Inducible gene expression in fetal thymic epithelium: A new BAC transgenic model.

The thymus is the site of T cell development. Several stromal and hematopoietic cell types are necessary for the proper function of thymic selection and eventually peripheral immunity. Thymic epithelial cells (TECs) are essential for T cell lineage commitment, expansion, and maturation in the thymus. We were interested in developing an in vivo model in which exogenous gene expression could be transiently induced in embryonic TEC (Tet-On system). To this end, we have generated a bacterial artificial chromosome (BAC) transgenic mouse line in which the reverse tetracycline-dependent transactivator (rtTA) is expressed under the control of the Foxn1 promoter, a transcriptional factor indispensable for TEC development. To analyze the expression pattern and efficiency of this novel mouse model, we crossed the Foxn1-rtTA founder with a Tet-Responsive Element (TRE)-LacZ GFP mouse reporter to obtain a double transgenic mouse. In the presence of doxycycline, rtTA can interact with TRE and induce the expression of GFP and LacZ. In this double transgenic mouse, we observed that GFP expression was high, inducible and limited to TEC in fetal thymus. In contrast, in adult thymus, when TEC development and maturation is completed, GFP was barely detectable. Therefore, Foxn1-rtTA represents a new and efficient transgenic mouse model to induce genes of interest specifically in fetal thymic epithelium. genesis 51:717-724. © 2013 Wiley Periodicals, Inc.

A deterministic model to assess the impact of AIDS interventions in countries with generalized HIV epidemic. An application to Botswana

General introductionThe Human Immunodeficiency/Acquired Immunodeficiency Syndrome (HIV/AIDS) epidemic, despite recent encouraging announcements by the World Health Organization (WHO) is still today one of the world's major health care challenges.The present work lies in the field of health care management, in particular, we aim to evaluate the behavioural and non-behavioural interventions against HIV/AIDS in developing countries through a deterministic simulation model, both in human and economic terms. We will focus on assessing the effectiveness of the antiretroviral therapies (ART) in heterosexual populations living in lesser developed countries where the epidemic has generalized (formerly defined by the WHO as type II countries). The model is calibrated using Botswana as a case study, however our model can be adapted to other countries with similar transmission dynamics.The first part of this thesis consists of reviewing the main mathematical concepts describing the transmission of infectious agents in general but with a focus on human immunodeficiency virus (HIV) transmission. We also review deterministic models assessing HIV interventions with a focus on models aimed at African countries. This review helps us to recognize the need for a generic model and allows us to define a typical structure of such a generic deterministic model.The second part describes the main feed-back loops underlying the dynamics of HIV transmission. These loops represent the foundation of our model. This part also provides a detailed description of the model, including the various infected and non-infected population groups, the type of sexual relationships, the infection matrices, important factors impacting HIV transmission such as condom use, other sexually transmitted diseases (STD) and male circumcision. We also included in the model a dynamic life expectancy calculator which, to our knowledge, is a unique feature allowing more realistic cost-efficiency calculations. Various intervention scenarios are evaluated using the model, each of them including ART in combination with other interventions, namely: circumcision, campaigns aimed at behavioral change (Abstain, Be faithful or use Condoms also named ABC campaigns), and treatment of other STD. A cost efficiency analysis (CEA) is performed for each scenario. The CEA consists of measuring the cost per disability-adjusted life year (DALY) averted. This part also describes the model calibration and validation, including a sensitivity analysis.The third part reports the results and discusses the model limitations. In particular, we argue that the combination of ART and ABC campaigns and ART and treatment of other STDs are the most cost-efficient interventions through 2020. The main model limitations include modeling the complexity of sexual relationships, omission of international migration and ignoring variability in infectiousness according to the AIDS stage.The fourth part reviews the major contributions of the thesis and discusses model generalizability and flexibility. Finally, we conclude that by selecting the adequate interventions mix, policy makers can significantly reduce the adult prevalence in Botswana in the coming twenty years providing the country and its donors can bear the cost involved.Part I: Context and literature reviewIn this section, after a brief introduction to the general literature we focus in section two on the key mathematical concepts describing the transmission of infectious agents in general with a focus on HIV transmission. Section three provides a description of HIV policy models, with a focus on deterministic models. This leads us in section four to envision the need for a generic deterministic HIV policy model and briefly describe the structure of such a generic model applicable to countries with generalized HIV/AIDS epidemic, also defined as pattern II countries by the WHO.

A MODEL OF CREATINE DEFICIENCY SYNDROMES IN 3D BRAIN CELL CULTURES BY KNOCKDOWN OF GAMT AND SLC6A8 GENES

La créatine joue un rôle essentiel dans le métabolisme cellulaire par sa conversion, par la creatine kinase, en phosphocreatine permettant la régénération de l'ATP. La synthèse de créatine, chez les mammifères, s'effectue par une réaction en deux étapes impliquant Γ arginine: glycine amidinotransférase (AGAT) et la guanidinoacétate méthyltransférase (GAMT). L'entrée de créatine dans les cellules s'effectue par son transporteur, SLC6A8. Les déficiences en créatine, dues au déficit en GAMT, AGAT ou SLC6A8, sont fréquentes et caractérisées par une absence ou une forte baisse de créatine dans le système nerveux central. Alors qu'il est connu que AGAT, GAMT et SLC6A8 sont exprimés par le cerveau, les conséquences des déficiences en créatine sur les cellules nerveuses sont peu comprises. Le but de ce travail était de développer de nouveaux modèles expérimentaux des déficiences en Cr dans des cultures 3D de cellules nerveuses de rat en agrégats au moyen de l'interférence à l'ARN appliquée aux gènes GAMT et SLC6A8. Des séquences interférentes (shRNAs) pour les gènes GAMT et SLC6A8 ont été transduites par des vecteurs viraux AAV (virus adéno-associés), dans les cellules nerveuses en agrégats. Nous avons ainsi démontré une baisse de l'expression de GAMT au niveau protéique (mesuré par western blot), et ARN messager (mesuré par qPCR) ainsi qu'une variation caractérisitique de créatine et guanidinoacétate (mesuré par spectrométrie de masse). Après avoir validé nos modèles, nous avons montré que les knockdown de GAMT ou SLC6A8 affectent le développement des astrocytes et des neurones ou des oligodendrocytes et des astrocytes, respectivement, ainsi qu'une augmentation de la mort cellulaire et des modifications dans le pattern d'activation des voies de signalisation impliquant caspase 3 et p38 MAPK, ayant un rôle dans le processus d'apoptose. - Creatine plays essential roles in energy metabolism by the interconversion, by creatine kinase, to its phosphorylated analogue, phosphocreatine, allowing the regeneration of ATP. Creatine is synthesized in mammals by a two step mechanism involving arginine:glycine amidinotransferase (AGAT) and guanidinoacetate methyltransferase (GAMT). Creatine is taken up by cells by a specific transporter, SLC6A8. Creatine deficiency syndromes, due to defects in GAMT, AGAT and SLC6A8, are among the most frequent inborn errors of metabolism, and are characterized by an absence or a severe decrease of creatine in central nervous system, which is the main tissue affected. While it is known that AGAT, GAMT and SLC6A8 are expressed in CNS, many questions remain on the specific effects of AGAT, GAMT and SLC6A8 deficiencies on brain cells. Our aim was to develop new experimental models of creatine deficiencies by knockdown of GAMT and SLC6A8 genes by RNAi in 3D organotypic rat brain cell cultures in aggregates. Specific shRNAs for the GAMT and SLC6A8 genes were transduced in brain cell aggregates by adeno-associated viruses (AAV). The AAV-transduced shRNAs were able to efficiently knockdown the expression of our genes of interest, as shown by a strong decrease of protein by western blotting, a decrease of mRNA by qPCR or characteristic variations of creatine and guanidinoacetate by tandem mass spectrometry. After having validated our experimental models, we have also shown that GAMT and SLC6A8 knockdown affected the development of astrocytes and neurons or oligodendrocytes and astrocytes, respectively. We also observed an increase of cell death and variations in activation pattern of caspase 3 and p38 MAPK pathways, involved in apoptosis, in our experimental model.

On optimal learning schedules and the marginal value of cumulative cultural evolution.

The age-dependent choice between expressing individual learning (IL) or social learning (SL) affects cumulative cultural evolution. A learning schedule in which SL precedes IL is supportive of cumulative culture because the amount of nongenetically encoded adaptive information acquired by previous generations can be absorbed by an individual and augmented. Devoting time and energy to learning, however, reduces the resources available for other life-history components. Learning schedules and life history thus coevolve. Here, we analyze a model where individuals may have up to three distinct life stages: "infants" using IL or oblique SL, "juveniles" implementing IL or horizontal SL, and adults obtaining material resources with learned information. We study the dynamic allocation of IL and SL within life stages and how this coevolves with the length of the learning stages. Although no learning may be evolutionary stable, we find conditions where cumulative cultural evolution can be selected for. In that case, the evolutionary stable learning schedule causes individuals to use oblique SL during infancy and a mixture between IL and horizontal SL when juvenile. We also find that the selected pattern of oblique SL increases the amount of information in the population, but horizontal SL does not do so.

Selective neurodegeneration induced in rotation-mediated aggregate cell cultures by a transient switch to stationary culture conditions: a potential model to study ischemia-related pathogenic mechanisms.

Aggregating brain cell cultures at an advanced maturational stage (20-21 days in vitro) were subjected for 1-3 h to anaerobic (hypoxic) and/or stationary (ischemic) conditions. After restoration of the normal culture conditions, cell loss was estimated by measuring the release of lactate dehydrogenase as well as the irreversible decrease of cell type-specific enzyme activities, total protein and DNA content. Ischemia for 2 h induced significant neuronal cell death. Hypoxia combined with ischemia affected both neuronal and glial cells to different degrees (GABAergic neurons>cholinergic neurons>astrocytes). Hypoxic and ischemic conditions greatly stimulated the uptake of 2-deoxy-D-glucose, indicating increased glucose consumption. Furthermore, glucose restriction (5.5 mM instead of 25 mM) dramatically increased the susceptibility of neuronal and glial cells to hypoxic and ischemic conditions. Glucose media concentrations below 2 mM caused selective neuronal cell death in otherwise normal culture conditions. GABAergic neurons showed a particularly high sensitivity to glucose restriction, hypoxia, and ischemia. The pattern of ischemia-induced changes in vitro showed many similarities to in vivo findings, suggesting that aggregating brain cell cultures provide a useful in vitro model to study pathogenic mechanisms related to brain ischemia.

Bristle pattern formation in the thorax formation of Drosophila: a systems level approach

A fundamental question in developmental biology is how tissues are patterned to give rise to differentiated body structures with distinct morphologies. The Drosophila wing disc offers an accessible model to understand epithelial spatial patterning. It has been studied extensively using genetic and molecular approaches. Bristle patterns on the thorax, which arise from the medial part of the wing disc, are a classical model of pattern formation, dependent on a pre-pattern of trans-activators and –repressors. Despite of decades of molecular studies, we still only know a subset of the factors that determine the pre-pattern. We are applying a novel and interdisciplinary approach to predict regulatory interactions in this system. It is based on the description of expression patterns by simple logical relations (addition, subtraction, intersection and union) between simple shapes (graphical primitives). Similarities and relations between primitives have been shown to be predictive of regulatory relationships between the corresponding regulatory factors in other Systems, such as the Drosophila egg. Furthermore, they provide the basis for dynamical models of the bristle-patterning network, which enable us to make even more detailed predictions on gene regulation and expression dynamics. We have obtained a data-set of wing disc expression patterns which we are now processing to obtain average expression patterns for each gene. Through triangulation of the images we can transform the expression patterns into vectors which can easily be analysed by Standard clustering methods. These analyses will allow us to identify primitives and regulatory interactions. We expect to identify new regulatory interactions and to understand the basic Dynamics of the regulatory network responsible for thorax patterning. These results will provide us with a better understanding of the rules governing gene regulatory networks in general, and provide the basis for future studies of the evolution of the thorax-patterning network in particular.

From a pattern-language to a pattern ontology approach for CSCL script design

Collaborative activities, in which students actively interact with each other, have proved to provide significant learning benefits. In Computer-Supported Collaborative Learning (CSCL), these collaborative activities are assisted by technologies. However, the use of computers does not guarantee collaboration, as free collaboration does not necessary lead to fruitful learning. Therefore, practitioners need to design CSCL scripts that structure the collaborative settings so that they promote learning. However, not all teachers have the technical and pedagogical background needed to design such scripts. With the aim of assisting teachers in designing effective CSCL scripts, we propose a model to support the selection of reusable good practices (formulated as patterns) so that they can be used as a starting point for their own designs. This model is based on a pattern ontology that computationally represents the knowledge captured on a pattern language for the design of CSCL scripts. A preliminary evaluation of the proposed approach is provided with two examples based on a set of meaningful interrelated patters computationally represented with the pattern ontology, and a paper prototyping experience carried out with two teaches. The results offer interesting insights towards the implementation of the pattern ontology in software tools.

Radioimmunotherapy of human colon carcinoma by 131I-labelled monoclonal anti-CEA antibodies in a nude mouse model.

A mixture of 3 MAbs directed against 3 different CEA epitopes was radiolabelled with 131I and used for the treatment of a human colon carcinoma transplanted s.c. into nude mice. Intact MAbs and F(ab')2 fragments were mixed because it had been shown by autoradiography that these 2 antibody forms can penetrate into different areas of the tumor nodule. Ten days after transplantation of colon tumor T380 a single dose of 600 microCi of 131I MAbs was injected i.v. The tumor grafts were well established (as evidenced by exponential growth in untreated mice) and their size continued to increase up to 6 days after radiolabelled antibody injection. Tumor shrinking was then observed lasting for 4-12 weeks. In a control group injected with 600 microCi of 131I coupled to irrelevant monoclonal IgG, tumor growth was delayed, but no regression was observed. Tumors of mice injected with the corresponding amount of unlabelled antibodies grew like those of untreated mice. Based on measurements of the effective whole-body half-life of injected 131I, the mean radiation dose received by the animals was calculated to be 382 rads for the antibody group and 478 rads for the normal IgG controls. The genetically immunodeficient animals exhibited no increase in mortality, and only limited bone-marrow toxicity was observed. Direct measurement of radioactivity in mice dissected 1, 3 and 7 days after 131I-MAb injection showed that 25, 7.2 and 2.2% of injected dose were recovered per gram of tumor, the mean radiation dose delivered to the tumor being thus more than 5,000 rads. These experiments show that therapeutic doses of radioactivity can be selectively directed to human colon carcinoma by i.v. injection of 131I-labelled anti-CEA MAbs.

A dynamic choice model of hybrid behavior in the attribute-space

This paper presents a dynamic choice model in the attributespace considering rational consumers that discount the future. In lightof the evidence of several state-dependence patterns, the model isfurther extended by considering a utility function that allows for thedifferent types of behavior described in the literature: pure inertia,pure variety seeking and hybrid. The model presents a stationaryconsumption pattern that can be inertial, where the consumer only buysone product, or a variety-seeking one, where the consumer buys severalproducts simultane-ously. Under the inverted-U marginal utilityassumption, the consumer behaves inertial among the existing brands forseveral periods, and eventually, once the stationary levels areapproached, the consumer turns to a variety-seeking behavior. An empiricalanalysis is run using a scanner database for fabric softener andsignificant evidence of hybrid behavior for most attributes is found,which supports the functional form considered in the theory.

Hybrid consumption paths in the attribute space: A model and application with scanner data

This paper presents and estimates a dynamic choice model in the attribute space considering rational consumers. In light of the evidence of several state-dependence patterns, the standard attribute-based model is extended by considering a general utility function where pure inertia and pure variety-seeking behaviors can be explained in the model as particular linear cases. The dynamics of the model are fully characterized by standard dynamic programming techniques. The model presents a stationary consumption pattern that can be inertial, where the consumer only buys one product, or a variety-seeking one, where the consumer shifts among varied products.We run some simulations to analyze the consumption paths out of the steady state. Underthe hybrid utility assumption, the consumer behaves inertially among the unfamiliar brandsfor several periods, eventually switching to a variety-seeking behavior when the stationary levels are approached. An empirical analysis is run using scanner databases for three different product categories: fabric softener, saltine cracker, and catsup. Non-linear specifications provide the best fit of the data, as hybrid functional forms are found in all the product categories for most attributes and segments. These results reveal the statistical superiority of the non-linear structure and confirm the gradual trend to seek variety as the level of familiarity with the purchased items increases.

Adaptive model selection using empirical complexities

Given $n$ independent replicates of a jointly distributed pair $(X,Y)\in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessingthe {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk.The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover.Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent,and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension.For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.

Generation of the primary hair follicle pattern.

Hair follicles are spaced apart from one another at regular intervals through the skin. Although follicles are predominantly epidermal structures, classical tissue recombination experiments indicated that the underlying dermis defines their location during development. Although many molecules involved in hair follicle formation have been identified, the molecular interactions that determine the emergent property of pattern formation have remained elusive. We have used embryonic skin cultures to dissect signaling responses and patterning outcomes as the skin spatially organizes itself. We find that ectodysplasin receptor (Edar)-bone morphogenetic protein (BMP) signaling and transcriptional interactions are central to generation of the primary hair follicle pattern, with restriction of responsiveness, rather than localization of an inducing ligand, being the key driver in this process. The crux of this patterning mechanism is rapid Edar-positive feedback in the epidermis coupled with induction of dermal BMP4/7. The BMPs in turn repress epidermal Edar and hence follicle fate. Edar activation also induces connective tissue growth factor, an inhibitor of BMP signaling, allowing BMP action only at a distance from their site of synthesis. Consistent with this model, transgenic hyperactivation of Edar signaling leads to widespread overproduction of hair follicles. This Edar-BMP activation-inhibition mechanism appears to operate alongside a labile prepattern, suggesting that Edar-mediated stabilization of beta-catenin active foci is a key event in determining definitive follicle locations.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical {\sc vc} dimension, empirical {\sc vc} entropy, andmargin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Atlas-based segmentation of pathological MR brain images using a model of lesion growth.

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.

Comparison of the polynomial model against explicit measurements of noise components for different mammography systems.

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 µGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed.Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure.