Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The discovery that mammalian cells have the ability to synthesize the free radical nitric oxide (NO) has stimulated an extraordinary impetus for scientific research in all the fields of biology and medicine. Since its early description as an endothelial-derived relaxing factor, NO has emerged as a fundamental signaling device regulating virtually every critical cellular function, as well as a potent mediator of cellular damage in a wide range of conditions. Recent evidence indicates that most of the cytotoxicity attributed to NO is rather due to peroxynitrite, produced from the diffusion-controlled reaction between NO and another free radical, the superoxide anion. Peroxynitrite interacts with lipids, DNA, and proteins via direct oxidative reactions or via indirect, radical-mediated mechanisms. These reactions trigger cellular responses ranging from subtle modulations of cell signaling to overwhelming oxidative injury, committing cells to necrosis or apoptosis. In vivo, peroxynitrite generation represents a crucial pathogenic mechanism in conditions such as stroke, myocardial infarction, chronic heart failure, diabetes, circulatory shock, chronic inflammatory diseases, cancer, and neurodegenerative disorders. Hence, novel pharmacological strategies aimed at removing peroxynitrite might represent powerful therapeutic tools in the future. Evidence supporting these novel roles of NO and peroxynitrite is presented in detail in this review.

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Nitric Oxide and Peroxynitrite in Health and Disease

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Modern Applied Statistics With S

The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.

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Building Predictive Models in R Using the caret Package

General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

Applied Predictive Modeling

The role of reactive oxygen-induced oxidative damage to lipids (i.e., lipid peroxidation, LP) and proteins has been strongly supported in previous work. Most notably, a number of free radical scavengers and lipid antioxidants have been demonstated to be neuroprotective in traumatic brain injury (TBI) models. However, the specific sources of reactive oxygen species (ROS), the time course of oxidative damage and its relationship to post-traumatic neurodegeneration in the injured brain have been incompletely defined. The present study was directed at an investigation of the role of the ROS, peroxynitrite (PON), in the acute pathophysiology of TBI and its temporal relationship to neurodegeneration in the context of the mouse model of diffuse head injury model. Male CF-1 mice were subjected to a moderately severe head injury and assessed at 1-, 3-, 6-, 12-, 24-, 48-, 72, 96- and 120-h post-injury for neurodegeneration using quantitative image analysis of silver staining and semi-quantitative analysis of PON-me...

/pdf/peroxynitrite-mediated-protein-nitration-and-lipid-4zttu8jg1w.pdf

Peroxynitrite-mediated protein nitration and lipid peroxidation in a mouse model of traumatic brain injury.

Three-dimensional (3D) cell culture is gaining acceptance in response to the need for cellular models that better mimic physiologic tissues. Spheroids are one such 3D model where clusters of cells will undergo self-assembly to form viable, 3D tumor-like structures. However, to date little is known about how spheroid biology compares to that of the more traditional and widely utilized 2D monolayer cultures. Therefore, the goal of this study was to characterize the phenotypic and functional differences between lung tumor cells grown as 2D monolayer cultures, versus cells grown as 3D spheroids. Eight lung tumor cell lines, displaying varying levels of epidermal growth factor receptor (EGFR) and cMET protein expression, were used to develop a 3D spheroid cell culture model using low attachment U-bottom plates. The 3D spheroids were compared with cells grown in monolayer for 1) EGFR and cMET receptor expression, as determined by flow cytometry, 2) EGFR and cMET phosphorylation by MSD assay, and 3) cell proliferation in response to epidermal growth factor (EGF) and hepatocyte growth factor (HGF). In addition, drug responsiveness to EGFR and cMET inhibitors (Erlotinib, Crizotinib, Cetuximab [Erbitux] and Onartuzumab [MetMab]) was evaluated by measuring the extent of cell proliferation and migration. Data showed that EGFR and cMET expression is reduced at day four of untreated spheroid culture compared to monolayer. Basal phosphorylation of EGFR and cMET was higher in spheroids compared to monolayer cultures. Spheroids showed reduced EGFR and cMET phosphorylation when stimulated with ligand compared to 2D cultures. Spheroids showed an altered cell proliferation response to HGF, as well as to EGFR and cMET inhibitors, compared to monolayer cultures. Finally, spheroid cultures showed exceptional utility in a cell migration assay. Overall, the 3D spheroid culture changed the cellular response to drugs and growth factors and may more accurately mimic the natural tumor microenvironment.

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Three-dimensional lung tumor microenvironment modulates therapeutic compound responsiveness in vitro--implication for drug development.

Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.

https://www.jstatsoft.org/article/view/v017i02/v17i02.pdf

ada: An R Package for Stochastic Boosting

Determining which predictors should be included in a model is becoming one of the most critical questions as data are becoming increasingly high-dimensional. The chapter demonstrates the negative effect of extra predictors on a number of models (Section 19.1), as well as discussing typical approaches to supervised feature selection such as wrapper and filter methods (Sections 19.2-19.4). The modeler should also be aware of the danger of selection bias and how to avoid it (Section 19.5). In Section 19.6 we present a case study to illustrate the feature selection methods. In the Computing Section (19.7) we demonstrate how to implement feature selection methodologies in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Kjell Johnson

Papers

Applied Predictive Modeling

Peroxynitrite-mediated protein nitration and lipid peroxidation in a mouse model of traumatic brain injury.

Three-dimensional lung tumor microenvironment modulates therapeutic compound responsiveness in vitro--implication for drug development.

ada: An R Package for Stochastic Boosting

An Introduction to Feature Selection