Predicting trabecular bone microdamage initiation and accumulation using a non-linear perfect damage model

Studies evaluating the mechanical behavior of the trabecular microstructure play an important role in our understanding of pathologies such as osteoporosis, and in increasing our understanding of bone fracture and bone adaptation. Understanding of such behavior in bone is important for predicting and providing early treatment of fractures. The objective of this study is to present a numerical model for studying the initiation and accumulation of trabecular bone microdamage in both the pre- and post-yield regions. A sub-region of human vertebral trabecular bone was analyzed using a uniformly loaded anatomically accurate microstructural three-dimensional finite element model. The evolution of trabecular bone microdamage was governed using a non-linear, modulus reduction, perfect damage approach derived from a generalized plasticity stress-strain law. The model introduced in this paper establishes a history of microdamage evolution in both the pre- and post-yield regions

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

A comparison of the spatial dependence of body mass index among adults and children in a Swiss general population.

BACKGROUND: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether BMI clusters among children and how age-specific BMI clusters are related remains unknown. We aimed to identify and compare the spatial dependence of BMI in adults and children in a Swiss general population, taking into account the area's income level. METHODS: Geo-referenced data from the Bus Santé study (adults, n=6663) and Geneva School Health Service (children, n=3601) were used. We implemented global (Moran's I) and local (local indicators of spatial association (LISA)) indices of spatial autocorrelation to investigate the spatial dependence of BMI in adults (35-74 years) and children (6-7 years). Weight and height were measured using standardized procedures. Five spatial autocorrelation classes (LISA clusters) were defined including the high-high BMI class (high BMI participant's BMI value correlated with high BMI-neighbors' mean BMI values). The spatial distributions of clusters were compared between adults and children with and without adjustment for area's income level. RESULTS: In both adults and children, BMI was clearly not distributed at random across the State of Geneva. Both adults' and children's BMIs were associated with the mean BMI of their neighborhood. We found that the clusters of higher BMI in adults and children are located in close, yet different, areas of the state. Significant clusters of high versus low BMIs were clearly identified in both adults and children. Area's income level was associated with children's BMI clusters. CONCLUSIONS: BMI clusters show a specific spatial dependence in adults and children from the general population. Using a fine-scale spatial analytic approach, we identified life course-specific clusters that could guide tailored interventions.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Brain surface non-rigid registration in auditory processing

Résumé: Le développement rapide de nouvelles technologies comme l'imagerie médicale a permis l'expansion des études sur les fonctions cérébrales. Le rôle principal des études fonctionnelles cérébrales est de comparer l'activation neuronale entre différents individus. Dans ce contexte, la variabilité anatomique de la taille et de la forme du cerveau pose un problème majeur. Les méthodes actuelles permettent les comparaisons interindividuelles par la normalisation des cerveaux en utilisant un cerveau standard. Les cerveaux standards les plus utilisés actuellement sont le cerveau de Talairach et le cerveau de l'Institut Neurologique de Montréal (MNI) (SPM99). Les méthodes de recalage qui utilisent le cerveau de Talairach, ou celui de MNI, ne sont pas suffisamment précises pour superposer les parties plus variables d'un cortex cérébral (p.ex., le néocortex ou la zone perisylvienne), ainsi que les régions qui ont une asymétrie très importante entre les deux hémisphères. Le but de ce projet est d'évaluer une nouvelle technique de traitement d'images basée sur le recalage non-rigide et utilisant les repères anatomiques. Tout d'abord, nous devons identifier et extraire les structures anatomiques (les repères anatomiques) dans le cerveau à déformer et celui de référence. La correspondance entre ces deux jeux de repères nous permet de déterminer en 3D la déformation appropriée. Pour les repères anatomiques, nous utilisons six points de contrôle qui sont situés : un sur le gyrus de Heschl, un sur la zone motrice de la main et le dernier sur la fissure sylvienne, bilatéralement. Evaluation de notre programme de recalage est accomplie sur les images d'IRM et d'IRMf de neuf sujets parmi dix-huit qui ont participés dans une étude précédente de Maeder et al. Le résultat sur les images anatomiques, IRM, montre le déplacement des repères anatomiques du cerveau à déformer à la position des repères anatomiques de cerveau de référence. La distance du cerveau à déformer par rapport au cerveau de référence diminue après le recalage. Le recalage des images fonctionnelles, IRMf, ne montre pas de variation significative. Le petit nombre de repères, six points de contrôle, n'est pas suffisant pour produire les modifications des cartes statistiques. Cette thèse ouvre la voie à une nouvelle technique de recalage du cortex cérébral dont la direction principale est le recalage de plusieurs points représentant un sillon cérébral. Abstract : The fast development of new technologies such as digital medical imaging brought to the expansion of brain functional studies. One of the methodolgical key issue in brain functional studies is to compare neuronal activation between individuals. In this context, the great variability of brain size and shape is a major problem. Current methods allow inter-individual comparisions by means of normalisation of subjects' brains in relation to a standard brain. A largerly used standard brains are the proportional grid of Talairach and Tournoux and the Montreal Neurological Insititute standard brain (SPM99). However, there is a lack of more precise methods for the superposition of more variable portions of the cerebral cortex (e.g, neocrotex and perisyvlian zone) and in brain regions highly asymmetric between the two cerebral hemipsheres (e.g. planum termporale). The aim of this thesis is to evaluate a new image processing technique based on non-linear model-based registration. Contrary to the intensity-based, model-based registration uses spatial and not intensitiy information to fit one image to another. We extract identifiable anatomical features (point landmarks) in both deforming and target images and by their correspondence we determine the appropriate deformation in 3D. As landmarks, we use six control points that are situated: one on the Heschl'y Gyrus, one on the motor hand area, and one on the sylvian fissure, bilaterally. The evaluation of this model-based approach is performed on MRI and fMRI images of nine of eighteen subjects participating in the Maeder et al. study. Results on anatomical, i.e. MRI, images, show the mouvement of the deforming brain control points to the location of the reference brain control points. The distance of the deforming brain to the reference brain is smallest after the registration compared to the distance before the registration. Registration of functional images, i.e fMRI, doesn't show a significant variation. The small number of registration landmarks, i.e. six, is obvious not sufficient to produce significant modification on the fMRI statistical maps. This thesis opens the way to a new computation technique for cortex registration in which the main directions will be improvement of the registation algorithm, using not only one point as landmark, but many points, representing one particular sulcus.

Modelling the curing process in magneto-sensitive polymers: Rate-dependence and shrinkage

This paper deals with a phenomenologically motivated magneto-viscoelastic coupled finite strain framework for simulating the curing process of polymers under the application of a coupled magneto-mechanical road. Magneto-sensitive polymers are prepared by mixing micron-sized ferromagnetic particles in uncured polymers. Application of a magnetic field during the curing process causes the particles to align and form chain-like structures lending an overall anisotropy to the material. The polymer curing is a viscoelastic complex process where a transformation from fluid. to solid occurs in the course of time. During curing, volume shrinkage also occurs due to the packing of polymer chains by chemical reactions. Such reactions impart a continuous change of magneto-mechanical properties that can be modelled by an appropriate constitutive relation where the temporal evolution of material parameters is considered. To model the shrinkage during curing, a magnetic-induction-dependent approach is proposed which is based on a multiplicative decomposition of the deformation gradient into a mechanical and a magnetic-induction-dependent volume shrinkage part. The proposed model obeys the relevant laws of thermodynamics. Numerical examples, based on a generalised Mooney-Rivlin energy function, are presented to demonstrate the model capacity in the case of a magneto-viscoelastically coupled load.

Objective analysis of gait coordination for total knee arthroplasty outcome

Introduction: Coordination is a strategy chosen by the central nervous system to control the movements and maintain stability during gait. Coordinated multi-joint movements require a complex interaction between nervous outputs, biomechanical constraints, and pro-prioception. Quantitatively understanding and modeling gait coordination still remain a challenge. Surgeons lack a way to model and appreciate the coordination of patients before and after surgery of the lower limbs. Patients alter their gait patterns and their kinematic synergies when they walk faster or slower than normal speed to maintain their stability and minimize the energy cost of locomotion. The goal of this study was to provide a dynamical system approach to quantitatively describe human gait coordination and apply it to patients before and after total knee arthroplasty. Methods: A new method of quantitative analysis of interjoint coordination during gait was designed, providing a general model to capture the whole dynamics and showing the kinematic synergies at various walking speeds. The proposed model imposed a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion of each individual. An integration of different analysis tools such as Harmonic analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. Ten patients were studied using an ambulatory gait device (Physilog®). Each participant was asked to perform two walking trials of 30m long at 3 different speeds and to complete an EQ-5D questionnaire, a WOMAC and Knee Society Score. Lower limbs rotations were measured by four miniature angular rate sensors mounted respectively, on each shank and thigh. The outcomes of the eight patients undergoing total knee arthroplasty, recorded pre-operatively and post-operatively at 6 weeks, 3 months, 6 months and 1 year were compared to 2 age-matched healthy subjects. Results: The new method provided coordination scores at various walking speeds, ranged between 0 and 10. It determined the overall coordination of the lower limbs as well as the contribution of each joint to the total coordination. The difference between the pre-operative and post-operative coordination values were correlated with the improvements of the subjective outcome scores. Although the study group was small, the results showed a new way to objectively quantify gait coordination of patients undergoing total knee arthroplasty, using only portable body-fixed sensors. Conclusion: A new method for objective gait coordination analysis has been developed with very encouraging results regarding the objective outcome of lower limb surgery.

The behavioral relevance and top-down susceptibility of early, low level multisensory interactions in humans

Abstract (English)General backgroundMultisensory stimuli are easier to recognize, can improve learning and a processed faster compared to unisensory ones. As such, the ability an organism has to extract and synthesize relevant sensory inputs across multiple sensory modalities shapes his perception of and interaction with the environment. A major question in the scientific field is how the brain extracts and fuses relevant information to create a unified perceptual representation (but also how it segregates unrelated information). This fusion between the senses has been termed "multisensory integration", a notion that derives from seminal animal single-cell studies performed in the superior colliculus, a subcortical structure shown to create a multisensory output differing from the sum of its unisensory inputs. At the cortical level, integration of multisensory information is traditionally deferred to higher classical associative cortical regions within the frontal, temporal and parietal lobes, after extensive processing within the sensory-specific and segregated pathways. However, many anatomical, electrophysiological and neuroimaging findings now speak for multisensory convergence and interactions as a distributed process beginning much earlier than previously appreciated and within the initial stages of sensory processing.The work presented in this thesis is aimed at studying the neural basis and mechanisms of how the human brain combines sensory information between the senses of hearing and touch. Early latency non-linear auditory-somatosensory neural response interactions have been repeatedly observed in humans and non-human primates. Whether these early, low-level interactions are directly influencing behavioral outcomes remains an open question as they have been observed under diverse experimental circumstances such as anesthesia, passive stimulation, as well as speeded reaction time tasks. Under laboratory settings, it has been demonstrated that simple reaction times to auditory-somatosensory stimuli are facilitated over their unisensory counterparts both when delivered to the same spatial location or not, suggesting that audi- tory-somatosensory integration must occur in cerebral regions with large-scale spatial representations. However experiments that required the spatial processing of the stimuli have observed effects limited to spatially aligned conditions or varying depending on which body part was stimulated. Whether those divergences stem from task requirements and/or the need for spatial processing has not been firmly established.Hypotheses and experimental resultsIn a first study, we hypothesized that auditory-somatosensory early non-linear multisensory neural response interactions are relevant to behavior. Performing a median split according to reaction time of a subset of behavioral and electroencephalographic data, we found that the earliest non-linear multisensory interactions measured within the EEG signal (i.e. between 40-83ms post-stimulus onset) were specific to fast reaction times indicating a direct correlation of early neural response interactions and behavior.In a second study, we hypothesized that the relevance of spatial information for task performance has an impact on behavioral measures of auditory-somatosensory integration. Across two psychophysical experiments we show that facilitated detection occurs even when attending to spatial information, with no modulation according to spatial alignment of the stimuli. On the other hand, discrimination performance with probes, quantified using sensitivity (d'), is impaired following multisensory trials in general and significantly more so following misaligned multisensory trials.In a third study, we hypothesized that behavioral improvements might vary depending which body part is stimulated. Preliminary results suggest a possible dissociation between behavioral improvements andERPs. RTs to multisensory stimuli were modulated by space only in the case when somatosensory stimuli were delivered to the neck whereas multisensory ERPs were modulated by spatial alignment for both types of somatosensory stimuli.ConclusionThis thesis provides insight into the functional role played by early, low-level multisensory interac-tions. Combining psychophysics and electrical neuroimaging techniques we demonstrate the behavioral re-levance of early and low-level interactions in the normal human system. Moreover, we show that these early interactions are hermetic to top-down influences on spatial processing suggesting their occurrence within cerebral regions having access to large-scale spatial representations. We finally highlight specific interactions between auditory space and somatosensory stimulation on different body parts. Gaining an in-depth understanding of how multisensory integration normally operates is of central importance as it will ultimately permit us to consider how the impaired brain could benefit from rehabilitation with multisensory stimula-Abstract (French)Background théoriqueDes stimuli multisensoriels sont plus faciles à reconnaître, peuvent améliorer l'apprentissage et sont traités plus rapidement comparé à des stimuli unisensoriels. Ainsi, la capacité qu'un organisme possède à extraire et à synthétiser avec ses différentes modalités sensorielles des inputs sensoriels pertinents, façonne sa perception et son interaction avec l'environnement. Une question majeure dans le domaine scientifique est comment le cerveau parvient à extraire et à fusionner des stimuli pour créer une représentation percep- tuelle cohérente (mais aussi comment il isole les stimuli sans rapport). Cette fusion entre les sens est appelée "intégration multisensorielle", une notion qui provient de travaux effectués dans le colliculus supérieur chez l'animal, une structure sous-corticale possédant des neurones produisant une sortie multisensorielle différant de la somme des entrées unisensorielles. Traditionnellement, l'intégration d'informations multisen- sorielles au niveau cortical est considérée comme se produisant tardivement dans les aires associatives supérieures dans les lobes frontaux, temporaux et pariétaux, suite à un traitement extensif au sein de régions unisensorielles primaires. Cependant, plusieurs découvertes anatomiques, électrophysiologiques et de neuroimageries remettent en question ce postulat, suggérant l'existence d'une convergence et d'interactions multisensorielles précoces.Les travaux présentés dans cette thèse sont destinés à mieux comprendre les bases neuronales et les mécanismes impliqués dans la combinaison d'informations sensorielles entre les sens de l'audition et du toucher chez l'homme. Des interactions neuronales non-linéaires précoces audio-somatosensorielles ont été observées à maintes reprises chez l'homme et le singe dans des circonstances aussi variées que sous anes- thésie, avec stimulation passive, et lors de tâches nécessitant un comportement (une détection simple de stimuli, par exemple). Ainsi, le rôle fonctionnel joué par ces interactions à une étape du traitement de l'information si précoce demeure une question ouverte. Il a également été démontré que les temps de réaction en réponse à des stimuli audio-somatosensoriels sont facilités par rapport à leurs homologues unisensoriels indépendamment de leur position spatiale. Ce résultat suggère que l'intégration audio- somatosensorielle se produit dans des régions cérébrales possédant des représentations spatiales à large échelle. Cependant, des expériences qui ont exigé un traitement spatial des stimuli ont produits des effets limités à des conditions où les stimuli multisensoriels étaient, alignés dans l'espace ou encore comme pouvant varier selon la partie de corps stimulée. Il n'a pas été établi à ce jour si ces divergences pourraient être dues aux contraintes liées à la tâche et/ou à la nécessité d'un traitement de l'information spatiale.Hypothèse et résultats expérimentauxDans une première étude, nous avons émis l'hypothèse que les interactions audio- somatosensorielles précoces sont pertinentes pour le comportement. En effectuant un partage des temps de réaction par rapport à la médiane d'un sous-ensemble de données comportementales et électroencépha- lographiques, nous avons constaté que les interactions multisensorielles qui se produisent à des latences précoces (entre 40-83ms) sont spécifique aux temps de réaction rapides indiquant une corrélation directe entre ces interactions neuronales précoces et le comportement.Dans une deuxième étude, nous avons émis l'hypothèse que si l'information spatiale devient perti-nente pour la tâche, elle pourrait exercer une influence sur des mesures comportementales de l'intégration audio-somatosensorielles. Dans deux expériences psychophysiques, nous montrons que même si les participants prêtent attention à l'information spatiale, une facilitation de la détection se produit et ce toujours indépendamment de la configuration spatiale des stimuli. Cependant, la performance de discrimination, quantifiée à l'aide d'un index de sensibilité (d') est altérée suite aux essais multisensoriels en général et de manière plus significative pour les essais multisensoriels non-alignés dans l'espace.Dans une troisième étude, nous avons émis l'hypothèse que des améliorations comportementales pourraient différer selon la partie du corps qui est stimulée (la main vs. la nuque). Des résultats préliminaires suggèrent une dissociation possible entre une facilitation comportementale et les potentiels évoqués. Les temps de réactions étaient influencés par la configuration spatiale uniquement dans le cas ou les stimuli somatosensoriels étaient sur la nuque alors que les potentiels évoqués étaient modulés par l'alignement spatial pour les deux types de stimuli somatosensorielles.ConclusionCette thèse apporte des éléments nouveaux concernant le rôle fonctionnel joué par les interactions multisensorielles précoces de bas niveau. En combinant la psychophysique et la neuroimagerie électrique, nous démontrons la pertinence comportementale des ces interactions dans le système humain normal. Par ailleurs, nous montrons que ces interactions précoces sont hermétiques aux influences dites «top-down» sur le traitement spatial suggérant leur occurrence dans des régions cérébrales ayant accès à des représentations spatiales de grande échelle. Nous soulignons enfin des interactions spécifiques entre l'espace auditif et la stimulation somatosensorielle sur différentes parties du corps. Approfondir la connaissance concernant les bases neuronales et les mécanismes impliqués dans l'intégration multisensorielle dans le système normale est d'une importance centrale car elle permettra d'examiner et de mieux comprendre comment le cerveau déficient pourrait bénéficier d'une réhabilitation avec la stimulation multisensorielle.

Wavelet analysis residual kriging vs. neural network residual kriging

This paper deals with the problem of spatial data mapping. A new method based on wavelet interpolation and geostatistical prediction (kriging) is proposed. The method - wavelet analysis residual kriging (WARK) - is developed in order to assess the problems rising for highly variable data in presence of spatial trends. In these cases stationary prediction models have very limited application. Wavelet analysis is used to model large-scale structures and kriging of the remaining residuals focuses on small-scale peculiarities. WARK is able to model spatial pattern which features multiscale structure. In the present work WARK is applied to the rainfall data and the results of validation are compared with the ones obtained from neural network residual kriging (NNRK). NNRK is also a residual-based method, which uses artificial neural network to model large-scale non-linear trends. The comparison of the results demonstrates the high quality performance of WARK in predicting hot spots, reproducing global statistical characteristics of the distribution and spatial correlation structure.

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Dynamic changes in brain functional connectivity during concurrent dual-task performance.

This study investigated the spatial, spectral, temporal and functional proprieties of functional brain connections involved in the concurrent execution of unrelated visual perception and working memory tasks. Electroencephalography data was analysed using a novel data-driven approach assessing source coherence at the whole-brain level. Three connections in the beta-band (18-24 Hz) and one in the gamma-band (30-40 Hz) were modulated by dual-task performance. Beta-coherence increased within two dorsofrontal-occipital connections in dual-task conditions compared to the single-task condition, with the highest coherence seen during low working memory load trials. In contrast, beta-coherence in a prefrontal-occipital functional connection and gamma-coherence in an inferior frontal-occipitoparietal connection was not affected by the addition of the second task and only showed elevated coherence under high working memory load. Analysis of coherence as a function of time suggested that the dorsofrontal-occipital beta-connections were relevant to working memory maintenance, while the prefrontal-occipital beta-connection and the inferior frontal-occipitoparietal gamma-connection were involved in top-down control of concurrent visual processing. The fact that increased coherence in the gamma-connection, from low to high working memory load, was negatively correlated with faster reaction time on the perception task supports this interpretation. Together, these results demonstrate that dual-task demands trigger non-linear changes in functional interactions between frontal-executive and occipitoparietal-perceptual cortices.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

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.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.