In the present study samples from four native Turkish Cattle Breeds / South Anatolian Red (n= 48), East Anatolian Red (n= 34), Anatolian Black (n= 42) and Turkish Grey (n=46) and elite bulls of Holstein (n=21) were genotyped with respect to two milk production enhancer genes, Prolactin (PRL) and Diacylglycerol acyltransferase (DGAT1), and one disease (Complex Vertebral Malformation) causing gene (SLC35A3). A allele frequency for PRL gene, believed to be positively associated with the milk yield in cattle, ranged between 0.5645 (Anatolian Black) - 0.7558 (South Anatolian Red). K allele frequency which is thought to be related with the milk fat content in cattle varied between 0.7794 (East Anatolian Red) - 0.9250 (Anatolian Black). Complex Vertebral Malformation gene was not observed in any of the examined individuals (n= 164), hence, SLC35A3 locus was monomorphic. Pairwise Fst values based on the two polymorphic loci revealed that breeds are not significantly different from each other with respect to these two genes. Correlations, but weak, between the PRL A allele frequency and milk yield and similarly DGAT1 K allele and milk fat content was observed, Principle Component Analysis generated two compound axis based on the two polymorphic loci. Positions of the breeds on the first axis were correlated with the milk fat content of the breeds, perfectly. Again, positions of the breeds on the second axis were correlated with the milk yield of the breeds. Furthermore, PCA revealed that both A of PRL and K of DGAT1 genes seemed to have contributions in milk yield Results are believed to be useful for the management efforts of Turkish native cattle breeds.

In this study, a 3D face representation and recognition method based on spherical harmonics expansion is proposed. The input data to the method is range image of the face. This data is called 2.5 dimensional. Input faces are manually marked on the two eyes, nose and chin points. In two dimensions, using the marker points, the human face is modeled as two concentric half ellipses for the selection of region of interest. These marker points are also used in three dimensions to register the faces so that the nose point tip is at the origin and the line across the two eyes lies parallel to the horizontal plane. A PCA based component analysis is done to further align the faces vertically. The aligned face is stitched and mapped to an ellipsoid and transformed using real spherical harmonics expansion. The real harmonics expansion coefficients are labeled and stored into a gallery. Using these coefficients as input, several classification algorithms are applied and the results are reported.

Investigations of the anticipated effects of alcohol indicate that cognitive frameworks are highly correlated with drinking and other variables associated with alcohol use, explaining up to 50% of the variance in drinking outcomes (Goldman, Darkes, & Del Boca, 1999; Goldman, 2002; Goldman et al., 2006; Goldman, Reich, & Darkes, 2006). Furthermore, alcohol expectancies appear to mediate the relationship between a variety of risk factors, such as sensation seeking, and alcohol outcomes (Darkes, Greenbaum, & Goldman, 2004). The current study examined the relationship of these cognitive networks with a physiological index of expectancy violation Participants were presented with statements reflecting a wide range of alcohol outcome effects, which either violated or confirmed the participant’s own set of alcohol expectancies, while the ERPs evoked by these stimuli were recorded. As predicted, the P300 amplitude elicited by negative alcohol expectancy stimuli was positively correlated with the degree of endorsement of positive/arousing expectancies on the self-report measure. That is, the higher the individual’s positive/arousing expectancies, the larger the P300 elicited by stimuli asserting the negative effects of alcohol. There was no significant correlation, however, between P300 amplitude elicited by positive alcohol expectancy stimuli and the degree of endorsement of negative/sedating expectancies on the selfreport measure. In addition, individual differences relating to alcohol expectancies were examined as well. These results were able to identify specific stimuli that violated expectancies for each individual, as well as those that tended to violate expectancies in systematic ways across subjects. These findings provide a way forward for more precise assessment and prediction based on the well developed cognitive model of Alcohol Expectancies. In sum, variations in the amplitude of the P300 were consistent with the model of Alcohol Expectancies. Words imputing negative/sedating effects of alcohol elicited a large P300 in individuals with higher positive alcohol expectancies. By indexing the brain’s electrophysiological response sensitive to expectancy violations, these findings demonstrate concordance between verbal measures of alcohol expectancies, which by their very nature are introspective, and a psychophysiological index of expectancy thought to operate automatically and to be independent of overt responding.

Participatory sensing (PS) systems are a new emerging sensing paradigm based on the participation of cellular users in a cooperative way. Due to the spatio-temporal granularity that a PS system can provide, it is now possible to detect and analyze events that occur at different scales, at a low cost. While PS systems present interesting characteristics, they also create new problems. Since the measuring devices are cheaper and they are in the hands of the users, PS systems face several design challenges related to the poor accuracy and high failure rate of the sensors, the possibility of malicious users tampering the data, the violation of the privacy of the users as well as methods to encourage the participation of the users, and the effective visualization of the data. This dissertation presents four main contributions in order to solve some of these challenges. This dissertation presents a framework to guide the design and implementation of PS applications considering all these aspects. The framework consists of five modules: sample size determination, data collection, data verification, data visualization, and density maps generation modules. The remaining contributions are mapped one-on-one to three of the modules of this framework: data verification, data visualization and density maps. Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. A new algorithm for spatial outliers detection and removal is proposed, implemented, and tested. This hybrid neighborhood-aware algorithm considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The experimental results show that the proposed algorithm performs as good as the best estimator while reducing the execution time considerably. The problem of data visualization in the context of PS application is also of special interest. The characteristics of a typical PS application imply the generation of multivariate time-space series with many gaps in time and space. Considering this, a new method is presented based on the kriging technique along with Principal Component Analysis and Independent Component Analysis. Additionally, a new technique to interpolate data in time and space is proposed, which is more appropriate for PS systems. The results indicate that the accuracy of the estimates improves with the amount of data, i.e., one variable, multiple variables, and space and time data. Also, the results clearly show the advantage of a PS system compared with a traditional measuring system in terms of the precision and spatial resolution of the information provided to the users. One key challenge in PS systems is that of the determination of the locations and number of users where to obtain samples from so that the variables of interest can be accurately represented with a low number of participants. To address this challenge, the use of density maps is proposed, a technique that is based on the current estimations of the variable. The density maps are then utilized by the incentive mechanism in order to encourage the participation of those users indicated in the map. The experimental results show how the density maps greatly improve the quality of the estimations while maintaining a stable and low total number of users in the system. P-Sense, a PS system to monitor pollution levels, has been implemented and tested, and is used as a validation example for all the contributions presented here. P-Sense integrates gas and environmental sensors with a cell phone, in order to monitor air quality levels.

Today’s process control industry, which is extensively automated, generates huge amounts of process data from the sensors used to monitor the processes. These data if effectively analyzed and interpreted can give a clearer picture of the performance of the underlying process and can be used for its proactive monitoring. With the great advancements in computing systems a new genre of process monitoring and fault detection systems are being developed which are essentially data-driven. The objectives of this research are to explore a set of data-driven methodologies with a motive to provide a predictive modeling framework and to apply it to process control. This project explores some of the data-driven methods being used in the process control industry, compares their performance, and introduces a novel method based on statistical process control techniques. To evaluate the performance of this novel predictive modeling technique called Multi-state PLS, a patented continuous process analytics technique that is being developed at Emerson Process Management, Austin, some extensive simulations were performed in MATLAB. A MATLAB Graphical User Interface has been developed for implementing the algorithm on the data generated from the simulation of a continuously stirred blending tank. The effects of noise, disturbances, and different excitations on the performance of this algorithm were studied through these simulations. The simulations have been performed first on a steady state system and then applied to a dynamic system .Based on the results obtained for the dynamic system, some modifications have been done in the algorithm to further improve the prediction performance when the system is in dynamic state. Future work includes implementing of the MATLAB based predictive modeling technique to real production data, assessing the performance of the algorithm and to compare with the performance for simulated data. / text

No description available.

The plant cell wall contains significant quantities of renewable polymers in the form of cellulose, hemicellulose, and lignin. These three renewable polymers have the potential to complement or replace synthetic polymers in a variety of applications. Rapidly determining the quantities of these polysaccharides in lignocellulosic biomass is important yet difficult since plant biomass is recalcitrant and highly variable in composition. Part of this contribution outlines a novel compositional analysis protocol using infrared spectroscopy and multivariate regression techniques that is rapid and inexpensive. Multivariate regression models based on calibration mixtures can be used to discern between populations of lignocellulosic biomass or to predict cellulose, hemicellulose, and lignin quantities. Thus, the compositional analysis step can be expedited so that other processes, like fractionation of the lignocellulose polymers, can be tuned accordingly to maximize the value of the final product. Hybrid materials were also generated using a variety of polymerization techniques and post-polymerization modifications. A novel controlled/living radical polymerization initiator was synthesized (2-bromo-2-methylpropane hydrazide) containing a hydrazide functionality that was covalently linked to the reducing-end of dextran. Despite the rapid coupling of the hydrazide- based initiator to the reducing-end of dextran, the instability of the alkyl bromide bond resulted in several unsuccessful attempts at Cu(0)-mediated controlled/living radical polymerization. Recommendations were given to improve the stability of this compound; however, an alternative approach to synthesizing hybrid copolymers was also investigated in parallel. Hyperbranched polymers were synthesized using commercially available vinyl and divinyl monomers in the presence of a cobalt(II) complex that enabled control over the size, architecture, and mol% of pendant vinyl groups amenable to post-polymerization modification. Modifying the ratio of divinyl monomer to cobalt(II) complex provided a series of hyperbranched polymers with variable morphology and mol% pendant vinyl groups. The pendant vinyl bonds were subsequently converted to amines via thiol additions with cysteamine. These amine functionalized hyperbranched polymers were then used in a subsequent reductive amination reaction with the reducing-end of dextran to produce the amphiphilic core-shell copolymer poly(methyl methacrylate-co-ethylene glycol dimethacrylate)-b-dextran. These amphiphilic copolymers mimicked the colloidal behaviour of conventional block copolymer micelles without requiring difficult syntheses or tedious self-assembly steps. / Thesis (Master, Chemical Engineering) -- Queen's University, 2012-02-28 11:20:01.568

Computed Tomography is one of the most important image modalities in medical imaging nowadays. Recent developments have led to a new acquisition technique called 'dual-energy', where images are taken with different x-ray spectra. This enables for the first time spectral information in the CT dataset. Our approach was to use an energy resolving detector (Medipix) and investigate its potential in the medical imaging domain. Images are taken in different energy bins. For acquisition of the data, a CT scanner called 'Medipix All Resolution System' (MARS) scanner was constructed. It was upgraded to achieve better image quality as well as faster scan time and a stable operation. In medical imaging, it is important to achieve a high contrast and a good detail recognition at a low dose. Therefore, it is common practice to use contrast agents to highlight certain regions of the body like e.g. the vascular system. But with a broad spectrum acquisition, it is often impossible to distinguish highly absorbing body elements like bones from the contrast agent. We target this problem by a contrast agent study using different energy bins. This so called spectral contrast agent study has been conducted with small animals using the MARS scanner. The data has been processed to create an optimal CT reconstruction. The image enhancement techniques consist of corrections for noisy pixels, intensity fluctuations and eliminating streaks in the sinograms to reduce ring artifacts. In order to evaluate the data, we used two methods of material identification. The material reconstruction method works on projection data and uses a maximum-likelihood estimation to reconstruct images of base materials. The second method, the principal component analysis (PCA), identifies the relevant information from the spectral dataset in a few derived variables that account for most of the variance in the dataset. This resulted in images with enhanced contrast and removed redundancies. It is possible to combine these images in one colour image where anatomical structures are shown in good detail and certain materials show up in different colors. Based on this new information from spectral data, we could show that it is possible to distinguish the spinal bone from contrast agent.

A systematic methodology for quantitative structure-activity relationship (QSAR) development in environmental toxicology is provided. The methodology is summarized in a strategy with six sequential steps. The strategy rests on two cornerstones, namely (1) the use of statistical design to select a series of representative compounds (the so-called training set) on which to base a QSAR, and (2) the multivariate modelling of the relationship between physicochemical and biological properties of the training set compounds. The first step of the strategy is the division of chemicals into classes of structurally similar compounds. Briefly, steps 2 to 6 are: (2) physico-chemical and structural characterization of the compounds in a class, (3) selection of a training set of representative compounds, (4) biological testing of the selected training set, (5) QSAR model development, and (6) experimental validation of the QSAR and predictions for non-tested compounds. The thesis summarizes the results obtained from the application of the strategy to the class of halogenated aliphatic compounds. Biological measurements were made in four biological test systems, reflecting acute toxicity, mutagenicity, relative cytotoxicity and genotoxicity. QSARs were developed relating each biological endpoint to the structural descriptors of the compounds. Multivariate PLS modelling was used in the data analysis. The developed QSARs were used for predicting the biological activity pattern of the non-tested compounds in the class. These predictions may be used as a starting point for a priority ranking for further biological testing of these compounds. The strategy has not been developed solely for establishing QSARs for the halogenated aliphatics class. On the contrary, this work is intended to demonstrate a generally applicable QSAR methodology. / <p>Diss. (sammanfattning) Umeå : Umeå universitet, 1991</p> / digitalisering@umu

This thesis investigates the application of principal component analysis to the Australian stock market using ASX200 index and its constituents from April 2000 to February 2014. The first ten principal components were retained to present the major risk sources in the stock market. We constructed portfolio based on each of the ten principal components and named these “principal portfolios