Feedback control architecture & the bacterial chemotaxis network

Bacteria move towards favourable and away from toxic environments by changing their swimming pattern. This response is regulated by the chemotaxis signalling pathway, which has an important feature: it uses feedback to ‘reset’ (adapt) the bacterial sensing ability, which allows the bacteria to sense a range of background environmental changes. The role of this feedback has been studied extensively in the simple chemotaxis pathway of Escherichia coli. However it has been recently found that the majority of bacteria have multiple chemotaxis homologues of the E. coli proteins, resulting in more complex pathways. In this paper we investigate the configuration and role of feedback in Rhodobacter sphaeroides, a bacterium containing multiple homologues of the chemotaxis proteins found in E. coli. Multiple proteins could produce different possible feedback configurations, each having different chemotactic performance qualities and levels of robustness to variations and uncertainties in biological parameters and to intracellular noise. We develop four models corresponding to different feedback configurations. Using a series of carefully designed experiments we discriminate between these models and invalidate three of them. When these models are examined in terms of robustness to noise and parametric uncertainties, we find that the non-invalidated model is superior to the others. Moreover, it has a ‘cascade control’ feedback architecture which is used extensively in engineering to improve system performance, including robustness. Given that the majority of bacteria are known to have multiple chemotaxis pathways, in this paper we show that some feedback architectures allow them to have better performance than others. In particular, cascade control may be an important feature in achieving robust functionality in more complex signalling pathways and in improving their performance

Testing the assumptions of linear prediction analysis in normal vowels

This paper develops an improved surrogate data test to show experimental evidence, for all the simple vowels of US English, for both male and female speakers, that Gaussian linear prediction analysis, a ubiquitous technique in current speech technologies, cannot be used to extract all the dynamical structure of real speech time series. The test provides robust evidence undermining the validity of these linear techniques, supporting the assumptions of either dynamical nonlinearity and/or non-Gaussianity common to more recent, complex, efforts at dynamical modelling speech time series. However, an additional finding is that the classical assumptions cannot be ruled out entirely, and plausible evidence is given to explain the success of the linear Gaussian theory as a weak approximation to the true, nonlinear/non-Gaussian dynamics. This supports the use of appropriate hybrid linear/nonlinear/non-Gaussian modelling. With a calibrated calculation of statistic and particular choice of experimental protocol, some of the known systematic problems of the method of surrogate data testing are circumvented to obtain results to support the conclusions to a high level of significance

Simplified methods of assessing the impact of grid frequency dynamics upon generating plants

The frequency of the national electricity grid is affected by fluctuations in supply and demand, and so continually "judders" in an essentially unpredictable fashion around 50 Hz. At present such perturbations do not seemingly affect Nuclear Electric as most of their plant is run at more or less constant load, but they would like to be able to offer the national grid a mode of operation in which they "followed" the grid frequency: i.e., as the frequency rose above or fell below 50 Hz, the plant's output would be adjusted so as to tend to restore the frequency to 50 Hz. The aim is to maintain grid frequency within 0.2 Hz of its notional value. Such a mode of operation, however, would cause a certain amount of damage to plant components owing to the consequent continual changes in temperature and pressure within them. Nuclear Electric currently have complex computational models of how plants will behave under these conditions, which allows them to compute plant data (e.g., reactor temperatures) from given grid frequency data. One approach to damage assessment would require several years'-worth of real grid data to be fed into this model and the corresponding damage computed (via "cycle distributions" created by their damage experts). The results of this analysis would demonstrate one of three possibilities: the damage may be acceptable under all reasonable operating conditions; or it may be acceptable except in the case of an exceptional abrupt change in grid frequency (caused by power transmission line failure, or another power station suddenly going off-line, for instance), in which case some kind of backup supply (e.g., gas boilers) would be required; or it may simply be unacceptable. However, their current model runs in approximately real time, making it inappropriate for such a large amount of data: our problem was to suggest alternative approaches. Specifically, we were asked the following questions: - Can component damage be reliably estimated directly from cycle distributions of grid frequency? i.e., are there maps from frequency cycle distributions to plant parameter cycle distributions? - Can a simple model of plant dynamics be used to assess the potential for such maps? - What methods can be used to select representative samples of grid frequency behaviour? - What weightings should be applied to the selections? - Is it possible to construct a "cycle transform" (Fourier transform) which will capture the essential features of grid frequency and which can then be inverted to generate simulated frequency transients? We did not consider this last question, other than to say "probably not". We were supplied with data of the actual grid frequency measurements for the evening of 29/7/95, and the corresponding plant responses (obtained using Nuclear Electric's current computational model). A simplified nonlinear mathematical model of the plant was also provided. Two main approaches were considered: statistical prediction and analytical modelling via a reduction of the simplified plant model

An investigation into the synthesis, structural characterisation, thermal and polymorphic behaviour of organic crystalline materials

The organic solid state appears in a complex number of forms. The design, synthesis and application of solid state organic materials have a big impact upon society, e.g. pharmaceuticals. Traditionally, the process of selecting active pharmaceutical ingredients (APIs) was limited to free drug or accepted salt formulations. The cocrystallisation of APIs with a former molecule significantly increases the developmental options for APIs. Many pharmaceutical solids are prepared as polycrystalline materials in order to deliver favourable physical properties, i.e. solubility, bioavailability and stability. In such cases, the development and application of structure solution techniques via powder X-ray diffraction (pxrd) has played an ever increasing pivotal role. In this thesis a number of new multi-component materials; oxamic acid:nicotinamide, oxamic acid:isonicotinamide, fumaric acid:nicotinamide, maleic acid:nicotinamide and maleic acid:isonicotinamide, will be synthesised, via a number of synthetic methods, and fully structurally characterised. A direct comparison of structures solved by powder and single crystal diffraction, have been made in order to evaluate the reliability of structure solution from pxrd in these types of materials. The thermal behaviour of molecular materials will be presented as significant structural information can be extracted from the anisotropic expansion of molecular materials. In conjunction with the research into new multi-component materials, the structure solution of oxamic acid via pxrd, single X-ray diffraction and neutron diffraction will be investigated. Small organic molecular materials like oxamic acid provide a challenge to the crystallographer due to the similarities in the electron density surrounding each functional group in the molecule

Immunological studies in extrinsic allergic alveolitis

Summary available: pp. xiv-xvii

Why do some asthma patients respond poorly to glucocorticoid therapy?

Glucocorticosteroids are the first-line therapy for controlling airway inflammation in asthma. They bind intracellular glucocorticoid receptors to trigger increased expression of anti-inflammatory genes and suppression of pro-inflammatory gene activation in asthmatic airways. In the majority of asthma patients, inhaled glucocorticoids are clinically efficacious, improving lung function and preventing exacerbations. However, 5–10 % of the asthmatic population respond poorly to high dose inhaled and then systemic glucocorticoids. These patients form a category of severe asthma associated with poor quality of life, increased morbidity and mortality, and constitutes a major societal and health care burden. Inadequate therapeutic responses to glucocorticoid treatment is also reported in other inflammatory conditions such as rheumatoid arthritis and inflammatory bowel disease; however, asthma represents the most studied steroid-refractory disease. Several cellular and molecular events underlying glucocorticoid resistance in asthma have been identified involving abnormalities of glucocorticoid receptor signaling pathways. These events have been strongly related to immunological dysregulation, genetic, and environmental factors such as cigarette smoking or respiratory infections. A better understanding of the multiple mechanisms associated with glucocorticoid insensitivity in asthma phenotypes could improve quality of life for people with asthma but would also provide transferrable knowledge for other inflammatory diseases. In this review, we provide an update on the molecular mechanisms behind steroid-refractory asthma. Additionally, we discuss some therapeutic options for treating those asthmatic patients who respond poorly to glucocorticoid therapy

Cytokines at the Interplay Between Asthma and Atherosclerosis?

Cardiovascular disease (CVD) is an important comorbidity in a number of chronic inflammatory diseases. However, evidence in highly prevalent respiratory disease such as asthma are still limited. Epidemiological and clinical data are not univocal in supporting the hypothesis that asthma and CVD are linked and the mechanisms of this relationship remain poorly defined. In this review, we explore the relationship between asthma and cardiovascular disease, with a specific focus on cytokine contribution to vascular dysfunction and atherosclerosis. This is important in the context of recent evidence linking broad inflammatory signaling to cardiovascular events. However inflammatory regulation in asthma is different to the one typically observed in atherosclerosis. We focus on the contribution of cytokine networks encompassing IL-4, IL-6, IL-9, IL-17A, IL-33 but also IFN-γ and TNF-α to vascular dysfunction in atherosclerosis. In doing so we highlight areas of unmet need and possible therapeutic implications

Communique: Reponse de la Haute Autorite a la question ecrite No. 51 de Mme Erisia Gennai Tonietti et M. Pedini. European Coal and Steel Community High Authority Information Service. 24 July 1962

Experience curves are widely used to predict the cost benefits of increasing the deployment of a technology. But how good are such forecasts? Can one predict their accuracy a priori? In this paper we answer these questions by developing a method to make distributional forecasts for experience curves. We test our method using a dataset with proxies for cost and experience for 51 products and technologies and show that it works reasonably well. The framework that we develop helps clarify why the experience curve method often gives similar results to simply assuming that costs decrease exponentially. To illustrate our method we make a distributional forecast for prices of solar photovoltaic modules