Physical Sciences and Mathematics Commons^™

Positron Emission Tomography In Oncology And Environmental Science, Samantha Delaney

Dissertations, Theses, and Capstone Projects

The last half century has played witness to the onset of molecular imaging for the clinical assessment of physiological targets. While several medical imaging modalities allow for the visualization of the functional and anatomical properties of humans and living systems, few offer accurate quantitation and the ability to detect biochemical processes with low-administered drug mass doses. This limits how physicians and scientists may diagnose and treat medical issues, such as cancer, disease, and foreign agents.

A promising alternative to extant invasive procedures and suboptimal imaging modalities to assess the nature of a biological environment is the use of positron emission …

Indolizine Donor-Based Dyes For Applications In Fluorescence Biological Imaging, William Meador

Honors Theses

NIR emissive fluorophores are intensely researched due to their potential to replace modern imaging procedures. Many molecular strategies have been employed in the literature to optimize fluorophores for deeper NIR absorption and emission, biocompatibility, and higher fluorescence quantum yields. Amongst the fluorophores studied to date, proaromatic indolizine donors are attractive alternatives to traditional alkyl amine and indoline based donors due to their 1) lower energy absorption and emission facilitated by proaromaticity, 2) large Stokes shifts due to increased dihedral angles about the π-system, 3) ease of functionalization and capacity for bioconjugation at the phenyl ring, and 4) potential for further …

Developing A Method To Track Marine Snow Aggregation Through Individual Collisions Using Stereoscopic Imaging, Riley Henning

Theses

The aggregation of individual phytoplankton into marine snow allows particles to sink more quickly, thus resulting in the transport of particulate organic carbon from surface waters to the deep ocean. Aggregate formation has previously been measured in experiments indirectly by quantifying how particle size or particle concentration changes over time. Here, I present my thesis in which I applied high-resolution imaging to quantify aggregate formation using two different methodologies.

We conducted experiments to investigate aggregate formation using stereoscopic imaging, tracking individual particles directly in a 3D volume. Phytoplankton cultures were rolled in cylindrical tanks and imaged by two cameras illuminated …

Using Fundamental Properties Of Light To Investigate Photonic Effects In Condensed Matter And Biological Tissues, Laura A. Sordillo

Dissertations and Theses

Light possesses characteristics such as polarization, wavelength and coherence. The interaction of light and matter, whether in a semiconductor or in a biological sample, can reveal important information about the internal properties of a system. My thesis focuses on two areas: photocarriers in gallium arsenide and biomedical optics. Varying the excitation wavelength can be used to study both biological tissue and condensed matter. I altered the excitation wavelengths to be in the longer near-infrared (NIR) optical windows, in the shortwave infrared (SWIR) range, a wavelength region previously thought to be unusable for medical imaging. With this method, I acquired high …

A Framework For The Statistical Analysis Of Mass Spectrometry Imaging Experiments, Kyle Bemis

Open Access Dissertations

Mass spectrometry (MS) imaging is a powerful investigation technique for a wide range of biological applications such as molecular histology of tissue, whole body sections, and bacterial films , and biomedical applications such as cancer diagnosis. MS imaging visualizes the spatial distribution of molecular ions in a sample by repeatedly collecting mass spectra across its surface, resulting in complex, high-dimensional imaging datasets. Two of the primary goals of statistical analysis of MS imaging experiments are classification (for supervised experiments), i.e. assigning pixels to pre-defined classes based on their spectral profiles, and segmentation (for unsupervised experiments), i.e. assigning pixels to newly …

Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li

Computer Science Theses & Dissertations

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …

Characterization Of The Desorption Electrospray Ionization Mechanism Using Microscopic Imaging Of The Sample Surface, Michael Craig Wood

Theses and Dissertations

Desorption electrospray ionization (DESI) is an ambient ionization technique for mass spectrometry. This solvent based desorption ion source has wide applicability in surface analysis with minimal sample preparation. Interest in improving detection limits, broadening applications, and increasing the spatial resolution for chemical imaging has led to studies of the DESI mechanism. An inverted microscope has been used to image interactions between the DESI spray and test analytes on a glass surface. Microscopic images recorded with millisecond time resolution have provided important insights into the processes governing analyte transport and desorption. These insights are the basis of a rivulet-based model for …

Associative Pattern Mining For Supervised Learning, Harpreet Singh

Doctoral Dissertations

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known …