The structural and functional diversity of naturally occurring antimicrobial peptides

Antimicrobial peptides occur in a diverse range of organisms from microorganisms to insects, plants and animals. Although they all have the common function of inhibiting or killing invading microorganisms they achieve this function using an extremely diverse range of structural motifs. Their sizes range from approximately 10-90 amino acids. Most carry an overall positive charge, reflecting a preferred mode of electrostatic interaction with negatively charged microbial membranes. This article describes the structural diversity of a representative set of antimicrobial peptides divided into five structural classes: those with agr-helical structure, those with bgr-sheet structure, those with mixed helical / bgr- sheet structure, those with irregular structure, and those incorporating a macrocyclic structure. There is a significant diversity in both the size and charge of molecules within each of these classes and between the classes. The common feature of their three-dimensional structures is, however, that they have a degree of amphipathic character in which there is separate localisation of hydrophobic regions and positively charged regions. An emerging trend amongst antimicrobial proteins is the discovery of more macrocyclic analogues. Cyclisation appears to impart an additional degree of stability on these molecules and minimizes proteolytic cleavage. In conclusion, there appear to be a number of promising opportunities for the development of novel clinically useful antimicrobial peptides based on knowledge of the structures of naturally occurring antimicrobial molecules.

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies. (C) 2003 Elsevier Inc. All rights reserved.

Genetic variation in foliar carbon isotope composition in relation to tree growth and foliar nitrogen concentration in clones of the F-1 hybrid between slash pine and Caribbean pine

The objectives of this study were: (1) to quantify the genetic variation in foliar carbon isotope composition (delta(13)C) of 122 clones of ca. 4-year-old F-1 hybrids between slash pine (Pinus elliottii Engelm var. elliottii) and Caribbean pine (Pinus caribaea var. hondurensis Barr.,et Golf.) grown at two field experimental sites with different water and nitrogen availability in southeast Queensland, Australia, in relation to tree growth and foliar nitrogen concentration (N-mass); and (2) to assess the potential of using delta(13)C measurements, in the foliage materials collected from the clone hedges at nursery and the 4-year-old tree canopies in the field, as an indirect index of tree water use efficiency for selecting elite F-1 hybrid pine clones with improved tree growth. There were significant differences in foliar delta(13)C between the nursery hedges and the 4-year-old tree canopies in the field, between the summer and winter seasons, between the two experimental sites, and between the upper outer and lower outer canopy positions sampled. This indicates that delta(13)C measurements in the foliage materials are significantly influenced by the sampling techniques and environmental conditions. Significant differences in foliar delta(13)C, at the upper outer canopy in both field experiments in summer and winter, were detected between the clones, and between the female parents of the clones. Clone means of tree height at age ca. 3 years were positively related to those of the upper outer canopy delta(13)C at both experimental sites in winter, but only for the wetter site in summer. There were positive, linear relationships between clone means of canopy delta(13)C and those of canopy N-mass, indicating that canopy photosynthetic capacity might be an important factor regulating the clonal variation in canopy delta(13)C. Significant correlations were found between clone means of canopy delta(13)C at both experimental sites in summer and winter, and between those at the upper outer and lower outer canopy positions. Mean clone delta(13)C for the nursery hedges was only positively related to mean clone stem diameter at 1.3 m height at age 3 years on the wetter site. The clone by site interaction for foliar delta(13)C at the upper outer canopy was significant only in summer. Overall, the relatively high genetic variance components for foliar delta(13)C and significant, positive correlations between clone means of foliar delta(13)C and tree growth have highlighted the potential of using foliar delta(13)C measurements for assisting in selection of the elite F-1 hybrid pine clones with improved tree growth. (C) 2002 Elsevier Science B.V. All rights reserved.

Therapeutic potential of venom peptides

Venomous animals have evolved a vast array of peptide toxins for prey capture and defence. These peptides are directed against a wide variety of pharmacological targets, making them an invaluable source of ligands for studying the properties of these targets in different experimental paradigms. A number of these peptides have been used in vivo for proof-of-concept studies, with several having undergone preclinical or clinical development for the treatment of pain, diabetes, multiple sclerosis and cardiovascular diseases. Here we survey the pharmacology of venom peptides and assess their therapeutic prospects.

CAUSE-FIT, POSITIVE ATTITUDES AND BEHAVIORS WITHIN HYBRID COLOMBIAN ORGANIZATIONS

ABSTRACTSocially oriented ventures have provided livelihoods and social recognition to disadvantaged communities in different corners of the world. In some cases, these ventures are the result of Corporate Social Responsibility (CSR) programs. In Latin America, this type of undertaking has responded positively to unmet social needs. The social cause drives these organizations and their human resources and they give high value to organizational cause-fit. This paper presents empirical evidence of the effects of perceived cause-fit on several worker attitudes and behaviors. Psychological contract theory was adopted as theoretical background. Employees working in a hybrid (for-profit/socially oriented) Colombian organization created by a CSR program participated in the survey. Data provided by 218 employees were analyzed using PLS structural equation modeling. The results suggest the ideological components of the employee-employer relationship predict positive attitudes and cooperative organizational behaviors towards hybrid organizations.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

A hybrid approach to multi-agent decision-making

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii)joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Assessment of guanylin peptides system in type 2 diabetes

Os doentes com diabetes mellitus tipo 2 apresentam predisposição para a retenção de sódio e são frequentemente hipertensos. No entanto, os mecanismos implicados na dificuldade do rim diabético em mobilizar o sódio são, ainda, pouco compreendidos. Os peptídeos da família das guanilinas estão envolvidos na regulação do transporte de electrólitos e água nos epitélios intestinal e renal, através da activação do receptor guanilato ciclase-C (GC-C) e subsequente libertação intracelular de GMPc. O objectivo do presente estudo foi a avaliação da actividade do sistema dos peptídeos das guanilinas (SPG) e do seu papel na regulação do balanço de sódio num modelo animal de diabetes tipo 2. Ratinhos machos C57BL/6 foram submetidos a uma dieta com alto teor de gordura e rica em hidratos de carbono simples (ratinhos diabéticos) ou a uma dieta normal (ratinhos controlo). A expressão renal e intestinal da guanilina (GN), uroguanilina (UGN) e do receptor GC-C assim como os níveis de GMPc na urina e plasma foram avaliados nos ratinhos controlo e diabéticos, durante a ingestão de dietas normo (NS) e hiper-salina (HS). Nos ratinhos diabéticos, durante a dieta NS verificou-se um aumento significativo da pressão arterial que foi acompanhado de redução da expressão do ARNm da GN, UGN e do GC-C no intestino e de aumento da expressão de ARNm da UGN no rim. A dieta HS induziu um aumento da expressão do ARNm da UGN no jejuno dos ratinhos controlo mas não nos diabéticos. Os ratinhos diabéticos apresentaram níveis urinários de GMPc inferiores aos controlos, em condições de dieta NS. Em conclusão, os nossos resultados sugerem que na diabetes tipo 2 ocorre uma redução da actividade intestinal do SPG que é acompanhada por um aumento compensatório da actividade renal do SPG. A diminuição da actividade do SPG intestinal na diabetes tipo 2 deve-se não só a uma redução da expressão dos peptídeos GN e UGN, mas também a uma redução da expressão do seu receptor, GC-C. Estes resultados sugerem que o SPG pode contribuir para a sensibilidade ao sódio na diabetes.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

A hybrid intelligent system for distributed dynamic scheduling

This chapter addresses the resolution of dynamic scheduling by means of meta-heuristic and multi-agent systems. Scheduling is an important aspect of automation in manufacturing systems. Several contributions have been proposed, but the problem is far from being solved satisfactorily, especially if scheduling concerns real world applications. The proposed multi-agent scheduling system assumes the existence of several resource agents (which are decision-making entities based on meta-heuristics) distributed inside the manufacturing system that interact with other agents in order to obtain optimal or near-optimal global performances.

Scheduling maintenance activities of electric power transmission networks using an hybrid constraint method

In this paper we present a Constraint Logic Programming (CLP) based model, and hybrid solving method for the Scheduling of Maintenance Activities in the Power Transmission Network. The model distinguishes from others not only because of its completeness but also by the way it models and solves the Electric Constraints. Specifically we present a efficient filtering algorithm for the Electrical Constraints. Furthermore, the solving method improves the pure CLP methods efficiency by integrating a type of Local Search technique with CLP. To test the approach we compare the method results with another method using a 24 bus network, which considerers 42 tasks and 24 maintenance periods.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.