Spatio-temporal correlations in aqueous systems: computational studies of static and dynamic heterogeneity by 2D-IR spectroscopy

Local heterogeneity is ubiquitous in natural aqueous systems. It can be caused locally by external biomolecular subsystems like proteins, DNA, micelles and reverse micelles, nanoscopic materials etc., but can also be intrinsic to the thermodynamic nature of the aqueous solution itself (like binary mixtures or at the gas-liquid interface). The altered dynamics of water in the presence of such diverse surfaces has attracted considerable attention in recent years. As these interfaces are quite narrow, only a few molecular layers thick, they are hard to study by conventional methods. The recent development of two dimensional infra-red (2D-IR) spectroscopy allows us to estimate length and time scales of such dynamics fairly accurately. In this work, we present a series of interesting studies employing two dimensional infra-red spectroscopy (2D-IR) to investigate (i) the heterogeneous dynamics of water inside reverse micelles of varying sizes, (ii) supercritical water near the Widom line that is known to exhibit pronounced density fluctuations and also study (iii) the collective and local polarization fluctuation of water molecules in the presence of several different proteins. The spatio-temporal correlation of confined water molecules inside reverse micelles of varying sizes is well captured through the spectral diffusion of corresponding 2D-IR spectra. In the case of supercritical water also, we observe a strong signature of dynamic heterogeneity from the elongated nature of the 2D-IR spectra. In this case the relaxation is ultrafast. We find remarkable agreement between the different tools employed to study the relaxation of density heterogeneity. For aqueous protein solutions, we find that the calculated dielectric constant of the respective systems unanimously shows a noticeable increment compared to that of neat water. However, the `effective' dielectric constant for successive layers shows significant variation, with the layer adjacent to the protein having a much lower value. Relaxation is also slowest at the surface. We find that the dielectric constant achieves the bulk value at distances more than 3 nm from the surface of the protein.

Big data analytics and visualization with spatio-temporal correlations for traffic accidents

Big data analytics for traffic accidents is a hot topic and has significant values for a smart and safe traffic in the city. Based on the massive traffic accident data from October 2014 to March 2015 in Xiamen, China, we propose a novel accident occurrences analytics method in both spatial and temporal dimensions to predict when and where an accident with a specific crash type will occur consequentially by whom. Firstly, we analyze and visualize accident occurrences in both temporal and spatial view. Second, we illustrate spatio-temporal visualization results through two case studies in multiple road segments, and the impact of weather on crash types. These findings of accident occurrences analysis and visualization would not only help traffic police department implement instant personnel assignments among simultaneous accidents, but also inform individual drivers about accident-prone sections and the time span which requires their most attention.

Débito cardíaco e fração de ejeção fetal por meio do spatio-temporal image correlation (STIC): comparação entre fetos masculinos e femininos

OBJETIVO: Comparar do débito cardíaco (DC) e a fração de ejeção (FE) do coração de fetos masculinos e femininos obtidos por meio da ultrassonografia tridimensional, utilizando o spatio-temporal image correlation (STIC). MÉTODOS: Realizou-se um estudo de corte transversal com 216 fetos normais, entre 20 a 34 semanas de gestação, sendo 108 masculinos e 108 femininos. Os volumes ventriculares no final da sístole e diástole foram obtidos por meio do STIC, sendo as avaliações volumétricas realizadas pelo virtual organ computer-aided analysis (VOCAL) com rotação de 30º. Para o cálculo do DC utilizou-se a fórmula: DC= volume sistólico/frequência cardíaca fetal, enquanto que para a FE utilizou-se a fórmula: FE= volume sistólico/volume diastólico final. O DC (combinado, feminino e masculino) e a FE (masculina e feminina) foram comparadas utilizando-se o teste t não pareado e ANCOVA. Foram criados gráficos de dispersão com os percentis 5, 50 e 95. RESULTADOS: A média do DC combinado, DC direito, DC esquerdo, FE direita e FE esquerda, para feminino e masculino, foram 240,07 mL/min; 122,67 mL/min; 123,40 mL/min; 72,84%; 67,22%; 270,56 mL/min; 139,22 mL/min; 131,34 mL/min; 70,73% e 64,76%, respectivamente; sem diferença estatística (P> 0,05). CONCLUSÕES: O DC e a FE fetal obtidos por meio da ultrassonografia tridimensional (STIC) não apresentaram diferença significativa em relação ao gênero.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Land Surface Temperature with Land Cover Dynamics:Multi-Resolution, Spatio-Temporal Data Analysis of Greater Bangalore

Bangalore is experiencing unprecedented urbanisation in recent times due to concentrated developmental activities with impetus on IT (Information Technology) and BT (Biotechnology) sectors. The concentrated developmental activities has resulted in the increase in population and consequent pressure on infrastructure, natural resources, ultimately giving rise to a plethora of serious challenges such as urban flooding, climate change, etc. One of the perceived impact at local levels is the increase in sensible heat flux from the land surface to the atmosphere, which is also referred as heat island effect. In this communication, we report the changes in land surface temperature (LST) with respect to land cover changes during 1973 to 2007. A novel technique combining the information from sub-pixel class proportions with information from classified image (using signatures of the respective classes collected from the ground) has been used to achieve more reliable classification. The analysis showed positive correlation with the increase in paved surfaces and LST. 466% increase in paved surfaces (buildings, roads, etc.) has lead to the increase in LST by about 2 ºC during the last 2 decades, confirming urban heat island phenomenon. LSTs’ were relatively lower (~ 4 to 7 ºC) at land uses such as vegetation (parks/forests) and water bodies which act as heat sinks.

Projecting low and extensive dimensional chaos from spatio-temporal dynamics

We review the spatio-temporal dynamical features of the Ananthakrishna model for the Portevin-Le Chatelier effect, a kind of plastic instability observed under constant strain rate deformation conditions. We then establish a qualitative correspondence between the spatio-temporal structures that evolve continuously in the instability domain and the nature of the irregularity of the scalar stress signal. Rest of the study is on quantifying the dynamical information contained in the stress signals about the spatio-temporal dynamics of the model. We show that at low applied strain rates, there is a one-to-one correspondence with the randomly nucleated isolated bursts of mobile dislocation density and the stress drops. We then show that the model equations are spatio-temporally chaotic by demonstrating the number of positive Lyapunov exponents and Lyapunov dimension scale with the system size at low and high strain rates. Using a modified algorithm for calculating correlation dimension density, we show that the stress-strain signals at low applied strain rates corresponding to spatially uncorrelated dislocation bands exhibit features of low dimensional chaos. This is made quantitative by demonstrating that the model equations can be approximately reduced to space independent model equations for the average dislocation densities, which is known to be low-dimensionally chaotic. However, the scaling regime for the correlation dimension shrinks with increasing applied strain rate due to increasing propensity for propagation of the dislocation bands. The stress signals in the partially propagating to fully propagating bands turn to have features of extensive chaos.

Spatio-temporal Map Formation Based on a Potential Function

We revisit the problem of temporal self organization using activity diffusion based on the neural gas (NGAS) algorithm. Using a potential function formulation motivated by a spatio-temporal metric, we derive an adaptation rule for dynamic vector quantization of data. Simulations results show that our algorithm learns the input distribution and time correlation much faster compared to the static neural gas method over the same data sequence under similar training conditions.

Spatio-temporal variability in ammonia oxidation and ammonia oxidising bacteria and archaea in coastal sediments of the Western English Channel

The abundance of ammonia-oxidising bacterial (AOB) and ammonia-oxidising archaeal (AOA) (amoA) genes and ammonia oxidation rates were compared bimonthly from July 2008 to May 2011 in 4 contrasting coastal sediments in the western English Channel. Despite a higher abundance of AOA amoA genes within all sediments and at all time-points, rates of ammonia oxidation correlated with AOB and not AOA amoA gene abundance. Sediment type was a major factor in determining both AOB amoA gene abundance and AOB community structure, possibly due to deeper oxygen penetration into the sandier sediments, increasing the area available for ammonia oxidation. Decreases in AOB amoA gene abundance were evident during summer and autumn, with maximum abundance and ammonia oxidation rates occurring in winter and early spring. PCR-DGGE of AOB amoA genes indicated that no seasonal changes to community composition occurred; however, a gradual movement in community composition occurred at 3 of the sites studied. The lack of correlation between AOA amoA gene abundance and ammonium oxidation rates, or any other environmental variable measured, may be related to the higher spatial variation amongst measurements, obscuring temporal trends, or the bimonthly sampling, which may have been too infrequent to capture temporal variability in the deposition of fresh organic matter. Alternatively, AOA may respond to changing substrate concentrations by an increase or decrease in transcript rather than gene abundance.

Spatio-temporal Rich Model Based Video Steganalysis on Cross Sections of Motion Vector Planes

A rich model based motion vector steganalysis benefiting from both temporal and spatial correlations of motion vectors is proposed in this work. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this work. Firstly, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring motion vectors for longer distances. Therefore, temporal motion vector dependency along side the spatial dependency is utilized for rigorous motion vector steganalysis. Secondly, unlike the filters previously used, which were heuristically designed against a specific motion vector steganography, a diverse set of many filters which can capture aberrations introduced by various motion vector steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent motion vector steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in motion vector steganalysis field including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.

The residual circulation of the Southern Ocean: which spatio-temporal scales are needed?

The Southern Ocean circulation consists of a complicated mixture of processes and phenomena that arise at different time and spatial scales which need to be parametrized in the state-of-the-art climate models. The temporal and spatial scales that give rise to the present-day residual mean circulation are here investigated by calculating the Meridional Overturning Circulation (MOC) in density coordinates from an eddy-permitting global model. The region sensitive to the temporal decomposition is located between 38°S and 63°S, associated with the eddy-induced transport. The ‘‘Bolus’’ component of the residual circulation corresponds to the eddy-induced transport. It is dominated by timescales between 1 month and 1 year. The temporal behavior of the transient eddies is examined in splitting the ‘‘Bolus’’ component into a ‘‘Seasonal’’, an ‘‘Eddy’’ and an ‘‘Inter-monthly’’ component, respectively representing the correlation between density and velocity fluctuations due to the average seasonal cycle, due to mesoscale eddies and due to large-scale motion on timescales longer than one month that is not due to the seasonal cycle. The ‘‘Seasonal’’ bolus cell is important at all latitudes near the surface. The ‘‘Eddy’’ bolus cell is dominant in the thermocline between 50°S and 35°S and over the whole ocean depth at the latitude of the Drake Passage. The ‘‘Inter-monthly’’ bolus cell is important in all density classes and is maximal in the Brazil–Malvinas Confluence and the Agulhas Return Current. The spatial decomposition indicates that a large part of the Eulerian mean circulation is recovered for spatial scales larger than 11.25°, implying that small-scale meanders in the Antarctic Circumpolar Current (ACC), near the Subantarctic and Polar Fronts, and near the Subtropical Front are important in the compensation of the Eulerian mean flow.

Spatio-temporal segregation and size distribution of fish assemblages as related to non-native species occurrence in the middle rio Doce Valley, MG, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Spatio-temporal variability of sugarcane yield efficiency in the state of Sao Paulo, Brazil

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of Sao Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of Sao Paulo.

Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of São Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of São Paulo.

Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Spatio-temporal generation regimes in quasi-CW Raman fiber lasers

We present experimental measurements of intensity spatiotemporal dynamics in quasi-CW Raman fiber laser. Depending on the power, the laser operates in different spatio-temporal regimes varying from partial mode-locking near the generation threshold to almost stochastic radiation and a generation of short-lived pulses at high power. The transitions between the generation regimes are evident in intensity spatio-temporal dynamics. Two-dimensional auto-correlation functions provide an additional insight into temporal and spatial properties of the observed regimes.