Síntese, caracterização e aplicação de hidrogéis nanoestruturados contendo nanoargila para melhorar a germinação e qualidade de muda de hortaliça

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Synthese und Charakterisierung einer Hydrogelmatrix für die Multianalyt-Sensorik

Gegenstand und Ziel der vorliegenden Arbeit war die Synthese und Charakterisierung einer Hydrogelmatrix, welche für die Herstellung eines vielseitig verwendbaren Sensors, der mehrere Analyten (Proteine, DNA etc.) in hoher Verdünnung (c0 < 50 fM) aus kleinsten Probenmengen (Volumina <20 nl) schnell (t < 1 min) und parallel nachweisen kann, Verwendung finden soll. Der Fokus der Arbeit lag dabei in der Synthese und Charakterisierung von Copolymeren als Hydrogelmatrix, welche geeignetes temperaturabhängiges Verhalten zeigen. Die Copolymere wurden in eine dreidimensionale Netzwerkstruktur überführt und auf einer Goldoberfläche kovalent angebunden, um Delamination zu vermeiden und die Untersuchung mittels Oberflächenplasmonenresonanz-Spektroskopie (SPR) und Optischer Wellenleiter-Spektroskopie (OWS) zu erlauben. Weiterhin wurde das temperaturabhängige Verhalten der Polymernetzwerke in Wasser mittels optischen Messungen (SPR/OWS) untersucht, um Erkenntnisse über die Quell- und Kollabiereigenschaften des Hydrogels zu gewinnen. Um temperaturschaltbare Hydrogele herzustellen, wurden auf N-Isopropylacrylamid (NIPAAm) basierende Polymere synthetisiert. Es wurde sowohl die für Hydrogele übliche Methode der freien radikalischen Vernetzungspolymerisation in Wasser, wie eine neue, auf Benzophenoneinheiten basierende Syntheseroute, welche die freie radikalische Polymerisation in organischem Medium nutzt, verwendet. Die synthetisierten Polymere sind Copolymere aus N‑Isopropylacrylamid (NIPAAm) und 4-Methacryloyloxybenzophenon (MABP). NIPAAm ist dabei für das temperaturschaltbare Verhalten der Gele verantwortlich und MABP dient als Photovernetzer. Weitere Copolymere, die neben den genannten Monomeren noch andere Funktionen, wie z.B. ionische Gruppen oder Aktivesterfunktionen enthalten, wurden ebenfalls synthetisiert. Das temperaturabhängige Quellverhalten in Bezug auf die chemische Zusammensetzung wurde mit der Oberflächenplasmonenresonanz-Spektroskopie (SPR) und Optischen Wellenleiter-Spektroskopie (OWS) untersucht. Es zeigte sich, dass die Anwesenheit von Salz im Hydrogel (Natriumacrylat als Monomer, P4S) Inhomogenität, in Form eines Brechungsindexgradienten senkrecht zur Substratoberfläche, hervorruft. Dies ist nicht der Fall, wenn statt des Salzes die Säure (Methacrylsäure als Monomer, P4A) verwendet wird. Durch die Inhomogenität lassen sich die Filme mit dem Natriummethacrylat nicht mehr mit dem, üblicherweise zur Auswertung genutzten, Kastenmodell beschreiben. Die Anwendung der Wentzel-Kramers-Brillouin-Näherung (WKB) auf die Messdaten führt hingegen zu dem gewünschten Ergebnis. Man findet ein kastenähnliches Brechungsindexprofil für das Hydrogel mit der Säure (P4A) und ein Gradientenprofil für das Gel mit dem Salz (P4S). Letzteres ist nicht nur hydrophiler und insgesamt stärker gequollen, sondern ragt auch weiter in die überstehende Wasserphase hinein. Anhand eines säurehaltigen Hydrogels (P8A) konnte der quellungshemmende Einfluss von hohen Salzkonzentrationen gezeigt werden. Weiterhin wurde während des Quellvorgangs eine gewisse Anisotropie gefunden, die aber im vollständig gequollenen und vollständig kollabierten Zustand nicht mehr vorliegt. Anhand eines Hydrogels ohne ionisierbare Gruppen (P9) wurde die Reversibilität des Quell- und Kollabiervorgangs gezeigt. Bei einem Vergleich zwischen einem säurehaltigen Hydrogel (P8A, Quellgrad von 7,3) und einem ohne ionisierbare Gruppen (P9, Quellgrad von 6,1), hat die Anwesenheit der 8 mol% Säuregruppen eine leichte Verstärkung der Quellung um den Faktor 1,2 bewirkt. Rasterkraftmikroskopische Untersuchungen (AFM) an diesen beiden Hydrogelen im getrockneten Zustand, haben gezeigt, dass nach dem Quellen, Kollabieren und Trocknen bei beiden Gelen Porenstrukturen sehr unterschiedlicher Ausmaße vorliegen.

Alpha, Omega end group functionalization of RAFT polymers based on pentafluorophenyl esters and methane thiosulfonates

While polymers with different functional groups along the backbone have intensively been investigated, there is still a challenge in orthogonal functionalization of the end groups. Such well-defined systems are interesting for the preparation of multiblock (co) polymers or polymer networks, for bio-conjugation or as model systems for examining the end group separation of isolated polymer chains. rnHere, Reversible Addition Fragmentation Chain Transfer (RAFT) polymerization was employed as method to investigate improved techniques for an a, w end group functionalization. RAFT produces polymers terminated in an R group and a dithioester-Z group, where R and Z stem from a suitable chain transfer agent (CTA). rnFor alpha end group functionalization, a CTA with an activated pentafluorophenyl (PFP) ester R group was designed and used for the polymerization of various methacrylate monomers, N-isopropylacrylamide and styrene yielding polymers with a PFP ester as a end group. This allowed the introduction of inert propyl amides, of light responsive diazo compounds, of the dyes NBD, Texas Red, or Oregon Green, of the hormone thyroxin and allowed the formation of multiblocks or peptide conjugates. rnFor w end group functionalization, problems of other techniques were overcome through an aminolysis of the dithioester in the presence of a functional methane thiosulfonate (MTS), yielding functional disulfides. These disulfides were stable under ambient conditions and could be cleaved on demand. Using MTS chemistry, terminal methyl disulfides (enabling self-assembly on planar gold surfaces and ligand substitution on gold and semiconductor nanoparticles), butynyl disulfide end groups (allowing the “clicking” of the polymers onto azide functionalized surfaces and the selective removal through reduction), the bio-target biotin, and the fluorescent dye Texas Red were introduced into polymers. rnThe alpha PFP amidation could be performed under mild conditions, without substantial loss of DTE. This way, a step-wise synthesis produced polymers with two functional end groups in very high yields. rnAs examples, polymers with an anchor group for both gold nanoparticles (AuNP) and CdSe / ZnS semi-conductor nanoparticles (QD) and with a fluorescent dye end group were synthesized. They allowed a NP decoration and enabled an energy transfer from QD to dye or from dye to AuNP. Water-soluble polymers were prepared with two different bio-target end groups, each capable of selectively recognizing and binding a certain protein. The immobilization of protein-polymer-protein layers on planar gold surfaces was monitored by surface plasmon resonance.Introducing two different fluorescent dye end groups enabled an energy transfer between the end groups of isolated polymer chains and created the possibility to monitor the behavior of single polymer chains during a chain collapse. rnThe versatility of the synthetic technique is very promising for applications beyond this work.

Synthese und Charakterisierung biologischer Hybridpolymere zur gentechnologischen Anwendung

KurzfassungrnrnZiel der vorliegenden Arbeit war es eine gezielte, hochspezifische Inhibierung der Proteinbiosynthese zu erreichen. Dies kann durch eine Blockierung des mRNA-Strangs durch komplementäre DNA/RNA-Stränge (ähnlich zur Antisense-Methode) oder durch die Hydrolyse des mRNA-Strangs mit Hilfe spezieller Enzyme (RNasen) realisiert werden. Da jedoch beide Methoden nicht zu zufriedenstellenden Ergebnissen führen, wäre deshalb eine Kombination aus beiden Methoden ideal, welche in einer spezifischen, gezielten und permanenten Ausschaltung der Proteinbiosynthese resultieren würde. Um dieses Ziel zu verwirklichen, ist es nötig, ein Molekül zu synthetisieren, welches in der Lage ist selektiv an einer spezifischen Position an den RNA-Strang zu hybridisieren und anschließend den RNA-Strang an dieser zu hydrolysieren. Der große Vorteil dieses Konzepts liegt darin, dass die DNA-Sequenz für die Hybridisierung an die entsprechende RNA maßgeschneidert hergestellt werden kann und somit jede RNA gezielt angesteuert werden kann, was letztendlich zu einer spezifischen Inhibierung der korrespondierenden Proteinbiosynthese führen soll.rnDurch die Verwendung und Optimierung der Nativen Chemischen Ligation (NCL) als Konjugationsmethode konnten zwei Biomakromoleküle in Form einer 46-basenlangen DNA (komplementär zum RNA-Strang) und einer 31-aminosäurelangen RNase kovalent verknüpft werden. Durch unterschiedliche chemische und molekularbiologische Analysemethoden, wie PAGE, GPC, CE, MALDI-ToF-MS etc., war es zudem möglich, die erfolgreiche Synthese dieses biologischen Hybridpolymers als monodisperses, reines Produkt zu bestätigen. rnDie Synthese des ca. 800-basenlangen RNA-Strangs, der als Modell-Matrize für die selektive und spezifische Degradierung durch das DNA-RNase-Konjugat dienen sollte, konnte unter Zuhilfenahme gentechnologischer Standard-Methoden erfolgreich bewerkstelligt werden. Weiterhin konnte durch die Verwendung der radioaktiven cDNA-Synthese gezeigt werden, dass das DNA-RNase-Konjugat an die gewünschte Stelle des RNA-Strangs hybridisiert. Die Identifizierung einer anschließenden spezifischen Hydrolyse des RNA-Strangs durch die an den DNA-Strang angeknüpfte RNase war aufgrund der geringen katalytischen Aktivität des Enzyms bisher allerdings nicht möglich.rn

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Ocular biomaterials and the anterior eye

Hydrogels, water swollen polymer matrices, have been utilised in many biomedical applications, as there is the potential to manipulate the properties for a given application by changing the chemical structure of the constituent monomers The eye provides an excellent site to examne the interaction between a synthetic material and a complex biological fluid without invasive surgery. There is a need for the development of new synthetic hydrogels for use in the anterior eye, Three applications of hydrogels in the eye were considered in this thesis. For some patients, the only hope of any visual improvement lies in the use of an artificial cornea, or keratoprosthesis, Preliminary investigations of a series of simple homogeneous hydrogel copolymers revealed that the mechanical properties required to withstand surgery and in eye stresses, were not achieved This lead to work on the development of semi-interpenetrating polymer networks based on the aforementioned copolymers, Manufacture of the device and cell response were also studied. Lasers have been employed in ocular surgery to correct refractive defects. If an irregular surface is ablated, an irregular surface is obtained. A hydrogel system was investigated that could be applied to the eye prior to ablation to create a smooth surface. Factors that may influence ablation rate were explored, Soft contact lenses can be used as a probe to study the interaction between synthetic materials and the biological constituents of tears. This has lead to the development of many sensitive analytical techniques for protein and lipid deposition, one of which is fluorescence spectrophotometry. Various commercially available soft contact lenses were worn for different periods of time and then analysed for protein and lipid deposition using fluorescence spectrophotometry, The influence of water content, degree of ionicity and the lens material on the level and type of deposition was investigated.

Novel imprinted polymers as artificial enzymes

Derivatives of L-histidine were investigated as suitable models for the Asp-His couple found in the catalytic triad of serine proteases. A combination of molecular dynamics and IH NMR spectroscopy suggested that the most populous conformations of N-acetyl-L-histidine and the N-acetyl-L-histidine anion were predominated by those in which the carboxylate group was gauche to the imidazole ring overcoming steric and electrostatic repulsion, suggesting there is an interaction between the carboxylate group and the imidazole ring. Kinetic studies, using imidazole, N-acetyl-L-histidine and the N-acetyl-L-histidine anion showed that in a DMSO/H20 9: 1 v/v solution, the N-acetyl-L-histidine anion catalysed the hydrolysis of p-nitrophenyl acetate at a greater rate than using either imidazole or N-acetyl-L-histidine as catalyst. This indicates that the carboxylate group affects the nucleophilicity of the unprotonated imidazole ring. 31P MAS NMR spectroscopy was investigated as a new technique for the study of the template molecule environment within the polymer networks. It was found that it was possible to distinguish between template associated with the polymer and that which was precipitated onto the surface, though it was not possible to distinguish between polymer within imprinted cavities and that which was not. Attempts to study the effect of the carboxylate group/imidazole ring interaction in the imprinted cavity of a molecularly imprinted polymer network were hindered by the method used to follow the reaction. It was found though that in a pH 8.0 buffered solution the presence of imprinted cavities increased the rate of reaction for those polymers derived from L-histidine. Some preliminary investigations into the design and synthesis of an MIP which would catalyse the oxy-Cope rearrangement were carried out but the results were inconclusive.

Novel hydrogels for keratoprosthesis

Hydrogels may be described as cross~linked hydrophilic polymers that swell but do not dissolve in water. They have been utilised in many biomedical applications, as there is the potential to manipulate the properties for a given application by changing the chemical structure of the constituent monomers. This project is focused on the development of novel hydrogels for keratoprosthesis (KPro). The most commonly used KPro model consists of a tansparent central stem witb a porous peripheral skirt. Clear poly (methyl methacrylate) (PMMA) core material used in the Strampelli KPros prosthesis has not been the cause of failure found in other core and skirt prostheses. However, epithelialization of this kind of solid, rigid optic material is clearly impossible. The approach to the development of a hydrogeJ for potential KPro use adopted in this work is to develop soft core material to mimic the properties of the natural cornea by incorporating some hydrophilic monomers such as N, N-dimethyacrylamide (NNSMA) N~vinyl pyrrolidone (NVP) and acryloylmorpholine (AMO) with methyl methactylate (MMA). Most of these materials have been used in other ophthalmic applications, such as contact lens. However, an unavoidable limitation of simple .MMA copolymers as conventional hydrogels is poor mechanical strength. The hydrogel for use in this application must be able to withstand the stresses involved during the surgical procedure involved with KPro surgery and the in situ stresses such as the deforming force of the eyelid during the blink cycle. Thus, semi-interpenetrating polymer networks (SIPNs) based on ester polyurethane, AMO, NVP and NNDMA were examined in this work and were found to have much improved mechanical properties at water contents between 40% and 70%. Polyethylene glycol monomethacrylate (PEG MA) was successfully incorporated in order to modulate protein deposition and cell adhesion. Porous peripheral skirts were fabricated using different types of porosigen. The water content mechanical properties, surface properties and cell response of these various materials have been investigated in this thesis. These studies demonstrated that simple hydrogel SIPNs which show isotropic mechanical behaviour, are not ideal KPro materials since they do not mimic the anisotropic behaviour of natural cornea. The final stage of the work has concentrated on the study of hydrogels reinforced with mesh materials. They offer a promising approach to making a hydrogel that is very flexible but strong under tension, thereby having mechanical properties closer to the natural cornea than has been previously possible.

Novel hydrogel polymers

Hydrogels may be conveniently described as hydrophilic polymers that are swollen by, but do not dissolve in water. In this work a series of copolymer hydrogels and semi-interpenetrating polymer networks based on the monomers 2-hydroxyethyl methacrylate, N-vinyl pyrrolidone and N'N' dimethyl acrylamide, together with some less hydrophilic hydroxyalkyl acrylates and methacrylates have been synthesised. Variations in structure and composition have been correlated both with the total equilibrium water content of the resultant hydrogel and with the more detailed water binding behaviour, as revealed by differential scanning calorimetry studies. The water binding characteristics of the hydrogels were found to be primarily a function of the water structuring groups present in gel. The water binding abilities of these groups were, however, modified by steric effects. The mechanical properties of the hydrogels were also investigated. These were found to be dependent on both the polymer composition and the amount and nature of the water present in the gels. In biological systems, composite formation provides a means of producing strong, high water content materials. As an analogy with these systems hydrogel composites were prepared. In an initial study of these materials the water binding and mechanical properties of semi-interpenetrating polymer networks of N'N'dimethyl acrylamide with cellulosic type materials, with polyurethanes and with ester containing polymers were examined. A preliminary investigation of surface properties of both the copolymers and semi-interpenetrating polymer networks has been completed, using both contact angle measurements and anchorage dependent fibroblast cells. Measurable differences in surface properties attributable to structural variations in the polymers were detected by droplet techniques in the dehydrated state. However, in the hydrated state these differences were masked by the water in the gels. The use of cells enabled the underlying differences to be probed and the nature of the water structuring group was again found to be the dominant factor.

The combination of smart hydrogels and fibre optic sensor technology for analyte detection

The subject of investigation of the present research is the use of smart hydrogels with fibre optic sensor technology. The aim was to develop a costeffective sensor platform for the detection of water in hydrocarbon media, and of dissolved inorganic analytes, namely potassium, calcium and aluminium. The fibre optic sensors in this work depend upon the use of hydrogels to either entrap chemotropic agents or to respond to external environmental changes, by changing their inherent properties, such as refractive index (RI). A review of current fibre optic technology for sensing outlined that the main principles utilised are either the measurement of signal loss or a change in wavelength of the light transmitted through the system. The signal loss principle relies on changing the conditions required for total internal reflection to occur. Hydrogels are cross-linked polymer networks that swell but do not dissolve in aqueous environments. Smart hydrogels are synthetic materials that exhibit additional properties to those inherent in their structure. In order to control the non-inherent properties, the hydrogels were fabricated with the addition of chemotropic agents. For the detection of water, hydrogels of low refractive index were synthesized using fluorinated monomers. Sulfonated monomers were used for their extreme hydrophilicity as a means of water sensing through an RI change. To enhance the sensing capability of the hydrogel, chemotropic agents, such as pH indicators and cobalt salts, were used. The system comprises of the smart hydrogel coated onto an exposed section of the fibre optic core, connected to the interrogation system measuring the difference in the signal. Information obtained was analysed using a purpose designed software. The developed sensor platform showed that an increase in the target species caused an increase in the signal lost from the sensor system, allowing for a detection of the target species. The system has potential applications in areas such as clinical point of care, water detection in fuels and the detection of dissolved ions in the water industry.

The “social” aspect of social-ecological systems: a critique of analytical frameworks and findings from a multisite study of coastal sustainability

We evaluate whether society can adequately be conceptualized as a component of social-ecological systems, given social theory and the current outputs of systems-based research. A mounting critique from the social sciences posits that resilience theory has undertheorized social entities with the concept of social-ecological systems. We trace the way that use of the term has evolved, relating to social science theory. Scientometic and network analysis provide a wide range of empirical data about the origin, growth, and use of this term in academic literature. A content analysis of papers in Ecology and Society demonstrates a marked emphasis in research on institutions, economic incentives, land use, population, social networks, and social learning. These findings are supported by a review of systems science in 18 coastal assessments. This reveals that a systems-based conceptualization tends to limit the kinds of social science research favoring quantitative couplings of social and ecological components and downplaying interpretive traditions of social research. However, the concept of social-ecological systems remains relevant because of the central insights concerning the dynamic coupling between humans and the environment, and its salient critique about the need for multidisciplinary approaches to solve real world problems, drawing on heuristic devices. The findings of this study should lead to more circumspection about whether a systems approach warrants such claims to comprehensiveness. Further methodological advances are required for interdisciplinarity. Yet there is evidence that systems approaches remain highly productive and useful for considering certain social components such as land use and hybrid ecological networks. We clarify advantages and restrictions of utilizing such a concept, and propose a reformulation that supports engagement with wider traditions of research in the social sciences.

Plásticos reforçados a base de tecidos híbridos: efeitos da anisotropia e geometria normativa na caracterização mecânica e da fratura

As most current studies, reinforced plastics have been, in recent years, a viable alternative in building structural elements of medium and large, since the lightness accompanied by high performance possible. The design of hybrid polymer composites (combination of different types of reinforcements) may enable structural applications thereof, facing the most severe service conditions. Within this class of composite materials, reinforced the underlying tissues hybrid high performance are taking space when your application requires high load bearing and high rigidity. The objective of this research work is to study the challenges in designing these fabrics bring these materials as to its mechanical characterization and fracture mechanisms involved. Some parameters associated with the process and / or form of hybridization stand out as influential factors in the final performance of the material such as the presence of anisotropy, so the fabric weave, the process of making the same, normative geometry of the specimens, among others. This sense, four laminates were developed based hybrid reinforcement fabrics involving AS4 carbon fiber, kevlar and glass 49-E as the matrix epoxy vinyl ester resin (DERAKANE 411-350). All laminates were formed each with four layers of reinforcements. Depending on the hybrid fabric, all the influencing factors mentioned above have been studied for laminates. All laminates were manufactured industrially used being the lamination process manual (hand-lay-up). All mechanical characterization and study of the mechanism of fracture (fracture mechanics) was developed for laminates subjected to uniaxial tensile test, bending in three and uniaxial compression. The analysis of fracture mechanisms were held involving the macroscopic, optical microscopy and scanning electron microscopy

Preparation and luminescent properties of inorganic polymer nano - Structured hybrid films doped with rare earth complexes

The inorganic/polymer hybrid films with good luminescent properties have been obtained by the sol - gel process via incorporating the polymer component doped with rare earth complexes. These films exhibit good toughness and transparency. Their fluorescence spectra and lifetimes indicate that they all have the characteristic luminescence of the central rare earth ions. The lifetimes of these films are longer than those of pure complexes. TEM have showed that the rare earth complexes are dispersed homogeneously in SiO2/PVB interpenetratiny networks, and the dispersed size is between 20 and 30 nn.

Hybrid system simulation of computer control applications over communication networks

Discrete event-driven simulations of digital communication networks have been used widely. However, it is difficult to use a network simulator to simulate a hybrid system in which some objects are not discrete event-driven but are continuous time-driven. A networked control system (NCS) is such an application, in which physical process dynamics are continuous by nature. We have designed and implemented a hybrid simulation environment which effectively integrates models of continuous-time plant processes and discrete-event communication networks by extending the open source network simulator NS-2. To do this a synchronisation mechanism was developed to connect a continuous plant simulation with a discrete network simulation. Furthermore, for evaluating co-design approaches in an NCS environment, a piggybacking method was adopted to allow the control period to be adjusted during simulations. The effectiveness of the technique is demonstrated through case studies which simulate a networked control scenario in which the communication and control system properties are defined explicitly.