Two-dimensional IR correlation spectroscopy: Sequential events in the unfolding process of the λ Cro-V55C repressor protein

A question often posed in protein folding/unfolding studies is whether the process is fully cooperative or whether it contains sequential elements. To address this question, one needs tools capable of resolving different events. It seems that, at least in certain cases, two-dimensional (2D) IR correlation spectroscopy can provide answers to this question. To illustrate this point, we have turned to the Cro-V55C dimer of the λ Cro repressor, a protein known to undergo thermal unfolding in two discrete steps through a stable equilibrium intermediate. The secondary structure of this intermediate is compatible with that of a partially unfolded protein and involves a reorganization of the N terminus, whereas the antiparallel β-ribbon formed by the C-terminal part of each subunit remains largely intact. To establish whether the unfolding process involves sequential events, we have performed a 2D correlation analysis of IR spectra recorded over the temperature range of 20–95°C. The 2D IR correlation analysis indeed provides evidence for a sequential formation of the stable intermediate, which is created in three (closely related) steps. A first step entails the unfolding of the short N-terminal β-strand, followed by the unfolding of the α-helices in a second step, and the third step comprises the reorganization of the remaining β-sheet and of some unordered segments in the protein. The complete unfolding of the stable intermediate at higher temperatures also undergoes sequential events that ultimately end with the breaking of the H bonds between the two β-strands at the dimer interface.

Systematic derivation of partition functions for ligand binding to two-dimensional lattices

The Ising problem consists in finding the analytical solution of the partition function of a lattice once the interaction geometry among its elements is specified. No general analytical solution is available for this problem, except for the one-dimensional case. Using site-specific thermodynamics, it is shown that the partition function for ligand binding to a two-dimensional lattice can be obtained from those of one-dimensional lattices with known solution. The complexity of the lattice is reduced recursively by application of a contact transformation that involves a relatively small number of steps. The transformation implemented in a computer code solves the partition function of the lattice by operating on the connectivity matrix of the graph associated with it. This provides a powerful new approach to the Ising problem, and enables a systematic analysis of two-dimensional lattices that model many biologically relevant phenomena. Application of this approach to finite two-dimensional lattices with positive cooperativity indicates that the binding capacity per site diverges as Na (N = number of sites in the lattice) and experiences a phase-transition-like discontinuity in the thermodynamic limit N → ∞. The zeroes of the partition function tend to distribute on a slightly distorted unit circle in complex plane and approach the positive real axis already for a 5×5 square lattice. When the lattice has negative cooperativity, its properties mimic those of a system composed of two classes of independent sites with the apparent population of low-affinity binding sites increasing with the size of the lattice, thereby accounting for a phenomenon encountered in many ligand-receptor interactions.

Spatially independent activity patterns in functional MRI data during the Stroop color-naming task

A method is given for determining the time course and spatial extent of consistently and transiently task-related activations from other physiological and artifactual components that contribute to functional MRI (fMRI) recordings. Independent component analysis (ICA) was used to analyze two fMRI data sets from a subject performing 6-min trials composed of alternating 40-sec Stroop color-naming and control task blocks. Each component consisted of a fixed three-dimensional spatial distribution of brain voxel values (a “map”) and an associated time course of activation. For each trial, the algorithm detected, without a priori knowledge of their spatial or temporal structure, one consistently task-related component activated during each Stroop task block, plus several transiently task-related components activated at the onset of one or two of the Stroop task blocks only. Activation patterns occurring during only part of the fMRI trial are not observed with other techniques, because their time courses cannot easily be known in advance. Other ICA components were related to physiological pulsations, head movements, or machine noise. By using higher-order statistics to specify stricter criteria for spatial independence between component maps, ICA produced improved estimates of the temporal and spatial extent of task-related activation in our data compared with principal component analysis (PCA). ICA appears to be a promising tool for exploratory analysis of fMRI data, particularly when the time courses of activation are not known in advance.

Two-dimensional protein crystallization via metal-ion coordination by naturally occurring surface histidines.

A powerful and potentially general approach to the targeting and crystallization of proteins on lipid interfaces through coordination of surface histidine residues to lipid-chelated divalent metal ions is presented. This approach, which should be applicable to the crystallization of a wide range of naturally occurring or engineered proteins, is illustrated here by the crystallization of streptavidin on a monolayer of an iminodiacetate-Cu(II) lipid spread at the air-water interface. This method allows control of the protein orientation at interfaces, which is significant for the facile production of highly ordered protein arrays and for electron density mapping in structural analysis of two-dimensional crystals. Binding of native streptavidin to the iminodiacetate-Cu lipids occurs via His-87, located on the protein surface near the biotin binding pocket. The two-dimensional streptavidin crystals show a previously undescribed microscopic shape that differs from that of crystals formed beneath biotinylated lipids.

Spin degree of freedom in two dimensional exciton condensates

We present a theoretical analysis of a spin-dependent multicomponent condensate in two dimensions. The case of a condensate of resonantly photoexcited excitons having two different spin orientations is studied in detail. The energy and the chemical potentials of this system depend strongly on the spin polarization. When electrons and holes are located in two different planes, the condensate can be either totally spin polarized or spin unpolarized, a property that is measurable. The phase diagram in terms of the total density and electron-hole separation is discussed.

Mapping Emotion Terms into Affective Space: Further Evidence for a Four-Dimensional Structure

Recent empirical work on the semantics of emotion terms across many different cultures and languages, using a theoretical componential approach, suggested that four dimensions are needed to parsimoniously describe the semantic space of the emotion domain as reflected in emotion terms (Fontaine, Scherer, Roesch, & Ellsworth, 2007; Fontaine, Scherer, & Soriano, 2013). In addition to valence, power, and arousal, a novelty dimension was discovered that mostly differentiated surprise from other emotions. Here, we further explore the existence and nature of the fourth dimension in semantic emotion space using a much larger and much more representative set of emotion terms. A group of 156 participants each rated 10 out of a set of 80 French emotion terms with respect to semantic meaning. The meaning of an emotion term was evaluated with respect to 68 emotion features representing the appraisal, action tendency, bodily reaction, expression, and feeling components of the emotion process. A principal component analysis confirmed the four-dimensional valence, power, arousal, and novelty structure. Moreover, this larger and much more representative set of emotion terms revealed that the novelty dimension not only differentiates surprise terms from other emotion terms, but also identifies substantial variation within the fear and joy emotion families.

R2D2 : a Fortran program for two-dimensional chemically reacting, hyperthermal, internal flows.

"January 1980."

Capillary phenomena in the framework of the two-dimensional density functional theory

We present results of application of the density functional theory (DFT) to adsorption and desorption in finite and infinite cylindrical pores accounting for the density distribution in radial and axial directions. Capillary condensation via formation of bridges is considered using canonical and grand canonical versions of the 2D DFT. The potential barrier of nucleation is determined as a function of the bulk pressure and the pore diameter. In the framework of the conventional assumptions on intermolecular interactions both 1D and 2D DFT versions lead to the same results and confirm the classical scenario of condensation and evaporation: the condensation occurs at the vapor-like spinodal point, and the evaporation corresponds to the equilibrium transition pressure. The analysis of experimental data on argon and nitrogen adsorption on MCM-41 samples seems to not completely corroborate this scenario, with adsorption branch being better described by the equilibrium pressure - diameter dependence. This points to the necessity of the further development of basic representations on the hysteresis phenomena.

Modeling of adsorption and nucleation in infinite cylindrical pores by two-dimensional density functional theory

In this paper, we present an analysis of argon adsorption in cylindrical pores having amorphous silica structure by means of a nonlocal density functional theory (NLDFT). In the modeling, we account for the radial and longitudinal density distributions, which allow us to consider the interface between the liquidlike and vaporlike fluids separated by a hemispherical meniscus in the canonical ensemble. The Helmholtz free energy of the meniscus was determined as a function of pore diameter. The canonical NLDFT simulations show the details of density rearrangement at the vaporlike and liquidlike spinodal points. The limits of stability of the smallest bridge and the smallest bubble were also determined with the canonical NLDFT. The energy of nucleation as a function of the bulk pressure and the pore diameter was determined with the grand canonical NLDFT using an additional external potential field. It was shown that the experimentally observed reversibility of argon adsorption isotherms at its boiling point up to the pore diameter of 4 nm is possible if the potential barrier of 22kT is overcome due to density fluctuations.

Using two dimensional sectional distributions to infer three dimensional volumetric distributions - Validation using tomography

The critical process parameter for mineral separation is the degree of mineral liberation achieved by comminution. The degree of liberation provides an upper limit of efficiency for any physical separation process. The standard approach to measuring mineral liberation uses mineralogical analysis based two-dimensional sections of particles which may be acquired using a scanning electron microscope and back-scatter electron analysis or from an analysis of an image acquired using an optical microscope. Over the last 100 years, mathematical techniques have been developed to use this two dimensional information to infer three-dimensional information about the particles. For mineral processing, a particle that contains more than one mineral (a composite particle) may appear to be liberated (contain only one mineral) when analysed using only its revealed particle section. The mathematical techniques used to interpret three-dimensional information belong, to a branch of mathematics called stereology. However methods to obtain the full mineral liberation distribution of particles from particle sections are relatively new. To verify these adjustment methods, we require an experimental method which can accurately measure both sectional and three dimensional properties. Micro Cone Beam Tomography provides such a method for suitable particles and hence, provides a way to validate methods used to convert two-dimensional measurements to three dimensional estimates. For this study ore particles from a well-characterised sample were subjected to conventional mineralogical analysis (using particle sections) to estimate three-dimensional properties of the particles. A subset of these particles was analysed using a micro-cone beam tomograph. This paper presents a comparison of the three-dimensional properties predicted from measured two-dimensional sections with the measured three-dimensional properties.

Comparison of two-dimensional speckle and tissue velocity based strain and validation with harmonic phase magnetic resonance imaging

Two-dimensional (2-D) strain (epsilon(2-D)) on the basis of speckle tracking is a new technique for strain measurement. This study sought to validate epsilon(2-D) and tissue velocity imaging (TVI)based strain (epsilon(TVI)) with tagged harmonic-phase (HARP) magnetic resonance imaging (MRI). Thirty patients (mean age. 62 +/- 11 years) with known or suspected ischemic heart disease were evaluated. Wall motion (wall motion score index 1.55 +/- 0.46) was assessed by an expert observer. Three apical images were obtained for longitudinal strain (16 segments) and 3 short-axis images for radial and circumferential strain (18 segments). Radial epsilon(TVI) was obtained in the posterior wall. HARP MRI was used to measure principal strain, expressed as maximal length change in each direction. Values for epsilon(2-D), epsilon(TVI), and HARP MRI were comparable for all 3 strain directions and were reduced in dysfunctional segments. The mean difference and correlation between longitudinal epsilon(2-D) and HARP MRI (2.1 +/- 5.5%, r = 0.51, p < 0.001) were similar to those between longitudinal epsilon(TVI), and HARP MRI (1.1 +/- 6.7%, r = 0.40, p < 0.001). The mean difference and correlation were more favorable between radial epsilon(2-D) and HARP MRI (0.4 +/- 10.2%, r = 0.60, p < 0.001) than between radial epsilon(TVI), and HARP MRI (3.4 +/- 10.5%, r = 0.47, p < 0.001). For circumferential strain, the mean difference and correlation between epsilon(2-D) and HARP MRI were 0.7 +/- 5.4% and r = 0.51 (p < 0.001), respectively. In conclusion, the modest correlations of echocardiographic and HARP MRI strain reflect the technical challenges of the 2 techniques. Nonetheless, epsilon(2-D) provides a reliable tool to quantify regional function, with radial measurements being more accurate and feasible than with TVI. Unlike epsilon(TVI), epsilon(2-D) provides circumferential measurements. (c) 2006 Elsevier Inc. All rights reserved.

Gaussian regression and optimal finite dimensional linear models

The problem of regression under Gaussian assumptions is treated generally. The relationship between Bayesian prediction, regularization and smoothing is elucidated. The ideal regression is the posterior mean and its computation scales as O(n³), where n is the sample size. We show that the optimal m-dimensional linear model under a given prior is spanned by the first m eigenfunctions of a covariance operator, which is a trace-class operator. This is an infinite dimensional analogue of principal component analysis. The importance of Hilbert space methods to practical statistics is also discussed.

GTM: the generative topographic mapping

This thesis describes the Generative Topographic Mapping (GTM) --- a non-linear latent variable model, intended for modelling continuous, intrinsically low-dimensional probability distributions, embedded in high-dimensional spaces. It can be seen as a non-linear form of principal component analysis or factor analysis. It also provides a principled alternative to the self-organizing map --- a widely established neural network model for unsupervised learning --- resolving many of its associated theoretical problems. An important, potential application of the GTM is visualization of high-dimensional data. Since the GTM is non-linear, the relationship between data and its visual representation may be far from trivial, but a better understanding of this relationship can be gained by computing the so-called magnification factor. In essence, the magnification factor relates the distances between data points, as they appear when visualized, to the actual distances between those data points. There are two principal limitations of the basic GTM model. The computational effort required will grow exponentially with the intrinsic dimensionality of the density model. However, if the intended application is visualization, this will typically not be a problem. The other limitation is the inherent structure of the GTM, which makes it most suitable for modelling moderately curved probability distributions of approximately rectangular shape. When the target distribution is very different to that, theaim of maintaining an `interpretable' structure, suitable for visualizing data, may come in conflict with the aim of providing a good density model. The fact that the GTM is a probabilistic model means that results from probability theory and statistics can be used to address problems such as model complexity. Furthermore, this framework provides solid ground for extending the GTM to wider contexts than that of this thesis.

Description and critical appraisal of principal components analysis (PCA) methodology applied to pulsed-field gel electrophoresis profiles of methicillin-resistant Staphylococcus aureus isolates

Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

Statnote 27: Principal components analysis (PCA)

In Statnotes 24 and 25, multiple linear regression, a statistical method that examines the relationship between a single dependent variable (Y) and two or more independent variables (X), was described. The principle objective of such an analysis was to determine which of the X variables had a significant influence on Y and to construct an equation that predicts Y from the X variables. ‘Principal components analysis’ (PCA) and ‘factor analysis’ (FA) are also methods of examining the relationships between different variables but they differ from multiple regression in that no distinction is made between the dependent and independent variables, all variables being essentially treated the same. Originally, PCA and FA were regarded as distinct methods but in recent times they have been combined into a single analysis, PCA often being the first stage of a FA. The basic objective of a PCA/FA is to examine the relationships between the variables or the ‘structure’ of the variables and to determine whether these relationships can be explained by a smaller number of ‘factors’. This statnote describes the use of PCA/FA in the analysis of the differences between the DNA profiles of different MRSA strains introduced in Statnote 26.