Local Salmonella immunostimulation recruits vaccine-specific CD8 T cells and increases regression of bladder tumor.

NlmCategory="UNASSIGNED">The efficacy of antitumoral responses can be increased using combinatorial vaccine strategies. We recently showed that vaccination could be optimized by local administration of diverse molecular or bacterial agents to target and augment antitumoral CD8 T cells in the genital mucosa (GM) and increase regression of cervical cancer in an animal model. Non muscle-invasive bladder cancer is another disease that is easily amenable to local therapies. In contrast to data obtained in the GM, in this study we show that intravesical (IVES) instillation of synthetic toll-like receptor (TLR) agonists only modestly induced recruitment of CD8 T cells to the bladder. However, IVES administration of Ty21a, a live bacterial vaccine against typhoid fever, was much more effective and increased the number of total and vaccine-specific CD8 T cells in the bladder approximately 10 fold. Comparison of chemokines induced in the bladder by either CpG (a TLR-9 agonist) or Ty21a highlighted the preferential increase in complement component 5a, CXCL5, CXCL2, CCL8, and CCL5 by Ty21a, suggesting their involvement in the attraction of T cells to the bladder. IVES treatment with Ty21a after vaccination also significantly increased tumor regression compared to vaccination alone, resulting in 90% survival in an orthotopic murine model of bladder cancer expressing a prototype tumor antigen. Our data demonstrate that combining vaccination with local immunostimulation may be an effective treatment strategy for different types of cancer and also highlight the great potential of the Ty21a vaccine, which is routinely used worldwide, in such combinatorial therapies.

Systematic analysis of factors associated with progression and regression of ulcerative colitis in 918 patients.

BACKGROUND: Studies that systematically assess change in ulcerative colitis (UC) extent over time in adult patients are scarce. AIM: To assess changes in disease extent over time and to evaluate clinical parameters associated with this change. METHODS: Data from the Swiss IBD cohort study were analysed. We used logistic regression modelling to identify factors associated with a change in disease extent. RESULTS: A total of 918 UC patients (45.3% females) were included. At diagnosis, UC patients presented with the following disease extent: proctitis [199 patients (21.7%)], left-sided colitis [338 patients (36.8%)] and extensive colitis/pancolitis [381 (41.5%)]. During a median disease duration of 9 [4-16] years, progression and regression was documented in 145 patients (15.8%) and 149 patients (16.2%) respectively. In addition, 624 patients (68.0%) had a stable disease extent. The following factors were identified to be associated with disease progression: treatment with systemic glucocorticoids [odds ratio (OR) 1.704, P = 0.025] and calcineurin inhibitors (OR: 2.716, P = 0.005). No specific factors were found to be associated with disease regression. CONCLUSIONS: Over a median disease duration of 9 [4-16] years, about two-thirds of UC patients maintained the initial disease extent; the remaining one-third had experienced either progression or regression of the disease extent.

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Effects of unit - based pricing on the waste collection demand : a meta - regression analysis

We perform a meta - analysis of 21 studies that estimate the elasticity of the price of waste collection demand upon waste quantities, a prior literature review having revealed that the price elasticity differs markedly. Based on a meta - regression with a total of 65 observations, we find no indication that municipal data give higher estimates for price elasticities than those associated with household data. Furthermore, there is no evidence that treating prices as exogenous underestimates the price elasticity. We find that much of the variation can be explained by sample size, the use of a weight - based as opposed to a volume - based pricing system, and the pricing of compostable waste. We also show that price elasticities determined in the USA and point estimations of elasticities are more elastic, but these effects are not robust to the changing of model specifications. Finally, our tests show that there is no evidence of publication bias while there is some evidence of the existence of genuine empirical effect.

A Hierarchical Approach for Multi-task Logistic Regression

Peer-reviewed

DFM(A)- aspects for a fixed electrical attenuator design

The goal of this paper is to describe a complete and extensive prototype design of a fixed electrical attenuator. The paper starts by describing the function and by giving some basic information about the attenuators. After a comprehensive description of the component, the facts of reverse engineering are discussed. The method itself is applied to ease manufacturing and design stages of this component. Information about materials and applied manufacturing technologies are also included in this report. By applying some specified DFMA-aspects the final design turned out to be a potential prototype device to be manufactured and for further analyse.

On Commutation and Conjugacy of Rational Languages and the Fixed Point Method

The research on language equations has been active during last decades. Compared to the equations on words the equations on languages are much more difficult to solve. Even very simple equations that are easy to solve for words can be very hard for languages. In this thesis we study two of such equations, namely commutation and conjugacy equations. We study these equations on some limited special cases and compare some of these results to the solutions of corresponding equations on words. For both equations we study the maximal solutions, the centralizer and the conjugator. We present a fixed point method that we can use to search these maximal solutions and analyze the reasons why this method is not successful for all languages. We give also several examples to illustrate the behaviour of this method.

Regulating Concessions of Toll Motorways, An Empirical Study on Fixed vs. Variable Term Contracts

Recent theoretical developments on concession contracts for long term infrastructure projects under uncertain demand show the benefits of allowing for flexible term contracts rather than fixing a rigid term. This study presents a simulation to compare both alternatives by using real data from the oldest Spanish toll motorway. For this purpose, we analyze how well the flexible term would have performed instead of the fixed length actually established. Our results show a huge reduction of the term of concession that would have dramatically decreased the firm’s benefits and the user’s overpayment due to the internalization of an unexpected traffic increase.

Regression analysis of compositional data when both the dependent variable and independent variable are components

It is well known that regression analyses involving compositional data need special attention because the data are not of full rank. For a regression analysis where both the dependent and independent variable are components we propose a transformation of the components emphasizing their role as dependent and independent variables. A simple linear regression can be performed on the transformed components. The regression line can be depicted in a ternary diagram facilitating the interpretation of the analysis in terms of components. An exemple with time-budgets illustrates the method and the graphical features

Capital Structure of Finnish SMEs and Financial Constraints

This thesis studies capital structure of Finnish small and medium sized enterprises. The specific object of the study is to test whether financial constraints have an effect on capital structure. In addition influences of several other factors were studied. Capital structure determinants are formulated based on three capital structure theories. The tradeoff theory and the agency theory concentrate on the search of optimal capital structure. The pecking order theory concerns favouring on financing source over another. The data of this study consists of financial statement data and results of corporate questionnaire. Regression analysis was used to find out the effects of several determinants. Regression models were formed based on the presented theories. Short and long term debt ratios were considered separately. The metrics of financially constrained firms was included in all models. It was found that financial constrains have a negative and significant effect to short term debt ratios. The effect was negative also to long term debt ratio but not statistically significant. Other considerable factors that influenced debt ratios were fixed assets, age, profitability, single owner and sufficiency of internal financing.

The causal factors of international inequality in CO2 emissions per capita: a regression-based inequality decomposition analysis

This paper uses the possibilities provided by the regression-based inequality decomposition (Fields, 2003) to explore the contribution of different explanatory factors to international inequality in CO2 emissions per capita. In contrast to previous emissions inequality decompositions, which were based on identity relationships (Duro and Padilla, 2006), this methodology does not impose any a priori specific relationship. Thus, it allows an assessment of the contribution to inequality of different relevant variables. In short, the paper appraises the relative contributions of affluence, sectoral composition, demographic factors and climate. The analysis is applied to selected years of the period 1993–2007. The results show the important (though decreasing) share of the contribution of demographic factors, as well as a significant contribution of affluence and sectoral composition.