The handaxe and the microscope: individual and social learning in a multidimensional model of adaptation

When individuals learn by trial-and-error, they perform randomly chosen actions and then reinforce those actions that led to a high payoff. However, individuals do not always have to physically perform an action in order to evaluate its consequences. Rather, they may be able to mentally simulate actions and their consequences without actually performing them. Such fictitious learners can select actions with high payoffs without making long chains of trial-and-error learning. Here, we analyze the evolution of an n-dimensional cultural trait (or artifact) by learning, in a payoff landscape with a single optimum. We derive the stochastic learning dynamics of the distance to the optimum in trait space when choice between alternative artifacts follows the standard logit choice rule. We show that for both trial-and-error and fictitious learners, the learning dynamics stabilize at an approximate distance of root n/(2 lambda(e)) away from the optimum, where lambda(e) is an effective learning performance parameter depending on the learning rule under scrutiny. Individual learners are thus unlikely to reach the optimum when traits are complex (n large), and so face a barrier to further improvement of the artifact. We show, however, that this barrier can be significantly reduced in a large population of learners performing payoff-biased social learning, in which case lambda(e) becomes proportional to population size. Overall, our results illustrate the effects of errors in learning, levels of cognition, and population size for the evolution of complex cultural traits. (C) 2013 Elsevier Inc. All rights reserved.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Social and individual learning of helping in humans and other species.

Helping behaviors can be innate, learned by copying others (cultural transmission) or individually learned de novo. These three possibilities are often entangled in debates on the evolution of helping in humans. Here we discuss their similarities and differences, and argue that evolutionary biologists underestimate the role of individual learning in the expression of helping behaviors in humans.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Nondeclarative learning in children with Specific Language Impairment: Predicting regularities in the visuomotor, phonological, and cognitive domains.

Ullman (2004) suggested that Specific Language Impairment (SLI) results from a general procedural learning deficit. In order to test this hypothesis, we investigated children with SLI via procedural learning tasks exploring the verbal, motor, and cognitive domains. Results showed that compared with a Control Group, the children with SLI (a) were unable to learn a phonotactic learning task, (b) were able but less efficiently to learn a motor learning task and (c) succeeded in a cognitive learning task. Regarding the motor learning task (Serial Reaction Time Task), reaction times were longer and learning slower than in controls. The learning effect was not significant in children with an associated Developmental Coordination Disorder (DCD), and future studies should consider comorbid motor impairment in order to clarify whether impairments are related to the motor rather than the language disorder. Our results indicate that a phonotactic learning but not a cognitive procedural deficit underlies SLI, thus challenging Ullmans' general procedural deficit hypothesis, like a few other recent studies.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Virulent bacterial infection improves aversive learning performance in Drosophila melanogaster.

Virulent infections are expected to impair learning ability, either as a direct consequence of stressed physiological state or as an adaptive response that minimizes diversion of energy from immune defense. This prediction has been well supported for mammals and bees. Here, we report an opposite result in Drosophila melanogaster. Using an odor-mechanical shock conditioning paradigm, we found that intestinal infection with bacterial pathogens Pseudomonas entomophila or Erwinia c. carotovora improved flies' learning performance after a 1h retention interval. Infection with P. entomophila (but not E. c. carotovora) also improved learning performance after 5 min retention. No effect on learning performance was detected for intestinal infections with an avirulent GacA mutant of P. entomophila or for virulent systemic (hemocoel) infection with E. c. carotovora. Assays of unconditioned responses to odorants and shock do not support a major role for changes in general responsiveness to stimuli in explaining the changes in learning performance, although differences in their specific salience for learning cannot be excluded. Our results demonstrate that the effects of pathogens on learning performance in insects are less predictable than suggested by previous studies, and support the notion that immune stress can sometimes boost cognitive abilities.

ACID-SENSING ION CHANNELS: functional and molecular characterization in putative nociceptors, and modulation by nerve injury and serine protease

Résumé Les canaux ioniques ASICs (acid-sensing ion channels) appartiennent à la famille des canaux ENaC/Degenerin. Pour l'instant, quatre gènes (1 à 4) ont été clonés dont certains présentent des variants d'épissage. Leur activation par une acidification rapide du milieu extracellulaire génère un courant entrant transitoire essentiellement sodique accompagné pour certains types d'ASICs d'une phase soutenue. Les ASICs sont exprimés dans le système nerveux, central (SNC) et périphérique (SNP). On leur attribue un rôle dans l'apprentissage, la mémoire et l'ischémie cérébrale au niveau central ainsi que dans la nociception (douleur aiguë et inflammatoire) et la méchanotransduction au niveau périphérique. Toutefois, les données sont parfois contradictoires. Certaines études suggèrent qu'ils sont des senseurs primordiaux impliqués dans la détection de l'acidification et la douleur. D'autres études suggèrent plutôt qu'ils ont un rôle modulateur inhibiteur dans la douleur. De plus, le fait que leur activation génère majoritairement un courant transitoire alors que les fibres nerveuses impliquées dans la douleur répondent à un stimulus nocif avec une adaptation lente suggère que leurs propriétés doivent être modulés par des molécules endogènes. Dans une première partie de ma thèse, nous avons abordé la question de l'expression fonctionnelle des ASICs dans les neurones sensoriels primaires afférents du rat adulte pour clarifier le rôle des ASICs dans les neurones sensoriels. Nous avons caractérisé leurs propriétés biophysiques et pharmacologiques par la technique du patch-clamp en configuration « whole-cell ». Nous avons pu démontrer que près de 60% des neurones sensoriels de petit diamètre expriment des courants ASICs. Nous avons mis en évidence trois types de courant ASIC dans ces neurones. Les types 1 et 3 ont des propriétés compatibles avec un rôle de senseur du pH alors que le type 2 est majoritairement activé par des pH inférieurs à pH6. Le type 1 est médié par des homomers de la sous-unité ASIC1 a qui sont perméables aux Ca2+. Nous avons étudié leur co-expression avec des marqueurs des nocicepteurs ainsi que la possibilité d'induire une activité neuronale suite à une acidification qui soit dépendante des ASICs. Le but était d'associer un type de courant ASIC avec une fonction potentielle dans les neurones sensoriels. Une majorité des neurones exprimant les courants ASIC co-expriment des marqueurs des nocicepteurs. Toutefois, une plus grande proportion des neurones exprimant le type 1 n'est pas associée à la nociception par rapport aux types 2 et 3. Nous avons montré qu'il est possible d'induire des potentiels d'actions suite à une acidification. La probabilité d'induction est proportionnelle à la densité des courants ASIC et à l'acidité de la stimulation. Puis, nous avons utilisé cette classification comme un outil pour appréhender les potentielles modulations fonctionnelles des ASICs dans un model de neuropathie (spared nerve injury). Cette approche fut complétée par des expériences de «quantitative RT-PCR ». En situation de neuropathie, les courants ASIC sont dramatiquement changés au niveau de leur expression fonctionnelle et transcriptionnelle dans les neurones lésés ainsi que non-lésés. Dans une deuxième partie de ma thèse, suite au test de différentes substances sécrétées lors de l'inflammation et l'ischémie sur les propriétés des ASICs, nous avons caractérisé en détail la modulation des propriétés des courants ASICs notamment ASIC1 par les sérines protéases dans des systèmes d'expression recombinants ainsi que dans des neurones d'hippocampe. Nous avons montré que l'exposition aux sérine-protéases décale la dépendance au pH de l'activation ainsi que la « steady-state inactivation »des ASICs -1a et -1b vers des valeurs plus acidiques. Ainsi, l'exposition aux serine protéases conduit à une diminution du courant quand l'acidification a lieu à partir d'un pH7.4 et conduit à une augmentation du courant quand l'acidification alleu à partir d'un pH7. Nous avons aussi montré que cette régulation a lieu des les neurones d'hippocampe. Nos résultats dans les neurones sensoriels suggèrent que certains courants ASICs sont impliqués dans la transduction de l'acidification et de la douleur ainsi que dans une des phases du processus conduisant à la neuropathie. Une partie des courants de type 1 perméables au Ca 2+ peuvent être impliqués dans la neurosécrétion. La modulation par les sérines protéases pourrait expliquer qu'en situation d'acidose les canaux ASICs soient toujours activables. Résumé grand publique Les neurones sont les principales cellules du système nerveux. Le système nerveux est formé par le système nerveux central - principalement le cerveau, le cervelet et la moelle épinière - et le système nerveux périphérique -principalement les nerfs et les neurones sensoriels. Grâce à leur nombreux "bras" (les neurites), les neurones sont connectés entre eux, formant un véritable réseau de communication qui s'étend dans tout le corps. L'information se propage sous forme d'un phénomène électrique, l'influx nerveux (ou potentiels d'actions). A la base des phénomènes électriques dans les neurones il y a ce que l'on appelle les canaux ioniques. Un canal ionique est une sorte de tunnel qui traverse l'enveloppe qui entoure les cellules (la membrane) et par lequel passent les ions. La plupart de ces canaux sont normalement fermés et nécessitent d'être activés pour s'ouvrire et générer un influx nerveux. Les canaux ASICs sont activés par l'acidification et sont exprimés dans tout le système nerveux. Cette acidification a lieu notamment lors d'une attaque cérébrale (ischémie cérébrale) ou lors de l'inflammation. Les expériences sur les animaux ont montré que les canaux ASICs avaient entre autre un rôle dans la mort des neurones lors d'une attaque cérébrale et dans la douleur inflammatoire. Lors de ma thèse je me suis intéressé au rôle des ASICs dans la douleur et à l'influence des substances produites pendant l'inflammation sur leur activation par l'acidification. J'ai ainsi pu montrer chez le rat que la majorité des neurones sensoriels impliqués dans la douleur ont des canaux ASICs et que l'activation de ces canaux induit des potentiels d'action. Nous avons opéré des rats pour qu'ils présentent les symptômes d'une maladie chronique appelée neuropathie. La neuropathie se caractérise par une plus grande sensibilité à la douleur. Les rats neuropathiques présentent des changements de leurs canaux ASICs suggérant que ces canaux ont une peut-être un rôle dans la genèse ou les symptômes de cette maladie. J'ai aussi montré in vitro qu'un type d'enryme produit lors de l'inflammation et l'ischémie change les propriétés des ASICs. Ces résultats confirment un rôle des ASICs dans la douleur suggérant notamment un rôle jusque là encore non étudié dans la douleur neuropathique. De plus, ces résultats mettent en évidence une régulation des ASICs qui pourrait être importante si elle se confirmait in vivo de part les différents rôles des ASICs. Abstract Acid-sensing ion channels (ASICs) are members of the ENaC/Degenerin superfamily of ion channels. Their activation by a rapid extracellular acidification generates a transient and for some ASIC types also a sustained current mainly mediated by Na+. ASICs are expressed in the central (CNS) and in the peripheral (PNS) nervous system. In the CNS, ASICs have a putative role in learning, memory and in neuronal death after cerebral ischemia. In the PNS, ASICs have a putative role in nociception (acute and inflammatory pain) and in mechanotransduction. However, studies on ASIC function are somewhat controversial. Some studies suggest a crucial role of ASICs in transduction of acidification and in pain whereas other studies suggest rather a modulatory inhibitory role of ASICs in pain. Moreover, the basic property of ASICs, that they are activated only transiently is irreconcilable with the well-known property of nociception that the firing of nociceptive fibers demonstrated very little adaptation. Endogenous molecules may exist that can modulate ASIC properties. In a first part of my thesis, we addressed the question of the functional expression of ASICs in adult rat dorsal root ganglion (DRG) neurons. Our goal was to elucidate ASIC roles in DRG neurons. We characterized biophysical and pharmacological properties of ASIC currents using the patch-clamp technique in the whole-cell configuration. We observed that around 60% of small-diameter sensory neurons express ASICs currents. We described in these neurons three ASIC current types. Types 1 and 3 have properties compatible with a role of pH-sensor whereas type 2 is mainly activated by pH lower than pH6. Type 1 is mediated by ASIC1a homomultimers which are permeable to Ca 2+. We studied ASIC co-expression with nociceptor markers. The goal was to associate an ASIC current type with a potential function in sensory neurons. Most neurons expressing ASIC currents co-expressed nociceptor markers. However, a higher proportion of the neurons expressing type 1 was not associated with nociception compared to type 2 and -3. We completed this approach with current-clamp measurements of acidification-induced action potentials (APs). We showed that activation of ASICs in small-diameter neurons can induce APs. The probability of AP induction is positively correlated with the ASIC current density and the acidity of stimulation. Then, we used this classification as a tool to characterize the potential functional modulation of ASICs in the spared nerve injury model of neuropathy. This approach was completed by quantitative RT-PCR experiments. ASICs current expression was dramatically changed at the functional and transcriptional level in injured and non-injured small-diameter DRG neurons. In a second part of my thesis, following an initial screening of the effect of various substances secreted during inflammation and ischemia on ASIC current properties, we characterized in detail the modulation of ASICs, in particular of ASIC1 by serine proteases in a recombinant expression system as well as in hippocampal neurons. We showed that protease exposure shifts the pH dependence of ASIC1 activation and steady-state inactivation to more acidic pH. As a consequence, protease exposure leads to a decrease in the current response if ASIC1 is activated by a pH drop from pH 7.4. If, however, acidification occurs from a basal pH of 7, protease-exposed ASIC1a shows higher activity than untreated ASIC1a. We provided evidence that this bi-directional regulation of ASIC1a function also occurs in hippocampal neurons. Our results in DRG neurons suggest that some ASIC currents are involved in the transduction of peripheral acidification and pain. Furthermore, ASICs may participate to the processes leading to neuropathy. Some Ca 2+-permeable type 1 currents may be involved in neurosecretion. ASIC modulation by serine proteases may be physiologically relevant, allowing ASIC activation under sustained slightly acidic conditions.

Learning in the working place: the educational potential of a multihead microscope in pathology postgraduate training.

Training future pathologists is an important mission of many hospital anatomic pathology departments. Apprenticeship-a process in which learning and teaching tightly intertwine with daily work, is one of the main educational methods in use in postgraduate medical training. However, patient care, including pathological diagnosis, often comes first, diagnostic priorities prevailing over educational ones. Recognition of the unique educational opportunities is a prerequisite for enhancing the postgraduate learning experience. The aim of this paper is to draw attention of senior pathologists with a role as supervisor in postgraduate training on the potential educational value of a multihead microscope, a common setting in pathology departments. After reporting on an informal observation of senior and junior pathologists' meetings around the multihead microscope in our department, we review the literature on current theories of learning to provide support to the high potential educational value of these meetings for postgraduate training in pathology. We also draw from the literature on learner-centered teaching some recommendations to better support learning in this particular context. Finally, we propose clues for further studies and effective instruction during meetings around a multihead microscope.

Mineral zoning and geochemistry of epithermal polymetallic Zn-Pb-Ag-Cu-Bi mineralization at Cerro de Pasco, Peru

The large Cerro de Pasco Cordilleran base metal deposit in central Peru is located on the eastern margin of a middle Miocene diatreme-dome complex and comprises two mineralization stages. The first stage consists of a large pyrite-quartz body replacing Lower Mesozoic Pucara carbonate rocks and, to a lesser extent, diatreme breccia. This body is composed of pyrite with pyrrhotite inclusions, quartz, and black and red chalcedony (containing hypogene hematite). At the contact with the pyrite-quartz body, the diatreme breccia is altered to pyrite-quartz-sericite-pyrite. This body was, in part, replaced by pipelike pyrrhotite bodies zoned outward to carbonate-replacement Zn-Pb ores hearing Fe-rich sphalerite (up to 24 mol % Fes). The second mineralization stage is partly superimposed on the first and consists of zoned east-west-trending Cu-Ag-(Au-Zn-Pb) enargite-pyrite veins hosted in the diatreme breccia in the western part of the deposit and well-zoned Zn-Pb-(Bi-Ag-Cu) carbonate-replacement orebodies; in both cases, sphalerite is Fe poor and the inner parts of the orebodies show typically advanced argillic alteration assemblages, including aluminum phosphate Sulfate (APS) minerals. The zoned enargite-pyrite veins display mineral zoning, from a core of enargite-pyrite +/- alunite with traces of Au, through an intermediate zone of tennantite, chalcopyrite, and Bi minerals to a poorly developed Outer zone hearing sphalerite-galena +/- kaolinite. The carbonate-hosted replacement ores are controlled along N 35 degrees E, N 90 degrees E, N 120 degrees E, and N 170 degrees E faults. They form well-zoned upward-flaring pipelike orebodies with a core of famatinite-pyrite and alunite, an intermediate zone with tetrahedrite-pyrite, chalcopyrite, matildite, cuprobismutite, emplectite, and other Bi minerals accompanied by APS minerals, kaolinite, and dickite, and an outer zone composed of Fe-poor sphalerite (in the range of 0.05-3.5 mol % Fes) and galena. The outermost zone consists of hematite, magnetite, and Fe-Mn-Zn-Ca-Mg carbonates. Most of the second-stage carbonate-replacement orebodies plunge between 25 degrees and 60 degrees to the west, suggesting that the hydrothermal fluids ascended from deeper levels and that no lateral feeding from the veins to the carbonate-replacement orebodies took place. In the Venencocha and Santa Rosa areas, located 2.5 km northwest of the Cerro de Pasco open pit and in the southern part of the deposit, respectively, advanced argillic altered dacitic domes and oxidized veins with advanced argillic alteration halos occur. The latter veins are possibly the oxidized equivalent of the second-stage enargite-pyrite veins located in the western part of the deposit. The alteration assemblage quartz-muscovite-pyrite associated with the pyrite-quartz body suggests that the first stage precipitated at slightly, acidic fin. The sulfide mineral assemblages define an evolutionary path close to the pyrite-pyrrhotite boundary and are characteristic of low-sulfidation states; they suggest that the oxidizing slightly acidic hydrothermal fluid was buffered by phyllite, shale, and carbonate host rock. However, the presence in the pyrite-quartz body of hematite within quartz suggests that, locally, the fluids were less buffered by the host rock. The mineral assemblages of the second mineralization stage are characteristic of high- to intermediate-sulfidation states. High-sulfidation states and oxidizing conditions were achieved and maintained in the cores of the second-stage orebodies, even in those replacing carbonate rocks. The observation that, in places, second-stage mineral assemblages are found in the inner and outer zones is explained in terms of the hydrothermal fluid advancing and waning. Microthermometric data from fluid inclusions in quartz indicate that the different ores of the first mineralization stage formed at similar temperatures and moderate salinities (200 degrees-275 degrees C and 0.2-6.8 wt % NaCl equiv in the pyrite-quartz body; 192 degrees-250 degrees C and 1.1-4.3 wt % NaCl equiv in the pyrrhotite bodies; and 183 degrees-212 degrees C and 3.2-4.0 wt % NaCl equiv in the Zn-Pb ores). These values are similar to those obtained for fluid inclusions in quartz and sphalerite from the second-stage ores (187 degrees-293 degrees C and 0.2-5.2 wt % NaCl equiv in the enargite-pyrite veins: 178 degrees-265 degrees C and 0.2-7.5 wt % NaCl equiv in quartz of carbonate-replacement orebodies; 168 degrees-999 degrees C and 3-11.8 wt % NaCl equiv in sphalerite of carbonate-replacement orebodies; and 245 degrees-261 degrees C and 3.2-7.7 wt % NaCl equiv in quartz from Venencocha). Oxygen and hydrogen isotope compositions oil kaolinite from carbonate-replacement orebodies (delta(18)O = 5.3-11.5%o, delta D = -82 to -114%o) and on alunite from the Venencocha and Santa Rosa areas (delta(18)O = 1.9-6.9%o, delta D = -56 to -73%o). Oxygen isotope compositions of quartz from the first and second stages have 6180 values from 9.1 to 1.7.8 per mil. Calculated fluids in equilibrium with kaolinite have delta(18)O values of 2.0 to 8.2 and delta D values of -69 to -97 per mil; values in equilibrium with alunite are -1.4 to -6.4 and -62 to -79 per mil. Sulfur isotope compositions of sulfides from both stages have a narrow range of delta(34)S values, between -3.7 and +4.2 per mil; values for sulfates from the second stage are between 4.2 and 31.2 per mil. These results define two mixing trends for the ore-forming fluids. The first trend reflects mixing between a moderately saline (similar to 10 wt % NaCl equiv) magmatic end member that had degassed (as indicated by the low delta D values) and meteoric water. The second mixing indicates condensation of magmatic vapor with HCl and SO(2) into meteoric water, which formed alunite. The hydrothermal system at Cerro de Pasco was emplaced at a shallow depth (similar to 500 m) in the epithermal and upper part of a porphyry environment. The similar temperatures and salinities obtained for the first stage and second stages, together with the stable isotope data, indicate that both stages are linked and represent successive stages of epithermal polymetallic mineralization in the upper part of a porphyry system.

Metabolic activation pattern of distinct hippocampal subregions during spatial learning and memory retrieval.

Activation dynamics of hippocampal subregions during spatial learning and their interplay with neocortical regions is an important dimension in the understanding of hippocampal function. Using the (14C)-2-deoxyglucose autoradiographic method, we have characterized the metabolic changes occurring in hippocampal subregions in mice while learning an eight-arm radial maze task. Autoradiogram densitometry revealed a heterogeneous and evolving pattern of enhanced metabolic activity throughout the hippocampus during the training period and on recall. In the early stages of training, activity was enhanced in the CA1 area from the intermediate portion to the posterior end as well as in the CA3 area within the intermediate portion of the hippocampus. At later stages, CA1 and CA3 activations spread over the entire longitudinal axis, while dentate gyrus (DG) activation occurred from the anterior to the intermediate zone. Activation of the retrosplenial cortex but not the amygdala was also observed during the learning process. On recall, only DG activation was observed in the same anterior part of the hippocampus. These results suggest the existence of a functional segmentation of the hippocampus, each subregion being dynamically but also differentially recruited along the acquisition, consolidation, and retrieval process in parallel with some neocortical sites.

Ibotenate Lesions of Hippocampus and/or Subiculum: Dissociating Components of Allocentric Spatial Learning

This study examined the effects of ibotenic acid-induced lesions of the hippocampus, subiculum and hippocampus +/- subiculum upon the capacity of rats to learn and perform a series of allocentric spatial learning tasks in an open-field water maze. The lesions were made by infusing small volumes of the neurotoxin at a total of 26 (hippocampus) or 20 (subiculum) sites intended to achieve complete target cell loss but minimal extratarget damage. The regional extent and axon-sparing nature of these lesions was evaluated using both cresyl violet and Fink - Heimer stained sections. The behavioural findings indicated that both the hippocampus and subiculum lesions caused impairment to the initial postoperative acquisition of place navigation but did not prevent eventual learning to levels of performance almost as effective as those of controls. However, overtraining of the hippocampus + subiculum lesioned rats did not result in significant place learning. Qualitative observations of the paths taken to find a hidden escape platform indicated that different strategies were deployed by hippocampal and subiculum lesioned groups. Subsequent training on a delayed matching to place task revealed a deficit in all lesioned groups across a range of sample choice intervals, but the subiculum lesioned group was less impaired than the group with the hippocampal lesion. Finally, unoperated control rats given both the initial training and overtraining were later given either a hippocampal lesion or sham surgery. The hippocampal lesioned rats were impaired during a subsequent retention/relearning phase. Together, these findings suggest that total hippocampal cell loss may cause a dual deficit: a slower rate of place learning and a separate navigational impairment. The prospect of unravelling dissociable components of allocentric spatial learning is discussed.