Factors influencing intracranial pressure (ICP) during percutaneous tracheostomy.

Objectives: Percutaneous tracheostomy (PT) is common on ICUs. An increase of intracranial pressure (ICP) can be observed in patients with acute cerebral diseases. Factors determining ICP increase remain unclear.

EcR recruits dMi-2 and increases efficiency of dMi-2-mediated remodelling to constrain transcription of hormone-regulated genes.

Gene regulation by steroid hormones plays important roles in health and disease. In Drosophila, the hormone ecdysone governs transitions between key developmental stages. Ecdysone-regulated genes are bound by a heterodimer of ecdysone receptor (EcR) and Ultraspiracle.

Relationship of Prolonged Operative Time and Comorbidities With Complications After Geriatric Ankle Fractures.

Background: The incidence of geriatric ankle fractures has increased during the last few decades. In contrast to younger patients, increased complication rates have been observed. Thus, the goal of the present study was to identify risk factors for perioperative complications following open reduction and internal fixation of geriatric ankle fractures.

Effect of percutaneous tracheostomy on intracerebral pressure and perfusion pressure in patients with acute cerebral dysfunction (TIP Trial): an observational study.

Background: Bedside percutaneous tracheostomy (PT) is very commonly used for patients who require prolonged mechanical ventilation. The effect of tracheostomy on intracranial pressure (ICP) is currently a subject of controversy. The aim of our study is to clarify the relation between PT and its effect on ICP and cerebral perfusion pressure.

PERMORY-MPI: a program for high-speed parallel permutation testing in genome-wide association studies.

Unlabelled: PERMORY is software for accelerated permutation testing of genome-wide association studies (GWAS). We have parallelized PERMORY using the Message-Passing Interface resulting in a nearly linear speedup. Furthermore, we added accelerated analysis of GWAS using quantitative phenotypes, and an accurate estimation of the effective number of independent tests.

PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing.

Motivation: In genome-wide association studies (GWAS) examining hundreds of thousands of genetic markers, the potentially high number of false positive findings requires statistical correction for multiple testing. Permutation tests are considered the gold standard for multiple testing correction in GWAS, because they simultaneously provide unbiased type I error control and high power. At the same time, they demand heavy computational effort, especially with large-scale datasets of modern GWAS.

Optimal robust two-stage designs for genome-wide association studies.

Optimal robust two-stage designs for genome-wide association studies are proposed using the maximum of the recessive, additive and dominant linear trend test statistics. These designs combine cost-saving two-stage genotyping with robustness against misspecification of the genetic model and are much more efficient than designs based on a single model specific test statistic in detecting multiple loci with different modes of inheritance. For given power of 90%, typical cost savings of 34% can be realised by increasing the total sample size by about 13% but genotyping only about half of the sample for the full marker set in the first stage and carrying forward about 0.

Optimal multistage designs--a general framework for efficient genome-wide association studies.

Genome-wide association studies (GWAS) have become increasingly affordable but they are still costly. Therefore, cost saving 2-stage designs were proposed in the literature. The restriction to 2 stages, however, seems artificial and does not exploit the full potential of the underlying methods.

Including sampling and phenotyping costs into the optimization of two stage designs for genomewide association studies.

We propose optimized two-stage designs for genome-wide case-control association studies, using a hypothesis testing paradigm. To save genotyping costs, the complete marker set is genotyped in a sub-sample only (stage I). On stage II, the most promising markers are then genotyped in the remaining sub-sample.