Microscopy image segmentation by active contour models

In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations.

Participatory Surveillance and Mathematical Models in Epidemiologic Research: Successes and Challenges

Theoretical epidemiology aims to understand the dynamics of diseases in populations and communities. Biological and behavioral processes are abstracted into mathematical formulations which aim to reproduce epidemiological observations. In this thesis a new system for the self-reporting of syndromic data — Influenzanet — is introduced and assessed. The system is currently being extended to address greater challenges of monitoring the health and well-being of tropical communities.(...)

Amyotrophic Lateral Sclerosis:Mammalian Cell Models,Copper-Zinc Superoxide Dismutaseand Biological Characteristics

"Amyotrophic Lateral Sclerosis (ALS) is the most severe and common adult onset disorder that affects motor neurons in the spinal cord, brainstem and cortex, resulting in progressive weakness and death from respiratory failure within two to five years of symptoms onset(...)

Enterprise reference lexicon building from business models

Nowadays, a significant increase on the demand for interoperable systems for exchanging data in business collaborative environments has been noticed. Consequently, cooperation agreements between each of the involved enterprises have been brought to light. However, due to the fact that even in a same community or domain, there is a big variety of knowledge representation not semantically coincident, which embodies the existence of interoperability problems in the enterprises information systems that need to be addressed. Moreover, in relation to this, most organizations face other problems about their information systems, as: 1) domain knowledge not being easily accessible by all the stakeholders (even intra-enterprise); 2) domain knowledge not being represented in a standard format; 3) and even if it is available in a standard format, it is not supported by semantic annotations or described using a common and understandable lexicon. This dissertation proposes an approach for the establishment of an enterprise reference lexicon from business models. It addresses the automation in the information models mapping for the reference lexicon construction. It aggregates a formal and conceptual representation of the business domain, with a clear definition of the used lexicon to facilitate an overall understanding by all the involved stakeholders, including non-IT personnel.

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

Real-time collaborative editing of OutSystems DSL models

Real-time collaborative editing systems are common nowadays, and their advantages are widely recognized. Examples of such systems include Google Docs, ShareLaTeX, among others. This thesis aims to adopt this paradigm in a software development environment. The OutSystems visual language lends itself very appropriate to this kind of collaboration, since the visual code enables a natural flow of knowledge between developers regarding the developed code. Furthermore, communication and coordination are simplified. This proposal explores the field of collaboration on a very structured and rigid model, where collaboration is made through the copy-modify-merge paradigm, in which a developer gets its own private copy from the shared repository, modifies it in isolation and later uploads his changes to be merged with modifications concurrently produced by other developers. To this end, we designed and implemented an extension to the OutSystems Platform, in order to enable real-time collaborative editing. The solution guarantees consistency among the artefacts distributed across several developers working on the same project. We believe that it is possible to achieve a much more intense collaboration over the same models with a low negative impact on the individual productivity of each developer.

Factores sociodemográficos na saúde oral : influências nos comportamentos de rotinas de saúde oral, frequências de idas a consultas, auto avaliação do estado de saúde oral e presença de dor em jovens de 12 anos

RESUMO - Introdução: A saúde oral é uma componente essencial na saúde geral e no bem-estar dos indivíduos. Sabe-se que os problemas de saúde oral afectam predominantemente os elementos de níveis socioeconómicos mais baixos, evidenciando a influência dos determinantes sociais da saúde na saúde oral das populações. Os objectivos deste estudo são caracterizar os comportamentos de rotinas diárias de higiene oral, frequências de idas a consultas de saúde oral, auto-avaliação do estado de saúde oral e percepção de dor na cavidade oral em crianças de 12 anos em Portugal e analisar a associação entre estes e os factores sociodemográficos. Métodos: Foi realizado um estudo observacional, transversal e analítico, abrangendo 1309 jovens e baseado em informação recolhida no III Estudo Nacional de Prevalência de Doenças Orais (ENPDO). Para além das estatísticas descritivas usuais, as estatísticas inferenciais basearam-se predominantemente em modelos de regressão logística binária. Resultados: Dos participantes, 70.6% (n=924) escova “duas ou mais vezes por dia” com associação com todas as variáveis sociodemográficas. Na análise multivariada, o género masculino (OR=2.088; IC95%: 1.574-2.770, em relação ao género feminino), a área de residência predominantemente rural ou mediamente urbana (OR= 1.800; IC95%: 2.587; OR=1.516; IC95%: 1.093-2.103, em relação a zonas predominantemente urbanas), a escolaridade da mãe ser o ensino básico (OR= 2.112; IC95%: 1.408-3.168, em relação ao ensino superior) e a actividade laboral do pai ser desempregado (OR= 1.938; IC95%: 1.280-2.934, em relação a ser trabalhador) foram as variáveis com mais impacto para a adopção de comportamentos de escovagem potencialmente inadequados (p<0.05). A maioria dos inquiridos (94.2%; n=1247) já tinham ido a uma consulta de saúde oral e 74.5% (n=860) nos últimos 12 meses, 95.5% (n=1250) encontram-se satisfeitos com a saúde oral e 44.5% (n=578) afirma ter tido algum tipo de dor na cavidade oral nos últimos 12 meses. Conclusão: Os resultados obtidos estão de acordo com a literatura em termos de factores de associação. Desta forma, a saúde oral nos jovens de 12 anos em Portugal, nos diversos contextos aqui analisados, pode ser considerada como satisfatória. A única excepção relevante é a componente da dor, com valores alarmantes embora de natureza mais subjectiva. A influência dos factores sociodemográficos sugere que futuras abordagens para a promoção da saúde oral tenham em conta os determinantes de saúde no delineamento de estratégias quer a nível individual quer a nível comunitário.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

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.

How can clusters help in the product type choice? The case of a specialized retail store

This work project (WP) is a study about a clustering strategy for Sport Zone. The general cluster study’s objective is to create groups such that within each group the individuals are similar to each other, but should be different among groups. The clusters creation is a mix of common sense, trial and error and some statistical supporting techniques. Our particular objective is to support category managers to better define the product type to be displayed in the stores’ shelves by doing store clusters. This research was carried out for Sport Zone, and comprises an objective definition, a literature review, the clustering activity itself, some factor analysis and a discriminant analysis to better frame our work. Together with this quantitative part, a survey addressed to category managers to better understand their key drivers, for choosing the type of product of each store, was carried out. Based in a non-random sample of 65 stores with data referring to 2013, the final result was the choice of 6 store clusters (Figure 1) which were individually characterized as the main outcome of this work. In what relates to our selected variables, all were important for the distinction between clusters, which proves the adequacy of their choice. The interpretation of the results gives category managers a tool to understand which products best fit the clustered stores. Furthermore, as a side finding thanks to the clusterization, a STP (Segmentation, Targeting and Positioning) was initiated, being this WP the first steps of a continuous process.

Optimal choice between even- and uneven-aged forestry: the case of non-industrial private forest owners

An infinite-horizon discrete time model with multiple size-class structures using a transition matrix is built to assess optimal harvesting schedules in the context of Non-Industrial Private Forest (NIPF) owners. Three model specifications accounting for forest income, financial return on an asset and amenity valuations are considered. Numerical simulations suggest uneven-aged forest management where a rational forest owner adapts her or his forest policy by influencing the regeneration of trees or adjusting consumption dynamics depending on subjective time preference and market return rate dynamics on the financial asset. Moreover she or he does not value significantly non-market benefits captured by amenity valuations relatively to forest income.

Simplified wind turbine models for wind energy integration into power systems

This paper analyses the boundaries of simplified wind turbine models used to represent the behavior of wind turbines in order to conduct power system stability studies. Based on experimental measurements, the response of recent simplified (also known as generic) wind turbine models that are currently being developed by the International Standard IEC 61400-27 is compared to complex detailed models elaborated by wind turbine manufacturers. This International Standard, whose Technical Committee was convened in October 2009, is focused on defining generic simulation models for both wind turbines (Part 1) and wind farms (Part 2). The results of this work provide an improved understanding of the usability of generic models for conducting power system simulations.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.