The synthesis and evaluation of small organic molecules as cholecystokinin antagonists

Cholecystokinin (CCK) is a peptide hormone, present in the alimentary and the CNS. It is the most abundant peptide in the brain. CCK has been implicated in a number of disorders. The link between CCK and anxiety was the basis for this research. A comprehensive discussion on the many types of CCK receptor antagonists is included. For the drug discovery process, a number of synthetic approaches have been investigated and alternative chemical approaches developed. 1,4-Benzodiazepine analogues were prepared, with substitutents In the 1,2 & 3- position of the benzodiazepine scaffold varied, and substituted 3-anilino benzodiazepines exhibited the greatest in vitro activity towards the CCKA receptor subtype. Through extensive screening, pyrazolinone-ureido derivatives were identified, optimised, SAR studied and re-screened. A comprehensive in vivo study on the most active analogue is included, which has a number of common structural features with L-36S, 260 including activity. Pyrazolinone-amide derivatives, bearing the tryptophan moiety were equally active. A number of existing and novel furan- 2(SH)-one building blocks were prepared, from which a selected mini-library of 4- amino-substituted furan-2(SH)-ones were prepared and evaluated. All synthesised compounds were evaluated in a CCK radiolabelled binding assay (CCKA & CCKB), with compounds demonstrating receptor selectivity and lead structures being discovered. The work in this thesis has identified a number of highly active prime structures, from which further investigations are essential in providing more in vitro & in vivo data and the need to prepare more analogues.

School mathematics and dyslexia: aspects of the interrelationship

This investigation sought to explore the nature and extent of school mathematical difficulties among the dyslexic population. Anecdotal reports have suggested that many dyslexics may have difficulties in arithmetic, but few systematic studies have previously been undertaken. The literature pertaining to dyslexia and school mathematics respectively is reviewed. Clues are sought in studies of dyscalculia. These seem inadequate in accounting for dyslexics' reported mathematical difficulties. Similarities between aspects of language and mathematics are examined for underlying commonalities that may partially account for concomitant problems in mathematics, in individuals with a written language dysfunction. The performance of children taught using different mathematics work-schemes is assessed to ascertain if these are associated with differential levels of achievement that may be reflected in the dyslexic population few are found. Findings from studies designed to assess the relationship between written language failure and achievement in mathematics are reported. Study 1 reveals large correlational differences between subtest scores (Wechsler Intelligence Scale for Children, Wechsler, 1976) and three mathematics tests, for young dyslexics and children without literacy difficulties. However, few differences are found between levels of attainment, at this age (6 ½ - 9 years). Further studies indicate that, for dyslexics, achievement in school mathematics, may be independent of measured intelligence, as is the case with their literacy skills. Studies 3 and 4 reveal that dyslexics' performances on a range of school mathematical topics gets relatively worse compared with that of Controls (age range 8 - 17 years), as they get older. Extensive item analyses reveal many errors relating strongly to known deficits in the dyslexics' learning style - poor short-term memory, sequencing skills and verbal labelling strategies. Subgroups of dyslexics are identified on the basis of mathematical performance. Tentative explanations, involving alternative neuropsychological approaches, are offered for the measured differences in attainment between these groups.

Classifying clinical decision making:a unifying approach

This is the first of two linked papers exploring decision making in nursing which integrate research evidence from different clinical and academic disciplines. Currently there are many decision-making theories, each with their own distinctive concepts and terminology, and there is a tendency for separate disciplines to view their own decision-making processes as unique. Identifying good nursing decisions and where improvements can be made is therefore problematic, and this can undermine clinical and organizational effectiveness, as well as nurses' professional status. Within the unifying framework of psychological classification, the overall aim of the two papers is to clarify and compare terms, concepts and processes identified in a diversity of decision-making theories, and to demonstrate their underlying similarities. It is argued that the range of explanations used across disciplines can usefully be re-conceptualized as classification behaviour. This paper explores problems arising from multiple theories of decision making being applied to separate clinical disciplines. Attention is given to detrimental effects on nursing practice within the context of multidisciplinary health-care organizations and the changing role of nurses. The different theories are outlined and difficulties in applying them to nursing decisions highlighted. An alternative approach based on a general model of classification is then presented in detail to introduce its terminology and the unifying framework for interpreting all types of decisions. The classification model is used to provide the context for relating alternative philosophical approaches and to define decision-making activities common to all clinical domains. This may benefit nurses by improving multidisciplinary collaboration and weakening clinical elitism.

Verification and validation of numerical models of the transport of insulation debris

A combination of the two-fluid and drift flux models have been used to model the transport of fibrous debris. This debris is generated during loss of coolant accidents in the primary circuit of pressurized or boiling water nuclear reactors, as high pressure steam or water jets can damage adjacent insulation materials including mineral wool blankets. Fibre agglomerates released from the mineral wools may reach the containment sump strainers, where they can accumulate and compromise the long-term operation of the emergency core cooling system. Single-effect experiments of sedimentation in a quiescent rectangular column and sedimentation in a horizontal flow are used to verify and validate this particular application of the multiphase numerical models. The utilization of both modeling approaches allows a number of pseudocontinuous dispersed phases of spherical wetted agglomerates to be modeled simultaneously. Key effects on the transport of the fibre agglomerates are particle size, density and turbulent dispersion, as well as the relative viscosity of the fluid-fibre mixture.

A közgazdaságtan metodológiájáról és a matematikai közgazdaságtanról a Neumann-modell ürügyén

A dolgozatban a Neumann-modell lehetséges elméleti és módszertani rokonságát elemezzük annak fényében, hogy mind a neoklasszikusok, mind a klasszikus hagyományokat felélesztő neoricardiánusok a magukénak vallják. Ennek során megvizsgáljuk a klasszikus és a neoklasszikus gazdaságfelfogás, az ex post és az ex ante szemléletű modellek közötti különbségeket, és azt a forradalmi jelentőségű módszertani változást, amely a sok szempontból joggal bírálható modern matematikai közgazdaságtan kialakulásához vezetett. Összevetjük Neumann modelljét az osztrák iskola árbeszámítási elméletével, a Walras­Cassel- és a Schlesinger­Wald-féle modellekkel, illetve a Ricardo, Marx, Dmitriev, Leontief nevekkel fémjelezhető klasszikus vonulat eredményeivel. Rámutatunk arra, hogy Neumann voltaképpen az "igazságos és értelmes gazdaság" ősi ideáját öntötte kora modern fizikájában honos matematikai modell formájába. /===/ The paper investigates the potential theoretical and methodological sources of inspiration of the von Neumann model, in view of the fact that both the neoclassical and the neo-Ricardian economists claim heritage to it. In the course of that the author assesses the main differences of the classical and neoclassical, the ex post and ex ante modeling approaches. He also confronts the von Neumann model with the Walras–Cassel and the Schlesinger–Wald models, and with models worked out in the classical tradition a’la Ricardo, Marx, Dmitriev and Leontief. He concludes that the Neumann-model is, in fact, nothing but a reformulation of a very old belief in a “just and reasonable economic system” based on the modern modeling approach of contemporary physics and mathematics.

Tax morale and tax evasion: Social preferences and bounded rationality

We study a family of models of tax evasion, where a flat-rate tax finances only the provision of public goods, neglecting audits and wage differences. We focus on the comparison of two modeling approaches. The first is based on optimizing agents, who are endowed with social preferences, their utility being the sum of private consumption and moral utility. The second approach involves agents acting according to simple heuristics. We find that while we encounter the traditionally shaped Laffer-curve in the optimizing model, the heuristics models exhibit (linearly) increasing Laffercurves. This difference is related to a peculiar type of behavior emerging within the heuristics based approach: a number of agents lurk in a moral state of limbo, alternating between altruism and selfishness.

Estimation of ravine sediment production and sediment dynamics in the lower Le Sueur River watershed, Minnesota

This study focuses on quantifying explicitly the sediment budget of deeply incised ravines in the lower Le Sueur River watershed, in southern Minnesota. High-rate-gully-erosion equations along with the Universal Soil Loss Equation (USLE) were implemented in a numerical modeling approach that is based on a time-integration of the sediment balance equations. The model estimates the rates of ravine width and depth change and the amount of sediment periodically flushing from the ravines. Components of the sediment budget of the ravines were simulated with the model and results suggest that the ravine walls are the major sediment source in the ravines. A sensitivity analysis revealed that the erodibility coefficients of the gully bed and wall, the local slope angle and the Manning’s coefficient are the key parameters controlling the rate of sediment production. Recommendations to guide further monitoring efforts in the watershed and increased detail modeling approaches are highlighted as a result of this modeling effort.

Projected Reorganization of Florida Bay Seagrass Communities in Response to the Increased Freshwater Inflow of Everglades Restoration

Historic changes in water-use management in the Florida Everglades have caused the quantity of freshwater inflow to Florida Bay to decline by approximately 60% while altering its timing and spatial distribution. Two consequences have been (1) increased salinity throughout the bay, including occurrences of hypersalinity, coupled with a decrease in salinity variability, and (2) change in benthic habitat structure. Restoration goals have been proposed to return the salinity climates (salinity and its variability) of Florida Bay to more estuarine conditions through changes in upstream water management, thereby returning seagrass species cover to a more historic state. To assess the potential for meeting those goals, we used two modeling approaches and long-term monitoring data. First, we applied the hydrological mass balance model FATHOM to predict salinity climate changes in sub-basins throughout the bay in response to a broad range of freshwater inflow from the Everglades. Second, because seagrass species exhibit different sensitivities to salinity climates, we used the FATHOM-modeled salinity climates as input to a statistical discriminant function model that associates eight seagrass community types with water quality variables including salinity, salinity variability, total organic carbon, total phosphorus, nitrate, and ammonium, as well as sediment depth and light reaching the benthos. Salinity climates in the western sub-basins bordering the Gulf of Mexico were insensitive to even the largest (5-fold) modeled increases in freshwater inflow. However, the north, northeastern, and eastern sub-basins were highly sensitive to freshwater inflow and responded to comparatively small increases with decreased salinity and increased salinity variability. The discriminant function model predicted increased occurrences ofHalodule wrightii communities and decreased occurrences of Thalassia testudinum communities in response to the more estuarine salinity climates. The shift in community composition represents a return to the historically observed state and suggests that restoration goals for Florida Bay can be achieved through restoration of freshwater inflow from the Everglades.

Geographic provenancing of unprocessed cotton using elemental analysis and stable isotope ratios

Cotton is the most abundant natural fiber in the world. Many countries are involved in the growing, importation, exportation and production of this commodity. Paper documentation claiming geographic origin is the current method employed at U.S. ports for identifying cotton sources and enforcing tariffs. Because customs documentation can be easily falsified, it is necessary to develop a robust method for authenticating or refuting the source of the cotton commodities. This work presents, for the first time, a comprehensive approach to the chemical characterization of unprocessed cotton in order to provide an independent tool to establish geographic origin. Elemental and stable isotope ratio analysis of unprocessed cotton provides a means to increase the ability to distinguish cotton in addition to any physical and morphological examinations that could be, and are currently performed. Elemental analysis has been conducted using LA-ICP-MS, LA-ICP-OES and LIBS in order to offer a direct comparison of the analytical performance of each technique and determine the utility of each technique for this purpose. Multivariate predictive modeling approaches are used to determine the potential of elemental and stable isotopic information to aide in the geographic provenancing of unprocessed cotton of both domestic and foreign origin. These approaches assess the stability of the profiles to temporal and spatial variation to determine the feasibility of this application. This dissertation also evaluates plasma conditions and ablation processes so as to improve the quality of analytical measurements made using atomic emission spectroscopy techniques. These interactions, in LIBS particularly, are assessed to determine any potential simplification of the instrumental design and method development phases. This is accomplished through the analysis of several matrices representing different physical substrates to determine the potential of adopting universal LIBS parameters for 532 nm and 1064 nm LIBS for some important operating parameters. A novel approach to evaluate both ablation processes and plasma conditions using a single measurement was developed and utilized to determine the "useful ablation efficiency" for different materials. The work presented here demonstrates the potential for an a priori prediction of some probable laser parameters important in analytical LIBS measurement.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Estimation of Ravine Sediment Production and Sediment Dynamics in the Lower Le Sueur River Watershed, Minnesota

This study focuses on quantifying explicitly the sediment budget of deeply incised ravines in the lower Le Sueur River watershed, in southern Minnesota. High-rate-gully-erosion equations along with the Universal Soil Loss Equation (USLE) were implemented in a numerical modeling approach that is based on a time-integration of the sediment balance equations. The model estimates the rates of ravine width and depth change and the amount of sediment periodically flushing from the ravines. Components of the sediment budget of the ravines were simulated with the model and results suggest that the ravine walls are the major sediment source in the ravines. A sensitivity analysis revealed that the erodibility coefficients of the gully bed and wall, the local slope angle and the Manning’s coefficient are the key parameters controlling the rate of sediment production. Recommendations to guide further monitoring efforts in the watershed and increased detail modeling approaches are highlighted as a result of this modeling effort.

Empirical likelihood based longitudinal studies

In longitudinal data analysis, our primary interest is in the regression parameters for the marginal expectations of the longitudinal responses; the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly for correlated discrete outcome data. Marginal modeling approaches such as generalized estimating equations (GEEs) have received much attention in the context of longitudinal regression. These methods are based on the estimates of the first two moments of the data and the working correlation structure. The confidence regions and hypothesis tests are based on the asymptotic normality. The methods are sensitive to misspecification of the variance function and the working correlation structure. Because of such misspecifications, the estimates can be inefficient and inconsistent, and inference may give incorrect results. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its characteristics and asymptotic properties. We also provide an algorithm based on EL principles for the estimation of the regression parameters and the construction of a confidence region for the parameter of interest. We extend our approach to variable selection for highdimensional longitudinal data with many covariates. In this situation it is necessary to identify a submodel that adequately represents the data. Including redundant variables may impact the model’s accuracy and efficiency for inference. We propose a penalized empirical likelihood (PEL) variable selection based on GEEs; the variable selection and the estimation of the coefficients are carried out simultaneously. We discuss its characteristics and asymptotic properties, and present an algorithm for optimizing PEL. Simulation studies show that when the model assumptions are correct, our method performs as well as existing methods, and when the model is misspecified, it has clear advantages. We have applied the method to two case examples.

Bridging the Scale Gap from Leaf to Canopy in Biosphere-Atmosphere Gas and Particle Exchanges

Terrestrial ecosystems, occupying more than 25% of the Earth's surface, can serve as

`biological valves' in regulating the anthropogenic emissions of atmospheric aerosol

particles and greenhouse gases (GHGs) as responses to their surrounding environments.

While the signicance of quantifying the exchange rates of GHGs and atmospheric

aerosol particles between the terrestrial biosphere and the atmosphere is

hardly questioned in many scientic elds, the progress in improving model predictability,

data interpretation or the combination of the two remains impeded by

the lack of precise framework elucidating their dynamic transport processes over a

wide range of spatiotemporal scales. The diculty in developing prognostic modeling

tools to quantify the source or sink strength of these atmospheric substances

can be further magnied by the fact that the climate system is also sensitive to the

feedback from terrestrial ecosystems forming the so-called `feedback cycle'. Hence,

the emergent need is to reduce uncertainties when assessing this complex and dynamic

feedback cycle that is necessary to support the decisions of mitigation and

adaptation policies associated with human activities (e.g., anthropogenic emission

controls and land use managements) under current and future climate regimes.

With the goal to improve the predictions for the biosphere-atmosphere exchange

of biologically active gases and atmospheric aerosol particles, the main focus of this

dissertation is on revising and up-scaling the biotic and abiotic transport processes

from leaf to canopy scales. The validity of previous modeling studies in determining

iv

the exchange rate of gases and particles is evaluated with detailed descriptions of their

limitations. Mechanistic-based modeling approaches along with empirical studies

across dierent scales are employed to rene the mathematical descriptions of surface

conductance responsible for gas and particle exchanges as commonly adopted by all

operational models. Specically, how variation in horizontal leaf area density within

the vegetated medium, leaf size and leaf microroughness impact the aerodynamic attributes

and thereby the ultrane particle collection eciency at the leaf/branch scale

is explored using wind tunnel experiments with interpretations by a porous media

model and a scaling analysis. A multi-layered and size-resolved second-order closure

model combined with particle

uxes and concentration measurements within and

above a forest is used to explore the particle transport processes within the canopy

sub-layer and the partitioning of particle deposition onto canopy medium and forest

oor. For gases, a modeling framework accounting for the leaf-level boundary layer

eects on the stomatal pathway for gas exchange is proposed and combined with sap

ux measurements in a wind tunnel to assess how leaf-level transpiration varies with

increasing wind speed. How exogenous environmental conditions and endogenous

soil-root-stem-leaf hydraulic and eco-physiological properties impact the above- and

below-ground water dynamics in the soil-plant system and shape plant responses

to droughts is assessed by a porous media model that accommodates the transient

water

ow within the plant vascular system and is coupled with the aforementioned

leaf-level gas exchange model and soil-root interaction model. It should be noted

that tackling all aspects of potential issues causing uncertainties in forecasting the

feedback cycle between terrestrial ecosystem and the climate is unrealistic in a single

dissertation but further research questions and opportunities based on the foundation

derived from this dissertation are also brie

y discussed.

Revisiting the Land Issue

This paper seeks to review the critical role of land in delivering sustainable development, focusing on the supply of affordable homes. It first presents a historical overview of debates on land reform, including nationalisation of development land and betterment, before reviewing the impact of land costs on housing delivery, using London as a case study. It then considers alternative policy approaches to ensuring the most effective use of land resources and development capacity, and sets out a programme embracing planning reform, public land acquisition, disposal and taxation.

Storytelling and story testing in domestication

The domestication of plants and animals marks one of the most significant transitions in human, and indeed global, history. Traditionally, study of the domestication process was the exclusive domain of archaeologists and agricultural scientists; today it is an increasingly multidisciplinary enterprise that has come to involve the skills of evolutionary biologists and geneticists. Although the application of new information sources and methodologies has dramatically transformed our ability to study and understand domestication, it has also generated increasingly large and complex datasets, the interpretation of which is not straightforward. In particular, challenges of equifinality, evolutionary variance, and emergence of unexpected or counter-intuitive patterns all face researchers attempting to infer past processes directly from patterns in data. We argue that explicit modeling approaches, drawing upon emerging methodologies in statistics and population genetics, provide a powerful means of addressing these limitations. Modeling also offers an approach to analyzing datasets that avoids conclusions steered by implicit biases, and makes possible the formal integration of different data types. Here we outline some of the modeling approaches most relevant to current problems in domestication research, and demonstrate the ways in which simulation modeling is beginning to reshape our understanding of the domestication process.