Improvement of a photoautotrophic chassis robustness for synthetic biology applications

Cyanobacteria are photoautotrophic microorganisms with great potential for the biotechnological industry due to their low nutrient requirements, photosynthetic capacities and metabolic plasticity. In biotechnology, the energy sector is one of the main targets for their utilization, especially to produce the so called third generation biofuels, which are regarded as one of the best replacements for petroleum-based fuels. Although, several issues could be solved, others arise from the use of cyanobacteria, namely the need for high amounts of freshwater and contamination/predation by other microorganisms that affect cultivation efficiencies. The cultivation of cyanobacteria in seawater could solve this issue, since it has a very stable and rich chemical composition. Among cyanobacteria, the model microorganism Synechocystis sp. PCC 6803 is one of the most studied with its genome fully sequenced and genomic, transcriptomic and proteomic data available to better predict its phenotypic behaviors/characteristics. Despite suitable for genetic engineering and implementation as a microbial cell factory, Synechocystis’ growth rate is negatively affected by increasing salinity levels. Therefore, it is important to improve. To achieve this, several strategies involving the constitutive overexpression of the native genes encoding the proteins involved in the production of the compatible solute glucosylglycerol were implemented, following synthetic biology principles. A preliminary transcription analysis of selected mutants revealed that the assembled synthetic devices are functional at the transcriptional level. However, under different salinities, the mutants did not show improved robustness to salinity in terms of growth, compared with the wild-type. Nevertheless, some mutants carrying synthetic devices appear to have a better physiological response under seawater’s NaCl concentration than in 0% (w/v) NaCl.

Biology of amazonian mosquitoes. III. Esterase isozymes in Anopheles darlingi.

Six esterase isozymes were studied during the development of Anopheles darlingi by using polyacrylamide gel electrophoresis and two different substrates, a-naphthylcelate and a-naphthylpropionate. Esterases 5 and 6'were detected in all developmental stages esterases 1 and 2 were more intensively stained if larvae, while esterases 3 and 4 were better visualized in pupae and adults. Strong differences in intensity of some of the isozymes were observed during the pupal stage.Four out of the six isozymes showed variation in the electrophoretic mobility. Esterase-2 was choosed for genetic studies, because was the best stained isozyme in the gels. Two codominant alleles {Est2*S and Est2*F) code for this polymorphic system, with the Est*S frequency equal to 0.521. Phenotypic distribution is in agreement with hardy-Weinberg expectations.

Reproductive biology of Bellucia (MELASTOMATACEAE)

The reproductive biology of five of the seven species of Bellucia (Melastomataceae), a genus of shrubs and small trees, was investigated in Amazonia. Sucessful fruit-set by Bellucia requires floral visitation by bees. The flowers are produced continuously all year, and are visited by a wide variety of female bees, the principal pollinators being Xylocopa, Centris, Ptilotopus, Epicharis, Eulaema, Bombus, and Oxaea. The floral attractants are color and the odor produced by the pollen, stamens, and petals; the reqard is pollen. Three species of Bellucia are self-incompatible. Indiscriminate visitor behavior and lack of phenological, morphological, or genetic barriers lead to hybridization between sympatric species of Bellucia, and no more than two species occupy the same habitat at anu one locality. Bellucia produces berries with numerous small seeds, and is dispersed by birds, bats, monkeys, tapirs, turtles, and ants. Seedling establisment requires full sunlight, and occurs on a variety of soil types. The reproductive strategy is interpreted as that of a pioneer species.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

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.

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.

Dynamic group formation in mobile computer supported collaborative learning environment

Forming suitable learning groups is one of the factors that determine the efficiency of collaborative learning activities. However, only a few studies were carried out to address this problem in the mobile learning environments. In this paper, we propose a new approach for an automatic, customized, and dynamic group formation in Mobile Computer Supported Collaborative Learning (MCSCL) contexts. The proposed solution is based on the combination of three types of grouping criteria: learner’s personal characteristics, learner’s behaviours, and context information. The instructors can freely select the type, the number, and the weight of grouping criteria, together with other settings such as the number, the size, and the type of learning groups (homogeneous or heterogeneous). Apart from a grouping mechanism, the proposed approach represents a flexible tool to control each learner, and to manage the learning processes from the beginning to the end of collaborative learning activities. In order to evaluate the quality of the implemented group formation algorithm, we compare its Average Intra-cluster Distance (AID) with the one of a random group formation method. The results show a higher effectiveness of the proposed algorithm in forming homogenous and heterogeneous groups compared to the random method.

Ergonomic evaluation of office workplaces with Rapid Office Strain Assessment (ROSA)

The regular use of the computer in the office contributed to the appearance of many risk factors related with work-related musculoskeletal disorders (WRMSD) such as maintaining static sitting postures for long time and awkward postures of the head, neck and upper limbs, leading to increased muscle activity in the cervical spine and shoulders. The objective of this study was to evaluate the presence of risk factors for WRMSD in an office using the Rapid Assessment Office Strain method (ROSA). Based on the results of this ergonomic evaluation, an occupational gym program was designed and implemented. Thirty-eight workplaces were evaluated using the observation of the tasks and pictures records in order to characterize those tasks in more detail. The ROSA tool was applied by an observer, who selected the appropriate score based on the worker's posture as well as the time spent in each posture. Scores were recorded for the sections of the method, specifically Chair, Monitor and Mouse and Keyboard and Telephone. The scores were recorded in a sheet developed for the method. The mean ROSA final score was 3.61 ± 0.64, for Chair section was 3.45 ± 0.55, to Monitor and Telephone section was 3.11 ± 0.61, and to Mouse and Keyboard section was 2.11 ± 0.31. The results led to understand that the analyzed tasks represent situations of risk of discomfort and, according to the methods guidelines, further research and modifications of the workplace may be necessary. It should be emphasized that these scores may not be related to the poor available equipment but with the need to optimize their use by the workers. It was noticed also that the interaction of workers with the tasks and the adopted sitting posture at the computer throughout the day have effects at a muscular level, essentially for the cervical area and shoulders. ROSA tool is an useful and easy method to assess several risk factors associated with WRMSD, also allowing the design of specific occupational gym programs.

Shear strengthening of reinforced concrete beams with SHCC-FRP panels

Tese de Doutoramento em Engenharia Civil

Da prova: na atuação policial e no âmbito da infração tributária

Relatório de atividade profissional de mestrado em Direito Judiciário

Impact performance prediction of injection-molded talc-filled polypropylene through thermomechanical environment assessment

Due to the fact that different injection molding conditions tailor the mechanical response of the thermoplastic material, such effect must be considered earlier in the product development process. The existing approaches implemented in different commercial software solutions are very limited in their capabilities to estimate the influence of processing conditions on the mechanical properties. Thus, the accuracy of predictive simulations could be improved. In this study, we demonstrate how to establish straightforward processing-impact property relationships of talc-filled injection-molded polypropylene disc-shaped parts by assessing the thermomechanical environment (TME). To investigate the relationship between impact properties and the key operative variables (flow rate, melt and mold temperature, and holding pressure), the design of experiments approach was applied to systematically vary the TME of molded samples. The TME is characterized on computer flow simulation outputsanddefined bytwo thermomechanical indices (TMI): the cooling index (CI; associated to the core features) and the thermo-stress index (TSI; related to the skin features). The TMI methodology coupled to an integrated simulation program has been developed as a tool to predict the impact response. The dynamic impact properties (peak force, peak energy, and puncture energy) were evaluated using instrumented falling weight impact tests and were all found to be similarly affected by the imposed TME. The most important molding parameters affecting the impact properties were found to be the processing temperatures (melt andmold). CI revealed greater importance for the impact response than TSI. The developed integrative tool provided truthful predictions for the envisaged impact properties.

Reproductive biology of Amasonia obovata Gleason (Laminaceae)

Floral mechanisms that ensure seed production via autogamy are more likely to occur in species growing in environments where pollination is scarce. Amasonia obovata was studied in the State of Mato Grosso-Brazil, from 2009 to 2012, to analyze the morphological and reproductive characteristics, aside from investigating the association of the reproductive success with the pollinator frequency and identity. The flowering and fruiting of A. obovata was concentrated in a period of five months during the rainy season. The dichogamy in flowers of A. obovata is not clearly defined, since the sexual functions were overlapped in the male and female phases. The species is self-compatible and not apomictic. The fruiting percentage obtained by hand self-pollination did not differ from cross-breeding (F = 0.74, P =0.39). In the observations from 2010 to 2012, a hummingbird (Thalurania furcata) legitimate visited 20-100% of the flowers in the male and female phases on different A. obovata plants. Due to the high frequency, this hummingbird was considered the single potential pollinator of the species. These findings show that a limited availability of pollinators may select for floral traits and plant mating strategies that lead to a system of self-fertilization.