Spatio-temporal Description Research Articles

Climate Change and Dynamics of the ITCZ on the Hydro-Climatic Regime of the Northern Ecosystems of the Gulf of Guinea: A Case of the Grand-Lahou Lagoon System

Atmospheric dynamics and climatology in West Africa are strongly dominated by the latitudinal migration of the ITCZ which imposes directly or indirectly determinism on coastal ecosystems. Thus, the Grand-Lahou lagoon system and its watershed are influenced by the ITCZ, whose seasonal study made it possible to understand its mode of action first on climatic factors, then hydroclimatic, and finally hydrological in the context of global changes. The study of these factors showed a differentiated impact of its migration on the hydrological regime defining a new configuration of the lagoon system and beyond; the coastal ecosystems of the northern coast of the Gulf of Guinea irrigated by numerous fluvial inputs. Salinity, a hydrological parameter of critical ecosystem importance, combined with transparency and depth, has enabled the spatio-temporal description of hydrology. To do this, a series of monthly measurements (in-situ) for spatial coverage of 25 stations was tested. Long before that, the climatic and hydroclimatic parameters were obtained respectively at SODEXAM, the meteorological site earth.nullschool.net and ONADE. This study brought together the dynamics of the ITCZ and the hydrological system of the lagoon.

Self-organization in natural swarms of Photinus carolinus synchronous fireflies.

Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior.

Numerical analysis of non-collinear coherently combined laser beams interaction with micro-structured targets

In this work, we numerically investigate the laser beam intensification in a non-collinear coherently combined (NCCC) laser beams–micro-structured target interaction system, in the presence of pulse duration variation, down to few-cycle regime. A 2D numerical model based on finite-difference time domain (FDTD) method is performed in order to provide a spatio-temporal description of the electromagnetic field distribution in the vicinity of the laser–target interaction point in case of NCCC of multiple laser pulses. The results demonstrate that non-collinear coherent and spectral beam combination of three laser pulses in a micro-structured cone target allow a significant increase of the field intensity while a reduction of temporal duration of the combined pulse occurs. This numerical model offers an effective solution to increase the laser intensity in a micro-structured cone target by employing coherent and spectral beam combination of multiple laser sources and it can be used as an alternative method for field intensity enhancement in various laser–matter interaction experiments in ultra-intense regime.

Spatio-temporal characterization of causal electrophysiological activity stimulated by single pulse focused ultrasound: an ex vivo study on hippocampal brain slices

Objective. The brain operates via generation, transmission and integration of neuronal signals and most neurological disorders are related to perturbation of these processes. Neurostimulation by focused ultrasound (FUS) is a promising technology with potential to rival other clinically used techniques for the investigation of brain function and treatment of numerous neurological diseases. The purpose of this study was to characterize spatial and temporal aspects of causal electrophysiological signals directly stimulated by short, single pulses of FUS on ex vivo mouse hippocampal brain slices. Approach. Microelectrode arrays (MEAs) are used to study the spatio-temporal dynamics of extracellular neuronal activities both at the single neuron and neural networks scales. Hence, MEAs provide an excellent platform for characterization of electrical activity generated, modulated and transmitted in response to FUS exposure. In this study, a novel mixed FUS/MEA platform was designed for the spatio-temporal description of the causal responses generated by single 1.78 MHz FUS pulses in ex vivo mouse hippocampal brain slices. Main results. Our results show that FUS pulses can generate local field potentials (LFPs), sustained by synchronized neuronal post-synaptic potentials, and reproducing network activities. LFPs induced by FUS stimulation were found to be repeatable to consecutive FUS pulses though exhibiting a wide range of amplitudes (50–600 μV), durations (20–200 ms), and response delays (10–60 ms). Moreover, LFPs were spread across the hippocampal slice following single FUS pulses thus demonstrating that FUS may be capable of stimulating different neural structures within the hippocampus. Significance. Current knowledge on neurostimulation by ultrasound describes neuronal activity generated by trains of repetitive ultrasound pulses. This novel study details the causal neural responses produced by single-pulse FUS neurostimulation while illustrating the distribution and propagation properties of this neural activity along major neural pathways of the hippocampus.

How Simulations of the Land Carbon Sink Are Biased by Ignoring Fluvial Carbon Transfers: A Case Study for the Amazon Basin

Summary Land-surface models are important tools for simulation of the past, present, and future capacity of terrestrial ecosystems to absorb anthropogenic CO2 emissions. However, fluvial carbon (C) transfers are presently neglected in these models. Using the Amazon basin as a case study, we show that this negligence leads to significant underestimation of the net uptake of atmospheric C while terrestrial C storage changes are overestimated. These biases arise from the fact that C—in reality, leached from soils and exported through the river network—is instead represented as partly being respired and partly being stored in soils. Moreover, these biases scale mainly to the fluvial C export to the coast, despite aquatic CO2 emission to the atmosphere being the major pathway of riverine C exports. We further show that fluvial C transfers may change significantly in response to changes in either hydrology or in atmospheric C uptake by vegetation.

Dense hard disk ordering: influence of bidispersity and quenched disorder

Using Monte Carlo simulations, the impact on structural ordering in two-dimensional systems via the interplay of size bidispersity and quenched disorder in the form of an externally applied spatially random potential, is studied for a system of hard disks. By scanning across a wide range of dense packing fractions, size ratios and roughness of the applied potential, the phase diagram is constructed, which demonstrates that both quenched and size disorders shift the onset of translational order to higher packings, while maintaining the presence of the intermediate hexatic phase. At larger disorder strengths, the signatures of structural order are absent within the range of investigated packing fractions. Further, the dynamics with increasing potential strength is analysed for the mono-component system to obtain a spatio-temporal description of the melting process. Finally, the influence of the externally rough field on the Mermin–Wagner fluctuations, characteristic to two-dimensional systems, is investigated.

A spatio-temporal and kinematic description of self-selected walking in adults with Achondroplasia

BackgroundAchondroplasia is characterised by a shorter appendicular limb to torso ratio, compared to age matched individuals of average stature (controls). Despite the well documented shorter leg length of individuals with compared to controls, there are few complete descriptions of gait kinematics reported for the population. AimThe aim of this study was to report the spatio-temporal and kinematic characteristics of self-selected walking (SSW) in a group with Achondroplasia (N = 10) and age matched group without Achondroplasia (controls, N = 17). MethodWhole body 3D analysis of both groups was conducted using a 14 camera VICON system. Spatio-temporal and kinematic variables were determined through a Plug-in-Gait model. SSW was obtained from an average of three trials equating to a total of ∼120 m walking. ResultsThe group with Achondroplasia were 23 % slower (P < 0.001), had a 29 % shorter stride length (P < 0.001) and a 13 % higher stride frequency (P < 0.001) compared to controls. There were no differences in time normalised temporal measures of left toe off (P = 0.365), right heel contact (P = 0.442) or the duration of double support (P = 0.588) between groups. A number of discrete joint kinematic differences existed between groups, resulting in the group with Achondroplasia having more ‘flexed’ lower limbs than controls throughout the gait cycle. ConclusionDifferences in absolute spatio-temporal variables between groups is likely due to the shorter leg length of the group with Achondroplasia, while their more flexed position of the lower limbs may facilitate toe-clearance during the swing phase.

From video pornography to cancer cells: a tensor framework for spatiotemporal description

Spatiotemporal description is a research field with applications in various areas such as video indexing, surveillance, human-computer interfaces, among others. Big Data problems in large databases are now being treated with Deep Learning tools, however we still have room for improvement in spatiotemporal handcraft description. Moreover, we still have problems that involve small data in which data augmentation and other techniques are not valid, or even, it is not worth the use of an expensive method. The main contribution of this work is the development of a framework for spatiotemporal representation using orientation tensors enabling dimension reduction and invariance. This is a multipurpose framework called Features As Spatiotemporal Tensors (FASTensor). We evaluate this framework in two different applications: Video Pornography classification and Cancer Cell classification. The latter one is also a contribution of this work, since we introduce a new dataset called Melanoma Cancer Cell (MCC). It is a small dataset with inherent difficulties in the acquisition process and its particular motion nature. The results are competitive, while also being ease to compute. Finally, our results in the MCC dataset can be used in other cancer cell treatment analysis.

PALEO-PGEM v1.0: a statistical emulator of Pliocene–Pleistocene climate

Abstract. We describe the development of the “Paleoclimate PLASIM-GENIE (Planet Simulator–Grid-Enabled Integrated Earth system model) emulator” PALEO-PGEM and its application to derive a downscaled high-resolution spatio-temporal description of the climate of the last 5×106 years. The 5×106-year time frame is interesting for a range of paleo-environmental questions, not least because it encompasses the evolution of humans. However, the choice of time frame was primarily pragmatic; tectonic changes can be neglected to first order, so that it is reasonable to consider climate forcing restricted to the Earth's orbital configuration, ice-sheet state, and the concentration of atmosphere CO2. The approach uses the Gaussian process emulation of the singular value decomposition of ensembles of the intermediate-complexity atmosphere–ocean GCM (general circulation model) PLASIM-GENIE. Spatial fields of bioclimatic variables of surface air temperature (warmest and coolest seasons) and precipitation (wettest and driest seasons) are emulated at 1000-year intervals, driven by time series of scalar boundary-condition forcing (CO2, orbit, and ice volume) and assuming the climate is in quasi-equilibrium. Paleoclimate anomalies at climate model resolution are interpolated onto the observed modern climatology to produce a high-resolution spatio-temporal paleoclimate reconstruction of the Pliocene–Pleistocene.

Modeling of air bubble dynamics during resin transfer molding by pore doublet model

The most limiting aspect in liquid composite molding processes is the porosity issue. A successful manufacturing design depends mainly on the accurate prediction of the dynamics of air bubbles while processing composite structures. Especially, while simulating with a constant injection flow rate, where the part is supposed to exhibit a uniform porosity distribution according to the capillary number theory as proposed in the literature, it is not true in real practice. With this motivation, we present here a new approach, based on intra and intertow flows competition, to quantify the porosity of resin transfer molded composite parts. Combining the numerical modeling of the resin transfer molding (RTM) process based on the control volume finite element and volume of fluid methods on one hand, and the pore doublet model (PDM) on the other hand, air bubble contents at dual scale of pores are modeled in the case of constant pressure and constant injection flow rate. The results of the developed modeling permit a detailed spatiotemporal description of the created compressed and transported micro and macro air bubbles, besides fibrous fabric saturation. Consequently, taking into account the compression and transport phenomena, the porosity distribution is no longer uniform in the part, but higher in the downstream than in the upstream. Furthermore, the void transport phenomenon is also considered using the PDM concept. To validate the proposed approach, the numerical and experimental data are compared.

Mapping of Shigella flexneri's tissue distribution and type III secretion apparatus activity during infection of the large intestine of guinea pigs.

ABSTRACT Shigella spp. are bacterial pathogens that invade the human colonic mucosa using a type III secretion apparatus (T3SA), a proteinaceous device activated upon contact with host cells. Active T3SAs translocate proteins that carve the intracellular niche of Shigella spp. Nevertheless, the activation state of the T3SA has not been addressed in vivo. Here, we used a green fluorescent protein transcription-based secretion activity reporter (TSAR) to provide a spatio-temporal description of S. flexneri T3SAs activity in the colon of Guinea pigs. First, we observed that early mucus release is triggered in the vicinity of luminal bacteria with inactive T3SA. Subsequent mucosal invasion showed bacteria with active T3SA associated with the brush border, eventually penetrating into epithelial cells. From 2 to 8 h post-challenge, the infection foci expanded, and these intracellular bacteria displayed homogeneously high-secreting activity, while extracellular foci within the lamina propria featured bacteria with low secretion activity. We also found evidence that within lamina propria macrophages, bacteria reside in vacuoles instead of accessing the cytosol. Finally, bacteria were cleared from tissues between 8 and 24 h post-challenge, highlighting the hit-and-run colonization strategy of Shigella. This study demonstrates how genetically encoded reporters can contribute to deciphering pathogenesis in vivo.

Agent-based model of multicellular tumor spheroid evolution including cell metabolism.

Computational models aiming at the spatio-temporal description of cancer evolution are a suitable framework for testing biological hypotheses from experimental data, and generating new ones. Building on our recent work (J. Theor. Biol. 389, 146 (2016)) we develop a 3D agent-based model, capable of tracking hundreds of thousands of interacting cells, over time scales ranging from seconds to years. Cell dynamics is driven by a Monte Carlo solver, incorporating partial differential equations to describe chemical pathways and the activation/repression of "genes", leading to the up- or down-regulation of specific cell markers. Each cell-agent of different kind (stem, cancer, stromal etc.) runs through its cycle, undergoes division, can exit to a dormant, senescent, necrotic state, or apoptosis, according to the inputs from its systemic network. The basic network at this stage describes glucose/oxygen/ATP cycling, and can be readily extended to cancer-cell specific markers. Eventual accumulation of chemical/radiation damage to each cell's DNA is described by a Markov chain of internal states, and by a damage-repair network, whose evolution is linked to the cell systemic network. Aimed at a direct comparison with experiments of tumorsphere growth from stem cells, the present model will allow to quantitatively study the role of transcription factors involved in the reprogramming and variable radio-resistance of simulated cancer-stem cells, evolving in a realistic computer simulation of a growing multicellular tumorsphere.

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed regionally in any brain region.The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The model is first estimated on a control population using longitudinal data, then, for each testing subject, the markers are computed cross-sectionally for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolutions.In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quite high. The model is also generative since it can be used to simulate plausible morphological trajectories associated with the disease.Our method quantifies two interpretable scalar imaging biomarkers assessing respectively the effects of aging and disease on brain morphology, at the individual and population level. These markers confirm the presence of an accelerated apparent aging component in Alzheimer's patients but they also highlight specific morphological changes that can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.

Experimental investigation of Taylor-Couette-Poiseuille flow at low Taylor and Reynolds numbers.

Flow patterns of a Taylor-Couette-Poiseuille flow were studied at low axial Reynolds and rotational Taylor numbers (Re ≤ 10.5, Ta ≤ 319). The radius ratio of the inner and outer cylinders was 0.804 and the ratio of the length of the annulus to the gap width was 44.5. Complete map of the studied flow regimes was elaborated. The axial and azimuthal components of the wall shear rate γ were measured at the outer fixed cylinder using a three-segment electrodiffusion probe. The components of the wall shear rate of helices have never been measured in previous investigations. Spatio-temporal description of multiple flow patterns was obtained using flow visualizations and simultaneous measurements of wall shear rate components. The flow structures include Taylor vortices, helices winding in the same direction as the base flow or in the opposite direction, helices that were stagnant or moving in the axial direction, smooth or with superposed azimuthal waves, among others. The influence of different flow structures on the wall shear stress components is discussed with and without axial base flow. It was found that the wall shear stress is a function of Ta but no significant dependence on Re was observed for the studied flow regimes and that the mean wall shear stress increases with the number of azimuthal waves. It was also noted that the ED probes provide a more detailed information about flow patterns than torque measurements and visualizations described in the literature.

Spatiotemporal Prediction of Tidal Currents Using Gaussian Processes

AbstractPredicting fast tidal currents can be a challenging task. Unlike tidal water levels, currents can vary sharply over short distances. The classical approach of harmonic analysis can analyze data at point locations and there is a need for a method that can handle spatiotemporal data, as well as be robust to the uncertainty and noise inevitable in real‐world measurements. In this work, we present a Bayesian machine learning (ML) approach to tackle the problem. The method is based on Gaussian processes, a nonparametric ML technique that uses a kernel function to capture structures in the data. A case study is performed using data from a validated numerical model simulating the tidal dynamics in the Pentland Firth region, UK. Several sampling strategies are explored and the case where measurement location is changed after every sampling is found to produce the lowest average error in the predictions. We show that the presented methodology using data from just a single moving data source can provide a better spatiotemporal description than traditional techniques using continuous data from a large number of locations. The work can be useful to developers of tidal energy farms, navigation, and other purposes.

Geostatistical interpolation by quantile kriging

Abstract. The widely applied geostatistical interpolation methods of ordinary kriging (OK) or external drift kriging (EDK) interpolate the variable of interest to the unknown location, providing a linear estimator and an estimation variance as measure of uncertainty. The methods implicitly pose the assumption of Gaussianity on the observations, which is not given for many variables. The resulting “best linear and unbiased estimator” from the subsequent interpolation optimizes the mean error over many realizations for the entire spatial domain and, therefore, allows a systematic under-(over-)estimation of the variable in regions of relatively high (low) observations. In case of a variable with observed time series, the spatial marginal distributions are estimated separately for one time step after the other, and the errors from the interpolations might accumulate over time in regions of relatively extreme observations. Therefore, we propose the interpolation method of quantile kriging (QK) with a two-step procedure prior to interpolation: we firstly estimate distributions of the variable over time at the observation locations and then estimate the marginal distributions over space for every given time step. For this purpose, a distribution function is selected and fitted to the observed time series at every observation location, thus converting the variable into quantiles and defining parameters. At a given time step, the quantiles from all observation locations are then transformed into a Gaussian-distributed variable by a 2-fold quantile–quantile transformation with the beta- and normal-distribution function. The spatio-temporal description of the proposed method accommodates skewed marginal distributions and resolves the spatial non-stationarity of the original variable. The Gaussian-distributed variable and the distribution parameters are now interpolated by OK and EDK. At the unknown location, the resulting outcomes are reconverted back into the estimator and the estimation variance of the original variable. As a summary, QK newly incorporates information from the temporal axis for its spatial marginal distribution and subsequent interpolation and, therefore, could be interpreted as a space–time version of probability kriging. In this study, QK is applied for the variable of observed monthly precipitation from raingauges in South Africa. The estimators and estimation variances from the interpolation are compared to the respective outcomes from OK and EDK. The cross-validations show that QK improves the estimator and the estimation variance for most of the selected objective functions. QK further enables the reduction of the temporal bias at locations of extreme observations. The performance of QK, however, declines when many zero-value observations are present in the input data. It is further revealed that QK relates the magnitude of its estimator with the magnitude of the respective estimation variance as opposed to the traditional methods of OK and EDK, whose estimation variances do only depend on the spatial configuration of the observation locations and the model settings.

Simplified Description of Heat Release Oscillations for Assessing Combustor Stability

A simplified description of heat release oscillations associated with vortex-driven combustion instabilities is developed that can greatly reduce the amount of data required for determining the linear stability of combustor eigenmodes. The simplification is achieved by first representing heat release fluctuations as sinusoidal oscillations and then modeling the amplitude of the oscillations using a heuristic assumption of proportionality between the mean and the fluctuating components of local heat release. It is shown that by simply knowing 1) the average heat release, and 2) the heat release at any particular phase of the oscillations, complete spatiotemporal description of heat release oscillations can be reconstructed, and the linear stability of a given eigenmode can be determined. The description is shown to correctly determine the linear stability of a 150 Hz vortex-driven marginal amplitude instability in a laboratory-scale dump combustor with local and global Rayleigh indices within 16% of the experimentally obtained values. The approach opens up the possibility of predicting modal stability in the future without experiments, by using other forms of data, such as computational data on time-averaged heat release distribution and physics-based modeling data on phase-specific heat release rates.

Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data.

Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.

A space-fractional Monodomain model for cardiac electrophysiology combining anisotropy and heterogeneity on realistic geometries

Classical models of electrophysiology do not typically account for the effects of high structural heterogeneity in the spatio-temporal description of excitation waves propagation. We consider a modification of the Monodomain model obtained by replacing the diffusive term of the classical formulation with a fractional power of the operator, defined in the spectral sense. The resulting nonlocal model describes different levels of tissue heterogeneity as the fractional exponent is varied. The numerical method for the solution of the fractional Monodomain relies on an integral representation of the nonlocal operator combined with a finite element discretisation in space, allowing to handle in a natural way bounded domains in more than one spatial dimension. Numerical tests in two spatial dimensions illustrate the features of the model. Activation times, action potential duration and its dispersion throughout the domain are studied as a function of the fractional parameter: the expected peculiar behaviour driven by tissue heterogeneities is recovered.

Grete Hermann as Neo-Kantian Philosopher of Space and Time Representation

Grete Hermann’s essay “Die naturphilosophischen Grundlagen der Quantenmechanik” (1935) has received much deserved scholarly attention in recent years. In this paper, I follow the lead of Elise Crull (2017) who sees in Hermann’s work the general outlines of a neo-Kantian interpretation of quantum theory. In full support of this view, I focus on Hermann’s central claim that limited spatio-temporal, and even analogically causal, representations of events exist within an overall relational structure of entangled quantum mechanical states that defy any unified spatio-temporal description. In my view, Hermann also advances an important transcendental argument that perspectival spatio-temporal representations of nature have their foundations in general relational networks that are not spatio-temporal. The key point is that the adoption of a perspectival system within the general network induces the representation but only for that context. These ideas are consistent with a perspectival subject–object principle in Kant and also with Weyl’s work on Lie groups and their representations.