Metabolomics is a post genomic field of research concerned with the study of low molecular weight compounds within a biological system permitting the investigation of the metabolite differences between natural and perturbed systems (such as cells, organs and tissues). Rapid identification and discrimination of biological samples based upon metabolic differences and physiological status in microbiology, mammalian systems (particularly for disease diagnosis), plants and food science is highly desirable. Volatile organic compound (VOC) profiling is a novel area of research where the composition of the VOCs emitted by the biological samples can be correlated to its origin and physiological status. The aim of this project was to investigate the applicability of VOC profiling as a potential complementary tool within metabolomics.In this project the discrimination of bacteria using a novel gas phase separation method was investigated and the development of VOC-based profiling tools for the collections of VOCs emitted from biological samples was also studied. The optimisation and validation of a high throughput method for VOC analysis was achieved and this was used to assess wound healing.VOC metabolite profiling was further extended to the discrimination of S. typhimurium contaminated meat; the study was conducted in parallel with metabolite profiling analysis for the analysis of non-volatile small molecules. Finally, enhanced vibrational spectroscopic techniques were applied to the characterisation and screening of dye molecules in contaminated foodstuffs using Raman spectroscopy. This thesis clearly demonstrates that VOC metabolic profiling is a complementary tool within the metabolomics toolbox, one of its great attractions is that it permits the characterisation of biological samples in a rapid and non-invasive manner. The technique provides detailed chemical information regarding the VOC composition present above the headspace of the sample and can be used to understand its physiological status and biological origin. VOCs metabolite profiling will become a valuable tool for non-invasive analysis of many biological systems. Raman spectroscopy is a sensitive and non-destructive technique which can generate detailed chemical and structural information regarding the analyte under investigation with little or no sample preparation needed. The effect of the weak Raman signal can be significantly amplified by coupling the analyte molecule to surfaces of nanoparticles and demonstrated that it is ideal for analysing aqueous dye solutions in a quantitative manner.

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

The study analyzed hydro-climatic and land use sensitivities of stormwater runoff and quality in the complex coastal urban watershed of Miami River Basin, Florida by developing a Storm Water Management Model (EPA SWMM 5). Regression-based empirical models were also developed to explain stream water quality in relation to internal (land uses and hydrology) and external (upstream contribution, seawater) sources and drivers in six highly urbanized canal basins of Southeast Florida. Stormwater runoff and quality were most sensitive to rainfall, imperviousness, and conversion of open lands/parks to residential, commercial and industrial areas. In-stream dissolved oxygen and total phosphorus in the watersheds were dictated by internal stressors while external stressors were dominant for total nitrogen and specific conductance. The research findings and tools will be useful for proactive monitoring and management of storm runoff and urban stream water quality under the changing climate and environment in South Florida and around the world.

Prostate cancer is the most common cancer in men and the third leading cause of cancer-related deaths in Canadian men. Despite hormone and radiation therapies, most patients progress to late-stage metastatic castrate-resistant prostate cancer (mCRPC). 177Lu-PSMA617 radioligand therapy (rLT) is a radioactive biochemical substance that targets the human prostate-specific membrane antigen (hPSMA). This rLT has been used in compassionate trials in mCRPC patients and has been demonstrated significant clinical efficacy. However, recent findings suggest that this efficacy is short-lived, and most patients exhibit tumor recurrence [96]. Here we establish a murine model to study the anti-tumor effects and the corresponding immune response of 177Lu-PSMA617 rLT on prostate cancer. We generated a doxycycline-inducible hPSMA-expressing murine prostate cancer (hPSMA TRAMP-C2) cell line with high binding responses to PSMA617. Using this system, we evaluated the in vitro and in vivo binding of 177Lu-PSMA617 to the hPSMA TRAMP-C2 cell line. Here, we show that the hPSMA TRAMP-C2 cell line expresses hPSMA upon doxycycline induction and that 177Lu-PSMA617 can bind to its target in vitro and in vivo. Together, these results show that the developed hPSMA TRAMP-C2 cell line can be used to investigate therapeutic and immunological responses targeted against PSMA in prostate cancer. / Graduate

Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this thesis is to further extend the concept of tensor decomposition through the use of function spaces. In doing so, we address the limitations of PCA to vector and matrix representations and the CP-Decomposition to tensor representations. Lastly, we provide an application in the context of professional stock car racing.

The diploma thesis is focused on the determination of basic nutritional properties of 26 samples of ground pepper from different countries and evaluates, whether the chemical composition of ground pepper is affected by the geographical origin. The amount of 19 nutritional properties were determined by advanced analytical methodes (ICP-OES, HPLC DAD, HPLC ELSD) together with other laboratory techniques. Final results were statistically processed by the methods of analysis of variance (ANOVA), cluster analysis, principal component analysis (PCA) and discriminant analysis (DA). Statistical evaluation confirmed, that the chemical composition is affected by the geographical origin. Analysis of variance (ANOVA) determined 14 parameters to statistically differ (p

Thesis (M.Sc. Agriculture (Animal Production)) -- University of Limpopo, 2020 / The smallholder dairy sector in South Africa is characterized by a low input production system and poor animal productivity. Research has been carried out to benchmark cow productivity on smallholder dairy herds; however, there is a paucity of information on the current status of breeding practices and the genetic consititution of cattle used in this production system. This information is vital for the development of sound and sustainable breeding programs for SHD production, which can have an enormous positive impact on food security and rural livelihoods. Thus, the aim of this study was to evaluate the levels of genetic diversity and population structure in South African smallholder dairy (SHD) herds using single nucleotide polymorphism (SNP) markers. A total of 192 animals from SHD dairy herds were genotyped using the GeneSeek® Genomic Profiler (GGP) 150K-BeadChip. Four specialized dairy breeds included the Ayrshire(n = 200), Holstein(n = 231), Jersey (n = 224) and Nguni (n = 209) were used as the reference populations. The mean MAF values ranged from 0.30 Ayshire (AYR), Jersey (JER), and Nguni (NGI) to 0.31 Holstein (HOL) and SHD between the populations. There were slight differences in the levels of genetic diversity ranged between 0.39 (JER and NGI) to 0.40 (AYR, HOL, and SHD). A moderate level of inbreeding (0.02) was observed in the SHD population, which results in high genetic diversity among this herds. Principal Component Analysis (PCA) revealed four homogeneous clusters comprising of AYR, HOL, JER, NGI, and a heterogeneous cluster of the SHD. The heterogeneity observed in the SHD population indicates widespread crossbreeding. The model-based cluster analysis corresponded with the PCA and pointed out the predominance of HOL, JER, with marginal gene flow from the AYR and NGI. These results have provided a useful insight into the genetic structure and prevailing breeding practices on South African SHD herds. / National Research Foundation (NRF), Agricultural Research Council (ARC) and University of Limpopo (UL)

The aim of this study is evaluation of using multi-dimensional statistical analyses methods as a tool for simulations of reliability of distribution network. Prefered methods are a cluster analysis (CLU) and a principal component analysis (PCA). CLU is used for a division of objects on the basis of their signs and a calculation of the distance between objects into groups whose characteristics should be similar. The readout can reveal a secret structure in data. PCA is used for a location of a structure in signs of multi-dimensional matrix data. Signs present separate quantities describing the given object. PCA uses a dissolution of a primary matrix data to structural and noise matrix data. It concerns the transformation of primary matrix data into new grid system of principal components. New conversion data are called a score. Principal components generating orthogonal system of new position. Distribution network from the aspect of reliability can be characterized by a number of new statistical quantities. Reliability indicators might be: interruption numbers, interruption time. Integral reliability indicators might be: system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI). In conclusion, there is a comparison of performed SAIFI simulation according to negatively binomial division and provided values from a distribution company. It is performed a test at description of sign dependences and outlet divisions.

Master's thesis "Human-Machine Interface Based on Gestures" depicts the theoretical background of the computer vision and gesture recognition. It describes more in detail different methods that were used to create the application. Practical part of this thesis consists of the description of the developed program and its functionality. Using this application, user should be able to control computer by gestures of both right and left hands and also his head. The program is primarily based on the skin detection that is followed by the recognition of palms and head gestures. There were used two essential methods for these actions, AdaBoost and PCA.

This master's thesis considers methods of face recognition. There are described methods with different approachs: knowledge-based methods, feature invariant approaches, template matching methods and appearance-based methods. This master's thesis is focused particulary on template matching method and statistical methods like a principal component analysis (PCA) and linear discriminant analysis (LDA). There are described in detail template matching methods like active shape models (ASM) and active appearance models (AAM).