Multilabeled classification approach to find a plaint source for terpenoids

Recently, we have built a classification model that is capable of assigning a given sesquiterpene lactone (STL) into exactly one tribe of the plant family Asteraceae from which the STL has been isolated. Although many plant species are able to biosynthesize a set of peculiar compounds, the occurrence of the same secondary metabolites in more than one tribe of Asteraceae is frequent. Building on our previous work, in this paper, we explore the possibility of assigning an STL to more than one tribe (class) simultaneously. When an object may belong to more than one class simultaneously, it is called multilabeled. In this work, we present a general overview of the techniques available to examine multilabeled data. The problem of evaluating the performance of a multilabeled classifier is discussed. Two particular multilabeled classification methods-cross-training with support vector machines (ct-SVM) and multilabeled k-nearest neighbors (M-L-kNN)were applied to the classification of the STLs into seven tribes from the plant family Asteraceae. The results are compared to a single-label classification and are analyzed from a chemotaxonomic point of view. The multilabeled approach allowed us to (1) model the reality as closely as possible, (2) improve our understanding of the relationship between the secondary metabolite profiles of different Asteraceae tribes, and (3) significantly decrease the number of plant sources to be considered for finding a certain STL. The presented classification models are useful for the targeted collection of plants with the objective of finding plant sources of natural compounds that are biologically active or possess other specific properties of interest.

A new approach to the granulation of beta-cyclodextrin inclusion complexes

Cyclodextrins (CDs) are annular oligosaccharides containing 6-12 glucose unities joined together by alpha-1,4 bonds. They have a conical-truncated shape with a lipophilic cavity in which different molecules can be included resulting in a stable inclusion complex. The cyclodextrins have been widely applied in pharmaceutical technology with the objective of increasing the solubility, stability and bioavailability of drugs in different pharmaceutical dosage forms, such as tablets. In order to obtain beta-CD tablets, liquid dispersions of drug/beta-CD are usually submitted to different drying processes, like spray-drying, freeze-drying or slow evaporation, being this dry material added to a number of excipients. However, such drying processes can generate particulate materials showing problems of flow and compressibility, needing their conversion into granulates by means of wetting with granulation liquid followed by additional drying. In this work, the main objective was to evaluate the preparation of tablets without the need of this additional drying step. For this purpose an aqueous dispersion containing acetaminophen/beta-CD complex and cornstarch was dried using a spouted bed and the obtained granules were compressed in tablets. Acetaminophen was used as model drug due to its low water solubility and the inexpensive and widely available cornstarch was chosen as excipient. Acetaminophen powder was added into a beta-cyclodextrin solution prepared in distilled water at 70 degrees C. Stirring was kept until this dispersion cooled to room temperature. Then cornstarch was added and the resulting dispersion was dried in spouted bed equipment. This material was compressed into tablets using an Erweka Korsh EKO tablet machine. This innovative approach allowed the tablets preparation process to be carried out with fewer steps and represents a technological reliable strategy to produce beta-cyclodextrin inclusion complexes tablets. (C) 2010 Elsevier By. All rights reserved.

A proteomic approach to identifying proteins differentially expressed in conidia and mycelium of the entomopathogenic fungus Metarhizium acridum

Metarhizium spp. is an important worldwide group of entomopathogenic fungi used as an interesting alternative to chemical insecticides in programs of agricultural pest and disease vector control. Metarhizium conidia are important in fungal propagation and also are responsible for host infection. Despite their importance, several aspects of conidial biology, including their proteome, are still unknown. We have established conidial and mycelial proteome reference maps for Metarhizium acridum using two-dimensional gel electrophoresis (2-DE) and matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF MS). In all, 1130 +/- 102 and 1200 +/- 97 protein spots were detected in ungerminated conidia and fast-growing mycelia, respectively. Comparison of the two protein-expression profiles reveled that only 35 % of the protein spots were common to both developmental stages. Out of 94 2-DE protein spots (65 from conidia, 25 from mycelia and two common to both) analyzed using mass spectrometry, seven proteins from conidia, 15 from mycelia and one common to both stages were identified. The identified protein spots exclusive to conidia contained sequences similar to known fungal stress-protector proteins (such as heat shock proteins (HSP) and 6-phosphogluconate dehydrogenase) plus the fungal allergen Alt a 7, actin and the enzyme cobalamin-independent methionine synthase. The identified protein spots exclusive to mycelia included proteins involved in several cell housekeeping biological processes. Three proteins (HSP 90, 6-phosphogluconate dehydrogenase and allergen Alt a 7) were present in spots in conidial and mycelial gels, but they differed in their locations on the two gels. (c) 2010 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Leukotriene B(4)-loaded microspheres as a new approach to enhance antimicrobial responses in Histoplasma capsulatum-infected mice

Histoplasmosis is a pulmonary disease characterised by chronic granulomatous and suppurative inflammatory reactions caused by Histoplasma capsulatum. Regarding new therapies to control fungal infections, the aim of this study was to investigate whether pulmonary administration of leukotriene B(4) (LTB(4))-loaded microspheres (MS) could confer protection to 5-lipoxygenase knockout (5-LO(-/-)) mice infected by H. capsulatum. In this study, MS containing LTB4 were administered intranasally to mice infected by H. capsulatum. On Day 14 after the infection, fungal recovery from the lungs and histology were evaluated and inflammatory cytokines were measured. Pulmonary administration of LTB(4)-loaded MS was able to reduce fungal recovery from infected lungs. Production of important inflammatory cytokines related to host defence was augmented following MS administration to the lungs. Lung histology also showed that infected mice presented a clear reduction in the fungal burden following the pulmonary release of LTB4 from MS. Our study provides evidence that the proposed biodegradable microparticulate system, which can release LTB4 to the lungs, can be employed as therapy, enhancing the antimicrobial activity of host cells during histoplasmosis. (C) 2009 Elsevier B.V. and the International Society of Chemotherapy. All rights reserved.

Influence of Plasma Protein Binding on Pharmacodynamics: Estimation of In Vivo Receptor Affinities of beta Blockers Using a New Mechanism-Based PK-PD Modelling Approach

The objective of this investigation was to examine in a systematic manner the influence of plasma protein binding on in vivo pharmacodynamics. Comparative pharmacokinetic-pharmacodynamic studies with four beta blockers were performed in conscious rats, using heart rate under isoprenaline-induced tachycardia as a pharmacodynamic endpoint. A recently proposed mechanism-based agonist-antagonist interaction model was used to obtain in vivo estimates of receptor affinities (K(B),(vivo)). These values were compared with in vitro affinities (K(B),(vitro)) on the basis of both total and free drug concentrations. For the total drug concentrations, the K(B),(vivo) estimates were 26, 13, 6.5 and 0.89 nM for S(-)-atenolol, S(-)-propranolol, S(-)-metoprolol and timolol. The K(B),(vivo) estimates on the basis of the free concentrations were 25, 2.0, 5.2 and 0.56 nM, respectively. The K(B),(vivo)-K(B),(vitro) correlation for total drug concentrations clearly deviated from the line of identity, especially for the most highly bound drug S(-)-propranolol (ratio K(B),(vivo)/K(B),(vitro) similar to 6.8). For the free drug, the correlation approximated the line of identity. Using this model, for beta-blockers the free plasma concentration appears to be the best predictor of in vivo pharmacodynamics. (C) 2008 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 98:3816-3828, 2009

Approach to site, C House, Cooparoo

Looking uphill towards house from road.

A two-phase approach for detecting recombination in nucleotide sequences

Genetic recombination can produce heterogeneous phylogenetic histories within a set of homologous genes. Delineating recombination events is important in the study of molecular evolution, as inference of such events provides a clearer picture of the phylogenetic relationships among different gene sequences or genomes. Nevertheless, detecting recombination events can be a daunting task, as the performance of different recombination-detecting approaches can vary, depending on evolutionary events that take place after recombination. We recently evaluated the effects of post-recombination events on the prediction accuracy of recombination-detecting approaches using simulated nucleotide sequence data. The main conclusion, supported by other studies, is that one should not depend on a single method when searching for recombination events. In this paper, we introduce a two-phase strategy, applying three statistical measures to detect the occurrence of recombination events, and a Bayesian phylogenetic approach in delineating breakpoints of such events in nucleotide sequences. We evaluate the performance of these approaches using simulated data, and demonstrate the applicability of this strategy to empirical data. The two-phase strategy proves to be time-efficient when applied to large datasets, and yields high-confidence results.

Exploring Change in Neanderthal Behavioural Complexity: An Innovative Approach to the Study of Neanderthal Behaviour

Diachronic approaches provide potential for a more sophisticated framework within which to examine change in Neanderthal behavioural complexity using archaeological proxies such as symbolic artefacts, faunal assemblages and technology. Analysis of the temporal appearance and distribution of such artefacts and assemblages provide the basis for identifying changes in Neanderthal behavioural complexity in terms of symbolism, faunal extraction and technology respectively. Although changes in technology and faunal extraction were examined in the wider study, only the results of the symbolic study are presented below to illustrate the potential of the approach.

The prediction of disease risk in genomic medicine

In recent years, the phrase 'genomic medicine' has increasingly been used to describe a new development in medicine that holds great promise for human health. This new approach to health care uses the knowledge of an individual's genetic make-up to identify those that are at a higher risk of developing certain diseases and to intervene at an earlier stage to prevent these diseases. Identifying genes that are involved in disease aetiology will provide researchers with tools to develop better treatments and cures. A major role within this field is attributed to 'predictive genomic medicine', which proposes screening healthy individuals to identify those who carry alleles that increase their susceptibility to common diseases, such as cancers and heart disease. Physicians could then intervene even before the disease manifests and advise individuals with a higher genetic risk to change their behaviour - for instance, to exercise or to eat a healthier diet - or offer drugs or other medical treatment to reduce their chances of developing these diseases. These promises have fallen on fertile ground among politicians, health-care providers and the general public, particularly in light of the increasing costs of health care in developed societies. Various countries have established databases on the DNA and health information of whole populations as a first step towards genomic medicine. Biomedical research has also identified a large number of genes that could be used to predict someone's risk of developing a certain disorder. But it would be premature to assume that genomic medicine will soon become reality, as many problems remain to be solved. Our knowledge about most disease genes and their roles is far from sufficient to make reliable predictions about a patient’s risk of actually developing a disease. In addition, genomic medicine will create new political, social, ethical and economic challenges that will have to be addressed in the near future.

Strategic argumentation: A game theoretical investigation

Argumentation is modelled as a game where the payoffs are measured in terms of the probability that the claimed conclusion is, or is not, defeasibly provable, given a history of arguments that have actually been exchanged, and given the probability of the factual premises. The probability of a conclusion is calculated using a standard variant of Defeasible Logic, in combination with standard probability calculus. It is a new element of the present approach that the exchange of arguments is analysed with game theoretical tools, yielding a prescriptive and to some extent even predictive account of the actual course of play. A brief comparison with existing argument-based dialogue approaches confirms that such a prescriptive account of the actual argumentation has been almost lacking in the approaches proposed so far.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

The Learning Tourism Destination: The potential of a learning organisation approach for improving the sustainability of tourism destinations

Globalisation, increasing complexity, and the need to address triple-bottom line sustainability has seen the proliferation of Learning Organisations (LO) who, by definition, have the capacity to anticipate environmental changes and economic opportunities and adapt accordingly. Such organisations use system dynamics modelling (SDM) for both strategic planning and the promotion of organisational learning. Although SDM has been applied in the context of tourism destination management for predictive reasons, the current literature does not analyse or recognise how this could be used as a foundation for an LO. This study introduces the concept of the Learning Tourism Destinations (LTD) and discusses, on the basis of a review of 6 case studies, the potential of SDM as a tool for the implementation and enhancement of collective learning processes. The results reveal that SDM is capable of promoting communication between stakeholders and stimulating organisational learning. It is suggested that the LTD approach be further utilised and explored.

A Step-by-Step Approach to Transit Oriented Development Project Delivery

A major challenge in successfully implementing transit-oriented development (TOD) is having a robust process that ensures effective appraisal, initiation and delivery of multi-stakeholder TOD projects. A step-by step project development process can assist in the methodic design, evaluation, and initiation of TOD projects. Successful TOD requires attention to transit, mixed-use development and public space. Brisbane, Australia provides a case-study where recent planning policies and infrastructure documents have laid a foundation for TOD, but where barriers lie in precinct level planning and project implementation. In this context and perhaps in others, the research effort needs to shift toward identification of appropriate project processes and strategies. This paper presents the outcomes of research conducted to date. Drawing on the mainstream approach to project development and financial evaluation for property projects, key steps for potential use in successful delivery of TOD projects have been identified, including: establish the framework; location selection; precinct context review; preliminary precinct design; the initial financial viability study; the decision stage; establishment of project structure; land acquisition; development application; and project delivery. The appropriateness of this mainstream development and appraisal process will be tested through stakeholder research, and the proposed process will then be refined for adoption in TOD projects. It is suggested that the criteria for successful TOD should be broadened beyond financial concerns in order to deliver public sector support for project initiation.