Retinoid -induced regression of human head and neck squamous cell carcinomas is associated with the induction of a pro -inflammatory response

Retinoids are Vitamin A derivatives that are effective chemopreventative and chemotherapeutic agents for head and neck squamous cell carcinomas (HNSCC). Despite the wide application of retinoids in cancer treatment, the mechanism by which retinoids inhibit head and neck squamous cell carcinomas is not completely understood. While in vitro models show that drugs affect cell proliferation and differentiation, in vivo models, such as tumor xenografts in nude mice drugs affect more complex parameters such as extracellular matrix formation, angiogenesis and inflammation. Therefore, we studied the effects of retinoids on the growth of the 22B HNSCC tumors using a xenograft model. In this system, retinoids had no effect on tumor cell differentiation but caused invasion of the tumor by inflammatory cells. Retinoid induced inflammation lead to tumor cell death and tumor regression. Therefore, we hypothesized that retinoids stimulated the 22B HNSCC xenografts to produce a pro-inflammatory signal such as chemokines that in turn activated host inflammatory responses. ^ We used real time quantitative RT-PCR to measure cytokine and chemokine expression in retinoid treated tumors. Treatment of tumors with an RAR-specific retinoid, LGD1550, had no effect on the expression of TNFα, IL-1α, GROα, IP-10, Rantes, MCP-1 and MIP-1α but induced IL-8 mRNA 5-fold. We further characterized the retinoid effect on IL-8 expression on the 22B HNSCC and 1483 HNSCC cells in vitro. Retinoids increased IL-8 expression and enhanced TNFα-dependent IL-8 induction. In addition, retinoids increased the basal and TNFα-dependent expression of MCP-1 but decreased the basal and TNFα dependent expression of IP-10. The effect of retinoids on IL-8 and MCP-1 expression was very rapid with increased levels of mRNA detected within 1–2 hours. This effect did not require new protein synthesis and did not result from mRNA stabilization. Both RAR and RXR ligands increased IL-8 expression whereas only RAR ligands activated MCP-1 expression. ^ We identified a functional retinoid response element in the IL-8 promoter that was located adjacent to the C/EBP-NFkB response element. TNFα treatment of the 22B cells caused rapid, transient and selective acetylation of regions of the IL-8 promoter associated with the NFkB response element. Co-treatment of the cells with retinoids plus TNF increased the acetylation of chromatin in this region without altering the kinetics of acetylation. These results demonstrate that ligand activated retinoid receptors can cooperate with NFkB in histone acetylation and chromatin remodeling. We believe that in certain HNSCC tumors this cooperation and the resulting enhancement of IL-8 expression can induce an inflammatory response that leads to tumor regression. We believe that the induction of inflammation in susceptible tumors, possibly coupled with cytotoxic interventions may be an important component in the use of retinoids to treat human squamous cancers. ^

Diagnostik von Regressionsschätzungen bei kleinen Stichproben (mit einem Exkurs zu logistischer Regression)

Wie alle anderen statistischen Verfahren konzentriert sich auch die Methode der Regression nur auf die Analyse ausgewählter Aspekte vorliegenden Datenmaterials. Entsprechend sind zu gegebenen Regressionsergebnissen ganz unterschiedliche Datenkonstellationen denkbar, wovon aber für die Interpretation der Ergebnisse nicht alle unproblematisch sind. So besteht besonders bei kleinen Stichproben die Gefahr, dass die Regressionsschätzung entscheidend von einzelnen Extremwerten abhängt, was die Verlässlichkeit der daraus abgeleiteten Schlussfolgerungen beeinträchtigt. In diesem Beitrag werden deshalb anhand von Beispielen einige einfache grafische und formale Instrumente zur Diagnose einflussreicher Datenpunkte in der linearen und logistischen Regression vorgestellt, die im Prozess der Datenanalyse standardmäßig angewendet werden sollten. Weiterhin werden nach Identifikation „atypischer“ Datenpunkte zu verfolgende Analysestrategien diskutiert.

From regression estimates to document tables: output formatting using -estout-

Postestimation processing and formatting of regression estimates for input into document tables are tasks that many of us have to do. However, processing results by hand can be laborious, and is vulnerable to error. There are therefore many benefits to automation of these tasks while at the same time retaining user flexibility in terms of output format. The estout package meets these needs. estout assembles a table of coefficients, "significance stars", summary statistics, standard errors, t/z statistics, p-values, confidence intervals, and other statistics calculated for up to twenty models previously fitted and stored by estimates store. It then writes the table to the Stata log and/or to a text file. The estimates are formatted optionally in several styles: html, LaTeX, or tab-delimited (for input into MS Excel or Word). There are a large number of options regarding which output is formatted and how. This talk will take users through a range of examples, from relatively basic simple applications to complex ones.

Measuring fixed costs for firms' use of a free trade agreement : threshold regression approach

In this paper, by employing the threshold regression method, we estimate the average tariff equivalent of fixed costs for the use of a free trade agreement (FTA) among all existing FTAs in the world. It is estimated to be 3.2%. This global estimate serves as a reference rate in the evaluation of each FTA’s fixed costs.

Kernel bandwidth estimation for moving object detection in non-stabilized cameras

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

Lazy Lasso for local regression

Locally weighted regression is a technique that predicts the response for new data items from their neighbors in the training data set, where closer data items are assigned higher weights in the prediction. However, the original method may suffer from overfitting and fail to select the relevant variables. In this paper we propose combining a regularization approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets. Results show that the proposed method outperforms linear and local models for several kinds of scenarios

Nutritive value of palm kernel meal in diets for growing rabbits

The aim of this work was to determine the nutritive value of palm kernel meal (PKM) in diets for growing rabbits. In Experiment 1, 20 New Zealand × Californian growing rabbits 50 d-old were used to determine energy, crude protein, fibre and fat digestibility of PKM. The nutritive value was estimated by the difference method using a basal diet and another diet made by substituting 200 g/kg of basal diet with PKM. Energy, crude protein, ether extract and neutral detergent fibre of PKM digestibilities were, respectively, 0.549 (±0.056, SE), 0.541 (±0.069), 0.850 (±0.048) and 0.430 (±0.101), and the digestible energy concentration was 10.9 MJ/kg (±1.03) DM. In Experiment 2, 412 rabbits were allocated at random to the two experimental diets to measure growing performance. Inclusion of 200 g PKM/kg in the diet did not affect feed or digestible energy intake but decreased slightly (by around 5%) average daily gain (P = 0.003) and feed efficiency (P < 0.001). Neither mortality nor Clostridium perfringens counts in soft faeces were affected by type of diet. Palm kernel meal can be considered a palatable source of fibre, protein and fat for rabbits and can substitute significant amounts of other fibrous ingredients in the diet without adverse effects on growth performance.

Area-Efficient Linear Regression Architecture for Real-Time Signal Processing on FPGAs

Linear regression is a technique widely used in digital signal processing. It consists on finding the linear function that better fits a given set of samples. This paper proposes different hardware architectures for the implementation of the linear regression method on FPGAs, specially targeting area restrictive systems. It saves area at the cost of constraining the lengths of the input signal to some fixed values. We have implemented the proposed scheme in an Automatic Modulation Classifier, meeting the hard real-time constraints this kind of systems have.

A Generalized Regression Methodology for Bivariate Heteroscedastic Data

We present a methodology for reducing a straight line fitting regression problem to a Least Squares minimization one. This is accomplished through the definition of a measure on the data space that takes into account directional dependences of errors, and the use of polar descriptors for straight lines. This strategy improves the robustness by avoiding singularities and non-describable lines. The methodology is powerful enough to deal with non-normal bivariate heteroscedastic data error models, but can also supersede classical regression methods by making some particular assumptions. An implementation of the methodology for the normal bivariate case is developed and evaluated.

Benefits of using a Wendland Kernel for free-surface flows

The aim of this paper Is lo discuss the influence of the selection of the interpolation kernel in the accuracy of the modeling of the internal viscous dissipation in Tree surface Hows, Simulations corresponding to a standing wave* for which an analytic solution available, are presented. Wendland and renormalized Gaussian kernels are considered. The differences in the flow pattern* and Internal dissipation mechanisms are documented for a range of Reynolds numbers. It is shown that the simulations with Wendland kernels replicate the dissipation mechanisms more accurately than those with a renormalized Gaussian kernel. Although some explanations are hinted we have Tailed to clarify which the core structural reasons for Mich differences are*

An automatic translation scheme from prolog to the andorra kernel language

The Andorra family of languages (which includes the Andorra Kernel Language -AKL) is aimed, in principie, at simultaneously supporting the programming styles of Prolog and committed choice languages. On the other hand, AKL requires a somewhat detailed specification of control by the user. This could be avoided by programming in Prolog to run on AKL. However, Prolog programs cannot be executed directly on AKL. This is due to a number of factors, from more or less trivial syntactic differences to more involved issues such as the treatment of cut and making the exploitation of certain types of parallelism possible. This paper provides basic guidelines for constructing an automatic compiler of Prolog programs into AKL, which can bridge those differences. In addition to supporting Prolog, our style of translation achieves independent and-parallel execution where possible, which is relevant since this type of parallel execution preserves, through the translation, the user-perceived "complexity" of the original Prolog program.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Simulation tools for statistical model comparison: an application to unobserved componenet models versus dynamic regression models

Aplicación de simulación de Monte Carlo y técnicas de Análisis de la Varianza (ANOVA) a la comparación de modelos estocásticos dinámicos para accidentes de tráfico.