Association of Hematological Variables with Team-Sport Specific Fitness Performance.

PURPOSE: We investigated association of hematological variables with specific fitness performance in elite team-sport players. METHODS: Hemoglobin mass (Hbmass) was measured in 25 elite field hockey players using the optimized (2 min) CO-rebreathing method. Hemoglobin concentration ([Hb]), hematocrit and mean corpuscular hemoglobin concentration (MCHC) were analyzed in venous blood. Fitness performance evaluation included a repeated-sprint ability (RSA) test (8 x 20 m sprints, 20 s of rest) and the Yo-Yo intermittent recovery level 2 (YYIR2). RESULTS: Hbmass was largely correlated (r = 0.62, P<0.01) with YYIR2 total distance covered (YYIR2TD) but not with any RSA-derived parameters (r ranging from -0.06 to -0.32; all P>0.05). [Hb] and MCHC displayed moderate correlations with both YYIR2TD (r = 0.44 and 0.41; both P<0.01) and RSA sprint decrement score (r = -0.41 and -0.44; both P<0.05). YYIR2TD correlated with RSA best and total sprint times (r = -0.46, P<0.05 and -0.60, P<0.01; respectively), but not with RSA sprint decrement score (r = -0.19, P>0.05). CONCLUSION: Hbmass is positively correlated with specific aerobic fitness, but not with RSA, in elite team-sport players. Additionally, the negative relationships between YYIR2 and RSA tests performance imply that different hematological mechanisms may be at play. Overall, these results indicate that these two fitness tests should not be used interchangeably as they reflect different hematological mechanisms.

Investigating the functions of subregions within anterior hippocampus.

Previous functional MRI (fMRI) studies have associated anterior hippocampus with imagining and recalling scenes, imagining the future, recalling autobiographical memories and visual scene perception. We have observed that this typically involves the medial rather than the lateral portion of the anterior hippocampus. Here, we investigated which specific structures of the hippocampus underpin this observation. We had participants imagine novel scenes during fMRI scanning, as well as recall previously learned scenes from two different time periods (one week and 30 min prior to scanning), with analogous single object conditions as baselines. Using an extended segmentation protocol focussing on anterior hippocampus, we first investigated which substructures of the hippocampus respond to scenes, and found both imagination and recall of scenes to be associated with activity in presubiculum/parasubiculum, a region associated with spatial representation in rodents. Next, we compared imagining novel scenes to recall from one week or 30 min before scanning. We expected a strong response to imagining novel scenes and 1-week recall, as both involve constructing scene representations from elements stored across cortex. By contrast, we expected a weaker response to 30-min recall, as representations of these scenes had already been constructed but not yet consolidated. Both imagination and 1-week recall of scenes engaged anterior hippocampal structures (anterior subiculum and uncus respectively), indicating possible roles in scene construction. By contrast, 30-min recall of scenes elicited significantly less activation of anterior hippocampus but did engage posterior CA3. Together, these results elucidate the functions of different parts of the anterior hippocampus, a key brain area about which little is definitely known.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Advantages of digital holographic microscopy for real-time full field absolute phase imaging

Different interferometric techniques were developed last decade to obtain full field, quantitative, and absolute phase imaging, such as phase-shifting, Fourier phase microscopy, Hilbert phase microscopy or digital holographic microscopy (DHM). Although, these techniques are very similar, DHM combines several advantages. In contrast, to phase shifting, DHM is indeed capable of single-shot hologram recording allowing a real-time absolute phase imaging. On the other hand, unlike to Fourier phase or Hilbert phase microscopy, DHM does not require to record in focus images of the specimen on the digital detector (CCD or CMOS camera), because a numerical focalization adjustment can be performed by a numerical wavefront propagation. Consequently, the depth of view of high NA microscope objectives is numerically extended. For example, two different biological cells, floating at different depths in a liquid, can be focalized numerically from the same digital hologram. Moreover, the numerical propagation associated to digital optics and automatic fitting procedures, permits vibrations insensitive full- field phase imaging and the complete compensation for a priori any image distortion or/and phase aberrations introduced for example by imperfections of holders or perfusion chamber. Examples of real-time full-field phase images of biological cells have been demonstrated. ©2008 COPYRIGHT SPIE

Usefulness of Mendelian randomization in observational epidemiology.

Mendelian randomization refers to the random allocation of alleles at the time of gamete formation. In observational epidemiology, this refers to the use of genetic variants to estimate a causal effect between a modifiable risk factor and an outcome of interest. In this review, we recall the principles of a "Mendelian randomization" approach in observational epidemiology, which is based on the technique of instrumental variables; we provide simulations and an example based on real data to demonstrate its implications; we present the results of a systematic search on original articles having used this approach; and we discuss some limitations of this approach in view of what has been found so far.

Sox10 promotes the formation and maintenance of giant congenital naevi and melanoma.

Giant congenital naevi are pigmented childhood lesions that frequently lead to melanoma, the most aggressive skin cancer. The mechanisms underlying this malignancy are largely unknown, and there are no effective therapies. Here we describe a mouse model for giant congenital naevi and show that naevi and melanoma prominently express Sox10, a transcription factor crucial for the formation of melanocytes from the neural crest. Strikingly, Sox10 haploinsufficiency counteracts Nras(Q61K)-driven congenital naevus and melanoma formation without affecting the physiological functions of neural crest derivatives in the skin. Moreover, Sox10 is also crucial for the maintenance of neoplastic cells in vivo. In human patients, virtually all congenital naevi and melanomas are SOX10 positive. Furthermore, SOX10 silencing in human melanoma cells suppresses neural crest stem cell properties, counteracts proliferation and cell survival, and completely abolishes in vivo tumour formation. Thus, SOX10 represents a promising target for the treatment of congenital naevi and melanoma in human patients.

From molecular action to physiological outputs: peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions.

Peroxisome proliferator-activated receptors (PPARs) compose a family of three nuclear receptors which act as lipid sensors to modulate gene expression. As such, PPARs are implicated in major metabolic and inflammatory regulations with far-reaching medical consequences, as well as in important processes controlling cellular fate. Throughout this review, we focus on the cellular functions of these receptors. The molecular mechanisms through which PPARs regulate transcription are thoroughly addressed with particular emphasis on the latest results on corepressor and coactivator action. Their implication in cellular metabolism and in the control of the balance between cell proliferation, differentiation and survival is then reviewed. Finally, we discuss how the integration of various intra-cellular signaling pathways allows PPARs to participate to whole-body homeostasis by mediating regulatory crosstalks between organs.

Assessment of airborne microorganisms by real-time PCR: optimistic findings and research challenges

Most airborne microorganisms are natural components of our ecosystem. Soil, vegetation and animals, including humans, are sources for aerial release of these living or dead cells. In the past, assessment of airborne microorganisms was mainly restricted to occupational health concerns. Indeed, in several occupations, exposure to very high concentrations of non-infectious airborne bacteria and fungi, result in allergenic, toxic or irritant reactions. Recently, the threat of bioterrorism and pandemics have highlighted the urgent need to increase knowledge of bioaerosol ecology. More fundamentally, airborne bacterial and fungal communities begin to draw much more consideration from environmental microbiologists, who have neglected this area for a long time. This increased interest of scientists is to a great part due to the development and use of real-time PCR techniques to identify and quantify airborne microorganisms. Even if the advantages of the PCR technology are obvious, researchers are confronted with new problems. This review describes the methodological state of the art in bioaerosols field and emphasizes the future challenges and perspectives of the real-time PCR-based methods for airborne microorganism studies.

Framingham risk score and alternatives for prediction of coronary heart disease in older adults.

BACKGROUND: Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS.¦METHODS: Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization.¦RESULTS: During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS.¦CONCLUSIONS: The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.

Analysis of antiviral immunity in primary and chronic infections

Several evidences in humans underscored the contribution of CD4 and CD8 T-cell responses in controlling viral and bacterial infections. However, CD4 and CD8 Τ cells have distinct and specific effector functions leading to a hierarchical importance in responding to different types of pathogens. In this context, the present work aimed to investigate distinct CD8 T-cell features potentially influencing T-cell efficacy against viral infection. To achieve this-objective, CD8 Τ cells derived from HIV-infected patients and healthy donors harbouring virus-specific immune responses or immunized with an HTV vaccine candidate were studied. In particular, we performed a comprehensive cross-sectional and longitudinal analysis to characterize the function, the phenotype and the functional avidity of HIV-specific CD8 Τ cells during acute (PHI) and chronic infection and, in particular, we investigated immunological parameters potentially associated with the functional avidity of HIV-specific CD8 Τ cells. In addition, we studied the expression pattern of co-inhibitory molecules and the influence of CD 160 on the functions of CD8 Τ cells in absence of chronic infections. From these analyses we observed that the functional avidity of HIV-specific CD8 T- cell responses was significantly lower in acute than in chronic infection, but was not different between chronic progressive and non-progressive patients. Functional avidity remained low after several years of antiretroviral therapy in PHI patients, but increased in patients experiencing a virus rebound following treatment interruption in association with a massive renewal of the global CD8 complementarity-determining region 3 of the TCR. The functional avidity was also directly associated to T-cell exhaustion. In individuals with no sign of HIV or Hepatitis A, Β or C virus infection, CD8 Τ cells expressed higher levels of co-inhibitory molecules than CD4 Τ cells and this was dependent on the stage of T-cell differentiation and activation. The expression of CD 160 impaired the proliferation capacity and IL-2 production of CD8 Τ cells and was reduced upon CD8 T-cell activation, entitling CD 160 as unique marker of CD8 T-cell exhaustion. The CD 160 blockade restored the proliferation capacity of virus-specific CD8 Τ cells providing a potential new target for immunotherapy. All together, these results expand our knowledge regarding the interplay between the immune system and the viruses. - De nombreuses études chez l'Homme ont mis en évidence la contribution des réponses cellulaires Τ CD4 et CD8 dans le contrôle des infections virales et bactériennes. En particulier, les lymphocytes Τ ont différentes fonctions effectrices spécifiques qui leur confèrent un rôle clé lors d'infections par différents pathogènes. Ce travail vise à étudier différentes caractéristiques des cellules Τ CD8 affectant l'efficacité des réponses cellulaires contre les virus. Pour atteindre cet objectif nous avons étudié les cellules Τ CD8 provenant de patients infectés par le VIH et de donneurs sains avec des réponses immunitaires naturelles ou vaccinales contre des virus. Nous avons effectué plusieurs analyses transversales et longitudinales des fonctions, du phénotype et de l'avidité fonctionnelle des lymphocytes Τ CD8 spécifiques au VIH au cours d'infections aiguës et chroniques; en particulier, nous avons étudié les paramètres immunologiques qui pourraient être associés à l'avidité fonctionnelle. De plus, nous avons investigué le profil d'expression des principales molécules co-inhibitrices et en particulier le rôle du CD 160 dans les fonctions des lymphocytes Τ CD8. Sur la base de ces analyses, nous avons constaté que l'avidité fonctionnelle des cellules Τ CD8 spécifiques au VIH était significativement plus faible lors infections aiguës que lors d'infections chroniques, mais n'était, par contre, pas différente entre les patients avec des infections chroniques progressives et non progressives. L'avidité fonctionnelle reste faible après plusieurs années de traitement antirétroviral, mais augmente chez les patients subissant un rebond viral, et donc exposés à des hautes virémies, suite à l'interruption du traitement. Cette augmentation d'avidité des lymphocytes Τ CD8, liée à un épuisement fonctionnel accru, était quantitativement directement associée à un renouvellement massif du TCR. Indépendamment de l'infection par le VIH, les cellules Τ CD8 expriment des niveaux plus élevés de molécules co-inhibitrices (PD-1, 2B4 et CD 160) par rapport aux cellules Τ CD4 et ceci dépend de leur stade de différenciation et d'activation. En particulier, CD 160 semble être un marqueur clé d'épuisement cellulaire des cellules Τ CD8, et donc une nouvelle cible potentielle pour l'immunothérapie, car a) son expression réduit la capacité proliférative et la production d'IL-2 b) CD 160 diminue suite à 1'activation et c) le blocage de CD 160 redonne la capacité proliférative aux cellules Τ CD8 spécifiques aux virus. - Le système immunitaire est un ensemble de cellules, tissus et organes indispensables pour limiter l'entrée des pathogènes à travers la peau et les muqueuses. Parmi les différentes cellules composant le système immunitaire, les cellules Τ CD4 et CD8 sont fondamentales pour le contrôle des infections virales et bactériennes. Les moyens pour combattre les différents pathogènes peuvent être cependant très variables. Les cellules Τ CD8, qui sont indispensables pour la lutte contre les virus, peuvent avoir différents niveaux de sensibilité; les cellules qui répondent à de faibles quantités d'antigène ont une forte sensibilité. Suite à une première infection virale, les cellules Τ CD8 ont une sensibilité plus faible que lors d'expositions répétées au même virus. En effet, la réexposition au pathogène induit une augmentation de sensibilité, grâce au recrutement et/ou à l'expansion de cellules Τ dotées d'une sensibilité plus élevée. Les cellules Τ CD8 avec une plus haute sensibilité semblent être caractérisées par une perte de fonctionnalité (épuisement fonctionnel associé à une haute expression de molécules dites inhibitrices). En absence d'infection, la fonction des molécules inhibitrices n'est pas encore clairement définie. Les cellules Τ CD8 montrent un niveau d'expression plus élevé de ces molécules par rapport aux cellules Τ CD4. Ceci dépend de l'état des cellules. Parmi ces molécules, le CD160 est associé à l'incapacité des cellules à proliférer et à produire de l'IL-2, une protéine importante pour la prolifération et la survie cellulaire. L'incapacité des cellules exprimant le CD 160 à proliférer en réponse à des virus peut être restaurée par le blocage fonctionnel du récepteur CD 160. Cette étude étoffe notre connaissance du rôle des cellules Τ CD8 ainsi que des conséquences induites par leur épuisement fonctionnel. Ces informations sont fondamentales pour le développement de nouvelles stratégies thérapeutiques et vaccinales.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Submillimeter three-dimensional coronary MR angiography with real-time navigator correction: comparison of navigator locations.

Three-dimensional free-breathing coronary magnetic resonance angiography was performed in eight healthy volunteers with use of real-time navigator technology. Images acquired with the navigator localized at the right hemidiaphragm and at the left ventricle were objectively compared. The diaphragmatic navigator was found to be superior for vessel delineation of middle to distal portions of the coronary arteries.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.