Matrix elements of U(2n) generators in a multishell spin-orbit basis. I. The del-operator MEs in a two-shell composite Gelfand-Paldus basis

This is the first in a series of three articles which aimed to derive the matrix elements of the U(2n) generators in a multishell spin-orbit basis. This is a basis appropriate to many-electron systems which have a natural partitioning of the orbital space and where also spin-dependent terms are included in the Hamiltonian. The method is based on a new spin-dependent unitary group approach to the many-electron correlation problem due to Gould and Paldus [M. D. Gould and J. Paldus, J. Chem. Phys. 92, 7394, (1990)]. In this approach, the matrix elements of the U(2n) generators in the U(n) x U(2)-adapted electronic Gelfand basis are determined by the matrix elements of a single Ll(n) adjoint tensor operator called the del-operator, denoted by Delta(j)(i) (1 less than or equal to i, j less than or equal to n). Delta or del is a polynomial of degree two in the U(n) matrix E = [E-j(i)]. The approach of Gould and Paldus is based on the transformation properties of the U(2n) generators as an adjoint tensor operator of U(n) x U(2) and application of the Wigner-Eckart theorem. Hence, to generalize this approach, we need to obtain formulas for the complete set of adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis. The nonzero shift coefficients are uniquely determined and may he evaluated by the methods of Gould et al. [see the above reference]. In this article, we define zero-shift adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis which are appropriate to the many-electron problem. By definition, these are proportional to the corresponding two-shell del-operator matrix elements, and it is shown that the Racah factorization lemma applies. Formulas for these coefficients are then obtained by application of the Racah factorization lemma. The zero-shift adjoint reduced Wigner coefficients required for this procedure are evaluated first. All these coefficients are needed later for the multishell case, which leads directly to the two-shell del-operator matrix elements. Finally, we discuss an application to charge and spin densities in a two-shell molecular system. (C) 1998 John Wiley & Sons.

Matrix elements of U(2n) generators in a multishell spin-orbit basis. III. General formulas

This is the third and final article in a series directed toward the evaluation of the U(2n) generator matrix elements (MEs) in a multishell spin/orbit basis. Such a basis is required for many-electron systems possessing a partitioned orbital space and where spin-dependence is important. The approach taken is based on the transformation properties of the U(2n) generators as an adjoint tensor operator of U(n) x U(2) and application of the Wigner-Eckart theorem. A complete set of adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis (which is appropriate to the many-electron problem) were obtained in the first and second articles of this series. Ln the first article we defined zero-shift coupling coefficients. These are proportional to the corresponding two-shell del-operator matrix elements. See P. J. Burton and and M. D. Gould, J. Chem. Phys., 104, 5112 (1996), for a discussion of the del-operator and its properties. Ln the second article of the series, the nonzero shift coupling coefficients were derived. Having obtained all the necessary coefficients, we now apply the formalism developed above to obtain the U(2n) generator MEs in a multishell spin-orbit basis. The methods used are based on the work of Gould et al. (see the above reference). (C) 1998 John Wiley & Sons, Inc.

Optimising fed-batch production of recombinant proteins using the baculovirus expression vector system

Fed-batch culture can offer significant improvement in recombinant protein production compared to batch culture in the baculovirus expression vector system (BEVS), as shown by Nguyen et al. (1993) and Bedard et al. (1994) among others. However, a thorough analysis of fed-batch culture to determine its limits in improving recombinant protein production over batch culture has yet to be performed. In this work, this issue is addressed by the optimisation of single-addition fed-batch culture. This type of fed-batch culture involves the manual addition of a multi-component nutrient feed to batch culture before infection with the baculovirus. The nutrient feed consists of yeastolate ultrafiltrate, lipids, amino acids, vitamins, trace elements, and glucose, which were added to batch cultures of Spodoptera frugiperda (Sf9) cells before infection with a recombinant Autographa californica nuclear polyhedrosis virus (Ac-NPV) expressing beta-galactosidase (beta-Gal). The fed-batch production of beta-Gal was optimised using response surface methods (RSM). The optimisation was performed in two stages, starting with a screening procedure to determine the most important variables and ending with a central-composite experiment to obtain a response surface model of volumetric beta-Gal production. The predicted optimum volumetric yield of beta-Gal in fed-batch culture was 2.4-fold that of the best yields in batch culture. This result was confirmed by a statistical analysis of the best fed-batch and batch data (with average beta-Gal yields of 1.2 and 0.5 g/L, respectively) obtained from this laboratory. The response surface model generated can be used to design a more economical fed-batch operation, in which nutrient feed volumes are minimised while maintaining acceptable improvements in beta-Gal yield. (C) 1998 John Wiley & Sons, Inc.

Computational chemistry on Fujitsu vector-parallel processors: Development and performance of applications software

In this and a preceding paper, we provide an introduction to the Fujitsu VPP range of vector-parallel supercomputers and to some of the computational chemistry software available for the VPP. Here, we consider the implementation and performance of seven popular chemistry application packages. The codes discussed range from classical molecular dynamics to semiempirical and ab initio quantum chemistry. All have evolved from sequential codes, and have typically been parallelised using a replicated data approach. As such they are well suited to the large-memory/fast-processor architecture of the VPP. For one code, CASTEP, a distributed-memory data-driven parallelisation scheme is presented. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

Surface modification of metals by calcium carbonate thin films on a layer-by-layer polyelectrolyte matrix

A multilayer organic film containing poly(acrylic acid) and chitosan was fabricated on a metallic support by means of the layer-by-layer technique. This film was used as a template for calcium carbonate crystallization and presents two possible binding sites where the nucleation may be initiated, either calcium ions acting as counterions of the polyelectrolyte or those trapped in the template gel network formed by the polyelectrolyte chains. Calcium carbonate formation was carried out by carbon dioxide diffusion, where CO, was generated from ammonium carbonate decomposition. The CaCO3 nanocrystals obtained, formed a dense, homogeneous, and continuous film. Vaterite and calcite CaCO3 crystalline forms were detected. (c) 2007 Elsevier B.V All rights reserved.

Development of novel bioanodes for ethanol biofuel cell using PAMAM dendrimers as matrix for enzyme immobilization

This paper describes the preparation and application of a novel bioanode for use in ethanol/O(2) biofuel cells based upon immobilization of alcohol dehydrogenase (ADH) and polyamidoamine (PAMAM) dendrimers onto carbon cloth platforms. The power density measurements indicated a direct relationship between the amount of anchored ADH and the anode power values, which increased upon enzyme loading. The power density values ranged from 0.04 to 0.28 mW cm(-2), and the highest power density was achieved with the bioanode prepared with 28 U of ADH, which provided a power density of 0.28 mW cm(-2) at 0.3 V. The latter power output values were the maximum observed, even for higher enzyme concentrations. Stability of the bioanodes was quite satisfactory, since there was no appreciable reduction of enzymatic activity during the measurements. The method of bioanode preparation described here has proven to be very effective. The PAMAM dendrimer represents a friendly environment for the immobilization of enzymes, and it is stable and capable of generating high power density compared to other immobilization methods. (C) 2010 Elsevier B.V. All rights reserved.

Refolding and purification of the human secreted group IID phospholipase A(2) expressed as inclusion bodies in Escherichia coli

The secreted phospholipases A(2) (sPLA(2)s) are water-soluble enzymes that bind to the surface of both artificial and biological lipid bilayers and hydrolyze the membrane phospholipids. The tissue expression pattern of the human group IID secretory phospholipase A(2) (hsPLA(2)-IID) suggests that the enzyme is involved in the regulation of the immune and inflammatory responses. With an aim to establish an expression system for the hsPLA(2)-IID in Escherichia coli, the DNA-coding sequence for hsPLA(2)-IID was subcloned into the vector pET3a, and expressed as inclusion bodies in E. coli (BL21). A protocol has been developed to refold the recombinant protein in the presence of guanidinium hydrochloride, using a size-exclusion chromatography matrix followed by dilution and dialysis to remove the excess denaturant. After purification by cation-exchange chromatography, far ultraviolet circular dichroism spectra of the recombinant hsPLA(2)-IID indicated protein secondary structure content similar to the homologous human group IIA secretory phospholipase A(2). The refolded recombinant hsPLA(2)-IID demonstrated Ca(2+)-dependent hydrolytic activity, as measuring the release free fatty acid from phospholipid liposomes. This protein expression and purification system may be useful for site-directed mutagenesis experiments of the hsPLA(2)-IID which will advance our understanding of the structure-function relationship and biological effects of the protein. (C) 2009 Elsevier Inc. All rights reserved.

EXPRESSION OF MEMBRANE TYPE 1 MATRIX METALLOPROTEINASE IN MEDULLARY THYROID CARCINOMA: PROGNOSTIC IMPLICATIONS

Background. Clinical and pathologic examinations cannot always provide a prognosis for patients with medullary thyroid carcinoma. Membrane type 1 matrix metalloproteinase (MT1-MMP) can act directly on carcinogenesis and takes part in 1 of the processes of metalloproteinase 2 activation, an enzyme related to prognostic impairment of patients with such tumor. Methods. Thirty-five patients who were submitted to surgery were followed up for an average of 74 months, Postoperative and final medical conditions were characterized for comparison with MT1-MMP immunostainings, performed in surgical paraffin blocks. A value of p < .05 was considered statistically significant. Results. Proposed index (association of proportion and intensity of immunostaining) and proportion of immunostained cells in primary specimens were correlated with cure or persistence after initial operations (p = .0216 and p = .0098, respectively). Conclusion. MT1-MMP immunostaining in primary tumor specimens is a new and complementary prognostic predictor in patients with medullary thyroid carcinomas. (C) 2009 Wiley Periodicals, Inc. Head Neck 32: 58-67, 2010

Endostatin- and interleukin-2-expressing retroviral bicistronic vector for gene therapy of metastatic renal cell carcinoma

Background Metastatic renal cell carcinoma (mRCC) is one of the most treatment-resistant malignancies. Despite all new therapeutic advances, almost all patients develop resistance to treatment and cure is rarely seen. In the present study, we evaluated the antitumor effect of a bicistronic retrovirus vector encoding both endostatin (ES) and interleukin (IL)-2 using an orthotopic metastatic RCC mouse model. Methods Balb/C-bearing Renca cells were treated with NIH/3T3-LendIRES-IL-2-SN cells. In the survival studies, mice were monitored daily until they died. At the end of the in vivo experiment, serum levels of IL-2 and ES were measured, the lung was weighed, and the number of metastatic nodules, nodule area, tumor vessels and proliferation of tumor-infiltrating Renca cells were determined. Results Inoculation of NIH/3T3-LendIRES-IL-2-SN cells resulted in an increase in ES and IL-2 levels in the treated group (p < 0.05). There was a significant decrease in lung wet weight, lung nodule area and tumor vessels in the treated group compared to the control group (p < 0.001). The proliferation of Renca cells in the bicistronic-treated group was significantly reduced compared to the control group (p < 0.05). Kaplan-Meier survival curves showed that the probability of survival was significantly higher for mice submitted to bicistronic therapy (log-rank test, p = 0.0016). Bicistronic therapy caused an increase in the infiltration of CD4, CD4 interferon (IFN)gamma-producing, CD8, CD8 IFN gamma-producing and natural killer (CD49b) cells. Conclusions Retroviral bicistronic gene transfer led to the secretion of functional ES and IL-2 that was sufficiently active to: (i) inhibit tumor angiogenesis and tumor cell proliferation and (ii) increase the infiltration of immune cells (C) Copyright. 2011 John Wiley & Sons, Ltd.

Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer`s Disease

Here, we examine morphological changes in cortical thickness of patients with Alzheimer`s disease (AD) using image analysis algorithms for brain structure segmentation and study automatic classification of AD patients using cortical and volumetric data. Cortical thickness of AD patients (n = 14) was measured using MRI cortical surface-based analysis and compared with healthy subjects (n = 20). Data was analyzed using an automated algorithm for tissue segmentation and classification. A Support Vector Machine (SVM) was applied over the volumetric measurements of subcortical and cortical structures to separate AD patients from controls. The group analysis showed cortical thickness reduction in the superior temporal lobe, parahippocampal gyrus, and enthorhinal cortex in both hemispheres. We also found cortical thinning in the isthmus of cingulate gyrus and middle temporal gyrus at the right hemisphere, as well as a reduction of the cortical mantle in areas previously shown to be associated with AD. We also confirmed that automatic classification algorithms (SVM) could be helpful to distinguish AD patients from healthy controls. Moreover, the same areas implicated in the pathogenesis of AD were the main parameters driving the classification algorithm. While the patient sample used in this study was relatively small, we expect that using a database of regional volumes derived from MRI scans of a large number of subjects will increase the SVM power of AD patient identification.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Integer matrix diagonalization

We consider algorithms for computing the Smith normal form of integer matrices. A variety of different strategies have been proposed, primarily aimed at avoiding the major obstacle that occurs in such computations-explosive growth in size of intermediate entries. We present a new algorithm with excellent performance. We investigate the complexity of such computations, indicating relationships with NP-complete problems. We also describe new heuristics which perform well in practice. Wie present experimental evidence which shows our algorithm outperforming previous methods. (C) 1997 Academic Press Limited.

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.