Role of intracellular cysteines in ENaC function

The epithelial sodium channel (ENaC) regulates the sodium reabsorption in the collecting duct principal cells of the nephron. ENaC is mainly regulated by hormones such as aldosterone and vasopressin, but also by serine proteases, Na+ and divalent cations. The crystallization of an ENaC/Deg member, the Acid Sensing Ion Channel, has been recently published but the pore-lining residues constitution of ENaC internal pore remains unclear. It has been reported that mutation aS589C of the selectivity filter on the aENaC subunit, a three residues G/SxS sequence, renders the channel permeant to divalent cations and sensitive to extracellular Cd2+. We have shown in the first part of my work that the side chain of aSer589 residue is not pointing toward the pore lumen, permitting the Cd2+ to permeate through the ion pore and to coordinate with a native cysteine, gCys546, located in the second transmembrane domain of the gENaC subunit. In a second part, we were interested in the sulfhydryl-reagent intracellular inhibition of ENaC-mediated Na+ current. Kellenberger et al. have shown that ENaC is rapidly and reversibly inhibited by internal sulfhydryl reagents underlying the involvement of intracellular cysteines in the internal regulation of ENaC. We set up a new approach comprising a Substituted Cysteine Analysis Method (SCAM) using intracellular MTSEA-biotin perfusion coupled to functional and biochemical assays. We were thus able to correlate the cysteine-modification of ENaC by methanethiosulfonate (MTS) and its effect on sodium current. This allowed us to determine the amino acids that are accessible to intracellular MTS and the one important for the inhibition of the channel. RESUME : Le canal épithélial sodique ENaC est responsable de la réabsorption du sodium dans les cellules principales du tubule collecteur rénal. Ce canal est essentiellement régulé par voie hormonale via l'aldostérone et la vasopressine mais également par des sérines protéases, le Na+ lui-même et certains cations divalents. La cristallisation du canal sodique sensible au pH acide, ASIC, un autre membre de la famille ENaC/Deg, a été publiée mais les acides aminés constituant le pore interne d'ENaC restent indéterminés. Il a été montré que la mutation aS589C du filtre de sélectivité de la sous-unité aENaC permet le passage de cations divalents et l'inhibition du canal par le Cd2+ extracellulaire. Dans un premier temps, nous avons montré que la chaîne latérale de la aSer589 n'est pas orientée vers l'intérieur du pore, permettant au Cd2+ de traverser le canal et d'interagir avec une cysteine native du second domaine transrnembranaire de la sous-unité γENaC, γCys546. Dans un second temps, nous nous sommes intéressés au mécanisme d'inhibition d'ENaC par les réactifs sulfhydryl internes. Kellenberger et al. ont montré l'implication de cystéines intracellulaires dans la régulation interne d'ENaC par les réactifs sulfhydryl. Nous avons mis en place une nouvelle approche couplant la méthode d'analyse par substitution de cystéines (SCAM) avec des perfusions intracellulaires de MTSEAbiotine. Ainsi, nous pouvons meure en corrélation les modifications des cystéines d'ENaC par les réactifs methanethiosulfonates (MTS) avec leur effet sur le courant sodique, et donc mettre en évidence les acides aminés accessibles aux MTS intracellulaires et ceux qui sont importants dans la fonction du canal.

Influence of reconstruction parameters during filtered backprojection and ordered-subset expectation maximization in the measurement of the left-ventricular volumes and function during gated SPECT.

A crucial method for investigating patients with coronary artery disease (CAD) is the calculation of the left ventricular ejection fraction (LVEF). It is, consequently, imperative to precisely estimate the value of LVEF--a process that can be done with myocardial perfusion scintigraphy. Therefore, the present study aimed to establish and compare the estimation performance of the quantitative parameters of the reconstruction methods filtered backprojection (FBP) and ordered-subset expectation maximization (OSEM). METHODS: A beating-heart phantom with known values of end-diastolic volume, end-systolic volume, and LVEF was used. Quantitative gated SPECT/quantitative perfusion SPECT software was used to obtain these quantitative parameters in a semiautomatic mode. The Butterworth filter was used in FBP, with the cutoff frequencies between 0.2 and 0.8 cycles per pixel combined with the orders of 5, 10, 15, and 20. Sixty-three reconstructions were performed using 2, 4, 6, 8, 10, 12, and 16 OSEM subsets, combined with several iterations: 2, 4, 6, 8, 10, 12, 16, 32, and 64. RESULTS: With FBP, the values of end-diastolic, end-systolic, and the stroke volumes rise as the cutoff frequency increases, whereas the value of LVEF diminishes. This same pattern is verified with the OSEM reconstruction. However, with OSEM there is a more precise estimation of the quantitative parameters, especially with the combinations 2 iterations × 10 subsets and 2 iterations × 12 subsets. CONCLUSION: The OSEM reconstruction presents better estimations of the quantitative parameters than does FBP. This study recommends the use of 2 iterations with 10 or 12 subsets for OSEM and a cutoff frequency of 0.5 cycles per pixel with the orders 5, 10, or 15 for FBP as the best estimations for the left ventricular volumes and ejection fraction quantification in myocardial perfusion scintigraphy.

A low dimensional Kalman filter for systems with lagged observables

This note describes how the Kalman filter can be modified to allow for thevector of observables to be a function of lagged variables without increasing the dimensionof the state vector in the filter. This is useful in applications where it is desirable to keepthe dimension of the state vector low. The modified filter and accompanying code (whichnests the standard filter) can be used to compute (i) the steady state Kalman filter (ii) thelog likelihood of a parameterized state space model conditional on a history of observables(iii) a smoothed estimate of latent state variables and (iv) a draw from the distribution oflatent states conditional on a history of observables.

Three-dimensional ordered-subset expectation maximization iterative protocol for evaluation of left ventricular volumes and function by quantitative gated SPECT: a dynamic phantom study.

The purposes of this study were to characterize the performance of a 3-dimensional (3D) ordered-subset expectation maximization (OSEM) algorithm in the quantification of left ventricular (LV) function with (99m)Tc-labeled agent gated SPECT (G-SPECT), the QGS program, and a beating-heart phantom and to optimize the reconstruction parameters for clinical applications. METHODS: A G-SPECT image of a dynamic heart phantom simulating the beating left ventricle was acquired. The exact volumes of the phantom were known and were as follows: end-diastolic volume (EDV) of 112 mL, end-systolic volume (ESV) of 37 mL, and stroke volume (SV) of 75 mL; these volumes produced an LV ejection fraction (LVEF) of 67%. Tomographic reconstructions were obtained after 10-20 iterations (I) with 4, 8, and 16 subsets (S) at full width at half maximum (FWHM) gaussian postprocessing filter cutoff values of 8-15 mm. The QGS program was used for quantitative measurements. RESULTS: Measured values ranged from 72 to 92 mL for EDV, from 18 to 32 mL for ESV, and from 54 to 63 mL for SV, and the calculated LVEF ranged from 65% to 76%. Overall, the combination of 10 I, 8 S, and a cutoff filter value of 10 mm produced the most accurate results. The plot of the measures with respect to the expectation maximization-equivalent iterations (I x S product) revealed a bell-shaped curve for the LV volumes and a reverse distribution for the LVEF, with the best results in the intermediate range. In particular, FWHM cutoff values exceeding 10 mm affected the estimation of the LV volumes. CONCLUSION: The QGS program is able to correctly calculate the LVEF when used in association with an optimized 3D OSEM algorithm (8 S, 10 I, and FWHM of 10 mm) but underestimates the LV volumes. However, various combinations of technical parameters, including a limited range of I and S (80-160 expectation maximization-equivalent iterations) and low cutoff values (< or =10 mm) for the gaussian postprocessing filter, produced results with similar accuracies and without clinically relevant differences in the LV volumes and the estimated LVEF.

Development of filter media treatments for liquid filtration

Woven monofilament, multifilament, and spun yarn filter media have long been the standard media in liquid filtration equipment. While the energy for a solid-liquid separation process is determined by the engineering work, it is the interface between the slurry and the equipment - the filter media - that greatly affects the performance characteristics of the unit operation. Those skilled in the art are well aware that a poorly designed filter medium may endanger the whole operation, whereas well-performing filter media can make the operation smooth and economical. As the mineral and pulp producers seek to produce ever finer and more refined fractions of their products, it is becoming increasingly important to be able to dewater slurries with average particle sizes around 1 ¿m using conventional, high-capacity filtration equipment. Furthermore, the surface properties of the media must not allow sticky and adhesive particles to adhere to the media. The aim of this thesis was to test how the dirt-repellency, electrical resistance and highpressure filtration performance of selected woven filter media can be improved by modifying the fabric or yarn with coating, chemical treatment and calendering. The results achieved by chemical surface treatments clearly show that the woven media surface properties can be modified to achieve lower electrical resistance and improved dirt-repellency. The main challenge with the chemical treatments is the abrasion resistance and, while the experimental results indicate that the treatment is sufficiently permanent to resist standard weathering conditions, they may still prove to be inadequately strong in terms of actual use.From the pressure filtration studies in this work, it seems obvious that the conventional woven multifilament fabrics still perform surprisingly well against the coated media in terms of filtrate clarity and cake build-up. Especially in cases where the feed slurry concentration was low and the pressures moderate, the conventional media seemed to outperform the coated media. In the cases where thefeed slurry concentration was high, the tightly woven media performed well against the monofilament reference fabrics, but seemed to do worse than some of the coated media. This result is somewhat surprising in that the high initial specific resistance of the coated media would suggest that the media will blind more easily than the plain woven media. The results indicate, however, that it is actually the woven media that gradually clogs during the coarse of filtration. In conclusion, it seems obvious that there is a pressure limit above which the woven media looses its capacity to keep the solid particles from penetrating the structure. This finding suggests that for extreme pressures the only foreseeable solution is the coated fabrics supported by a strong enough woven fabric to hold thestructure together. Having said that, the high pressure filtration process seems to follow somewhat different laws than the more conventional processes. Based on the results, it may well be that the role of the cloth is most of all to support the cake, and the main performance-determining factor is a long life time. Measuring the pore size distribution with a commercially available porometer gives a fairly accurate picture of the pore size distribution of a fabric, but failsto give insight into which of the pore sizes is the most important in determining the flow through the fabric. Historically air, and sometimes water, permeability measures have been the standard in evaluating media filtration performance including particle retention. Permeability, however, is a function of a multitudeof variables and does not directly allow the estimation of the effective pore size. In this study a new method for estimating the effective pore size and open pore area in a densely woven multifilament fabric was developed. The method combines a simplified equation of the electrical resistance of fabric with the Hagen-Poiseuille flow equation to estimate the effective pore size of a fabric and the total open area of pores. The results are validated by comparison to the measured values of the largest pore size (Bubble point) and the average pore size. The results show good correlation with measured values. However, the measured and estimated values tend to diverge in high weft density fabrics. This phenomenon is thought to be a result of a more tortuous flow path of denser fabrics, and could most probably be cured by using another value for the tortuosity factor.

Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

Sidelobe elimination for generalized synthetic discriminant functions by a two-filter correlation and subsequent postprocessing of the intensity distributions

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

Microtubule Associated Protein 1b (MAP1B) Is a Marker of the Microtubular Cytoskeleton in Podocytes but Is Not Essential for the Function of the Kidney Filtration Barrier in Mice.

Podocytes are essential for the function of the kidney glomerular filter. A highly differentiated cytoskeleton is requisite for their integrity. Although much knowledge has been gained on the organization of cortical actin networks in podocyte's foot processes, less is known about the molecular organization of the microtubular cytoskeleton in primary processes and the cell body. To gain an insight into the organization of the microtubular cytoskeleton of the podocyte, we systematically analyzed the expression of microtubule associated proteins (Maps), a family of microtubules interacting proteins with known functions as regulator, scaffold and guidance proteins. We identified microtubule associated protein 1b (MAP1B) to be specifically enriched in podocytes in human and rodent kidney. Using immunogold labeling in electron microscopy, we were able to demonstrate an enrichment of MAP1B in primary processes. A similar association of MAP1B with the microtubule cytoskeleton was detected in cultured podocytes. Subcellular distribution of MAP1B HC and LC1 was analyzed using a double fluorescent reporter MAP1B fusion protein. Subsequently we analyzed mice constitutively depleted of MAP1B. Interestingly, MAP1B KO was not associated with any functional or structural alterations pointing towards a redundancy of MAP proteins in podocytes. In summary, we established MAP1B as a specific marker protein of the podocyte microtubular cytoskeleton.

Estimation of aircraft aerodynamic derivatives using Extended Kalman Filter

Design of flight control laws, verification of performance predictions, and the implementation of flight simulations are tasks that require a mathematical model of the aircraft dynamics. The dynamical models are characterized by coefficients (aerodynamic derivatives) whose values must be determined from flight tests. This work outlines the use of the Extended Kalman Filter (EKF) in obtaining the aerodynamic derivatives of an aircraft. The EKF shows several advantages over the more traditional least-square method (LS). Among these the most important are: there are no restrictions on linearity or in the form which the parameters appears in the mathematical model describing the system, and it is not required that these parameters be time invariant. The EKF uses the statistical properties of the process and the observation noise, to produce estimates based on the mean square error of the estimates themselves. Differently, the LS minimizes a cost function based on the plant output behavior. Results for the estimation of some longitudinal aerodynamic derivatives from simulated data are presented.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Characterization of Novel Fabry Pérot Filter Arrays for Nanospectrometers in Medical Applications

Optische Spektroskopie ist eine sehr wichtige Messtechnik mit einem hohen Potential für zahlreiche Anwendungen in der Industrie und Wissenschaft. Kostengünstige und miniaturisierte Spektrometer z.B. werden besonders für moderne Sensorsysteme “smart personal environments” benötigt, die vor allem in der Energietechnik, Messtechnik, Sicherheitstechnik (safety and security), IT und Medizintechnik verwendet werden. Unter allen miniaturisierten Spektrometern ist eines der attraktivsten Miniaturisierungsverfahren das Fabry Pérot Filter. Bei diesem Verfahren kann die Kombination von einem Fabry Pérot (FP) Filterarray und einem Detektorarray als Mikrospektrometer funktionieren. Jeder Detektor entspricht einem einzelnen Filter, um ein sehr schmales Band von Wellenlängen, die durch das Filter durchgelassen werden, zu detektieren. Ein Array von FP-Filter wird eingesetzt, bei dem jeder Filter eine unterschiedliche spektrale Filterlinie auswählt. Die spektrale Position jedes Bandes der Wellenlänge wird durch die einzelnen Kavitätshöhe des Filters definiert. Die Arrays wurden mit Filtergrößen, die nur durch die Array-Dimension der einzelnen Detektoren begrenzt werden, entwickelt. Allerdings erfordern die bestehenden Fabry Pérot Filter-Mikrospektrometer komplizierte Fertigungsschritte für die Strukturierung der 3D-Filter-Kavitäten mit unterschiedlichen Höhen, die nicht kosteneffizient für eine industrielle Fertigung sind. Um die Kosten bei Aufrechterhaltung der herausragenden Vorteile der FP-Filter-Struktur zu reduzieren, wird eine neue Methode zur Herstellung der miniaturisierten FP-Filtern mittels NanoImprint Technologie entwickelt und präsentiert. In diesem Fall werden die mehreren Kavitäten-Herstellungsschritte durch einen einzigen Schritt ersetzt, die hohe vertikale Auflösung der 3D NanoImprint Technologie verwendet. Seit dem die NanoImprint Technologie verwendet wird, wird das auf FP Filters basierende miniaturisierte Spectrometer nanospectrometer genannt. Ein statischer Nano-Spektrometer besteht aus einem statischen FP-Filterarray auf einem Detektorarray (siehe Abb. 1). Jeder FP-Filter im Array besteht aus dem unteren Distributed Bragg Reflector (DBR), einer Resonanz-Kavität und einen oberen DBR. Der obere und untere DBR sind identisch und bestehen aus periodisch abwechselnden dünnen dielektrischen Schichten von Materialien mit hohem und niedrigem Brechungsindex. Die optischen Schichten jeder dielektrischen Dünnfilmschicht, die in dem DBR enthalten sind, entsprechen einen Viertel der Design-Wellenlänge. Jeder FP-Filter wird einer definierten Fläche des Detektorarrays zugeordnet. Dieser Bereich kann aus einzelnen Detektorelementen oder deren Gruppen enthalten. Daher werden die Seitenkanal-Geometrien der Kavität aufgebaut, die dem Detektor entsprechen. Die seitlichen und vertikalen Dimensionen der Kavität werden genau durch 3D NanoImprint Technologie aufgebaut. Die Kavitäten haben Unterschiede von wenigem Nanometer in der vertikalen Richtung. Die Präzision der Kavität in der vertikalen Richtung ist ein wichtiger Faktor, der die Genauigkeit der spektralen Position und Durchlässigkeit des Filters Transmissionslinie beeinflusst.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

An exploration of the equivalent weights particle filter

Particle filters are fully non-linear data assimilation techniques that aim to represent the probability distribution of the model state given the observations (the posterior) by a number of particles. In high-dimensional geophysical applications the number of particles required by the sequential importance resampling (SIR) particle filter in order to capture the high probability region of the posterior, is too large to make them usable. However particle filters can be formulated using proposal densities, which gives greater freedom in how particles are sampled and allows for a much smaller number of particles. Here a particle filter is presented which uses the proposal density to ensure that all particles end up in the high probability region of the posterior probability density function. This gives rise to the possibility of non-linear data assimilation in large dimensional systems. The particle filter formulation is compared to the optimal proposal density particle filter and the implicit particle filter, both of which also utilise a proposal density. We show that when observations are available every time step, both schemes will be degenerate when the number of independent observations is large, unlike the new scheme. The sensitivity of the new scheme to its parameter values is explored theoretically and demonstrated using the Lorenz (1963) model.

On the application of optimal wavelet filter banks for ECG signal classification

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

The effect of the equivalent-weights particle filter on dynamical balance in a primitive equation model

The disadvantage of the majority of data assimilation schemes is the assumption that the conditional probability density function of the state of the system given the observations [posterior probability density function (PDF)] is distributed either locally or globally as a Gaussian. The advantage, however, is that through various different mechanisms they ensure initial conditions that are predominantly in linear balance and therefore spurious gravity wave generation is suppressed. The equivalent-weights particle filter is a data assimilation scheme that allows for a representation of a potentially multimodal posterior PDF. It does this via proposal densities that lead to extra terms being added to the model equations and means the advantage of the traditional data assimilation schemes, in generating predominantly balanced initial conditions, is no longer guaranteed. This paper looks in detail at the impact the equivalent-weights particle filter has on dynamical balance and gravity wave generation in a primitive equation model. The primary conclusions are that (i) provided the model error covariance matrix imposes geostrophic balance, then each additional term required by the equivalent-weights particle filter is also geostrophically balanced; (ii) the relaxation term required to ensure the particles are in the locality of the observations has little effect on gravity waves and actually induces a reduction in gravity wave energy if sufficiently large; and (iii) the equivalent-weights term, which leads to the particles having equivalent significance in the posterior PDF, produces a change in gravity wave energy comparable to the stochastic model error. Thus, the scheme does not produce significant spurious gravity wave energy and so has potential for application in real high-dimensional geophysical applications.