Ergonomics and Usability – key factors in Knowledge Society

During the recent years human society evolved from the “industrial society age” and transitioned into the “knowledge society age”. This means that knowledge media support migrated from “pen and paper” to computer-based Information Systems. Due to this fact Ergonomics has assumed an increasing importance, as a science/technology that deals with the problem of adapting the work to the man, namely in terms of Usability. This paper presents some relevant Ergonomics, Usability and User-centred design concepts regarding Information Systems.

Methods and challenges for the health impact assessment of vaccination programs in Latin America

ABSTRACT OBJECTIVE To describe methods and challenges faced in the health impact assessment of vaccination programs, focusing on the pneumococcal conjugate and rotavirus vaccines in Latin America and the Caribbean. METHODS For this narrative review, we searched for the terms "rotavirus", "pneumococcal", "conjugate vaccine", "vaccination", "program", and "impact" in the databases Medline and LILACS. The search was extended to the grey literature in Google Scholar. No limits were defined for publication year. Original articles on the health impact assessment of pneumococcal and rotavirus vaccination programs in Latin America and the Caribbean in English, Spanish or Portuguese were included. RESULTS We identified 207 articles. After removing duplicates and assessing eligibility, we reviewed 33 studies, 25 focusing on rotavirus and eight on pneumococcal vaccination programs. The most frequent studies were ecological, with time series analysis or comparing pre- and post-vaccination periods. The main data sources were: health information systems; population-, sentinel- or laboratory-based surveillance systems; statistics reports; and medical records from one or few health care services. Few studies used primary data. Hospitalization and death were the main outcomes assessed. CONCLUSIONS Over the last years, a significant number of health impact assessments of pneumococcal and rotavirus vaccination programs have been conducted in Latin America and the Caribbean. These studies were carried out few years after the programs were implemented, meet the basic methodological requirements and suggest positive health impact. Future assessments should consider methodological issues and challenges arisen in these first studies conducted in the region.

Sistema Multi-Agente de prestação de serviços de seguimento adaptados aos requisitos do utilizador

Mestrado em Engenharia Electrotécnica e de Computadores - Área de Especialização de Telecomunicações

Nanotechnology and antioxidant therapy: An emerging approach for neurodegenerative diseases

The efficacy, cellular uptake and specific transport of dietary antioxidants to target organs, tissues and cells remains the most important setback for their application in the treatment of oxidative-stress related disorders and in particular in neurodegenerative diseases, as brain targeting remains a still unsolved challenge. Nanotechnology based delivery systems can be a solution for the above mentioned problems, specifically in the case of targeting dietary antioxidants with neuroprotective activity. Nanotechnology-based delivery systems can protect antioxidants from degradation, improve their physicochemical drug-like properties and in turn their bioavailability. The impact of nanomedicine in the improvement of the performance of dietary antioxidants, as protective agents in oxidative- stress events, specifically through the use of drug delivery systems, is highlighted in this review as well as the type of nanomaterials regularly used for drug delivery purposes. From the data one can conclude that the research combining (dietary) antioxidants and nanotechnology, namely as a therapeutic solution for neurodegenerative diseases, is still in a very early stage. So, a huge research area remains to be explored that hopefully will yield new and effective neuroprotective therapeutic agents in a foreseeable future.

Integrated data model and DSL modifications

Companies are increasingly more and more dependent on distributed web-based software systems to support their businesses. This increases the need to maintain and extend software systems with up-to-date new features. Thus, the development process to introduce new features usually needs to be swift and agile, and the supporting software evolution process needs to be safe, fast, and efficient. However, this is usually a difficult and challenging task for a developer due to the lack of support offered by programming environments, frameworks, and database management systems. Changes needed at the code level, database model, and the actual data contained in the database must be planned and developed together and executed in a synchronized way. Even under a careful development discipline, the impact of changing an application data model is hard to predict. The lifetime of an application comprises changes and updates designed and tested using data, which is usually far from the real, production, data. So, coding DDL and DML SQL scripts to update database schema and data, is the usual (and hard) approach taken by developers. Such manual approach is error prone and disconnected from the real data in production, because developers may not know the exact impact of their changes. This work aims to improve the maintenance process in the context of Agile Platform by Outsystems. Our goal is to design and implement new data-model evolution features that ensure a safe support for change and a sound migration process. Our solution includes impact analysis mechanisms targeting the data model and the data itself. This provides, to developers, a safe, simple, and guided evolution process.

Development of human central nervous system 3D in vitro models for preclinical research

Neurological disorders are a major concern in modern societies, with increasing prevalence mainly related with the higher life expectancy. Most of the current available therapeutic options can only control and ameliorate the patients’ symptoms, often be-coming refractory over time. Therapeutic breakthroughs and advances have been hampered by the lack of accurate central nervous system (CNS) models. The develop-ment of these models allows the study of the disease onset/progression mechanisms and the preclinical evaluation of novel therapeutics. This has traditionally relied on genetically engineered animal models that often diverge considerably from the human phenotype (developmentally, anatomically and physiologically) and 2D in vitro cell models, which fail to recapitulate the characteristics of the target tissue (cell-cell and cell-matrix interactions, cell polarity). The in vitro recapitulation of CNS phenotypic and functional features requires the implementation of advanced culture strategies that enable to mimic the in vivo struc-tural and molecular complexity. Models based on differentiation of human neural stem cells (hNSC) in 3D cultures have great potential as complementary tools in preclinical research, bridging the gap between human clinical studies and animal models. This thesis aimed at the development of novel human 3D in vitro CNS models by integrat-ing agitation-based culture systems and a wide array of characterization tools. Neural differentiation of hNSC as 3D neurospheres was explored in Chapter 2. Here, it was demonstrated that human midbrain-derived neural progenitor cells from fetal origin (hmNPC) can generate complex tissue-like structures containing functional dopaminergic neurons, as well as astrocytes and oligodendrocytes. Chapter 3 focused on the development of cellular characterization assays for cell aggregates based on light-sheet fluorescence imaging systems, which resulted in increased spatial resolu-tion both for fixed samples or live imaging. The applicability of the developed human 3D cell model for preclinical research was explored in Chapter 4, evaluating the poten-tial of a viral vector candidate for gene therapy. The efficacy and safety of helper-dependent CAV-2 (hd-CAV-2) for gene delivery in human neurons was evaluated, demonstrating increased neuronal tropism, efficient transgene expression and minimal toxicity. The potential of human 3D in vitro CNS models to mimic brain functions was further addressed in Chapter 5. Exploring the use of 13C-labeled substrates and Nucle-ar Magnetic Resonance (NMR) spectroscopy tools, neural metabolic signatures were evaluated showing lineage-specific metabolic specialization and establishment of neu-ron-astrocytic shuttles upon differentiation. Chapter 6 focused on transferring the knowledge and strategies described in the previous chapters for the implementation of a scalable and robust process for the 3D differentiation of hNSC derived from human induced pluripotent stem cells (hiPSC). Here, software-controlled perfusion stirred-tank bioreactors were used as technological system to sustain cell aggregation and dif-ferentiation. The work developed in this thesis provides practical and versatile new in vitro ap-proaches to model the human brain. Furthermore, the culture strategies described herein can be further extended to other sources of neural phenotypes, including pa-tient-derived hiPSC. The combination of this 3D culture strategy with the implemented characterization methods represents a powerful complementary tool applicable in the drug discovery, toxicology and disease modeling.

Frequency-domain receiver design for doubly-selective channels

This work is devoted to the broadband wireless transmission techniques, which are serious candidates to be implemented in future broadband wireless and cellular systems, aiming at providing high and reliable data transmission and concomitantly high mobility. In order to cope with doubly-selective channels, receiver structures based on OFDM and SC-FDE block transmission techniques, are proposed, which allow cost-effective implementations, using FFT-based signal processing. The first subject to be addressed is the impact of the number of multipath components, and the diversity order, on the asymptotic performance of OFDM and SC-FDE, in uncoded and for different channel coding schemes. The obtained results show that the number of relevant separable multipath components is a key element that influences the performance of OFDM and SC-FDE schemes. Then, the improved estimation and detection performance of OFDM-based broadcasting systems, is introduced employing SFN (Single Frequency Network) operation. An initial coarse channel is obtained with resort to low-power training sequences estimation, and an iterative receiver with joint detection and channel estimation is presented. The achieved results have shown very good performance, close to that with perfect channel estimation. The next topic is related to SFN systems, devoting special attention to time-distortion effects inherent to these networks. Typically, the SFN broadcast wireless systems employ OFDM schemes to cope with severely time-dispersive channels. However, frequency errors, due to CFO, compromises the orthogonality between subcarriers. As an alternative approach, the possibility of using SC-FDE schemes (characterized by reduced envelope fluctuations and higher robustness to carrier frequency errors) is evaluated, and a technique, employing joint CFO estimation and compensation over the severe time-distortion effects, is proposed. Finally, broadband mobile wireless systems, in which the relative motion between the transmitter and receiver induces Doppler shift which is different or each propagation path, is considered, depending on the angle of incidence of that path in relation to the direction of travel. This represents a severe impairment in wireless digital communications systems, since that multipath propagation combined with the Doppler effects, lead to drastic and unpredictable fluctuations of the envelope of the received signal, severely affecting the detection performance. The channel variations due this effect are very difficult to estimate and compensate. In this work we propose a set of SC-FDE iterative receivers implementing efficient estimation and tracking techniques. The performance results show that the proposed receivers have very good performance, even in the presence of significant Doppler spread between the different groups of multipath components.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Plataforma de adquisición de imágenes en escenarios virtuales para uso en sistemas de visión

En aquest projecte presentem un mètode per generar bases de imatges de vianants, requerides per a l'entrenament o validació de sistemes d'aprenentatge basats en exemples, en un entorn virtual. S'ha desenvolupat una plataforma que permet simular una navegació d'una càmara en una escena virtual i recuperar el fluxe de vídeo amb el seu groundtruth. Amb l'ús d'aquesta plataforma es suprimeix el procés d'anotació, necesari per obtenir el groundtruth en entorns reals, i es redueixen els costos al treballar en un entorn virtual.

Species delimitation: A case study in a problematic ant taxon

Species delimitation has been invigorated as a discipline in systematics by an influx of new character sets, analytical methods, and conceptual advances. We use genetic data from 68 markers, combined with distributional, bioclimatic, and coloration information, to hypothesize boundaries of evolutionarily independent lineages (species) within the widespread and highly variable nominal fire ant species Solenopsis saevissima, a member of a species group containing invasive pests as well as species that are models for ecological and evolutionary research. Our integrated approach uses diverse methods of analysis to sequentially test whether populations meet specific operational criteria (contingent properties) for candidacy as morphologically cryptic species, including genetic clustering, monophyly, reproductive isolation, and occupation of distinctive niche space. We hypothesize that nominal S. saevissima comprises at least 4-6 previously unrecognized species, including several pairs whose parapatric distributions implicate the development of intrinsic premating or postmating barriers to gene flow. Our genetic data further suggest that regional genetic differentiation in S. saevissima has been influenced by hybridization with other nominal species occurring in sympatry or parapatry, including the quite distantly related Solenopsis geminata. The results of this study illustrate the importance of employing different classes of genetic data (coding and noncoding regions and nuclear and mitochondrial DNA [mtDNA] markers), different methods of genetic data analysis (tree-based and non-tree based methods), and different sources of data (genetic, morphological, and ecological data) to explicitly test various operational criteria for species boundaries in clades of recently diverged lineages, while warning against over reliance on any single data type (e.g., mtDNA sequence variation) when drawing inferences.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Hybridisation and diversification in the adaptive radiation of clownfishes.

BackgroundThe importance of hybridisation during species diversification has long been debated among evolutionary biologists. It is increasingly recognised that hybridisation events occurred during the evolutionary history of numerous species, especially during the early stages of adaptive radiation. We study the effect of hybridisation on diversification in the clownfishes, a clade of coral reef fish that diversified through an adaptive radiation process. While two species of clownfish are likely to have been described from hybrid specimens, the occurrence and effect of hybridisation on the clade diversification is yet unknown.ResultsWe generate sequences of three mitochondrial genes to complete an existing dataset of nuclear sequences and document cytonuclear discordance at a node, which shows a drastic increase of diversification rate. Then, using a tree-based jack-knife method, we identify clownfish species likely stemming from hybridisation events. Finally, we use molecular cloning and identify the putative parental species of four clownfish specimens that display the morphological characteristics of hybrids.ConclusionsOur results show that consistently with the syngameon hypothesis, hybridisation events are linked with a burst of diversification in the clownfishes. Moreover, several recently diverged clownfish lineages likely originated through hybridisation, which indicates that diversification, catalysed by hybridisation events, may still be happening.

Fast docking using the CHARMM force field with EADock DSS.

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 Å RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011.

Effects of phylogenetic signal on ancestral state reconstruction.

One of the standard tools used to understand the processes shaping trait evolution along the branches of a phylogenetic tree is the reconstruction of ancestral states (Pagel 1999). The purpose is to estimate the values of the trait of interest for every internal node of a phylogenetic tree based on the trait values of the extant species, a topology and, depending on the method used, branch lengths and a model of trait evolution (Ronquist 2004). This approach has been used in a variety of contexts such as biogeography (e.g., Nepokroeff et al. 2003, Blackburn 2008), ecological niche evolution (e.g., Smith and Beaulieu 2009, Evans et al. 2009) and metabolic pathway evolution (e.g., Gabaldón 2003, Christin et al. 2008). Investigations of the factors affecting the accuracy with which ancestral character states can be reconstructed have focused in particular on the choice of statistical framework (Ekman et al. 2008) and the selection of the best model of evolution (Cunningham et al. 1998, Mooers et al. 1999). However, other potential biases affecting these methods, such as the effect of tree shape (Mooers 2004), taxon sampling (Salisbury and Kim 2001) as well as reconstructing traits involved in species diversification (Goldberg and Igić 2008), have also received specific attention. Most of these studies conclude that ancestral character states reconstruction is still not perfect, and that further developments are necessary to improve its accuracy (e.g., Christin et al. 2010). Here, we examine how different estimations of branch lengths affect the accuracy of ancestral character state reconstruction. In particular, we tested the effect of using time-calibrated versus molecular branch lengths and provide guidelines to select the most appropriate branch lengths to reconstruct the ancestral state of a trait.