242 research outputs found
Prevention of colorectal cancer in Scotland: strategies for those at increased genetic risk
The identification of people at increased genetic risk of colorectal cancer and the
provision of appropriate clinical screening represents one approach to the prevention of
colorectal cancer in the Scottish population. This thesis aims to contribute to current
knowledge regarding the available tools for identifying those at increased genetic risk
in a population, namely genetic testing and family history assessment.Key issues relating to the use of family history in this context were addressed through
the analysis of a unique data set, comprising family history information reported by a
colorectal cancer case or control subject at interview and the results of record linkage
of this data to the Scottish Cancer Registry. Retrospective family history case-control
analysis showed that individuals with an affected first-degree relative were at an
increased risk of developing colorectal cancer (ORimh 2.14, 95% CI = 1.11, 4.14).
Prevalence of such a family history in control subjects was 9.4% (95% CI = 4.9, 13.9).
Substantial under-reporting of family history was evident, with sensitivity of interview
as a means of determining a history of colorectal cancer in a first-degree relative being
approximately 0.55 for both cases and controls. These studies illustrate the potential
advantages of targeting people with a family history, but also highlight some of the
limitations of such an approach.The genetic epidemiology of the mismatch repair genes hMLH 1 and hMSH2 and their
association with colorectal cancer was considered in a systematic literature review.
Although conventional epidemiological studies are lacking, there is compelling
evidence to implicate mutations in these genes in the aetiology of a sub-set of
colorectal cancers, with penetrance of approximately 80% in males and 40% in
females. A total of 550 different published gene variants were identified, and this high
degree of heterogeneity was illustrated in a unique database. This review indicates that
carriers of mismatch repair gene mutations merit particular consideration in the context
of colorectal cancer prevention through targeting people at increased genetic risk.Accordingly, the challenge of identifying asymptomatic mismatch repair gene mutation
carriers in Scotland was addressed through the development of a computer model of
cascade genetic testing, a strategy in which a mutation is identified in one family
member and systematically traced through a pedigree. The model predicts that
application of cascade genetic testing to colorectal cancer cases < 55 years of age over
a twenty- year period would involve testing 7142 patients and 849 relatives of known
carriers, and would identify 321.2 (95%CI = 305.3, 337.1) asymptomatic mutation
carriers, representing approximately 27% of the estimated 1209 carriers in Scotland.
Model outcomes were highly sensitive to the prevalence and penetrance of mutations,
and the participation rates of those offered testing. Overall, outcomes from this
computer model suggest that cascade genetic testing is potentially a useful means of
identifying asymptomatic mismatch repair gene mutation carriers in Scotland. Followup
work should ensure that it is also of practical importance as a tool for planning
research and health policy.Identification and screening of mismatch repair gene mutation carriers is an important
approach to colorectal cancer prevention, but is only relevant to a minority of people at
increased genetic risk. Hence, despite inherent limitations, family history remains a
crucial tool for genetic risk assessment in a population. An integrated approach to the
prevention of colorectal cancer through targeting people at increased genetic risk can
potentially provide substantial health benefits to a sub-group of the population, and thus
contribute to the overall prevention of colorectal cancer in Scotland
Mode-locking in advection-reaction-diffusion systems: an invariant manifold perspective
Fronts propagating in two-dimensional advection-reaction-diffusion (ARD)
systems exhibit rich topological structure. When the underlying fluid flow is
periodic in space and time, the reaction front can lock to the driving
frequency. We explain this mode-locking phenomenon using so-called burning
invariant manifolds (BIMs). In fact, the mode-locked profile is delineated by a
BIM attached to a relative periodic orbit (RPO) of the front element dynamics.
Changes in the type (and loss) of mode-locking can be understood in terms of
local and global bifurcations of the RPOs and their BIMs. We illustrate these
concepts numerically using a chain of alternating vortices in a channel
geometry.Comment: 9 pages, 13 figure
Accelerating the XGBoost algorithm using GPU computing
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3x and 6x using a Titan X compared to a 4 core i7 CPU, and 1.2x using a Titan X compared to 2x Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks
Accelerating the XGBoost algorithm using GPU computing
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3x and 6x using a Titan X compared to a 4 core i7 CPU, and 1.2x using a Titan X compared to 2x Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks
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