Treatment of Batch in the Detection, Calibration, and Quantification of Immunoassays in Large-scale Epidemiologic Studies

Many laboratory assays measure biomarkers via a 2-stage process. Direct measurement yields relative measures that are subsequently transformed to the unit of interest by using a calibration experiment. The calibration experiment is performed within the main experiment and uses a validation set for which true values are known and relative values are measured by assays to estimate the relation between relative and absolute values. Immunoassays, polymerase chain reaction, and chromatographic approaches are among assays performed in this manner. METHODS: For studies with multiple batches, data from more than a single calibration experiment are available. Conventionally, calibration of assays based on the standard curve is performed specific to each batch; the calibration experiment from each batch is used to calibrate each batch independently. This batch-specific approach incorporates batch variability but, due to the small number of calibration measurements in each batch, may not be best suited for this purpose. RESULTS: Mixed-effects models have been described to address interassay variability and to provide a measure of quality assurance. Conversely, when interbatch variability is negligible, a model that does not incorporate batch effect may be used to estimate an overall calibration curve. CONCLUSION: We explore approaches for use of calibration data in studies with many batches. Using a real data example with biomarker and outcome information, we show that risk estimates may vary depending on the calibration approach used. We demonstrate the potential for bias when using simulations. Under minimal interbatch variability, as seen in our data, conventional batch-specific calibration does not best use information available in the data and results in attenuated risk estimates

The fidelity of dynamic signaling by noisy biomolecular networks

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

3D global and regional patterns of human fetal subplate growth determined in utero

The waiting period of subplate evolution is a critical phase for the proper formation of neural connections in the brain. During this time, which corresponds to 15 to 24 postconceptual weeks (PCW) in the human fetus, thalamocortical and cortico-cortical afferents wait in and are in part guided by molecules embedded in the extracellular matrix of the subplate. Recent advances in fetal MRI techniques now allow us to study the developing brain anatomy in 3D from in utero imaging. We describe a reliable segmentation protocol to delineate the boundaries of the subplate from T2-W MRI. The reliability of the protocol was evaluated in terms of intra-rater reproducibility on a subset of the subjects. We also present the first 3D quantitative analyses of temporal changes in subplate volume, thickness, and contrast from 18 to 24 PCW. Our analysis shows that firstly, global subplate volume increases in proportion with the supratentorial volume; the subplate remained approximately one-third of supratentorial volume. Secondly, we found both global and regional growth in subplate thickness and a linear increase in the median and maximum subplate thickness through the waiting period. Furthermore, we found that posterior regions—specifically the occipital pole, ventral occipito-temporal region, and planum temporale—of the developing brain underwent the most statistically significant increases in subplate thickness. During this period, the thickest region was the developing somatosensory/motor cortex. The subplate growth patterns reported here may be used as a baseline for comparison to abnormal fetal brain development

A module-based analytical strategy to identify novel disease-associated genes shows an inhibitory role for interleukin 7 Receptor in allergic inflammation

<p>Abstract</p> <p>Background</p> <p>The identification of novel genes by high-throughput studies of complex diseases is complicated by the large number of potential genes. However, since disease-associated genes tend to interact, one solution is to arrange them in modules based on co-expression data and known gene interactions. The hypothesis of this study was that such a module could be a) found and validated in allergic disease and b) used to find and validate one ore more novel disease-associated genes.</p> <p>Results</p> <p>To test these hypotheses integrated analysis of a large number of gene expression microarray experiments from different forms of allergy was performed. This led to the identification of an experimentally validated reference gene that was used to construct a module of co-expressed and interacting genes. This module was validated in an independent material, by replicating the expression changes in allergen-challenged CD4⁺cells. Moreover, the changes were reversed following treatment with corticosteroids. The module contained several novel disease-associated genes, of which the one with the highest number of interactions with known disease genes, <it>IL7R</it>, was selected for further validation. The expression levels of <it>IL7R </it>in allergen challenged CD4⁺cells decreased following challenge but increased after treatment. This suggested an inhibitory role, which was confirmed by functional studies.</p> <p>Conclusion</p> <p>We propose that a module-based analytical strategy is generally applicable to find novel genes in complex diseases.</p

Azimuthal anisotropy and correlations at large transverse momenta in p+pp+p and Au+Au collisions at sNN\sqrt{s_{_{NN}}}= 200 GeV

Results on high transverse momentum charged particle emission with respect to the reaction plane are presented for Au+Au collisions at $\sqrt{s_{_{NN}}}$= 200 GeV. Two- and four-particle correlations results are presented as well as a comparison of azimuthal correlations in Au+Au collisions to those in $p+p$ at the same energy. Elliptic anisotropy, $v_2$, is found to reach its maximum at $p_t \sim 3$ GeV/c, then decrease slowly and remain significant up to $p_t\approx 7$ -- 10 GeV/c. Stronger suppression is found in the back-to-back high-$p_t$ particle correlations for particles emitted out-of-plane compared to those emitted in-plane. The centrality dependence of $v_2$ at intermediate $p_t$ is compared to simple models based on jet quenching.Comment: 4 figures. Published version as PRL 93, 252301 (2004

Azimuthal anisotropy in Au+Au collisions at sqrtsNN = 200 GeV

The results from the STAR Collaboration on directed flow (v_1), elliptic flow (v_2), and the fourth harmonic (v_4) in the anisotropic azimuthal distribution of particles from Au+Au collisions at sqrtsNN = 200 GeV are summarized and compared with results from other experiments and theoretical models. Results for identified particles are presented and fit with a Blast Wave model. Different anisotropic flow analysis methods are compared and nonflow effects are extracted from the data. For v_2, scaling with the number of constituent quarks and parton coalescence is discussed. For v_4, scaling with v_2^2 and quark coalescence is discussed.Comment: 26 pages. As accepted by Phys. Rev. C. Text rearranged, figures modified, but data the same. However, in Fig. 35 the hydro calculations are corrected in this version. The data tables are available at http://www.star.bnl.gov/central/publications/ by searching for "flow" and then this pape

Building effective service linkages in primary mental health care: a narrative review part 2

<p>Abstract</p> <p>Background</p> <p>Primary care services have not generally been effective in meeting mental health care needs. There is evidence that collaboration between primary care and specialist mental health services can improve clinical and organisational outcomes. It is not clear however what factors enable or hinder effective collaboration. The objective of this study was to examine the factors that enable effective collaboration between specialist mental health services and primary mental health care.</p> <p>Methods</p> <p>A narrative and thematic review of English language papers published between 1998 and 2009. An expert reference group helped formulate strategies for policy makers. Studies of descriptive and qualitative design from Australia, New Zealand, UK, Europe, USA and Canada were included. Data were extracted on factors reported as enablers or barriers to development of service linkages. These were tabulated by theme at clinical and organisational levels and the inter-relationship between themes was explored.</p> <p>Results</p> <p>A thematic analysis of 30 papers found the most frequently cited group of factors was "partnership formation", specifically role clarity between health care workers. Other factor groups supporting clinical partnership formation were staff support, clinician attributes, clinic physical features and evaluation and feedback. At the organisational level a supportive institutional environment of leadership and change management was important. The expert reference group then proposed strategies for collaboration that would be seen as important, acceptable and feasible. Because of the variability of study types we did not exclude on quality and findings are weighted by the number of studies. Variability in local service contexts limits the generalisation of findings.</p> <p>Conclusion</p> <p>The findings provide a framework for health planners to develop effective service linkages in primary mental health care. Our expert reference group proposed five areas of strategy for policy makers that address organisational level support, joint clinical problem solving, local joint care guidelines, staff training and supervision and feedback.</p