Interactions between learning and immunity in Drosophila melanogaster

Learning and immunity are two adaptive traits with roles in central aspects of an organism's life: learning allows adjusting behaviours in changing environments, while immunity protects the body integrity against parasites and pathogens. While we know a lot about how these two traits interact in vertebrates, the interactions between learning and immunity remain poorly explored in insects. During my PhD, I studied three possible ways in which these two traits interact in the model system Drosophila melanogaster, a model organism in the study of learning and in the study of immunity. Learning can affect the behavioural defences against parasites and pathogens through the acquisition of new aversions for contaminated food for instance. This type of learning relies on the ability to associate a food-related cue with the visceral sickness following ingestion of contaminated food. Despite its potential implication in infection prevention, the existence of pathogen avoidance learning has been rarely explored in invertebrates. In a first part of my PhD, I tested whether D. melanogaster, which feed on food enriched in microorganisms, innately avoid the orally-acquired 'novel' virulent pathogen Pseudomonas entomophila, and whether it can learn to avoid it. Although flies did not innately avoid this pathogen, they decreased their preference for contaminated food over time, suggesting the existence of a form of learning based likely on infection-induced sickness. I further found that flies may be able to learn to avoid an odorant which was previously associated with the pathogen, but this requires confirmation with additional data. If this is confirmed, this would be the first time, to my knowledge, that pathogen avoidance learning is reported in an insect. The detrimental effect of infection on cognition and more specifically on learning ability is well documented in vertebrates and in social insects. While the underlying mechanisms are described in detail in vertebrates, experimental investigations are lacking in invertebrates. In a second part of my PhD, I tested the effect of an oral infection with natural pathogens on associative learning of D. melanogaster. By contrast with previous studies in insects, I found that flies orally infected with the virulent P. entomophila learned better the association of an odorant with mechanical shock than uninfected flies. The effect seems to be specific to a gut infection, and so far I have not been able to draw conclusions on the respective contributions of the pathogen's virulence and of the flies' immune activity in this effect. Interestingly, infected flies may display an increased sensitivity to physical pain. If the learning improvement observed in infected flies was due partially to the activity of the immune system, my results would suggest the existence of physiological connections between the immune system and the nervous system. The basis of these connections would then need to be addressed. Learning and immunity are linked at the physiological level in social insects. Physiological links between traits often result from the expression of genetic links between these traits. However, in social insects, there is no evidence that learning and immunity may be involved in an evolutionary trade-off. I previously reported a positive effect of infection on learning in D. melanogaster. This might suggest that a positive genetic link could exist between learning and immunity. We tested this hypothesis with two approaches: the diallel cross design with inbred lines, and the isofemale lines design. The two approaches provided consistent results: we found no additive genetic correlation between learning and resistance to infection with the diallel cross, and no genetic correlation in flies which are not yet adapted to laboratory conditions in isofemale lines. Consistently with the literature, the two studies suggested that the positive effect of infection on learning I observed might not be reflected by a positive evolutionary link between learning and immunity. Nevertheless, the existence of complex genetic relationships between the two traits cannot be excluded. - L'apprentissage et l'immunité sont deux caractères à valeur adaptative impliqués dans des aspects centraux de la vie d'un organisme : l'apprentissage permet d'ajuster les comportements pour faire face aux changements de l'environnement, tandis que l'immunité protège l'intégrité corporelle contre les attaques des parasites et des pathogènes. Alors que les interactions entre l'apprentissage et l'immunité sont bien documentées chez les vertébrés, ces interactions ont été très peu étudiées chez les insectes. Pendant ma thèse, je me suis intéressée à trois aspects des interactions possibles entre l'apprentissage et l'immunité chez la mouche du vinaigre Drosophila melanogaster, qui est un organisme modèle dans l'étude à la fois de l'apprentissage et de l'immunité. L'apprentissage peut affecter les défenses comportementales contre les parasites et les pathogènes par l'acquisition de nouvelles aversions pour la nourriture contaminée par exemple. Ce type d'apprentissage repose sur la capacité à associer une caractéristique de la nourriture avec la maladie qui suit l'ingestion de cette nourriture. Malgré les implications potentielles pour la prévention des infections, l'évitement appris des pathogènes a été rarement étudié chez les invertébrés. Dans une première partie de ma thèse, j'ai testé si les mouches, qui se nourrissent sur des milieux enrichis en micro-organismes, évitent de façon innée un 'nouveau' pathogène virulent Pseudomonas entomophila, et si elles ont la capacité d'apprendre à l'éviter. Bien que les mouches ne montrent pas d'évitement inné pour ce pathogène, elles diminuent leur préférence pour de la nourriture contaminée dans le temps, suggérant l'existence d'une forme d'apprentissage basée vraisemblablement sur la maladie générée par l'infection. J'ai ensuite observé que les mouches semblent être capables d'apprendre à éviter une odeur qui était au préalable associée avec ce pathogène, mais cela reste à confirmer par la collecte de données supplémentaires. Si cette observation est confirmée, cela sera la première fois, à ma connaissance, que l'évitement appris des pathogènes est décrit chez un insecte. L'effet détrimental des infections sur la cognition et plus particulièrement sur les capacités d'apprentissage est bien documenté chez les vertébrés et les insectes sociaux. Alors que les mécanismes sous-jacents sont détaillés chez les vertébrés, des études expérimentales font défaut chez les insectes. Dans une seconde partie de ma thèse, j'ai mesuré les effets d'une infection orale par des pathogènes naturels sur les capacités d'apprentissage associatif de la drosophile. Contrairement aux études précédentes chez les insectes, j'ai trouvé que les mouches infectées par le pathogène virulent P. entomophila apprennent mieux à associer une odeur avec des chocs mécaniques que des mouches non infectées. Cet effet semble spécifique à l'infection orale, et jusqu'à présent je n'ai pas pu conclure sur les contributions respectives de la virulence du pathogène et de l'activité immunitaire des mouches dans cet effet. De façon intéressante, les mouches infectées pourraient montrer une plus grande réactivité à la douleur physique. Si l'amélioration de l'apprentissage observée chez les mouches infectées était due en partie à l'activité du système immunitaire, mes résultats suggéreraient l'existence de connections physiologiques entre le système immunitaire et le système nerveux. Les mécanismes de ces connections seraient à explorer. L'apprentissage et l'immunité sont liés sur un plan physiologique chez les insectes sociaux. Les liens physiologiques entre les caractères résultent souvent de l'expression de liens entre ces caractères au niveau génétique. Cependant, chez les insectes sociaux, il n'y a pas de preuve que l'apprentissage et l'immunité soient liés par un compromis évolutif. J'ai précédemment rapporté un effet positif de l'infection sur l'apprentissage chez la drosophile. Cela pourrait suggérer qu'une relation génétique positive existerait entre l'apprentissage et l'immunité. Nous avons testé cette hypothèse par deux approches : le croisement diallèle avec des lignées consanguines, et les lignées isofemelles. Les deux approches ont fournies des résultats similaires : nous n'avons pas détecté de corrélation génétique additive entre l'apprentissage et la résistance à l'infection avec le croisement diallèle, et pas de corrélation génétique chez des mouches non adaptées aux conditions de laboratoire avec les lignées isofemelles. En ligne avec la littérature, ces deux études suggèrent que l'effet positif de l'infection sur l'apprentissage que j'ai précédemment observé ne refléterait pas un lien évolutif positif entre l'apprentissage et l'immunité. Néanmoins, l'existence de relations génétiques complexes n'est pas exclue.

Reducing the learning curve for the treatment of morphoeic (sclerosing) basal cell carcinoma of the face.

The treatment of morphoeic (or sclerosing) basal cell carcinoma (mBCC) of the face is associated with high rates of incomplete excision and recurrence. A principal risk factor for incomplete resection is the grade of surgeon. We did a prospective, randomised study of 40 consecutive patients with mBCC of the face. The extent of the tumour was assessed under standard conditions by consultant surgeons and compared with assessments by resident surgeons with the help of the Varioscope, a combination of microscope and loupe glasses with strong illumination and a maximal magnification of 7x. The data from a former retrospective study of all excisions of mBCC of the face during a five-year period at the hospital served as control. Residents with the support of the Varioscope achieved a rate of incomplete excisions similar to that of consultants under standard conditions. There was a significant reduction of the rate of incomplete resections by resident surgeons thanks to high magnification and good lighting (p=0.02). High magnification and good lighting were useful in learning how to recognise skin changes associated with mBCC of the face and achieving a low rate of incomplete excisions.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task.

In an uncertain environment, probabilities are key to predicting future events and making adaptive choices. However, little is known about how humans learn such probabilities and where and how they are encoded in the brain, especially when they concern more than two outcomes. During functional magnetic resonance imaging (fMRI), young adults learned the probabilities of uncertain stimuli through repetitive sampling. Stimuli represented payoffs and participants had to predict their occurrence to maximize their earnings. Choices indicated loss and risk aversion but unbiased estimation of probabilities. BOLD response in medial prefrontal cortex and angular gyri increased linearly with the probability of the currently observed stimulus, untainted by its value. Connectivity analyses during rest and task revealed that these regions belonged to the default mode network. The activation of past outcomes in memory is evoked as a possible mechanism to explain the engagement of the default mode network in probability learning. A BOLD response relating to value was detected only at decision time, mainly in striatum. It is concluded that activity in inferior parietal and medial prefrontal cortex reflects the amount of evidence accumulated in favor of competing and uncertain outcomes.

Dynamics of the recruitment of lateral amygdala neurons following successive fear learning to different conditional stimuli

In fear conditioning, an animal learns to associate an unconditioned stimulus (US), such as a shock, and a conditioned stimulus (CS), such as a tone, so that the presentation of the CS alone can trigger conditioned responses. Recent research on the lateral amygdala has shown that following cued fear conditioning, only a subset of higher-excitable neurons are recruited in the memory trace. Their selective deletion after fear conditioning results in a selective erasure of the fearful memory. I hypothesize that the recruitment of highly excitable neurons depends on responsiveness to stimuli, intrinsic excitability and local connectivity. In addition, I hypothesize that neurons recruited for an initial memory also participate in subsequent memories, and that changes in neuronal excitability affect secondary fear learning. To address these hypotheses, I will show that A) a rat can learn to associate two successive short-term fearful memories; B) neuronal populations in the LA are competitively recruited in the memory traces depending on individual neuronal advantages, as well as advantages granted by the local network. By performing two successive cued fear conditioning experiments, I found that rats were able to learn and extinguish the two successive short-term memories, when tested 1 hour after learning for each memory. These rats were equipped with a system of stable extracellular recordings that I developed, which allowed to monitor neuronal activity during fear learning. 233 individual putative pyramidal neurons could modulate their firing rate in response to the conditioned tone (conditioned neurons) and/or non- conditioned tones (generalizing neurons). Out of these recorded putative pyramidal neurons 86 (37%) neurons were conditioned to one or both tones. More precisely, one population of neurons encoded for a shared memory while another group of neurons likely encoded the memories' new features. Notably, in spite of a successful behavioral extinction, the firing rate of those conditioned neurons in response to the conditioned tone remained unchanged throughout memory testing. Furthermore, by analyzing the pre-conditioning characteristics of the conditioned neurons, I determined that it was possible to predict neuronal recruitment based on three factors: 1) initial sensitivity to auditory inputs, with tone-sensitive neurons being more easily recruited than tone- insensitive neurons; 2) baseline excitability levels, with more highly excitable neurons being more likely to become conditioned; and 3) the number of afferent connections received from local neurons, with neurons destined to become conditioned receiving more connections than non-conditioned neurons. - En conditionnement de la peur, un animal apprend à associer un stimulus inconditionnel (SI), tel un choc électrique, et un stimulus conditionné (SC), comme un son, de sorte que la présentation du SC seul suffit pour déclencher des réflexes conditionnés. Des recherches récentes sur l'amygdale latérale (AL) ont montré que, suite au conditionnement à la peur, seul un sous-ensemble de neurones plus excitables sont recrutés pour constituer la trace mnésique. Pour apprendre à associer deux sons au même SI, je fais l'hypothèse que les neurones entrent en compétition afin d'être sélectionnés lors du recrutement pour coder la trace mnésique. Ce recrutement dépendrait d'un part à une activation facilité des neurones ainsi qu'une activation facilité de réseaux de neurones locaux. En outre, je fais l'hypothèse que l'activation de ces réseaux de l'AL, en soi, est suffisante pour induire une mémoire effrayante. Pour répondre à ces hypothèses, je vais montrer que A) selon un processus de mémoire à court terme, un rat peut apprendre à associer deux mémoires effrayantes apprises successivement; B) des populations neuronales dans l'AL sont compétitivement recrutées dans les traces mnésiques en fonction des avantages neuronaux individuels, ainsi que les avantages consentis par le réseau local. En effectuant deux expériences successives de conditionnement à la peur, des rats étaient capables d'apprendre, ainsi que de subir un processus d'extinction, pour les deux souvenirs effrayants. La mesure de l'efficacité du conditionnement à la peur a été effectuée 1 heure après l'apprentissage pour chaque souvenir. Ces rats ont été équipés d'un système d'enregistrements extracellulaires stables que j'ai développé, ce qui a permis de suivre l'activité neuronale pendant l'apprentissage de la peur. 233 neurones pyramidaux individuels pouvaient moduler leur taux d'activité en réponse au son conditionné (neurones conditionnés) et/ou au son non conditionné (neurones généralisant). Sur les 233 neurones pyramidaux putatifs enregistrés 86 (37%) d'entre eux ont été conditionnés à un ou deux tons. Plus précisément, une population de neurones code conjointement pour un souvenir partagé, alors qu'un groupe de neurones différent code pour de nouvelles caractéristiques de nouveaux souvenirs. En particulier, en dépit d'une extinction du comportement réussie, le taux de décharge de ces neurones conditionné en réponse à la tonalité conditionnée est resté inchangée tout au long de la mesure d'apprentissage. En outre, en analysant les caractéristiques de pré-conditionnement des neurones conditionnés, j'ai déterminé qu'il était possible de prévoir le recrutement neuronal basé sur trois facteurs : 1) la sensibilité initiale aux entrées auditives, avec les neurones sensibles aux sons étant plus facilement recrutés que les neurones ne répondant pas aux stimuli auditifs; 2) les niveaux d'excitabilité des neurones, avec les neurones plus facilement excitables étant plus susceptibles d'être conditionnés au son ; et 3) le nombre de connexions reçues, puisque les neurones conditionné reçoivent plus de connexions que les neurones non-conditionnés. Enfin, nous avons constaté qu'il était possible de remplacer de façon satisfaisante le SI lors d'un conditionnement à la peur par des injections bilatérales de bicuculline, un antagoniste des récepteurs de l'acide y-Aminobutirique.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

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.