Le système nerveux central est la cible la plus probable d'effets biologiques dûs à l'exposition aux radiofréquences (RF) de la téléphonie mobile. Plusieurs études sur l’EEG (électroencéphalogramme) ont montré des variations dans le spectre de la bande alpha pendant et / ou après l'exposition aux radiofréquences, avec les yeux fermés ou pendant le sommeil. Dans ce contexte, l'observation de l'activité électrique spontanée des réseaux neuronaux sous exposition aux radiofréquences représente un outil efficace pour détecter de possibles effets des RF de faible niveau sur le système nerveux. Dans ce travail de thèse, nous avons développé un dispositif expérimental dédié à l'exposition dans la gamme des GHz de réseaux neuronaux et permettant simultanément l’enregistrement de l'activité électrique des neurones. Une cellule électromagnétique transversale (TEM) a été utilisée afin d'exposer les réseaux neuronaux aux signaux GSM-1800 à un niveau de DAS de 3,2 W / kg. L'enregistrement de l'activité électrique neuronale et la détection en termes de spikes et bursts sous exposition ont été réalisées à l'aide de réseaux de micro-électrodes (MEAs). Ce travail démontre la faisabilité de l’étude (culture de réseaux de neurones primaires, enregistrement de l'activité électrique et analyse des signaux obtenus sous exposition aux radiofréquences) et expose des résultats préliminaires. Dans l'expérience principale (16 cultures), il y avait une diminution réversible de 30% du taux moyen de spikes (MFR) et de bursts (BR) pendant les 3 min d’exposition aux RF. Des expériences supplémentaires sont nécessaires pour mieux caractériser cet effet, notamment en termes d'élévation de la température au niveau microscopique. / The central nervous system is the most likely target of mobile telephony radiofrequency field (RF) exposure in terms of biological effects. Several EEG (electroencephalography) studies have reported variations in the alpha-band power spectrum during and/or after RF exposure, in resting EEG and during sleep. In this context, the observation of the spontaneous electrical activity of neuronal networks under RF exposure can be an efficient tool to detect the occurrence of low-level RF effects on the nervous system. In this thesis research work we developed a dedicated experimental setup in the GHz range for the simultaneous exposure of neuronal networks and monitoring of electrical activity. A transverse electromagnetic (TEM) cell was used to expose the neuronal networks to GSM-1800 signals at a SAR level of 3.2 W/kg. Recording of the neuronal electrical activity and detection of the extracellular spikes and bursts under exposure were performed using Micro Electrode Arrays (MEAs). This work provides the proof of feasibility and preliminary results of the integrated investigation regarding exposure setup, culture of the neuronal network, recording of the electrical activity and analysis of the signals obtained under RF exposure. In the main experiment (16 cultures), there was a 30% reversible decrease in mean firing rate (MFR) and bursting rate (BR) during the 3 min exposures to RF. Additional experiments are needed to further characterize this effect, especially in terms of temperature elevation at the microscopic level.

Die Kartographie ist eine Wissenschaft, die in ihrem Charakter starke interdisziplinäre Züge aufweist. Sie zeigt sich in den verschiedensten Facetten und wird darum in den unterschiedlichsten Wissenschaften angewandt. Markantester Charakter ist, schon per Definition, die Modellierung von geowissenschaftlichen Ereignissen und Sachverhalten. „A unique facility for the creation and manipulation of visual or virtual representations of geospace – maps – to permit the exploration, analysis, understanding and communication of information about that space.“(ICA 2003) Aus dieser Definition wird die Charakteristik einer Kommunikationswissenschaft (Brassel) deutlich. Gerade seit dem Paradigmenwechsel der 1970er Jahre fließen zahlreiche weitere Aspekte wie Informatik, Semiotik und Psychologie in das Verständnis von Kartographie ein. Dadurch wird die Karte nicht mehr als reines graphisches Mittel verstanden, sondern als Träger und Übermittler von Informationen verstanden. Der Kartennutzer und dessen Verständnis von Karten rücken dabei immer weiter in den Vordergrund und werden „Ziel“ der kartographischen Verarbeitung. Aus diesem Verständnis heraus, möchte ich in der folgenden Arbeit einen relativ neuen Einfluss und Aspekt der Kartographie vorstellen. Es handelt sich um das Modell der Self-Organizing Maps (SOM), welches erstmalig Anfang der 1980er Jahre von Teuvo Kohonen vorgestellt wurde und deshalb auch, von einigen Autoren, als Kohonenmaps bezeichnet wird. Dem Typus nach, handelt es sich dabei um künstliche neuronale Netze, welche dem Nervensystem des menschlichen Gehirns nachempfunden sind und damit allgemein als eine Art selbständiger, maschineller Lernvorgang angesehen werden können. Im Speziellen sind Self-Organizing Maps ein unüberwachtes Lernverfahren, das in der Lage ist völlig unbekannte Eingabewerte zu erkennen und zu verarbeiten. Durch diese Eigenschaft eignen sie sich als optimales Werkzeug für Data Mining sowie zur Visualisierung von hochdimensionalen Daten. Eine Vielzahl von Wissenschaftlern hat diesen Vorteil bereits erkannt und das Modell in ihre Arbeit einbezogen oder auf dessen Verwendbarkeit analysiert. Deshalb möchte in dieser Arbeit, einige dieser Verwendungsmöglichkeiten und den daraus resultierenden Vorteil für die Kartographie aufzeigen.:1.) Einleitung ...........................................................................................2 2.) Aufbau und Funktionsweise von SOM ............................................ 5 2.1.) Was sind Self-Organizing Maps? ................................................5 2.2.) Funktionsweise ............................................................................7 2.3.) Visualisierung des trainierten Kohonen-Netz .......................... 11 2.4.) Software ..................................................................................... 12 3. Möglichkeiten für die Kartographie................................................ 14 3.1 Geowissenschaftliches Data Mining ........................................... 15 3.2 Visualisierung von Daten............................................................. 17 4. explorative Datenanalyse geographischer Daten .......................... 19 4.1 SOM als Geovisualisierung .......................................................... 19 4.1.1 U-Matrix-Darstellung .............................................................22 4.1.2 Projektionen (Netzdarstellungen) ........................................26 4.1.3 2D & 3D-Plots .........................................................................28 4.1.4 Komponentenebenen ...........................................................29 4.2 Geo-SOM & andere Möglichkeiten zur Verarbeitung von geowissenschaftlichen Daten ................................................... 32 4.2.1 Hierarchische SOMs ...............................................................33 4.2.2 Geo-enforced SOM ................................................................34 4.2.3 Geo-SOM ................................................................................35 4.3 SOM & GIS .................................................................................... 38 5. Datenverarbeitende Anwendungen ............................................... 40 5.1 Klassifizierung von Fernerkundungsdaten................................. 40 5.2 Kantendetektion in Satellitenbildern......................................... 43 5.3 Auswertung von Zeitreihen & Monitoring................................. 47 5.4 Klassifikation von SAR-Daten...................................................... 49 5.5 Generalisierung............................................................................ 50 5.6 Problem des Handlungsreisenden (Travelling Salesman Problem)..................................................................................... 52 6. SOM als Kartenmetapher zur Visualisierung nicht-geographischer Daten .............................................................................................. 54 7. Zusammenfassung............................................................................ 62 X. Quellenverzeichnis ........................................................................... 63 X.I Literaturnachweise ....................................................................... 63 X.II Lehrinhalte aus dem Internet ..................................................... 69 X.III Softwarelösungen ...................................................................... 69

No description available.

Over the last decade advances in multineuron simultaneous recording techniques have produced huge amounts of data. This has led to the investigation of probable temporal relationships between spike times of neurons as manifestations of the underlying network structure. But the huge dimensionality of data makes the search for patterns difficult. Although this difficulty may be surpassed by employing massive computing resources, understanding the significance and relation of these temporal patterns to the underlying network structure and the causative activity is still difficult. To find such relationships in networks of excitatory neurons, a simplified network structure of feedforward chains called "Synfire chains" has been frequently employed. But in a recurrently connected network where activity from feedback connections is comparable to the feedforward chain, the basic assumptions underlying synfire chains are violated. In the first part of this thesis we propose the first-spike-latency based analysis as a low complexity method of studying the temporal relationships between neurons. Firstly, spike latencies being temporal delays measured at a particular epoch of time (onset of activity after a quiescent period) are a small subset of all the temporal information available in spike trains, thereby hugely reducing the amount of data that needs to be analyzed. We also define for the first time, "Synconset waves and chains" as a sequence of first-spike-times and the causative neuron chain. Using simulations, we show the efficacy of the synconset paradigm in unraveling feedforward chains of excitatory neurons even in a recurrent network. We further create a framework for going back and forth between network structure and the observed first-spike-latency patterns. To quantify these associations between network structure and dynamics we propose a likelihood measure based on Bayesian reasoning. This quantification is agnostic to the methods of association used and as such can be used with any of the existing approaches. We also show the benefits of such an analysis when the recorded data is subsampled, as is the case with most physiological recordings. In the subsequent part of our thesis we show two sample applications of first-spike-latency analysis on data acquired from multielectrode arrays. Our first application dwells on the intricacies of extracting first-spike-latency patterns from multineuron recordings using recordings of glutamate injured cultures. We study the significance of these patterns extracted vis-a-vis patterns that may be obtained from exponential spike latency distributions and show the differences between patterns obtained in injured and control cultures. In a subsequent application, we study the evolution of latency patterns over several days during the lifetime of a dissociated hippocampal culture.

One of the defining characteristics of the brain is its plasticity, which is the ability to alter and reorganize neuronal circuits. The brain is constantly being shaped and moulded by the external world through endogenous factors like neurotransmitters, growth factors and circulating hormones. 17β-estradiol, which is the most potent estrogen among the group of ovarian steroid hormones, has widespread effects throughout the central nervous system. Apart from its actions on regions of the brain concerned with reproduction, estradiol has profound effects on brain areas not classically associated with reproductive function like cerebral cortex, midbrain, brainstem, hippocampus and spinal cord. This enables the hormone to influence learning and memory, emotions, affective state, cognition, motor coordination and pain sensitivity. Estradiol exerts these effects by regulating gene expression via intracellular estrogen receptors. In addition to this, the hormone interacts with receptors at the cell membrane to rapidly alter the electrical activity of neurons and astrocytes, and regulate second messenger systems. The aim of this study was to investigate the cellular and functional effects of estradiol on neuronal networks and on signaling between neurons and astrocytes in primary mixed hippocampal cultures. Estradiol is proconvulsant; it increases neuronal excitability and decreases the threshold for seizures. This property of estradiol is instrumental in precipitating catamenial seizures in women with epilepsy. These are epileptic seizures influenced by cyclical hormone changes and occur in over one-third to half of women with epilepsy. In the first part of the work, the effects of 24-hour estradiol treatment on hippocampal neurons were investigated using fluorescence imaging and electrophysiological techniques. Further, the ability of gabapentin, an antiepileptic drug sometimes used to treat hormone sensitive seizures, to counteract the effects of estradiol was studied. Synaptic vesicles were labeled by uptake of FM 1-43, and high K+- triggered exocytotic release was monitored by fluorescence imaging. The reduction in intensity of FM 1-43 fluorescence, which is a measure of vesicular release, was enhanced by estradiol, suggesting that estradiol upregulates the exocytotic machinery. The high K+-evoked intracellular Ca2+ rise in neurons, studied by loading the neurons with the Ca2+ indicator dye fluo-3 AM, was potentiated following estradiol treatment. Electrophysiological recordings from neurons following estradiol treatment showed an increase in the frequency of miniature excitatory postsynaptic currents (mEPSCs) and a larger number of mEPSC events with a predominant NMDA component. Many of the estradiol-induced excitatory effects on the neuronal network were abolished by incubating the cultures with a combination of estradiol and gabapentin suggesting a mechanism of action for the drug in the treatment of hormone sensitive seizures. Glial cells were once regarded as passive, supportive elements in the nervous system. This view of glial cells has drastically changed over the past decade and it is now known that glial cells are dynamic signaling elements in the brain. In view of the emerging importance of glia in the physiology of the nervous system and accumulating evidence of direct effects of steroid hormones on these cells, the subsequent part of the work delves into the consequences of 24-hour estradiol treatment on astrocytes and neuron-to-astrocyte signaling. Estrogen receptors have been described on both neurons and astrocytes in the hippocampus suggesting a complex interplay between the two cell types in mediating the effects of the hormone. Astrocytes sense and respond to neuronal activity with a rise in intracellular calcium concentration, ([Ca2+]i). Astrocyte ([Ca2+]i) transients can modulate neuronal activity, indicating a bi-directional form of communication between neurons and astrocytes. Using simultaneous electrophysiology and calcium imaging techniques, neuronal activity-evoked ([Ca2+]i) changes in fluo-3 AM loaded astrocytes were monitored. Action potential firing in neurons, elicited by injecting depolarizing current pulses, was associated with ([Ca2+]i) elevations in adjacent astrocytes which could be blocked by 200 µM MCPG and also 1 µM TTX. Comparison of astrocytic ([Ca2+]i) transients in control and estradiol treated cultures revealed that the amplitude of the ([Ca2+]i) transient, the number of responsive astrocytes and the ([Ca2+]i) wave velocity were all significantly reduced in estradiol treated cultures. ([Ca2+]i) rise in astrocytes in response to local application of the metabotropic glutamate receptor agonist t-ACPD was attenuated in estradiol treated cultures suggesting functional changes in the astrocyte metabotropic glutamate receptor following 24-hour treatment with estradiol. Since astrocytes can modulate synaptic transmission by release of glutamate, the attenuated ([Ca2+]i) response seen following estradiol treatment could have functional consequences on astrocyte-neuron signaling. The acute effects of estradiol on astrocyte-to-astrocyte and astrocyte-to-neuron signaling have been addressed in the next part of the study. Bidirectional communication between neurons and astrocytes involves integration of neuronal inputs by astrocytes, and release of gliotransmitters that modulate neuronal excitability and synaptic transmission. In addition to its rapid actions on neuronal electrical activity, estradiol can rapidly alter astrocyte ([Ca2+]i) levels through a plasma membrane-associated estrogen receptor. The functional consequences of acute estradiol treatment (5 min) on astrocyte-astrocyte and astrocyte-neuron communication were investigated using calcium imaging and electrophysiological techniques. Mechanical stimulation of an astrocyte evoked a ([Ca2+]i) rise in the stimulated astrocyte, which propagated to the surrounding astrocytes as a ([Ca2+]i) wave. Following acute treatment with estradiol, the amplitude of the ([Ca2+]i) elevation in astrocytes around the stimulated astrocyte was attenuated. Further, estradiol inhibited the ([Ca2+]i) rise in individual astrocytes in response to the metabotropic glutamate receptor agonist, t-ACPD. Mechanical stimulation of astrocytes induced ([Ca2+]i) elevations and electrophysiological responses in adjacent neurons. Estradiol rapidly attenuated the astrocyte-evoked glutamate-mediated ([Ca2+]i) rise and slow inward current in neurons. Also, the incidence of astrocyte-induced increase in spontaneous postsynaptic current frequency was reduced in presence of estradiol. The effects of estradiol were stereo-specific and reversible following washout. These findings indicate that the regulation of neuronal excitability and synaptic transmission by astrocytes is sensitive to rapid estradiol mediated hormonal control.

Une des capacités cérébrales les plus fondamentales, qui est essentiel pour tous les fonctions cognitifs de haut niveau, est de garder des informations pertinentes de tâche pendant les périodes courtes de temps; on connaît cette capacité comme la mémoire de travail (WM). Dans des décennies récentes, accumule là l'évidence d'activité pertinente de tâche dans le cortex préfrontal (PFC) de primates pendant les périodes de "delay" de tâches de "delay-response", impliquant ainsi que PFC peut maintenir des informations sensorielles et ainsi la fonction comme un module de WM. Pour la récupération d'informationssensorielles de l'activité de réseau après que le stimulus sensoriel n'est plus présent il est impératif que l'état du réseau au moment de la récupération soit corrélé avec son état au moment de la compensation de stimulus. Un extrême, en vue dans les modèles informatiques de WM, est la coexistence d'attracteurs multiples. Dans cette approche la dynamique de réseau a une multitude d'états stables possibles, qui correspondent aux états différents de mémoire et un stimulus peut forcer le réseau à changer à un tel état stable. Autrement, même en absence d'attracteurs multiples, si la dynamique du réseau estchaotique alors les informations sur des événements passés peuvent être extraites de l'état du réseau, à condition que la durée typique de l'autocorrélation (AC) de dynamique neuronale soit assez grande. Dans la première partie de cette thèse, j'étudie un modèle à base d'attracteur de mémoire d'un emplacement spatial, pour examiner le rôle des non-linéarités de courbes de f-I neuronales dans des mécanismes de WM. Je fournis une théorie analytique et des résultats de simulations montrant que ces nonlinéarités, plutôt que les constants de temps synaptic ou neuronal, peuvent être la base de mécanismes de réseau WM. Dans la deuxième partie j'explore des facteurs contrôlant la durée d'ACs neuronales dans ungrand réseau "balanced" affichant la dynamique chaotique. Je développe une théorie de moyen champ (MF) décrivant l'ACs en termes de plusieurs paramètres d'ordre. Alors, je montre qu'en dehors de la proximité au point de transition-à-chaos, qui peut augmenter la largeur de la courbe d'AC, l'existence de motifs de connectivité peut causer des corrélations de longue durée dans l'état du réseau. / One of the most fundamental brain capabilities, that is vital for any high level cognitive function, is to store task-relevant information for short periods of time; this capability is known as working memory (WM). In recent decades there is accumulating evidence of taskrelevant activity in the prefrontal cortex (PFC) of primates during delay periods of delayedresponse tasks, thus implying that PFC is able to maintain sensory information and so function as a WM module. For retrieval of sensory information from network activity after the sensory stimulus is no longer present it is imperative that the state of the network at the time of retrieval be correlated with its state at the time of stimulus offset. One extreme, prominent in computational models of WM, is the co-existence of multiple attractors. In this approach the network dynamics has a multitude of possible steady states, which correspond to different memory states, and a stimulus can force the network to shift to one such steady state. Alternatively, even in the absence of multiple attractors, if the dynamics of the network is chaotic then information about past events can be extracted from the state of the network, provided that the typical time scale of the autocorrelation (AC) of neuronal dynamics is large enough. In the first part of this thesis I study an attractor-based model of memory of a spatial location to investigate the role of non-linearities of neuronal f-I curves in WM mechanisms. I provide an analytic theory and simulation results showing that these nonlinearities, rather than synaptic or neuronal time constants, can be the basis of WM network mechanisms. In the second part I explore factors controlling the time scale of neuronal ACs in a large balanced network displaying chaotic dynamics. I develop a mean-field (MF) theory describing the ACs in terms of several order parameters. Then, I show that apart from the proximity to the transition-to-chaos point, which can increase the width of the AC curve, the existence of connectivity motifs can cause long-time correlations in the state of the network.

No description available.

Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.

El disseny molecular de sistemes d’interès per a la química mèdica i per al disseny de fàrmacs sempre s’ha trobat molt lligat a la disponibilitat sintètica dels resultats. Des del moment que la química combinatòria s’incorpora dins de l’esquema sintètic, canvia el paper que ha de jugar la química computacional: la diversitat d’estructures possibles a sintetitzar fa necessària la introducció de mètodes, com el cribratge virtual, que permetin avaluar la viabilitat de grans quimioteques virtuals amb un temps raonable. Els mètodes quimioinformàtics responen a la necessitat anterior, posant a l’abast de l’usuari mètodes eficaços per a la predicció teòrica d’activitats biològiques o propietats d’interès. Dins d’aquests destaquen els mètodes basats en la relació quantitativa d’estructura-activitat (QSAR). Aquests han demostrat ser eficaços per l’establiment de models de predicció en l’àmbit farmacològic i biomèdic. S’ha avaluat la utilització de mètodes QSAR no lineals en la teràpia fotodinàmica del càncer, donat que és una de les línies de recerca d’interès del Grup d’Enginyeria Molecular (GEM) de l’IQS. El disseny de fotosensibilitzadors es pot realitzar a partir de la predicció de propietats fisicoquímiques (com l’espectre d’absorció i la hidrofobicitat del sistema molecular), i de l’estudi de la seva localització subcel•lular preferent, la qual ha demostrat recentment jugar un paper molt important en l’eficàcia del procés global. Per altra banda, les xarxes neuronals artificials són actualment un dels mètodes més ben valorats per a l’establiment de models QSAR no lineals. Donat l’interès de disposar d’un programari capaç d’aplicar aquests mètodes i que, a més, sigui prou versàtil i adaptable com per poder-se aplicar a diferents problemes, s’ha desenvolupat el programari ArIS. Aquest inclou els principals mètodes de xarxes neuronals artificials, per realitzar tasques de classificació i predicció quantitativa, necessaris per a l’estudi de problemes d’interès, com és la predicció de l’activitat anti-VIH d’anàlegs de l’AZT, l’optimització de formulacions químiques o el reconeixement estructural de grans sistemes moleculars / El diseño molecular de sistemas de interés para la química médica y para el diseño de fármacos siempre ha estado condicionado por la disponibilidad sintética de los resultados. Desde el momento en que la química combinatoria se incorpora en el esquema sintético, cambia el papel de la química computacional: la diversidad de estructuras que pueden sintetizarse hace necesaria la introducción de métodos, como el cribado virtual, que permitan evaluar la viabilidad de grandes quimiotecas virtuales en un tiempo razonable. Los métodos quimioinformáticos responden a la necesidad anterior, ofreciendo al usuario métodos eficaces para la predicción teórica de actividades biológicas o propiedades de interés. Entre ellos destacan los métodos basados en la relación cuantitativa de estructura-actividad (QSAR), que han demostrado ser eficaces para establecer modelos de predicción en el ámbito farmacológico y biomédico. Se ha evaluado la utilización de métodos QSAR no lineales en terapia fotodinámica del cáncer, dado que es una de las líneas de investigación de interés del Grup d’Enginyeria Molecular (GEM) del IQS. El diseño de fotosensibilizadores se puede realizar a partir de la predicción de propiedades fisicoquímicas (como su espectro de absorción o su hidrofobicidad) y del estudio de su localización subcelular preferente, la cual ha demostrado recientemente jugar un papel muy importante en la eficacia del proceso global. Por otro lado, las redes neuronales artificiales son actualmente uno de los métodos mejor valorados para establecer modelos QSAR no lineales. Es por ello que resulta muy interesante disponer de un programa capaz de aplicar estos métodos y que, además, sea lo suficientemente versátil y adaptable como para poder aplicarse a distintos problemas, según las necesidades del usuario. Por este motivo se ha desarrollado el programa ArIS, el cual incluye los principales métodos de redes neuronales artificiales para realizar tareas de clasificación y predicción cuantitativa, necesarios para el estudio de problemas de interés como la predicción de la actividad anti-VIH de análogos del AZT, la optimización de formulaciones químicas o el reconocimiento estructural de grandes sistemas moleculares. / Molecular modelling of interesting systems for medicinal chemistry and drug design highly depends on availability of synthetic results. Since combinatorial chemistry was incorporated into the synthetic scheme, the role of computational chemistry has changed: the structural diversity of candidates to be synthesized requires the introduction of computational methods which are able to screen large virtual libraries. Answering to this requirement, chemoinformatics offers many kinds of different methods for predicting biological activities and molecular properties. One of the most relevant techniques among them is Quantitative Structure-Activity Relationships (QSAR), which can be used to establish prediction models for both, pharmacological and biomedical sectors. The use of non- linear QSAR methods has been evaluated in photodynamic therapy of cancer, one of the research areas of the Grup d’Enginyeria Molecular (GEM) at IQS. Molecular design of photosensitizers can be performed by computational studies of their physicochemical properties (absorption spectra or hydrophobicity, for example) and subcellular localization, which becomes a key factor in the efficacy of the overall process. Furthermore, artificial neural networks are nowadays rated as one of the very best methods for establishing non-linear QSAR models. Developing software that includes all these methods would be certainly interesting. Implemented algorithms should be versatile and easily adaptable for their use in any problems. We have developed ArIS software, which includes the most important methods of artificial neural networks for classification and quantitative prediction. ArIS has been used to predict anti-HIV activity of AZT-analogues, for optimization of chemical formulations and for structural recognition in large molecular systems, among others.

En el sector de la promoció construcció, i en especial, en el subsector de la promoció construcció d'habitatges, l'empresari ha de tenir un bon coneixement de les variables d'entorn ja que la consideració de les mateixes seran fonamentals a l'hora de prendre decisions sobre planificació estratègica. En l'actualitat vivim una fase de canvis socioeconòmics que dificulten la previsió del comportament futur de les variables d'entorn. Per tant, el subjecte decisor es troba en un ambient d'incertesa que s'aguditza per la majoritària presència de factors qualitatius difícils de quantificar. Llavors, l'empresari promotor constructor haurà de recórrer a tècniques operatives de gestió que tinguin present aquesta situació i això serà possible a partir de les eines que ens ofereix la lògica borrosa. Aquesta tesi s'ha estructurat en tres parts: En la primera part, exposem les característiques específiques i l'evolució del sector. En la segona part, expliquem la metodologia i, en la tercera part, exposem diverses aplicacions de la metodologia borrosa per l'establiment de noves estratègies de gestió aplicades al sector objecte d'estudi. / In the management of promoters builders business area, specially in the subarea of house promotion building, to gather a large information of the variables around becomes essential. Their being taken into consideration is fundamental when considering decisions upon strategic foresight. Today, socioeconomic changes make difficult to forecast the future behaviour of the variables around and the uncertain atmosphere intensifies due to the majority presence of qualitative factors hard to quantify. In the management of promoters builders business it turns as necessary to apply to operative techniques that take into account the present situation. This is possible by using the tools fuzzy logics grant us. The first part of this thesis gives specific characteristics of the area and its evolution. Methodology is explained in the second part and in the third part different practical examples of fuzzy logic methodology are presented to establish new management strategies applied to this field.