Real exchange rate and elasticity of labour supply in a balance-of-payments-constrained macrodynamics

A macrodynamic model is proposed in which the real exchange rate and the elasticity of labour supply interact defining different trajectories of growth and income distribution in a developing economy. Growth depends on imports of capital goods which are paid with exports (there are no capital flows) and hence is constrained by equilibrium in current account. The role of the elasticity of labour supply is to prevent the real exchange rate from appreciating as the economy grows, thereby sustaining international competitiveness. The model allows for endogenous technological change and considers the impact of migration from the subsistence to the modern sector on the cumulative (Kaldor-Verdoorn) process of learning.

A spectral clustering algorithm for manufacturing cell formation

A graph clustering algorithm constructs groups of closely related parts and machines separately. After they are matched for the least intercell moves, a refining process runs on the initial cell formation to decrease the number of intercell moves. A simple modification of this main approach can deal with some practical constraints, such as the popular constraint of bounding the maximum number of machines in a cell. Our approach makes a big improvement in the computational time. More importantly, improvement is seen in the number of intercell moves when the computational results were compared with best known solutions from the literature. (C) 2009 Elsevier Ltd. All rights reserved.

Geospatial clustering in data-rich environments: Features and issues

Geospatial clustering must be designed in such a way that it takes into account the special features of geoinformation and the peculiar nature of geographical environments in order to successfully derive geospatially interesting global concentrations and localized excesses. This paper examines families of geospaital clustering recently proposed in the data mining community and identifies several features and issues especially important to geospatial clustering in data-rich environments.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

A mixture model-based approach to the clustering of microarray expression data

Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.

Conformationally constrained macrocycles that mimic tripeptide beta-strands in water and aprotic solvents

The beta-strand conformation is unknown for short peptides in aqueous solution, yet it is a fundamental building block in proteins and the crucial recognition motif for proteolytic enzymes that enable formation and turnover of all proteins. To create a generalized scaffold as a peptidomimetic that is preorganized in a beta-strand, we individually synthesized a series of 15-22-membered macrocyclic analogues of tripeptides and analyzed their structures. Each cycle is highly constrained by two trans amide bonds and a planar aromatic ring with a short nonpeptidic linker between them. A measure of this ring strain is the restricted rotation of the component tyrosinyl aromatic ring (DeltaG(rot) 76.7 kJ mol(-1) (16-membered ring), 46.1 kJ mol(-1) (17-membered ring)) evidenced by variable temperature proton NMR spectra (DMF-d(7), 200-400 K). Unusually large amide coupling constants ((3)J(NH-CHalpha) 9-10 Hz) corresponding to large dihedral angles were detected in both protic and aprotic solvents for these macrocycles, consistent with a high degree of structure in solution. The temperature dependence of all amide NH chemical shifts (Deltadelta/T7-12 ppb/deg) precluded the presence of transannular hydrogen bonds that define alternative turn structures. Whereas similar sized conventional cyclic peptides usually exist in solution as an equilibrium mixture of multiple conformers, these macrocycles adopt a well-defined beta-strand structure even in water as revealed by 2-D NMR spectral data and by a structure calculation for the smallest (15-membered) and most constrained macrocycle. Macrocycles that are sufficiently constrained to exclusively adopt a beta-strand-mimicking structure in water may be useful pre-organized and generic templates for the design of compounds that interfere with beta-strand recognition in biology.

Regioselective synthesis of antiparallel loops on a macrocyclic scaffold constrained by oxazoles and thiazoles

[GRAPHICS] The regioselective syntheses and structures are reported for two tris-macrocylic compounds, each possessing two antiparallel loops on a macrocyclic scaffold constrained by two oxazoles and two thiazoles. NMR solution structures show the loops projecting from the same face of the macrocycle. Such molecules are shown to be prototypes for mimicking multiple loops of proteins.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.

Synthesis, stability, antiviral activity, and protease-bound structures of substrate-mimicking constrained macrocyclic inhibitors of HIV-1 protease

Three new peptidomimetics (1-3) have been developed with highly stable and conformationally constrained macrocyclic components that replace tripeptide segments of protease substrates. Each compound inhibits both HIV-1 protease and viral replication (HIV-I, HIV-2) at nanomolar concentrations without cytotoxicity to uninfected cells below 10 mu M. Their activities against HIV-1 protease (K-i 1.7 nM (1), 0.6 nM (2), 0.3 nM (3)) are 1-2 orders of magnitude greater than their antiviral potencies against HIV-1-infected primary peripheral blood mononuclear cells (IC50 45 nM (1), 56 nM (2), 95 nM (3)) or HIV-1-infected MT2 cells (IC50 90 nM (1), 60 nM (2)), suggesting suboptimal cellular uptake. However their antiviral potencies are similar to those of indinavir and amprenavir under identical conditions. There were significant differences in their capacities to inhibit the replication of HIV-1 and HIV-2 in infected MT2 cells, 1 being ineffective against HIV-2 while 2 was equally effective against both virus types. Evidence is presented that 1 and 2 inhibit cleavage of the HIV-1 structural protein precursor Pr55(gag) to p24 in virions derived from chronically infected cells, consistent with inhibition of the viral protease in cells. Crystal structures refined to 1.75 Angstrom (1) and 1.85 Angstrom (2) for two of the macrocyclic inhibitors bound to HIV-1 protease establish structural mimicry of the tripeptides that the cycles were designed to imitate. Structural comparisons between protease-bound macrocyclic inhibitors, VX478 (amprenavir), and L-735,524 (indinavir) show that their common acyclic components share the same space in the active site of the enzyme and make identical interactions with enzyme residues. This substrate-mimicking minimalist approach to drug design could have benefits in the context of viral resistance, since mutations which induce inhibitor resistance may also be those which prevent substrate processing.