Paracoccidioides brasiliensis AND Paracoccidioides lutzii, A SECRET LOVE AFFAIR

SUMMARYTo commemorate Prof. Carlos da Silva Lacaz's centennial anniversary, the authors have written a brief account of a few, out of hundreds, biological, ecological, molecular and phylogenetic studies that led to the arrival of Paracoccidioides lutzii, hidden for more than a century within Paracoccidioides brasiliensis. Lacaz's permanent interest in this fungus, and particularly his conviction on the benefits that research on paracoccidioidomycosis would bring to patients, were pivotal in the development of the field.

Health expenditures' coping strategies: A study on African low-income countries

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Perfil sociodemográfico e saúde mental em cuidadores informais de idosos no município de São Paulo-Brasil: desafios para as políticas públicas

RESUMO: INTRODUÇÃO: O rápido envelhecimento populacional, o aumento da prevalência de transtornos neuropsiquiátricos, o aumento das taxas de morbilidade clínica e incapacidade entre idosos de países em desenvolvimento têm trazido preocupações sobre a saúde mental e sobrecarga de cuidadores informais. Está bem estabelecida a elevada prevalência de transtornos mentais comuns (TMC) associada à adversidade socioeconômica, baixo nível educacional, estresse e gênero. Idosos e cuidadores vivendo em comunidade compartilham fatores de risco para morbilidade física e psiquiátrica. Adicionalmente, os cuidadores tem uma tripla carga, sendo simultaneamente familiares, trabalhadores leigos em saúde sem suporte dos serviços de saúde e assistência social e um paciente com necessidades não atendidas. O cuidador informal é o principal provedor de cuidado em todos os países. OBJETIVOS: Acessar perfil sociodemográfico, níveis de transtorno mental comum (TMC) e sobrecarga em cuidadores, características do cuidado e prevalência de demência e depressão no idosos, numa área carente da região oeste de São Paulo –Brasil. MÉTODO: Esta pesquisa transversal deriva do São Paulo Ageing and Health Study (SPAH) que incluiu idosos com 65 anos ou mais e seus respectivos cuidadores. Os participantes foram identificados por arrolamento domiciliar e entrevistadas em suas casas com protocolo padronizado de pesquisa. O instrumento utilizado para acessar os transtornos mentais comuns, foi o Self Rating Questionnaire SRQ-20.A sobrecarga foi quantificada pelo Zarit Caregiver Burden Scale. Diagnósticos psicogeriátricos foram mensurados através do SRQ-20 e critérios do CID-10 e do DSM-IV. 8 RESULTADOS: 588 cuidadores e respectivos idosos foram incluídos. Nos idosos, a prevalência de demência foi 15,9%, de depressão pelo CiD-10 9.9% e de TMC 39,25% Nos cuidadores, a prevalência de TMC foi de 55,1% e 32,8% dos cuidadores apresentaram sobrecarga elevada. O perfil do cuidador foi filha,com idade em torno dos 49 anos, casada e com baixo nível educacional.------------------ABSTRACT: BACKGROUND: With the fast population aging, growing prevalence of neuropsychiatric disorders, clinical morbidity and disability among the elderly particularly in low income countries (LAMIC), has brought concerns about informal caregiver Mental Health and Burden. It is well established the high prevalence of Common Mental Disorders (CMD) associated to socioeconomic adversity, low educational attainment, stress and gender. Community-dwelling elders and caregivers share risk factors for physical and psychiatric morbidity. In addition, caregivers have a triple strain, being simultaneously, family members, lay health workers with lack of support from health and social work services and a hidden patient with unmet needs. The world main source of caregiving relies on informal caregiver. AIMS: To assess 1) the sociodemographic profile, levels of CMD and burden among caregivers, and 2) the characteristics of care and prevalence of dementia and depression in elderly in a socioeconomic underprivileged area in western region of Sao Paulo – Brazil. METHOD: The present investigation is a cross-sectional part of Sao Paulo Ageing and Health Study (SPAH) which included participants aged 65 or older and their respective caregivers. Participants were identified by household enrollment and interviewed in their homes using a standardized research protocol. The assessment of common mental disorders was performed with the Self Rating Questionnaire – 20 (SRQ-20), used to establish psychiatric caseness. The assessment of burden was performed with Zarit Caregiver Burden Scale. Dementia and psychogeriatric diagnosis were reached through ICD-10, SRQ-20 and DSM-IV criteria. 10 RESULTS: 588 caregivers and respective elderly relatives were included. Prevalence of dementia was 15.9%, ICD-10 depression 9.9% and CMD 39.3% among the elderlys. Common mental disorder prevalence in caregivers was 55.1% and high burden was reached in 32.8% of the caregiver sample. Most of the caregivers were married and co-resident daughters with a mean age of 49 years (CI 95% - 48.7 to 51).

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

The grapevine defences

Grapevine is one of the most cultivated fruit crop worldwide with Vitis vinifera being the species with the highest economical importance due to the high quality standards of its berries. Nonetheless, it is also the most susceptible Vitis species to fungal pathogens. Among others, relevant fungal diseases currently threatening grapevine cultures are powdery mildew, caused by Erysiphe necator, and esca, a disease complex comprised of several fungi in which Phaeomoniella chlamydospora and Phaeoacremonium aleophilum participate.(...)

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Enhancement of an implemented rating model and impact on existing risk management processes: A hands-on approach from financial supplier risk management within the automotive industry

The purpose of this paper is to conduct a methodical drawback analysis of a financial supplier risk management approach which is currently implemented in the automotive industry. Based on identified methodical flaws, the risk assessment model is further developed by introducing a malus system which incorporates hidden risks into the model and by revising the derivation of the most central risk measure in the current model. Both methodical changes lead to significant enhancements in terms of risk assessment accuracy, supplier identification and workload efficiency.

The establishment mode choice of German multinational enterprises in Brazil: A comparative approach

We examine the drivers behind the establishment mode choice of German multinational enterprises (MNEs) in the sectors of Automotive, Chemicals and Mechanical Engineering in Brazil for the years 1993-2013 using a novel sample of primary data obtained directly from German MNEs. Based on prevalent theories found in the literature, we test the most common hypotheses on our sample. Firms with high R&D activities and firms with prior market knowledge in Brazil in from of previous sales offices are more likely to enter Brazil by a Greenfield investment. We also show that it is the specific private ownership of the German so-called hidden champions that drive those specific SMEs to enter Brazil by Greenfield, a sneaking suspicion that has been made before. Finally, we show that the establishment mode choice between Brazil and the USA only deviates to a low extent, with German MNEs preferring to enter Brazil by Greenfield and the USA by M&A. Thereby, we provide valuable insights for future research in this field.

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

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 human-computer 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 visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) 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. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

O desafio da sustentabilidade e da solidariedade na prática dos profissionais de turismo. O turismo de voluntariado no Brasil

Tese de Doutoramento em Geografia - Geografia Humana

Search for long-lived, weakly interacting particles that decay to displaced hadronic jets in proton--proton collisions at s√=8 TeV with the ATLAS detector

A search for the decay of neutral, weakly interacting, long-lived particles using data collected by the ATLAS detector at the LHC is presented. This analysis uses the full dataset recorded in 2012: 20.3 fb−1 of proton--proton collision data at s√=8 TeV. The search employs techniques for reconstructing decay vertices of long-lived particles decaying to jets in the inner tracking detector and muon spectrometer. Signal events require at least two reconstructed vertices. No significant excess of events over the expected background is found, and limits as a function of proper lifetime are reported for the decay of the Higgs boson and other scalar bosons to long-lived particles and for Hidden Valley Z′ and Stealth SUSY benchmark models. The first search results for displaced decays in Z′ and Stealth SUSY models are presented. The upper bounds of the excluded proper lifetimes are the most stringent to date.

Brazil's global aspirations and their implications for the relationship with Argentina in the framework of MERCOSUR

Dissertação de mestrado em Relações Internacionais

Associations, causation and model in psychiatry

This paper discusses models, associations and causation in psychiatry. The different types of association (linear, positive, negative, exponential, partial, U shaped relationship, hidden and spurious) between variables involved in mental disorders are presented as well as the use of multiple regression analysis to disentangle interrelatedness amongst multiple variables. A useful model should have internal consistency, external validity and predictive power; be dynamic in order to accommodate new sound knowledge; and should fit facts rather than they other way around. It is argued that whilst models are theoretical constructs they also convey a style of reasoning and can change clinical practice. Cause and effect are complex phenomena in that the same cause can yield different effects. Conversely, the same effect can have a different range of causes. In mental disorders and human behaviour there is always a chain of events initiated by the indirect and remote cause; followed by intermediate causes; and finally the direct and more immediate cause. Causes of mental disorders are grouped as those: (i) which are necessary and sufficient; (ii) which are necessary but not sufficient; and (iii) which are neither necessary nor sufficient, but when present increase the risk for mental disorders.