Gene expression profiling in human pathogenic dermatophytes in vitro and in vivo

Objectives: Dermatophytes are highly specialized fungi which are the most common agents of superficial mycoses in humans and animals. The particular ability of these microorganisms to invade and multiply within keratinized host structures is presumably linked to their secreted keratinolytic activity, which is therefore a major putative virulence attribute of these fungi. The overall adaptation and transcriptional response of dermatophytes during protein degradation and/or infection is largely unknown. Methods: A Trichophyton rubrum cDNA microarray was developed and used for the transcriptional analysis of T. rubrum and Arthroderma benhamiae cells during growth on protein substrates. Moreover, the gene expression profile in A. benhamiae cells was monitored during infection of guinea pigs. Results: T. rubrum and A. benhamiae cells activate a large set of genes encoding secreted endo- and exoproteases during growth on soy and keratin. In addition, other specifically induced factors with potential implication in protein utilization were identified, e.g. multiple transporters, metabolic enzymes, transcription factors and hypothetical proteins with unknown function. Notably however, the protease gene expression profile in the fungal cells during infection was significantly different from the pattern elicited during in vitro growth on keratin. Conclusions: Our results suggest specific functions of individual proteases during infection, which may not be restricted to the degradation of keratin. This first, broad in vivo transcriptional profiling approach in dermatophytes gives new molecular insights into pathogenicity associated adaptation mechanisms that make these microorganisms the most successful causitive agents of superficial mycoses.

Behavioural queueing : cellular automata and a laboratory experiment

Dans cette thèse, nous étudions les aspects comportementaux d'agents qui interagissent dans des systèmes de files d'attente à l'aide de modèles de simulation et de méthodologies expérimentales. Chaque période les clients doivent choisir un prestataire de servivce. L'objectif est d'analyser l'impact des décisions des clients et des prestataires sur la formation des files d'attente. Dans un premier cas nous considérons des clients ayant un certain degré d'aversion au risque. Sur la base de leur perception de l'attente moyenne et de la variabilité de cette attente, ils forment une estimation de la limite supérieure de l'attente chez chacun des prestataires. Chaque période, ils choisissent le prestataire pour lequel cette estimation est la plus basse. Nos résultats indiquent qu'il n'y a pas de relation monotone entre le degré d'aversion au risque et la performance globale. En effet, une population de clients ayant un degré d'aversion au risque intermédiaire encoure généralement une attente moyenne plus élevée qu'une population d'agents indifférents au risque ou très averses au risque. Ensuite, nous incorporons les décisions des prestataires en leur permettant d'ajuster leur capacité de service sur la base de leur perception de la fréquence moyenne d'arrivées. Les résultats montrent que le comportement des clients et les décisions des prestataires présentent une forte "dépendance au sentier". En outre, nous montrons que les décisions des prestataires font converger l'attente moyenne pondérée vers l'attente de référence du marché. Finalement, une expérience de laboratoire dans laquelle des sujets jouent le rôle de prestataire de service nous a permis de conclure que les délais d'installation et de démantèlement de capacité affectent de manière significative la performance et les décisions des sujets. En particulier, les décisions du prestataire, sont influencées par ses commandes en carnet, sa capacité de service actuellement disponible et les décisions d'ajustement de capacité qu'il a prises, mais pas encore implémentées. - Queuing is a fact of life that we witness daily. We all have had the experience of waiting in line for some reason and we also know that it is an annoying situation. As the adage says "time is money"; this is perhaps the best way of stating what queuing problems mean for customers. Human beings are not very tolerant, but they are even less so when having to wait in line for service. Banks, roads, post offices and restaurants are just some examples where people must wait for service. Studies of queuing phenomena have typically addressed the optimisation of performance measures (e.g. average waiting time, queue length and server utilisation rates) and the analysis of equilibrium solutions. The individual behaviour of the agents involved in queueing systems and their decision making process have received little attention. Although this work has been useful to improve the efficiency of many queueing systems, or to design new processes in social and physical systems, it has only provided us with a limited ability to explain the behaviour observed in many real queues. In this dissertation we differ from this traditional research by analysing how the agents involved in the system make decisions instead of focusing on optimising performance measures or analysing an equilibrium solution. This dissertation builds on and extends the framework proposed by van Ackere and Larsen (2004) and van Ackere et al. (2010). We focus on studying behavioural aspects in queueing systems and incorporate this still underdeveloped framework into the operations management field. In the first chapter of this thesis we provide a general introduction to the area, as well as an overview of the results. In Chapters 2 and 3, we use Cellular Automata (CA) to model service systems where captive interacting customers must decide each period which facility to join for service. They base this decision on their expectations of sojourn times. Each period, customers use new information (their most recent experience and that of their best performing neighbour) to form expectations of sojourn time at the different facilities. Customers update their expectations using an adaptive expectations process to combine their memory and their new information. We label "conservative" those customers who give more weight to their memory than to the xiv Summary new information. In contrast, when they give more weight to new information, we call them "reactive". In Chapter 2, we consider customers with different degree of risk-aversion who take into account uncertainty. They choose which facility to join based on an estimated upper-bound of the sojourn time which they compute using their perceptions of the average sojourn time and the level of uncertainty. We assume the same exogenous service capacity for all facilities, which remains constant throughout. We first analyse the collective behaviour generated by the customers' decisions. We show that the system achieves low weighted average sojourn times when the collective behaviour results in neighbourhoods of customers loyal to a facility and the customers are approximately equally split among all facilities. The lowest weighted average sojourn time is achieved when exactly the same number of customers patronises each facility, implying that they do not wish to switch facility. In this case, the system has achieved the Nash equilibrium. We show that there is a non-monotonic relationship between the degree of risk-aversion and system performance. Customers with an intermediate degree of riskaversion typically achieve higher sojourn times; in particular they rarely achieve the Nash equilibrium. Risk-neutral customers have the highest probability of achieving the Nash Equilibrium. Chapter 3 considers a service system similar to the previous one but with risk-neutral customers, and relaxes the assumption of exogenous service rates. In this sense, we model a queueing system with endogenous service rates by enabling managers to adjust the service capacity of the facilities. We assume that managers do so based on their perceptions of the arrival rates and use the same principle of adaptive expectations to model these perceptions. We consider service systems in which the managers' decisions take time to be implemented. Managers are characterised by a profile which is determined by the speed at which they update their perceptions, the speed at which they take decisions, and how coherent they are when accounting for their previous decisions still to be implemented when taking their next decision. We find that the managers' decisions exhibit a strong path-dependence: owing to the initial conditions of the model, the facilities of managers with identical profiles can evolve completely differently. In some cases the system becomes "locked-in" into a monopoly or duopoly situation. The competition between managers causes the weighted average sojourn time of the system to converge to the exogenous benchmark value which they use to estimate their desired capacity. Concerning the managers' profile, we found that the more conservative Summary xv a manager is regarding new information, the larger the market share his facility achieves. Additionally, the faster he takes decisions, the higher the probability that he achieves a monopoly position. In Chapter 4 we consider a one-server queueing system with non-captive customers. We carry out an experiment aimed at analysing the way human subjects, taking on the role of the manager, take decisions in a laboratory regarding the capacity of a service facility. We adapt the model proposed by van Ackere et al (2010). This model relaxes the assumption of a captive market and allows current customers to decide whether or not to use the facility. Additionally the facility also has potential customers who currently do not patronise it, but might consider doing so in the future. We identify three groups of subjects whose decisions cause similar behavioural patterns. These groups are labelled: gradual investors, lumpy investors, and random investor. Using an autocorrelation analysis of the subjects' decisions, we illustrate that these decisions are positively correlated to the decisions taken one period early. Subsequently we formulate a heuristic to model the decision rule considered by subjects in the laboratory. We found that this decision rule fits very well for those subjects who gradually adjust capacity, but it does not capture the behaviour of the subjects of the other two groups. In Chapter 5 we summarise the results and provide suggestions for further work. Our main contribution is the use of simulation and experimental methodologies to explain the collective behaviour generated by customers' and managers' decisions in queueing systems as well as the analysis of the individual behaviour of these agents. In this way, we differ from the typical literature related to queueing systems which focuses on optimising performance measures and the analysis of equilibrium solutions. Our work can be seen as a first step towards understanding the interaction between customer behaviour and the capacity adjustment process in queueing systems. This framework is still in its early stages and accordingly there is a large potential for further work that spans several research topics. Interesting extensions to this work include incorporating other characteristics of queueing systems which affect the customers' experience (e.g. balking, reneging and jockeying); providing customers and managers with additional information to take their decisions (e.g. service price, quality, customers' profile); analysing different decision rules and studying other characteristics which determine the profile of customers and managers.

Clinical Prognostic Factors Predicting Outcomes Of Patients With Recurrent Gbm And Nomograms

BACKGROUND: Prognostic models and nomograms were recently developed to predict survival of patients with newly diagnosed glioblastoma multiforme (GBM).1 To improve predictions, models should be updated with the most recent patient and disease information. Nomograms predicting patient outcome at the time of disease progression are required. METHODS: Baseline information from 299 patients with recurrent GBM recruited in 8 phase I or II trials of the EORTC Brain Tumor Group was used to evaluate clinical parameters as prognosticators of patient outcome. Univariate (log rank) and multivariate (Cox models) analyses were made to assess the ability of patients' characteristics (age, sex, performance status [WHO PS], and MRC neurological deficit scale), disease history (prior treatments, time since last treatment or initial diagnosis, and administration of steroids or antiepileptics) and disease characteristics (tumor size and number of lesions) to predict progression free survival (PFS) and overall survival (OS). Bootstrap technique was used for models internal validation. Nomograms were computed to provide individual patients predictions. RESULTS: Poor PS and more than 1 lesion had a significant prognostic impact for both PFS and OS. Antiepileptic drug use was significantly associated with worse PFS. Larger tumors (split by the median of the largest tumor diameter >42.5 mm) and steroid use had shorter OS. Age, sex, neurologic deficit, prior therapies, and time since last therapy or initial diagnosis did not show independent prognostic value for PFS or OS. CONCLUSIONS: This analysis confirms that PS but not age is a major prognostic factor for PFS and OS. Multiple or large tumors and the need to administer steroids significantly increase the risk of progression and death. Nomograms at the recurrence could be used to obtain accurate predictions for the design of new targeted therapy trials or retrospective analyses. (1. T. Gorlia et al., Nomograms for predicting survival of patients with newly diagnosed glioblastoma. Lancet Oncol 9 (1): 29-38, 2008.)

Minimal information about T cell assays: the process of reaching the community of T cell immunologists in cancer and beyond.

Many assays to evaluate the nature, breadth, and quality of antigen-specific T cell responses are currently applied in human medicine. In most cases, assay-related protocols are developed on an individual laboratory basis, resulting in a large number of different protocols being applied worldwide. Together with the inherent complexity of cellular assays, this leads to unnecessary limitations in the ability to compare results generated across institutions. Over the past few years a number of critical assay parameters have been identified which influence test performance irrespective of protocol, material, and reagents used. Describing these critical factors as an integral part of any published report will both facilitate the comparison of data generated across institutions and lead to improvements in the assays themselves. To this end, the Minimal Information About T Cell Assays (MIATA) project was initiated. The objective of MIATA is to achieve a broad consensus on which T cell assay parameters should be reported in scientific publications and to propose a mechanism for reporting these in a systematic manner. To add maximum value for the scientific community, a step-wise, open, and field-spanning approach has been taken to achieve technical precision, user-friendliness, adequate incorporation of concerns, and high acceptance among peers. Here, we describe the past, present, and future perspectives of the MIATA project. We suggest that the approach taken can be generically applied to projects in which a broad consensus has to be reached among scientists working in fragmented fields, such as immunology. An additional objective of this undertaking is to engage the broader scientific community to comment on MIATA and to become an active participant in the project.

Selection via simulated individual BLUP based on family genotypic effects in sugarcane

The objective of this work was to propose a new selection strategy for the initial stages of sugarcane improvement, based on the methodology 'simulated individual BLUP (BLUPIS)', which promotes a dynamic allocation of individuals selected in each full-sib family, using BLUP as a base for both the genotypic effects of the referred families and plot effects. The method proposed applies to single full-sib families or those obtained from unbalanced or balanced diallel crosses, half-sib families and self-pollinated families. BLUPIS indicates the number of individuals to be selected within each family, the total number of clones to be advanced, and the number of families to contribute with selected individuals. Correlation between BLUPIS and true BLUP was 0.96, by method validation. Additionally, BLUPIS allows the identification of which replication contains the best individuals of each family.

Development of stable cell lines for production or regulated expression using matrix attachment regions.

One of the major hurdles of isolating stable, inducible or constitutive high-level producer cell lines is the time-consuming selection procedure. Given the variation in the expression levels of the same construct in individual clones, hundreds of clones must be isolated and tested to identify one or more with the desired characteristics. Various boundary elements (BEs), matrix attachment regions, and locus control regions (LCRs) were screened for their ability to augment the expression of heterologous genes in Chinese hamster ovary (CHO) cells. Of the chromatin elements assayed, the chicken lysozyme matrix-attachment region (MAR) was the only element to significantly increase stable reporter expression. We found that the use of the MAR increases the proportion of high-producing clones, thus reducing the number of clones that need to be screened. These benefits are observed both for constructs with MARs flanking the transgene expression cassette, as well as when constructs are co-transfected with the MAR on a separate plasmid. Moreover, the MAR was co-transfected with a multicomponent regulatable beta-galactosidase expression system in C2C12 cells and several clones exhibiting regulated expression were identified. Hence, MARs are useful in the development of stable cell lines for production or regulated expression.

Intravitreal ranibizumab for AMD : the individual rhythm of OCT guided retreatment intervals

Purpose: We intended to determine whether the need for retreatmentwith intravitreal ranibizumab follows an individual rhythm in patientswith exudative AMD. Setting: Prospective mono-centre cohort study.Methods: Prospective study. 48 patients with exudative AMD. 3 loadingdoses of ranibizumab, followed by a 12 months PRN regimen guided byearly exudative signs on SD-OCT. An intensified follow-up allowed todetect recurrences early.Results:Mean VA improved by 6.4 letters at month 3 and by 13.1 letters atmonth 12, with a mean of 8.0 injections (range 0-12) during the maintenancephase. The intra-individual variance of the intervals was relativelysmall and ranged within 20% of themean interval in 91% of patients.Thefirst interval was within 1 week of the mean interval in 84% of patients.The retreatment criteria were stable in 89% of patients.Conclusion: The relative stability of the intra-individual intervalsmay allowsimplifying the care for AMDpatients.Theremay be a predictive role for thefirst interval after the loading phase. The functional results of this PRNregimenwith early retreatmentwere excellent. Financial disclosure:None.

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.

quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation.

quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo.

Agents anti-infectieux et fonction réna!e: vers des posologies sur mesure [Antimicrobial agents and renal elimination: towards individual dosage adjustment?].

The efficacy and safety of anti-infective treatments are associated with the drug blood concentration profile, which is directly correlated with a dosing adjustment to the individual patient's condition. Dosing adjustments to the renal function recommended in reference books are often imprecise and infrequently applied in clinical practice. The recent generalisation of the KDOQI (Kidney Disease Outcome Quality Initiative) staging of chronically impaired renal function represents an opportunity to review and refine the dosing recommendations in patients with renal insufficiency. The literature has been reviewed and compared to a predictive model of the fraction of drug cleared by the kidney based on the Dettli's principle. Revised drug dosing recommendations integrating these predictive parameters are proposed.

Nutrigenomics and nutrigenetics: the emerging faces of nutrition.

The recognition that nutrients have the ability to interact and modulate molecular mechanisms underlying an organism's physiological functions has prompted a revolution in the field of nutrition. Performing population-scaled epidemiological studies in the absence of genetic knowledge may result in erroneous scientific conclusions and misinformed nutritional recommendations. To circumvent such issues and more comprehensively probe the relationship between genes and diet, the field of nutrition has begun to capitalize on both the technologies and supporting analytical software brought forth in the post-genomic era. The creation of nutrigenomics and nutrigenetics, two fields with distinct approaches to elucidate the interaction between diet and genes but with a common ultimate goal to optimize health through the personalization of diet, provide powerful approaches to unravel the complex relationship between nutritional molecules, genetic polymorphisms, and the biological system as a whole. Reluctance to embrace these new fields exists primarily due to the fear that producing overwhelming quantities of biological data within the confines of a single study will submerge the original query; however, the current review aims to position nutrigenomics and nutrigenetics as the emerging faces of nutrition that, when considered with more classical approaches, will provide the necessary stepping stones to achieve the ambitious goal of optimizing an individual's health via nutritional intervention.

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.