On the hierarchical classification of G protein-coupled receptors

MOTIVATION: G protein-coupled receptors (GPCRs) play an important role in many physiological systems by transducing an extracellular signal into an intracellular response. Over 50% of all marketed drugs are targeted towards a GPCR. There is considerable interest in developing an algorithm that could effectively predict the function of a GPCR from its primary sequence. Such an algorithm is useful not only in identifying novel GPCR sequences but in characterizing the interrelationships between known GPCRs. RESULTS: An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Hierarchical approach to project planning:the case of a petroleum pipeline construction

Projects exposed to an uncertain environment must be adapted to deal with the effective integration of various planning elements and the optimization of project parameters. Time, cost, and quality are the prime objectives of a project that need to be optimized to fulfill the owner's goal. In an uncertain environment, there exist many other conflicting objectives that may also need to be optimized. These objectives are characterized by varying degrees of conflict. Moreover, an uncertain environment also causes several changes in the project plan throughout its life, demanding that the project plan be totally flexible. Goal programming (GP), a multiple criteria decision making technique, offers a good solution for this project planning problem. There the planning problem is considered from the owner's perspective, which leads to classifying the project up to the activity level. GP is applied separately at each level, and the formulated models are integrated through information flow. The flexibility and adaptability of the models lies in the ease of updating the model parameters at the required level through changing priorities and/or constraints and transmitting the information to other levels. The hierarchical model automatically provides integration among various element of planning. The proposed methodology is applied in this paper to plan a petroleum pipeline construction project, and its effectiveness is demonstrated.

Quantifying the complexity of chaos in multibasin multidimensional dynamics of molecular systems

The simulated classical dynamics of a small molecule exhibiting self-organizing behavior via a fast transition between two states is analyzed by calculation of the statistical complexity of the system. It is shown that the complexity of molecular descriptors such as atom coordinates and dihedral angles have different values before and after the transition. This provides a new tool to identify metastable states during molecular self-organization. The highly concerted collective motion of the molecule is revealed. Low-dimensional subspaces dynamics is found sensitive to the processes in the whole, high-dimensional phase space of the system. © 2004 Wiley Periodicals, Inc.

Hierarchical macroporous-mesoporous SBA-15 sulfonic acid catalysts for biodiesel synthesis

Hierarchical macroporous-mesoporous SBA-15 silicas have been synthesised via dual-templating routes employing liquid crystalline surfactants and polystyrene beads. These offer high surface areas and well-defined, interconnecting macro- and mesopore networks with respective narrow size distributions around 300 nm and 3-5 nm for polystyrene:tetraethoxysilane ratios ≥2:1. Subsequent functionalisation with propylsulfonic acid yields the first organized, macro-mesoporous solid acid catalyst. The enhanced mass transport properties of these new bi-modal solid acid architectures confer significant rate enhancements in the transesterification of bulky glyceryl trioctanoate, and esterification of long chain palmitic acid, over pure mesoporous analogues. This paves the way to the wider application of hierarchical catalysts in biofuel synthesis and biomass conversion. © 2010 The Royal Society of Chemistry.

Hierarchical porous materials:catalytic applications

In this review, we discuss the phenomenon of complementary macropore incorporation into mesoporous and/or microporous solids in order to enhance their catalytic performance in fuels and chemicals synthesis. © The Royal Society of Chemistry 2013.

Hierarchical macroporous mesoporous materials for biodiesel synthesis

The combination of dwindling oil reserves and growing concerns over carbon dioxide emissions and associated climate change is driving the urgent development of routes to utilize renewable feedstocks as sustainable sources of fuels. Catalysis has a rich history of facilitating energy efficient selective molecular transformations and contributes to 90% of chemical manufacturing processes and to more than 20% of all industrial products. In a post-petroleum era catalysis will be central to overcoming the engineering and scientific barriers to economically feasible routes to bio-fuels. This article will highlight some of the recent developments in the development of solid acid and base catalysts for the transesterification of oils to biodiesel. Particular attention will be paid to the challenges faced when developing new catalysts and importance of considering the design of pore architectures to improve in-pore diffusion of bulky substrates. © 2011 Materials Research Society.

Hierarchical nanoporous solid base catalysts for biofuels

Novel macroporous solid bases have been developed as alternative clean technologies to existing commercial homogeneous catalysts for the production of biodiesel from triglycerides; the latter suffer process disadvantages including complex separation and associated saponification and engine corrosion, and are unsuitable for continuous operation. To this end, tuneable macroporous MgAl hydrotalcites have been prepared by an alkali-free route and characterised by TGA, XRD, SEM and XPS. The macropore architecture improves diffusion of bulky triglyceride molecules to the active base sites, increasing activity. Lamellar and macroporous hydrotalcites will be compared for the transesterification of both model and plant oil feedstocks, and structure-reactivity relations identified.

Hierarchical porous catalysts tailored for biodiesel production

There is a pressing need for sustainable transportation fuels to combat both climate change and dwindling fossil fuel reserves. Biodiesel, synthesised from non-food plant (e.g., Jatropha curcas) or algal crops is one possible solution, but its energy efficient production requires design of new solid catalysts optimized for the bulky triglyceride and fatty acid feedstocks. Here we report on the synthesis of hierarchical macroporous-mesoporous silica and alumina architectures, and their subsequent functionalization by propylsulfonic acid groups or alkaline earth oxides to generate novel solid acid and base catalysts. These materials possess high surface areas and well-defined, interconnected macro-mesopore networks with respective narrow pore size distributions tuneable around 300 nm and 5 nm. Their high conductivity and improved mass transport characteristics enhance activity towards transesterification of bulky tricaprylin and palmitic acid esterification, over mesoporous analogues. This opens the way to the wider application of hierarchical catalysts in biofuel synthesis and biomass conversion.

Hierarchical control and performance regimes in local government

Purpose – The purpose of this paper is to consider hierarchical control as a mode of governance, and analyses the extent of control exhibited by central government over local government through the best value (BV) and comprehensive performance assessment (CPA) performance regimes. Design/methodology/approach – This paper utilises Ouchi's framework and, specifically, his articulation of bureaucratic or hierarchical control in the move towards achievement of organisational objectives. Hierarchical control may be inferred from the extent of “command and control” by Central Government, use of rewards and sanctions, and alignment to government priorities and discrimination of performance. Findings – CPA represents a more sophisticated performance regime than BV in the governance of local authorities by central government. In comparison to BV, CPA involved less scope for dialogue with local government prior to introduction, closer inspection of and direction of support toward poorer performing authorities, and more alignment to government priorities in the weightings attached to service blocks. Originality/value - The paper focuses upon the hierarchic/bureaucratic mode of governance as articulated by Ouchi and expands on this mode in order to analyse shifts in performance regimes in the public sector.

Surface modification of natural fibers using bacteria:depositing bacterial cellulose onto natural fibers to create hierarchical fiber reinforced nanocomposites

Triggered biodegradable composites made entirely from renewable resources are urgently sought after to improve material recyclability or be able to divert materials from waste streams. Many biobased polymers and natural fibers usually display poor interfacial adhesion when combined in a composite material. Here we propose a way to modify the surfaces of natural fibers by utilizing bacteria (Acetobacter xylinum) to deposit nanosized bacterial cellulose around natural fibers, which enhances their adhesion to renewable polymers. This paper describes the process of modifying large quantities of natural fibers with bacterial cellulose through their use as substrates for bacteria during fermentation. The modified fibers were characterized by scanning electron microscopy, single fiber tensile tests, X-ray photoelectron spectroscopy, and inverse gas chromatography to determine their surface and mechanical properties. The practical adhesion between the modified fibers and the renewable polymers cellulose acetate butyrate and poly(L-lactic acid) was quantified using the single fiber pullout test. © 2008 American Chemical Society.

Bridging the gap between stylistic and cognitive approaches to authorship analysis using systemic functional linguistics and multidimensional analysis

In the present state of the art of authorship attribution there seems to be an opposition between two approaches: cognitive and stylistic methodologies. It is proposed in this article that these two approaches are complementary and that the apparent gap between them can be bridged using Systemic Functional Linguistics (SFL) and in particular some of its theoretical constructions, such as codal variation. This article deals with the theoretical explanation of why such a theory would solve the debate between the two approaches and shows how these two views of authorship attribution are indeed complementary. Although the article is fundamentally theoretical, two example experimental trials are reported to show how this theory can be developed into a workable methodology of doing authorship attribution. In Trial 1, a SFL analysis was carried out on a small dataset consisting of three 300-word texts collected from three different authors whose socio-demographic background matched across a number of parameters. This trial led to some conclusions about developing a methodology based on SFL and suggested the development of another trial, which might hint at a more accurate and useful methodology. In Trial 2, Biber's (1988) multidimensional framework is employed, and a final methodology of authorship analysis based on this kind of analysis is proposed for future research. © 2013, EQUINOX PUBLISHING.

Multicriteria Evaluation and Optimization of Hierarchical Systems

It is shown that any multicriteria problem can be represented by a hierarchical system. Separate properties of the object are evaluated at the lower level of the system, using a criteria vector, and a composition mechanism is used to evaluate the object as a whole at the upper level. The paper proposes a method to solve complex multicriteria problems of evaluation and optimization. It is based on nested scalar convolutions of vector- valued criteria and allows simple structural and parametrical synthesis of multicriteria hierarchical systems.

The New Software Package for Dynamic Hierarchical Clustering for Circles Types of Shapes

In data mining, efforts have focused on finding methods for efficient and effective cluster analysis in large databases. Active themes of research focus on the scalability of clustering methods, the effectiveness of methods for clustering complex shapes and types of data, high-dimensional clustering techniques, and methods for clustering mixed numerical and categorical data in large databases. One of the most accuracy approach based on dynamic modeling of cluster similarity is called Chameleon. In this paper we present a modified hierarchical clustering algorithm that used the main idea of Chameleon and the effectiveness of suggested approach will be demonstrated by the experimental results.

Using the Agglomerative Method of Hierarchical Clustering as a Data Mining Tool in Capital Market

The purpose of this paper is to explain the notion of clustering and a concrete clustering method- agglomerative hierarchical clustering algorithm. It shows how a data mining method like clustering can be applied to the analysis of stocks, traded on the Bulgarian Stock Exchange in order to identify similar temporal behavior of the traded stocks. This problem is solved with the aid of a data mining tool that is called XLMiner™ for Microsoft Excel Office.