Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices

Genome-scale metabolic networks can be reconstructed. The systemic biochemical properties of these networks can now be studied. Here, genome-scale reconstructed metabolic networks were analysed using singular value decomposition (SVD). All the individual biochemical conversions contained in a reconstructed metabolic network are described by a stoichiometric matrix (S). SVD of S led to the definition of the underlying modes that characterize the overall biochemical conversions that take place in a network and rank-ordered their importance. The modes were shown to correspond to systemic biochemical reactions and they could be used to identify the groups and clusters of individual biochemical reactions that drive them. Comparative analysis of the Escherichia coli, Haemophilus influenzae, and Helicobacter pylori genome-scale metabolic networks showed that the four dominant modes in all three networks correspond to: (1) the conversion of ATP to ADP, (2) redox metabolism of NADP, (3) proton-motive force, and (4) inorganic phosphate metabolism. The sets of individual metabolic reactions deriving these systemic conversions, however, differed among the three organisms. Thus, we can now define systemic metabolic reactions, or eigen-reactions, for the study of systems biology of metabolism and have a basis for comparing the overall properties of genome-specific metabolic networks.     

Singular value decomposition analysis of the secondary structure features contributing to the circular dichroism spectra of model proteins

Amyloid fibril formation occurs in restricted environment, such as the interface between intercellular fluids and bio-membranes. Conformational interconversion from α-helix to β-structure does not progress in fluids; however, it can occur after sedimentary aggregation during amyloid fibril formation induced by heat treatment of hen egg white lysozyme (HEWL). Secondary structures of various proteins and denatured proteins titrated with 2,2,2-trifluoroethanol (TFE) were examined using their CD spectra. Gaussian peak/trough and singular value decompositions (SVD) showed that the spectral pattern of the α-helix comprised a sharp trough at wavelength 207 nm and a broad trough at 220 nm. Conversely, we distinguished two patterns for β-sheet—a spread barrel type, corresponding to ConA, and a tightly weaved type, corresponding to the soybean trypsin inhibitor. Herein, we confirmed that the spectral/conformational interconversion of the heat-treated HEWL was not observed in the dissolved fluid.     

Observation of multiple intermediates in alpha-synuclein fibril formation by singular value decomposition analysis

One of the most well known characteristics for Parkinson's disease (PD) is a polymerization of wild-type or mutant alpha-synuclein into aggregates and fibrils, commonly observed as Lewy bodies and Lewy neuritis in PD patients. Although numerous studies on alpha-synuclein fibrillation have been reported, the molecular mechanisms of aggregation and fibrillation are not well understood yet. In the present study, structural properties and propensities to form fibrils of wild-type, A30P, E46K, and A53T alpha-synucleins were investigated using fluorescence and circular dichroism (CD) methods. The results from these studies were analyzed using singular value decomposition (SVD) method which estimates a number of conformationally independent species for a given process. The time-dependent CD spectra of the wild-type alpha-synuclein indicated a multi-step process in the fibril formation, and SVD analysis using the time-dependent CD spectra revealed that five or nine intermediates were formed at the early stage of fibrillation.     

Bayesian modeling of embryonic growth using latent variables

In a growth model, individuals move progressively through a series of states in which each state is indicative of developmental status. Interest lies in estimating the rate of progression through each state while incorporating covariates that might affect the transition rates. We develop a Bayesian discrete-time multistate growth model for inference from cross-sectional data with unknown initiation times. For each subject, data are collected at only one time point at which we observe the state as well as covariates that measure developmental progress. We link the developmental progress variables to an underlying latent growth variable that can also affect the state transition rates. A subject with slow latent growth will then have relatively small developmental progress covariates and move through state transitions slowly. We then examine the association between latent growth and the probability of future events in a novel study of embryonic development and pregnancy loss. Using a Markov chain Monte Carlo (MCMC) algorithm for posterior computation, we found evidence in favor of a previously hypothesized but unproven association between slow growth early in pregnancy and increased risk of future spontaneous abortion.     

Reliability of ultrasound speckle tracking with singular value decomposition for quantifying displacement in the carpal tunnel

Inhibited movement patterns of carpal tunnel structures have been found in carpal tunnel syndrome (CTS) patients. Motion analysis on ultrasound images allows us to non-invasively study the (relative) movement of carpal tunnel structures and recently a speckle tracking method using singular value decomposition (SVD) has been proposed to optimize this tracking. This study aims to assess the reliability of longitudinal speckle tracking with SVD in both healthy volunteers and patients with CTS.Images from sixteen healthy volunteers and twenty-two CTS patients were used. Ultrasound clips of the third superficial flexor tendon and surrounding subsynovial connective tissue (SSCT) were acquired during finger flexion-extension. A custom made tracking algorithm was used for the analysis. Intra-class correlation coefficients (ICCs) were calculated using a single measure, two-way random model with absolute agreement and Bland-Altman plots were added for graphical representation.ICC values varied between 0.73 and 0.95 in the control group and 0.66–0.98 in the CTS patients, with the majority of the results classified as good to excellent. Tendon tracking showed higher reliability values compared to the SSCT, but values between the control and CTS groups were comparable.Speckle tracking with SVD can reliably be used to analyze longitudinal movement of anatomical structures with different sizes and compositions within the context of the carpal tunnel in both a healthy as well as a pathological state. Based on these results, this technique also holds relevant potential for areas where ultrasound based dynamic imaging requires quantification of motion.     

Meta-analysis for surrogacy: accelerated failure time models and semicompeting risks modeling

There has been great recent interest in the medical and statistical literature in the assessment and validation of surrogate endpoints as proxies for clinical endpoints in medical studies. More recently, authors have focused on using metaanalytical methods for quantification of surrogacy. In this article, we extend existing procedures for analysis based on the accelerated failure time model to this setting. An advantage of this approach relative to proportional hazards model is that it allows for analysis in the semicompeting risks setting, where we model the region where the surrogate endpoint occurs before the true endpoint. Several estimation methods and attendant inferential procedures are presented. In addition, between- and within-trial methods for evaluating surrogacy are developed; a novel principal components procedure is developed for quantifying trial-level surrogacy. The methods are illustrated by application to data from several studies in colorectal cancer.     

Self-efficacy difference among patients with cancer with different socioeconomic status: Application of latent class analysis and standardization and decomposition analysis

IntroductionAlthough the relationship between partial socioeconomic status (SES) and self-efficacy has been studied in previous studies, few research have examined self-efficacy difference among patients with cancer with different SES.MethodsA cross-sectional survey involving 764 patients with cancer was completed. Latent class analysis (LCA) was applied to identify distinct groups of patients with cancer using four SES indicators (education, income, employment status and health insurance status). Standardization and decomposition analysis (SDA) was then used to examine differences in patients’ self-efficacy among SES groups and the components of the differences attributed to confounding factors, such as gender, age, anxiety, depression and social support.ResultsParticipants were classified into four distinctive SES groups via using LCA method, and the observed self-efficacy level significantly varied by SES groups; as theorized, higher self-efficacy was associated with higher SES. The self-efficacy differences by SES groups were decomposed into “real” group differences and factor component effects that are attributed to group differences in confounding factor compositions.ConclusionSelf-efficacy significantly varies by SES. Social support significantly confounded the observed differences in self-efficacy between different SES groups among Chinese patients with cancer.     

Automatic identification of discrete substates in proteins: Singular value decomposition analysis of time-averaged crystallographic refinements

The singular value decomposition (SVD) provides a method for decomposing a molecular dynamics trajectory into fundamental modes of atomic motion. The right singular vectors are projections of the protein conformations onto these modes showing the protein motion in a generalized low-dimensional basis. Statistical analysis of the right singular vectors can be used to classify discrete configurational substates in the protein. The configuration space portraits formed from the right singular vectors can also be used to visualize complex high-dimensional motion and to examine the extent of configuration space sampling by the simulation. © 1995 Wiley-Liss, Inc.     

Improving the objectivity of sustainability indices by a novel approach for combining contrasting effects: Happy Planet Index revisited

Measuring complex and rather intuitive qualities such as sustainability requires combining many different measures together. These measures often quantify contrasting effects. The resulting composite index then also depends not only on the component indices but also on the way that these have been combined together. An example of such a measure is the Happy Planet Index (HPI) that aggregates information on positive qualities like life-expectancy and human well-being with negative ones like ecological footprint to rank countries according to their sustainability. However, since component indices are often mutually correlated and feature quite different distributions of entities ranked, elaborate rules are used in the process of combination. As a result, the resulting composite index may look somewhat contrived and its rankings may depend heavily on subjective parameters in the combination process. We propose a geometrically motivated parameter-free method for combining indices with contrasting effects together. The method is independent of the number of contrasting indices to be combined and eliminates mutual correlation between component indices by using Singular Value Decomposition (SVD) analysis. As an example of its use, we revisit the Happy Planet Index and demonstrate the impact of adding new component indices to HPI on ranking nations by their sustainability.     

A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease

For many diseases the infection status of individuals cannot be observed directly, but can only be inferred from biomarkers that are subject to measurement error. Diagnosis of infection based on observed symptoms can itself be regarded as an imperfect test of infection status. The temporal relationship between infection and marker outcomes may be complex, especially for recurrent diseases where individuals can experience multiple bouts of infection. We propose an approach that first models the unobserved longitudinal infection status of individuals conditional on relevant covariates, and then jointly models the longitudinal sequence of biomarker outcomes conditional on infection status and covariate information through time, thus resulting in a joint model for longitudinal infection and biomarker sequences. This model can be used to investigate the temporal dynamics of infection, and to evaluate the usefulness of biomarkers for monitoring purposes. Our work is motivated and illustrated by a longitudinal study of bovine digital dermatitis (BDD) on commercial dairy farms in North West England and North Wales, in which the infection of interest is Treponeme spp., and the biomarkers of interest are a continuous enzyme-linked immunosorbent assay test outcome and a dichotomous outcome, foot lesion status. BDD is known to be one of the possible causes of foot lesions in cows.     

Analytical trapping: extraction of time-independent structures from time-dependent crystallographic data

All chemical and biological reactions involve atomic motion, embodied in dynamic structural changes. Identifying these changes is the goal of time-resolved crystallography. The "raw" output of a time-resolved macromolecular crystallography experiment is the time-dependent set of difference electron density maps that span the desired time range and display the time-dependent changes in density (and underlying structure) as the reaction progresses. The goal is to interpret such data in terms of a small number of crystallographically refinable, time-independent structures, each associated with a reaction intermediate; to establish the pathways and rate coefficients by which the intermediates interconvert; and thus to establish a chemical kinetic mechanism. We review briefly the various strategies that may be used to achieve this goal and concentrate on two promising advances: singular value decomposition and cluster analysis. The strategies are illustrated by using data on the photocycle of the bacterial blue light photoreceptor, photoactive yellow protein.     

Human serum albumin binding ibuprofen: A 3D description of the unfolding pathway in urea

Small angle X-ray scattering (SAXS) technique, supported by light scattering measurements and spectroscopic data (circular dichroism and fluorescence) allowed us to restore the 3D structure at low resolution of defatted human serum albumin (HSA) in interaction with ibuprofen. The data were carried out on a set of HSA solutions with urea concentrations between 0.00 and 9.00 M. The Singular Value Decomposition method, applied to the complete SAXS data set allowed us to distinguish three different states in solution. In particular a native conformation N (at 0.00 M urea), an intermediate I1 (at 6.05 M urea) and an unfolded structure U (at 9.00 M urea) were recognized. The low-resolution structures of these states were obtained by exploiting both ab initio and rigid body fitting methods. In particular, for the protein without denaturant, a conformation recently described (Leggio et al., PCCP, 2008, 10, 6741–6750), very similar to the crystallographic heart shape, with only a slight reciprocal movement of the three domains, was confirmed. The I1 structure was instead characterized by only a closed domain (domain III) and finally, the recovered structure of the U state revealed the characteristic feature of a completely open state. A direct comparison with the free HSA pointed out that the presence of the ibuprofen provokes a shift of the equilibrium towards higher urea concentrations without changing the unfolding sequence. The work represents a type of analysis which could be exploited in future investigations on proteins in solution, in the binding of drugs or endogenous compounds and in the pharmacokinetic properties as well as in the study of allosteric effects, cooperation or anticooperation mechanisms.