Priorities to promote participant engagement in the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network

BACKGROUND: Engaging diverse populations in cancer genomics research is of critical importance and is a fundamental goal of the NCI Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. Established as part of the Cancer Moonshot, PE-CGS is a consortium of stakeholders including clinicians, scientists, genetic counselors, and representatives of potential study participants and their communities. Participant engagement is an ongoing, bidirectional, and mutually beneficial interaction between study participants and researchers. PE-CGS sought to set priorities in participant engagement for conducting the network\u27s research. METHODS: PE-CGS deliberatively engaged its stakeholders in the following four-phase process to set the network\u27s research priorities in participant engagement: (i) a brainstorming exercise to elicit potential priorities; (ii) a 2-day virtual meeting to discuss priorities; (iii) recommendations from the PE-CGS External Advisory Panel to refine priorities; and (iv) a virtual meeting to set priorities. RESULTS: Nearly 150 PE-CGS stakeholders engaged in the process. Five priorities were set: (i) tailor education and communication materials for participants throughout the research process; (ii) identify measures of participant engagement; (iii) identify optimal participant engagement strategies; (iv) understand cancer disparities in the context of cancer genomics research; and (v) personalize the return of genomics findings to participants. CONCLUSIONS: PE-CGS is pursuing these priorities to meaningfully engage diverse and underrepresented patients with cancer and posttreatment cancer survivors as participants in cancer genomics research and, subsequently, generate new discoveries. IMPACT: Data from PE-CGS will be shared with the broader scientific community in a manner consistent with participant informed consent and community agreement

Factors affecting the accuracy of urine-based biomarkers of BSE

<p>Abstract</p> <p>Background</p> <p>Transmissible spongiform encephalopathy diseases are untreatable, uniformly fatal degenerative syndromes of the central nervous system that can be transmitted both within as well as between species. The bovine spongiform encephalopathy (BSE) epidemic and the emergence of a new human variant of Creutzfeldt-Jakob disease (vCJD), have profoundly influenced beef production processes as well as blood donation and surgical procedures. Simple, robust and cost effective diagnostic screening and surveillance tools are needed for both the preclinical and clinical stages of TSE disease in order to minimize both the economic costs and zoonotic risk of BSE and to further reduce the risk of secondary vCJD.</p> <p>Objective</p> <p>Urine is well suited as the matrix for an ante-mortem test for TSE diseases because it would permit non-invasive and repeated sampling. In this study urine samples collected from BSE infected and age matched control cattle were screened for the presence of individual proteins that exhibited disease specific changes in abundance in response to BSE infection that might form the basis of such an ante-mortem test.</p> <p>Results</p> <p>Two-dimensional differential gel electrophoresis (2D-DIGE) was used to identify proteins exhibiting differential abundance in two sets of cattle. The known set consisted of BSE infected steers and age matched controls throughout the course of the disease. The blinded unknown set was composed of BSE infected and control samples of both genders, a wide range of ages and two different breeds. Multivariate analyses of individual protein abundance data generated classifiers comprised of the proteins best able to discriminate between the samples based on disease state, breed, age and gender.</p> <p>Conclusion</p> <p>Despite the presence of confounding factors, the disease specific changes in abundance exhibited by a panel of urine proteins permitted the creation of classifiers able to discriminate between control and infected cattle with a high degree of accuracy.</p

The identification of disease-induced biomarkers in the urine of BSE infected cattle

<p>Abstract</p> <p>Background</p> <p>The bovine spongiform encephalopathy (BSE) epidemic and the emergence of a new human variant of Creutzfeldt-Jakob Disease (vCJD) have led to profound changes in the production and trade of agricultural goods. The rapid tests currently approved for BSE monitoring in slaughtered cattle are all based on the detection of the disease related isoform of the prion protein, PrP^d, in brain tissue and consequently are only suitable for post-mortem diagnosis. Objectives: In instances such as assessing the health of breeding stock for export purposes where post-mortem testing is not an option, there is a demand for an ante-mortem test based on a matrix or body fluid that would permit easy access and repeated sampling. Urine and urine based analyses would meet these requirements.</p> <p>Results</p> <p>Two dimensional differential gel eletrophoresis (2D-DIGE) and mass spectrometry analyses were used to identify proteins exhibiting differential abundance in the urine of BSE infected cattle and age matched controls over the course of the disease. Multivariate analyses of protein expression data identified a single protein able to discriminate, with 100% accuracy, control from infected samples. In addition, a subset of proteins were able to predict with 85% ± 13.2 accuracy the time post infection that the samples were collected.</p> <p>Conclusion</p> <p>These results suggest that in principle it is possible to identify biomarkers in urine useful in the diagnosis, prognosis and monitoring of disease progression of transmissible spongiform encephalopathy diseases (TSEs).</p

Towards an Efficient Finite Element Method for the Integral Fractional Laplacian on Polygonal Domains

We explore the connection between fractional order partial differential equations in two or more spatial dimensions with boundary integral operators to develop techniques that enable one to efficiently tackle the integral fractional Laplacian. In particular, we develop techniques for the treatment of the dense stiffness matrix including the computation of the entries, the efficient assembly and storage of a sparse approximation and the efficient solution of the resulting equations. The main idea consists of generalising proven techniques for the treatment of boundary integral equations to general fractional orders. Importantly, the approximation does not make any strong assumptions on the shape of the underlying domain and does not rely on any special structure of the matrix that could be exploited by fast transforms. We demonstrate the flexibility and performance of this approach in a couple of two-dimensional numerical examples

Composites Curriculum Development: tackling the skills gap in UK advanced composites

The aim of this project is to generate an industrially relevant and academically rigorous curriculum which could be deployed to tackle the significant skills gap in composites professionals, vital for delivering on the UK’s National Composite Strategy and allowing the industry to grow to its full potential, forecast by the Composites Leadership Forum to grow by a factor of 5 by 2030. A Masters’ level curriculum of short, industrially focused units has been specified and a small number of trial units developed. Engagement of academics in this novel collaborative curriculum development, utilising each institution’s expertise, has been very good and feedback from industry and participants in pilot units has been positive. Consortium participants are investigating numerous options for developing this further and have begun to put plans in place for the next stage

Apraxia and motor dysfunction in corticobasal syndrome

Background: Corticobasal syndrome (CBS) is characterized by multifaceted motor system dysfunction and cognitive disturbance; distinctive clinical features include limb apraxia and visuospatial dysfunction. Transcranial magnetic stimulation (TMS) has been used to study motor system dysfunction in CBS, but the relationship of TMS parameters to clinical features has not been studied. The present study explored several hypotheses; firstly, that limb apraxia may be partly due to visuospatial impairment in CBS. Secondly, that motor system dysfunction can be demonstrated in CBS, using threshold-tracking TMS, and is linked to limb apraxia. Finally, that atrophy of the primary motor cortex, studied using voxel-based morphometry analysis (VBM), is associated with motor system dysfunction and limb apraxia in CBS.   Methods: Imitation of meaningful and meaningless hand gestures was graded to assess limb apraxia, while cognitive performance was assessed using the Addenbrooke's Cognitive Examination - Revised (ACE-R), with particular emphasis placed on the visuospatial subtask. Patients underwent TMS, to assess cortical function, and VBM.   Results: In total, 17 patients with CBS (7 male, 10 female; mean age 64.4+/2 6.6 years) were studied and compared to 17 matched control subjects. Of the CBS patients, 23.5% had a relatively inexcitable motor cortex, with evidence of cortical dysfunction in the remaining 76.5% patients. Reduced resting motor threshold, and visuospatial performance, correlated with limb apraxia. Patients with a resting motor threshold <50% performed significantly worse on the visuospatial sub-task of the ACE-R than other CBS patients. Cortical function correlated with atrophy of the primary and pre-motor cortices, and the thalamus, while apraxia correlated with atrophy of the pre-motor and parietal cortices.   Conclusions: Cortical dysfunction appears to underlie the core clinical features of CBS, and is associated with atrophy of the primary motor and pre-motor cortices, as well as the thalamus, while apraxia correlates with pre-motor and parietal atrophy

A preliminary study of genetic factors that influence susceptibility to bovine tuberculosis in the British cattle herd

Associations between specific host genes and susceptibility to Mycobacterial infections such as tuberculosis have been reported in several species. Bovine tuberculosis (bTB) impacts greatly the UK cattle industry, yet genetic predispositions have yet to be identified. We therefore used a candidate gene approach to study 384 cattle of which 160 had reacted positively to an antigenic skin test (‘reactors’). Our approach was unusual in that it used microsatellite markers, embraced high breed diversity and focused particularly on detecting genes showing heterozygote advantage, a mode of action often overlooked in SNP-based studies. A panel of neutral markers was used to control for population substructure and using a general linear model-based approach we were also able to control for age. We found that substructure was surprisingly weak and identified two genomic regions that were strongly associated with reactor status, identified by markers INRA111 and BMS2753. In general the strength of association detected tended to vary depending on whether age was included in the model. At INRA111 a single genotype appears strongly protective with an overall odds ratio of 2.2, the effect being consistent across nine diverse breeds. Our results suggest that breeding strategies could be devised that would appreciably increase genetic resistance of cattle to bTB (strictly, reduce the frequency of incidence of reactors) with implications for the current debate concerning badger-culling

A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available. © 2013 Capra et al

A New Hybrid Method for Gland Segmentation in Histology Images

Gland segmentation has become an important task in biomedical image analysis. An accurate gland segmentation could be instrumental in designing of personalised treatments, potentially leading to improved patient survival rate. Different gland instance segmentation architectures have been tested in the work reported here. A hybrid method that combines two-level classification has been described. The proposed method achieved very good image-level classification results with 100% classification accuracy on the available test data. Therefore, the overall performance of the proposed hybrid method highly depends on the results of the pixel-level classification. Diverse image features reflecting various morphological gland structures visible in histology images have been tested in order to improve the performance of the gland instance segmentation. Based on the reported experimental results, the hybrid approach, which combines two-level classification, achieved overall the best results among the tested methods