Quantum theory beyond the physical : information in context

Measures and theories of information abound, but there are few formalised methods for treating the contextuality that can manifest in different information systems. Quantum theory provides one possible formalism for treating information in context. This paper introduces a quantum-like model of the human mental lexicon, and shows one set of recent experimental data suggesting that concept combinations can indeed behave non-separably. There is some reason to believe that the human mental lexicon displays entanglement.

The ghrelin receptor isoforms (GHS-R1a and GHS-R1b) and GPR39 : an investigation into receptor dimerisation

Prostate cancer is the second most common cause of cancer-related deaths in Western males. Current diagnostic, prognostic and treatment approaches are not ideal and advanced metastatic prostate cancer is incurable. There is an urgent need for improved adjunctive therapies and markers for this disease. GPCRs are likely to play a significant role in the initiation and progression of prostate cancer. Over the last decade, it has emerged that G protein coupled receptors (GPCRs) are likely to function as homodimers and heterodimers. Heterodimerisation between GPCRs can result in the formation of novel pharmacological receptors with altered functional outcomes, and a number of GPCR heterodimers have been implicated in the pathogenesis of human disease. Importantly, novel GPCR heterodimers represent potential new targets for the development of more specific therapeutic drugs. Ghrelin is a 28 amino acid peptide hormone which has a unique n-octanoic acid post-translational modification. Ghrelin has a number of important physiological roles, including roles in appetite regulation and the stimulation of growth hormone release. The ghrelin receptor is the growth hormone secretagogue receptor type 1a, GHS-R1a, a seven transmembrane domain GPCR, and GHS-R1b is a C-terminally truncated isoform of the ghrelin receptor, consisting of five transmembrane domains. Growing evidence suggests that ghrelin and the ghrelin receptor isoforms, GHS-R1a and GHS-R1b, may have a role in the progression of a number of cancers, including prostate cancer. Previous studies by our research group have shown that the truncated ghrelin receptor isoform, GHS-R1b, is not expressed in normal prostate, however, it is expressed in prostate cancer. The altered expression of this truncated isoform may reflect a difference between a normal and cancerous state. A number of mutant GPCRs have been shown to regulate the function of their corresponding wild-type receptors. Therefore, we investigated the potential role of interactions between GHS-R1a and GHS-R1b, which are co-expressed in prostate cancer and aimed to investigate the function of this potentially new pharmacological receptor. In 2005, obestatin, a 23 amino acid C-terminally amidated peptide derived from preproghrelin was identified and was described as opposing the stimulating effects of ghrelin on appetite and food intake. GPR39, an orphan GPCR which is closely related to the ghrelin receptor, was identified as the endogenous receptor for obestatin. Recently, however, the ability of obestatin to oppose the effects of ghrelin on appetite and food intake has been questioned, and furthermore, it appears that GPR39 may in fact not be the obestatin receptor. The role of GPR39 in the prostate is of interest, however, as it is a zinc receptor. Zinc has a unique role in the biology of the prostate, where it is normally accumulated at high levels, and zinc accumulation is altered in the development of prostate malignancy. Ghrelin and zinc have important roles in prostate cancer and dimerisation of their receptors may have novel roles in malignant prostate cells. The aim of the current study, therefore, was to demonstrate the formation of GHS-R1a/GHS-R1b and GHS-R1a/GPR39 heterodimers and to investigate potential functions of these heterodimers in prostate cancer cell lines. To demonstrate dimerisation we first employed a classical co-immunoprecipitation technique. Using cells co-overexpressing FLAG- and Myc- tagged GHS-R1a, GHS-R1b and GPR39, we were able to co-immunoprecipitate these receptors. Significantly, however, the receptors formed high molecular weight aggregates. A number of questions have been raised over the propensity of GPCRs to aggregate during co-immunoprecipitation as a result of their hydrophobic nature and this may be misinterpreted as receptor dimerisation. As we observed significant receptor aggregation in this study, we used additional methods to confirm the specificity of these putative GPCR interactions. We used two different resonance energy transfer (RET) methods; bioluminescence resonance energy transfer (BRET) and fluorescence resonance energy transfer (FRET), to investigate interactions between the ghrelin receptor isoforms and GPR39. RET is the transfer of energy from a donor fluorophore to an acceptor fluorophore when they are in close proximity, and RET methods are, therefore, applicable to the observation of specific protein-protein interactions. Extensive studies using the second generation bioluminescence resonance energy transfer (BRET2) technology were performed, however, a number of technical limitations were observed. The substrate used during BRET2 studies, coelenterazine 400a, has a low quantum yield and rapid signal decay. This study highlighted the requirement for the expression of donor and acceptor tagged receptors at high levels so that a BRET ratio can be determined. After performing a number of BRET2 experimental controls, our BRET2 data did not fit the predicted results for a specific interaction between these receptors. The interactions that we observed may in fact represent ‘bystander BRET’ resulting from high levels of expression, forcing the donor and acceptor into close proximity. Our FRET studies employed two different FRET techniques, acceptor photobleaching FRET and sensitised emission FRET measured by flow cytometry. We were unable to observe any significant FRET, or FRET values that were likely to result from specific receptor dimerisation between GHS-R1a, GHS-R1b and GPR39. While we were unable to conclusively demonstrate direct dimerisation between GHS-R1a, GHS-R1b and GPR39 using several methods, our findings do not exclude the possibility that these receptors interact. We aimed to investigate if co-expression of combinations of these receptors had functional effects in prostate cancers cells. It has previously been demonstrated that ghrelin stimulates cell proliferation in prostate cancer cell lines, through ERK1/2 activation, and GPR39 can stimulate ERK1/2 signalling in response to zinc treatments. Additionally, both GHS-R1a and GPR39 display a high level of constitutive signalling and these constitutively active receptors can attenuate apoptosis when overexpressed individually in some cell types. We, therefore, investigated ERK1/2 and AKT signalling and cell survival in prostate cancer the potential modulation of these functions by dimerisation between GHS-R1a, GHS-R1b and GPR39. Expression of these receptors in the PC-3 prostate cancer cell line, either alone or in combination, did not alter constitutive ERK1/2 or AKT signalling, basal apoptosis or tunicamycin-stimulated apoptosis, compared to controls. In summary, the potential interactions between the ghrelin receptor isoforms, GHS-R1a and GHS-R1b, and the related zinc receptor, GPR39, and the potential for functional outcomes in prostate cancer were investigated using a number of independent methods. We did not definitively demonstrate the formation of these dimers using a number of state of the art methods to directly demonstrate receptor-receptor interactions. We investigated a number of potential functions of GPR39 and GHS-R1a in the prostate and did not observe altered function in response to co-expression of these receptors. The technical questions raised by this study highlight the requirement for the application of extensive controls when using current methods for the demonstration of GPCR dimerisation. Similar findings in this field reflect the current controversy surrounding the investigation of GPCR dimerisation. Although GHS-R1a/GHS-R1b or GHS-R1a/GPR39 heterodimerisation was not clearly demonstrated, this study provides a basis for future investigations of these receptors in prostate cancer. Additionally, the results presented in this study and growing evidence in the literature highlight the requirement for an extensive understanding of the experimental method and the performance of a range of controls to avoid the spurious interpretation of data gained from artificial expression systems. The future development of more robust techniques for investigating GPCR dimerisation is clearly required and will enable us to elucidate whether GHS-R1a, GHS-R1b and GPR39 form physiologically relevant dimers.

Electron paramagnetic resonance, NIR studies on zoisite, clinozoisite and chrom-zoisite minerals

A zoisite group of mineral samples from different localities are used in the present study. An EPR study on powdered samples confirms the presence of Mn(II), Fe(III) and Cr(III) in the minerals. NIR studies confirm the presence of these ions in the minerals.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Discovering characteristics of stochastic collections of process models

Process models in organizational collections are typically modeled by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models, in the form of Petri nets, from small samples of a collection for accurate estimations of the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrary-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections.We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Shrinking neighborhood evolution : a novel stochastic algorithm for numerical optimization

Focusing on the conditions that an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. The principle is discussed in the theoretical model of DSZ algorithm, from which we present the practical model of DSZ algorithm. Practical model efficiency is demonstrated by the comparison with the similar algorithms such as Enhanced simulated annealing (ESA), Monte Carlo simulated annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategies how to optimize the high-dimensional unconstrained problem using DSZ algorithm.

A Stochastic View of Optimal Regret through Minimax Duality

We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over joint distributions of the adversary's action sequence, of the difference between a sum of minimal expected losses and the minimal empirical loss. We show that the optimal regret has a natural geometric interpretation, since it can be viewed as the gap in Jensen's inequality for a concave functional--the minimizer over the player's actions of expected loss--defined on a set of probability distributions. We use this expression to obtain upper and lower bounds on the regret of an optimal strategy for a variety of online learning problems. Our method provides upper bounds without the need to construct a learning algorithm; the lower bounds provide explicit optimal strategies for the adversary. Peter L. Bartlett, Alexander Rakhlin

Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations

Maximum-likelihood estimates of the parameters of stochastic differential equations are consistent and asymptotically efficient, but unfortunately difficult to obtain if a closed-form expression for the transitional probability density function of the process is not available. As a result, a large number of competing estimation procedures have been proposed. This article provides a critical evaluation of the various estimation techniques. Special attention is given to the ease of implementation and comparative performance of the procedures when estimating the parameters of the Cox–Ingersoll–Ross and Ornstein–Uhlenbeck equations respectively.