Basis set effects on the energy and hardness profiles of the hydrogen fluoride dimer

In earlier work, the present authors have shown that hardness profiles are less dependent on the level of calculation than energy profiles for potential energy surfaces (PESs) having pathological behaviors. At variance with energy profiles, hardness profiles always show the correct number of stationary points. This characteristic has been used to indicate the existence of spurious stationary points on the PESs. In the present work, we apply this methodology to the hydrogen fluoride dimer, a classical difficult case for the density functional theory methods

Large TCR diversity of virus-specific CD8 T cells provides the mechanistic basis for massive TCR renewal after antigen exposure.

Ex vivo analysis of virus-specific CD8 T cell populations by anchored PCR has shown that the CD8 TCR repertoire was less oligoclonal (seven to nine clonotypes per individual epitope) than previously thought. In the current study, TCR diversity was investigated by assessing both the overall TCR β-chain variable regions usage as well as the CDR3 regions in ex vivo-isolated CMV- and EBV-specific CD8 T cells from 27 healthy donors. The average number of clonotypes specific to most single viral epitopes comprised between 14 and 77. Changes in the CD8 TCR repertoire were also longitudinally assessed under conditions of HIV-1 chronic infection (i.e., in patients with suppressed virus replication and after treatment interruption and Ag re-exposure). The results showed that a large renewal (≤80%) of the TRB repertoire occurred after Ag re-exposure and was eventually associated with an increased T cell recognition functional avidity. These results demonstrate that the global CD8 TCR repertoire is much more diverse (≤9-fold) than previously estimated and provide the mechanistic basis for supporting massive repertoire renewal during chronic virus infection and Ag re-exposure.

Crystal structure of yeast peroxisomal multifunctional enzyme: structural basis for substrate specificity of (3R)-hydroxyacyl-CoA dehydrogenase units.

(3R)-hydroxyacyl-CoA dehydrogenase is part of multifunctional enzyme type 2 (MFE-2) of peroxisomal fatty acid beta-oxidation. The MFE-2 protein from yeasts contains in the same polypeptide chain two dehydrogenases (A and B), which possess difference in substrate specificity. The crystal structure of Candida tropicalis (3R)-hydroxyacyl-CoA dehydrogenase AB heterodimer, consisting of dehydrogenase A and B, determined at the resolution of 2.2A, shows overall similarity with the prototypic counterpart from rat, but also important differences that explain the substrate specificity differences observed. Docking studies suggest that dehydrogenase A binds the hydrophobic fatty acyl chain of a medium-chain-length ((3R)-OH-C10) substrate as bent into the binding pocket, whereas the short-chain substrates are dislocated by two mechanisms: (i) a short-chain-length 3-hydroxyacyl group ((3R)-OH-C4) does not reach the hydrophobic contacts needed for anchoring the substrate into the active site; and (ii) Leu44 in the loop above the NAD(+) cofactor attracts short-chain-length substrates away from the active site. Dehydrogenase B, which can use a (3R)-OH-C4 substrate, has a more shallow binding pocket and the substrate is correctly placed for catalysis. Based on the current structure, and together with the structure of the 2-enoyl-CoA hydratase 2 unit of yeast MFE-2 it becomes obvious that in yeast and mammalian MFE-2s, despite basically identical functional domains, the assembly of these domains into a mature, dimeric multifunctional enzyme is very different.

The genetic basis of sleep disorders.

The contribution of genes, environment and gene-environment interactions to sleep disorders is increasingly recognized. Well-documented familial and twin sleep disorder studies suggest an important influence of genetic factors. However, only few sleep disorders have an established genetic basis including four rare diseases that may result from a single gene mutation: fatal familial insomnia, familial advanced sleep-phase syndrome, chronic primary insomnia, and narcolepsy with cataplexy. However, most sleep disorders are complex in terms of their genetic susceptibility together with the variable expressivity of the phenotype even within a same family. Recent linkage, genome-wide and candidate gene association studies resulted in the identification of gene mutations, gene localizations, or evidence for susceptibility genes and/or loci in several sleep disorders. Molecular techniques including mainly genome-wide linkage and association studies are further required to identify the contribution of new genes. These identified susceptibility genetic determinants will provide clues to better understand pathogenesis of sleep disorders, to assess the risk for diseases and also to find new drug targets to treat and to prevent the underlying conditions. We reviewed here the role of genetic basis in most of key sleep disorders.

Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity.

Sarcomas are heterogeneous and aggressive mesenchymal tumors. Histological grading has so far been the best predictor for metastasis-free survival, but it has several limitations, such as moderate reproducibility and poor prognostic value for some histological types. To improve patient grading, we performed genomic and expression profiling in a training set of 183 sarcomas and established a prognostic gene expression signature, complexity index in sarcomas (CINSARC), composed of 67 genes related to mitosis and chromosome management. In a multivariate analysis, CINSARC predicts metastasis outcome in the training set and in an independent 127 sarcomas validation set. It is superior to the Fédération Francaise des Centres de Lutte Contre le Cancer grading system in determining metastatic outcome for sarcoma patients. Furthermore, it also predicts outcome for gastrointestinal stromal tumors (GISTs), breast carcinomas and lymphomas. Application of the signature will permit more selective use of adjuvant therapies for people with sarcomas, leading to decreased iatrogenic morbidity and improved outcomes for such individuals.

Molecular basis of selective PPARgamma modulation for the treatment of Type 2 diabetes.

Peroxisome proliferator-activated receptors (PPARs) (alpha, beta/delta and gamma) are lipid sensors capable of adapting gene expression to integrate various lipid signals. As such, PPARs are also very important pharmaceutical targets, and specific synthetic ligands exist for the different isotypes and are either currently used or hold promises in the treatment of major metabolic disorders. In particular, compounds of the class of the thiazolinediones (TZDs) are PPARgamma agonists and potent insulin-sensitizers. The specific but still broad expression patterns of PPARgamma, as well as its implication in numerous pathways, constitutes also a disadvantage regarding drug administration, since this potentially increases the chance to generate side-effects through the activation of the receptor in tissues or cells not affected by the disease. Actually, numerous side effects associated with the administration of TZDs have been reported. Today, a new generation of PPARgamma modulators is being actively developed to activate the receptor more specifically, in a cell and time-dependent manner, in order to induce a specific subset of target genes only and modulate a restricted number of metabolic pathways. We will discuss here why and how the development of such selective PPARgamma modulators is possible, and summarize the results obtained with the published molecules.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Fear and anxiety at the basis of adolescent externalizing and internalizing behaviors: a case study.

Juvenile delinquency is rarely associated with success in psychotherapeutic treatment. Up until now, few data have been recorded regarding possible overlaps or common features of conduct disorders with anxiety disorders. This case report of a delinquent adolescent's presenting an obsessive-compulsive disorder discusses possible underlying common features of externalizing and internalizing disorders, mainly in terms of fear and anxiety regulation. The successful psychotherapy is discussed with regard to efficient psychological assessment and treatment of delinquent adolescents, and it underlies the importance of detailed analysis of psychopathology in cases of juvenile delinquency.

Neuromodulation by oxytocin and vasopressin in the central nervous system as a basis for their rapid behavioral effects.

The last several years have seen an increasing number of studies that describe effects of oxytocin and vasopressin on the behavior of animals or humans. Studies in humans have reported behavioral changes and, through fMRI, effects on brain function. These studies are paralleled by a large number of reports, mostly in rodents, that have also demonstrated neuromodulatory effects by oxytocin and vasopressin at the circuit level in specific brain regions. It is the scope of this review to give a summary of the most recent neuromodulatory findings in rodents with the aim of providing a potential neurophysiological basis for their behavioral effects. At the same time, these findings may point to promising areas for further translational research towards human applications.

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.

Molecular basis of leukocyte rolling on PSGL-1. Predominant role of core-2 O-glycans and of tyrosine sulfate residue 51.

Interactions between the leukocyte adhesion receptor L-selectin and P-selectin glycoprotein ligand-1 play an important role in regulating the inflammatory response by mediating leukocyte tethering and rolling on adherent leukocytes. In this study, we have examined the effect of post-translational modifications of PSGL-1 including Tyr sulfation and presentation of sialylated and fucosylated O-glycans for L-selectin binding. The functional importance of these modifications was determined by analyzing soluble L-selectin binding and leukocyte rolling on CHO cells expressing various glycoforms of PSGL-1 or mutant PSGL-1 targeted at N-terminal Thr or Tyr residues. Simultaneous expression of core-2 beta1,6-N-acetylglucosaminyltransferase and fucosyltransferase VII was required for optimal L-selectin binding to PSGL-1. Substitution of Thr-57 by Ala but not of Thr-44, strongly decreased L-selectin binding and leukocyte rolling on PSGL-1. Substitution of Tyr by Phe revealed that PSGL-1 Tyr-51 plays a predominant role in mediating L-selectin binding and leukocyte rolling whereas Tyr-48 has a minor role, an observation that contrasts with the pattern seen for the interactions between PSGL-1 and P-selectin where Tyr-48 plays a key role. Molecular modeling analysis of L-selectin and P-selectin interactions with PSGL-1 further supported these observations. Additional experiments showed that core-2 O-glycans attached to Thr-57 were also of critical importance in regulating the velocity and stability of leukocyte rolling. These observations pinpoint the structural characteristics of PSGL-1 that are required for optimal interactions with L-selectin and may be responsible for the specific kinetic and mechanical bond properties of the L-selectin-PSGL-1 adhesion receptor-counterreceptor pair.