Monitoring the functional connectivity between brain networks is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded EEG such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. Here we utilize mutual information (MI) as the metric for determining nonlinear statistical dependencies between electroencephalographic (EEG) channels. Previous work investigating MI between EEG channels in subjects with widespread diseases of the cerebral cortex had subjects simply rest quietly with their eyes closed. In motor disorders such as Parkinson’s disease (PD), abnormalities are only expected during performance of motor tasks, but this makes the assumption of stationarity of relationships between EEG channels untenable. We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). After suitable thresholding of the MI values between channels in the temporally segmented EEG, graphical theoretical analysis approaches are applied to the derived networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects on and off medication performing a motor task involving either their right hand only or both hands simultaneously. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference between subject groups. This proposed segmentation/MI network method appears to be a promising approach for EEG analysis.

Regression models with a scalar response and a functional predictor have been extensively studied. One approach is to approximate the functional predictor using basis function or eigenfunction expansions. In the expansion, the coefficient vector can either be fixed or random. The random coefficient vector is also known as random effects and thus the regression models are in a mixed effects framework. The random effects provide a model for the within individual covariance of the observations. But it also introduces an additional parameter into the model, the covariance matrix of the random effects. This additional parameter complicates the covariance matrix of the observations. Possibly, the covariance parameters of the model are not identifiable. We study identifiability in normal linear mixed effects models. We derive necessary and sufficient conditions of identifiability, particularly, conditions of identifiability for the regression models with a scalar response and a functional predictor using random effects. We study the regression model using the eigenfunction expansion approach with random effects. We assume the random effects have a general covariance matrix and the observed values of the predictor are contaminated with measurement error. We propose methods of inference for the regression model's functional coefficient. As an application of the model, we analyze a biological data set to investigate the dependence of a mouse's wheel running distance on its body mass trajectory.

In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.

In this thesis I describe a functional genomics resource in which each yeast gene, with its native promoter and 3’UTR, is cloned on a uniquely barcoded low-copy vector. We refer to this resource as the Molecular Barcoded Yeast ORF (MoBY-ORF) library 1.0. Each gene carried by MoBY-ORF 1.0 should mimic its native expression and thus is best suited for complementation cloning. The vector backbone of MoBY-ORF 1.0 is compatible with the mating-assisted genetically integrated cloning (MAGIC) system for recombination cloning in bacterial cells, which allows the transfer of the ORF fragment and its barcoded cassette to other vector backbones. Taking advantage of the MAGIC system, we created a multi-copy version of the library, which we refer to as MoBY-ORF 2.0. I used MoBY-ORF 1.0 to map drug resistant mutants by complementation cloning with a barcode microarray readout. I investigated several drugs with known targets in my proof-of-principle experiments and showed the feasibility of this method. I identified a single mutation that causes resistance to two different natural products, theopalauamide and stichloroside. By doing so, I was able to link these two chemicals to their cellular target, ergosterol. In fact, theopalauamide represents a new class of sterol binding chemical. I also describe the use of MoBY-ORF 2.0 to clone dosage suppressors of conditional temperature-sensitive mutants. By doing so, and combing our own data with published literature, we showed that dosage suppression interactions often overlap with protein-protein interactions and negative genetic interactions but not positive interactions; however the majority of dosage suppression interactions are unique and thus they represent an unique edge on a global functional interaction map. We also describe the first genome-wide dosage suppressor interaction map of budding yeast.

The subject of this work is functional equations with direction towards linear functional equations. The .rst part describes function sets where iterates of the functions converge to a .xed point. In the second part the convergence property is used to provide solutions to linear functional equations by de.ning solutions as in.nite sums. Furthermore, this work contains some transforms to linear form, examples of functions that belong to di¤erent classes and corresponding linear functional equations. We use Mathematica to generate solutions and solve itera- tively equations.

Two Texas fed beef and cow/bull packing plants were surveyed for high pH beef carcasses as well as the evaluation of functional properties of high pH beef in whole muscle beef jerky, frankfurters, and snack stick production. An estimated 42% of cow carcasses were of 6.0 muscle pH or greater as well as exhibiting darker, less red colored lean. Jerky high in pH from cow inside rounds resulted in the greatest percent moisture and least percent protein compared to other treatments. High pH cooked jerky were higher in (P < 0.05) pH and water activity and lower in percent fat and shear force values. In cooked jerky slices, fed-high resulted in the highest percent MPR and cook yield, but was the most tender. In contrast, fed-normal resulted in the least water activity, MPR and toughest jerky slices. In frankfurter production, emulsion stability and hydration values was highest for C (1.98mL) followed by 100H (3.37mL) that decreased as percent high pH decreased. As storage day increased, frankfurter pH decreased, especially by day 56 (5.67) where LAB and APC counts had reached a log of 6.0 indicative of spoilage by day 28 and became lighter and less red in color with minimal lipid oxidation. 100H was harder and less cohesive with trained panelists reported containing at least 50% high pH meat was harder. The pH and internal color of cooked snack sticks significantly increased and became lighter and redder as percent high pH meat increased. Even though water activity compared to a whole muscle dry product or an emulsified, water added product ranged from 0.85 to 0.86 (P > 0.05), minimal (P greater than 0.05) lipid oxidation occurred. 100N resulted in the least percentage of fat but 3% more (P less than 0.05) protein and highest shear force values. Overall, beef raw materials high in pH may be better suited in a semi-dry fermented product.

Organic semiconductors have generated huge interested in recent years for low-cost and flexible electronics. Current and future device applications for semiconducting polymers include light-emitting diodes, thin-film transistors, photovoltaic cells, chemical and biological sensors, photodetectors, lasers, and memories. The performance of conjugated polymer devices depends on two major factors: the chain conformation in polymer film and the device architecture. Highly ordered chain structure usually leads to much improved performance by enhancing interchain interaction to facilitate carrier transport. The goal of this research is to improve the performance of organic devices with the nanoimprint lithography. The work begins with the controlling of polymer chain orientation in patterned nanostructures through nanoimprint mold design and process parameter manipulation, and studying the effect of chain ordering on material properties. After that, step-and-repeat thermal nanoimprint technique for large-scale continuous manufacturing of conjugated polymer nanostructures is developed. The actual chain orientation of molecular groups in polymer micro- and nanostructures patterning by nanoimprint is complicated. However, this information is crucial for intelligently controlling the electrical and photophysical properties of conjugated polymers by nanoimprint. Systematic investigation of polymer chain configuration by Raman spectroscopy is carried out to understand how nanoimprint process parameters, such as mold pattern size, temperature, and polymer molecular weight, affects polymer chain configuration. The results indicate that chain orientation in nanoimprinted polymer micro- and nanostructures is highly related to the nanoimprint temperature and the dimensions of the mold structures. The ability to create nanoscale polymer micro- and nanostructures and manipulate their internal chain conformation establishes an original experimental platform that enables studying the properties of functional polymers at the micro- and nanoscale and understanding their fundamental structure-property relationships. In addition to the impact on basic research, the techniques developed in this work are important in applied research and development. Large-area conjugated polymer micro- and nanostructures can be easily fabricated by thermal step-and-repeat nanoimprint for organic flat-panel displays, organic circuits and organic solar panels. The ability to manipulate chain orientation through nanoimprint presents a new route to fine-tune the electrical and photophysical properties of conjugated polymers, which can lead to improved performance for all organic electronics. The techniques developed here also allow for easy incorporation of other micro- and nanoscale soft functional polymers in miniaturized devices and systems for new applications in electronics, photonics, sensors and bioengineering.

Thesis--Ph. D., University of Hong Kong, 1983.

We develop the dual system encryption methodology to provide fully secure functional encryption systems. We introduce new proof techniques and explore their applications, resulting in systems that advance the state of the art in terms of functionality, security, and efficiency. Our approach constructs versatile tools for adapting the dual system encryption methodology to new functionalities and efficiency goals. As particular demonstrations of our techniques, we obtain fully secure ciphertext-policy attribute-based encryption systems in the single authority and decentralized settings. Our work has provided the first fully secure attribute-based encryption schemes as well as the first decentralized schemes achieving desired levels of flexibility. / text

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