Exploring ionic liquids′ unique stimuli to elucidate uncharacterised cellular and molecular mechanisms in filamentous fungi

The emergence of new fungal pathogens, either of plants or animals, and the increasing number of reported cases of resistant human pathogenic strains to the available antifungal drugs reinforces the need for better understanding the biology of filamentous fungi. Conventional drugs target components of the fungal membrane or cell wall, therefore identifying novel intracellular targets, yet unique to fungi, is a global priority.(...)

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.

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.

Flood resilient housing recovery models: a theoretical case study in Maldives

Natural disasters are events that cause general and widespread destruction of the built environment and are becoming increasingly recurrent. They are a product of vulnerability and community exposure to natural hazards, generating a multitude of social, economic and cultural issues of which the loss of housing and the subsequent need for shelter is one of its major consequences. Nowadays, numerous factors contribute to increased vulnerability and exposure to natural disasters such as climate change with its impacts felt across the globe and which is currently seen as a worldwide threat to the built environment. The abandonment of disaster-affected areas can also push populations to regions where natural hazards are felt more severely. Although several actors in the post-disaster scenario provide for shelter needs and recovery programs, housing is often inadequate and unable to resist the effects of future natural hazards. Resilient housing is commonly not addressed due to the urgency in sheltering affected populations. However, by neglecting risks of exposure in construction, houses become vulnerable and are likely to be damaged or destroyed in future natural hazard events. That being said it becomes fundamental to include resilience criteria, when it comes to housing, which in turn will allow new houses to better withstand the passage of time and natural disasters, in the safest way possible. This master thesis is intended to provide guiding principles to take towards housing recovery after natural disasters, particularly in the form of flood resilient construction, considering floods are responsible for the largest number of natural disasters. To this purpose, the main structures that house affected populations were identified and analyzed in depth. After assessing the risks and damages that flood events can cause in housing, a methodology was proposed for flood resilient housing models, in which there were identified key criteria that housing should meet. The same methodology is based in the US Federal Emergency Management Agency requirements and recommendations in accordance to specific flood zones. Finally, a case study in Maldives – one of the most vulnerable countries to sea level rise resulting from climate change – has been analyzed in light of housing recovery in a post-disaster induced scenario. This analysis was carried out by using the proposed methodology with the intent of assessing the resilience of the newly built housing to floods in the aftermath of the 2004 Indian Ocean Tsunami.

Multipacket reception in the presence of in-band full-duplex communication

In-Band Full-DupleX (IB-FDX) is defined as the ability for nodes to transmit and receive signals simultaneously on the same channel. Conventional digital wireless networks do not implement it, since a node’s own transmission signal causes interference to the signal it is trying to receive. However, recent studies attempt to overcome this obstacle, since it can potentially double the spectral efficiency of current wireless networks. Different mechanisms exist today that are able to reduce a significant part of the Self- Interference (SI), although specially tuned Medium Access Control (MAC) protocols are required to optimize its use. One of IB-FDX’s biggest problems is that the nodes’ interference range is extended, meaning the unusable space for other transmissions and receptions is broader. This dissertation proposes using MultiPacket Reception (MPR) to address this issue and adapts an already existing Single-Carrier with Frequency-Domain Equalization (SC-FDE) receiver to IB-FDX. The performance analysis suggests that MPR and IB-FDX have a strong synergy and are able to achieve higher data rates, when used together. Using analytical models, the optimal transmission patterns and transmission power were identified, which maximize the channel capacity with the minimal energy consumption. This was used to define a new MAC protocol, named Full-duplex Multipacket reception Medium Access Control (FM-MAC). FM-MAC was designed for a single-hop cellular infrastructure, where the Access Point (AP) and the terminals implement both IB-FDX and MPR. It divides the coverage range of the AP into a closer Full-DupleX (FDX) zone and a farther Half-DupleX (HDX) zone and adds a tunable fairness mechanism to avoid terminal starvation. Simulation results show that this protocol provides efficient support for both HDX and FDX terminals, maximizing its capacity when more FDX terminals are used.

The cellular basis of a congenital heart defect Drosophila

RESUMO: Mutações em genes envolvidos na formação do coração e anomalias em qualquer etapa deste processo causam frequentemente malformações cardíacas, que representam o tipo mais comum de defeitos em neonatais, afetando cerca de 1% dos nascimentos por ano. Assim, estima-se que 20 milhões de pessoas sejam portadoras de um defeito cardíaco congénito. O coração da Drosophila melanogaster (mosca-da-fruta), denominado vaso dorsal, é um órgão relativamente simples que actua como uma bomba muscular, contraindo automaticamente para permitir a circulação da hemolinfa através do corpo. A formação do vaso dorsal na mosca é muito semelhante ao desenvolvimento do coração em vertebrados, representando por isso, um poderoso modelo para estudar a rede de genes e os padrões regulatórios relacionados com o desenvolvimento deste órgão. Anteriormente, nós identificámos um gene em Drosophila, darhgef10, fortemente expresso no coração em desenvolvimento e cuja deleção induz anormalidades cardíacas subtis mas prevalentes. Os mutantes para darhgef10 são viáveis e férteis no ambiente controlado de laboratório. Este trabalho teve como objectivos caracterizar fenotipicamente os mutantes nulos para darhgef10, determinar a localização subcelular da proteína dArhgef10 e investigar a base celular subjacente ao defeito no alinhamento dos cardioblastos observado nos mutantes. Os nossos resultados revelaram que a deleção de darhgef10 provoca uma severa redução da viabilidade, sem no entanto comprometer o tempo de desenvolvimento e a longevidade. Por outro lado, o aumento da expressão de darhgef10 em músculos, glândulas salivares e no disco imaginal do olho afeta drasticamente a integridade destes tecidos. A expressão ectópica de darhgef10 in vitro e in vivo revelou que a proteína está localiza no citoplasma com enriquecimento junto à membrana celular, com associação à actina F. Live imaging de embriões mutantes para darhgef10 revelou que os defeitos observados no coração podem estar associados a um defeito na adesão dos músculos alary e/ou das células pericardiais ao vaso dorsal. O homólogo humano de darhgef10, ARHGEF10, também é expresso no coração e está associação a uma maior susceptibilidade para a ocorrência de acidentes vasculares cerebrais aterotrombóticos, sugerindo que o que aprendemos sobre darhgef10 em Drosophila pode ter implicações do ponto de vista clínico para a saúde humana. ----------------------------- ABSTRACT: Mutations in genes controlling heart development and abnormalities in any of its steps frequently cause cardiac malformations, the most common type of birth defects in humans, affecting nearly 1% of births per year. Hence around 20 million adults are expected to live with a congenital heart defect. The Drosophila melanogaster heart, called dorsal vessel, is a relatively simple organ that acts as a muscular pump contracting automatically to allow the circulation of hemolymph. Drosophila heart formation shares many similarities with heart development in vertebrates providing a powerful system to study gene networks and regulatory pathways involved in heart development. We have previously identified a Drosophila gene, darhgef10, which is strongly expressed in the developing heart and when deleted, leads to flies with highly prevalent yet subtle heart abnormalities, compatible with unchallenged life in the laboratory. Our aims were to phenotypically characterize homozygous null darhgef10 mutants, characterize the subcellular localization of dArhgef10 and to study the cellular basis of the misaligned cardioblasts defect. We found that about half of darhgef10 mutants die during development. However, the survivors surprisingly have a nearly normal developmental time, adult locomotor behavior and total lifespan. Detection of transgene-derived dArhgef10 protein in vitro and in vivo using custom antibodies revealed a cytosolic protein slightly enriched in the cellular membranes and associated with F-actin. Tissue-specific darhgef10 expression disrupts the normal morphology of developing muscles, salivary glands and the eye. Live imaging of darhgef10 mutant embryos revealed that heart defect could be caused by a reduced capacity of attachment of pericardial cells and/or alary muscle to dorsal vessel. The human homolog of darhgef10 is also expressed in the heart and is a susceptibility gene for atherothrombotic stroke, suggesting that what we learn about the function of this gene and its phenotypes in Drosophila could have implications to human health.

The future of airline business models:

This research is titled “The Future of Airline Business Models: Which Will Win?” and it is part of the requirements for the award of a Masters in Management from NOVA BSE and another from Luiss Guido Carlo University. The purpose is to elaborate a complete market analysis of the European Air Transportation Industry in order to predict which Airlines, strategies and business models may be successful in the next years. First, an extensive literature review of the business model concept has been done. Then, a detailed overview of the main European Airlines and the strategies that they have been implementing so far has been developed. Finally, the research is illustrated with three case studies

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

Understanding multi-asset factor models: Factor exposure interpretation

Field lab: Business project

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.