sequential pattern mining based on Support index and Event index algorithm proposes two new parameters support index and event index which are used to scrutinize the sequences extracted from the database. A data structure is also proposed to represent the spatio-temporal data for efficient pattern mining. Findings: The proposed algorithm generates the interesting set of frequent sequential patterns. The proposed algorithm is compared with Slicing-STS-Miner and MST-ITP and the experimental results proved that the proposed algorithm performs well with the order of two to three. Application/Improvements: The proposed algorithm uses frequency-based measures rather than density-based measures. Frequency-based measures take less computational time when compared to density-based measures. The proposed technique is suitable for extracting knowledge in the form of sequential patterns from spatio-temporal point databases.

Chaos and extreme events in an azimuthally polarized Nd:GdVO4 laser with pump modulation.

Nonlinear dynamics and extreme events were explored in a spatially distributed polarization laser based on an azimuthally polarized Nd:GdVO4 laser with pump modulation. When the modulation frequency approached the relaxation oscillation frequency, a period-doubling route to chaos was observed with an increasing modulation depth, and spatiotemporal extreme events occurred with the generation of the chaos. An efficient azimuthal polarization was kept, even though the intensity fluctuated in the chaotic region.

An Efficient Distributed Traffic Events Generator for Smart Highways

This paper deals with a spatiotemporal traffic events generator for real highway networks. The goal is to use the event generator to test real-time and batch traffic analysis applications. In this context, we represent a highway network as an oriented graph based on the geographic data of the different sensors locations. The traffic is generated based on a socio-cultural calendar using a virtual clock to speed up the simulation process. In order to enable our generator to support the global worldwide highway networks, we propose a dynamic sized distributed architecture based on multi-agent systems. In this platform, we distinguish the physical model based on sensors from the logical model based on an oriented graph. The architecture of the simulator and the results of some of its implementations applied to the Moroccan highway network are presented.

Enabling Next Generation Logistics and Planning for Smarter Societies

Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UK's major financial center. The results presented in the paper have been obtained using 500,000 tweets.

RFID Ownership Transfer with Positive Secrecy Capacity Channels.

RFID ownership transfer protocols (OTPs) transfer tag ownership rights. Recently, there has been considerable interest in such protocols; however, guaranteeing privacy for symmetric-key settings without trusted third parties (TTPs) is a challenge still unresolved. In this paper, we address this issue and show that it can be solved by using channels with positive secrecy capacity. We implement these channels with noisy tags and provide practical values, thus proving that perfect secrecy is theoretically possible. We then define a communication model that captures spatiotemporal events and describe a first example of symmetric-key based OTP that: (i) is formally secure in the proposed communication model and (ii) achieves privacy with a noisy tag wiretap channel without TTPs.

Brain Network Activation Analysis Utilizing Spatiotemporal Features for Event Related Potentials Classification.

The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs) using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA) analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool.Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject's spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation) and their utility as a classification tool were examined in the context of Target-Novel classification.Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200, and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95). The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits).Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.

PerSE: visual analytics for calendar related spatiotemporal periodicity detection and analysis

Periodicity is embedded in all societies. As most of us organize our lives based on temporal structures, it is hard to imagine what life would be like without it. We experience periodicity through naturally occurring rhythms that exist in nature, such as sunrise/sunset, seasonal changes in the weather, and the tides. We also experience it through abstract means via cultural, political, religious ties, such as the weekend, Independence Day, and Ramadan. Forms of periodicity, like the examples above, are foundational to making sense of human activity because they provide contextual rationale and frame normaility. However, disparate calendars (e.g. Islamic vs. Gregorian), localized idiosyncrasies, and other variables greatly complicate the analytical ability to uncover and understand human activity at a given time within a specified region. We have developed PerSE (Periodicity in Spatiotemporal Events): a web application designed to aid users in the detection and analysis of calendar related periodicity in spatiotemporal event data sets via exploratory user interaction. PerSE is composed of several crossfiltering views: the Map, Attribute View, Time-Wheel, Timeline, and Table. Users interactively set and release filters on one or more of the views to detect and analyze calendar related periodicity. This paper illustrates the utility of PerSE through an in-depth description of the tool and proof of concept usage example.

The Transcription Factor ATHB5 Affects GA-Mediated Plasticity in Hypocotyl Cell Growth during Seed Germination.

Gibberellic acid (GA)-mediated cell expansion initiates the seed-to-seedling transition in plants and is repressed by DELLA proteins. Using digital single-cell analysis, we identified a cellular subdomain within the midhypocotyl, whose expansion drives the final step of this developmental transition under optimal conditions. Using network inference, the transcription factor ATHB5 was identified as a genetic factor whose localized expression promotes GA-mediated expansion specifically within these cells. Both this protein and its putative growth-promoting target EXPANSIN3 are repressed by DELLA, and coregulated at single-cell resolution during seed germination. The cellular domains of hormone sensitivity were explored within the Arabidopsis (Arabidopsis thaliana) embryo by putting seeds under GA-limiting conditions and quantifying cellular growth responses. The middle and upper hypocotyl have a greater requirement for GA to promote cell expansion than the lower embryo axis. Under these conditions, germination was still completed following enhanced growth within the radicle and lower axis. Under GA-limiting conditions, the athb5 mutant did not show a phenotype at the level of seed germination, but it did at a cellular level with reduced cell expansion in the hypocotyl relative to the wild type. These data reveal that the spatiotemporal cell expansion events driving this transition are not determinate, and the conditional use of GA-ATHB5-mediated hypocotyl growth under optimal conditions may be used to optionally support rapid seedling growth. This study demonstrates that multiple genetic and spatiotemporal cell expansion mechanisms underlie the seed to seedling transition in Arabidopsis.

Online Spatial Event Forecasting in Microblogs

Event forecasting from social media data streams has many applications. Existing approaches focus on forecasting temporal events (such as elections and sports) but as yet cannot forecast spatiotemporal events such as civil unrest and influenza outbreaks, which are much more challenging. To achieve spatiotemporal event forecasting, spatial features that evolve with time and their underlying correlations need to be considered and characterized. In this article, we propose novel batch and online approaches for spatiotemporal event forecasting in social media such as Twitter. Our models characterize the underlying development of future events by simultaneously modeling the structural contexts and their spatiotemporal burstiness based on different strategies. Both batch and online-based inference algorithms are developed to optimize the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental evaluations on two different domains demonstrate the effectiveness of our proposed approach.

Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps

The spatial and temporal distribution of forest fires displays a complex pattern which strongly influences the forest landscape and the neighbouring anthropogenic development. Statistical methods developed for spatio-temporal stochastic point processes can be employed to find a structure, detect over-densities and trends in forest fire risk and address towards prevention and forecasting measures. The present study considers the Portuguese mapped burnt areas official geodatabase resulting from interpreted satellite measurements, covering the period 1990–2013. The main goal is to detect whether space and time act independently or whether, conversely, neighbouring events are also closer in time, interacting to generate clusters. To this purpose, the following statistical methods were applied: (1) the geographically weighted summary statistics, to explore how the average burned area vary locally through the investigated region; (2) the bivariate K-function, to test the space–time interaction and the spatial attraction/independency between fires of different size; and (3) the space–time kernel density, allowing elaborating smoothed density surfaces and representing over-densities of large versus medium versus small fires and on north versus south region. The proposed approach successfully allowed finding and mapping spatio-temporal patterns within this large data series. Specifically, medium fires tend to aggregate around small fires, while large fires aggregate at a larger distance and longer times, indicating that the return time following these events is longer than for small and medium fires. The density maps shows that hot spots are present almost each year in the northern region, with a higher concentration in the northern areas, while the southern half of the country counts lower surface densities of fires, which are mainly concentrated in the central period (2000–2007).

A Porcine Urinary Bladder Matrix Does Not Recapitulate the Spatiotemporal Macrophage Response of Muscle Regeneration after Volumetric Muscle Loss Injury

Volumetric muscle loss (VML) results in irrecoverable loss of muscle tissue making its repair challenging. VML repair with acellular extracellular matrix (ECM) scaffolds devoid of exogenous cells has shown improved muscle function, but limited de novo muscle fiber regeneration. On the other hand, studies using minced autologous and free autologous muscle grafts have reported appreciable muscle regeneration. This raises the fundamental question whether an acellular ECM scaffold can orchestrate the spatiotemporal cellular events necessary for appreciable muscle fiber regeneration. This study compares the macrophage and angiogenic responses including the remodeling outcomes of a commercially available porcine urinary bladder matrix, MatriStem™, and autologous muscle grafts. The early heightened and protracted M1 response of the scaffold indicates that the scaffold does not recapitulate the spatiotemporal macrophage response of the autograft tissue. Additionally, the scaffold only supports limited de novo muscle fiber formation and regressing vessel density. Furthermore, scaffold remodeling is accompanied by increased presence of transforming growth factor and α-smooth muscle actin, which is consistent with remodeling of the scaffold into a fibrotic scar-like tissue. The limited muscle formation and scaffold-mediated fibrosis noted in this study corroborates the findings of recent studies that investigated acellular ECM scaffolds (devoid of myogenic cells) for VML repair. Taken together, acellular ECM scaffolds when used for VML repair will likely remodel into a fibrotic scar-like tissue and support limited de novo muscle fiber regeneration primarily in the proximity of the injured musculature. This is a work of the US Government and is not subject to copyright protection in the USA. Foreign copyrights may apply. Published by S. Karger AG, Basel