Using a matrix in life transition research

Life transitions can be planned or can occur unexpectedly. They can cause a major change to a person's life patterns and well-being. Older adulthood is a time for many life transitions as a result of changes in life roles and health status. In this exploratory study, the authors investigate the transition involved in driving cessation for older people. In analyzing and organizing the data, they develop a matrix that incorporated descriptive and temporal factors associated with the transition. This matrix is useful in organizing and communicating the findings as a whole and could be used in describing individual experiences. It might be of use for the organization of qualitative data about other life transitions such as illness, retirement, and the development and adoption of new behaviors.

Mechanism of adhesion and detachment at the anterior end of Merizocotyle icopae (Monogenea : Monocotylidae) including ultrastructure of the anterior adhesive matrix

The anterior adhesive mechanism was studied for Merizocotyle icopae (Monogenea: Monocotylidae). Adult anterior apertures can open and close. In addition, duct endings terminating within the apertures are everted or retracted depending on the stage of attachment. Adhesive in adults is synthesized from all 3 secretory types (rod-shaped, small and large spheroidal bodies) found within anterior apertures. All exit together and undergo mixing to produce the adhesive matrix, a process that depletes duct contents. A greater number of ducts carrying rod-shaped bodies is depleted than ducts containing spheroidal bodies which changes the ratio of secretory types present on detachment. Detachment involves elongation of duct endings and secretion of additional matrix as the worm pulls away from the substrate. The change in secretory type ratio putatively modifies the properties of the secreted matrix enabling detachment. Only after detachment do ducts refill. During attachment, individual secretory bodies undergo morphological changes. The larval and adult adhesive matrix differs. Anterior adhesive in oncomiracidia does not show fibres with banding whereas banded fibres comprise a large part of adult adhesive. The data Suggest that this is the result of adult spheroidal secretions modifying the way in which the adult adhesive matrix forms.

Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported

Analysis of population pharmacokinetic data using NONMEM and WinBUGS

The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.

Factors important to the secretion and matrix deposition of tissue transglutaminase

Data suggest that for TG2 to be secreted, an intact N-terminal FN binding site (for which TG2 has high affinity) is required, however interaction of TG2 with its high affinity binding partners presents both in the intracellular and extracellular space as well as with specific cell surface receptors may also be involved in this process. Using a site-directed mutagenesis approach, the effects of specific mutations of TG2 on its translocation to the cell surface and secretion into the ECM have been investigated. Mutations include those affecting FN binding (FN1), HSPGs binding (HS1, HS2) GTP/GDP binding site (GTP1, 2) as well as N-terminal and C-terminal domains (TG2 deletion mutants N, and C). By performing transglutaminase activity assays, cell surface protein biotinylation and verifying distribution of TG2 mutants in the ECM we demonstrated that one of the potential heparan sulfate binding site mutants (HS2 mutant) is secreted at the cell surface in a much reduced manner and is less deposited into the ECM than the HS1 mutant. The HS2 mutant showed a low affinity for binding to a heparin sepharose column demonstrating this mutation site may be a potential heparan binding site of TG2. Analogous peptides to this site were shown to have some efficiency in the inhibition of the binding of the FN-TG2 complex to cell surface heparan sulfates in a cell adhesion assay indicating the peptide to be representative of the novel heparin binding site within TG2. The GTP binding site mutants GTP1 and GTP2 exhibited low specific activity however, GTP2 showed more secretion to the cell surface in comparison to GTP1. The FN1 binding mutant did not greatly affect TG2 activity nor did it alter TG2 secretion at the cell surface and deposition into the ECM indicating that fibronectin binding at this site on the enzyme is not an important factor. Interestingly an intact N-terminus (?1-15) appeared to be essential for enzyme externalisation. Removal of the first 15 amino acids (N-terminal mutant) abolished TG2 secretion to the cell surface as well as deposition into the ECM. In addition it reduced the enzymes affinity for binding to heparin. In contrast, deletion of the C-terminal TG2 domain (?594-687) increased enzyme secretion to the cell surface. Consistent with the data presented in this thesis we speculate that TG2 must fulfill two requirements to be successfully secreted from cells. The findings indicate that the closed conformation of the enzyme as well as intact N-terminal tail and a novel HS binding site within the TG2 molecule are key elements for the enzyme’s localisation at the cell surface and its deposition into the extracellular matrix. The importance of understanding the interactions between TG2, heparan sulfates and other TG2 binding partners at the cell surface could have an impact on the design of novel strategies for enzyme inhibition which could be important in the control of extracellular TG2 related diseases.

Use of a solvent-free dry matrix coating for quantitative matrix-assisted laser desorption ionization imaging of 4-bromophenyl-1,4-diazabicyclo(3.2.2)nonane-4-carboxylate in rat brain and quantitative analysis of the drug from laser microdissected tissue regions

A dry matrix application for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was used to profile the distribution of 4-bromophenyl-1,4-diazabicyclo(3.2.2)nonane-4-carboxylate, monohydrochloride (BDNC, SSR180711) in rat brain tissue sections. Matrix application involved applying layers of finely ground dry alpha-cyano-4-hydroxycinnamic acid (CHCA) to the surface of tissue sections thaw mounted onto MALDI targets. It was not possible to detect the drug when applying matrix in a standard aqueous-organic solvent solution. The drug was detected at higher concentrations in specific regions of the brain, particularly the white matter of the cerebellum. Pseudomultiple reaction monitoring imaging was used to validate that the observed distribution was the target compound. The semiquantitative data obtained from signal intensities in the imaging was confirmed by laser microdissection of specific regions of the brain directed by the imaging, followed by hydrophilic interaction chromatography in combination with a quantitative high-resolution mass spectrometry method. This study illustrates that a dry matrix coating is a valuable and complementary matrix application method for analysis of small polar drugs and metabolites that can be used for semiquantitative analysis.

Sequential, Bayesian geostatistics:a principled method for large data sets

The principled statistical application of Gaussian random field models used in geostatistics has historically been limited to data sets of a small size. This limitation is imposed by the requirement to store and invert the covariance matrix of all the samples to obtain a predictive distribution at unsampled locations, or to use likelihood-based covariance estimation. Various ad hoc approaches to solve this problem have been adopted, such as selecting a neighborhood region and/or a small number of observations to use in the kriging process, but these have no sound theoretical basis and it is unclear what information is being lost. In this article, we present a Bayesian method for estimating the posterior mean and covariance structures of a Gaussian random field using a sequential estimation algorithm. By imposing sparsity in a well-defined framework, the algorithm retains a subset of “basis vectors” that best represent the “true” posterior Gaussian random field model in the relative entropy sense. This allows a principled treatment of Gaussian random field models on very large data sets. The method is particularly appropriate when the Gaussian random field model is regarded as a latent variable model, which may be nonlinearly related to the observations. We show the application of the sequential, sparse Bayesian estimation in Gaussian random field models and discuss its merits and drawbacks.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.

The importance of the tissue transglutaminase-fibronectin heterocomplex in the RGD-independent cell adhesion and fibronectin matrix deposition

Tissue transglutaminase (TG2) has been reported as a wound response protein. Once over-expressed by cells under stress such as during wound healing or following tissue damage, TG2 can be secreted and deposited into extracellular matrix, where it forms a heterocomplex (TG-FN) with the abundant matrix protein fibronectin (FN). A further cellular response elicited after tissue damage is that of matrix remodelling leading to the release of the Arg-Gly-Asp (RGD) containing matrix fragments by matrix matelloproteinases (MMPs). These peptides are able to block the interaction between integrin cell surface receptors and ECM proteins, leading to the loss of cell adhesion and ultimately Anoikis. This study provides a mechanism for TG2, as a stress-induced matrix protein, in protecting the cells from the RGD-dependent loss of cell adhesion and rescuing the cells from Anoikis. Mouse fibroblasts were used as a major model for this study, including different types of cell surface receptor knockout mouse embryonic fibroblasts (MEFs) (such as syndecan-4, a5, ß1 or ß3 integrins). In addition specific syndecan-2 targetting siRNAs, ß1 integrin and a4ß1 integrin functional blocking antibodies, and a specific targeting peptide against a5ß1 integrin A5-1 were used to investigate the involvement of these receptors in the RGD-independent cell adhesion on TG-FN. Crucial for TG-FN to compensate the RGD-independent cell adhesion and actin cytoskeleton formation is the direct interaction between the heparan sulfate chains of syndecan-4 and TG2, which elicits the inside-out signalling of a5ß1 integrin and the intracellular activation of syndecan-2 by protein kinase C a (PKCa). By using specific inhibitors, a cell-permeable inhibiting peptide and the detection of the phosphorylation sites for protein kinases and/or the translocation of PKCa via Western blotting, the activation of PKCa, focal adhesion kinase (FAK), ERK1/2 and Rho kinase (ROCK) were confirmed as downstream signalling molecules. Importantly, this study also investigated the influence of TG-FN on matrix turnover and demonstrated that TG-FN can restore the RGD-independent FN deposition process via an a5ß1 integrin and syndecan-4/2 co-signalling pathway linked by PKCa in a transamidating-independent manner. These data provide a novel function for TG2 in wound healing and matrix turnover which is a key event in a number of both physiological and pathological processes.

Exploratory database visualisation: the application and assessment of data and dimensionality reduction

This thesis describes the development of a complete data visualisation system for large tabular databases, such as those commonly found in a business environment. A state-of-the-art 'cyberspace cell' data visualisation technique was investigated and a powerful visualisation system using it was implemented. Although allowing databases to be explored and conclusions drawn, it had several drawbacks, the majority of which were due to the three-dimensional nature of the visualisation. A novel two-dimensional generic visualisation system, known as MADEN, was then developed and implemented, based upon a 2-D matrix of 'density plots'. MADEN allows an entire high-dimensional database to be visualised in one window, while permitting close analysis in 'enlargement' windows. Selections of records can be made and examined, and dependencies between fields can be investigated in detail. MADEN was used as a tool for investigating and assessing many data processing algorithms, firstly data-reducing (clustering) methods, then dimensionality-reducing techniques. These included a new 'directed' form of principal components analysis, several novel applications of artificial neural networks, and discriminant analysis techniques which illustrated how groups within a database can be separated. To illustrate the power of the system, MADEN was used to explore customer databases from two financial institutions, resulting in a number of discoveries which would be of interest to a marketing manager. Finally, the database of results from the 1992 UK Research Assessment Exercise was analysed. Using MADEN allowed both universities and disciplines to be graphically compared, and supplied some startling revelations, including empirical evidence of the 'Oxbridge factor'.

Exploratory data analysis with non-linear and missing data in geochemistry

Exploratory analysis of data seeks to find common patterns to gain insights into the structure and distribution of the data. In geochemistry it is a valuable means to gain insights into the complicated processes making up a petroleum system. Typically linear visualisation methods like principal components analysis, linked plots, or brushing are used. These methods can not directly be employed when dealing with missing data and they struggle to capture global non-linear structures in the data, however they can do so locally. This thesis discusses a complementary approach based on a non-linear probabilistic model. The generative topographic mapping (GTM) enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate more structure than a two dimensional principal components plot. The model can deal with uncertainty, missing data and allows for the exploration of the non-linear structure in the data. In this thesis a novel approach to initialise the GTM with arbitrary projections is developed. This makes it possible to combine GTM with algorithms like Isomap and fit complex non-linear structure like the Swiss-roll. Another novel extension is the incorporation of prior knowledge about the structure of the covariance matrix. This extension greatly enhances the modelling capabilities of the algorithm resulting in better fit to the data and better imputation capabilities for missing data. Additionally an extensive benchmark study of the missing data imputation capabilities of GTM is performed. Further a novel approach, based on missing data, will be introduced to benchmark the fit of probabilistic visualisation algorithms on unlabelled data. Finally the work is complemented by evaluating the algorithms on real-life datasets from geochemical projects.

Addressing missing data in geochemistry: a non-linear approach

Exploratory analysis of petroleum geochemical data seeks to find common patterns to help distinguish between different source rocks, oils and gases, and to explain their source, maturity and any intra-reservoir alteration. However, at the outset, one is typically faced with (a) a large matrix of samples, each with a range of molecular and isotopic properties, (b) a spatially and temporally unrepresentative sampling pattern, (c) noisy data and (d) often, a large number of missing values. This inhibits analysis using conventional statistical methods. Typically, visualisation methods like principal components analysis are used, but these methods are not easily able to deal with missing data nor can they capture non-linear structure in the data. One approach to discovering complex, non-linear structure in the data is through the use of linked plots, or brushing, while ignoring the missing data. In this paper we introduce a complementary approach based on a non-linear probabilistic model. Generative topographic mapping enables the visualisation of the effects of very many variables on a single plot, while also dealing with missing data. We show how using generative topographic mapping also provides an optimal method with which to replace missing values in two geochemical datasets, particularly where a large proportion of the data is missing.

Mechanical properties and the evolution of matrix molecules in PTFE upon irradiation with MeV alpha particles

The morphology, chemical composition, and mechanical properties in the surface region of α-irradiated polytetrafluoroethylene (PTFE) have been examined and compared to unirradiated specimens. Samples were irradiated with 5.5 MeV 4He2+ ions from a tandem accelerator to doses between 1 × 106 and 5 × 1010 Rad. Static time-of-flight secondary ion mass spectrometry (ToF-SIMS), using a 20 keV C60+ source, was employed to probe chemical changes as a function of a dose. Chemical images and high resolution spectra were collected and analyzed to reveal the effects of a particle radiation on the chemical structure. Residual gas analysis (RGA) was utilized to monitor the evolution of volatile species during vacuum irradiation of the samples. Scanning electron microscopy (SEM) was used to observe the morphological variation of samples with increasing a particle dose, and nanoindentation was engaged to determine the hardness and elastic modulus as a function of a dose. The data show that PTFE nominally retains its innate chemical structure and morphology at a doses <109 Rad. At α doses ≥109 Rad the polymer matrix experiences increased chemical degradation and morphological roughening which are accompanied by increased hardness and declining elasticity. At  α doses >1010 Rad the polymer matrix suffers severe chemical degradation and material loss. Chemical degradation is observed in ToF-SIMS by detection of ions that are indicative of fragmentation, unsaturation, and functionalization of molecules in the PTFE matrix. The mass spectra also expose the subtle trends of crosslinking within the α-irradiated polymer matrix. ToF-SIMS images support the assertion that chemical degradation is the result of a particle irradiation and show morphological roughening of the sample with increased a dose. High resolution SEM images more clearly illustrate the morphological roughening and the mass loss that accompanies high doses of a particles. RGA confirms the supposition that the outcome of chemical degradation in the PTFE matrix with continuing irradiation is evolution of volatile species resulting in morphological roughening and mass loss. Finally, we reveal and discuss relationships between chemical structure and mechanical properties such as hardness and elastic modulus.

Key Agreement Protocol (KAP) Based on Matrix Power Function

* Work is partially supported by the Lithuanian State Science and Studies Foundation.