Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Biomarkers which predict patient’s survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis. In this paper a novel method is proposed to detect the prognostic biomarkers ofsurvival in colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes classifier. One dimensional discrete wavelet transform (DWT) is normally used to reduce the dimensionality of biomedical data. In this study one dimensional continuous wavelet transform (CWT) was proposed to extract the features of colorectal cancer data. One dimensional CWT has no ability to reduce dimensionality of data, but captures the missing features of DWT, and is complementary part of DWT. Genetic algorithm was performed on extracted wavelet coefficients to select the optimized features, using Bayes classifier to build its fitness function. The corresponding protein markers were located based on the position of optimized features. Kaplan-Meier curve and Cox regression model 2 were used to evaluate the performance of selected biomarkers. Experiments were conducted on colorectal cancer dataset and several significant biomarkers were detected. A new protein biomarker CD46 was found to significantly associate with survival time

Dimension-adaptive bounds on compressive FLD Classification

Efficient dimensionality reduction by random projections (RP) gains popularity, hence the learning guarantees achievable in RP spaces are of great interest. In finite dimensional setting, it has been shown for the compressive Fisher Linear Discriminant (FLD) classifier that forgood generalisation the required target dimension grows only as the log of the number of classes and is not adversely affected by the number of projected data points. However these bounds depend on the dimensionality d of the original data space. In this paper we give further guarantees that remove d from the bounds under certain conditions of regularity on the data density structure. In particular, if the data density does not fill the ambient space then the error of compressive FLD is independent of the ambient dimension and depends only on a notion of ‘intrinsic dimension'

CD55 is over-expressed in the tumour environment

CD55 is a protein that protects cells from complement-mediated attack. 791Tgp72 is an antigen which has been used succesfully as a target for both tumour imaging and cancer vaccines. 791Tgp72 has recently been identified as CD55. Quantitative expression of CD55 in the tumour environment was therefore studied. Tumour cells showed a 4–100-fold increase in CD55 cell surface expression when compared to normal cells. Immunohistochemical staining of colorectal tumours also revealed high expression of CD55 in the stroma. To examine the source of this stromal CD55 the ability of both epithelial cells and endothelial cells to produce extracellular CD55 was measured. Tumour cell lines deposit CD55 into their extracellular matrix (ECM) in direct proportion to their cell surface expression. In contrast the ECM from HUVEC cells contained large amounts of CD55 despite expressing low levels of CD55 on their cell surface. Furthermore expression of CD55 on HUVEC cells was increased by exposure to VEGF. Although it remains unclear why CD55 is upregulated in the tumour environment its high level of expression on tumour cells and associated endothelium may explain why it is a good target for both imaging and immunotherapy. © 2001 Cancer Research Campaign http://www.bjcancer.co

AutoClickChem: Click Chemistry in Silico

Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu

Reciprocity in the Co-Production of Public Services: The Role of Volunteering through Community Time Exchange?

Time Credits are a form of community currency based upon the reciprocal exchange of time and represent an interpretation of ‘time banking’ by a UK social enterprise, Spice. This article sets out the contribution made by research on Time Credits to the theory and practice of co-production in public services. Time Credits are intended to improve wellbeing through volunteering and ultimately increase economic participation. There is a focus on communities exhibiting high levels of deprivation within a small Cambridgeshire town (Wisbech, UK) which is geographically isolated and characterised by low-skilled, agri-food based employment opportunities that attracted high levels of inward migration from the A8 EU accession countries. In separating the rhetoric from the reality of co-production, the research aims to shed some light upon the extent to which such initiatives can realistically engender a shift towards a more reciprocal economy in the context of an ongoing programme of fiscal austerity

Protecting Free-Living Dormice: Molecular Identification of Cestode Parasites in Captive Dormice (Muscardinus avellanarius) Destined for Reintroduction

The success of any population translocation programme relies heavily on the measures implemented to control and monitor the spread of disease. Without these measures, programmes run the risk of releasing immunologically naïve species or, more dangerously, introducing novel infectious agents to native populations. As a precaution, a reintroduction programme for the common or hazel dormouse, Muscardinus avellanarius, in England screens dormice before release following captive breeding. Using PCR sequencing of a range of genes, we tested whether the same species of tapeworm(s) were present in captive and free-living dormice. Whilst only Rodentolepis straminea were identified in free-living dormice, cestode ova found in a captive individual produced a molecular match closely related to Hymenolepis microstoma and a previously unrecorded Rodentolepis species. To prevent putting at risk the free-living population, we recommended the continued treatment of dormice showing tapeworm infection before release. Our work demonstrates how molecular techniques can be used to inform reintroduction programmes, reduce risk from disease and increase chances of reintroduction success

Total loss of MHC class I is an independent indicator of good prognosis in breast cancer

Tumours can be recognised by CTL and NK cells. CTL recognition depends on expression of MHC Class I loaded with peptides from tumour antigens. In contrast, loss of MHC Class I results in NK activation. In our study a large set of samples from patients with primary operable invasive breast cancer was evaluated for the expression of MHC Class I heavy and light by immunohistochemical staining of 439 breast carcinomas in a tissue microarray. Forty-seven percent (206 of 439) of breast carcinomas were considered negative for HLA Class I heavy chain (HC10), whereas lack of anti-β2m-antibody staining was observed in 39% (167 of 424) of tumours, with only 3% of the β2m-negative tumours expressing detectable HLA Class I heavy chain. Correlation with patient outcome showed direct relationship between patient survival and HLA-negative phenotype (log rank = 0.004). A positive relationship was found between the intensity of expression of MHC Class I light and heavy chains expression and histological grade of invasive tumour (p < 0.001) and Nottingham Prognostic Index (p < 0.001). To investigate whether HLA Class I heavy and light chains expression had independent prognostic significance, Cox multivariate regression analysis, including the parameters of tumour size, lymph node stage, grade and intensity of HC10 and anti-β2m staining, was carried out. In our analysis, lymph node stage (p < 0.001), tumour grade (p = 0.005) and intensity of MHC Class I light and heavy chains expression were shown to be independent prognostic factors predictive of overall survival (p-values HC10 = 0.047 and β2m = 0.018)

What are the Characteristics of Respondents using Different Devices in Mixed-device Online Surveys? Evidence from Six UK Surveys

There is a move towards online data collection across the world. Online survey response is complicated by respondents using different devices. So far, no research has been conducted in the UK to study characteristics of people using different devices in mixed‐device online surveys. This analysis uses all publicly available UK social surveys with an online component: Understanding Society Innovation Panel, Community Life Survey, European Social Survey, 1958 National Child Development Study and the Second Longitudinal Study of Young People in England. Descriptive analysis and logistic regressions are used to explore significant correlates of device use in online surveys. The results of bivariate analysis suggest that age, gender, marital status, employment, religion, household size, children in household, household income, number of cars and frequency of internet use are significantly associated with device used across surveys. The associations with age, gender, employment status, household size and education are consistent with the findings from other countries. The knowledge about respondents' characteristics using different devices in online surveys in the UK will help to understand better the response process in online surveys and to target certain subgroups more effectively. It is also important for designs of online surveys, understanding of data quality and post‐survey adjustments