The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

Telomere length attrition, a marker of biological senescence, is inversely correlated with triglycerides and cholesterol in South Asian males with type 2 diabetes mellitus

South Asians have a higher risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) than white Caucasians, for a given BMI. Premature biological ageing, assessed by reduction in telomere length (TL), may be mediated by factors resulting from altered metabolic profiles associated with obesity. We hypothesise that ethnicity and metabolic status represent detrimental factors contributing to premature biological ageing. Therefore we assessed TL in two South Asian, age and BMI-matched cohorts [T2DM (n = 142) versus non-T2DM (n = 76)] to determine the effects of BMI, gender, lipid and CVD profile on biological ageing. Genomic DNA was obtained from the UKADS cohort; biochemical and anthropometric data was collected and TL was measured by quantitative real-time PCR. Our findings indicated a gender-specific effect with reduced TL in T2DM men compared with non-T2DM men (P = 0.006). Additionally, in T2DM men, TL was inversely correlated with triglycerides and total cholesterol (r = -0.419, P <0.01; r = -0.443, P <0.01). In summary, TL was reduced amongst South Asian T2DM men and correlated with triglycerides and total cholesterol. This study highlights enhanced biological ageing among South Asian, T2DM men, which appears to be tracked by changes in lipids and BMI, suggesting that raised lipids and BMI may directly contribute to premature ageing.

Signal processing for molecular and cellular biological physics:an emerging field

Recent advances in our ability to watch the molecular and cellular processes of life in action-such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer-raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

Reporter ion-based mass spectrometry approaches for the detection of non-enzymatic protein modifications in biological samples

Development of mass spectrometry techniques to detect protein oxidation, which contributes to signalling and inflammation, is important. Label-free approaches have the advantage of reduced sample manipulation, but are challenging in complex samples owing to undirected analysis of large data sets using statistical search engines. To identify oxidised proteins in biological samples, we previously developed a targeted approach involving precursor ion scanning for diagnostic MS3 ions from oxidised residues. Here, we tested this approach for other oxidations, and compared it with an alternative approach involving the use of extracted ion chromatograms (XICs) generated from high-resolution MSMS data using very narrow mass windows. This accurate mass XIC data methodology was effective at identifying nitrotyrosine, chlorotyrosine, and oxidative deamination of lysine, and for tyrosine oxidations highlighted more modified peptide species than precursor ion scanning or statistical database searches. Although some false positive peaks still occurred in the XICs, these could be identified by comparative assessment of the peak intensities. The method has the advantage that a number of different modifications can be analysed simultaneously in a single LC-MSMS run. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine. Biological significance: The use of accurate mass extracted product ion chromatograms to detect oxidised peptides could improve the identification of oxidatively damaged proteins in inflammatory conditions. © 2013 Elsevier B.V.

Assessment of physical and hydrological properties of biological soil crusts using x-ray microtomography and modelling

Biological soil crusts (BSCs) are formed by aggregates of soil particles and communities of microbial organisms and are common in all drylands. The role of BSCs on infiltration remains uncertain due to the lack of data on their role in affecting soil physical properties such as porosity and structure. Quantitative assessment of these properties is primarily hindered by the fragile nature of the crusts. Here we show how the use of a combination of non-destructive imaging X-ray microtomography (XMT) and Lattice Boltzmann method (LBM) enables quantification of key soil physical parameters and the modeling of water flow through BSCs samples from Kalahari Sands, Botswana. We quantify porosity and flow changes as a result of mechanical disturbance of such a fragile cyanobacteria-dominated crust. Results show significant variations in porosity between different types of crusts and how they affect the flow and that disturbance of a cyanobacteria-dominated crust results in the breakdown of larger pore spaces and reduces flow rates through the surface layer. We conclude that the XMT–LBM approach is well suited for study of fragile surface crust samples where physical and hydraulic properties cannot be easily quantified using conventional methods.

Characterisation of electrospun polystyrene scaffolds for three-dimensional in vitro biological studies

The purpose of this study was to produce a well-characterised electrospun polystyrene scaffold which could be used routinely for three-dimensional (3D) cell culture experimentation. A linear relationship (p<0.01p<0.01) between three principal process variables (applied voltage, working distance and polymer concentration) and fibre diameter was reliably established enabling a mathematical model to be developed to standardise the electrospinning process. Surface chemistry and bulk architecture were manipulated to increase wetting and handling characteristics, respectively. X-ray photoelectron spectroscopy (XPS) confirmed the presence of oxygen-containing groups after argon plasma treatment, resulting in a similar surface chemistry to treated tissue culture plastic. The bulk architecture of the scaffolds was characterised by scanning electron microscopy (SEM) to assess the alignment of both random and aligned electrospun fibres, which were calculated to be 0.15 and 0.66, respectively. This compared to 0.51 for collagen fibres associated with native tissue. Tensile strength and strain of approximately of 0.15 MPa and 2.5%, respectively, allowed the scaffolds to be routinely handled for tissue culture purposes. The efficiency of attachment of smooth muscle cells to electrospun scaffolds was assessed using a modified 3-[4,5-dimethyl(thiazol-2yl)-3,5-diphery] tetrazolium bromide assay and cell morphology was assessed by phalloidin-FITC staining of F-actin. Argon plasma treatment of electrospun polystyrene scaffold resulted in significantly increased cell attachment (p<0.05p<0.05). The alignment factors of the actin filaments were 0.19 and 0.74 for the random and aligned scaffold respectively, compared to 0.51 for the native tissue. The data suggests that electrospinning of polystyrene generates 3D scaffolds which complement polystyrene used in 2D cell culture systems.

Antagonistic effects of two novel GIP analogs, (Hyp3)GIP and (Hyp3)GIPLys16PAL, on the biological actions of GIP and longer-term effects in diabetic ob/ob mice

This study examines the actions of the novel enzyme-resistant, NH 2-terminally modified GIP analog (Hyp3)GIP and its fatty acid-derivatized analog (Hyp3)GIPLys16PAL. Acute effects are compared with the established GIP receptor antagonist (Pro3)GIP. All three peptides exhibited DPP IV resistance, and significantly inhibited GIP stimulated cAMP formation and insulin secretion in GIP receptor-transfected fibroblasts and in clonal pancreatic BRIN-BD11 cells, respectively. Likewise, in obese diabetic ob/ob mice, intraperitoneal administration of GIP analogs significantly inhibited the acute antihyperglycemic and insulin-releasing effects of native GIP. Administration of once daily injections of (Hyp 3)GIP or (Hyp3)GIPLys16PAL for 14 days resulted in significantly lower plasma glucose levels (P < 0.05) after (Hyp 3)GIP on days 12 and 14 and enhanced glucose tolerance (P < 0.05) and insulin sensitivity (P < 0.05 to P < 0.001) in both groups by day 14. Both (Hyp3)GIP and (Hyp3)GIPLys16PAL treatment also reduced pancreatic insulin (P < 0.05 to P < 0.01) without affecting islet number. These data indicate that (Hyp3)GIP and (Hyp 3)GIPLys16PAL function as GIP receptor antagonists with potential for ameliorating obesity-related diabetes. Acylation of (Hyp 3)GIP to extend bioactivity does not appear to be of any additional benefit. Copyright © 2007 the American Physiological Society.

Novel dipeptidyl peptidase IV resistant analogues of glucagon-like peptide-1(7-36)amide have preserved biological activities in vitro conferring improved glucose-lowering action in vivo

Although the incretin hormone glucagon-like peptide-1 (GLP-1) is a potent stimulator of insulin release, its rapid degradation in vivo by the enzyme dipeptidyl peptidase IV (DPP IV) greatly limits its potential for treatment of type 2 diabetes. Here, we report two novel Ala8-substituted analogues of GLP-1, (Abu8)GLP-1 and (Val8)GLP-1 which were completely resistant to inactivation by DPP IV or human plasma. (Abu8)GLP-1 and (Val8)GLP-1 exhibited moderate affinities (IC50: 4.76 and 81.1 nM, respectively) for the human GLP-1 receptor compared with native GLP-1 (IC50: 0.37 nM). (Abu8)GLP-1 and (Val8)GLP-1 dose-dependently stimulated cAMP in insulin-secreting BRIN BD11 cells with reduced potency compared with native GLP-1 (1.5- and 3.5-fold, respectively). Consistent with other mechanisms of action, the analogues showed similar, or in the case of (Val8)GLP-1 slightly impaired insulin releasing activity in BRIN BD11 cells. Using adult obese (ob/ob) mice, (Abu8 )GLP-1 had similar glucose-lowering potency to native GLP-1 whereas the action of (Val8)GLP-1 was enhanced by 37%. The in vivo insulin-releasing activities were similar. These data indicate that substitution of Ala8 in GLP-1 with Abu or Val confers resistance to DPP IV inactivation and that (Val8)GLP-1 is a particularly potent N-terminally modified GLP-1 analogue of possible use in type 2 diabetes.

LEAPS:a semantic web and linked data framework for the Algal biomass domain

In this paper we present, LEAPS, a Semantic Web and Linked data framework for searching and visualising datasets from the domain of Algal biomass. LEAPS provides tailored interfaces to explore algal biomass datasets via REST services and a SPARQL endpoint for stakeholders in the domain of algal biomass. The rich suite of datasets include data about potential algal biomass cultivation sites, sources of CO2, the pipelines connecting the cultivation sites to the CO2 sources and a subset of the biological taxonomy of algae derived from the world's largest online information source on algae.

Low-Power Tracking Image Sensor Based on Biological Models of Attention

This paper presents implementation of a low-power tracking CMOS image sensor based on biological models of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing "smart" image sensor architecture, where all image processing is implemented on the sensor focal plane, the proposed imager allows reduction of the amount of data transmitted from the sensor array to external processing units and thus provides real time operation. The imager operation and architecture are based on the models taken from biological systems, where data sensed by many millions of receptors should be transmitted and processed in real time. The imager architecture is optimized to achieve low-power dissipation both in acquisition and tracking modes of operation. The tracking concept is presented, the system architecture is shown and the circuits description is discussed.

Bottleneck Problem Solution using Biological Models of Attention in High Resolution Tracking Sensors

Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in performance, caused by the requirement to process a large amount of information in parallel. The same problem exists in biological vision systems, where the information, sensed by many millions of receptors should be transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real time salient target tracking and proposes a simple solution by employing models of attention, found in biological systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are discussed and the architecture of the proposed imager is shown.

Novel ageing-biomarker discovery using data-intensive technologies

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for biomarker discovery; (1) microarray analyses and/or proteomics in cell systems e.g. endothelial progenitor cells or T cell ageing including a stress model; and (2) investigation of cellular material and plasma directly from tightly-defined proband subsets of different ages using proteomic, transcriptomic and miR array. The first approach provided longitudinal insight into endothelial progenitor and T cell ageing.This review describes the strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miR, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages. It considers the challenges associated with integrating multiple models and pilot studies as rational biomarkers for a large cohort study. From this approach, a number of high-throughput methods were developed to evaluate novel, putative biomarkers of ageing in the MARK-AGE cohort.

Systematic review and analysis of evidences on clinical efficacy and cost-effectiveness of biological drugs for the treatment of Ankylosing Spondylitis

Technology: Infliximab and comparator biological such as adalimumab, etanercept, golimumab. Conditions: Ankylosing spondylitis (AS) Issue: Infliximab is registered to be used in patients with AS. The aim of the Report is to evaluate the clinical efficacy and safety of infliximab and comparator biologicals for the treatment of adult AS. Methods: Systematic literature review and analysis as well as meta-analysis (direct and indirect comparison) of published randomised controlled clinical trials (RCT) were performed, all relevant health economics literature were identified ad analysed. Results: Clinical efficacy of biological therapies is based on good clinical evidences regarding to all clinical efficacy endpoints (ASAS20, ASAS40, ASAS 5/6, and BASDAI 50% response). Altogether, 22 trials are included in our meta-analysis, 12 infliximab, 3 adalimumab studies, 6 etanercept and 1 golimumab. Efficacy of biological treatments for the treatment of AS has been established by clinical scientific evidences, significant improvement at all outcomes considered was confirmed. According to the results of indirect comparison, there were no significant difference between biological treatments and placebo in terms of safety and tolerability endpoints. We found no significant difference between the clinical efficacy and safety of infliximab, adalimumab, etanercept and golimumab therapies. Cost-utility analysis of adalimumab and/or infliximab, etanercept and golimumab treatment for AS were performed in the UK, Canada, The Netherlands, Germany, Spain and France. There are no cost-utility studies from Eastern Central Europe. Implications for decision making: Efficacy of infliximab and comparator biologicals for the treatment of Ankylosing Spondylitis (AS) was proved by clinical evidence, significant improvement at all outcomes considered was confirmed. We found no significant differences in efficacy and safety of different biological treatments. Health economics results suggest that biological therapies are cost-effective alternatives for the treatment of AS in group of developed high income countries. There is a lack of health economics results in Central-Eastern European countries however these data are more and more required by governments and funders as part of the company economic dossiers.

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

Ranked search on data graphs

Graph-structured databases are widely prevalent, and the problem of effective search and retrieval from such graphs has been receiving much attention recently. For example, the Web can be naturally viewed as a graph. Likewise, a relational database can be viewed as a graph where tuples are modeled as vertices connected via foreign-key relationships. Keyword search querying has emerged as one of the most effective paradigms for information discovery, especially over HTML documents in the World Wide Web. One of the key advantages of keyword search querying is its simplicity—users do not have to learn a complex query language, and can issue queries without any prior knowledge about the structure of the underlying data. The purpose of this dissertation was to develop techniques for user-friendly, high quality and efficient searching of graph structured databases. Several ranked search methods on data graphs have been studied in the recent years. Given a top-k keyword search query on a graph and some ranking criteria, a keyword proximity search finds the top-k answers where each answer is a substructure of the graph containing all query keywords, which illustrates the relationship between the keyword present in the graph. We applied keyword proximity search on the web and the page graph of web documents to find top-k answers that satisfy user’s information need and increase user satisfaction. Another effective ranking mechanism applied on data graphs is the authority flow based ranking mechanism. Given a top- k keyword search query on a graph, an authority-flow based search finds the top-k answers where each answer is a node in the graph ranked according to its relevance and importance to the query. We developed techniques that improved the authority flow based search on data graphs by creating a framework to explain and reformulate them taking in to consideration user preferences and feedback. We also applied the proposed graph search techniques for Information Discovery over biological databases. Our algorithms were experimentally evaluated for performance and quality. The quality of our method was compared to current approaches by using user surveys.