Evaluating a transfer gradient assumption in a fomite-mediated microbial transmission model using an experimental and Bayesian approach

Current microbial exposure models assume that microbial exchange follows a concentration gradient during hand-to-surface contacts. Our objectives were to evaluate this assumption using transfer efficiency experiments and to evaluate a model's ability to explain concentration changes using approximate Bayesian computation (ABC) on these experimental data. Experiments were conducted with two phages (MS2, ΦX174) simultaneously to study bidirectional transfer. Concentrations on the fingertip and surface were quantified before and after fingertip-to-surface contacts. Prior distributions for surface and fingertip swabbing efficiencies and transfer efficiency were used to estimate concentrations on the fingertip and surface post contact. To inform posterior distributions, Euclidean distances were calculated for predicted detectable concentrations (log10 PFU cm−2) on the fingertip and surface post contact in comparison with experimental values. To demonstrate the usefulness of posterior distributions in calibrated model applications, posterior transfer efficiencies were used to estimate rotavirus infection risks for a fingertip-to-surface and subsequent fingertip-to-mouth contact. Experimental findings supported the transfer gradient assumption. Through ABC, the model explained concentration changes more consistently when concentrations on the fingertip and surface were similar. Future studies evaluating microbial transfer should consider accounting for differing fingertip-to-surface and surface-to-fingertip transfer efficiencies and extend this work for other microbial types

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

<p>Abstract</p> <p>Background</p> <p>High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.</p> <p>Results</p> <p>We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.</p> <p>Conclusions</p> <p>Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.</p

Extended analysis of benchmark datasets for Agilent two-color microarrays

<p>Abstract</p> <p>Background</p> <p>As part of its broad and ambitious mission, the MicroArray Quality Control (MAQC) project reported the results of experiments using External RNA Controls (ERCs) on five microarray platforms. For most platforms, several different methods of data processing were considered. However, there was no similar consideration of different methods for processing the data from the Agilent two-color platform. While this omission is understandable given the scale of the project, it can create the false impression that there is consensus about the best way to process Agilent two-color data. It is also important to consider whether ERCs are representative of all the probes on a microarray.</p> <p>Results</p> <p>A comparison of different methods of processing Agilent two-color data shows substantial differences among methods for low-intensity genes. The sensitivity and specificity for detecting differentially expressed genes varies substantially for different methods. Analysis also reveals that the ERCs in the MAQC data only span the upper half of the intensity range, and therefore cannot be representative of all genes on the microarray.</p> <p>Conclusion</p> <p>Although ERCs demonstrate good agreement between observed and expected log-ratios on the Agilent two-color platform, such an analysis is incomplete. Simple loess normalization outperformed data processing with Agilent's Feature Extraction software for accurate identification of differentially expressed genes. Results from studies using ERCs should not be over-generalized when ERCs are not representative of all probes on a microarray.</p

Sensory Measurements: Coordination and Standardization

Do sensory measurements deserve the label of “measurement”? We argue that they do. They fit with an epistemological view of measurement held in current philosophy of science, and they face the same kinds of epistemological challenges as physical measurements do: the problem of coordination and the problem of standardization. These problems are addressed through the process of “epistemic iteration,” for all measurements. We also argue for distinguishing the problem of standardization from the problem of coordination. To exemplify our claims, we draw on olfactory performance tests, especially studies linking olfactory decline to neurodegenerative disorders

Seismic reflection images of a near-axis melt sill within the lower crust at the Juan de Fuca ridge

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 460 (2009): 89-93, doi:10.1038/nature08095.The oceanic crust extends over two thirds of the Earth’s solid surface and is generated along mid-ocean ridges from melts derived from the upwelling mantle. The upper and mid crust are constructed by dyking and seafloor eruptions originating from magma accumulated in mid-crustal lenses at the spreading axis, but the style of accretion of the lower oceanic crust is actively debated. Models based on geological and petrological data from ophiolites propose that the lower oceanic crust is accreted from melt sills intruded at multiple levels between the Moho transition zone (MTZ) and the mid-crustal lens, consistent with geophysical studies that suggest the presence of melt within the lower crust. However, seismic images of molten sills within the lower crust have been elusive. To date only seismic reflections from mid-crustal melt lenses and sills within the MTZ have been described, suggesting that melt is efficiently transported through the lower crust. Here we report deep crustal seismic reflections off the southern Juan de Fuca Ridge that we interpret as originating from a molten sill presently accreting the lower oceanic crust. The sill sits 5-6 km beneath the seafloor and 850-900 m above the MTZ, and it is located 1.4-3.2 km off thespreading axis. Our results provide evidence for the existence of low permeability barriers to melt migration within the lower section of modern oceanic crust forming at intermediate-to-fast spreading rates, as inferred from ophiolite studies.This research was supported by grants form the US NSF

Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data

<p>Abstract</p> <p>Background</p> <p>Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC.</p> <p>Results</p> <p>A total of 293 arrays were used in the intra- and inter-platform analysis. A hierarchical cluster analysis shows distinct differences in the measured intensities among the five platforms. A number of genes show a small fold-change in one platform and a large fold-change in another platform, even though the correlations between platforms are high. An analysis of variance shows thirty percent of gene expressions of the samples show inconsistent patterns across the five platforms. We illustrated that POG does not reflect the accuracy of a selected gene list. A non-overlapping gene can be truly differentially expressed with a stringent cut, and an overlapping gene can be non-differentially expressed with non-stringent cutoff. In addition, POG is an unusable selection criterion. POG can increase or decrease irregularly as cutoff changes; there is no criterion to determine a cutoff so that POG is optimized.</p> <p>Conclusion</p> <p>Using various statistical methods we demonstrate that there are differences in the intensities measured by different platforms and different sites within platform. Within each platform, the patterns of expression are generally consistent, but there is site-by-site variability. Evaluation of data analysis methods for use in regulatory decision should take no treatment effect into consideration, when there is no treatment effect, "a fold-change cutoff with a non-stringent p-value cutoff" could result in 100% false positive error selection.</p

Frozen magma lenses below the oceanic crust

Author Posting. © The Authors, 2005. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 436 (2005): 1149-1152, doi:10.1038/nature03944.The Earth's oceanic crust crystallizes from magmatic systems generated at mid-ocean ridges. Whereas a single magma body residing within the mid-crust is thought to be responsible for the generation of the upper oceanic crust, it remains unclear if the lower crust is formed from the same magma body, or if it mainly crystallizes from magma lenses located at the base of the crust. Thermal modelling, tomography, compliance and wide-angle seismic studies, supported by geological evidence, suggest the presence of gabbroic-melt accumulations within the Moho transition zone in the vicinity of fast- to intermediate-spreading centres. Until now, however, no reflection images have been obtained of such a structure within the Moho transition zone. Here we show images of groups of Moho transition zone reflection events that resulted from the analysis of approximately 1,500 km of multichannel seismic data collected across the intermediate-spreading-rate Juan de Fuca ridge. From our observations we suggest that gabbro lenses and melt accumulations embedded within dunite or residual mantle peridotite are the most probable cause for the observed reflectivity, thus providing support for the hypothesis that the crust is generated from multiple magma bodies

Evaluating methods for ranking differentially expressed genes applied to microArray quality control data

<p>Abstract</p> <p>Background</p> <p>Statistical methods for ranking differentially expressed genes (DEGs) from gene expression data should be evaluated with regard to high sensitivity, specificity, and reproducibility. In our previous studies, we evaluated eight gene ranking methods applied to only Affymetrix GeneChip data. A more general evaluation that also includes other microarray platforms, such as the Agilent or Illumina systems, is desirable for determining which methods are suitable for each platform and which method has better inter-platform reproducibility.</p> <p>Results</p> <p>We compared the eight gene ranking methods using the MicroArray Quality Control (MAQC) datasets produced by five manufacturers: Affymetrix, Applied Biosystems, Agilent, GE Healthcare, and Illumina. The area under the curve (AUC) was used as a measure for both sensitivity and specificity. Although the highest AUC values can vary with the definition of "true" DEGs, the best methods were, in most cases, either the weighted average difference (WAD), rank products (RP), or intensity-based moderated <it>t </it>statistic (ibmT). The percentages of overlapping genes (POGs) across different test sites were mainly evaluated as a measure for both intra- and inter-platform reproducibility. The POG values for WAD were the highest overall, irrespective of the choice of microarray platform. The high intra- and inter-platform reproducibility of WAD was also observed at a higher biological function level.</p> <p>Conclusion</p> <p>These results for the five microarray platforms were consistent with our previous ones based on 36 real experimental datasets measured using the Affymetrix platform. Thus, recommendations made using the MAQC benchmark data might be universally applicable.</p

Peripheral T-lymphocytes express WNT7A and its restoration in leukemia-derived lymphoblasts inhibits cell proliferation

<p>Abstract</p> <p>Background</p> <p>WNT7a, a member of the Wnt ligand family implicated in several developmental processes, has also been reported to be dysregulated in some types of tumors; however, its function and implication in oncogenesis is poorly understood. Moreover, the expression of this gene and the role that it plays in the biology of blood cells remains unclear. In addition to determining the expression of the <it>WNT7A </it>gene in blood cells, in leukemia-derived cell lines, and in samples of patients with leukemia, the aim of this study was to seek the effect of this gene in proliferation.</p> <p>Methods</p> <p>We analyzed peripheral blood mononuclear cells, sorted CD3 and CD19 cells, four leukemia-derived cell lines, and blood samples from 14 patients with Acute lymphoblastic leukemia (ALL), and 19 clinically healthy subjects. Reverse transcription followed by quantitative Real-time Polymerase chain reaction (qRT-PCR) analysis were performed to determine relative <it>WNT7A </it>expression. Restoration of WNT7a was done employing a lentiviral system and by using a recombinant human protein. Cell proliferation was measured by addition of WST-1 to cell cultures.</p> <p>Results</p> <p>WNT7a is mainly produced by CD3 T-lymphocytes, its expression decreases upon activation, and it is severely reduced in leukemia-derived cell lines, as well as in the blood samples of patients with ALL when compared with healthy controls (<it>p </it>≤0.001). By restoring <it>WNT7A </it>expression in leukemia-derived cells, we were able to demonstrate that WNT7a inhibits cell growth. A similar effect was observed when a recombinant human WNT7a protein was used. Interestingly, restoration of <it>WNT7A </it>expression in Jurkat cells did not activate the canonical Wnt/β-catenin pathway.</p> <p>Conclusions</p> <p>To our knowledge, this is the first report evidencing quantitatively decreased <it>WNT7A </it>levels in leukemia-derived cells and that <it>WNT7A </it>restoration in T-lymphocytes inhibits cell proliferation. In addition, our results also support the possible function of <it>WNT7A </it>as a tumor suppressor gene as well as a therapeutic tool.</p

Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples

<p>Abstract</p> <p>Background</p> <p>Readthrough fusions across adjacent genes in the genome, or transcription-induced chimeras (TICs), have been estimated using expressed sequence tag (EST) libraries to involve 4-6% of all genes. Deep transcriptional sequencing (RNA-Seq) now makes it possible to study the occurrence and expression levels of TICs in individual samples across the genome.</p> <p>Methods</p> <p>We performed single-end RNA-Seq on three human prostate adenocarcinoma samples and their corresponding normal tissues, as well as brain and universal reference samples. We developed two bioinformatics methods to specifically identify TIC events: a targeted alignment method using artificial exon-exon junctions within 200,000 bp from adjacent genes, and genomic alignment allowing splicing within individual reads. We performed further experimental verification and characterization of selected TIC and fusion events using quantitative RT-PCR and comparative genomic hybridization microarrays.</p> <p>Results</p> <p>Targeted alignment against artificial exon-exon junctions yielded 339 distinct TIC events, including 32 gene pairs with multiple isoforms. The false discovery rate was estimated to be 1.5%. Spliced alignment to the genome was less sensitive, finding only 18% of those found by targeted alignment in 33-nt reads and 59% of those in 50-nt reads. However, spliced alignment revealed 30 cases of TICs with intervening exons, in addition to distant inversions, scrambled genes, and translocations. Our findings increase the catalog of observed TIC gene pairs by 66%.</p> <p>We verified 6 of 6 predicted TICs in all prostate samples, and 2 of 5 predicted novel distant gene fusions, both private events among 54 prostate tumor samples tested. Expression of TICs correlates with that of the upstream gene, which can explain the prostate-specific pattern of some TIC events and the restriction of the <it>SLC45A3-ELK4 </it>e4-e2 TIC to <it>ERG</it>-negative prostate samples, as confirmed in 20 matched prostate tumor and normal samples and 9 lung cancer cell lines.</p> <p>Conclusions</p> <p>Deep transcriptional sequencing and analysis with targeted and spliced alignment methods can effectively identify TIC events across the genome in individual tissues. Prostate and reference samples exhibit a wide range of TIC events, involving more genes than estimated previously using ESTs. Tissue specificity of TIC events is correlated with expression patterns of the upstream gene. Some TIC events, such as <it>MSMB-NCOA4</it>, may play functional roles in cancer.</p