Substance abuse is the overindulgence in and dependence on a drug or chemical leading to detrimental effects on the individual’s health and the welfare of those surrounding him or her. Logistic regression analysis is an important tool used in the analysis of the relationship between various explanatory variables and nominal response variables. The objective of this study is to use this statistical method to determine the factors which are considered to be significant contributors to the use or abuse of substances in school-aged children and also determine what measures can be implemented to minimize their effect. The logistic regression model was used to build models for the three main types of substances used in this study; Tobacco, Alcohol and Drugs and this facilitated the identification of the significant factors which seem to influence their use in children.

A novel imaging technology, digital breast tomosynthesis (DBT), is a technique that overcomes the tissue superposition limitation of conventional mammography by acquiring a limited number of X-ray projections from a narrow angular range, and combining these projections to reconstruct a pseudo-3D image. The emergence of DBT as a potential replacement or adjunct to mammographic screening mandates that solutions be found to two of its major limitations, namely X-ray scatter and mono-energetic reconstruction methods. A multi-faceted software-based approach to enhance the image quality of DBT imaging has the potential to increase the sensitivity and specificity of breast cancer detection and diagnosis. A scatter correction (SC) algorithm and a spectral reconstruction (SR) algorithm are both ready for implementation and clinical evaluation in a DBT system and exhibit the potential to improve image quality. A principal component analysis (PCA) based model of breast shape and a PCA model of X-ray scatter optimize the SC algorithm for the clinical realm. In addition, a comprehensive dosimetric characterization of a FDA approved DBT system has also been performed, and the feasibility of a new dual-spectrum, single-acquisition DBT imaging technique has also been evaluated.

Research into traumatic brain injury (TBI) mechanisms is essential for the development of methods to prevent its occurrence. One of the most common ways to incur a TBI is from falls, especially for the young and very old. The purpose of this thesis was to investigate how the acceleration loading curves influenced the occurrence of different types of TBI, namely: epidural hematoma, subdural hematoma, subarachnoid hemorrhage, and contusion. This investigation was conducted in three parts. The first study conducted reconstructions of 20 TBI cases with varying outcomes using MADYMO, Hybrid III, and finite element methodologies. This study provided a dataset of threshold values for each of the TBI injuries measured in parameters of strain and stress. The results of this study indicated that using a combined reconstructive approach produces results which are in keeping with the literature for TBI. The second study examined how the characteristics of the loading curves which were produced from each reconstruction influenced the outcome using a principal components analysis. It was found that the duration of the event accounted for much of the variance in the results, followed with the acceleration components. Different curve characteristics also accounted for differing amounts of variance in each of the lesion types. Study 3 examined how the dynamic response of the impact influenced where in the brain a subdural hematoma (SDH) could occur. It was found that the largest magnitudes of acceleration produced SDH in the parietal lobe, and the lowest in the occipital lobe. Overall this thesis examined the mechanism of injury for TBI using a large dataset with methodologies which complement each other’s limitations. As a result in depth information of the nature of TBI was attained and information provided which may be used to improve future protection and standard development.

Following Merton (1942) science studies has moved from the philosophy of science to a more sociologically minded analysis of scientific activity. This largely involves a shift away from questions that bear on the context of justification – a question of rationality and philosophy, to those that deal with the context of discovery. This thesis investigates changes in science studies in three papers: sociocultural evolutionary theories of scientific change; general trends in science studies - especially concerning the sociology of science; and a principle component analysis (PCA) that details the development and interaction between research programmes in science studies. This thesis describes the proliferation of research programmes in science studies and uses evolutionary theory to make sense of the pattern of change.

Quantification of natural organic matter (NOM) in water is limited by the complex and varied nature of compounds found in natural waters. Current characterization techniques, which identify and quantify fractions of NOM, are often expensive and time consuming suggesting the need for rapid and accurate characterization methods. In this work, principal component analysis of fluorescence excitation-emission matrices (FEEM-PCA) was investigated as a NOM characterization technique. Through the use of jar tests and disinfection by-product formation tests, FEEM-PCA was shown to be a good surrogate for disinfection by-product precursors. FEEM-PCA was also applied in order to characterize differences in humic-like, protein-like, and Rayleigh scattering between multiple source waters and due to differing treatment processes. A decrease in Rayleigh scattering influence was observed for a deep lake intake, and multiple processes were found to significantly affect humic-like substances, protein-like, and Rayleigh scattering fractions.

Following Merton (1942) science studies has moved from the philosophy of science to a more sociologically minded analysis of scientific activity. This largely involves a shift away from questions that bear on the context of justification – a question of rationality and philosophy, to those that deal with the context of discovery. This thesis investigates changes in science studies in three papers: sociocultural evolutionary theories of scientific change; general trends in science studies - especially concerning the sociology of science; and a principle component analysis (PCA) that details the development and interaction between research programmes in science studies. This thesis describes the proliferation of research programmes in science studies and uses evolutionary theory to make sense of the pattern of change.

Quantification of natural organic matter (NOM) in water is limited by the complex and varied nature of compounds found in natural waters. Current characterization techniques, which identify and quantify fractions of NOM, are often expensive and time consuming suggesting the need for rapid and accurate characterization methods. In this work, principal component analysis of fluorescence excitation-emission matrices (FEEM-PCA) was investigated as a NOM characterization technique. Through the use of jar tests and disinfection by-product formation tests, FEEM-PCA was shown to be a good surrogate for disinfection by-product precursors. FEEM-PCA was also applied in order to characterize differences in humic-like, protein-like, and Rayleigh scattering between multiple source waters and due to differing treatment processes. A decrease in Rayleigh scattering influence was observed for a deep lake intake, and multiple processes were found to significantly affect humic-like substances, protein-like, and Rayleigh scattering fractions.

Antioxidants are molecules capable of stabilizing and preventing oxidation. Certain peptides, protein hydrolysates, have shown antioxidant capacities, which are obtained once liberated from the native protein structure. Soy protein isolates (SPI) were enzymatically hydrolyzed by pepsin and pancreatin mixtures. The soy protein hydrolysates (SPH) were fractionated with sequential ultrafiltration (UF) and nanofiltration (NF) membrane steps. Heat pre-treatment of SPI at 95 degrees celsius (C) for 5 min prior to enzymatic hydrolysis was investigated for its effect on peptide distribution and antioxidant capacity. SPH were subjected to UF with a 10 kDa molecular weight cut off (MWCO) polysulfone membrane. UF permeate fractions (lower molecular weight than 10 kDa) were fractionated by NF with a thin film composite membrane (2.5 kDa MWCO) at pH 4 and 8. Similar peptide content and antioxidant capacity (α=0.05) were obtained in control and pre-heated SPH when comparing the respective UF and NF permeate and retentate fractions produced. FCR antioxidant capacities of the SPH fractions were significantly lower than their ORAC antioxidant capacities, and the distribution among the UF and NF fractions was generally different. Most UF and NF fractions displayed higher antioxidant capacities when compared to the crude SPI hydrolysates, showing the importance of molecular weight on antioxidant capacity of peptides. The permeate fractions produced by NF at pH 8 displayed the highest antioxidant capacity, expressed in terms of Trolox equivalents (TE) per total solids (TS): 5562 μmol TE/g TS for control SPH, and 5187 μmol TE/g TS for pre-heated SPH. Due to the improvement in antioxidant capacity of peptides by NF at pH 8, the potential for NF as a viable industrial fractionation process was demonstrated. Principal component analysis (PCA) of fluorescence excitation-emission matrix (EEM) data for UF and NF peptide fractions, followed by multi-linear regression analysis, was assessed for its potential to monitor and identify the contributions to ORAC and FCR, two in vitro antioxidant capacity assays, of SPH during membrane fractionation. Two statistically significant principal components (PCs) were obtained for UF and NF peptide fractions. Multi-linear regression models (MLRM) were developed to estimate their fluorescence and PCA-captured ORAC (ORAC-FPCA) and FCR (FCR-FPCA) antioxidant capacities. The ORAC-FPCA and FCR-FPCA antioxidant capacities for NF samples displayed strong, linear relationships at different pH conditions (R-squared>0.99). Such relationships are believed to reflect the individual and relative combined contributions of tryptophan and tyrosine residues present in the SPH fractions to ORAC and FCR antioxidant capacities. Therefore, the proposed method provides a tool for the assessment of fundamental parameters of antioxidant capacities captured by ORAC and FCR assays.

In pattern recognition, when data has different structures in different parts of the input space, fitting one global model can be slow and inaccurate. Learning methods can quickly learn the structure of the data in local regions, consequently, offering faster and more accurate model fitting. Breaking training data set into smaller subsets may lead to curse of dimensionality problem, as a training sample subset may not be enough for estimating the required set of parameters for the submodels. Increasing the size of training data may not be at hand in many situations. Interestingly, the data in local regions becomes more correlated. Therefore, by decorrelation methods we can reduce data dimensions and hence the number of parameters. In other words, we can find uncorrelated low dimensional subspaces that capture most of the data variability. The current subspace modelling methods have proved better performance than the global modelling methods for the given type of training data structure. Nevertheless these methods still need more research work as they are suffering from two limitations 2 There is no standard method to specify the optimal number of subspaces. &sup2 / There is no standard method to specify the optimal dimensionality for each subspace. In the current models these two parameters are determined beforehand. In this dissertation we propose and test algorithms that try to find a suboptimal number of principal subspaces and a suboptimal dimensionality for each principal subspaces automatically.

国立情報学研究所で電子化したコンテンツを使用している。