The hypoxia-inducible factor 1 (HIF-1) plays a critical role in cellular responses to hypoxia. The aim of the present study was to evaluate which genes are induced by hypoxia, and whether this induction is mediated by HIF-1, by expression microarray analysis of wt and HIF-1alpha null mouse fibroblasts. Forty-five genes were up-regulated by hypoxia and 40 (89%) of these were regulated by HIF-1. Of the 114 genes down-regulated by hypoxia, 19 (17%) were HIF-1-dependent. All glycolytic enzymes were strongly up-regulated by hypoxia in a HIF-1-dependent manner. Genes already known to be related to hypoxia, such as glucose transporter 1, BNIP3, and hypoxia-induced gene 1, were induced. In addition, multiple new HIF-1-regulated genes were identified, including genes involved in metabolism (adenylate kinase 4, galactokinase), apoptosis (galectin-3 and gelsolin), and invasion (RhoA). Genes down-regulated by hypoxia were involved in cytoskeleton maintenance (Rho kinase), mRNA processing (heterogeneous nuclear ribonucleoprotein H1 and splicing factor), and DNA repair (REV3). Furthermore, seven cDNAs from genes with unknown function or expressed sequence tags (ESTs) were up-regulated and 27 such cDNAs were down-regulated. In conclusion, hypoxia causes down- rather than up-regulation of gene expression and HIF-1 seems to play a major role in the regulation of hypoxia-induced genes.

Survival probabilities were high among HIV-infected patients initiating HAART at an early stage of infection. The best therapy strategy is therefore to start HAART at this stage of infection. However, deferring HAART in patients with high CD4 cell counts may be clinically more appropriate given toxicity and adherence problems. The lack of any change in non-HIV-related mortality suggests that toxicity has not yet become a major risk factor for death.

Summary.  A class of Shewhart‐type distribution‐free control charts is considered. A key advantage of these charts is that the in‐control run length distribution is the same for all continuous process distributions. Exact expressions for the run length distribution and the average run length (ARL) are derived and properties of the charts are studied via evaluations of the run length distribution probabilities and the ARL. Tables are provided for implementation for some typical ARL values and false alarm rates. The charts proposed are preferable from a robustness point of view, have attractive ARL properties and would be particularly useful in situations where one uses a classical Shewhart  X‐chart. A numerical illustration is given.

Nonparametric or distribution-free charts can be useful in statistical process control problems when there is limited or lack of knowledge about the underlying process distribution. In this paper, a phase II Shewhart-type chart is considered for location, based on reference data from phase I analysis and the well-known Mann-Whitney statistic. Control limits are computed using Lugannani-Rice-saddlepoint, Edgeworth, and other approximations along with Monte Carlo estimation. The derivations take account of estimation and the dependence from the use of a reference sample. An illustrative numerical example is presented. The in-control performance of the proposed chart is shown to be much superior to the classical ShewhartX chart. Further comparisons on the basis of some percentiles of the out-of-control conditional run length distribution and the unconditional out-of-control ARL show that the proposed chart is almost as good as the ShewhartX chart for the normal distribution, but is more powerful for a heavy-tailed distribution such as the Laplace, or for a skewed distribution such as the Gamma. Interactive software, enabling a complete implementation of the chart, is made available on a website.

Sample size determination methods are considered for hypothesis testing about location parameters of two continuous distributions using independent random samples and the Mann-Whitney-Wilcoxon (MWW) test, for a specified shift, size, and power. These methods are based on the so-called "Noether's formula," derived from a normal approximation to the power of the MWW test, and a pilot sample from each of the distributions. Compared with alternate methods, including the one using bootstrap similar to that of Hamilton and Collings (1991) the new methods are shown to be at least as good in terms of accuracy and substantially more efficient in terms of speed and variability. The simpler method, using linearly smoothed empirical cdf's of the pilot samples and requiring no bootstrapping, is recommended for practical use. Extensions and adaptations for obtaining an upper confidence bound on the sample size and to general linear rank tests are indicated.