Probabilistic methods for drug discovery

This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large multi-dimensional datasets. The advantage of such a framework to other techniques currently available to the domain experts is that the user is directly involved in the data mining process and advanced machine learning algorithms are employed for better projection. A hierarchical visualisation model guided by a domain expert allows them to obtain an informed segmentation of the input space. Two other components of this thesis exploit properties of these principled probabilistic projection algorithms to develop a guided mixture of local experts algorithm which provides robust prediction and a model to estimate feature saliency simultaneously with the training of a projection algorithm.Local models are useful since a single global model cannot capture the full variability of a heterogeneous data space such as the chemical space. Probabilistic hierarchical visualisation techniques provide an effective soft segmentation of an input space by a visualisation hierarchy whose leaf nodes represent different regions of the input space. We use this soft segmentation to develop a guided mixture of local experts (GME) algorithm which is appropriate for the heterogeneous datasets found in chemoinformatics problems. Moreover, in this approach the domain experts are more involved in the model development process which is suitable for an intuition and domain knowledge driven task such as drug discovery. We also derive a generative topographic mapping (GTM) based data visualisation approach which estimates feature saliency simultaneously with the training of a visualisation model.

The European Community and innovation policy : reorienting towards diffusion

This study aims to explore the position of diffusion oriented support mechanisms in European Community (EC) innovation policy. With the shift from the traditional linear model towards an integrative approach to innovation, the role of diffusion of technologies and knowledge, achieved greater weight. This shift in both the thinking of academic experts, and of national policy makers, induced EC policy makers to appeal for similar changes in Community innovation policy. From the mid-1980s, the Commission of the European Communities, the key actor in EC policy making, thought to move its innovation policy away from the traditional science push approach. This study shows that in the implementation of programmes for research, technology and innovation, the traditional linear model is still dominant. The core research and technological development programmes still operate from a science push concept of innovation, mainly due to their pre-competitive nature. The case of SPRINT illustrates that policy programmes with an integrated innovation perspective can be successful at Community level. However the programme operates in a relatively isolated position from overall research and technological development policy. The case of BRITE-EURAM illustrates the difficulties of collaborative research programmes, the bulk of EC support mechanisms, to move away from the traditional model. The study shows how conflicting policy objectives arising from the different policy networks that shape EC policy making, in combination with a lack of co-ordination in those policy domains, hinder the emergence of the integrated approach. Consequently EC diffusion policy, implemented from the perspective of the linear model, will have a sub-optimal impact on the competitiveness of European industries.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Objectives: Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data. Methods: We used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers. Results: The accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03). Conclusions: These results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

Developing and leveraging human capital resource to promote service quality:testing a theory of performance

Grounded in Vroom’s motivational framework of performance, we examine the interactive influence of collective human capital (ability) and aggregated service orientation (motivation) on the cross-level relationship between high-performance work systems (HPWS) and individual-level service quality. Results of hierarchical linear modeling (HLM) revealed that HPWS related to collective human capital and aggregated service orientation, which in turn related to individual-level service quality. Furthermore, both HLM and ordinary least squares regression analyses revealed a cross-level interaction effect of collective human capital and aggregated service orientation such that high levels of collective human capital and aggregated service orientation influence individual-level service quality.

When creativity enhances sales effectiveness:the moderating role of leader–member exchange

This study extends research on creativity by exploring the boundary conditions of the creativity-job effectiveness relationship. Building on social exchange theory, we argue that the extent to which employee creativity is related to sales - an objective work effectiveness measure - depends on the quality of leader-member exchange (LMX). We hypothesize that the relationship between creativity and sales is significant and positive when LMX is high, but not when LMX is low. Hierarchical linear modelling analysis provided support for the interaction hypothesis in a sample of 151 sales agents and 26 supervisors drawn from both pharmaceutical and insurance companies. Results showed that sales agents who were more creative generated higher sales only when they had high quality LMX. An ad-hoc qualitative study provided a more detailed understanding of the moderator role played by LMX. Copyright © 2012 John Wiley & Sons, Ltd.

Petroleum pipeline construction planning:a conceptual framework

A cross-country pipeline construction project is exposed to an uncertain environment due to its enormous size (physical, manpower requirement and financial value), complexity in design technology and involvement of external factors. These uncertainties can lead to several changes in project scope during the process of project execution. Unless the changes are properly controlled, the time, cost and quality goals of the project may never be achieved. A methodology is proposed for project control through risk analysis, contingency allocation and hierarchical planning models. Risk analysis is carried out through the analytic hierarchy process (AHP) due to the subjective nature of risks in construction projects. The results of risk analysis are used to determine the logical contingency for project control with the application of probability theory. Ultimate project control is carried out by hierarchical planning model which enables decision makers to take vital decisions during the changing environment of the construction period. Goal programming (GP), a multiple criteria decision-making technique, is proposed for model formulation because of its flexibility and priority-base structure. The project is planned hierarchically in three levels—project, work package and activity. GP is applied separately at each level. Decision variables of each model are different planning parameters of the project. In this study, models are formulated from the owner's perspective and its effectiveness in project control is demonstrated.

Wave kinetics of random fibre lasers

Traditional wave kinetics describes the slow evolution of systems with many degrees of freedom to equilibrium via numerous weak non-linear interactions and fails for very important class of dissipative (active) optical systems with cyclic gain and losses, such as lasers with non-linear intracavity dynamics. Here we introduce a conceptually new class of cyclic wave systems, characterized by non-uniform double-scale dynamics with strong periodic changes of the energy spectrum and slow evolution from cycle to cycle to a statistically steady state. Taking a practically important example—random fibre laser—we show that a model describing such a system is close to integrable non-linear Schrödinger equation and needs a new formalism of wave kinetics, developed here. We derive a non-linear kinetic theory of the laser spectrum, generalizing the seminal linear model of Schawlow and Townes. Experimental results agree with our theory. The work has implications for describing kinetics of cyclical systems beyond photonics.

Body iron is associated with cognitive executive planning function in college women

Evidence of the relationship between altered cognitive function and depleted Fe status is accumulating in women of reproductive age but the degree of Fe deficiency associated with negative neuropsychological outcomes needs to be delineated. Data are limited regarding this relationship in university women in whom optimal cognitive function is critical to academic success. The aim of the present study was to examine the relationship between body Fe, in the absence of Fe-deficiency anaemia, and neuropsychological function in young college women. Healthy, non-Anaemic undergraduate women (n 42) provided a blood sample and completed a standardised cognitive test battery consisting of one manual (Tower of London (TOL), a measure of central executive function) and five computerised (Bakan vigilance task, mental rotation, simple reaction time, immediate word recall and two-finger tapping) tasks. Women's body Fe ranged from - 4·2 to 8·1 mg/kg. General linear model ANOVA revealed a significant effect of body Fe on TOL planning time (P= 0.002). Spearman's correlation coefficients showed a significant inverse relationship between body Fe and TOL planning time for move categories 4 (r - 0.39, P= 0.01) and 5 (r - 0.47, P= 0.002). Performance on the computerised cognitive tasks was not affected by body Fe level. These findings suggest that Fe status in the absence of anaemia is positively associated with central executive function in otherwise healthy college women. Copyright © The Authors 2012.

Assembly accuracy analysis for small components with a planar surface in large-scale metrology

Large-scale mechanical products, such as aircraft and rockets, consist of large numbers of small components, which introduce additional difficulty for assembly accuracy and error estimation. Planar surfaces as key product characteristics are usually utilised for positioning small components in the assembly process. This paper focuses on assembly accuracy analysis of small components with planar surfaces in large-scale volume products. To evaluate the accuracy of the assembly system, an error propagation model for measurement error and fixture error is proposed, based on the assumption that all errors are normally distributed. In this model, the general coordinate vector is adopted to represent the position of the components. The error transmission functions are simplified into a linear model, and the coordinates of the reference points are composed by theoretical value and random error. The installation of a Head-Up Display is taken as an example to analyse the assembly error of small components based on the propagation model. The result shows that the final coordination accuracy is mainly determined by measurement error of the planar surface in small components. To reduce the uncertainty of the plane measurement, an evaluation index of measurement strategy is presented. This index reflects the distribution of the sampling point set and can be calculated by an inertia moment matrix. Finally, a practical application is introduced for validating the evaluation index.

Műszaki és gazdasági hatékonyság Koopmans termeléselméletében (Technical and economic efficiency in Koopmans' theory of production)

Koopmans gyakorlati problémák megoldása során szerzett tapasztalatait általánosítva fogott hozzá a lineáris tevékenységelemzési modell kidolgozásához. Meglepődve tapasztalta, hogy a korabeli közgazdaságtan nem rendelkezett egységes, kellően egzakt termeléselmélettel és fogalomrendszerrel. Úttörő dolgozatában ezért - mintegy a lineáris tevékenységelemzési modell elméleti kereteként - lerakta a technológiai halmazok fogalmán nyugvó axiomatikus termeléselmélet alapjait is. Nevéhez fűződik a termelési hatékonyság és a hatékonysági árak fogalmának egzakt definíciója, s az egymást kölcsönösen feltételező viszonyuk igazolása a lineáris tevékenységelemzési modell keretében. A hatékonyság manapság használatos, pusztán műszaki szempontból értelmezett definícióját Koopmans csak sajátos esetként tárgyalta, célja a gazdasági hatékonyság fogalmának a bevezetése és elemzése volt. Dolgozatunkban a lineáris programozás dualitási tételei segítségével rekonstruáljuk ez utóbbira vonatkozó eredményeit. Megmutatjuk, hogy egyrészt bizonyításai egyenértékűek a lineáris programozás dualitási tételeinek igazolásával, másrészt a gazdasági hatékonysági árak voltaképpen a mai értelemben vett árnyékárak. Rámutatunk arra is, hogy a gazdasági hatékonyság értelmezéséhez megfogalmazott modellje az Arrow-Debreu-McKenzie-féle általános egyensúlyelméleti modellek közvetlen előzményének tekinthető, tartalmazta azok szinte minden lényeges elemét és fogalmát - az egyensúlyi árak nem mások, mint a Koopmans-féle hatékonysági árak. Végezetül újraértelmezzük Koopmans modelljét a vállalati technológiai mikroökonómiai leírásának lehetséges eszközeként. Journal of Economic Literature (JEL) kód: B23, B41, C61, D20, D50. /===/ Generalizing from his experience in solving practical problems, Koopmans set about devising a linear model for analysing activity. Surprisingly, he found that economics at that time possessed no uniform, sufficiently exact theory of production or system of concepts for it. He set out in a pioneering study to provide a theoretical framework for a linear model for analysing activity by expressing first the axiomatic bases of production theory, which rest on the concept of technological sets. He is associated with exact definition of the concept of production efficiency and efficiency prices, and confirmation of their relation as mutual postulates within the linear model of activity analysis. Koopmans saw the present, purely technical definition of efficiency as a special case; he aimed to introduce and analyse the concept of economic efficiency. The study uses the duality precepts of linear programming to reconstruct the results for the latter. It is shown first that evidence confirming the duality precepts of linear programming is equal in value, and secondly that efficiency prices are really shadow prices in today's sense. Furthermore, the model for the interpretation of economic efficiency can be seen as a direct predecessor of the Arrow–Debreu–McKenzie models of general equilibrium theory, as it contained almost every essential element and concept of them—equilibrium prices are nothing other than Koopmans' efficiency prices. Finally Koopmans' model is reinterpreted as a necessary tool for microeconomic description of enterprise technology.

Principal component and second generation wavelet analysis of Treasury yield curve evolution

Prices of U.S. Treasury securities vary over time and across maturities. When the market in Treasurys is sufficiently complete and frictionless, these prices may be modeled by a function time and maturity. A cross-section of this function for time held fixed is called the yield curve; the aggregate of these sections is the evolution of the yield curve. This dissertation studies aspects of this evolution. ^ There are two complementary approaches to the study of yield curve evolution here. The first is principal components analysis; the second is wavelet analysis. In both approaches both the time and maturity variables are discretized. In principal components analysis the vectors of yield curve shifts are viewed as observations of a multivariate normal distribution. The resulting covariance matrix is diagonalized; the resulting eigenvalues and eigenvectors (the principal components) are used to draw inferences about the yield curve evolution. ^ In wavelet analysis, the vectors of shifts are resolved into hierarchies of localized fundamental shifts (wavelets) that leave specified global properties invariant (average change and duration change). The hierarchies relate to the degree of localization with movements restricted to a single maturity at the base and general movements at the apex. Second generation wavelet techniques allow better adaptation of the model to economic observables. Statistically, the wavelet approach is inherently nonparametric while the wavelets themselves are better adapted to describing a complete market. ^ Principal components analysis provides information on the dimension of the yield curve process. While there is no clear demarkation between operative factors and noise, the top six principal components pick up 99% of total interest rate variation 95% of the time. An economically justified basis of this process is hard to find; for example a simple linear model will not suffice for the first principal component and the shape of this component is nonstationary. ^ Wavelet analysis works more directly with yield curve observations than principal components analysis. In fact the complete process from bond data to multiresolution is presented, including the dedicated Perl programs and the details of the portfolio metrics and specially adapted wavelet construction. The result is more robust statistics which provide balance to the more fragile principal components analysis. ^

Essays in environmental economic valuation and decision making in the presence of an environmental disaster

The first essay developed a respondent model of Bayesian updating for a double-bound dichotomous choice (DB-DC) contingent valuation methodology. I demonstrated by way of data simulations that current DB-DC identifications of true willingness-to-pay (WTP) may often fail given this respondent Bayesian updating context. Further simulations demonstrated that a simple extension of current DB-DC identifications derived explicitly from the Bayesian updating behavioral model can correct for much of the WTP bias. Additional results provided caution to viewing respondents as acting strategically toward the second bid. Finally, an empirical application confirmed the simulation outcomes. The second essay applied a hedonic property value model to a unique water quality (WQ) dataset for a year-round, urban, and coastal housing market in South Florida, and found evidence that various WQ measures affect waterfront housing prices in this setting. However, the results indicated that this relationship is not consistent across any of the six particular WQ variables used, and is furthermore dependent upon the specific descriptive statistic employed to represent the WQ measure in the empirical analysis. These results continue to underscore the need to better understand both the WQ measure and its statistical form homebuyers use in making their purchase decision. The third essay addressed a limitation to existing hurricane evacuation modeling aspects by developing a dynamic model of hurricane evacuation behavior. A household's evacuation decision was framed as an optimal stopping problem where every potential evacuation time period prior to the actual hurricane landfall, the household's optimal choice is to either evacuate, or to wait one more time period for a revised hurricane forecast. A hypothetical two-period model of evacuation and a realistic multi-period model of evacuation that incorporates actual forecast and evacuation cost data for my designated Gulf of Mexico region were developed for the dynamic analysis. Results from the multi-period model were calibrated with existing evacuation timing data from a number of hurricanes. Given the calibrated dynamic framework, a number of policy questions that plausibly affect the timing of household evacuations were analyzed, and a deeper understanding of existing empirical outcomes in regard to the timing of the evacuation decision was achieved.

Explaining the effects of class size on faculty and students

This study explored the effects of class size on faculty and students. Specifically, it examined the relationship of class size and students' participation in class, faculty interactive styles, and academic environment and how these behaviors affected student achievement (percentage of students passing). The sample was composed of 629 students in 30 sections of Algebra I at a large, urban community college. A survey was administered to the students to solicit their perceptions on their participation in class, their faculty interaction style, and the academic environment in their classes. Selected classes were observed to triangulate the findings. The relationship of class size to student participation, faculty interactive styles, and academic environment was determined by using hierarchical linear modeling (HLM). A significant difference was found on the participation of students related to class size. Students in smaller classes participated more and were more engaged than students in larger classes. Regression analysis using the same variables in small and large classes showed that faculty interactive styles significantly predicted student achievement. Stepwise regression analyses of student and faculty background variables showed that (a) students' estimate of GPA was significantly related to their achievement (r = .63); (b) older students reported more participation than did younger ones, (c) students in classes taught by female, Hispanic faculty earned higher passing grades, and (d) students' participation was greater with adjunct professors. Class observations corroborated these findings. The analysis and observational data provided sufficient evidence to warrant the conclusion that small classes were not always most effective in promoting achievement. It was found that small classes may be an artifact of ineffectual teaching, actual or by reputation. While students in small classes participate and are more engaged than students in larger classes, the class-size effect is essentially due to what happens in instruction to promote learning. The interaction of the faculty with students significantly predicted students' achievement regardless of class size. Since college students select their own classes, students do not register for classes taught by faculty with poor teaching reputation, thereby leading to small classes. Further studies are suggested to determine reasons why classes differ in size.