Electronic energy transfer processes and charge carrier transport in pi-conjugated polymers

Conjugated polymers have attracted tremendous academical and industrial research interest over the past decades due to the appealing advantages that organic / polymeric materials offer for electronic applications and devices such as organic light emitting diodes (OLED), organic field effect transistors (OFET), organic solar cells (OSC), photodiodes and plastic lasers. The optimization of organic materials for applications in optoelectronic devices requires detailed knowledge of their photophysical properties, for instance energy levels of excited singlet and triplet states, excited state decay mechanisms and charge carrier mobilities. In the present work a variety of different conjugated (co)polymers, mainly polyspirobifluorene- and polyfluorene-type materials, was investigated using time-resolved photoluminescence spectroscopy in the picosecond to second time domain to study their elementary photophysical properties and to get a deeper insight into structure-property relationships. The experiments cover fluorescence spectroscopy using Streak Camera techniques as well as time-delayed gated detection techniques for the investigation of delayed fluorescence and phosphorescence. All measurements were performed on the solid state, i.e. thin polymer films and on diluted solutions. Starting from the elementary photophysical properties of conjugated polymers the experiments were extended to studies of singlet and triplet energy transfer processes in polymer blends, polymer-triplet emitter blends and copolymers. The phenomenon of photonenergy upconversion was investigated in blue light-emitting polymer matrices doped with metallated porphyrin derivatives supposing an bimolecular annihilation upconversion mechanism which could be experimentally verified on a series of copolymers. This mechanism allows for more efficient photonenergy upconversion than previously reported for polyfluorene derivatives. In addition to the above described spectroscopical experiments, amplified spontaneous emission (ASE) in thin film polymer waveguides was studied employing a fully-arylated poly(indenofluorene) as the gain medium. It was found that the material exhibits a very low threshold value for amplification of blue light combined with an excellent oxidative stability, which makes it interesting as active material for organic solid state lasers. Apart from spectroscopical experiments, transient photocurrent measurements on conjugated polymers were performed as well to elucidate the charge carrier mobility in the solid state, which is an important material parameter for device applications. A modified time-of-flight (TOF) technique using a charge carrier generation layer allowed to study hole transport in a series of spirobifluorene copolymers to unravel the structure-mobility relationship by comparison with the homopolymer. Not only the charge carrier mobility could be determined for the series of polymers but also field- and temperature-dependent measurements analyzed in the framework of the Gaussian disorder model showed that results coincide very well with the predictions of the model. Thus, the validity of the disorder concept for charge carrier transport in amorphous glassy materials could be verified for the investigated series of copolymers.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Gaussian phase-space representations for fermions

We introduce a positive phase-space representation for fermions, using the most general possible multimode Gaussian operator basis. The representation generalizes previous bosonic quantum phase-space methods to Fermi systems. We derive equivalences between quantum and stochastic moments, as well as operator correspondences that map quantum operator evolution onto stochastic processes in phase space. The representation thus enables first-principles quantum dynamical or equilibrium calculations in many-body Fermi systems. Potential applications are to strongly interacting and correlated Fermi gases, including coherent behavior in open systems and nanostructures described by master equations. Examples of an ideal gas and the Hubbard model are given, as well as a generic open system, in order to illustrate these ideas.

A scalarization technique for computing the power and exponential moments of gaussian random matrices

We consider the problems of computing the power and exponential moments EXs and EetX of square Gaussian random matrices X=A+BWC for positive integer s and real t, where W is a standard normal random vector and A, B, C are appropriately dimensioned constant matrices. We solve the problems by a matrix product scalarization technique and interpret the solutions in system-theoretic terms. The results of the paper are applicable to Bayesian prediction in multivariate autoregressive time series and mean-reverting diffusion processes.

Derivations of variational gaussian process approximation framework

Recently, within the VISDEM project (EPSRC funded EP/C005848/1), a novel variational approximation framework has been developed for inference in partially observed, continuous space-time, diffusion processes. In this technical report all the derivations of the variational framework, from the initial work, are provided in detail to help the reader better understand the framework and its assumptions.

A basis function approach to Bayesian inference in diffusion processes

In this paper, we present a framework for Bayesian inference in continuous-time diffusion processes. The new method is directly related to the recently proposed variational Gaussian Process approximation (VGPA) approach to Bayesian smoothing of partially observed diffusions. By adopting a basis function expansion (BF-VGPA), both the time-dependent control parameters of the approximate GP process and its moment equations are projected onto a lower-dimensional subspace. This allows us both to reduce the computational complexity and to eliminate the time discretisation used in the previous algorithm. The new algorithm is tested on an Ornstein-Uhlenbeck process. Our preliminary results show that BF-VGPA algorithm provides a reasonably accurate state estimation using a small number of basis functions.

Gaussian process approximations of stochastic differential equations

Stochastic differential equations arise naturally in a range of contexts, from financial to environmental modeling. Current solution methods are limited in their representation of the posterior process in the presence of data. In this work, we present a novel Gaussian process approximation to the posterior measure over paths for a general class of stochastic differential equations in the presence of observations. The method is applied to two simple problems: the Ornstein-Uhlenbeck process, of which the exact solution is known and can be compared to, and the double-well system, for which standard approaches such as the ensemble Kalman smoother fail to provide a satisfactory result. Experiments show that our variational approximation is viable and that the results are very promising as the variational approximate solution outperforms standard Gaussian process regression for non-Gaussian Markov processes.

Optimal design for correlated processes with input-dependent noise

Optimal design for parameter estimation in Gaussian process regression models with input-dependent noise is examined. The motivation stems from the area of computer experiments, where computationally demanding simulators are approximated using Gaussian process emulators to act as statistical surrogates. In the case of stochastic simulators, which produce a random output for a given set of model inputs, repeated evaluations are useful, supporting the use of replicate observations in the experimental design. The findings are also applicable to the wider context of experimental design for Gaussian process regression and kriging. Designs are proposed with the aim of minimising the variance of the Gaussian process parameter estimates. A heteroscedastic Gaussian process model is presented which allows for an experimental design technique based on an extension of Fisher information to heteroscedastic models. It is empirically shown that the error of the approximation of the parameter variance by the inverse of the Fisher information is reduced as the number of replicated points is increased. Through a series of simulation experiments on both synthetic data and a systems biology stochastic simulator, optimal designs with replicate observations are shown to outperform space-filling designs both with and without replicate observations. Guidance is provided on best practice for optimal experimental design for stochastic response models. © 2013 Elsevier Inc. All rights reserved.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Geochemistry Of Acid Mine Drainage From A Coal Mining Area And Processes Controlling Metal Attenuation In Stream Waters, Southern Brazil.

Acid drainage influence on the water and sediment quality was investigated in a coal mining area (southern Brazil). Mine drainage showed pH between 3.2 and 4.6 and elevated concentrations of sulfate, As and metals, of which, Fe, Mn and Zn exceeded the limits for the emission of effluents stated in the Brazilian legislation. Arsenic also exceeded the limit, but only slightly. Groundwater monitoring wells from active mines and tailings piles showed pH interval and chemical concentrations similar to those of mine drainage. However, the river and ground water samples of municipal public water supplies revealed a pH range from 7.2 to 7.5 and low chemical concentrations, although Cd concentration slightly exceeded the limit adopted by Brazilian legislation for groundwater. In general, surface waters showed large pH range (6 to 10.8), and changes caused by acid drainage in the chemical composition of these waters were not very significant. Locally, acid drainage seemed to have dissolved carbonate rocks present in the local stratigraphic sequence, attenuating the dispersion of metals and As. Stream sediments presented anomalies of these elements, which were strongly dependent on the proximity of tailings piles and abandoned mines. We found that precipitation processes in sediments and the dilution of dissolved phases were responsible for the attenuation of the concentrations of the metals and As in the acid drainage and river water mixing zone. In general, a larger influence of mining activities on the chemical composition of the surface waters and sediments was observed when enrichment factors in relation to regional background levels were used.

Construction Of A Manual Of Work Processes And Techniques From Centro De Dispensação De Medicamentos De Alto Custo (cedmac), Hospital De Clínicas, Unicamp.

The Centers for High Cost Medication (Centros de Medicação de Alto Custo, CEDMAC), Health Department, São Paulo were instituted by project in partnership with the Clinical Hospital of the Faculty of Medicine, USP, sponsored by the Foundation for Research Support of the State of São Paulo (Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP) aimed at the formation of a statewide network for comprehensive care of patients referred for use of immunobiological agents in rheumatological diseases. The CEDMAC of Hospital de Clínicas, Universidade Estadual de Campinas (HC-Unicamp), implemented by the Division of Rheumatology, Faculty of Medical Sciences, identified the need for standardization of the multidisciplinary team conducts, in face of the specificity of care conducts, verifying the importance of describing, in manual format, their operational and technical processes. The aim of this study is to present the methodology applied to the elaboration of the CEDMAC/HC-Unicamp Manual as an institutional tool, with the aim of offering the best assistance and administrative quality. In the methodology for preparing the manuals at HC-Unicamp since 2008, the premise was to obtain a document that is participatory, multidisciplinary, focused on work processes integrated with institutional rules, with objective and didactic descriptions, in a standardized format and with electronic dissemination. The CEDMAC/HC-Unicamp Manual was elaborated in 10 months, with involvement of the entire multidisciplinary team, with 19 chapters on work processes and techniques, in addition to those concerning the organizational structure and its annexes. Published in the electronic portal of HC Manuals in July 2012 as an e-Book (ISBN 978-85-63274-17-5), the manual has been a valuable instrument in guiding professionals in healthcare, teaching and research activities.

Agrin And Perlecan Mediate Tumorigenic Processes In Oral Squamous Cell Carcinoma.

Oral squamous cell carcinoma is the most common type of cancer in the oral cavity, representing more than 90% of all oral cancers. The characterization of altered molecules in oral cancer is essential to understand molecular mechanisms underlying tumor progression as well as to contribute to cancer biomarker and therapeutic target discovery. Proteoglycans are key molecular effectors of cell surface and pericellular microenvironments, performing multiple functions in cancer. Two of the major basement membrane proteoglycans, agrin and perlecan, were investigated in this study regarding their role in oral cancer. Using real time quantitative PCR (qRT-PCR), we showed that agrin and perlecan are highly expressed in oral squamous cell carcinoma. Interestingly, cell lines originated from distinct sites showed different expression of agrin and perlecan. Enzymatically targeting chondroitin sulfate modification by chondroitinase, oral squamous carcinoma cell line had a reduced ability to adhere to extracellular matrix proteins and increased sensibility to cisplatin. Additionally, knockdown of agrin and perlecan promoted a decrease on cell migration and adhesion, and on resistance of cells to cisplatin. Our study showed, for the first time, a negative regulation on oral cancer-associated events by either targeting chondroitin sulfate content or agrin and perlecan levels.

Non-gaussian Spatial Correlations Dramatically Weaken Localization.

We perform variational studies of the interaction-localization problem to describe the interaction-induced renormalizations of the effective (screened) random potential seen by quasiparticles. Here we present results of careful finite-size scaling studies for the conductance of disordered Hubbard chains at half-filling and zero temperature. While our results indicate that quasiparticle wave functions remain exponentially localized even in the presence of moderate to strong repulsive interactions, we show that interactions produce a strong decrease of the characteristic conductance scale g^{*} signaling the crossover to strong localization. This effect, which cannot be captured by a simple renormalization of the disorder strength, instead reflects a peculiar non-Gaussian form of the spatial correlations of the screened disordered potential, a hitherto neglected mechanism to dramatically reduce the impact of Anderson localization (interference) effects.