Reconstructing time-dependent dynamics

The usefulness of the information extracted from biomedical data relies heavily on the underlying theory of the methods used in its extraction. The assumptions of stationarity and autonomicity traditionally applied to dynamical systems break down when considering living systems, due to their inherent time-variability. Living systems are thermodynamically open, and thus constantly interacting with their environment. This results in highly nonlinear, time-dependent dynamics. The aim of signal analysis is to gain insight into the behaviour of the system from which the signal originated. Here, various analysis methods for the characterization of signals and their underlying non-autonomous dynamics are presented, incorporating time-frequency analysis, time-domain decomposition of nonlinear modes, and methods to study mutual interactions and couplings using dynamical Bayesian inference, wavelet-bispectral and time-localised coherence, and entropy and information-based analysis. The recent introduction of chronotaxic systems provides a theoretical framework in which dynamical systems can have amplitudes and frequencies which are time-varying, yet stable, matching well the characteristics of living systems. We demonstrate that considering this theory of chronotaxic systems whilst applying the presented methods results in an approach for the reconstruction of the dynamics of living systems across many scales

Mineralogic variability of the Kelso Dunes, Mojave Desert, California derived from Thermal Infrared Multispectral Scanner (TIMS) data

Mineral identification and mapping of alluvial material using thermal infrared (TIR) remote sensing is extremely useful for tracking sediment transport, assessing the degree of weathering and locating sediment sources. As a result of the linear relation between a mineral's percentage in a given area (image pixel) and the depth of its diagnostic spectral features, TIR spectra can be deconvolved in order to ascertain mineralogic percentages. Typical complications such as vegetation, particle size and thermal shadowing are minimized upon examination of dunes. Actively saltating dunes contain little to no vegetation, are very well sorted and lack the thermal shadows that arise from rocky terrain. The primary focus of this work was to use the Kelso Dunes as a test location for an accuracy analysis of temperature/emissivity separation and linear unmixing algorithms. Accurate determination of ground temperature and component discrimination will become key products of future ASTER data. A decorrelation stretch of the TIMS image showed clear color variations within the active dunes. Samples collected from these color units were analyzed for mineralogy, grain size, and separated into endmembers. This analysis not only revealed that the dunes contained significant mineralogic variation, but were more immature (low quartz percentage) than previously reported. Unmixing of the TIMS data using the primary mineral endmembers produced unique variations within the dunes and may indicate near, rather than far, source locales for the dunes. The Kelso Dunes lie in the eastern Mojave Desert, California, approximately 95 km west of the Colorado River. The primary dune field is contained within a topographic basin bounded by the Providence, Granite Mountains, with the active region marked by three northeast trending linear ridges. Although active, the dunes appear to lie at an opposing regional wind boundary which produces little net movement of the crests. Previous studies have estimated the dunes range from 70% to 90% quartz mainly derived from a source 40 km to the west. The dune field is assumed to have formed in a much more arid climate than present, with the age of the deposit estimated at greater than 100,000 years

Detecting chronotaxic systems from single-variable time series with separable amplitude and phase

The recent introduction of chronotaxic systems provides the means to describe nonautonomous systems with stable yet time-varying frequencies which are resistant to continuous external perturbations. This approach facilitates realistic characterization of the oscillations observed in living systems, including the observation of transitions in dynamics which were not considered previously. The novelty of this approach necessitated the development of a new set of methods for the inference of the dynamics and interactions present in chronotaxic systems. These methods, based on Bayesian inference and detrended fluctuation analysis, can identify chronotaxicity in phase dynamics extracted from a single time series. Here, they are applied to numerical examples and real experimental EEG data. We also review the current methods, including their assumptions and limitations, elaborate on their implementation, and discuss future perspectives

The role of the C-terminal lysine of S100P in S100P-induced cell migration and metastasis

S100P protein is a potent inducer of metastasis in a model system, and its presence in cancer cells of patients is strongly associated with their reduced survival times. A well-established Furth Wistar rat metastasis model system, methods for measuring cell migration, and specific inhibitors were used to study pathways of motility-driven metastasis. Cells expressing C-terminal mutant S100P proteins display markedly-reduced S100P-driven metastasis in vivo and cell migration in vitro. These cells fail to display the low focal adhesion numbers observed in cells expressing wild-type S100P, and the mutant S100P proteins exhibit reduced biochemical interaction with non-muscle myosin heavy chain isoform IIA in vitro. Extracellular inhibitors of the S100P-dependent plasminogen activation pathway reduce, but only in part, wild-type S100P-dependent cell migration; they are without effect on S100P-negative cells or cells expressing C-terminal mutant S100P proteins and have no effect on the numbers of focal adhesions. Recombinant wild-type S100P protein, added extracellularly to S100P-negative cells, stimulates cell migration, which is abolished by these inhibitors. The results identify at least two S100P-dependent pathways of migration, one cell surface and the other intracellularly-linked, and identify its C-terminal lysine as a target for inhibiting multiple migration-promoting activities of S100P protein and S100P-driven metastasis

Empirical likelihood estimation of the spatial quantile regression

The spatial quantile regression model is a useful and flexible model for analysis of empirical problems with spatial dimension. This paper introduces an alternative estimator for this model. The properties of the proposed estimator are discussed in a comparative perspective with regard to the other available estimators. Simulation evidence on the small sample properties of the proposed estimator is provided. The proposed estimator is feasible and preferable when the model contains multiple spatial weighting matrices. Furthermore, a version of the proposed estimator based on the exponentially tilted empirical likelihood could be beneficial if model misspecification is suspect

First star-forming structures in fuzzy cosmic filaments

For abstract see published article

On spatial adaptivity and interpolation when using the method of lines

The solution of time-dependent partial differential equations with discrete time static remeshing is considered within a method of lines framework. Numerical examples in one and two space dimensions are used to show that spatial interpolation error may have an important impact on the efficiency of integration. Analysis of a simple problem and of the time integration method is used to confirm the experimental results and a computational test for monitoring the impact of this error is derived and tested