Soluble sugars and germination of Annona emarginata (Schltdl.) H. Rainer seeds submitted to immersion in GA3 up to different water contents

The objective of this study was to evaluate the effect of different water contents achieved by Annona emarginata (Schltdl.) H. Rainer seeds during immersion in GA3 solutions, in variation of soluble sugars levels and germination. Seeds with 10% of initial water content were submitted to imbibition in GA3 solutions with concentrations of 0; 250; 500; 750 and 1000 mg L-1 and when they reached the water content of 15%, 20%, 25%, 30% and 35%, the quantification of soluble sugars levels and germination test were performed. Seeds immersed up to they reach 15% of water with GA3 and immersed up to the water acquisition of 20% without GA3, presented higher soluble sugars levels and germination percentage, which were decreased when the seeds reached 30% and 35% of water, independently of the presence of the plant growth regulator. It was conclude that different water contents reached by the seeds in immersion treatments with GA3 affect the soluble sugars levels and germination percentage of Annona emarginata seeds. Thus, in treatments with Annona emarginata, the seeds must remain immersed in water without GA3 up to they reach 20% of water, as higher water contents (35%) reduce the soluble sugars levels and the seed germination percentage.

Impact on costs of a case management intervention for emergency department frequent users

Background: In most of the emergency departments (ED) in developed countries, a subset of patients visits the ED frequently. Despite their small numbers, these patients are the source of a disproportionally high number of all ED visits, and use a significant proportion of healthcare resources. They place a heavy economic burden on hospital and healthcare system budgets overall. In order to improve the management of these patients, the University hospital of Lausanne, Switzerland implemented a case management intervention (CM) between May 2012 and July 2013. In this randomized controlled trial, 250 frequent ED users (visits>5 during previous 12 months) were allocated to either the CM group or the standard ED care (SC) group and followed up for 12 months. The first result of the CM was to reduce significantly the ED visits. The present study examined whether the CM intervention also reduced the costs generated by the ED frequent users not only from the hospital perspective, but also from the healthcare system perspective. Methods: Cost data were obtained from the hospital's analytical accounting system and from health insurances. Multivariate linear models including a fixed effect "group" and socio-demographic characteristics and health-related variables were run.

The ramp-up of new products in the outbound logistics

Tässä diplomityössä on perehdytty kaaoksen ja oppivan organisaation mekaniikkaan ja olemukseen. Niitä on sovellettu uuden tuotteen saattamiseen logistiikan toimitusketjuun Nokia Oyj:n Networks toimialaryhmässä. Samalla on pyritty löytämään ratkaisuja toiminnan parantamiseksi. Kaaosjohtaminen tarjoaa prosessijohtamista täydentävän tavan mallintaa muutosta ja selittää sen aikaansaamia ilmiöitä. Näiden kahden viitekehyksen avulla on pyritty pureutumaan laajaan ja moniulotteiseen ongelmakenttään. Muotoaan hakevassa organisaatiossa kaaoksen elementit ovat voimakkaasti läsnä. Kaaoksen hallintaan liittyy saumattomasti itseorganisoituminen ja oppiminen. Oppinen ei ole organisaatiolle yhtä luontainen kyky kuin se on yksilölle. Se vaatii organisaation jäseniltä oman roolinsa tiedostamista ja tiedon jakamista. Organisaation tavoitteet saattavat yksilön näkökulmasta tuntua joskus epäkonkreettisilta ja samalla muutokset hallitsemattomilta. Markkinoiden ja teknologian muuttuminen on ollut erityisen nopeaa telekommunikaatioalalla. Näissä olosuhteissa on tärkeää pyrkiä oppivaan organisaatioon, joka tukee organisaation toimintaa läpi muutoksen.

A Reappraisal of the Allocation Puzzle through the Portfolio Approach

Paradoxically, high-growth, high-investment developing countries tend to experience capital outflows. This paper shows that this allocation puzzle can be explained simply by introducing uninsurable idiosyncratic investment risk in the neoclassical growth model with international trade in bonds, and by taking into account not only TFP catch-up, but also the capital wedge, that is, the distortions on the return to capital. The model fits the two following facts, documented on a sample of 67 countries between 1980 and 2003: (i) TFP growth is positively correlated with capital outflows in a sample including creditor countries; (ii) the long-run level of capital per efficient unit of labor is positively correlated with capital outflows. Consistently, we show that the capital flows predicted by the model are positively correlated with the actual ones in this sample once the capital wedge is accounted for. The fact that Asia dominates global imbalances can be explained by its relatively low capital wedge.

A phase IIa randomized clinical study testing GNbAC1, a humanized monoclonal antibody against the envelope protein of multiple sclerosis associated endogenous retrovirus in multiple sclerosis patients - A twelve month follow-up.

GNbAC1 is a humanized monoclonal antibody targeting MSRV-Env, an endogenous retroviral protein, which is expressed in multiple sclerosis (MS) lesions, is pro-inflammatory and inhibits oligodendrocyte precursor cell differentiation. This paper describes the open-label extension up to 12months of a trial testing GNbAC1 in 10 MS patients at 2 and 6mg/kg. The primary objective was to assess GNbAC1 safety, and other objectives were pharmacokinetic and pharmacodynamic assessments. During the extended study, no safety issues occurred in the 8 remaining patients. No anti-GNbAC1 antibodies were detected. GNbAC1 appears well tolerated.

Caloric restriction induces energy-sparing alterations in skeletal muscle contraction, fiber composition and local thyroid hormone metabolism that persist during catch-up fat upon refeeding.

Weight regain after caloric restriction results in accelerated fat storage in adipose tissue. This catch-up fat phenomenon is postulated to result partly from suppressed skeletal muscle thermogenesis, but the underlying mechanisms are elusive. We investigated whether the reduced rate of skeletal muscle contraction-relaxation cycle that occurs after caloric restriction persists during weight recovery and could contribute to catch-up fat. Using a rat model of semistarvation-refeeding, in which fat recovery is driven by suppressed thermogenesis, we show that contraction and relaxation of leg muscles are slower after both semistarvation and refeeding. These effects are associated with (i) higher expression of muscle deiodinase type 3 (DIO3), which inactivates tri-iodothyronine (T3), and lower expression of T3-activating enzyme, deiodinase type 2 (DIO2), (ii) slower net formation of T3 from its T4 precursor in muscles, and (iii) accumulation of slow fibers at the expense of fast fibers. These semistarvation-induced changes persisted during recovery and correlated with impaired expression of transcription factors involved in slow-twitch muscle development. We conclude that diminished muscle thermogenesis following caloric restriction results from reduced muscle T3 levels, alteration in muscle-specific transcription factors, and fast-to-slow fiber shift causing slower contractility. These energy-sparing effects persist during weight recovery and contribute to catch-up fat.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Incidence and consequence of major bleeding in primary percutaneous intervention for ST-elevation myocardial infarction in the era of radial access: an analysis of the international randomized Acute myocardial infarction Treated with primary angioplasty and intravenous enoxaparin Or unfractionated heparin to Lower ischemic and bleeding events at short- and Long-term follow-up trial.

AIMS: The aims of the study are to compare the outcome with and without major bleeding and to identify the independent correlates of major bleeding complications and mortality in patients described in the ATOLL study. METHODS: The ATOLL study included 910 patients randomly assigned to either 0.5 mg/kg intravenous enoxaparin or unfractionated heparin before primary percutaneous coronary intervention. Incidence of major bleeding and ischemic end points was assessed at 1 month, and mortality, at 1 and 6 months. Patients with and without major bleeding complication were compared. A multivariate model of bleeding complications at 1 month and mortality at 6 months was realized. Intention-to-treat and per-protocol analyses were performed. RESULTS: The most frequent bleeding site appears to be the gastrointestinal tract. Age >75 years, cardiac arrest, and the use of insulin or >1 heparin emerged as independent correlates of major bleeding at 1 month. Patients presenting with major bleeding had significantly higher rates of adverse ischemic complications. Mortality at 6 months was higher in bleeders. Major bleeding was found to be one of the independent correlates of 6-month mortality. The addition or mixing of several anticoagulant drugs was an independent factor of major bleeding despite the predominant use of radial access. CONCLUSIONS: This study shows that major bleeding is independently associated with poor outcome, increasing ischemic events, and mortality in primary percutaneous coronary intervention performed mostly with radial access.

Cancer testiculaire: un modèle pour optimiser le suivi radiologique [Testicular cancer: a model to optimize the radiological follow-up].

Le cancer testiculaire, bien que peu fréquent, revêt une importance particulière en oncologie ; il représente actuellement un modèle pour optimiser un suivi radiologique tout en essayant de diminuer l'apparition de tumeurs radio-induites.En effet, cette pathologie présente un taux très élevé de survie nécessitant, au vu du jeune âge des patients, des bilans radiologiques à long terme, auxquels pourront être liés des effets secondaires, en particulier les tumeurs secondaires.Afin de diminuer cela, les recommandations de prise en charge ont évolué et les protocoles de radiologie s'améliorent afin d'exposer à moins de rayonnements ionisants pour un résultat identique.Il est donc devenu primordial de maintenir un suivi optimal tout en essayant d'en minimiser la toxicité. Despite being rare cancers, testicular seminoma and non-seminoma play an important role in oncology: they represent a model on how to optimize radiological follow-up, aiming at a lowest possible radiation exposure and secondary cancer risk. Males diagnosed with testicular cancer undergo frequently prolonged follow-up with CT-scans with potential toxic side effects, in particular secondary cancers. To reduce the risks linked to ionizing radiation, precise follow-up protocols have been developed. The number of recommended CT-scanners has been significantly reduced over the last 10 years. The CT scanners have evolved technically and new acquisition protocols have the potential to reduce the radiation exposure further.

The specificity of the familial aggregation of early-onset bipolar disorder: A controlled 10-year follow-up study of offspring of parents with mood disorders.

BACKGROUND: Two major sources of heterogeneity of mood disorders that have been demonstrated in clinical, family and genetic studies are the mood disorder subtype (i.e. bipolar (BPD) and major depressive disorder (MDD)) and age of onset of mood episodes. Using a prospective high-risk study design, our aims were to test the specificity of the parent-child transmission of BPD and MDD and to establish the risk of psychopathology in offspring in function of the age of onset of the parental disorder. METHODS: Clinical information was collected on 208 probands (n=81 with BPD, n=64 with MDD, n=63 medical controls) as well as their 202 spouses and 372 children aged 6-17 years at study entry. Parents and children were directly interviewed every 3 years (mean duration of follow-up=10.6 years). Parental age of onset was dichotomized at age 21. RESULTS: Offspring of parents with early onset BPD entailed a higher risk of BPD HR=7.9(1.8-34.6) and substance use disorders HR=5.0(1.1-21.9) than those with later onset and controls. Depressive disorders were not significantly increased in offspring regardless of parental mood disorder subtype or age of onset. LIMITATIONS: Limited sample size, age of onset in probands was obtained retrospectively, age of onset in co-parents was not adequately documented, and a quarter of the children had no direct interview. CONCLUSIONS: Our results provide support for the independence of familial aggregation of BPD from MDD and the heterogeneity of BPD based on patterns of onset. Future studies should further investigate correlates of early versus later onset BPD.