Análise mecânica e microestrutural das ligas de cobalto-cromo de uso odontológico após sucessivas refundições

The cobalt-chromium alloy is extensively used in the Odontology for the confection of metallic scaffolding in partial removable denture. During the last few years, it has been reported an increasing number of premature imperfections, with a few months of prosthesis use. The manufacture of these components is made in prosthetic laboratories and normally involves recasting, using parts of casting alloy and parts of virgin alloy. Therefore, the objective of the present study was to analyze the mechanical properties of a commercial cobalt-chromium alloy of odontological use after successive recasting, searching information to guide the dental prosthesis laboratories in the correct manipulation of the cobalt-chromium alloy in the process of casting and the possible limits of recasting in the mechanical properties of this material. Seven sample groups were confectioned, each one containing five test bodies, divided in the following way: G1: casting only with virgin alloy; G2: casting with 50% of the alloy of the G1 + 50% of virgin alloy; G3: casting with 50% of the alloy of the G2 + 50% of virgin alloy; G4: casting with 50% of the alloy of the G3 + 50% of virgin alloy; G5: 50% of alloy of the G4 + 50% of virgin alloy; G6: 50% of alloy of the G5 + 50% of virgin alloy and finally the G7, only with recasting alloy. The modifications in the mechanical behavior of the alloy were evaluated. Moreover, it was carried the micro structural characterization of the material by optic and electronic scanning microscopy, and X ray diffraction.and fluorescence looking into the correlatation of the mechanical alterations with structural modifications of the material caused by successive recasting process. Generally the results showed alterations in the fracture energy of the alloy after successive recasting, resulting mainly of the increasing presence of pores and large voids, characteristic of the casting material. Thus, the interpretation of the results showed that the material did not reveal significant differences with respect to the tensile strength or elastic limit, as a function of successive recasting. The elastic modulus increased from the third recasting cycle on, indicating that the material can be recast only twice. The fracture energy of the material decreased, as the number of recasting cycles increased. With respect to the microhardness, the statistical analyses showedno significant differences. Electronic scanning microscopy revealed the presence of imperfections and defects, resulting of the recasting process. X ray diffraction and fluorescence did not show alterations in the composition of the alloy or the formation of crystalline phases between the analyzed groups. The optical micrographs showed an increasing number of voids and porosity as the material was recast. Therefore, the general conclusion of this study is that the successive recasting of of Co-Cr alloys affects the mechanical properties of the material, consequently leading to the failure of the prosthetic work. Based on the results, the best recommendadition is that the use of the material should be limited to two recasting cycles

Motivating the use of public transit through a mobile glanceable display

With increasing concerns about the impact of global warming on human life, policy makers around the world and researchers have sought for technological solutions that have the potential to attenuate this process. This thesis describes the design and evaluation of an information appliance that aims to increase the use of public transportation. We developed a mobile glanceable display that, being aware of the user’s transportation routines, provides awareness cues about bus arrival time, grounded upon the vision of Ambient Intelligence. We present the design process we followed, from ideation to building a prototype and conducting a field study, and conclude with a set of guidelines for the design of relevant personal information systems. More specifically we seek to test the following hypotheses: 1) That the tangible prototype that provides ambient cues will be used more frequently than a similar purpose mobile app, 2) That the tangible prototype will reduce the waiting time at the bus stop, 3) That the tangible prototype will result to reduced anxiety on passengers, 4) That the tangible prototype will result to an increase in the perceived reliability of the transit service, 5) That the tangible prototype will enhance users’ efficiency in reading the bus schedules and 6) That the tangible prototype will make individuals more likely to use public transit. In a field study, we compare the tangible prototype against the mobile app and a control condition where participants were given no external support in obtaining bus arrival information, other than their existing routines. Using qualitative and quantitative data, we test the aforementioned hypotheses and explore users’ reactions to the prototype we developed.

The impact of denture related disease on the oral microbiome of denture wearers

Advances in healthcare over the last 100 years has resulted in an ever increasing elderly population. This presents greater challenges for adequate systemic and oral healthcare delivery. With increasing age there is a natural decline in oral health, leading to the loss of teeth and ultimately for some having to wear denture prosthesis. It is currently estimated that approximately one fifth of the UK and US populations have some form of removable prosthesis. The microbiology of denture induced mucosal inflammation is a pivotal factor to consider in denture care management, similar to many other oral diseases of microbial influence, such as caries, gingivitis and periodontitis. Dentures support the growth of microbial biofilms, structures commonly known as denture plaque. Microbiologically, denture stomatitis (DS) is a disease primarily considered to be of yeast aetiology, with the literature disproportionately focussed on Candida spp. However, the denture surface is capable of carrying up to 1011 microbes per milligram, the majority of which are bacteria. Thus it is apparent that denture plaque is more diverse than we assume. There is a fundamental gap in our understanding of the bacterial composition of denture plaque and the role that they may play in denture related disease such as DS. This is categorised as inflammation of the oral mucosa, a disease affecting around half of all denture wearers. It has been proposed that bacteria and fungi interact on the denture surface and that these polymicrobial interactions lead to synergism and increased DS pathogenesis. Therefore, understanding the denture microbiome composition is the key step to beginning to understand disease pathogenesis, and ultimately help improve treatments and identify novel targets for therapeutic and preventative strategies. A group of 131 patients were included within this study in which they provided samples from their dentures, palatal mucosa, saliva and dental plaque. Microbes residing on the denture surface were quantified using standard Miles and Misra culture technique which investigated the presence of Candida, aerobes and anaerobes. These clinical samples also underwent next generation sequencing using the Miseq Illumina platform to give a more global representation of the microbes present at each of these sites in the oral cavity of these denture wearers. This data was then used to compare the composition and diversity of denture, mucosal and dental plaque between one another, as well as between healthy and diseased individuals. Additional comparisons included denture type and the presence or absence of natural teeth. Furthermore, microbiome data was used to assess differences between patients with varying levels of oral hygiene. The host response to the denture microbiome was investigated by screening the patients saliva for the presence and quantification of a range of antimicrobial peptides that are associated with the oral cavity. Based on the microbiome data an in vitro biofilm model was developed that reflected the composition of denture plaque. These biofilms were then used to assess quantitative and compositional changes over time and in response to denture cleansing treatments. Finally, the systemic implications of denture plaque were assessed by screening denture plaque samples for the presence of nine well known respiratory pathogens using quantitative PCR. The results from this study have shown that the bacterial microbiome composition of denture wearers is not consistent throughout the mouth and varies depending on sample site. Moreover, the presence of natural dentition has a significant impact on the microbiome composition. As for healthy and diseased patients the data suggests that compositional changes responsible for disease progression are occurring at the mucosa, and that dentures may in fact be a reservoir for these microbes. In terms of denture hygiene practices, sleeping with a denture in situ was found to be a common occurrence. Furthermore, significant shifts in denture microbiome composition were found in these individuals when compared to the denture microbiome of those that removed their denture at night. As for the host response, some antimicrobial peptides were found to be significantly reduced in the absence of natural dentition, indicating that the oral immune response is gradually impaired with the loss of teeth. This study also identified potentially serious systemic implications in terms of respiratory infection, as 64.6% of patients carried respiratory pathogens on their denture. In conclusion, this is the first study to provide a detailed understanding of the oral microbiome of denture wearers, and has provided evidence that DS development is more complex than simply a candidal infection. Both fungal and bacterial kingdoms clearly play a role in defining the progression of DS. The biofilm model created in this study demonstrated its potential as a platform to test novel actives. Future use of this model will aid in greater understanding of host: biofilm interactions. Such findings are applicable to oral health and beyond, and may help to identify novel therapeutic targets for the treatment of DS and other biofilm associated diseases.

Motivating the use of public transit through a mobile glanceable display

With increasing concerns about the impact of global warming on human life, policy makers around the world and researchers have sought for technological solutions that have the potential to attenuate this process. This thesis describes the design and evaluation of an information appliance that aims to increase the use of public transportation. We developed a mobile glanceable display that, being aware of the user’s transportation routines, provides awareness cues about bus arrival time, grounded upon the vision of Ambient Intelligence. We present the design process we followed, from ideation to building a prototype and conducting a field study, and conclude with a set of guidelines for the design of relevant personal information systems. More specifically we seek to test the following hypotheses: 1) That the tangible prototype that provides ambient cues will be used more frequently than a similar purpose mobile app, 2) That the tangible prototype will reduce the waiting time at the bus stop, 3) That the tangible prototype will result to reduced anxiety on passengers, 4) That the tangible prototype will result to an increase in the perceived reliability of the transit service, 5) That the tangible prototype will enhance users’ efficiency in reading the bus schedules and 6) That the tangible prototype will make individuals more likely to use public transit. In a field study, we compare the tangible prototype against the mobile app and a control condition where participants were given no external support in obtaining bus arrival information, other than their existing routines. Using qualitative and quantitative data, we test the aforementioned hypotheses and explore users’ reactions to the prototype we developed.

Load Profile Analysis Tool for Electrical Appliances in Households

This paper presents a methodology to forecast the hourly and daily consumption in households. The methodology was validated for households in Lisbon region, Portugal. The paper shows that the forecast tool allows obtaining satisfactory results for forecasting. Models of demand response allow the support of consumer’s decision in exchange for an economic benefit by the redefinition of load profile or changing the appliance consumption period. It is also in the interest of electric utilities to take advantage of these changes, particularly when consumers have an action on the demand-side management or production. Producers need to understand the load profile of households that are connected to a smart grid, to promote a better use of energy, as well as optimize the use of micro-generation from renewable sources, not only to delivering to the network but also in self-consumption.

Follicular wave synchronization on in vivo embryo production in Santa Ines sheep.

This study aimed to evaluate two hormonal protocols for synchronization of follicular wave emergence on in vivo embryo production in Santa Ines sheep under tropical conditions. The greater PRCL rate in GT probably contributed to the smaller number of viable embryos. Thus, it is suggested the appliance indicated the GEm protocol for in vivo embryo production in Santa Ines sheep under tropical conditions.

Laying the path to consumer-level immersive simulation environment

Since the end of the long winter of virtual reality (VR) at the beginning of the 2010 decade, many improvements have been made in terms of hardware technologies and software platforms performances and costs. Many expect such trend will continue, pushing the penetration rate of virtual reality headsets to skyrocket at some point in the future, just as mobile platforms did before. In the meantime, virtual reality is slowly transitioning from a specialized laboratory-only technology, to a consumer electronics appliance, opening interesting opportunities and challenges. In this transition, two interesting research questions amount to how 2D-based content and applications may benefit (or be hurt) by the adoption of 3D-based immersive environments and to how to proficiently support such integration. Acknowledging the relevance of the former, we here consider the latter question, focusing our attention on the diversified family of PC-based simulation tools and platforms. VR-based visualization is, in fact, widely understood and appreciated in the simulation arena, but mainly confined to high performance computing laboratories. Our contribution here aims at characterizing the simulation tools which could benefit from immersive interfaces, along with a general framework and a preliminary implementation which may be put to good use to support their transition from uniquely 2D to blended 2D/3D environments.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.