Microfluidics: a new look at cell migration analysis

This thesis explores the development and employment of microfluidic devices as a tool for studying the effect of the surrounding environment on embryonic stem cells during the migration phenomena. Different single-cell microchips were designed and manufactured to study mouse embryonic fibroblasts (MEFs) migration towards an environmental variation (increase of serum concentration in the culture medium) that was expected to function as a motility stimuli. Considering the experimental, cells were injected into the microchips chambers and individually isolated by dedicated cell traps with view to a single-cell analysis. Once fribroblasts were attached to the surface, culture medium with an increased serum level was subsequently injected in an adjacent chamber to promote the formation of a serum concentration gradient. The gradient established between the chambers could be sensed by the fibroblasts and thus triggered the cells mobilization towards and in the direction of the richer serum medium. Additionally, the experiment allowed the observation of MEFs’ structural reorganization when migrating through micro-tunnels containing widths below the cell size, suggesting a cytoskeleton rearrangement on account of the nutritional stimulus introduced. Furthermore, results indicate that fibronectin promotes MEFs adhesion to the substrate and that MEFs migration is characterized as haptotactic.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Neural correlates of face familiarity in institutionally reared children with distinctive, atypical social behavior

Although the impact of early adverse experience on neural processing of face familiarity has been studied, research has not taken into account disordered child behavior. This work compared the neural processing of familiar versus strangers' faces in 47 institutionalized children with a mean age of 54 months to determine the effects of (a) the presence versus absence of atypical social behavior and (b) inhibited versus indiscriminant atypical behavior. Results revealed a pattern of cortical hypoactivation in institutionalized children manifesting atypical social behavior and that inhibited children displayed larger neural response to a caregiver's face than to the stranger's, while indiscriminant children did not discriminate between stimuli. These findings suggest that neural correlates of face familiarity are associated with social functioning in institutionalized children.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

[Letter to the editor] Sustained effects of a neural-based intervention in a refractory case of Tourette syndrome

There are only a few treatments available for Tourette syndrome (TS). These treatments frequently do notwork in patients with moderate to severe TS [1]. Neuroimaging studies show a correlation between tics severity and increased activation over motor pathways, along with reduced activation over the control areas of the cortico-striato-thalamo-cortical circuits [2]. Moreover, the temporal pattern of tic generation suggests that cortical activation especially in the SMA precedes subcortical activation [3]. Following this assumption, here we explored the brain effects of 10-daily sessions of cathodal transcranial Direct Current Stimulation (tDCS) delivered over the pre-SMA in a patient with refractory and severe TS and also assessed whether those changes were long lasting (up to 6 months).

Migration and mental health: Japanese Brazilians in Japan and in Brazil

OBJECTIVE: Brazil is the country with the largest community of Japanese descendants in the world, from a migration movement that started in 1908. However, more recently (1988), a movement in the opposite direction began. Many of these descendants went to Japan for work purposes and suffered mental distress. Some of them sought treatment in Japan, while others returned to Brazil to seek treatment. The aim of the present study was to compare the sociodemographic profile and diagnoses of Japanese Brazilian psychiatric outpatients in Japan (remaining group) and in Brazil (returning group). METHOD: All consecutive Japanese Brazilian outpatients who received care from the psychiatric units in Japan and Brazil from April 1997 to April 2000 were compared. The diagnoses were based on ICD-10 and were made by psychiatrists. Sociodemographic data and diagnoses in Brazil and Japan were compared by means of the Chi-Squared Test. RESULTS: The individuals who returned to Brazil were mostly male and unmarried, had lived alone in Japan, had stayed there for short periods and were classified in the schizophrenia group. The individuals who remained in Japan were mostly female and married, were living with family or friends, had stayed there for long periods and were classified in the anxiety group. Logistic regression showed that the most significant factors associated with the returning group were that they had lived alone and stayed for short periods (OR = 0.93 and 40.21, respectively). CONCLUSION: We conclude that living with a family and having a network of friends is very important for mental health in the context evaluated.

High-aspect-ratio tridimensional electrode arrays for neural applications

Tese de Doutoramento em Engenharia Biomédica.

Participación de las células de la cresta neural en el desarrollo de anomalías inducidas por agentes exógenos (síndrome fetal alcohólico y embriopatía por ácido retinoico)

En la etapa embrionaria temprana de los vertebrados, las células de la cresta neural (CCN) se segregan del tubo neural y se distribuyen con patrones temporales y espaciales muy precisos, contribuyendo a la formación de muchos derivados: neuronas y glía del sistema nervioso periférico, parte del sistema endócrino, sistema pigmentario y la mayor parte de los tejidos cráneo-faciales. Las bases morfogenéticas de esta movilización de las CCN se asocian con la disponibilidad de componentes de la matriz extracelular, con fenómenos de apoptosis selectiva, y con la expresión de genes homeóticos, de proteínas transportadoras de retinoides y de receptores de ácido retinoico (AR). En consecuencia, el mecanismo que define el comportamiento de las CCN migratorias asume una especial importancia, desde que cualquier fallo producido en estos "reguladores topográficos" podrá alterar la ordenada traslocación de CN, induciendo una dismorfogénesis. (...) Objetivos: 1) Analizar el comportamiento dinámico de la etapa migratoria temprana de las CCN de niveles cefálico y troncal expuestas a etanol o AR in vitro. 2) Evaluar la posible reversibilidad de los efectos del etanol y del AR sobre la morfología y dinámica migratoria de las CCN de niveles cefálico y troncal in vitro. 3) Analizar los componentes del citoesqueleto asociados con los cambios de forma y motilidad celular en CCN de niveles cefálico y troncal expuestas a etanol y AR in vitro. Los resultados del proyecto permitirán aportar al conocimiento de los mecanismos básicos que regulan la movilidad de poblaciones celulares de gran importancia para el desarrollo humano. Los datos obtenidos servirán de base para futuros enfoques de biología molecular tendientes a innovar aspectos del diagnóstico, pronóstico y prevención de patologías humanas de creciente prevalencia provocadas por el etanol (FAS) y los retinoides (RAE).