Reducing the Probability of False Positive Research Findings by Pre-Publication Validation - Experience with a Large Multiple Sclerosis Database

*Objective*
We have assessed the utility of a pre-publication validation policy in reducing the probability of publishing false positive research findings. 
*Study design and setting*
The large database of the Sylvia Lawry Centre for Multiple Sclerosis Research was split in two parts: one for hypothesis generation and a validation part for confirmation of selected results. We present case studies from 5 finalized projects that have used the validation policy and results from a simulation study.
*Results*
In one project, the "relapse and disability" project as described in section II (example 3), findings could not be confirmed in the validation part of the database. The simulation study showed that the percentage of false positive findings can exceed 20% depending on variable selection. 
*Conclusion*
We conclude that the validation policy has prevented the publication of at least one research finding that could not be validated in an independent data set (and probably would have been a "true" false-positive finding) over the past three years, and has led to improved data analysis, statistical programming, and selection of hypotheses. The advantages outweigh the lost statistical power inherent in the process

Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction

Sensing is one of the most fundamental tasks for the monitoring, forecasting and control of complex, spatio-temporal systems. In many applications, a limited number of sensors are mobile and move with the dynamics, with examples including wearable technology, ocean monitoring buoys, and weather balloons. In these dynamic systems (without regions of statistical-independence), the measurement time history encodes a significant amount of information that can be extracted for critical tasks. Most model-free sensing paradigms aim to map current sparse sensor measurements to the high-dimensional state space, ignoring the time-history all together. Using modern deep learning architectures, we show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic trajectory information can be mapped to full state-space estimates. Indeed, we demonstrate that by leveraging mobile sensor trajectories with shallow recurrent decoder networks, we can train the network (i) to accurately reconstruct the full state space using arbitrary dynamical trajectories of the sensors, (ii) the architecture reduces the variance of the mean-square error of the reconstruction error in comparison with immobile sensors, and (iii) the architecture also allows for rapid generalization (parameterization of dynamics) for data outside the training set. Moreover, the path of the sensor can be chosen arbitrarily, provided training data for the spatial trajectory of the sensor is available. The exceptional performance of the network architecture is demonstrated on three applications: turbulent flows, global sea-surface temperature data, and human movement biomechanics.Comment: 11 pages, 5 figures, 2 table

Transcriptional profiling avian beta-defensins in chicken oviduct epithelial cells before and after infection with Salmonella enterica serovar Enteritidis

<p>Abstract</p> <p>Background</p> <p><it>Salmonella enterica </it>serovar Enteritidis (SE) colonizes the ovary and oviduct of chickens without causing overt clinical signs which can lead to SE-contamination of the content and membrane of shell-eggs as well as hatchery eggs. The organism utilizes the <it>Salmonella </it>Pathogenicity Island-2 encoded type III secretion system (T3SS-2) to promote persistence in the oviduct of laying hens. In this study, reverse transcriptase-polymerase chain reaction (RT-PCR) was carried out to determine the expression profiles of 14 known avian beta defensins (AvBDs) in primary chicken oviduct epithelial cells (COEC) before and after infections with a wild type SE strain and T3SS mutant SE strains carrying an inactivated <it>sipA </it>or <it>pipB </it>gene.</p> <p>Results</p> <p>Based on the expression levels in uninfected COEC, AvBDs can be loosely grouped into three categories with AvBD4-5 and AvBD9-12 being constitutively expressed at high levels; AvBD1, AvBD3, and AvBD13-14 at moderate levels; and AvBD2 and AvBD6-8 at minimal levels. Infection with the wild type SE strain temporarily repressed certain highly expressed AvBDs and induced the expression of minimally expressed AvBDs. The <it>pipB </it>mutant, compared to the wild type strain, had reduced suppressive effect on the expression of highly expressed AvBDs. Moreover, the <it>pipB </it>mutant elicited significantly higher levels of the minimally expressed AvBDs than the wild type SE or the <it>sipA </it>mutant did.</p> <p>Conclusion</p> <p>Chicken oviduct epithelial cells express most of the known AvBD genes in response to SE infection. PipB, a T3SS-2 effector protein, plays a role in dampening the β-defensin arm of innate immunity during SE invasion of chicken oviduct epithelium.</p

The multiple sclerosis risk sharing scheme monitoring study - early results and lessons for the future

Background: Risk sharing schemes represent an innovative and important approach to the problems of rationing and achieving cost-effectiveness in high cost or controversial health interventions. This study aimed to assess the feasibility of risk sharing schemes, looking at long term clinical outcomes, to determine the price at which high cost treatments would be acceptable to the NHS. Methods: This case study of the first NHS risk sharing scheme, a long term prospective cohort study of beta interferon and glatiramer acetate in multiple sclerosis ( MS) patients in 71 specialist MS centres in UK NHS hospitals, recruited adults with relapsing forms of MS, meeting Association of British Neurologists (ABN) criteria for disease modifying therapy. Outcome measures were: success of recruitment and follow up over the first three years, analysis of baseline and initial follow up data and the prospect of estimating the long term cost-effectiveness of these treatments. Results: Centres consented 5560 patients. Of the 4240 patients who had been in the study for a least one year, annual review data were available for 3730 (88.0%). Of the patients who had been in the study for at least two years and three years, subsequent annual review data were available for 2055 (78.5%) and 265 (71.8%) patients respectively. Baseline characteristics and a small but statistically significant progression of disease were similar to those reported in previous pivotal studies. Conclusion: Successful recruitment, follow up and early data analysis suggest that risk sharing schemes should be able to deliver their objectives. However, important issues of analysis, and political and commercial conflicts of interest still need to be addressed

The Inheritance of Resistance Alleles in Multiple Sclerosis

Multiple sclerosis (MS) is a complex trait in which alleles at or near the class II loci HLA-DRB1 and HLA-DQB1 contribute significantly to genetic risk. HLA-DRB1*15 and HLA-DRB1*17-bearing haplotypes and interactions at the HLA-DRB1 locus increase risk of MS but it has taken large samples to identify resistance HLA-DRB1 alleles. In this investigation of 7,093 individuals from 1,432 MS families, we have assessed the validity, mode of inheritance, associated genotypes, and the interactions of HLA-DRB1 resistance alleles. HLA-DRB1*14-, HLA-DRB1*11-, HLA-DRB1*01-, and HLA-DRB1*10-bearing haplotypes are protective overall but they appear to operate by different mechanisms. The first type of resistance allele is characterised by HLA-DRB1*14 and HLA-DRB1*11. Each shows a multiplicative mode of inheritance indicating a broadly acting suppression of risk, but a different degree of protection. In contrast, a second type is exemplified by HLA-DRB1*10 and HLA-DRB1*01. These alleles are significantly protective when they interact specifically in trans with HLA-DRB1*15-bearing haplotypes. HLA-DRB1*01 and HLA-DRB1*10 do not interact with HLA-DRB1*17, implying that several mechanisms may be operative in major histocompatibility complex–associated MS susceptibility, perhaps analogous to the resistance alleles. There are major practical implications for risk and for the exploration of mechanisms in animal models. Restriction of antigen presentation by HLA-DRB1*15 seems an improbably simple mechanism of major histocompatibility complex–associated susceptibility

Prognosis of the individual course of disease - steps in developing a decision support tool for Multiple Sclerosis

<p>Abstract</p> <p>Background</p> <p>Multiple sclerosis is a chronic disease of uncertain aetiology. Variations in its disease course make it difficult to impossible to accurately determine the prognosis of individual patients. The Sylvia Lawry Centre for Multiple Sclerosis Research (SLCMSR) developed an "online analytical processing (OLAP)" tool that takes advantage of extant clinical trials data and allows one to model the near term future course of this chronic disease for an individual patient.</p> <p>Results</p> <p>For a given patient the most similar patients of the SLCMSR database are intelligently selected by a model-based matching algorithm integrated into an OLAP-tool to enable real time, web-based statistical analyses. The underlying database (last update April 2005) contains 1,059 patients derived from 30 placebo arms of controlled clinical trials. Demographic information on the entire database and the portion selected for comparison are displayed. The result of the statistical comparison is provided as a display of the course of Expanded Disability Status Scale (EDSS) for individuals in the database with regions of probable progression over time, along with their mean relapse rate. Kaplan-Meier curves for time to sustained progression in the EDSS and time to requirement of constant assistance to walk (EDSS 6) are also displayed. The software-application OLAP anticipates the input MS patient's course on the basis of baseline values and the known course of disease for similar patients who have been followed in clinical trials.</p> <p>Conclusion</p> <p>This simulation could be useful for physicians, researchers and other professionals who counsel patients on therapeutic options. The application can be modified for studying the natural history of other chronic diseases, if and when similar datasets on which the OLAP operates exist.</p

Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease

PMCID: PMC3710212This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited