Building hippocampal circuits to learn and remember: insights into the development of human memory

The hippocampal formation is essential for the processing of episodic memories for autobiographical events that happen in unique spatiotemporal contexts. Interestingly, before 2 years of age, children are unable to form or store episodic memories for recall later in life, a phenomenon known as infantile amnesia. From 2 to 7 years of age, there are fewer memories than predicted based on a forgetting function alone, a phenomenon known as childhood amnesia. Here, we discuss the postnatal maturation of the primate hippocampal formation with the goal of characterizing the development of the neurobiological substrates thought to subserve the emergence of episodic memory. Distinct regions, layers and cells of the hippocampal formation exhibit different profiles of structural and molecular development during early postnatal life. The protracted period of neuronal addition and maturation in the dentate gyrus is accompanied by the late maturation of specific layers in different hippocampal regions that are located downstream from the dentate gyrus, particularly CA3. In contrast, distinct layers in several hippocampal regions, particularly CA1, which receive direct projections from the entorhinal cortex, exhibit an early maturation. In addition, hippocampal regions that are more highly interconnected with subcortical structures, including the subiculum, presubiculum, parasubiculum and CA2, mature even earlier. These findings, together with our studies of the development of human spatial memory, support the hypothesis that the differential maturation of distinct hippocampal circuits might underlie the differential emergence of specific "hippocampus-dependent" memory processes, culminating in the emergence of episodic memory concomitant with the maturation of all hippocampal circuits.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Looking Back: Historical Hall, 1899-1904; State Historical, Memorial and Art Building, 1905-1999; Ola Babcock Miller Building, 1999-Present, June 2013

For more than 80 years, visitors to the Iowa State Historical, Memorial, and Art Building were treated to the state’s collection of historic documents, literature, portraits, and historical, geological, and archeological artifacts. Those who visited might have memories of the spectacular sand paintings by Iowan Andrew Clemens, the variety of taxidermy Iowa animals, the pioneer Conestoga wagon in the basement, the biplane hanging from the dome ceiling, the odd display by the medical library of things removed from stomachs, or the Native American display on the third floor. This booklet is a look back at the origins of the museum. It includes some of the Historical Department reports, legislation passed by the general assembly, newspaper and magazine articles, and photographs pertaining to the museum and library. It is not intended to be an exhaustive review and documentation of displays and exhibits. It is a brief overview of the building’s history and some photographs that may bring back memories, for some, of a field trip as a student. This booklet has been created from a variety of source materials: photographs, newspaper articles, and various reports. The following have contributed: State Library of Iowa, Iowa State Historical Society, the Iowa Judicial Branch, Susan Wallace, Helen Dagley, Barb Corson, Jerome Thompson, Pam Rees, Georgiann Fischer, and Jason Mrachina.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Brain lactate kinetics: Modeling evidence for neuronal lactate uptake upon activation.

A critical issue in brain energy metabolism is whether lactate produced within the brain by astrocytes is taken up and metabolized by neurons upon activation. Although there is ample evidence that neurons can efficiently use lactate as an energy substrate, at least in vitro, few experimental data exist to indicate that it is indeed the case in vivo. To address this question, we used a modeling approach to determine which mechanisms are necessary to explain typical brain lactate kinetics observed upon activation. On the basis of a previously validated model that takes into account the compartmentalization of energy metabolism, we developed a mathematical model of brain lactate kinetics, which was applied to published data describing the changes in extracellular lactate levels upon activation. Results show that the initial dip in the extracellular lactate concentration observed at the onset of stimulation can only be satisfactorily explained by a rapid uptake within an intraparenchymal cellular compartment. In contrast, neither blood flow increase, nor extracellular pH variation can be major causes of the lactate initial dip, whereas tissue lactate diffusion only tends to reduce its amplitude. The kinetic properties of monocarboxylate transporter isoforms strongly suggest that neurons represent the most likely compartment for activation-induced lactate uptake and that neuronal lactate utilization occurring early after activation onset is responsible for the initial dip in brain lactate levels observed in both animals and humans.

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.

Développement d'une méthode microgravimétrique urbaine : intégration dans un SIG et application au projet du M2 lausannois

Résumé La réalisation d'une seconde ligne de métro (M2) dès 2004, passant dans le centre ville de Lausanne, a été l'opportunité de développer une méthodologie concernant des campagnes microgravimétriques dans un environnement urbain perturbé. Les corrections topographiques prennent une dimension particulière dans un tel milieu, car de nombreux objets non géologiques d'origine anthropogénique comme toutes sortes de sous-sols vides viennent perturber les mesures gravimétriques. Les études de génie civil d'avant projet de ce métro nous ont fournis une quantité importante d'informations cadastrales, notamment sur les contours des bâtiments, sur la position prévue du tube du M2, sur des profondeurs de sous-sol au voisinage du tube, mais aussi sur la géologie rencontré le long du corridor du M2 (issue des données lithologiques de forages géotechniques). La planimétrie des sous-sols a été traitée à l'aide des contours des bâtiments dans un SIG (Système d'Information Géographique), alors qu'une enquête de voisinage fut nécessaire pour mesurer la hauteur des sous-sols. Il a été alors possible, à partir d'un MNT (Modèle Numérique de Terrain) existant sur une grille au mètre, de mettre à jour celui ci avec les vides que représentent ces sous-sols. Les cycles de mesures gravimétriques ont été traités dans des bases de données Ac¬cess, pour permettre un plus grand contrôle des données, une plus grande rapidité de traitement, et une correction de relief rétroactive plus facile, notamment lorsque des mises à jour de la topographie ont lieu durant les travaux. Le quartier Caroline (entre le pont Bessières et la place de l'Ours) a été choisi comme zone d'étude. Le choix s'est porté sur ce quartier du fait que, durant ce travail de thèse, nous avions chronologiquement les phases pré et post creusement du tunnel du M2. Cela nous a permis d'effectuer deux campagnes gravimétriques (avant le creu¬sement durant l'été 2005 et après le creusement durant l'été 2007). Ces réitérations nous ont permis de tester notre modélisation du tunnel. En effet, en comparant les mesures des deux campagnes et la réponse gravifique du modèle du tube discrétisé en prismes rectangulaires, nous avons pu valider notre méthode de modélisation. La modélisation que nous avons développée nous permet de construire avec détail la forme de l'objet considéré avec la possibilité de recouper plusieurs fois des interfaces de terrains géologiques et la surface topographique. Ce type de modélisation peut s'appliquer à toutes constructions anthropogéniques de formes linéaires. Abstract The realization of a second underground (M2) in 2004, in downtown Lausanne, was the opportunity to develop a methodology of microgravity in urban environment. Terrain corrections take on special meaning in such environment. Many non-geologic anthropogenic objects like basements act as perturbation of gravity measurements. Civil engineering provided a large amount of cadastral informations, including out¬lines of buildings, M2 tube position, depths of some basements in the vicinity of the M2 corridor, and also on the geology encountered along the M2 corridor (from the lithological data from boreholes). Geometry of basements was deduced from building outlines in a GIS (Geographic Information System). Field investigation was carried out to measure or estimate heights of basements. A DEM (Digital Elevation Model) of the city of Lausanne is updated from voids of basements. Gravity cycles have been processed in Access database, to enable greater control of data, enhance speed processing, and retroactive terrain correction easier, when update of topographic surface are available. Caroline area (between the bridge Saint-Martin and Place de l'Ours) was chosen as the study area. This area was in particular interest because it was before and after digging in this thesis. This allowed us to conduct two gravity surveys (before excavation during summer 2005 and after excavation during summer 2007). These re-occupations enable us to test our modélisation of the tube. Actually, by comparing the difference of measurements between the both surveys and the gravity response of our model (by rectangular prisms), we were able to validate our modeling. The modeling method we developed allows us to construct detailed shape of an object with possibility to cross land geological interfaces and surface topography. This type of modélisation can be applied to all anthropogenic structures.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.

List of books by Iowa authors : collected by the Auxiliary Committee of the Iowa Commission, Louisiana Purchase Exposition, for the exhibit of books by Iowa authors in the Iowa State Building, St. Louis, 1904

This is a list of Iowa authors that grew out of the efforts of the auxiliary committee of the Iowa Commission of the Louisiana Purchase Exposition to bring together a collection of books by Iowa authors for the Iowa State building at the St. Louis Exposition.