The down-regulation of the catalytic subunit of the mitochondrial H + -ATP synthase (B-F1-ATPase) is a hallmark of most human carcinomas. This characteristic of the cancer cell provides a proteomic signature of cellular bioenergetics that can predict the prognosis of colon, lung, and breast cancer patients. Here we show that the in vivo tumor glucose uptake of lung carcinomas, as assessed by positron emission tomography in 110 patients using 2-deoxy-2-[18 F]fluoro-D-glucose as probe, inversely correlates with the bioenergetic signature determined by immunohistochemical analysis in tumor surgical specimens. Further, we show that inhibition of the activity of oxidative phosphorylation by incubation of cancer cells with oligomycin triggers a rapid increase in their rates of aerobic glycolysis. Moreover, we show that the cellular expression level of the B-F1-ATPase protein of mitochondrial oxidative phosphorylation inversely correlates (P < 0.001) with the rates of aerobic glycolysis in cancer cells. The results highlight the relevance of the alteration of the bioenergetic function of mitochondria for glucose capture and consumption by aerobic glycolysis in carcinomas. [Cancer Res 2007;67(19):9013-7]

A principal component method for multivariate functional data is proposed. Data can be arranged in a matrix whose elements are functions so that for each individual a vector of p functions is observed. This set of p curves is reduced to a small number of transformed functions, retaining as much information as possible. The criterion to measure the information loss is the integrated variance. Under mild regular conditions, it is proved that if the original functions are smooth this property is inherited by the principal components. A numerical procedure to obtain the smooth principal components is proposed and the goodness of the dimension reduction is assessed by two new measures of the proportion of explained variability. The method performs as expected in various controlled simulated data sets and provides interesting conclusions when it is applied to real data sets.

The Hájek-Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon-Nikodym density for each measure with respect to the other one) or mutually singular. Unlike the case of finite dimensional Gaussian measures, there are non-trivial examples of both situations when dealing with Gaussian stochastic processes. This paper provides:(a) Explicit expressions for the optimal (Bayes) rule and the minimal classification error probability in several relevant problems of supervised binary classification of mutually absolutely continuous Gaussian processes. The approach relies on some classical results in the theory of Reproducing Kernel Hilbert Spaces (RKHS).(b) An interpretation, in terms of mutual singularity, for the "near perfect classification" phenomenon described by Delaigle and Hall (2012). We show that the asymptotically optimal rule proposed by these authors can be identified with the sequence of optimal rules for an approximating sequence of classification problems in the absolutely continuous case.(c) A new model-based method for variable selection in binary classification problems, which arises in a very natural way from the explicit knowledge of the RN-derivatives and the underlying RKHS structure. Different classifiers might be used from the selected variables. In particular, the classical, linear finite-dimensional Fisher rule turns out to be consistent under some standard conditions on the underlying functional model.

No abstract

Given a random sample taken on a compact domain S ⊂ R d , the authors propose a new method for testing the hypothesis of uniformity of the underlying distribution. The test statistic is based on the distance of every observation to the boundary of S. The proposed test has a number of interesting properties, namely: Unlike most available methods, it is feasible and particularly suitable for high dimensional data, it is distribution-free for a wide range of choices of S, it can be adapted to the case that the support S is unknown and also allows for one-sided versions. Moreover, the results suggest that, in some cases, this procedure does not suffer from the well-known "curse of dimensionality". The authors study the properties of this test from both a theoretical and practical point of view. In particular, an extensive simulation study is given in order to compare the performance of our methods with some recent alternative procedures. The conclusions suggest that the proposed test provides quite a satisfactory balance between statistical power, computational simplicity, and flexibility of application for different dimensions and supports. Title in French: we can supply thisRésumé : Given a random sample taken on a compact domain S ⊂ R d , the authors propose a new method for testing the hypothesis of uniformity of the underlying distribution. The test statistic is based on the distance of every observation to the boundary of S. The proposed test has a number of interesting properties, namely: Unlike most available methods, it is feasible and particularly suitable for high dimensional data, it is distribution-free for a wide range of choices of S, it can be adapted to the case that the support S is unknown and also allows for one-sided versions. Moreover, the results suggest that, in some cases, this procedure does not suffer from the well-known "curse of dimensionality". The authors study the properties of this test from both a theoretical and practical point of view. In particular, an extensive simulation study is given in order to compare the performance of our methods with some recent alternative procedures. The conclusions suggest that the proposed test provides quite a satisfactory balance between statistical power, computational simplicity, and flexibility of application for different dimensions and supports.