GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Influence of bedrock structures on the spatial pattern of erosional landforms in small alpine catchments

Structural settings and lithological characteristics are traditionally assumed to influence the development of erosional landforms, such as gully networks and rock couloirs, in steep mountain rock basins. The structural control of erosion of two small alpine catchments of distinctive rock types is evaluated by comparing the correspondences between the orientations of their gullies and rock couloirs with (1) the sliding orientations of potential slope failures mechanisms, and (2) the orientation of the maximum joint frequency, this latter being considered as the direction exploited primarily by erosion and mass wasting processes. These characteristic orientations can be interpreted as structural weaknesses contributing to the initiation and propagation of erosion. The morphostructural analysis was performed using digital elevation models and field observations. The catchment comprised of magmatic intrusive rocks shows a clear structural control, mostly expressed through potential wedges failure. Such joint configurations have a particular geometry that encourages the development of gullies in hard rock, e.g. through enhanced gravitational and hydrological erosional processes. In the catchment underlain by sedimentary rocks, penetrative joints that act as structural weaknesses seem to be exploited by gullies and rock couloirs. However, the lithological setting and bedding configuration prominently control the development of erosional landforms, and influence not only the local pattern of geomorphic features, but the general morphology of the catchment. The orientations of the maximum joint frequency are clearly associated with the gully network, suggesting that its development is governed by anisotropy in rock strength. These two catchments are typical of bedrock-dominated basins prone to intense processes of debris supply. This study suggests a quantitative approach for describing the relationship between bedrock jointing and geomorphic features geometry. Incorporation of bedrock structure can be relevant when studying processes governing the transfer of clastic material, for the assessment of sediment yields and in landforms evolution models.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Using a NPWE model observer to assess suitable image quality for a digital mammography quality assurance programme.

A method of objectively determining imaging performance for a mammography quality assurance programme for digital systems was developed. The method is based on the assessment of the visibility of a spherical microcalcification of 0.2 mm using a quasi-ideal observer model. It requires the assessment of the spatial resolution (modulation transfer function) and the noise power spectra of the systems. The contrast is measured using a 0.2-mm thick Al sheet and Polymethylmethacrylate (PMMA) blocks. The minimal image quality was defined as that giving a target contrast-to-noise ratio (CNR) of 5.4. Several evaluations of this objective method for evaluating image quality in mammography quality assurance programmes have been considered on computed radiography (CR) and digital radiography (DR) mammography systems. The measurement gives a threshold CNR necessary to reach the minimum standard image quality required with regards to the visibility of a 0.2-mm microcalcification. This method may replace the CDMAM image evaluation and simplify the threshold contrast visibility test used in mammography quality.

A comprehensive model for quantum noise characterization in digital mammography.

NlmCategory="UNASSIGNED">A version of cascaded systems analysis was developed specifically with the aim of studying quantum noise propagation in x-ray detectors. Signal and quantum noise propagation was then modelled in four types of x-ray detectors used for digital mammography: four flat panel systems, one computed radiography and one slot-scan silicon wafer based photon counting device. As required inputs to the model, the two dimensional (2D) modulation transfer function (MTF), noise power spectra (NPS) and detective quantum efficiency (DQE) were measured for six mammography systems that utilized these different detectors. A new method to reconstruct anisotropic 2D presampling MTF matrices from 1D radial MTFs measured along different angular directions across the detector is described; an image of a sharp, circular disc was used for this purpose. The effective pixel fill factor for the FP systems was determined from the axial 1D presampling MTFs measured with a square sharp edge along the two orthogonal directions of the pixel lattice. Expectation MTFs were then calculated by averaging the radial MTFs over all possible phases and the 2D EMTF formed with the same reconstruction technique used for the 2D presampling MTF. The quantum NPS was then established by noise decomposition from homogenous images acquired as a function of detector air kerma. This was further decomposed into the correlated and uncorrelated quantum components by fitting the radially averaged quantum NPS with the radially averaged EMTF(2). This whole procedure allowed a detailed analysis of the influence of aliasing, signal and noise decorrelation, x-ray capture efficiency and global secondary gain on NPS and detector DQE. The influence of noise statistics, pixel fill factor and additional electronic and fixed pattern noises on the DQE was also studied. The 2D cascaded model and decompositions performed on the acquired images also enlightened the observed quantum NPS and DQE anisotropy.

An adapted model of ex vivo vein perfusion: the key to understand vessel wall remodeling

Objective: Saphenous vein graft bypass remains the salvage option when¦endovascular procedure has failed or was contraindicated due to extensive¦occlusive lesions. However, pathological wall remodeling leading leading to¦graft failure is one of the most limiting factors of this therapy. Therefore, the¦understanding of this remodeling process of human vein is essential to the design¦of future effective therapeutics and it requires an adapted model of ex-vivo vein¦perfusion.¦Methods: We have developed an ex vivo vein support system (EVVSS), which¦uses standardized and controlled hemodynamic parameters for the pulsatile¦perfusion of saphenous vein segments. The morphological and molecular¦parameters involved in the remodeling process under an arterial shear stress¦associated to low (7 mm Hg) or high (70 mm Hg) pressure conditions can be¦analyzed.¦Results: Histomorphometric analysis showed that the vein segments perfused¦during 7 days under high pressure undergo a significant neointima development¦compared to veins exposed to low pressure conditions. The application of an¦arterial shear stress in the vein under low pressure induced an elevation of the¦MMP-2 and MMP-9 expression, activity and transcription. The application of¦higher pressure is associated to increased MMP2 expression and transcription¦and MMP9 transcription. TIMP1 expression and transcription were initiated by¦the application of an arterial shear stress but not modified by the modification¦of the pressure. However, TIMP2 expression was increased under high¦pressure conditions but its transcription was inhibited by arterial shear stress,¦independently of the pressure. The values of transcription and expression of¦PAI-1 were not modified by high pressure. Eph-B4 transcription and expression¦were significantly decreased under arterial shear stress.¦Conclusion: These data show that our EVVSS is a valuable setting to study¦ex vivo remodeling of human saphenous veins submitted to arterial conditions.¦The intimal hyperplasia as well as MMP 2, 9 and TIMP 2 seem to be influenced¦by the pressure.

ARCHITECTING USER-CENTRIC PRIVACY-AS-A-SET-OF-SERVICES: DIGITAL IDENTITY RELATED PRIVACY FRAMEWORK

AbstractDigitalization gives to the Internet the power by allowing several virtual representations of reality, including that of identity. We leave an increasingly digital footprint in cyberspace and this situation puts our identity at high risks. Privacy is a right and fundamental social value that could play a key role as a medium to secure digital identities. Identity functionality is increasingly delivered as sets of services, rather than monolithic applications. So, an identity layer in which identity and privacy management services are loosely coupled, publicly hosted and available to on-demand calls could be more realistic and an acceptable situation. Identity and privacy should be interoperable and distributed through the adoption of service-orientation and implementation based on open standards (technical interoperability). Ihe objective of this project is to provide a way to implement interoperable user-centric digital identity-related privacy to respond to the need of distributed nature of federated identity systems. It is recognized that technical initiatives, emerging standards and protocols are not enough to guarantee resolution for the concerns surrounding a multi-facets and complex issue of identity and privacy. For this reason they should be apprehended within a global perspective through an integrated and a multidisciplinary approach. The approach dictates that privacy law, policies, regulations and technologies are to be crafted together from the start, rather than attaching it to digital identity after the fact. Thus, we draw Digital Identity-Related Privacy (DigldeRP) requirements from global, domestic and business-specific privacy policies. The requirements take shape of business interoperability. We suggest a layered implementation framework (DigldeRP framework) in accordance to model-driven architecture (MDA) approach that would help organizations' security team to turn business interoperability into technical interoperability in the form of a set of services that could accommodate Service-Oriented Architecture (SOA): Privacy-as-a-set-of- services (PaaSS) system. DigldeRP Framework will serve as a basis for vital understanding between business management and technical managers on digital identity related privacy initiatives. The layered DigldeRP framework presents five practical layers as an ordered sequence as a basis of DigldeRP project roadmap, however, in practice, there is an iterative process to assure that each layer supports effectively and enforces requirements of the adjacent ones. Each layer is composed by a set of blocks, which determine a roadmap that security team could follow to successfully implement PaaSS. Several blocks' descriptions are based on OMG SoaML modeling language and BPMN processes description. We identified, designed and implemented seven services that form PaaSS and described their consumption. PaaSS Java QEE project), WSDL, and XSD codes are given and explained.

Development and experimental validation of a finite element model of total ankle replacement.

Total ankle replacement remains a less satisfactory solution compared to other joint replacements. The goal of this study was to develop and validate a finite element model of total ankle replacement, for future testing of hypotheses related to clinical issues. To validate the finite element model, an experimental setup was specifically developed and applied on 8 cadaveric tibias. A non-cemented press fit tibial component of a mobile bearing prosthesis was inserted into the tibias. Two extreme anterior and posterior positions of the mobile bearing insert were considered, as well as a centered one. An axial force of 2kN was applied for each insert position. Strains were measured on the bone surface using digital image correlation. Tibias were CT scanned before implantation, after implantation, and after mechanical tests and removal of the prosthesis. The finite element model replicated the experimental setup. The first CT was used to build the geometry and evaluate the mechanical properties of the tibias. The second CT was used to set the implant position. The third CT was used to assess the bone-implant interface conditions. The coefficient of determination (R-squared) between the measured and predicted strains was 0.91. Predicted bone strains were maximal around the implant keel, especially at the anterior and posterior ends. The finite element model presented here is validated for future tests using more physiological loading conditions.

Image quality assessment in digital mammography: part I. Technical characterization of the systems.

In many European countries, image quality for digital x-ray systems used in screening mammography is currently specified using a threshold-detail detectability method. This is a two-part study that proposes an alternative method based on calculated detectability for a model observer: the first part of the work presents a characterization of the systems. Eleven digital mammography systems were included in the study; four computed radiography (CR) systems, and a group of seven digital radiography (DR) detectors, composed of three amorphous selenium-based detectors, three caesium iodide scintillator systems and a silicon wafer-based photon counting system. The technical parameters assessed included the system response curve, detector uniformity error, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Approximate quantum noise limited exposure range was examined using a separation of noise sources based upon standard deviation. Noise separation showed that electronic noise was the dominant noise at low detector air kerma for three systems; the remaining systems showed quantum noise limited behaviour between 12.5 and 380 µGy. Greater variation in detector MTF was found for the DR group compared to the CR systems; MTF at 5 mm(-1) varied from 0.08 to 0.23 for the CR detectors against a range of 0.16-0.64 for the DR units. The needle CR detector had a higher MTF, lower NNPS and higher DQE at 5 mm(-1) than the powder CR phosphors. DQE at 5 mm(-1) ranged from 0.02 to 0.20 for the CR systems, while DQE at 5 mm(-1) for the DR group ranged from 0.04 to 0.41, indicating higher DQE for the DR detectors and needle CR system than for the powder CR phosphor systems. The technical evaluation section of the study showed that the digital mammography systems were well set up and exhibiting typical performance for the detector technology employed in the respective systems.

Augmented myocardial methionine-enkephalin in a murine model of cardiac angiotensin II-overexpression.

INTRODUCTION: The endogenous opioid system has been reported to interact with both the cardiac sympathetic and renin-angiotensin systems in exerting a local regulatory action on the heart. The goal of this investigation was to examine how cardiac levels of enkephalin production are altered in the development of normotensive primary hypertrophy due to elevated intra-cardiac angiotensin II (Ang II) production. METHODS: Atrial and ventricular methionine-enkephalin (ME) levels were measured by quantitative radioimmunoassay in 14 and 28-week-old male transgenic mice (TG1306/1R) and control mice. The TG1306/1R exhibit cardiac specific Ang II overexpression and cardiac hypertrophy, but not hypertension. RESULTS: TG1306/1R mice had significantly higher heart/body weight ratios (15-20%) than control littermates at both 14 (p=0.02) and 28 weeks (p=0.04). Relative to controls, ME content was significantly elevated (approximately two-fold) in atria and ventricles in the older 28-week TG1306/1R mice only. A significant inverse correlation between heart size and ME level was observed for 28-week TG1306/1R only. CONCLUSIONS: We have provided evidence that a marked elevation of myocardial enkephalin level is observed in the established (but not early) phase of cardiac hypertrophy associated with cardiac-specific Ang II-overexpression. This study identifies a potentially important relationship between two endogenous peptidergic signalling systems involved in the regulation of growth and function of the hypertrophic heart.

High elevation Plantago lanceolata plants are less resistant to herbivory than their low elevation conspecifics: is it just temperature?

Traits that mediate species interactions are evolutionarily shaped by biotic and abiotic drivers, yet we know relatively little about the relative importance of these factors. Herbivore pressure, along with resource availability and third-party' mutualists, are hypothesized to play a major role in the evolution of plant defence traits. Here, we used the model system Plantago lanceolata, which grows along steep elevation gradients in the Swiss Alps, to investigate the effect of elevation, herbivore pressure, mycorrhizal inoculation and temperature on plant resistance. Over a 1200 m elevation gradient, the levels of herbivory and iridoid glycosides (IGs) declined with increasing elevation. By planting seedlings at three different elevations, we further showed that both low-elevation growing conditions and mycorrhizal inoculation resulted in increased plant resistance to herbivores. Finally, using a temperature-controlled experiment comparing high- and low-elevation ecotypes, we showed that high-elevation ecotypes are less resistant to herbivory, and that lower temperatures impair IGs deployment after herbivore attack. We thus propose that both lower herbivore pressure, and colder temperatures relax the defense syndrome of high elevation plants.

Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.

Autophagy induction does not protect retina against apoptosis in ischemia/reperfusion model.

The role played by autophagy after ischemia/reperfusion (I/R) in the retina remains unknown. Our study investigated whether ischemic injury in the retina, which causes an energy crisis, would induce autophagy. Retinal ischemia was induced by elevation of the intraocular pressure and modulation of autophagic markers was analyzed at the protein levels in an early and late phase of recovery. Following retinal ischemia an increase in LC3BII was first observed in the early phase of recovery but did not stay until the late phase of recovery. Post-ischemic induction of autophagy by intravitreal rapamycin administration did not provide protection against the lesion induced by the ischemic stress. On the contrary, an increase in the number of apoptotic cells was observed following I/R in the rapamycin treated retinas.

Spatial analysis of erythrocyte membrane fluctuations by digital holographic microscopy.

Red blood cell (RBC) membrane fluctuations provide important insights into cell states. We present a spatial analysis of red blood cell membrane fluctuations by using digital holographic microscopy (DHM). This interferometric and dye-free technique, possessing nanometric axial and microsecond temporal sensitivities enables to measure cell membrane fluctuations (CMF) on the whole cell surface. DHM acquisition is combined with a model which allows extracting the membrane fluctuation amplitude, while taking into account cell membrane topology. Uneven distribution of CMF amplitudes over the RBC surface is observed, showing maximal values in a ring corresponding to the highest points on the RBC torus as well as in some scattered areas in the inner region of the RBC. CMF amplitudes of 35.9+/-8.9 nm and 4.7+/-0.5 nm (averaged over the cell surface) were determined for normal and ethanol-fixed RBCs, respectively.