Expression of smooth muscle and extracellular matrix proteins in relation to airway function in asthma

Background: Smooth muscle content is increased within the airway wall in patients with asthma and is likely to play a role in airway hyperresponsiveness. However, smooth muscle cells express several contractile and structural proteins, and each of these proteins may influence airway function distinctly. Objective: We examined the expression of contractile and structural proteins of smooth muscle cells, as well as extracellular matrix proteins, in bronchial biopsies of patients with asthma, and related these to lung function, airway hyperresponsiveness, and responses to deep inspiration. Methods: Thirteen patients with asthma (mild persistent, atopic, nonsmoking) participated in this cross-sectional study. FEV1 % predicted, PC20 methacholine, and resistance of the respiratory system by the forced oscillation technique during tidal breathing and deep breath were measured. Within 1 week, a bronchoscopy was performed to obtain 6 bronchial biopsies that were immunuhistochemically stained for alpha-SM-actin, desmin, myosin light chain kinase (MLCK), myosin, calponin, vimentin, elastin, type III collagen, and fibronectin. The level of expression was determined by automated densitometry. Results: PC20 methacholine was inversely related to the expression of alpha-smooth muscle actin (r = -0.62), desmin (r = -0.56), and elastin (r = -0.78). In addition, FEV1% predicted was positively related and deep inspiration-induced bronchodilation inversely related to desmin (r = -0.60), MLCK (r = -0.60), and calponin (r = -0.54) expression. Conclusion: Airway hyperresponsiveness, FEV1% predicted, and airway responses to deep inspiration are associated with selective expression of airway smooth muscle proteins and components of the extracellular matrix.

Lp95, a novel leptospiral protein that binds extracellular matrix components and activates e-selectin on endothelial cells

Objectives: The study of a predicted outer membrane leptospiral protein encoded by the gene LIC12690 in mediating the adhesion process. Methods: The gene was cloned and expressed in Escherichia coli BL21 (SI) strain by using the expression vector pAE. The recombinant protein tagged with N-terminal hexahistidine was purified by metal-charged chromatography and used to assess its ability to activate human umbilical vein endothelial cells (HUVECs). Results: The recombinant leptospiral protein of 95 kDa, named Lp95, activated E-selectin in a dose-dependent fashion but not the intercellular adhesion molecule 1 (ICAM-1). In addition, we show that pathogenic and non-pathogenic Leptospira are both capable to stimulate endothelium E-selectin and ICAM-1, but the pathogenic L. interrogans serovar Copenhageni strain promotes a statistically significant higher activation than the non-pathogenic L. biflexa serovar Patoc (P < 0.01). The Lp95 was identified in vivo in the renal tubules of animal during experimental infection with L. interrogans. The whole Lp95 as well as its fragments, the C-terminal containing the domain of unknown function (DUF), the N-terminal and the central overlap regions bind laminin and fibronectin ECM molecules, being the binding stronger with the DUF containing fragment. Conclusion: This is the first leptospiral protein capable to mediate the adhesion to ECM components and the activation of HUVECS, thus suggesting its participation in the pathogenesis of Leptospira. (C) 2009 The British Infection Society. Published by Elsevier Ltd. All rights reserved.

Matrix-product codes over Fq

Codes C-1,...,C-M of length it over F-q and an M x N matrix A over F-q define a matrix-product code C = [C-1 (...) C-M] (.) A consisting of all matrix products [c(1) (...) c(M)] (.) A. This generalizes the (u/u + v)-, (u + v + w/2u + v/u)-, (a + x/b + x/a + b + x)-, (u + v/u - v)- etc. constructions. We study matrix-product codes using Linear Algebra. This provides a basis for a unified analysis of /C/, d(C), the minimum Hamming distance of C, and C-perpendicular to. It also reveals an interesting connection with MDS codes. We determine /C/ when A is non-singular. To underbound d(C), we need A to be 'non-singular by columns (NSC)'. We investigate NSC matrices. We show that Generalized Reed-Muller codes are iterative NSC matrix-product codes, generalizing the construction of Reed-Muller codes, as are the ternary 'Main Sequence codes'. We obtain a simpler proof of the minimum Hamming distance of such families of codes. If A is square and NSC, C-perpendicular to can be described using C-1(perpendicular to),...,C-M(perpendicular to) and a transformation of A. This yields d(C-perpendicular to). Finally we show that an NSC matrix-product code is a generalized concatenated code.

Purification and characterisation of functional early pregnancy factor expressed in Sf9 insect cells and in Escherichia coli

Early pregnancy factor (EPF) is a secreted protein with growth regulatory and immunomodulatory properties. It is an extracellular form of the mitochondrial matrix protein chaperonin 10 (Cpn10), a molecular chaperone. An understanding of the mechanism of action of EPF and an exploration of therapeutic potential has been limited by availability of purified material. The present study was undertaken to develop a simple high-yielding procedure for preparation of material for structure/function studies, which could be scaled up for therapeutic application. Human EPF was expressed in Sf9 insect cells by baculovirus infection and in Escherichia coli using a heat inducible vector. A modified molecule with an additional N-terminal alanine was also expressed in E coli. The soluble protein was purified from cell lysates via anion exchange (negative-binding mode), cation exchange, and hydrophobic interaction chromatography, yielding similar to42 and 36 mg EPF from 300 ml bacterial and I L Sf9 cultures, respectively. The preparations were highly purified ( greater than or equal to99% purity on SDS-PAGE for the bacterial products and greater than or equal to97% for that of insect cells) and had the expected mass and heptameric structure under native conditions, as determined by mass spectrometry and gel permeation chromatography, respectively. All recombinant preparations exhibited activity in the EPF bioassay, the rosette inhibition test, with similar potency both to each other and to the native molecule. In two in vivo assays of immuno suppressive activity, the delayed-type hypersensitivity reaction and experimental autoimmune encephalomyelitis, the insect cell and modified bacterial products, both with N-terminal additions (acetylation or amino acid), exhibited similar levels of suppressive activity, but the bacterial product with no N-terminal modification had no effect in either assay. Studies by others have shown that N-terminal addition is not necessary for Cpn10 activity. By defining techniques for facile production of molecules with and without immunosuppressive properties, the present studies make it possible to explore mechanisms underlying the distinction between EPF and Cpn10 activity. (C) 2003 Elsevier Inc. All rights reserved.

Extracellular matrix and platelet function in patients with musculocontractural Ehlers-Danlos syndrome caused by mutations in the CHST14 gene.

We report on a consanguineous, Afghani family with two sisters affected with characteristic facial features, multiple contractures, progressive joint and skin laxity, hemorrhagic diathesis following minor trauma and multisystem fragility-related manifestations suggestive of a diagnosis of musculocontractural Ehlers-Danlos syndrome (EDS). This novel form of connective tissue disorder was recently reported in patients of Japanese, Turkish, and Indian descent who were formerly classified as having EDS type VIB and has now been recognized to be a part of spectrum including patients previously classified as having adducted thumb-clubfoot syndrome. We identified a previously unreported mutation in the CHST14 gene, which codes for the enzyme dermatan 4-O-sulfotransferase. We discuss the prenatal presentation, detailed clinical manifestations, and neurological findings in two sisters with this newly described musculocontractural EDS-CHST14 type. We demonstrate that fibroblasts from one of our patients produce more chondroitin sulfate than normal and show lower than normal deposition of collagens I and II and fibrillin 1-containing microfibrills. These findings suggest that the imbalance in the glycosaminoglycan content in developing tissues might interfere with normal deposition of other extracellular matrix components and ultimately contribute to the development of the phenotype observed in these patients. Furthermore, we ruled out the contribution of intrinsic platelet factors to the bleeding diathesis observed in some affected individuals. © 2012 Wiley Periodicals, Inc.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Design for Customer Needs: Utilization of Quality Function Deployment in Product Development

Kansainvälisen kaupan kiristyessä yrityksien kyky täyttää asiakasketjunsa lailliset, sosiaaliset ja toiminnalliset asiakastarpeet tulee punnituksi. Globaalisuuden lisääntyessä asiakasketju voi sisältää toimintoja samanaikaisesti yli sadassa maassa. Jotta asiakasketjun tarpeet voidaan sisällyttää tuotteeseen tehokkaasti yhä useammat yritykset ovat siirtyneet käyttämään Quality Function Deployment nimistä projektijohto- ja laatutyökalua. Quality Function Deployment työkalu auttaa yritystä muuntamaan sisäisten ja ulkoisten asiakkaittensa tarpeet, tuotefunktioiksi ja tuotespesifikaatioiksi. Näin tehdessä voidaan uuden tuotteen kehitysaikaa ja hintaa alentaa merkittävästi suunnittelmalla tuote alunalkaen paremmin. QFD:tä on käytetty useissa yrityksissä Aasiassa, Pohjois-Amerikassa ja Euroopassa, sen kehittämisen jälkeen Japanissa 1960 luvulla. Tämä diplomityö antaa teoreettisen ja käytännön kuvauksen siitä miten QFD:tä kannatta käyttää ja mitä sen avulla voidaan saavuttaa vastaten kysymykseen "miten minä, ja yritykseni hyötyy jos käytän QFD:tä".

How can we agree on some product agnostic repository function fundamentals?

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

A singular vector perspective of 4D-Var: Filtering and interpolation

Four-dimensional variational data assimilation (4D-Var) combines the information from a time sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrievals. It is shown that the 4D-Var analysis increments can be written as a linear combination of the singular vectors of a matrix which is a function of both the observational and the forecast model systems. This formulation is used to consider the filtering and interpolating aspects of 4D-Var using idealized case-studies based on a simple model of baroclinic instability. The results of the 4D-Var case-studies exhibit the reconstruction of the state in unobserved regions as a consequence of the interpolation of observations through time. The results also exhibit the filtering of components with small spatial scales that correspond to noise, and the filtering of structures in unobserved regions. The singular vector perspective gives a very clear view of this filtering and interpolating by the 4D-Var algorithm and shows that the appropriate specification of the a priori statistics is vital to extract the largest possible amount of useful information from the observations. Copyright © 2005 Royal Meteorological Society

Rational conversion of substrate and product specificity in a Salvia monoterpene synthase: structural insights into the evolution of terpene synthase function

Terpene synthases are responsible for the biosynthesis of the complex chemical defense arsenal of plants and microorganisms. How do these enzymes, which all appear to share a common terpene synthase fold, specify the many different products made almost entirely from one of only three substrates? Elucidation of the structure of 1,8-cineole synthase from Salvia fruticosa (Sf-CinS1) combined with analysis of functional and phylogenetic relationships of enzymes within Salvia species identified active-site residues responsible for product specificity. Thus, Sf-CinS1 was successfully converted to a sabinene synthase with a minimum number of rationally predicted substitutions, while identification of the Asn side chain essential for water activation introduced 1,8-cineole and alpha-terpineol activity to Salvia pomifera sabinene synthase. A major contribution to product specificity in Sf-CinS1 appears to come from a local deformation within one of the helices forming the active site. This deformation is observed in all other mono- or sesquiterpene structures available, pointing to a conserved mechanism. Moreover, a single amino acid substitution enlarged the active-site cavity enough to accommodate the larger farnesyl pyrophosphate substrate and led to the efficient synthesis of sesquiterpenes, while alternate single substitutions of this critical amino acid yielded five additional terpene synthases.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.