Time series analysis of jaw muscle contraction and tissue deformation during mastication in miniature pigs

Masticatory muscle contraction causes both jaw movement and tissue deformation during function. Natural chewing data from 25 adult miniature pigs were studied by means of time series analysis. The data set included simultaneous recordings of electromyography (EMG) from bilateral masseter (MA), zygomaticomandibularis (ZM) and lateral pterygoid muscles, bone surface strains from the left squamosal bone (SQ), condylar neck (CD) and mandibular corpus (MD), and linear deformation of the capsule of the jaw joint measured bilaterally using differential variable reluctance transducers. Pairwise comparisons were examined by calculating the cross-correlation functions. Jaw-adductor muscle activity of MA and ZM was found to be highly cross-correlated with CD and SQ strains and weakly with MD strain. No muscle’s activity was strongly linked to capsular deformation of the jaw joint, nor were bone strains and capsular deformation tightly linked. Homologous muscle pairs showed the greatest synchronization of signals, but the signals themselves were not significantly more correlated than those of non-homologous muscle pairs. These results suggested that bone strains and capsular deformation are driven by different mechanical regimes. Muscle contraction and ensuing reaction forces are probably responsible for bone strains, whereas capsular deformation is more likely a product of movement.

Probabilistic Envelope Curves for Extreme Rainfall Events - Curve Inviluppo Probabilistiche per Precipitazioni Estreme

A regional envelope curve (REC) of flood flows summarises the current bound on our experience of extreme floods in a region. RECs are available for most regions of the world. Recent scientific papers introduced a probabilistic interpretation of these curves and formulated an empirical estimator of the recurrence interval T associated with a REC, which, in principle, enables us to use RECs for design purposes in ungauged basins. The main aim of this work is twofold. First, it extends the REC concept to extreme rainstorm events by introducing the Depth-Duration Envelope Curves (DDEC), which are defined as the regional upper bound on all the record rainfall depths at present for various rainfall duration. Second, it adapts the probabilistic interpretation proposed for RECs to DDECs and it assesses the suitability of these curves for estimating the T-year rainfall event associated with a given duration and large T values. Probabilistic DDECs are complementary to regional frequency analysis of rainstorms and their utilization in combination with a suitable rainfall-runoff model can provide useful indications on the magnitude of extreme floods for gauged and ungauged basins. The study focuses on two different national datasets, the peak over threshold (POT) series of rainfall depths with duration 30 min., 1, 3, 9 and 24 hrs. obtained for 700 Austrian raingauges and the Annual Maximum Series (AMS) of rainfall depths with duration spanning from 5 min. to 24 hrs. collected at 220 raingauges located in northern-central Italy. The estimation of the recurrence interval of DDEC requires the quantification of the equivalent number of independent data which, in turn, is a function of the cross-correlation among sequences. While the quantification and modelling of intersite dependence is a straightforward task for AMS series, it may be cumbersome for POT series. This paper proposes a possible approach to address this problem.

An optimal estimator for the correlation of CMB anisotropies with Large Scale Structures and its application to WMAP-7year and NVSS

In the thesis we present the implementation of the quadratic maximum likelihood (QML) method, ideal to estimate the angular power spectrum of the cross-correlation between cosmic microwave background (CMB) and large scale structure (LSS) maps as well as their individual auto-spectra. Such a tool is an optimal method (unbiased and with minimum variance) in pixel space and goes beyond all the previous harmonic analysis present in the literature. We describe the implementation of the QML method in the {\it BolISW} code and demonstrate its accuracy on simulated maps throughout a Monte Carlo. We apply this optimal estimator to WMAP 7-year and NRAO VLA Sky Survey (NVSS) data and explore the robustness of the angular power spectrum estimates obtained by the QML method. Taking into account the shot noise and one of the systematics (declination correction) in NVSS, we can safely use most of the information contained in this survey. On the contrary we neglect the noise in temperature since WMAP is already cosmic variance dominated on the large scales. Because of a discrepancy in the galaxy auto spectrum between the estimates and the theoretical model, we use two different galaxy distributions: the first one with a constant bias $b$ and the second one with a redshift dependent bias $b(z)$. Finally, we make use of the angular power spectrum estimates obtained by the QML method to derive constraints on the dark energy critical density in a flat $\Lambda$CDM model by different likelihood prescriptions. When using just the cross-correlation between WMAP7 and NVSS maps with 1.8° resolution, we show that $\Omega_\Lambda$ is about the 70\% of the total energy density, disfavouring an Einstein-de Sitter Universe at more than 2 $\sigma$ CL (confidence level).

Monitoring kinetic processes in polymeric colloids by fluorescence correlation spectroscopy

Polymerbasierte Kolloide mit Groen im Nanometerbereich werden als aussichts- reiche Kandidaten fur die Verkapselung und den Transport von pharmazeutischen Wirkstoen angesehen. Daher ist es wichtig die physikalischen Prozesse, die die Bil- dung, Struktur und kinetische Stabilitat der polymerbasierten Kolloide beein ussen, besser zu verstehen. Allerdings ist die Untersuchung dieser Prozesse fur nanome- tergroe Objekte kompliziert und erfordert fortgeschrittene Techniken. In dieser Arbeit beschreibe ich Untersuchungen, bei denen Zwei-Farben-Fluoreszenzkreuz- korrelationsspektroskopie (DC FCCS) genutzt wurde, um Informationen uber die Wechselwirkung und den Austausch von dispergierten, nanometergroen Kolloiden zu bekommen. Zunachst habe ich den Prozess der Polymernanopartikelherstellung aus Emul- sionstropfen untersucht, welcher einen der am haugsten angewendeten Prozesse der Nanopartikelformulierung darstellt. Ich konnte zeigen, dass mit DC FCCS eindeutig und direkt Koaleszenz zwischen Emulsionstropfen gemessen werden kann. Dies ist von Interesse, da Koaleszenz als Hauptgrund fur die breite Groenverteilung der nalen Nanopartikel angesehen wird. Weiterhin habe ich den Austausch von Mizellen bildenden Molekulen zwischen amphiphilen Diblock Kopolymermizellen untersucht. Als Modellsystem diente ein Linear-Burste Block Kopolymer, welches Mizellen mit einer dichten und kurzen Korona bildet. Mit Hilfe von DC FCCS konnte der Austausch in verschiedenen Losungsmitteln und bei verschiedenen Temperaturen beobachtet werden. Ich habe herausgefunden, dass in Abhangigkeit der Qualitat des Losungsmittels die Zeit des Austausches um Groenordnungen verschoben werden kann, was eine weitreichende Einstellung der Austauschkinetik ermoglicht. Eine Eigenschaft die all diese Kolloide gemeinsam haben ist ihre Polydispersitat. Im letzten Teil meiner Arbeit habe ich am Beispiel von Polymeren als Modellsystem untersucht, welchen Eekt Polydispersitat und die Art der Fluoreszenzmarkierung auf FCS Experimente haben. Eine Anpassung des klassischen FCS Modells kann die FCS Korrelationskurven dieser Systeme beschreiben. Die Richtigkeit meines Ansatzes habe ich mit dem Vergleich zur Gel-Permeations-Chromatographie und Brownschen Molekulardynamiksimulationen bestatigt.

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.

Hydrodynamic correlation functions of a driven granular fluid in steady state

We study a homogeneously driven granular fluid of hard spheres at intermediate volume fractions and focus on time-delayed correlation functions in the stationary state. Inelastic collisions are modeled by incomplete normal restitution, allowing for efficient simulations with an event-driven algorithm. The incoherent scattering function Fincoh(q,t ) is seen to follow time-density superposition with a relaxation time that increases significantly as the volume fraction increases. The statistics of particle displacements is approximately Gaussian. For the coherent scattering function S(q,ω), we compare our results to the predictions of generalized fluctuating hydrodynamics, which takes into account that temperature fluctuations decay either diffusively or with a finite relaxation rate, depending on wave number and inelasticity. For sufficiently small wave number q we observe sound waves in the coherent scattering function S(q,ω) and the longitudinal current correlation function Cl(q,ω). We determine the speed of sound and the transport coefficients and compare them to the results of kinetic theory.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Artificial oxygen fluxes measured by the eddy correlation method using stirring-sensitive oxygen microsensor and oxygen optodes in a flume experiment

In the last decade, the aquatic eddy correlation (EC) technique has proven to be a powerful approach for non-invasive measurements of oxygen fluxes across the sediment water interface. Fundamental to the EC approach is the correlation of turbulent velocity and oxygen concentration fluctuations measured with high frequencies in the same sampling volume. Oxygen concentrations are commonly measured with fast responding electrochemical microsensors. However, due to their own oxygen consumption, electrochemical microsensors are sensitive to changes of the diffusive boundary layer surrounding the probe and thus to changes in the ambient flow velocity. The so-called stirring sensitivity of microsensors constitutes an inherent correlation of flow velocity and oxygen sensing and thus an artificial flux which can confound the benthic flux determination. To assess the artificial flux we measured the correlation between the turbulent flow velocity and the signal of oxygen microsensors in a sealed annular flume without any oxygen sinks and sources. Experiments revealed significant correlations, even for sensors designed to have low stirring sensitivities of ~0.7%. The artificial fluxes depended on ambient flow conditions and, counter intuitively, increased at higher velocities because of the nonlinear contribution of turbulent velocity fluctuations. The measured artificial fluxes ranged from 2 - 70 mmol m**-2 d**-1 for weak and very strong turbulent flow, respectively. Further, the stirring sensitivity depended on the sensor orientation towards the flow. Optical microsensors (optodes) that should not exhibit a stirring sensitivity were tested in parallel and did not show any significant correlation between O2 signals and turbulent flow. In conclusion, EC data obtained with electrochemical sensors can be affected by artificial flux and we recommend using optical microsensors in future EC-studies. Flume experiments were conducted in February 2013 at the Institute for Environmental Sciences, University of Koblenz-Landau Landau. Experiments were performed in a closed oval-shaped acrylic glass flume with cross-sectional width of 4 cm and height of 10 cm and total length of 54 cm. The fluid flow was induced by a propeller driven by a motor and mean flow velocities of up to 20 cm s-1 were generated by applying voltages between 0 V and 4 V DC. The flume was completely sealed with an acrylic glass cover. Oxygen sensors were inserted through rubber seal fittings and allowed positioning the sensors with inclinations to the main flow direction of ~60°, ~95° and ~135°. A Clark type electrochemical O2 microsensor with a low stirring sensitivity (0.7%) was tested and a fast-responding needle-type O2 optode (PyroScience GmbH, Germany) was used as reference as optodes should not be stirring sensitive. Instantaneous three-dimensional flow velocities were measured at 7.4 Hz using stereoscopic particle image velocimetry (PIV). The velocity at the sensor tip was extracted. The correlation of the fluctuating O2 sensor signals and the fluctuating velocities was quantified with a cross-correlation analysis. A significant cross-correlation is equivalent to a significant artificial flux. For a total of 18 experiments the flow velocity was adjusted between 1.7 and 19.2 cm s**-1, and 3 different orientations of the electrochemical sensor were tested with inclination angles of ~60°, ~95° and ~135° with respect to the main flow direction. In experiments 16-18, wavelike flow was induced, whereas in all other experiments the motor was driven by constant voltages. In 7 experiments, O2 was additionally measured by optodes. Although performed simultaneously with the electrochemical sensor, optode measurements are listed as separate experiments (denoted by the attached 'op' in the filename), because the velocity time series was extracted at the optode tip, located at a different position in the flume.

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance.

HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ?traditional? set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified, easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

On the construction of correlation functions for the integrable supersymmetric fermion models

We review the recent progress on the construction of the determinant representations of the correlation functions for the integrable supersymmetric fermion models. The factorizing F-matrices (or the so-called F-basis) play an important role in the construction. In the F-basis, the creation (and the annihilation) operators and the Bethe states of the integrable models are given in completely symmetric forms. This leads to the determinant representations of the scalar products of the Bethe states for the models. Based on the scalar products, the determinant representations of the correlation functions may be obtained. As an example, in this review, we give the determinant representations of the two-point correlation function for the U-q(gl(2 vertical bar 1)) (i.e. q-deformed) supersymmetric t-J model. The determinant representations are useful for analyzing physical properties of the integrable models in the thermodynamical limit.

Possibilities and limitations of coded aperture imaging

We have simulated the performance of various apertures used in Coded Aperture Imaging - optically. Coded pictures of extended and continuous-tone planar objects from the Annulus, Twin Annulus, Fresnel Zone Plate and the Uniformly Redundant Array have been decoded using a noncoherent correlation process. We have compared the tomographic capabilities of the Twin Annulus with the Uniformly Redundant Arrays based on quadratic residues and m-sequences. We discuss the ways of reducing the 'd. c.' background of the various apertures used. The non-ideal System-Point-Spread-Function inherent in a noncoherent optical correlation process produces artifacts in the reconstruction. Artifacts are also introduced as a result of unwanted cross-correlation terms from out-of-focus planes. We find that the URN based on m-sequences exhibits good spatial resolution and out-of-focus behaviour when imaging extended objects.

Autonomic, biometric and oculomotor correlates of myopia in young adults

The principal work reported in this thesis is the examination of autonomic profile of ciliary muscle innervation as a risk factor in myopia development. Deficiency in sympathetic inhibitory control of accommodation has been proposed as a contributory factor in the development of late onset myopia (LOM). Complementary measurements of ocular biometry, oculomotor function and dynamic accommodation response were carried out on the same subject cohort, thus allowing cross-correlation of these factors with. autonomic profile. Subjects were undergraduate and postgraduate students of Aston University. A 2.5 year longitudinal study of refractive error progression in 40 subjects revealed the onset of LOM in 10, initially emmetropic, young adult subjects (age range 18-24 years) undertaking substantial amounts of near work. A controlled, double blind experimental protocol was conducted concurrently to measure post-task open-loop accommodative regression following distance (0 D) or near (3 D above baseline tonic accommodation) closed-loop tasks of short (10 second) or long (3 minute) duration. Closed-loop tasks consisted of observation of a high contrast Maltese cross target; open-loop conditions were imposed by observation of a 0.2 c/deg Difference of Gaussian target. Accommodation responses were recorded continuously at 42 Hz using a modified Shin-Nippon SRW-5000 open-view infra-red optometer. Blockade of the sympathetic branch of accommodative control was achieved by topical instillation of the non-selective b-adrenoceptor antagonist timolol maleate. Betaxolol hydrochloride (non-selective b1-adrenoceptor antagonist) and normal saline were employed as control agents. Retarded open-loop accommodative regression under b2 blockade following the 3 minute near task indicated the presence of sympathetic facility. Sympathetic inhibitory facility in accommodation control was found in similar proportions between LOM and stable emmetropic subjects. A cross-sectional study (N=60) of autonomic profile showed that sympathetic innervation of ciliary muscle is present in similar proportions between emmetropes, early-, and late-onset myopes. Sympathetic facility was identified in 27% of emmetropes, 21% of EOMs and 29% of LOMs.