Intention recognition, commitment and their roles in the evolution of cooperation

Dissertação para obtenção do Grau de Doutor em Informática

Bacterial peptidoglycan biosynthesis and recognition by the innate immune system

Dissertation presented to obtain the Ph.D degree in Biology

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional Biochemistry

Software for pattern recognition of the larvae of Aedes aegypti and Aedes albopictus

Software for pattern recognition of the larvae of mosquitoes Aedes aegypti and Aedes albopictus, biological vectors of dengue and yellow fever, has been developed. Rapid field identification of larva using a digital camera linked to a laptop computer equipped with this software may greatly help prevention campaigns.

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.

G-quadruplex ligands for cancer therapy

DNA may fold into a diversity of structures and topologies such as duplexes and triplexes. Some specific guanine-rich DNA sequences may even fold into a higher order structures denominated guanine G-quadruplexes (G4). These G-quadruplex forming sequences have shown biological interest since were found in telomeres and in promoter region of oncogenes. Thus, these G4 forming sequences have been explored as therapeutic targets for cancer therapy, since G4 formation was demonstrated to inhibit RNA-polymerase and telomerase activity. However, the G4 structures are transient and are only formed under specific conditions. Hence the main objective of this work is to develop new G4-specific ligands which may potentially find applications in the therapeutic area. Several potential G4-binding ligands were synthesized and characterized. The synthesis of these compounds consisted on a procedure based on van Leusen chemistry and a cross-coupling reaction through C-H activation, affording phenanthroline compounds (Phen-1, 50%; Phen-2, 20%), phenyl (Iso-1, 61%; Iso-2, 21%; Ter-1, 85%; Ter-2, 35%), and quinolyl (Quin-1, 85%; Quin-2, 45%) compounds. Screening assays for selecting the potential G4 compounds were performed by FRET-melting, G4-FID, CD-melting and DSF. Qualitative biophysical studies were performed by fluorescence and CD spectroscopy. Two high-specific G-quadruplex ligands, Phen-1 and Phen-2, were found to effectively bind telomeric and c-myc G4 structures. Phen-1 was found to stabilize parallel telomeric 22AG and c-myc sequence by 4.1 and 4.3 ˚C, respectively. Phen-2 also displayed high affinity towards 22AG (

The role of Hedgehog signaling during zebrafish larvae fin fold regeneration

Several studies have demonstrated that although the structure of the adult and larval zebrafish caudal fin is different, there are similarities at the cellular and molecular level that turn larval zebrafish fin fold a useful model to study the basic principles of regeneration. In this process, while the essential role for Hedgehog (Hh) signaling is well established in the adult zebrafish caudal fin system, its involvement in juvenile tissue regeneration is still unknown. The aim of this Master thesis was therefore to evaluate the contribution of the Hh signaling pathway to the larval zebrafish fin fold regeneration process. Accordingly, we analyzed the expression of several Hh signaling components through in situ hybridization. Here, we showed that several of these genes are effectively expressed in the larval regenerating fin tissue, suggesting a role for Hh signaling also during larval regeneration. However, divergence in the regulation of few Hh signaling components appears to exist between the adult and larval zebrafish fin regeneration processes. Nevertheless, similarly to adult caudal fin regeneration, when Hh signaling was blocked, by using cyclopamine, the larval fin fold regenerative outgrowth is severely impaired. Since larval zebrafish fin fold is ciliated, and primary cilia are closely related to Hh signaling regulation in vertebrate systems, we further addressed the role of primary cilia during larval fin fold regeneration process. To this end, we used the zebrafish iguana mutant, in which primary cilia are not formed, to study the modulation of Hh signaling expression during larval fin fold regeneration in the absence of primary cilia. Here, we found that several genes were expressed with a delay, coincident with the delay in the mutant fin fold regeneration observed in previous work. We show that Hh signaling in the fin fold is crucial to promote cell proliferation. When Hh signaling is blocked using cyclopamine there is a strong blockage of cell proliferation and regeneration is also blocked. Surprisingly, in iguana mutants where Hh signaling is impaired but not totally blocked, cell proliferation is not detected but regeneration still occurs. This raises the question about the requirement of cell proliferation in larvae fin fold regeneration. By blocking the cell cycle using aphidicolin we demonstrate that cell proliferation is not necessary for zebrafish larvae fin fold regeneration.

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.

Vision-based portuguese sign language recognition system

Vision-based hand gesture recognition is an area of active current research in computer vision and machine learning. Being a natural way of human interaction, it is an area where many researchers are working on, with the goal of making human computer interaction (HCI) easier and natural, without the need for any extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them, for example, to convey information. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. Hand gestures are a powerful human communication modality with lots of potential applications and in this context we have sign language recognition, the communication method of deaf people. Sign lan- guages are not standard and universal and the grammars differ from country to coun- try. In this paper, a real-time system able to interpret the Portuguese Sign Language is presented and described. Experiments showed that the system was able to reliably recognize the vowels in real-time, with an accuracy of 99.4% with one dataset of fea- tures and an accuracy of 99.6% with a second dataset of features. Although the im- plemented solution was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.