Ana M. G. Guerreiro

Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.

Image processing for eye detection and classification of the gaze direction

The vision has many sensors responsible for capturing information that is sent to the brain. The gaze reflects its attention, intention and interest of the brain towards the outside world. Therefore, the detection of the gaze direction is a promising alternative for the simulation programs, virtual reality applications and human-machine special communication. Cheaper devices to capture images and increase the power processing of personal computers motivate studies that allow human-machine interactivity. The application of techniques to detect the gaze direction has the possibility of improving significantly the interaction between people with motor deficiency and personal computers. The objective of this work is to provide a system that uses techniques of digital image processing to classify the gaze direction. The results show the complexity and efficiency of a system that performs not only the acquisition of images but also their classification by using artificial neural networks.

Classification of cardiac arrhythmias using competitive networks

Information generated by sensors that collect a patients vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Hospital automation system RFID-based: Technology embedded in smart devices (cards, tags and bracelets)

RFID is a technology being adopted in many business fields, especially in the medical field. This work has the objective to present a system for automation of a hospital clinical analysis laboratory. This system initially uses contactless smart cards to store patients data and for authentication of hospital employees in the system. The proposed system also uses RFID tags stuck to containers containing patients collected samples for the correct identification of the patient who gave away the samples. This work depicts a hospital laboratory workflow, presents the system modeling and deals with security matters related to information stored in the smart cards.

AngelCare mobile system: Homecare patient monitoring using Bluetooth and GPRS.

The quick progress in technology has brought new paradigms to the computing area, bringing with them many benefits to society. The paradigm of ubiquitous computing brings innovations applying computing in peoples daily life without being noticed. For this, it has used the combination of several existing technologies like wireless communications and sensors. Several of the benefits have reached the medical area, bringing new methods of surgery, appointments and examinations. This work presents telemedicine software that adds the idea of ubiquity to the medical area, innovating the relation between doctor and patient. It also brings security and confidence to a patient being monitored in homecare.

Modeling of medical care with stochastic Petri Nets

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

An extended model for a spiking neuron class

This paper proposes an extension to the model of a spiking neuron for information processing in artificial neural networks, developing a new approach for the dynamic threshold of the integrate-and-fire neuron. This new approach invokes characteristics of biological neurons such as the behavior of chemical synapses and the receptor field. We demonstrate how such a digital model of spiking neurons can solve complex nonlinear classification with a single neuron, performing experiments for the classical XOR problem. Compared with rate-coded networks and the classical integrate-and-fire model, the trained network demonstrated faster information processing, requiring fewer neurons and shorter learning periods. The extended model validates all the logic functions of biological neurons when such functions are necessary for the proper flow of binary codes through a neural network.

Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach

INTRODUCTION: Automatic detection of blood components is an important topic in the field of hematology. Segmentation is an important step because it allows components to be grouped into common areas and processed separately. This paper proposes a method for the automatic segmentation and classification of blood components in microscopic images using a general and automatic fuzzy approach. METHODS: During pre-processing, the supports of the fuzzy sets are automatically calculated based on the histogram peaks in the green channel of the RGB image and the Euclidean distance between the leukocyte nuclei centroids and the remaining pixels. During processing, fuzzification associates the degree of pertinence of the gray level of each pixel in the regions defined in the histogram with the proximity of the leukocyte nucleus centroid closest to the pixel. The fuzzy rules are then applied, and the image is defuzzified, resulting in the classification of four regions: leukocyte nuclei, leukocyte cytoplasm, erythrocytes and blood plasma. In post-processing, false positives are reduced and the leukocytes (including the nucleus and cytoplasm), erythrocytes and blood plasma are segmented. RESULTS: A total of 530 microscopic images of blood smears were processed, and the results were compared with the results of manual segmentation by experts and the accuracy rates of other approaches. CONCLUSION: The method demonstrated average accuracy rates of 97.31% for leukocytes, 95.39% for erythrocytes and 95.06% for blood plasma, avoiding the limitations found in the literature and contributing to the practice of the segmentation of blood components.

Analysing some R-Implications and its application in fuzzy mathematical morphology

The fuzzy mathematical morphology extends the binary morphological operators to gray-scale and coloured images using concepts of fuzzy logic. To define the morphological operators of fuzzy erosion and dilatation it is used the implications and conjunctions respectively. This work presents an analysis of some R-implications to verify if the pairs of implications and T-norms (conjunctions) were adjunctions. It was used a fuzzy application developed in the Matlab for implementation and tests with the respectively results.

S2DIA: A Diagnostic System for Diabetes mellitus using SANA platform

Currently, Diabetes is a very common disease around the world, and with an increase in sedentary lifestyles, obesity and an aging population the number of people with Diabetes worldwide will increase by more than 50%. In this context, the MIT (Massachusetts Institute of Technology) developed the SANA platform, which brings the benefits of information technology to the field of healthcare. It offers healthcare delivery in remote areas, improves patient access to medical specialists for faster, higher quality, and more cost effective diagnosis and intervention. For these reasons, we developed a system for diagnosis of Diabetes using the SANA platform, called S2DIA. It is the first step towards knowing the risks for type 2 Diabetes, and it will be evaluated, especially, in remote/poor areas of Brazil.