The effect of temperature on certain aspects of the metabolism of psychrophiles

The refrigeration of foodstuffs at 0-1

Ensemble Learning of Colorectal Cancer Survival Rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved.Comment: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, pp. 82 - 86, 201

Ensemble learning of colorectal cancer survival rates

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates

This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight into the biochemical and immunological status of patients at the point of tumour removal along with information about tumour classification and post-operative survival. The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours. We ask whether it is possible to predict survival rate more accurately using a selection of machine learning techniques applied to subsets of data to gain a deeper understanding of the relationships between a patient’s biochemical markers and survival. We use a range of feature selection and single classification techniques to predict the 5 year survival rate of TNM stage 2 and 3 patients which initially produces less than ideal results. The performance of each model individually is then compared with subsets of the data where agreement is reached for multiple models. This novel method of selective ensembling demonstrates that significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. Finally we point at a possible method to identify whether a patients prognosis can be accurately predicted or not

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.Comment: Federated Conference on Computer Science and Information Systems (FedCSIS), pp 187-191, 201

The role of MUC1 and MUC3 in the biology and prognosis of colorectal cancer

BACKGROUND: MUC1 and MUC3 are from a large family of glycoproteins with an aberrant expression profile in various malignancies. Much interest has been focused on the role of these proteins in the development and progression of colorectal cancer; however, no previous studies have included the highly confounding variable of vascular invasion in their survival analysis. Using high throughput tissue microarray technology we assessed the prognostic value of MUC1 and MUC3 expression in the largest cohort of colorectal cancer patients to date. We propose that tumours lacking expression of MUC1 and MUC3 will be more likely to metastasise, due to previously observed loss of cell-cell adhesion, and this will therefore lead to more aggressive cancers with poorer prognosis. METHODS: A tissue micro-array was prepared from tumour samples of 462 consecutive patients undergoing resection of a primary colorectal cancer. A comprehensive prospectively recorded data base with mean follow up of 75 months was collected and included common clinicopathological variables and disease specific survival. Immunohistochemical analysis of MUC1 and MUC3 expression was performed using antibodies NCL-MUC1 and 1143/B7 respectively, results were correlated with the variables within the database. RESULTS: Positive expression of MUC1 and MUC3 was seen in 32% and 74% of tumours respectively. On univariate analysis no correlation was seen with either MUC1 or MUC3 and any of the clinicopathological variables including tumour grade and stage, vascular invasion and tumour type. Kaplan-Meier analysis demonstrated a significant reduction in disease specific survival with MUC1 positive tumours (p = 0.038), this was not seen with MUC3 (p = 0.552). On multivariate analysis, using Cox proportional hazards model, MUC1 expression was shown to be an independent marker of prognosis (HR 1.339, 95%CI 1.002–1.790, p = 0.048). CONCLUSION: MUC1 expression in colorectal cancer is an independent marker of poor prognosis, even when vascular invasion is included in the analysis. These results support previous studies suggesting a role for MUC1 in colorectal cancer development possibly through its effects on cell adhesion

Data refinement for true concurrency

The majority of modern systems exhibit sophisticated concurrent behaviour, where several system components modify and observe the system state with fine-grained atomicity. Many systems (e.g., multi-core processors, real-time controllers) also exhibit truly concurrent behaviour, where multiple events can occur simultaneously. This paper presents data refinement defined in terms of an interval-based framework, which includes high-level operators that capture non-deterministic expression evaluation. By modifying the type of an interval, our theory may be specialised to cover data refinement of both discrete and continuous systems. We present an interval-based encoding of forward simulation, then prove that our forward simulation rule is sound with respect to our data refinement definition. A number of rules for decomposing forward simulation proofs over both sequential and parallel composition are developed

Evidence that the p53 negative / Bcl-2 positive phenotype is an independent indicator of good prognosis in colorectal cancer: A tissue microarray study of 460 patients

BACKGROUND: Advances in our understanding of the molecular biology of colorectal cancer have fuelled the search for novel molecular prognostic markers to complement existing staging systems. Markers assessed in combination may perform better than those considered individually. Using high-throughput tissue microarray technology, we describe the prognostic value of combined p53 / Bcl-2 status in colorectal cancer. PATIENTS AND METHODS: Tumour samples from 462 patients who underwent elective surgery to resect a primary colorectal cancer between 1994 and 2000 (mean follow-up of 75 months) were assembled in tissue microarray format. Clinico-pathological data including tumour grade, stage, vascular invasion status along with disease specific survival data has been collected prospectively. Immunohistochemical analysis of p53 and Bcl-2 expression was performed using antibodies DO-7 (p53) and 124 (Bcl-2), and results correlated with known clinico-pathological variables and outcomes. RESULTS: Abnormal nuclear p53 accumulation and Bcl-2 overexpression were detected in 221/445 (49.6%) and199/437 (45.5%) tumours respectively, with a significant inverse correlation between the two markers (p = 0.023). On univariate analysis no correlations were found between either marker and standard clinico-pathological variables, however nuclear p53 expression was associated with a significantly reduced survival (p = 0.024). Combined analysis of the two markers indicated that 112/432 (24.2%) cases displayed a p53(-)/Bcl-2(+) phenotype, this occurring more frequently in earlier stage tumours. Kaplan-Meier analysis revealed a significant survival advantage in these p53(-)/Bcl-2(+) tumours compared with the remaining cases (p = 0.0032). On multivariate analysis using the Cox proportional hazards model, neither p53 expression nor Bcl-2 expression alone were of independent prognostic significance, however the combined p53(-)/Bcl-2(+) phenotype was significantly associated with a good prognosis in this series (HR 0.659, 95%CI 0.452–0.959, p = 0.029). CONCLUSION: Patient stratification by combined p53 / Bcl-2 phenotype provides stage-independent prognostic information in colorectal cancer. Specifically, that up to a quarter of patients display a good prognosis p53(-)/Bcl-2(+) phenotype. This may indicate a more clinically indolent phenotype and a subset of patients for whom less aggressive adjuvant treatment appropriate

The wheat Phs-A1 pre-harvest sprouting resistance locus delays the rate of seed dormancy loss and maps 0.3 cM distal to the PM19 genes in UK germplasm

The precocious germination of cereal grains before harvest, also known as pre-harvest sprouting, is an important source of yield and quality loss in cereal production. Pre-harvest sprouting is a complex grain defect and is becoming an increasing challenge due to changing climate patterns. Resistance to sprouting is multi-genic, although a significant proportion of the sprouting variation in modern wheat cultivars is controlled by a few major quantitative trait loci, including Phs-A1 in chromosome arm 4AL. Despite its importance, little is known about the physiological basis and the gene(s) underlying this important locus. In this study, we characterized Phs-A1 and show that it confers resistance to sprouting damage by affecting the rate of dormancy loss during dry seed after-ripening. We show Phs-A1 to be effective even when seeds develop at low temperature (13 °C). Comparative analysis of syntenic Phs-A1 intervals in wheat and Brachypodium uncovered ten orthologous genes, including the Plasma Membrane 19 genes (PM19-A1 and PM19-A2) previously proposed as the main candidates for this locus. However, high-resolution fine-mapping in two bi-parental UK mapping populations delimited Phs-A1 to an interval 0.3 cM distal to the PM19 genes. This study suggests the possibility that more than one causal gene underlies this major pre-harvest sprouting locus. The information and resources reported in this study will help test this hypothesis across a wider set of germplasm and will be of importance for breeding more sprouting resilient wheat varieties