Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Learning collaboration in General Internal Medicine: a pilot project for undergraduate students in nursing, physiotherapy and medicine

Background: A form of education called Interprofessional Education (IPE) occurs when two or more professions learn with, from and about each other. The purpose of IPE is to improve collaboration and the quality of care. Today, IPE is considered as a key educational approach for students in the health professions. IPE is highly effective when delivered in active patient care, such as in clinical placements. General internal medicine (GIM) is a core discipline where hospital-based clinical placements are mandatory for students in many health professions. However, few interprofessional (IP) clinical placements in GIM have been implemented. We designed such a placement. Placement design: The placement took place in the Department of Internal Medicine at the CHUV. It involved students from nursing, physiotherapy and medicine. The students were in their last year before graduation. Students formed teams consisting of one student from each profession. Each team worked in the same unit and had to take care of the same patient. The placement lasted three weeks. It included formal IP sessions, the most important being facilitated discussions or "briefings" (3x/w) during which the students discussed patient care and management. Four teams of students eventually took part in this project. Method: We performed a type of evaluation research called formative evaluation. This aimed at (1) understanding the educational experience and (2) assessing the impact of the placement on student learning. We collected quantitative data with pre-post clerkship questionnaires. We also collected qualitative data with two Focus Groups (FG) discussions at the end of the placement. The FG were audiotaped and transcribed. A thematic analysis was then performed. Results: We focused on the qualitative data, since the quantitative data lacked of statistical power due to the small numbers of students (N = 11). Five themes emerged from the FG analysis: (1) Learning of others' roles, (2) Learning collaborative competences, (3) Striking a balance between acquiring one's own professional competences and interprofessional competences, (4) Barriers to apply learnt IP competences in the future and (5) Advantages and disadvantages of IP briefings. Conclusions: Our IP clinical placement in GIM appeared to help students learn other professionals' roles and collaborative skills. Some challenges (e.g. finding the same patient for each team) were identified and will require adjustments.

Aversive Stimuli and Functional Networks of Fear Learning in PTSD : 734

Background: One characteristic of post traumatic stress disorder is an inability to adapt to a safe environment i.e. to change behavior when predictions of adverse outcomes are not met. Recent studies have also indicated that PTSD patients have altered pain processing, with hyperactivation of the putamen and insula to aversive stimuli (Geuze et al, 2007). The present study examined neuronal responses to aversive and predicted aversive events. Methods: Twenty-four trauma exposed non-PTSD controls and nineteen subjects with PTSD underwent fMRI imaging during a partial reinforcement fear conditioning paradigm, with a mild electric shock as the unconditioned stimuli (UCS). Three conditions were analyzed: actual presentations of the UCS, events when a UCS was expected, but omitted (CS+), and events when the UCS was neither expected nor delivered (CS-). Results: The UCS evoked significant alterations in the pain matrix consisting of the brainstem, the midbrain, the thalamus, the insula, the anterior and middle cingulate and the contralateral somatosensory cortex. PTSD subjects displayed bilaterally elevated putamen activity to the electric shock, as compared to controls. In trials when USC was expected, but omitted, significant activations were observed in the brainstem, the midbrain, the anterior insula and the anterior cingulate. PTSD subjects displayed similar activations, but also elevated activations in the amygdala and the posterior insula. Conclusions: These results indicate altered fear and safety learning in PTSD, and neuronal activations are further explored in terms of functional connectivity using psychophysiological interaction analyses.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Monitoring compliance in resistant hypertension: an important step in patient management.

Poor compliance with antihypertensive drug regimens is one recognized cause of inadequate blood pressure control. Compliance is difficult to measure, so poor adherence to treatment remains largely undiagnosed in clinical practice. When the therapeutic response to a drug is not the one expected, it is a major challenge for many physicians to decide whether the patient is a non-responder or a non-complier. Poor compliance is therefore often incorrectly interpreted as a lack of response to treatment. Not detecting non-compliance can lead to the wrong measures being taken. Electronic monitoring of compliance provides important longitudinal information about drug-intake behaviour that cannot be obtained in the clinic. Such monitoring can improve both compliance and blood pressure control, and help physicians to make more rational therapeutic decisions. A reliable assessment of compliance could have a great impact on medical costs by preventing unnecessary investigations or dose adaptations in patients who are not taking their drugs adequately, or potentially reducing the number of hospitalizations. Side-effects and lack of effectiveness are two frequent causes of poor compliance. The right choice of antihypertensive drug can therefore contribute to compliance. In this respect, it is important to find a drug regimen that is effective, long-acting and well tolerated. Long-acting antihypertensive drugs that provide good blood pressure control beyond the 24-h dosing period should perhaps be considered as drugs of choice in non-compliant patients with hypertension because they help to prevent the consequences of occasional drug omissions.

Decoding sequence learning from single-trial intracranial EEG in humans.

We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep) in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT) using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep) or a later consolidated phase (day 2, after sleep), whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence). Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition) at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

Cellular and molecular mechanisms underlying the effects of sleep loss on hippocampal synaptic plasticity

SUMMARY : The function of sleep for the organism is one of the most persistent and perplexing questions in biology. Current findings lead to the conclusion that sleep is primarily for the brain. In particular, a role for sleep in cognitive aspects of brain function is supported by behavioral evidence both in humans and animals. However, in spite of remarkable advancement in the understanding of the mechanisms underlying sleep generation and regulation, it has been proven difficult to determine the neurobiological mechanisms underlying the beneficial effect of sleep, and the detrimental impact of sleep loss, on learning and memory processes. In my thesis, I present results that lead to several critical steps forward in the link between sleep and cognitive function. My major result is the molecular identification and physiological analysis of a protein, the NR2A subunit of NMDA receptor (NMDAR), that confers sensitivity to sleep loss to the hippocampus, a brain structure classically involved in mnemonic processes. Specifically, I used a novel behavioral approach to achieve sleep deprivation in adult C57BL6/J mice, yet minimizing the impact of secondary factors associated with the procedure,.such as stress. By using in vitro electrophysiological analysis, I show, for the first time, that sleep loss dramatically affects bidirectional plasticity at CA3 to CA1 synapses in the hippocampus, a well established cellular model of learning and memory. 4-6 hours of sleep loss elevate the modification threshold for bidirectional synaptic plasticity (MT), thereby promoting long-term depression of CA3 to CA 1 synaptic strength after stimulation in the theta frequency range (5 Hz), and rendering long-term potentiation induction.more difficult. Remarkably, 3 hours of recovery sleep, after the deprivation, reset the MT at control values, thus re-establishing the normal proneness of synapses to undergo long-term plastic changes. At the molecular level, these functional changes are paralleled by a change in the NMDAR subunit composition. In particular, the expression of the NR2A subunit protein of NMDAR at CA3 to CA1 synapses is selectively and rapidly increased by sleep deprivation, whereas recovery sleep reset NR2A synaptic content to control levels. By using an array of genetic, pharmacological and computational approaches, I demonstrate here an obligatory role for NR2A-containing NMDARs in conveying the effect of sleep loss on CA3 to CAl MT. Moreover, I show that a genetic deletion of the NR2A subunit fully preserves hippocampal plasticity from the impact of sleep loss, whereas it does not alter sleepwake behavior and homeostatic response to sleep deprivation. As to the mechanism underlying the effects of the NR2A subunit on hippocampal synaptic plasticity, I show that the increased NR2A expression after sleep loss distinctly affects the contribution of synaptic and more slowly recruited NMDAR pools activated during plasticity-induction protocols. This study represents a major step forward in understanding the mechanistic basis underlying sleep's role for the brain. By showing that sleep and sleep loss affect neuronal plasticity by regulating the expression and function of a synaptic neurotransmitter receptor, I propose that an important aspect of sleep function could consist in maintaining and regulating protein redistribution and ion channel trafficking at central synapses. These findings provide a novel starting point for investigations into the connections between sleep and learning, and they may open novel ways for pharmacological control over hippocampal .function during periods of sleep restriction. RÉSUMÉ DU PROJET La fonction du sommeil pour l'organisme est une des questions les plus persistantes et difficiles dans la biologie. Les découvertes actuelles mènent à la conclusion que le sommeil est essentiel pour le cerveau. En particulier, le rôle du sommeil dans les aspects cognitifs est soutenu par des études comportementales tant chez les humains que chez les animaux. Cependant, malgré l'avancement remarquable dans la compréhension des mécanismes sous-tendant la génération et la régulation du sommeil, les mécanismes neurobiologiques qui pourraient expliquer l'effet favorable du sommeil sur l'apprentissage et la mémoire ne sont pas encore clairs. Dans ma thèse, je présente des résultats qui aident à clarifier le lien entre le sommeil et la fonction cognitive. Mon résultat le plus significatif est l'identification moléculaire et l'analyse physiologique d'une protéine, la sous-unité NR2A du récepteur NMDA, qui rend l'hippocampe sensible à la perte de sommeil. Dans cette étude, nous avons utilisé une nouvelle approche expérimentale qui nous a permis d'induire une privation de sommeil chez les souris C57BL6/J adultes, en minimisant l'impact de facteurs confondants comme, par exemple, le stress. En utilisant les techniques de l'électrophysiologie in vitro, j'ai démontré, pour la première fois, que la perte de sommeil est responsable d'affecter radicalement la plasticité bidirectionnelle au niveau des synapses CA3-CA1 de l'hippocampe. Cela correspond à un mécanisme cellulaire de l'apprentissage et de la mémoire bien établi. En particulier, 4-6 heures de privation de sommeil élèvent le seuil de modification pour la plasticité synaptique bidirectionnelle (SM). Comme conséquence, la dépression à long terme de la transmission synaptique est induite par la stimulation des fibres afférentes dans la bande de fréquences thêta (5 Hz), alors que la potentialisation à long terme devient plus difficile. D'autre part, 3 heures de sommeil de récupération sont suffisant pour rétablir le SM aux valeurs contrôles. Au niveau moléculaire, les changements de la plasticité synaptiques sont associés à une altération de la composition du récepteur NMDA. En particulier, l'expression synaptique de la protéine NR2A du récepteur NMDA est rapidement augmentée de manière sélective par la privation de sommeil, alors que le sommeil de récupération rétablit l'expression de la protéine au niveau contrôle. En utilisant des approches génétiques, pharmacologiques et computationnelles, j'ai démontré que les récepteurs NMDA qui expriment la sous-unité NR2A sont responsables de l'effet de la privation de sommeil sur le SM. De plus, nous avons prouvé qu'une délétion génétique de la sous-unité NR2A préserve complètement la plasticité synaptique hippocampale de l'impact de la perte de sommeil, alors que cette manipulation ne change pas les mécanismes de régulation homéostatique du sommeil. En ce qui concerne les mécanismes, j'ai .découvert que l'augmentation de l'expression de la sous-unité NR2A au niveau synaptique modifie les propriétés de la réponse du récepteur NMDA aux protocoles de stimulations utilisés pour induire la plasticité. Cette étude représente un pas en avant important dans la compréhension de la base mécaniste sous-tendant le rôle du sommeil pour le cerveau. En montrant que le sommeil et la perte de sommeil affectent la plasticité neuronale en régulant l'expression et la fonction d'un récepteur de la neurotransmission, je propose qu'un aspect important de la fonction du sommeil puisse être finalisé au règlement de la redistribution des protéines et du tracking des récepteurs aux synapses centraux. Ces découvertes fournissent un point de départ pour mieux comprendre les liens entre le sommeil et l'apprentissage, et d'ailleurs, ils peuvent ouvrir des voies pour des traitements pharmacologiques dans le .but de préserver la fonction hippocampale pendant les périodes de restriction de sommeil.

The influence of learning on evolution: a mathematical framework.

The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Intrahippocampal cholinergic grafts in aged rats compensate impairments in a radial maze and in a place learning task

Age-related cognitive impairments were studied in rats kept in semi-enriched conditions during their whole life, and tested during ontogeny and adult life in various classical spatial tasks. In addition, the effect of intrahippocampal grafts of fetal septal-diagonal band tissue, rich in cholinergic neurons, was studied in some of these subjects. The rats received bilateral cell suspensions when aged 23-24 months. Starting 4 weeks after grafting, they were trained during 5 weeks in an 8-arm maze made of connected plexiglass tunnels. No age-related impairment was detected during the first eight trials, when the maze shape was that of a classical radial maze in which the rats had already been trained when young. The older rats were impaired when the task was made more difficult by rendering two arms parallel to each other. They developed an important neglect of one of the parallel tunnels resulting in a high amount of errors before completion of the task. In addition, the old rats developed a systematic response pattern of visits to adjacent arms in a sequence, which was not observed in the younger subjects. None of these behaviours were observed in the old rats with a septal transplant. Sixteen weeks after grafting, another experiment was conducted in a homing hole board task. Rats were allowed to escape from a large circular arena through one hole out of many, and to reach home via a flexible tube under the table. The escape hole was at a fixed position according to distant room cues, and olfactory cues were made irrelevant by rotating the table between the trials. An additional cue was placed on the escape position. No age-related difference in escape was observed during training. During a probe trial with no hole connected and no proximal cue present, the old untreated rats were less clearly focussed on the training sector than were either the younger or the grafted old subjects. Taken together, these experiments indicate that enriched housing conditions and spatial training during adult life do not protect against all age-related deterioration in spatial ability. However, it might be that the considerable improvement observed in the grafted subjects results from an interaction between the graft treatment and the housing conditions.

Radio-immunothérapie du lymphome folliculaire: un pas vers la guérison [Radioimmunotherapy of follicular lymphoma: a step towards cure?]

Advanced stage follicular lymphoma is incurable by conventional treatment. Important progress has been observed with the development of new therapies based on monoclonal antibodies and on the use of radioimmunotherapy (RIT) in the treatment of non-Hodgkin lymphomas (NHL). Rituximab in combination with chemotherapy in the upfront setting significantly improved treatment outcome as compared with chemotherapy alone. Different studies also indicate that RIT has an important role in the management of NHL and could be beneficial in combination with chemotherapy. These two new treatment options have clearly distinctive mechanisms of action, rituximab being an exclusively biological treatment and RIT adding targeted systemic radiation therapy. Both RIT and the unlabeled antibody treatments might be further improved by different strategies including repetition of RIT or combination of different antibodies. We present here our experience with RIT using 131I-tositumomab (Bexxar) and discuss different topics regarding RIT, like the use of different antibodies, the best choice of the radioisotope or the place of radio-imaging. From the therapeutic point of view, we argue that the debate should not be as to which one among antibody immunotherapy or RIT should be best added to chemotherapy, but that all three treatments might be optimally combined with the aim to get the highest chance of cure for advanced stage follicular lymphoma.

Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging.

Auditory spatial representations are likely encoded at a population level within human auditory cortices. We investigated learning-induced plasticity of spatial discrimination in healthy subjects using auditory-evoked potentials (AEPs) and electrical neuroimaging analyses. Stimuli were 100 ms white-noise bursts lateralized with varying interaural time differences. In three experiments, plasticity was induced with 40 min of discrimination training. During training, accuracy significantly improved from near-chance levels to approximately 75%. Before and after training, AEPs were recorded to stimuli presented passively with a more medial sound lateralization outnumbering a more lateral one (7:1). In experiment 1, the same lateralizations were used for training and AEP sessions. Significant AEP modulations to the different lateralizations were evident only after training, indicative of a learning-induced mismatch negativity (MMN). More precisely, this MMN at 195-250 ms after stimulus onset followed from differences in the AEP topography to each stimulus position, indicative of changes in the underlying brain network. In experiment 2, mirror-symmetric locations were used for training and AEP sessions; no training-related AEP modulations or MMN were observed. In experiment 3, the discrimination of trained plus equidistant untrained separations was tested psychophysically before and 0, 6, 24, and 48 h after training. Learning-induced plasticity lasted <6 h, did not generalize to untrained lateralizations, and was not the simple result of strengthening the representation of the trained lateralizations. Thus, learning-induced plasticity of auditory spatial discrimination relies on spatial comparisons, rather than a spatial anchor or a general comparator. Furthermore, cortical auditory representations of space are dynamic and subject to rapid reorganization.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.