Allergen databases

Allergies represent a significant medical and industrial problem. Molecular and clinical data on allergens are growing exponentially and in this article we have reviewed nine specialized allergen databases and identified data sources related to protein allergens contained in general purpose molecular databases. An analysis of allergens contained in public databases indicates a high level of redundancy of entries and a relatively low coverage of allergens by individual databases. From this analysis we identify current database needs for allergy research and, in particular, highlight the need for a centralized reference allergen database.

Constitutive mutants of the GM-CSF receptor reveal multiple pathways leading to myeloid cell survival, proliferation, and granulocyte-macrophage differentiation

Activation of the granulocyte-macrophage colony-stimulating factor (GM-CSF) family of receptors promotes the survival, proliferation, and differentiation of cells of the myeloid compartment. Several signaling pathways are activated downstream of the receptor, however it is not clear how these induce specific biologic outcomes. We have previously identified 2 classes of constitutively active mutants of the shared signaling subunit, human (h) betac, of the human GM-CSF/interieukin-3 (IL-3)/IL-5 receptors that exhibit different modes of signaling. In a factor-dependent bipotential myeloid cell line, FDB1, an activated mutant containing a substitution in the transmembrane domain (V449E) induces factor-independent proliferation and survival, while mutants in the extracellular domain induce factor-independent granulocyte-macrophage differentiation. Here we have used further mutational analysis to demonstrate that there are nonredundant functions for several regions of the cytoplasmic domain with regard to mediating proliferation, viability, and differentiation, which have not been revealed by previous studies with the wild-type GM-CSF receptor. This unique lack of redundancy has revealed an association of a conserved membrane-proximal region with viability signaling and a critical but distinct role for tyrosine 577 in the activities of each class of mutant.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Global Analysis of Protein Palmitoylation in African Trypanosomes

Many eukaryotic proteins are posttranslationally modified by the esterification of cysteine thiols to long-chain fatty acids. This modification, protein palmitoylation, is catalyzed by a large family of palmitoyl acyltransferases that share an Asp-His-His-Cys Cys-rich domain but differ in their subcellular localizations and substrate specificities. In Trypanosoma brucei, the flagellated protozoan parasite that causes African sleeping sickness, protein palmitoylation has been observed for a few proteins, but the extent and consequences of this modification are largely unknown. We undertook the present study to investigate T. brucei protein palmitoylation at both the enzyme and substrate levels. Treatment of parasites with an inhibitor of total protein palmitoylation caused potent growth inhibition, yet there was no effect on growth by the separate, selective inhibition of each of the 12 individual T. brucei palmitoyl acyltransferases. This suggested either that T. brucei evolved functional redundancy for the palmitoylation of essential palmitoyl proteins or that palmitoylation of some proteins is catalyzed by a noncanonical transferase. To identify the palmitoylated proteins in T. brucei, we performed acyl biotin exchange chemistry on parasite lysates, followed by streptavidin chromatography, two-dimensional liquid chromatography-tandem mass spectrometry protein identification, and QSpec statistical analysis. A total of 124 palmitoylated proteins were identified, with an estimated false discovery rate of 1.0%. This palmitoyl proteome includes all of the known palmitoyl proteins in procyclic-stage T. brucei as well as several proteins whose homologues are palmitoylated in other organisms. Their sequences demonstrate the variety of substrate motifs that support palmitoylation, and their identities illustrate the range of cellular processes affected by palmitoylation in these important pathogens.

The Provisional Paediatric Rheumatology International Trials Organisation/American College of Rheumatology/European League Against Rheumatism disease activity core set for the evaluation of response to therapy in juvenile dermatomyositis: A prospective validation study

Objective. To validate a core set of outcome measures for the evaluation of response to treatment in patients with juvenile dermatomyositis (DM). Methods. In 2001, a preliminary consensus-derived core set for evaluating response to therapy in juvenile DM was established. In the present study, the core set was validated through an evidence-based, large-scale data collection that led to the enrollment of 294 patients from 36 countries. Consecutive patients with active disease were assessed at baseline and after 6 months. The validation procedures included assessment of feasibility, responsiveness, discriminant and construct ability, concordce in the evaluation of response to therapy between physicians and parents, redundancy, internal consistency, and ability to predict a therapeutic response. Results. The following clinical measures were found to be feasible, and to have good construct validity, discriminative ability, and internal consistency; furthermore, they were not redundant, proved responsive to clinically important changes in disease activity, and were associated strongly with treatment outcome and thus were included in the final core set: 1) physician`s global assessment of disease activity, 2) muscle strength, 3) global disease activity measure, 4) parent`s global assessment of patient`s well-being, 5) functional ability, and 6) health-related quality of life. Conclusion. The members of the Paediatric Rheumatology International Trials Organisation, with the endorsement of the American College of Rheumatology and the European Leauge Against Rheumatism, propose a core set of criteria for the evaluation of response of therapy that is scientifically and clinically relevant and statistically validated. The core set will help standardize the conduct and reporting of clinical trials and assist practitioners in deciding whether a child with juvenile DM has responded adequately to therapy.

Prediction of Golgi Type II membrane proteins based on their transmembrane domains

Motivation: A major issue in cell biology today is how distinct intracellular regions of the cell, like the Golgi Apparatus, maintain their unique composition of proteins and lipids. The cell differentially separates Golgi resident proteins from proteins that move through the organelle to other subcellular destinations. We set out to determine if we could distinguish these two types of transmembrane proteins using computational approaches. Results: A new method has been developed to predict Golgi membrane proteins based on their transmembrane domains. To establish the prediction procedure, we took the hydrophobicity values and frequencies of different residues within the transmembrane domains into consideration. A simple linear discriminant function was developed with a small number of parameters derived from a dataset of Type II transmembrane proteins of known localization. This can discriminate between proteins destined for Golgi apparatus or other locations (post-Golgi) with a success rate of 89.3% or 85.2%, respectively on our redundancy-reduced data sets.

The role of growth hormone in fetal development

Studies across several species, particularly the mouse, show that growth hormone (GH, somatotrophin) is an important determinant of litter size, and to a lesser extent, of birth length. GH acts at all stages of development, from ovulation through preimplantation development to the late fetus, with actions on both embryo/fetus and mother contributing to successful fetal development. The fact that these are not more obvious in vivo is likely a result of redundancy of cytokine hormone action, particularly in relation to prolactin, which shares common actions and receptor locations with GH. (C) 2002 Elsevier Science Ltd. All rights reserved.

Warranty and optimal reliability improvement through product development

For products sold with warranty, the warranty servicing cost can be reduced by improving product reliability through a development process. However, this increases the unit manufacturing cost. Optimal development must achieve a trade-off between these two costs. The outcome of the development process is uncertain and needs to be taken into account in the determination of the optimal development effort. The paper develops a model where this uncertainty is taken into account. (C) 2003 Elsevier Ltd. All rights reserved.

Quase-verdade e pragmatismo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Side information creation for efficient Wyner-Ziv video coding: Classifying and reviewing

Video coding technologies have played a major role in the explosion of large market digital video applications and services. In this context, the very popular MPEG-x and H-26x video coding standards adopted a predictive coding paradigm, where complex encoders exploit the data redundancy and irrelevancy to 'control' much simpler decoders. This codec paradigm fits well applications and services such as digital television and video storage where the decoder complexity is critical, but does not match well the requirements of emerging applications such as visual sensor networks where the encoder complexity is more critical. The Slepian Wolf and Wyner-Ziv theorems brought the possibility to develop the so-called Wyner-Ziv video codecs, following a different coding paradigm where it is the task of the decoder, and not anymore of the encoder, to (fully or partly) exploit the video redundancy. Theoretically, Wyner-Ziv video coding does not incur in any compression performance penalty regarding the more traditional predictive coding paradigm (at least for certain conditions). In the context of Wyner-Ziv video codecs, the so-called side information, which is a decoder estimate of the original frame to code, plays a critical role in the overall compression performance. For this reason, much research effort has been invested in the past decade to develop increasingly more efficient side information creation methods. This paper has the main objective to review and evaluate the available side information methods after proposing a classification taxonomy to guide this review, allowing to achieve more solid conclusions and better identify the next relevant research challenges. After classifying the side information creation methods into four classes, notably guess, try, hint and learn, the review of the most important techniques in each class and the evaluation of some of them leads to the important conclusion that the side information creation methods provide better rate-distortion (RD) performance depending on the amount of temporal correlation in each video sequence. It became also clear that the best available Wyner-Ziv video coding solutions are almost systematically based on the learn approach. The best solutions are already able to systematically outperform the H.264/AVC Intra, and also the H.264/AVC zero-motion standard solutions for specific types of content. (C) 2013 Elsevier B.V. All rights reserved.

Estudo de uma central hidroeléctrica com base em turbina hidráulica reversível

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.

Detection and diagnosis solutions for fault-tolerant VSI

This paper presents solutions for fault detection and diagnosis of two-level, three phase voltage-source inverter (VSI) topologies with IGBT devices. The proposed solutions combine redundant standby VSI structures and contactors (or relays) to improve the fault-tolerant capabilities of power electronics in applications with safety requirements. The suitable combination of these elements gives the inverter the ability to maintain energy processing in the occurrence of several failure modes, including short-circuit in IGBT devices, thus extending its reliability and availability. A survey of previously developed fault-tolerant VSI structures and several aspects of failure modes, detection and isolation mechanisms within VSI is first discussed. Hardware solutions for the protection of power semiconductors with fault detection and diagnosis mechanisms are then proposed to provide conditions to isolate and replace damaged power devices (or branches) in real time. Experimental results from a prototype are included to validate the proposed solutions.

An unsupervised approach to feature discretization and selection

Many learning problems require handling high dimensional datasets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words representation in text classification problems and gene expression data for tumor detection/classification. Usually, among the high number of features characterizing the instances, many may be irrelevant (or even detrimental) for the learning tasks. It is thus clear that there is a need for adequate techniques for feature representation, reduction, and selection, to improve both the classification accuracy and the memory requirements. In this paper, we propose combined unsupervised feature discretization and feature selection techniques, suitable for medium and high-dimensional datasets. The experimental results on several standard datasets, with both sparse and dense features, show the efficiency of the proposed techniques as well as improvements over previous related techniques.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.