A non-Markovian model for cell population growth: speed of convergence and central limit theorem

In De Gunst (1989) a stochastic model was developed for the growth of a batch culture of plant cells. In this paper the mathematical properties of the model are considered. We investigate the asymptotic behaviour of the population growth as predicted by the model when the initial cell number of population members tends to infinity. In particular it is shown that the total cell number, which is a non-Markovian counting process, converges almost surely, uniformly on the real line to a non-random function and the rate of convergence is established. Moreover, a central limit theorem is proved. Computer simulations illustrate the behaviour of the process. The model is graphically compared with experimental data

Wild Bootstrap for Counting Process-Based Statistics

The wild bootstrap is a popular resampling method in the context of time-to-event data analyses. Previous works established the large sample properties of it for applications to different estimators and test statistics. It can be used to justify the accuracy of inference procedures such as hypothesis tests or time-simultaneous confidence bands. This paper consists of two parts: in Part~I, a general framework is developed in which the large sample properties are established in a unified way by using martingale structures. The framework includes most of the well-known non- and semiparametric statistical methods in time-to-event analysis and parametric approaches. In Part II, the Fine-Gray proportional sub-hazards model exemplifies the theory for inference on cumulative incidence functions given the covariates. The model falls within the framework if the data are censoring-complete. A simulation study demonstrates the reliability of the method and an application to a data set about hospital-acquired infections illustrates the statistical procedure.Comment: 2 parts, 115 pages, 2 figures, 13 table

Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data

Fine-Gray models specify the subdistribution hazards for one out of multiple competing risks to be proportional. The estimators of parameters and cumulative incidence functions under Fine-Gray models have a simpler structure when data are censoring-complete than when they are more generally incomplete. This paper considers the case of incomplete data but it exploits the above-mentioned simpler estimator structure for which there exists a wild bootstrap approach for inferential purposes. The present idea is to link the methodology under censoring-completeness with the more general right-censoring regime with the help of multiple imputation. In a simulation study, this approach is compared to the estimation procedure proposed in the original paper by Fine and Gray when it is combined with a bootstrap approach. An application to a data set about hospital-acquired infections illustrates the method.Comment: 32 pages, 2 figures, 1 tabl

Functional analysis of cancer-associated EGFR mutants using a cellular assay with YFP-tagged EGFR intracellular domain

<p>Abstract</p> <p>Background</p> <p>The presence of EGFR kinase domain mutations in a subset of NSCLC patients correlates with the response to treatment with the EGFR tyrosine kinase inhibitors gefitinib and erlotinib. Although most EGFR mutations detected are short deletions in exon 19 or the L858R point mutation in exon 21, more than 75 different EGFR kinase domain residues have been reported to be altered in NSCLC patients. The phenotypical consequences of different EGFR mutations may vary dramatically, but the majority of uncommon EGFR mutations have never been functionally evaluated.</p> <p>Results</p> <p>We demonstrate that the relative kinase activity and erlotinib sensitivity of different EGFR mutants can be readily evaluated using transfection of an YFP-tagged fragment of the EGFR intracellular domain (YFP-EGFR-ICD), followed by immunofluorescence microscopy analysis. Using this assay, we show that the exon 20 insertions Ins770SVD and Ins774HV confer increased kinase activity, but no erlotinib sensitivity. We also show that, in contrast to the common L858R mutation, the uncommon exon 21 point mutations P848L and A859T appear to behave like functionally silent polymorphisms.</p> <p>Conclusion</p> <p>The ability to rapidly obtain functional information on EGFR variants of unknown relevance using the YFP-EGFR-ICD assay might prove important in the future for the management of NSCLC patients bearing uncommon EGFR mutations. In addition, our assay may be used to determine the response of resistant EGFR mutants to novel second-generation TKIs.</p

A very brief description of LOFAR - the Low Frequency Array

LOFAR (Low Frequency Array) is an innovative radio telescope optimized for the frequency range 30-240 MHz. The telescope is realized as a phased aperture array without any moving parts. Digital beam forming allows the telescope to point to any part of the sky within a second. Transient buffering makes retrospective imaging of explosive short-term events possible. The scientific focus of LOFAR will initially be on four key science projects (KSPs): 1) detection of the formation of the very first stars and galaxies in the universe during the so-called epoch of reionization by measuring the power spectrum of the neutral hydrogen 21-cm line (Shaver et al. 1999) on the ~5' scale; 2) low-frequency surveys of the sky with of order $10^8$ expected new sources; 3) all-sky monitoring and detection of transient radio sources such as gamma-ray bursts, x-ray binaries, and exo-planets (Farrell et al. 2004); and 4) radio detection of ultra-high energy cosmic rays and neutrinos (Falcke & Gorham 2003) allowing for the first time access to particles beyond 10^21 eV (Scholten et al. 2006). Apart from the KSPs open access for smaller projects is also planned. Here we give a brief description of the telescope.Comment: 2 pages, IAU GA 2006, Highlights of Astronomy, Volume 14, K.A. van der Hucht, e