O uso clínico dos anti-inflamatórios não esteróides

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378)

Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.

ART 2-A: An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition

This article introduces ART 2-A, an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large-scale neural computation.

Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System

A Fuzzy ART model capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns is described. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns. The generalization to learning both analog and binary input patterns is achieved by replacing appearances of the intersection operator (n) in AHT 1 by the MIN operator (Λ) of fuzzy set theory. The MIN operator reduces to the intersection operator in the binary case. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy set theory play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Learning stops when the input space is covered by boxes. With fast learning and a finite input set of arbitrary size and composition, learning stabilizes after just one presentation of each input pattern. A fast-commit slow-recode option combines fast learning with a forgetting rule that buffers system memory against noise. Using this option, rare events can be rapidly learned, yet previously learned memories are not rapidly erased in response to statistically unreliable input fluctuations.

A Recurrent Cooperative/Competitive Field for Segmentation of Magnetic Resonance Brain Imagery

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, and neighbor interactions. This network is presented as an example of applying a recurrent cooperative/competitive field (RCCF) to a problem with multiple conflicting constraints. Simulations of the network and its phase plane analysis are presented.

A Neural Pattern Generator that Exhibits Frequency-Dependent In-Phase and Anti-Phase Oscillations

This article describes a. neural pattern generator based on a cooperative-competitive feedback neural network. The two-channel version of the generator supports both in-phase and anti-phase oscillations. A scalar arousal level controls both the oscillation phase and frequency. As arousal increases, oscillation frequency increases and bifurcations from in-phase to anti-phase, or anti-phase to in-phase oscillations can occur. Coupled versions of the model exhibit oscillatory patterns which correspond to the gaits used in locomotion and other oscillatory movements by various animals.

Adaptive Resonance Associative Map: A Hierarchical ART System for Fast Stable Associative Learning

This paper introduces a new class of predictive ART architectures, called Adaptive Resonance Associative Map (ARAM) which performs rapid, yet stable heteroassociative learning in real time environment. ARAM can be visualized as two ART modules sharing a single recognition code layer. The unit for recruiting a recognition code is a pattern pair. Code stabilization is ensured by restricting coding to states where resonances are reached in both modules. Simulation results have shown that ARAM is capable of self-stabilizing association of arbitrary pattern pairs of arbitrary complexity appearing in arbitrary sequence by fast learning in real time environment. Due to the symmetrical network structure, associative recall can be performed in both directions.

Practical assessment of hardware limitations on power aware wireless sensor networks - An anti-windup approach

This work considers the effect of hardware constraints that typically arise in practical power-aware wireless sensor network systems. A rigorous methodology is presented that quantifies the effect of output power limit and quantization constraints on bit error rate performance. The approach uses a novel, intuitively appealing means of addressing the output power constraint, wherein the attendant saturation block is mapped from the output of the plant to its input and compensation is then achieved using a robust anti-windup scheme. A priori levels of system performance are attained using a quantitative feedback theory approach on the initial, linear stage of the design paradigm. This hybrid design is assessed experimentally using a fully compliant 802.15.4 testbed where mobility is introduced through the use of autonomous robots. A benchmark comparison between the new approach and a number of existing strategies is also presented.

Design, synthesis and development of novel indolocarbazole derivatives as potential anti-cancer agents

This thesis describes work carried out on the design of new routes to a range of bisindolylmaleimide and indolo[2,3-a]carbazole analogs, and investigation of their potential as successful anti-cancer agents. Following initial investigation of classical routes to indolo[2,3-a]pyrrolo[3,4-c]carbazole aglycons, a new strategy employing base-mediated condensation of thiourea and guanidine with a bisindolyl β-ketoester intermediate afforded novel 5,6-bisindolylpyrimidin-4(3H)-ones in moderate yields. Chemical diversity within this H-bonding scaffold was then studied by substitution with a panel of biologically relevant electrophiles, and by reductive desulfurisation. Optimisation of difficult heterogeneous literature conditions for oxidative desulfurisation of thiouracils was also accomplished, enabling a mild route to a novel 5,6-bisindolyluracil pharmacophore to be developed within this work. The oxidative cyclisation of selected acyclic bisindolyl systems to form a new planar class of indolo[2,3-a]pyrimido[5,4-c]carbazoles was also investigated. Successful conditions for this transformation, as well as the limitations currently prevailing for this approach are discussed. Synthesis of 3,4-bisindolyl-5-aminopyrazole as a potential isostere of bisindolylmaleimide agents was encountered, along with a comprehensive derivatisation study, in order to probe the chemical space for potential protein backbone H-bonding interactions. Synthesis of a related 3,4-arylindolyl-5-aminopyrazole series was also undertaken, based on identification of potent kinase inhibition within a closely related heterocyclic template. Following synthesis of approximately 50 novel compounds with a diversity of H-bonding enzyme-interacting potential within these classes, biological studies confirmed that significant topo II inhibition was present for 9 lead compounds, in previously unseen pyrazolo[1,5-a]pyrimidine, indolo[2,3-c]carbazole and branched S,N-disubstituted thiouracil derivative series. NCI-60 cancer cell line growth inhibition data for 6 representative compounds also revealed interesting selectivity differences between each compound class, while a new pyrimido[5,4-c]carbazole agent strongly inhibited cancer cell division at 10 µM, with appreciable cytotoxic activity observed across several tumour types.

Design, synthesis and evaluation of novel ellipticine derivatives and analogues as anti-cancer agents

This thesis describes work carried out on the synthesis of novel 5- and 11-substituted ellipticines and derivatives of the ellipticine analogues, isoellipticine and deazaellipticine, followed by investigation of their potential as anti-cancer agents. Preparation of the key 5- and 11-substituted ellipticine targets involved the development of regiospecific, sequential alkylation reactions with alkenyllithium and Grignard reagents. Investigation of these novel reactions resulted in a new route towards 5-substituted ellipticines via Grignard reaction with vinylmagnesium bromide. These novel 5-vinylellipticine derivatives were further functionalised in an ozonolysis reaction, followed by oxidation to give a range of novel 5-substituted ellipticines. Less success was encountered in the 11-substituted ellipticine series, however preparation of these derivatives using a previously published route was accomplished, and the resulting 11-formylellipticine was further derivatised to give a panel of novel 9- and 11-substituted ellipticines, incorporating amide, carboxylate, imine and amine functionality. The successful route towards 5-substituted ellipticines was applied to the preparation of a range of novel 11-substituted isoellipticines and 6-substituted deazaellipticines, the first time substantial synthesis has been undertaken with these analogues. In addition to this, the first preparation of isoellipticinium salts is described, and a panel of novel isoellipticinium, 7 formylisoellipticinium and 7-hydroxyisoellipticinium salts were synthesised in good yields. Biological evaluation of a panel of 43 novel ellipticine, isoellipticine and deazaellipticine derivatives was accomplished with a topoisomerase II decatenation assay and submission to the NCI 60-cell line screen. Four novel isoellipticine topoisomerase II inhibitors were identified from the decatenation assay, with strong activity at 10 μM. In addition to this, NCI screening identified five highly cytotoxic ellipticine and isoellipticine compounds with remarkable selectivity profiles for different cancer types. These novel lead compounds represent new templates for further research and synthesis.

Characterising novel anti-biofilm targets for the treatment of chronic Pseudomonas aeruginosa infection in the cystic fibrosis lung

The global rise in antibiotic resistance is a significant problem facing healthcare professionals. In particular within the cystic fibrosis (CF) lung, bacteria can establish chronic infection and resistance to a wide array of antibiotic therapies. One of the principle pathogens associated with chronic infection in the CF lung is Pseudomonas aeruginosa. P. aeruginosa can establish chronic infection in the CF lung partly through the use of the biofilm mode of growth. This biofilm mode of growth offers a considerable degree of protection from a wide variety of challenges such as the host immune system or antibiotic therapy. The threat posed by the emergence of chronic pathogens is prompting the development of next generation antimicrobials. The biofilm mode of growth is often central to the establishment of chronic infection and the development of antibiotic resistance. Thus, targeting biofilm formation has emerged as one of the principle strategies for the development of next generation antimicrobials. In this thesis two separate approaches were used to identify potential anti - biofilm targets. The first strategy focused on the identification of novel genes with a role in a biofilm formation. High throughput screening identified almost 300 genes which had a role in biofilm formation. A number of these genes were characterised at a phenotypic and a molecular level. The second strategy focused on the identification of compounds capable of inhibiting biofilm formation. A collection of marine sponge isolated bacteria were screened for the ability to inhibit the central pathway regulating biofilm formation, quorum sensing. A number of distinct isolates were identified that had quorum sensing inhibition activity from which, a Pseudomonas isolate was selected for further characterisation. A specific compound capable of inhibiting quorum sensing was identified using chemical analytical technologies in the supernatant of this marine isolate.

The balance between the pro-inflammatory effect of plasma noradrenaline and the anti-inflammatory effect of neuronal noradrenaline determines the peripheral effect of noradrenaline

Perfusion experiments on an isolated, canine lateral saphenous vein segment preparation have shown that noradrenaline causes potent, flow dependent effects, at a threshold concentration comparable to that of plasma noradrenaline, when it stimulates the segment by diffusion from its microcirculation (vasa vasorum). The effects caused are opposite to those neuronal noradrenaline causes in vivo and that, in the light of the principle that all information is transmitted in patterns that need contrast to be detected – star patterns need darkness, sound patterns, quietness – has generated the hypothesis that plasma noradrenaline provides the obligatory contrast tissues need to detect and respond to the regulatory information encrypted in the diffusion pattern of neuronal noradrenaline. Based on the implications of that hypothesis, the controlled variable of the peripheral noradrenergic system is believed to be the maintenance of a set point balance between the contrasting effects of plasma and neuronal noradrenaline on a tissue. The hypothalamic sympathetic centres are believed to monitor that balance through the level of afferent sympathetic traffic they receive from a tissue and to correct any deviation it detects in the balance by adjusting the level of efferent sympathetic input it projects to the tissue. The failure of the centres to maintain the correct balance, for reasons intrinsic or extrinsic to themselves, is believed to be responsible for degenerative and genetic disorders. When the failure causes the balance to be polarised in favour of the effect of plasma noradrenaline that is believed to cause inflammatory diseases like dilator cardiac failure, renal hypertension, varicose veins and aneurysms; when it causes it to be polarised in favour of the effect of neuronal noradrenaline that is believed to cause genetic diseases like hypertrophic cardiopathy, pulmonary hypertension and stenoses and when, in pregnancy, a factor causes the polarity to favour plasma noradrenaline in all the maternal tissues except the uterus and conceptus, where it favours neuronal noradrenaline, that is believed to cause preeclampsia.