Using quantitative analysis to implement autonomic IT systems

The software underpinning today’s IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.

Quantitative methods in neuropathology

The last decade has seen a considerable increase in the application of quantitative methods in the study of histological sections of brain tissue and especially in the study of neurodegenerative disease. These disorders are characterised by the deposition and aggregation of abnormal or misfolded proteins in the form of extracellular protein deposits such as senile plaques (SP) and intracellular inclusions such as neurofibrillary tangles (NFT). Quantification of brain lesions and studying the relationships between lesions and normal anatomical features of the brain, including neurons, glial cells, and blood vessels, has become an important method of elucidating disease pathogenesis. This review describes methods for quantifying the abundance of a histological feature such as density, frequency, and 'load' and the sampling methods by which quantitative measures can be obtained including plot/quadrat sampling, transect sampling, and the point-quarter method. In addition, methods for determining the spatial pattern of a histological feature, i.e., whether the feature is distributed at random, regularly, or is aggregated into clusters, are described. These methods include the use of the Poisson and binomial distributions, pattern analysis by regression, Fourier analysis, and methods based on mapped point patterns. Finally, the statistical methods available for studying the degree of spatial correlation between pathological lesions and neurons, glial cells, and blood vessels are described.

Appraising qualitative research for inclusion in systematic reviews:A quantitative and qualitative comparison of three methods

Objective: Qualitative research is increasingly valued as part of the evidence for policy and practice, but how it should be appraised is contested. Various appraisal methods, including checklists and other structured approaches, have been proposed but rarely evaluated. We aimed to compare three methods for appraising qualitative research papers that were candidates for inclusion in a systematic review of evidence on support for breast-feeding. Method: A sample of 12 research papers on support for breast-feeding was appraised by six qualitative reviewers using three appraisal methods: unprompted judgement, based on expert opinion; a UK Cabinet Office quality framework; and CASP, a Critical Appraisal Skills Programme tool. Papers were assigned, following appraisals, to 1 of 5 categories, which were dichotomized to indicate whether or not papers should be included in a systematic review. Patterns of agreement in categorization of papers were assessed quantitatively using κ statistics, and qualitatively using cross-case analysis. Results: Agreement in categorizing papers across the three methods was slight (κ =0.13; 95% CI 0.06-0.24). Structured approaches did not appear to yield higher agreement than that by unprompted judgement. Qualitative analysis revealed reviewers' dilemmas in deciding between the potential impact of findings and the quality of the research execution or reporting practice. Structured instruments appeared to make reviewers more explicit about the reasons for their judgements. Conclusions: Structured approaches may not produce greater consistency of judgements about whether to include qualitative papers in a systematic review. Future research should address how appraisals of qualitative research should be incorporated in systematic reviews. © The Royal Society of Medicine Press Ltd 2007.

The copper cladding of steel by the submerged arc welding method

The literature available on submerged arc welding of copper and copper alloys, submerged arc welding with strip electrodes, and related areas has been reviewed in depth. Copper cladding of mild steel substrates by deposition from strip electrodes using the submerged arc welding process has been successful. A wide range of parameters, and several fluxes have been investigated. The range of deposit compositions is 66.4% Cu to 95.7% Cu. The weld beads have been metallographically examined using optical and electron microscopy. Equating weld beads to a thermodynamical equivalent of iron has proven to be an accurate and simplified means of handling quantitative data for multicomponent welds. Empirical equations derived using theoretical considerations characterize the weld bead dimensions as functions of the welding parameters and hence composition. The melting rate for strip electrodes is dependent upon the current-voltage product. Weld nugget size is increased by increased thermal transfer efficiencies resulting from stirring which is current dependent. The presence of Fe2O3 in a flux has been demonstrated to diminish electrode melting rate and drastically increase penetration, making flux choice the prime consideration in cladding operations. A theoretical model for welding with strip electrodes and the submerged arc process is presented.

A quantitative analysis of optic nerve axons in elderly control subjects and patients with Alzheimer's disease

Objective: To study the density and cross-sectional area of axons in the optic nerve in elderly control subjects and in cases of Alzheimer's disease (AD) using an image analysis system. Methods: Sections of optic nerves from control and AD patients were stained with toluidine blue to reveal axon profiles. Results: The density of axons was reduced in both the center and peripheral portions of the optic nerve in AD compared with control patients. Analysis of axons with different cross-sectional areas suggested a specific loss of the smaller sized axons in AD, i.e., those with areas less that 1.99 μm2. An analysis of axons >11 μm2 in cross-sectional area suggested no specific loss of the larger axons in this group of patients. Conclusions: The data suggest that image analysis provides an accurate and reproducible method of quantifying axons in the optic nerve. In addition, the data suggest that axons are lost throughout the optic nerve with a specific loss of the smaller-sized axons. Loss of the smaller axons may explain the deficits in color vision observed in a significant proportion of patients with AD.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

Concepts and methods of quantitative assessment of risk to groundwater resources

This work presents a two-dimensional approach of risk assessment method based on the quantification of the probability of the occurrence of contaminant source terms, as well as the assessment of the resultant impacts. The risk is calculated using Monte Carlo simulation methods whereby synthetic contaminant source terms were generated to the same distribution as historically occurring pollution events or a priori potential probability distribution. The spatial and temporal distributions of the generated contaminant concentrations at pre-defined monitoring points within the aquifer were then simulated from repeated realisations using integrated mathematical models. The number of times when user defined ranges of concentration magnitudes were exceeded is quantified as risk. The utilities of the method were demonstrated using hypothetical scenarios, and the risk of pollution from a number of sources all occurring by chance together was evaluated. The results are presented in the form of charts and spatial maps. The generated risk maps show the risk of pollution at each observation borehole, as well as the trends within the study area. This capability to generate synthetic pollution events from numerous potential sources of pollution based on historical frequency of their occurrence proved to be a great asset to the method, and a large benefit over the contemporary methods.

Quantitative interpretation of diffusion-ordered NMR spectra:can we rationalize small molecule diffusion coefficients?

Appealingly simple: A new method is described that allows the diffusion coefficient of a small molecule to be estimated given only the molecular weight and the viscosity of the solvent used. This method makes possible the quantitative interpretation of the diffusion domain of diffusion-ordered NMR spectra (see picture). © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

White matter pathology in progressive supranuclear palsy (PSP):a quantitative study of 8 cases

Aims: To quantify white matterpathology in progressive supranuclear palsy (PSP). Material: Histological sections of white matter of 8 PSP and 8 control cases \Method: Densities and spatial patterns of vacuolation, glial cell nuclei, and glial inclusions (GI) were measured in 8cortical and subcortical fiber tracts. Results: No GI wereobserved in control fiber tracts. Densities of vacuoles and glial cell nuclei were greater in PSP than in controls. In PSP, density of vacuoles was greatest in the alveus, frontopontine fibers (FPF), and central tegmental tract (CTT), and densities of glial cell nuclei were greater in cortical than subcortical regions.The highest densities of GI were observed in the basal ganglia, FPF, cerebellum, andsuperior frontal gyrus (SFG). Vacuoles, glialcells and GI were distributed randomly, uniformly,in regularly distributed clusters, or in large clusters across fiber tracts. GI wermore frequently distributed in regular clusters than the vacuoles and glial cell nuclei.Vacuoles, glial cell nuclei, and GI were not spatially correlated. Conclusions: The data suggest significant degeneration of white matter in PSP, vacuolation being related to neuronal loss in adjacent gray matterregions,GI the result of abnormal tau released from damaged axons, and gliosis a responseto these changes. © 2013.

A method for systematic artifact selection decision making

Artifact selection decisions typically involve the selection of one from a number of possible/candidate options (decision alternatives). In order to support such decisions, it is important to identify and recognize relevant key issues of problem solving and decision making (Albers, 1996; Harris, 1998a, 1998b; Jacobs & Holten, 1995; Loch & Conger, 1996; Rumble, 1991; Sauter, 1999; Simon, 1986). Sauter classifies four problem solving/decision making styles: (1) left-brain style, (2) right-brain style, (3) accommodating, and (4) integrated (Sauter, 1999). The left-brain style employs analytical and quantitative techniques and relies on rational and logical reasoning. In an effort to achieve predictability and minimize uncertainty, problems are explicitly defined, solution methods are determined, orderly information searches are conducted, and analysis is increasingly refined. Left-brain style decision making works best when it is possible to predict/control, measure, and quantify all relevant variables, and when information is complete. In direct contrast, right-brain style decision making is based on intuitive techniques—it places more emphasis on feelings than facts. Accommodating decision makers use their non-dominant style when they realize that it will work best in a given situation. Lastly, integrated style decision makers are able to combine the left- and right-brain styles—they use analytical processes to filter information and intuition to contend with uncertainty and complexity.

Quantitative analysis of multi-spectral fundus images

We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity:in silico bioinformatic step-by-step guide using quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Quantitative structure-activity relationships and the prediction of MHC supermotifs

The underlying assumption in quantitative structure–activity relationship (QSAR) methodology is that related chemical structures exhibit related biological activities. We review here two QSAR methods in terms of their applicability for human MHC supermotif definition. Supermotifs are motifs that characterise binding to more than one allele. Supermotif definition is the initial in silico step of epitope-based vaccine design. The first QSAR method we review here—the additive method—is based on the assumption that the binding affinity of a peptide depends on contributions from both amino acids and the interactions between them. The second method is a 3D-QSAR method: comparative molecular similarity indices analysis (CoMSIA). Both methods were applied to 771 peptides binding to 9 HLA alleles. Five of the alleles (A*0201, A* 0202, A*0203, A*0206 and A*6802) belong to the HLA-A2 superfamily and the other four (A*0301, A*1101, A*3101 and A*6801) to the HLA-A3 superfamily. For each superfamily, supermotifs defined by the two QSAR methods agree closely and are supported by many experimental data.

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.