Knee disorders in primary care: design and patient selection of the HONEUR knee cohort.

BACKGROUND: Knee complaints are a frequent reason for consultation in general practice. These patients constitute a specific population compared to secondary care patients. However, information to base treatment decisions on is generally derived from specialistic settings. Our cohort study is aimed at collecting knowledge about prognosis and prognostic factors of knee complaints presented in a primary care setting. This paper describes the methods used for data collection, and discusses potential selectiveness of patient recruitment. METHODS: This is a descriptive prospective cohort study with one-year follow-up. 40 Dutch GPs recruited consecutive patients with incident knee complaints aged 12 years and above from October 2001 to October 2003. Patients were assessed with questionnaires and standardised physical examinations. Additional measurements of subgroups included MRI for recent knee traumas and device assessed function measurements for non-traumatic patients. After the inclusion period we retrospectively searched the computerized medical files of participating GPs to obtain a sample to determine possible selective recruitment. We assessed differences in proportions of gender, traumatic onset of injury and age groups between participants and non-participants using Odds Ratios (OR) and 95% confidence intervals. RESULTS: We recruited 1068 patients. In a sample of 310 patients visiting the GP, we detected some selective recruitment, indicating an underrepresentation of patients aged 12 to 35 years (OR 1.70; 1.15-2.77), especially among men (OR 2.16; 1.12-4.18). The underrepresentation of patients with traumatic onset of injury was not statistically significant. CONCLUSION: This cohort is unique in its size, setting, and its range of both age and type of knee complaints. We believe the detected selective recruitment is unlikely to introduce significant bias, as the cohort will be divided into subgroups according to age group or traumatic onset of injury for future analyses. However, the underrepresentation of men in the age group of 12 to 35 years of age warrants caution. Based on the available data, we believe our cohort is an acceptable representation of patients with new knee complaints consulting the GP, and we expect no problems with extrapolation of the results to the general Dutch population

Geographic distribution at subspecies resolution level: closely related Rhodopirellula species in European coastal sediments.

Members of the marine genus Rhodopirellula are attached living bacteria and studies based on cultured Rhodopirellula strains suggested that three closely related species R. baltica, 'R. europaea' and 'R. islandica' have a limited geographic distribution in Europe. To address this hypothesis, we developed a nested PCR for a single gene copy detection of a partial acetyl CoA synthetase (acsA) from intertidal sediments collected all around Europe. Furthermore, we performed growth experiments in a range of temperature, salinity and light conditions. A combination of Basic Local Alignment Search Tool (BLAST) and Minimum Entropy Decomposition (MED) was used to analyze the sequences with the aim to explore the geographical distribution of the species and subspecies. MED has been mainly used for the analysis of the 16S rRNA gene and here we propose a protocol for the analysis of protein-coding genes taking into account the degeneracy of the codons and a possible overestimation of functional diversity. The high-resolution analysis revealed differences in the intraspecies community structure in different geographic regions. However, we found all three species present in all regions sampled and in agreement with growth experiments we demonstrated that Rhodopirellula species do not have a limited geographic distribution in Europe

Role of Cell-to-Cell Variability in Activating a Positive Feedback Antiviral Response in Human Dendritic Cells

In the first few hours following Newcastle disease viral infection of human monocyte-derived dendritic cells, the induction of IFNB1 is extremely low and the secreted type I interferon response is below the limits of ELISA assay. However, many interferon-induced genes are activated at this time, for example DDX58 (RIGI), which in response to viral RNA induces IFNB1. We investigated whether the early induction of IFNBI in only a small percentage of infected cells leads to low level IFN secretion that then induces IFN-responsive genes in all cells. We developed an agent-based mathematical model to explore the IFNBI and DDX58 temporal dynamics. Simulations showed that a small number of early responder cells provide a mechanism for efficient and controlled activation of the DDX58-IFNBI positive feedback loop. The model predicted distributions of single cell responses that were confirmed by single cell mRNA measurements. The results suggest that large cell-to-cell variation plays an important role in the early innate immune response, and that the variability is essential for the efficient activation of the IFNB1 based feedback loop

Ecosystem restoration strengthens pollination network resilience and function.

Land degradation results in declining biodiversity and the disruption of ecosystem functioning worldwide, particularly in the tropics. Vegetation restoration is a common tool used to mitigate these impacts and increasingly aims to restore ecosystem functions rather than species diversity. However, evidence from community experiments on the effect of restoration practices on ecosystem functions is scarce. Pollination is an important ecosystem function and the global decline in pollinators attenuates the resistance of natural areas and agro-environments to disturbances. Thus, the ability of pollination functions to resist or recover from disturbance (that is, the functional resilience) may be critical for ensuring a successful restoration process. Here we report the use of a community field experiment to investigate the effects of vegetation restoration, specifically the removal of exotic shrubs, on pollination. We analyse 64 plant-pollinator networks and the reproductive performance of the ten most abundant plant species across four restored and four unrestored, disturbed mountaintop communities. Ecosystem restoration resulted in a marked increase in pollinator species, visits to flowers and interaction diversity. Interactions in restored networks were more generalized than in unrestored networks, indicating a higher functional redundancy in restored communities. Shifts in interaction patterns had direct and positive effects on pollination, especially on the relative and total fruit production of native plants. Pollinator limitation was prevalent at unrestored sites only, where the proportion of flowers producing fruit increased with pollinator visitation, approaching the higher levels seen in restored plant communities. Our results show that vegetation restoration can improve pollination, suggesting that the degradation of ecosystem functions is at least partially reversible. The degree of recovery may depend on the state of degradation before restoration intervention and the proximity to pollinator source populations in the surrounding landscape. We demonstrate that network structure is a suitable indicator for pollination quality, highlighting the usefulness of interaction networks in environmental management

Organic Farming Improves Pollination Success in Strawberries

Pollination of insect pollinated crops has been found to be correlated to pollinator abundance and diversity. Since organic farming has the potential to mitigate negative effects of agricultural intensification on biodiversity, it may also benefit crop pollination, but direct evidence of this is scant. We evaluated the effect of organic farming on pollination of strawberry plants focusing on (1) if pollination success was higher on organic farms compared to conventional farms, and (2) if there was a time lag from conversion to organic farming until an effect was manifested. We found that pollination success and the proportion of fully pollinated berries were higher on organic compared to conventional farms and this difference was already evident 2–4 years after conversion to organic farming. Our results suggest that conversion to organic farming may rapidly increase pollination success and hence benefit the ecosystem service of crop pollination regarding both yield quantity and quality

deFuse: An Algorithm for Gene Fusion Discovery in Tumor RNA-Seq Data

Gene fusions created by somatic genomic rearrangements are known to play an important role in the onset and development of some cancers, such as lymphomas and sarcomas. RNA-Seq (whole transcriptome shotgun sequencing) is proving to be a useful tool for the discovery of novel gene fusions in cancer transcriptomes. However, algorithmic methods for the discovery of gene fusions using RNA-Seq data remain underdeveloped. We have developed deFuse, a novel computational method for fusion discovery in tumor RNA-Seq data. Unlike existing methods that use only unique best-hit alignments and consider only fusion boundaries at the ends of known exons, deFuse considers all alignments and all possible locations for fusion boundaries. As a result, deFuse is able to identify fusion sequences with demonstrably better sensitivity than previous approaches. To increase the specificity of our approach, we curated a list of 60 true positive and 61 true negative fusion sequences (as confirmed by RT-PCR), and have trained an adaboost classifier on 11 novel features of the sequence data. The resulting classifier has an estimated value of 0.91 for the area under the ROC curve. We have used deFuse to discover gene fusions in 40 ovarian tumor samples, one ovarian cancer cell line, and three sarcoma samples. We report herein the first gene fusions discovered in ovarian cancer. We conclude that gene fusions are not infrequent events in ovarian cancer and that these events have the potential to substantially alter the expression patterns of the genes involved; gene fusions should therefore be considered in efforts to comprehensively characterize the mutational profiles of ovarian cancer transcriptomes

Microarray scanner calibration curves: characteristics and implications

BACKGROUND: Microarray-based measurement of mRNA abundance assumes a linear relationship between the fluorescence intensity and the dye concentration. In reality, however, the calibration curve can be nonlinear. RESULTS: By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under 18 PMT gains, we were able to evaluate the differences in calibration characteristics of Cy5 and Cy3. First, the calibration curve for the same dye under the same PMT gain is nonlinear at both the high and low intensity ends. Second, the degree of nonlinearity of the calibration curve depends on the PMT gain. Third, the two PMTs (for Cy5 and Cy3) behave differently even under the same gain. Fourth, the background intensity for the Cy3 channel is higher than that for the Cy5 channel. The impact of such characteristics on the accuracy and reproducibility of measured mRNA abundance and the calculated ratios was demonstrated. Combined with simulation results, we provided explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. We further demonstrated that although Lowess normalization effectively eliminates the intensity-dependence of ratio bias, the systematic deviation from true ratios largely remained. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias. CONCLUSION: It is preferable to scan microarray slides at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy

Unsupervised assessment of microarray data quality using a Gaussian mixture model

<p>Abstract</p> <p>Background</p> <p>Quality assessment of microarray data is an important and often challenging aspect of gene expression analysis. This task frequently involves the examination of a variety of summary statistics and diagnostic plots. The interpretation of these diagnostics is often subjective, and generally requires careful expert scrutiny.</p> <p>Results</p> <p>We show how an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and the naïve Bayes model can be used to automate microarray quality assessment. The method is flexible and can be easily adapted to accommodate alternate quality statistics and platforms. We evaluate our approach using Affymetrix 3' gene expression and exon arrays and compare the performance of this method to a similar supervised approach.</p> <p>Conclusion</p> <p>This research illustrates the efficacy of an unsupervised classification approach for the purpose of automated microarray data quality assessment. Since our approach requires only unannotated training data, it is easy to customize and to keep up-to-date as technology evolves. In contrast to other "black box" classification systems, this method also allows for intuitive explanations.</p