<p>   </p> <p>Ambient Mass Spectrometry Imaging (MSI) is a powerful analytical tool in biomedical research that enables simultaneous label-free spatial mapping of hundreds of molecules in biological samples under native conditions. Nanospray desorption electrospray ionization (nano-DESI) is an emergent ambient MSI technique developed in 2010 that uses localized liquid extraction of molecules directly from surfaces. Like other liquid-extraction based techniques, nano-DESI relies on gentle removal of molecules from surfaces and soft ionization. High sensitivity and spatial resolution, versatility of the solvent composition, which may be used to tailor the extraction and ionization of selected molecules, quantification capabilities at the single-pixel level as well as compensation for matrix effects by adding a known standard to the solvent, and online derivatization are key features of nano-DESI MSI that position it as a unique analytical tool for studying biological systems. </p> <p>Despite the advantages that nano-DESI provides, there are still challenges associated with the structural characterization, extraction, and detection of certain molecular classes. Therefore, my dissertation research has focused on addressing these analytical challenges by developing innovative approaches that substantially enhance the performance of the nano-DESI technique in the study of complex biological systems. </p> <p>In this thesis, a systematic study of the solvent composition is carried out to aid in the detection of neutral lipids such as triglycerides thereby expanding the molecular coverage of nano-DESI experiments. Taking advantage of the versatility of the solvent composition, I developed an approach for the online derivatization of unsaturated lipids into lipid hydroperoxides using the reaction of singlet oxygen with C=C bonds. This method further expands the specificity of nano-DESI MSI by enabling the detection and imaging of positional lipid isomers. To aid in the analysis of complex mixtures and provide additional structural information in the form of collision cross sections, coupling of nano-DESI with a drift-tube ion mobility spectrometry is also reported along with examples of the powerful capabilities of this platform. Lastly, nano-DESI MSI is used to address the complexity in the analysis of individual skeletal muscle fibers. This collaborative project involves the development of a robust image registration approach of immunofluorescence imaging and high-spatial resolution nano-DESI MSI to obtain accurate chemical maps specific to each fiber type. The developments described in this thesis are key to understanding the dynamic metabolic processes on a molecular level with an unprecedented specificity and sensitivity.</p> <p>  </p>

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 desorption that differs significantly from the widely-accepted momentum transfer model.

Marine organisms are widely recognized sources of an impressive array of structurally unusual compounds. Marine natural products have exhibited interesting biomedical activities, provided targets for synthetic organic chemists, and afforded opportunities for elucidation of enzymatic mechanisms involved in biosyntheses of these molecules. Secondary metabolite pathways probably evolved to mediate interactions between organisms in their natural habitats; however, the ecological functions of natural products remain poorly understood for the vast majority of cases. In the present series of investigations, I evaluate the hypothesis that macroalgal natural products play a role in defending these organisms against potentially pathogenic microbes in the marine environment. Further, I combine these ecology-driven investigations with evaluation of algal natural products as sources of novel human drugs. This combined approach resulted in discovery of 15 novel natural products from two tropical red algae, Callophycus serratus and an unidentified crustose red alga. These new molecules included seven novel carbon-carbon connectivity patterns, not previously reported in the synthetic or natural product literature, illustrating the abundance of secondary metabolite diversity among marine macroalgae. Further, many compounds exhibited both biomedical and ecological activities, suggesting the synergistic potential of combined biomedical/ecological investigations in providing drug leads as well as insights into the natural functions of secondary metabolites. Bromophycolides and callophycoic acids, natural products from C. serratus, inhibited growth of the marine fungal pathogen Lindra thalassiae. Spatially-resolved desorption ionization mass spectrometry (DESI-MS) revealed that antifungal natural products were found at specific sites on algal surfaces. The heterogeneous presentation of antimicrobial chemical defenses on host surfaces suggests the potential importance of spatial scale in understanding host-pathogen interactions, and illustrates the capacity of mass spectrometry imaging in understanding chemically-mediated biological processes. Finally, assessment of antimicrobial chemical defenses among extracts from 72 collections of tropical red algae revealed that nearly all algae were defended against at least one marine pathogen or saprophyte and further suggested the untapped potential of ecological investigations in the discovery of novel chemistry.

(EN) Presented PhD thesis was aimed at the utilization of desorption ionization mass spectrometric techniques to study different types of biological samples. An important part of this thesis was the construction of a universal platform for ambient ionization techniques (AIT) in mass spectrometry (MS) for the experiments performed in combination with desorption electrospray ionization (DESI) and desorption atmospheric pressure photoionization (DAPPI). Initially manual platform for AIT was during the development rebuild into a motorized platform operated by software via user interface of the mass spectrometer. Using the motorized platform it was possible to carry out a one- dimensional (1-D) and two dimensional (2-D) samples analysis with a defined step size of imaging. Platform designed for AIT in MS was tested in DESI-MS mode by model analyte Rhodamine B and by plant samples which were represented by Aconitum plicatum. The platform was also tested on biological samples of vernix caseosa using DAPPI-MS ion source. Model analyte rhodamine B was in DESI-MS technique used to study the basic desorption ionization processes occurring in the DESI during the sample analysis. In order to highlight the surface structures the colloidal solution of ferrite nanoparticles was added into the sprayed liquid. For...

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Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities necessitates the use of mathematical and computational techniques for quicker, more accurate and more robust analysis of medical imagery. The fields of computer vision and machine learning provide a rich set of techniques that are useful in medical image analysis, in tasks ranging from segmentation to classification and population analysis, notably by integrating the qualitative knowledge of experts in anatomy and the pathologies of various disorders and making it applicable to the analysis of medical imagery going forward. The object of the proposed research is exactly to explore various computer vision and machine learning methods with a view to the improved analysis of multiple modalities of brain and cardiac imagery, towards enabling the clinical goals of studying schizophrenia, brain tumors (meningiomas and gliomas in particular) and cardiovascular disorders. In the first project, a framework is proposed for the segmentation of tubular, branched anatomical structures. The framework uses the tubular surface model which yields computational advantages and further incorporates a novel automatic branch detection algorithm. It is successfully applied to the segmentation of neural fiber bundles and blood vessels. In the second project, a novel population analysis framework is built using the shape model proposed as part of the first project. This framework is applied to the analysis of neural fiber bundles towards the detection and understanding of schizophrenia. In the third and final project, the use of mass spectrometry imaging for the analysis of brain tumors is motivated on two fronts, towards the offline classification analysis of the data, as well as the end application of intraoperative detection of tumor boundaries. SVMs are applied for the classification of gliomas into one of four subtypes towards application in building appropriate treatment plans, and multiple statistical measures are studied with a view to feature extraction (or biomarker detection). The problem of intraoperative tumor boundary detection is formulated as a detection of local minima of the spatial map of tumor cell concentration which in turn is modeled as a function of the mass spectra, via regression techniques.

Bruhn (2008) and Griffiths (1978) have referred in passing to Messiaen's use of non-Western content as an appropriation, but a consideration of its potential moral and aesthetic failings within the scope of modern literature on artistic cultural appropriation is an underexplored topic. Messiaen's first encounter with India came during his student years, by way of a Sanskrit version of Saṅgītaratnākara (c. 1240 CE) written by the thirteenth-century Hindu musicologist Śārṅgadeva. I examine Messiaen's use of Indian deśītālas within a cultural appropriation context. Non-Western music provided a safe space for him to explore the familiar, and served as validation for previously held creative interests, prompting the expansion and development of rhythmic techniques from the unfamiliar. Chapter 1 examines the different forms of artistic cultural appropriation, drawing on the ideas of James O. Young and Conrad G. Brunk (2012) and Bruce H. Ziff and Pratima V. Rao (1997). I consider the impact of power dynamic inequality between 'insider' and 'outsider' cultures. I evaluate the relation between aesthetic errors and authenticity. Chapter 2 considers the internal and external factors and that prompted Messiaen to draw on non-Western rhythm. I examine Messiaen's appropriation of Indian rhythm in relation to Bloomian poetic misreading, and whether his appropriation of Indian rhythm reveals an authentic intention. Chapter 3 analyses Messiaen's interpretation of Śārṅgadeva's 120 deśītālas and its underlying Hindu symbolism. Chapter 4 contextualises Messiaen's Japanese poem Sept haïkaï (1962) in relation to other European Orientalist artworks of the late-nineteenth and early-twentieth centuries, and also in relation to Michael Sullivan's (1987: 209) three-tiered definitions of japonism.

<div> Advancements in cancer treatments have increased rapidly in recent years, but cures remain elusive. Surgical tumor resection is a central treatment for many solid malignancies. Residual tumor at surgical margins leads to tumor recurrence. Novel tools for assessing residual tumor at surgical margins could improve surgical outcomes by helping to maximize the extent of resection. Ambient ionization-mass spectrometry (MS) methods generate and analyze ions from minimally prepared samples in near-real-time (e.g. seconds to minutes). These methods leverage the high sensitivity and specificity of mass spectrometry for analyzing gas phase ions and generating those ions quickly and with minimal sample preparation. Recent work has shown that differential profiles of ions, corresponding to phospholipids and small metabolites, are detected from cancerous and their respective normal tissue with ambient ionization-MS methods. When properly implemented, ambient ionization-MS could be used to assess for tumor at surgical margins and provide a molecular diagnosis during surgery. </div><div><br></div><div>The research herein reports efforts in developing rapid intraoperative ambient ionization-MS methods for the molecular assessment of cancerous tissues. Touch spray (TS) ionization and desorption electrospray ionization (DESI) were utilized to analyze kidney cancer and brain cancer.</div><div><br></div><div> As a demonstration of the applicability of TS-MS to provide diagnostic information from fresh surgical tissues, TS-MS was used to rapidly analyze renal cell carcinoma and healthy renal tissue biopsies obtained from human subjects undergoing nephrectomy surgery. Differential phospholipid profiles were identified using principal component analysis (PCA), and the significant ions were characterized using multiple stages of mass spectrometry and high resolution/exact mass MS. The same TS-MS analyzed renal tissues were subsequently analyzed with DESI-MS imaging to corroborate the TS-MS results, and the significant DESI-MS ions were also characterized with MS.</div><div><br></div><div>Significant efforts were made in developing and evaluating a standalone intraoperative DESI-MS system for analyzing brain tissue biopsies during brain tumor surgery. The intraoperative DESI-MS system consists of a linear trap quadrupole mass spectrometer placed on a custom-machined cart that contains all hardware for operating the mass spectrometer. This instrument was operated in the neurosurgical suites at Indiana University School of Medicine to rapidly analyze brain tissue biopsies obtained from glioma resection surgeries. A DESI-MS library of normal brain tissue and glioma was used to statistically classify the brain tissue biopsies collected in the operating room. Multivariate statistical methodologies were employed to predict the disease state and tumor cell percentage of the samples. A DESI-MS assay for detecting 2-hydroxyglutarate (2HG), the oncometabolic product of the isocitrate dehydrogenase (IDH) mutation (a key glioma prognostic marker), was developed and applied to determine the IDH mutation status during the surgical resection. The strengths, weaknesses, and areas of future work in this field are discussed. </div><div><br></div>

Masters of Science / Observations that push the boundaries have historically fuelled scientific breakthroughs, and these observations frequently involve phenomena that were previously unseen and unidentified. Data sets have increased in size and quality as modern technology advances at a record pace. Finding these elusive phenomena within these large data sets becomes a tougher challenge with each advancement made. Fortunately, machine learning techniques have proven to be extremely valuable in detecting outliers within data sets. Astronomaly is a framework that utilises machine learning techniques for anomaly detection in astronomy and incorporates active learning to provide target specific results. It is used here to evaluate whether machine learning techniques are suitable to detect anomalies within the optical astronomical data obtained from the Dark Energy Camera Legacy Survey. Using the machine learning algorithm isolation forest, Astronomaly is applied on subsets of the Dark Energy Camera Legacy Survey (DECaLS) data set. The pre-processing stage of Astronomaly had to be significantly extended to handle real survey data from DECaLS, with the changes made resulting in up to 10% more sources having their features extracted successfully. For the top 500 sources returned, 292 were ordinary sources, 86 artefacts and masked sources and 122 were interesting anomalous sources. A supplementary machine learning algorithm known as active learning enhances the identification probability of outliers in data sets by making it easier to identify target specific sources. The addition of active learning further increases the amount of interesting sources returned by almost 40%, with 273 ordinary sources, 56 artefacts and 171 interesting anomalous sources returned. Among the anomalies discovered are some merger events that have been successfully identified in known catalogues and several candidate merger events that have not yet been identified in the literature. The results indicate that machine learning, in combination with active learning, can be effective in detecting anomalies in actual data sets. The extensions integrated into Astronomaly pave the way for its application on future surveys like the Vera C. Rubin Observatory Legacy Survey of Space and Time.