Expression of prox1, lymphatic endothelial nuclear transcription factor, in Kaposiform hemangioendothelioma and tufted angioma.

Kaposiform hemangioendothelioma (KHE) and tufted angioma (TA) are rare tumors mainly occurring in early childhood. Our recent results showed that ectopic overexpression of human Prox1 gene, a lymphatic endothelial nuclear transcription factor, promoted an aggressive behavior in 2 murine models of KHE. This dramatic Prox1-induced phenotype prompted us to investigate immunohistochemical staining pattern of Prox1, podoplanin (D2-40), LYVE-1, and Prox1/CD34 as well as double immunofluorescent staining pattern of LYVE-1/CD31 in KHE and TA, compared with other pediatric vascular tumors. For this purpose, we examined 75 vascular lesions: KHE (n=18), TA (n=13), infantile hemangioma (n=13), pyogenic granuloma (n=18), and granulation tissue (n=13). Overall, KHE and TA shared an identical endothelial immunophenotype: the neoplastic spindle cells were Prox1, podoplanin, LYVE-1, CD31, and CD34, whereas endothelial cells within glomeruloid foci were Prox1, podoplanin, LYVE-1, CD31, and CD34. The lesional cells of all infantile hemangiomas and pyogenic granulomas were negative for Prox1 in the presence of positive internal control. These findings provide immunophenotypic evidence to support a preexisting notion that KHE and TA are closely related, if not identical. Overall, our results show, for the first time, that Prox1 is an immunohistochemical biomarker helpful in confirming the diagnosis of KHE/TA and in distinguishing it from infantile hemangioma and pyogenic granuloma.

Dynamic responses of oxygen uptake at the onset and end of moderate and heavy exercise in trained subjects.

Inconsistencies about dynamic asymmetry between the on- and off-transient responses in VO2 are found in the literature. Therefore the purpose of this study was to examine VO2 on- and off-transients during moderate- and heavy-intensity cycling exercise in trained subjects. Ten men underwent an initial incremental test for the estimation of ventilatory threshold (VT) and, on different days, two bouts of square-wave exercise at moderate (<VT) and heavy (>VT) intensities. VO2 kinetics in exercise and recovery were better described by a single exponential model (<VT), or by a double exponential with two time delays (>VT). For moderate exercise, we found a symmetry of VO2 kinetics between the on- and off-transients (i.e., fundamental component), consistent with a system manifesting linear control dynamics. For heavy exercise, a slow component superimposed on the fundamental phase was expressed in both the exercise and recovery, with similar parameter estimates. But the on-transient values of the time constant were appreciably faster than the associated off-transient, and independent of the work rate imposed (<VT and >VT). Our results do not support a dynamically linear system model of VO2 during cycling exercise in the heavy-intensity domain.

Dynamic responses of O2 uptake at the onset and end of exercise in trained subjects.

Inconsistencies about dynamic asymmetry between the on- and off-transient responses in .VO2 are found in the literature. Therefore the purpose of this study was to examine .VO2on- and off-transients during moderate- and heavy-intensity cycling exercise in trained subjects. Ten men underwent an initial incremental test for the estimation of ventilatory threshold (VT) and, on different days, two bouts of square-wave exercise at moderate (<VT) and heavy (>VT) intensities. .VO2 kinetics in exercise and recovery were better described by a single exponential model (<VT) or by a double exponential with two time delays (>VT). For moderate exercise, we found a symmetry of .VO2 kinetics between the on- and off-transients (i.e., fundamental component), consistent with a system manifesting linear control dynamics. For heavy exercise, a slow component superimposed on the fundamental phase was expressed in both the exercise and recovery, with similar parameter estimates. But the on-transient values of the time constant were appreciably faster than the associated off-transient, and independent of the work rate imposed (<VT and >VT). Our results do not support a dynamically linear system model of .VO2 during cycling exercise in the heavy-intensity domain.

Peripheral inflammation increases the damage in animal models of nigrostriatal dopaminergic neurodegeneration: possible implication in Parkinson's disease incidence.

Inflammatory processes described in Parkinson’s disease (PD) and its animal models appear to be important in the progression of the pathogenesis, or even a triggering factor. Here we review that peripheral inflammation enhances the degeneration of the nigrostriatal dopaminergic system induced by different insults; different peripheral inflammations have been used, such as IL-1β and the ulcerative colitis model, as well as insults to the dopaminergic system such as 6-hydroxydopamine or lipopolysaccharide. In all cases, an increased loss of dopaminergic neurons was described; inflammation in the substantia nigra increased, displaying a great activation of microglia along with an increase in the production of cytokines such as IL-1β and TNF-α. Increased permeability or disruption of the BBB, with overexpression of the ICAM-1 adhesion molecule and infiltration of circulating monocytes into the substantia nigra, is also involved, since the depletion of circulating monocytes prevents the effects of peripheral inflammation. Data are reviewed in relation to epidemiological studies of PD.

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Dissolution of Dynamic Electronic Institutions, a first approach: Relevant factors and causes

In the context of the digital business ecosystems, small organizations cooperate between them in order to achieve common goals or offer new services for expanding their markets. There are different approaches for these cooperation models such as virtual enterprises, virtual organizations or dynamic electronic institutions which in their lifecycle have in common a dissolution phase. However this phase has not been studied deeply in the current literature and it lacks formalization. In this paper a first approach for achieving and managing the dissolution phase is proposed, as well as a CBR process in order to support it in a multi-agent system

Reassessing the role of Staphylococcus aureus clumping factor and fibronectin-binding protein by expression in Lactococcus lactis.

Since Staphylococcus aureus expresses multiple pathogenic factors, studying their individual roles in single-gene-knockout mutants is difficult. To circumvent this problem, S. aureus clumping factor A (clfA) and fibronectin-binding protein A (fnbA) genes were constitutively expressed in poorly pathogenic Lactococcus lactis using the recently described pOri23 vector. The recombinant organisms were tested in vitro for their adherence to immobilized fibrinogen and fibronectin and in vivo for their ability to infect rats with catheter-induced aortic vegetations. In vitro, both clfA and fnbA increased the adherence of lactococci to their specific ligands to a similar extent as the S. aureus gene donor. In vivo, the minimum inoculum size producing endocarditis in &gt; or =80% of the rats (80% infective dose [ID80]) with the parent lactococcus was &gt; or =10(7) CFU. In contrast, clfA-expressing and fnbA-expressing lactococci required only 10(5) CFU to infect the majority of the animals (P &lt; 0.00005). This was comparable to the infectivities of classical endocarditis pathogens such as S. aureus and streptococci (ID80 = 10(4) to 10(5) CFU) in this model. The results confirmed the role of clfA in endovascular infection, but with a much higher degree of confidence than with single-gene-inactivated staphylococci. Moreover, they identified fnbA as a critical virulence factor of equivalent importance. This was in contrast to previous studies that produced controversial results regarding this very determinant. Taken together, the present observations suggest that if antiadhesin therapy were to be developed, at least both of the clfA and fnbA products should be blocked for the therapy to be effective.

Granulocyte colony-stimulating factor in the treatment of high-risk febrile neutropenia: a multicenter randomized trial.

BACKGROUND Granulocyte colony-stimulating factors (G-CSFs) have been shown to help prevent febrile neutropenia in certain subgroups of cancer patients undergoing chemotherapy, but their role in treating febrile neutropenia is controversial. The purpose of our study was to evaluate-in a prospective multicenter randomized clinical trial-the efficacy of adding G-CSF to broad-spectrum antibiotic treatment of patients with solid tumors and high-risk febrile neutropenia. METHODS A total of 210 patients with solid tumors treated with conventional-dose chemotherapy who presented with fever and grade IV neutropenia were considered to be eligible for the trial. They met at least one of the following high-risk criteria: profound neutropenia (absolute neutrophil count <100/mm(3)), short latency from previous chemotherapy cycle (<10 days), sepsis or clinically documented infection at presentation, severe comorbidity, performance status of 3-4 (Eastern Cooperative Oncology Group scale), or prior inpatient status. Eligible patients were randomly assigned to receive the antibiotics ceftazidime and amikacin, with or without G-CSF (5 microg/kg per day). The primary study end point was the duration of hospitalization. All P values were two-sided. RESULTS Patients randomly assigned to receive G-CSF had a significantly shorter duration of grade IV neutropenia (median, 2 days versus 3 days; P = 0.0004), antibiotic therapy (median, 5 days versus 6 days; P = 0.013), and hospital stay (median, 5 days versus 7 days; P = 0.015) than patients in the control arm. The incidence of serious medical complications not present at the initial clinical evaluation was 10% in the G-CSF group and 17% in the control group (P = 0.12), including five deaths in each study arm. The median cost of hospital stay and the median overall cost per patient admission were reduced by 17% (P = 0.01) and by 11% (P = 0.07), respectively, in the G-CSF arm compared with the control arm. CONCLUSIONS Adding G-CSF to antibiotic therapy shortens the duration of neutropenia, reduces the duration of antibiotic therapy and hospitalization, and decreases hospital costs in patients with high-risk febrile neutropenia.

Research letter: is neuroticism a risk factor for postpartum depression?

Although the relationship between personality and depressive illness is complex (Shea, 2005), there is empirical evidence that some personality features such as neuroticism, harm avoidance, introversion, dependency, self-criticism or perfectionism are related to depressive illness risk (Gunderson et al. 1999).

Comparison of drug survival rates for tumor necrosis factor antagonists in rheumatoid arthritis.

BACKGROUND Persistence of anti-tumor necrosis factor (TNF) therapy in rheumatoid arthritis (RA) is an overall marker of treatment success. OBJECTIVE To assess the survival of anti-TNF treatment and to define the potential predictors of drug discontinuation in RA, in order to verify the adequacy of current practices. DESIGN An observational, descriptive, longitudinal, retrospective study. SETTING The Hospital Clínico Universitario de Valladolid, Valladolid, Spain. PATIENTS RA patients treated with anti-TNF therapy between January 2011 and January 2012. MEASUREMENTS Demographic information and therapy assessments were gathered from medical and pharmaceutical records. Data is expressed as means (standard deviations) for quantitative variables and frequency distribution for qualitative variables. Kaplan-Meier survival analysis was used to assess persistence, and Cox multivariate regression models were used to assess potential predictors of treatment discontinuation. RESULTS In total, 126 treatment series with infliximab (n = 53), etanercept (n = 51) or adalimumab (n = 22) were administered to 91 patients. Infliximab has mostly been used as a first-line treatment, but it was the drug with the shortest time until a change of treatment. Significant predictors of drug survival were: age; the anti-TNF agent; and the previous response to an anti-TNF drug. LIMITATION The small sample size. CONCLUSION The overall efficacy of anti-TNF drugs diminishes with time, with infliximab having the shortest time until a change of treatment. The management of biologic therapy in patients with RA should be reconsidered in order to achieve disease control with a reduction in costs.

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

Combined simulation and mutagenesis analyses reveal the involvement of key residues for peroxisome proliferator-activated receptor alpha helix 12 dynamic behavior.

The dynamic properties of helix 12 in the ligand binding domain of nuclear receptors are a major determinant of AF-2 domain activity. We investigated the molecular and structural basis of helix 12 mobility, as well as the involvement of individual residues with regard to peroxisome proliferator-activated receptor alpha (PPARalpha) constitutive and ligand-dependent transcriptional activity. Functional assays of the activity of PPARalpha helix 12 mutants were combined with free energy molecular dynamics simulations. The agreement between the results from these approaches allows us to make robust claims concerning the mechanisms that govern helix 12 functions. Our data support a model in which PPARalpha helix 12 transiently adopts a relatively stable active conformation even in the absence of a ligand. This conformation provides the interface for the recruitment of a coactivator and results in constitutive activity. The receptor agonists stabilize this conformation and increase PPARalpha transcription activation potential. Finally, we disclose important functions of residues in PPARalpha AF-2, which determine the positioning of helix 12 in the active conformation in the absence of a ligand. Substitution of these residues suppresses PPARalpha constitutive activity, without changing PPARalpha ligand-dependent activation potential.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Study and implementation of some quantitative trading models

Quantitative or algorithmic trading is the automatization of investments decisions obeying a fixed or dynamic sets of rules to determine trading orders. It has increasingly made its way up to 70% of the trading volume of one of the biggest financial markets such as the New York Stock Exchange (NYSE). However, there is not a signi cant amount of academic literature devoted to it due to the private nature of investment banks and hedge funds. This projects aims to review the literature and discuss the models available in a subject that publications are scarce and infrequently. We review the basic and fundamental mathematical concepts needed for modeling financial markets such as: stochastic processes, stochastic integration and basic models for prices and spreads dynamics necessary for building quantitative strategies. We also contrast these models with real market data with minutely sampling frequency from the Dow Jones Industrial Average (DJIA). Quantitative strategies try to exploit two types of behavior: trend following or mean reversion. The former is grouped in the so-called technical models and the later in the so-called pairs trading. Technical models have been discarded by financial theoreticians but we show that they can be properly cast into a well defined scientific predictor if the signal generated by them pass the test of being a Markov time. That is, we can tell if the signal has occurred or not by examining the information up to the current time; or more technically, if the event is F_t-measurable. On the other hand the concept of pairs trading or market neutral strategy is fairly simple. However it can be cast in a variety of mathematical models ranging from a method based on a simple euclidean distance, in a co-integration framework or involving stochastic differential equations such as the well-known Ornstein-Uhlenbeck mean reversal ODE and its variations. A model for forecasting any economic or financial magnitude could be properly defined with scientific rigor but it could also lack of any economical value and be considered useless from a practical point of view. This is why this project could not be complete without a backtesting of the mentioned strategies. Conducting a useful and realistic backtesting is by no means a trivial exercise since the \laws" that govern financial markets are constantly evolving in time. This is the reason because we make emphasis in the calibration process of the strategies' parameters to adapt the given market conditions. We find out that the parameters from technical models are more volatile than their counterpart form market neutral strategies and calibration must be done in a high-frequency sampling manner to constantly track the currently market situation. As a whole, the goal of this project is to provide an overview of a quantitative approach to investment reviewing basic strategies and illustrating them by means of a back-testing with real financial market data. The sources of the data used in this project are Bloomberg for intraday time series and Yahoo! for daily prices. All numeric computations and graphics used and shown in this project were implemented in MATLAB^R scratch from scratch as a part of this thesis. No other mathematical or statistical software was used.

Structural determinants for membrane association and dynamic organization of the hepatitis C virus NS3-4A complex.

Hepatitis C virus (HCV) NS3-4A is a membrane-associated multifunctional protein harboring serine protease and RNA helicase activities. It is an essential component of the HCV replication complex and a prime target for antiviral intervention. Here, we show that membrane association and structural organization of HCV NS3-4A are ensured in a cooperative manner by two membrane-binding determinants. We demonstrate that the N-terminal 21 amino acids of NS4A form a transmembrane alpha-helix that may be involved in intramembrane protein-protein interactions important for the assembly of a functional replication complex. In addition, we demonstrate that amphipathic helix alpha(0), formed by NS3 residues 12-23, serves as a second essential determinant for membrane association of NS3-4A, allowing proper positioning of the serine protease active site on the membrane. These results allowed us to propose a dynamic model for the membrane association, processing, and structural organization of NS3-4A on the membrane. This model has implications for the functional architecture of the HCV replication complex, proteolytic targeting of host factors, and drug design.