Produção de anticorpos monoclonais murinos dirigidos contra antígenos de células-tronco adulta de origem humana e de coelho

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Avaliação auditiva em pacientes diabéticos do tipo 1 e do tipo 2

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Desenvolvimento e aperfeiçoamento de protocolos de conjugação de nanomarcadores luminescentes com sistemas biológicos para aplicação em imunoensaios

Pós-graduação em Química - IBILCE

Síntese e caracterização de híbridos orgânico-inorgânicos luminescentes para aplicação em ensaios biológicos

Pós-graduação em Química - IBILCE

Fusão de métodos baseados em minúcias e em cristas para reconhecimento de impressões digitais

Pós-graduação em Ciência da Computação - IBILCE

Detecção e quantificação de corantes de importância ambiental empregando sensores químicos baseados em fibras ópticas e polímeros molecularmente impressos

Pós-graduação em Química - IQ

Morphology and Anatomy of Stem Mines in Cipocereus minensis (Wender.) Ritter (Cactaceae), an Endemic Species to Eastern Brazil

Plant mines are structures with the form of a cavity caused by consumption of host plant tissue by the insect's miner larvae. Plant mines are more common in leaves, but in Cipocereus minensis, a species in which the leaves are modified spines, the miner activity is restricted to the stem. The aim of this paper was to document the morphological and anatomical differences in the infected and uninfected stems of C. minensis due to the feeding habit of the mining agent. Fresh tissue samples of non-mined and mined young stem of C minensis were collected and examined in transverse sections. We hypothesize that the infection begins following mating when the females scratch the surface of the stem or while they feed on fruits and lay eggs, which subsequently develop into larvae, invading the cactus stem. The insect's miner larvae had mostly consumed the parenchyma tissue towards the center of the stem, and periderm formed along the entire path of the insect. This meristematic tissue or "wound periderm" is a common response for compartmentalization to isolate the damaged tissue, in this case the incubating chamber, in which the eggs will be placed. There were no signs of consumption of vascular tissue in the infested samples, further suggesting a compartmentalized infestation. The nest chamber was found in the stem pith region, with periderm surrounding an insect's miner pupa inside identified as a member of the Cerambycidae. The mining insect depends on a host plant to complete the life cycle; however, the nature of this partnership and the long-term effects of the insect on the plant tissue are unknown. The complex mechanisms by which herbivorous insects control the morphogenesis of the plant host are discussed. We propose that C. minensis has a recognition system to identify insect attack and evaluate the effectiveness of early response triggering compartmentalized defense mechanisms by protecting the injured area with a new layer of periderm.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Smile detection for user interfaces

[EN]Perceptual User Interfaces (PUIs) aim at facilitating human-computer interaction with the aid of human-like capacities (computer vision, speech recognition, etc.). In PUIs, the human face is a central element, since it conveys not only identity but also other important information, particularly with respect to the user’s mood or emotional state. This paper describes both a face detector and a smile detector for PUIs. Both are suitable for real-time interaction.

Progetto di reti Sensori Wireless e tecniche di Fusione Sensoriale

Ambient Intelligence (AmI) envisions a world where smart, electronic environments are aware and responsive to their context. People moving into these settings engage many computational devices and systems simultaneously even if they are not aware of their presence. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. The dependence on a large amount of fixed and mobile sensors embedded into the environment makes of Wireless Sensor Networks one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes, simple devices that typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. In order to handle the large amount of data generated by a WSN several multi sensor data fusion techniques have been developed. The aim of multisensor data fusion is to combine data to achieve better accuracy and inferences than could be achieved by the use of a single sensor alone. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas: Multimodal Surveillance and Activity Recognition. Novel techniques to handle data from a network of low-cost, low-power Pyroelectric InfraRed (PIR) sensors are presented. Such techniques allow the detection of the number of people moving in the environment, their direction of movement and their position. We discuss how a mesh of PIR sensors can be integrated with a video surveillance system to increase its performance in people tracking. Furthermore we embed a PIR sensor within the design of a Wireless Video Sensor Node (WVSN) to extend its lifetime. Activity recognition is a fundamental block in natural interfaces. A challenging objective is to design an activity recognition system that is able to exploit a redundant but unreliable WSN. We present our activity in building a novel activity recognition architecture for such a dynamic system. The architecture has a hierarchical structure where simple nodes performs gesture classification and a high level meta classifiers fuses a changing number of classifier outputs. We demonstrate the benefit of such architecture in terms of increased recognition performance, and fault and noise robustness. Furthermore we show how we can extend network lifetime by performing a performance-power trade-off. Smart objects can enhance user experience within smart environments. We present our work in extending the capabilities of the Smart Micrel Cube (SMCube), a smart object used as tangible interface within a tangible computing framework, through the development of a gesture recognition algorithm suitable for this limited computational power device. Finally the development of activity recognition techniques can greatly benefit from the availability of shared dataset. We report our experience in building a dataset for activity recognition. Such dataset is freely available to the scientific community for research purposes and can be used as a testbench for developing, testing and comparing different activity recognition techniques.

New platforms for optical biosensing

Physicochemical experimental techniques combined with the specificity of a biological recognition system have resulted in a variety of new analytical devices known as biosensors. Biosensors are under intensive development worldwide because they have many potential applications, e.g. in the fields of clinical diagnostics, food analysis, and environmental monitoring. Much effort is spent on the development of highly sensitive sensor platforms to study interactions on the molecular scale. In the first part, this thesis focuses on exploiting the biosensing application of nanoporous gold (NPG) membranes. NPG with randomly distributed nanopores (pore sizes less than 50 nm) will be discussed here. The NPG membrane shows unique plasmonic features, i.e. it supports both propagating and localized surface plasmon resonance modes (p SPR and l-SPR, respectively), both offering sensitive probing of the local refractive index variation on/in NPG. Surface refractive index sensors have an inherent advantage over fluorescence optical biosensors that require a chromophoric group or other luminescence label to transduce the binding event. In the second part, gold/silica composite inverse opals with macroporous structures were investigated with bio- or chemical sensing applications in mind. These samples combined the advantages of a larger available gold surface area with a regular and highly ordered grating structure. The signal of the plasmon was less noisy in these ordered substrate structures compared to the random pore structures of the NPG samples. In the third part of the thesis, surface plasmon resonance (SPR) spectroscopy was applied to probe the protein-protein interaction of the calcium binding protein centrin with the heterotrimeric G-protein transducin on a newly designed sensor platform. SPR spectroscopy was intended to elucidate how the binding of centrin to transducin is regulated towards understanding centrin functions in photoreceptor cells.

Audiological results with Baha in conductive and mixed hearing loss

The level of improvement in the audiological results of Baha(®) users mainly depends on the patient's preoperative hearing thresholds and the type of Baha sound processor used. This investigation shows correlations between the preoperative hearing threshold and postoperative aided thresholds and audiological results in speech understanding in quiet of 84 Baha users with unilateral conductive hearing loss, bilateral conductive hearing loss and bilateral mixed hearing loss. Secondly, speech understanding in noise of 26 Baha users with different Baha sound processors (Compact, Divino, and BP100) is investigated. Linear regression between aided sound field thresholds and bone conduction (BC) thresholds of the better ear shows highest correlation coefficients and the steepest slope. Differences between better BC thresholds and aided sound field thresholds are smallest for mid-frequencies (1 and 2 kHz) and become larger at 0.5 and 4 kHz. For Baha users, the gain in speech recognition in quiet can be expected to lie in the order of magnitude of the gain in their hearing threshold. Compared to its predecessor sound processors Baha(®) Compact and Baha(®) Divino, Baha(®) BP100 improves speech understanding in noise significantly by +0.9 to +4.6 dB signal-to-noise ratio, depending on the setting and the use of directional microphone. For Baha users with unilateral and bilateral conductive hearing loss and bilateral mixed hearing loss, audiological results in aided sound field thresholds can be estimated with the better BC hearing threshold. The benefit in speech understanding in quiet can be expected to be similar to the gain in their sound field hearing threshold. The most recent technology of Baha sound processor improves speech understanding in noise by an order of magnitude that is well perceived by users and which can be very useful in everyday life.

Content Classification of Multimedia Documents using Partitions of Low-Level Features

Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogram and color wavelet), and “audio words” based on low-level spectral features (spectral envelope and spectral flatness) for non-speech audio. Such audio and video words provide a means to represent the different modalities in a uniform way. The frequencies of the word analogues represent audio-visual documents: the standard bag-of-words approach. Support vector machines are used for supervised classification in a 1 vs. n setting. Classification based on speech outperforms all other single modalities. Combining speech with non-speech audio improves classification. Classification is further improved by supplementing speech and non-speech audio with video words. Optimal F-scores range between 62% and 94% corresponding to 50% - 84% above chance. The optimal combination of modalities depends on the category to be recognized. The construction of audio and video words from low-level features provide a good basis for the integration of speech, non-speech audio and video.

Intelligent Virtual Patients for Training Clinical Skills

The article presents the design process of intelligent virtual human patients that are used for the enhancement of clinical skills. The description covers the development from conceptualization and character creation to technical components and the application in clinical research and training. The aim is to create believable social interactions with virtual agents that help the clinician to develop skills in symptom and ability assessment, diagnosis, interview techniques and interpersonal communication. The virtual patient fulfills the requirements of a standardized patient producing consistent, reliable and valid interactions in portraying symptoms and behaviour related to a specific clinical condition.

Single beam survey data and links to sea-bottom video files off Helgoland

Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.