MACHINE LEARNING APPLIED TO RAMAN SPECTRA OF PANCREATIC CANCER CELLS TO IDENTIFY MOLECULAR SIGNATURES, PATTERNS AND PROTEIN EXPRESSIONS.

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States, with the life expectancy for patients diagnosed in the late stage ranging from 6-12 months. This study is part of a group effort to examine the hypothesis that, difficult to detect, tumor initiating cells (TIC) exist in small numbers in the solid tumors and are responsible for the cancer’s progression and relapse. The pancreatic cell line MIA PaCa-2 is seeded and grown in 90 Pa 3D fibrin gels for 10 days then samples are collected and Raman spectra obtained from a 784nm laser in the 150-1800nm and 2500-3500nm ranges. These spectra are analyzed via combinations of data preprocessing, wavelength or dimension reduction and machine learning classification algorithms. We extend to other cell lines such as CFPAC-1 and PANC-1. Support vector machine (SVM) and k-nearest neighbors (kNN) supervised machine learning classifiers are applied to the raw and pre-processed data sets and with various statistical and machine learning dimension reduction protocols. These combinations are compared to determine which performed best at classifying cancer and normal cell samples, and which led to selection of the same or similar dimensions. Identification of the best performing dimensions/wavelengths is then attempted from the Raman spectra by comparing them to existing biological molecule Raman databases to identify the patterns in the spectra and any unique molecular signatures or protein expressions that could prove useful to better understanding and therefore treating pancreatic cancer

LASER ABLATION-RESONANCE ENHANCED PHOTOIONIZATION MASS SPECTROMETRY (LA-REPMS) OF PARTICLE-BASED ASSAYS TO IMPROVE EARLY DETECTION OF CANCER

Early detection of cancer has a drastic impact on the successful treatment of the disease. However, detection of early signs of cancer is a challenge especially for a type of cancer such as epithelial ovarian cancer (EOC), with few or no symptoms at the early-stages. Development of a noninvasive method that can improve the detection of biomarkers with sufficient selectivity, sensitivity, and reproducibility is a promising approach to overcome the challenges of early detection. This study aims to develop novel optical and mass spectrographic techniques to detect biomolecules in complex matrices. To accomplish this, Laser Ablation-Resonance Enhanced Photoionization Mass Spectrometry is combined with nano- and micro-particle immunoassay to improve the detectability in a complex media. While there are many commercial mass spectrometry configurations available, none of them meet our specific needs, so a significant portion of the effort in this research to date has been dedicated to designing and building the custom apparatus to meet our needs. We present an overview of the design, testing, and preliminary studies on biomolecules

Novel mechanism for the generation of human xeno-autoantibodies against the nonhuman sialic acid N-glycolylneuraminic acid

The nonhuman sialic acid N-glycolylneuraminic acid (Neu5Gc) is metabolically incorporated into human tissues from certain mammalian-derived foods, and this occurs in the face of an anti-Neu5Gc “xeno-autoantibody” response. Given evidence that this process contributes to chronic inflammation in some diseases, it is important to understand when and how these antibodies are generated in humans. We show here that human anti-Neu5Gc antibodies appear during infancy and correlate with weaning and exposure to dietary Neu5Gc. However, dietary Neu5Gc alone cannot elicit anti-Neu5Gc antibodies in mice with a humanlike Neu5Gc deficiency. Other postnatally appearing anti-carbohydrate antibodies are likely induced by bacteria expressing these epitopes; however, no microbe is known to synthesize Neu5Gc. Here, we show that trace exogenous Neu5Gc can be incorporated into cell surface lipooligosaccharides (LOS) of nontypeable Haemophilus influenzae (NTHi), a human-specific commensal/pathogen. Indeed, infant anti-Neu5Gc antibodies appear coincident with antibodies against NTHi. Furthermore, NTHi that express Neu5Gc-containing LOS induce anti-Neu5Gc antibodies in Neu5Gc-deficient mice, without added adjuvant. Finally, Neu5Gc from baby food is taken up and expressed by NTHi. As the flora residing in the nasopharynx of infants can be in contact with ingested food, we propose a novel model for how NTHi and dietary Neu5Gc cooperate to generate anti-Neu5Gc antibodies in humans

Incidence and Tracking of Escherichia coli O157:H7 in a Major Produce Production Region in California

Fresh vegetables have become associated with outbreaks caused by Escherichia coli O157:H7 (EcO157). Between 1995–2006, 22 produce outbreaks were documented in the United States, with nearly half traced to lettuce or spinach grown in California. Outbreaks between 2002 and 2006 induced investigations of possible sources of pre-harvest contamination on implicated farms in the Salinas and San Juan valleys of California, and a survey of the Salinas watershed. EcO157 was isolated at least once from 15 of 22 different watershed sites over a 19 month period. The incidence of EcO157 increased significantly when heavy rain caused an increased flow rate in the rivers. Approximately 1000 EcO157 isolates obtained from cultures of>100 individual samples were typed using Multi-Locus Variable-number-tandem-repeat Analysis (MLVA) to assist in identifying potential fate and transport of EcO157 in this region. A subset of these environmental isolates were typed by Pulse Field Gel Electrophoresis (PFGE) in order to make comparisons with human clinical isolates associated with outbreak and sporadic illness. Recurrence of identical and closely related EcO157 strains from specific locations in the Salinas and San Juan valleys suggests that transport of the pathogen is usually restricted. In a preliminary study, EcO157 was detected in water at multiple locations in a low-flow creek only within 135 meters of a point source. However, possible transport up to 32 km was detected during periods of higher water flow associated with flooding. During the 2006 baby spinach outbreak investigation, transport was also detected where water was unlikely to be involved. These results indicate that contamination of the environment is a dynamic process involving multiple sources and methods of transport. Intensive studies of the sources, incidence, fate and transport of EcO157 near produce production are required to determine the mechanisms of pre-harvest contamination and potential risks for human illness

IMPROVING SPECTRAL ANALYSIS WITH THE APPLICATION OF MACHINE LEARNING: STUDY OF LASER-INDUCED BREAKDOWN SPECTROSCOPY (LIBS) AND RAMAN SPECTROSCOPY WITH CLASSIFICATION AND CLUSTERING TECHNIQUES.

AN ABSTRACT OF THE THESIS OFChristopher T. Mandrell, for the Master of Science degree in Physics, presented on April 8, 2020, at Southern Illinois University Carbondale.TITLE: IMPROVING SPECTRAL ANALYSIS WITH THE APPLICATION OF MACHINE LEARNING: STUDY OF LASER-INDUCED BREAKDOWN SPECTROSCOPY (LIBS) AND RAMAN SPECTROSCOPY WITH CLASSIFICATION AND CLUSTERING TECHNIQUESMAJOR PROFESSOR: Dr. Poopalasingam SivakumarAtomic and molecular spectroscopy, in the form of LIBS emissions and Raman scattering, respectively, are tools that provide a vast amount of information with little to no sample preparation. For this reason, these techniques are finding their way into a wide range of fields. However, each spectrum is notoriously complicated to analyze, with many complex interactions at play. Machine learning is the result of work on artificial intelligence. It provides tools to train a computer to look for connections in complex data sets that would likely be missed, or not even looked for, by other analytical methods. The combination of highly informative yet complex data with an analysis that is specifically designed to probe highly complex data for meaningful information is a logical step in the analysis of these spectra. Here we apply statistical analysis and classification algorithms to Raman spectra of pancreatic cancer cells and clustering algorithms to LIBS spectra of Mars Curiosity Rover simulants and Raman spectra of Mars Perseverance Rover simulants. We report here high accuracy in the classification of different types of pancreatic cancer cells, and informative clustering of the two Mars rovers’ simulant data

LASER ABLATION-RESONANCE ENHANCED PHOTOIONIZATION MASS SPECTROMETRY (LA-REPMS) OF PARTICLE-BASED ASSAYS TO IMPROVE EARLY DETECTION OF CANCER

Early detection of cancer has a drastic impact on the successful treatment of the disease. However, detection of early signs of cancer is a challenge especially for a type of cancer such as epithelial ovarian cancer (EOC), with few or no symptoms at the early-stages. Development of a noninvasive method that can improve the detection of biomarkers with sufficient selectivity, sensitivity, and reproducibility is a promising approach to overcome the challenges of early detection. This study aims to develop novel optical and mass spectrographic techniques to detect biomolecules in complex matrices. To accomplish this, Laser Ablation-Resonance Enhanced Photoionization Mass Spectrometry is combined with nano- and micro-particle immunoassay to improve the detectability in a complex media. While there are many commercial mass spectrometry configurations available, none of them meet our specific needs, so a significant portion of the effort in this research to date has been dedicated to designing and building the custom apparatus to meet our needs. We present an overview of the design, testing, and preliminary studies on biomolecules

Observation of a Signal Suppressing Effect in a Binary Mixture of Glycol-Water Contamination in Engine Oil with Fourier-Transform Infrared Spectroscopy

An in-depth experimental study of the matrix effect of antifreeze (ethylene glycol) and water contamination of engine oil through FT-IR spectroscopy. With a comparison of the percent by volume concentration of contaminated fresh 15W-40 engine oil, there appeared to be a noticeable reduction in the O–H stretching signal in the infrared spectrum when ethylene glycol based antifreeze was included as a contaminant. The contaminants of distilled water, a 50/50 mixture of water and commercial ethylene glycol antifreeze, and straight ethylene glycol antifreeze were compared and a signal reduction in the O–H stretch was clearly evident when glycol was present. Doubling the volume of the 50/50 mixture as compared to water alone still resulted in a weaker O–H stretching signal. The possibility that this signal reduction was due to the larger ethylene glycol molecule having fewer O–H bonds in a given sample size was eliminated by comparing samples with the same number of O–H bonds per unit volume. The strong hydrogen bonding between that of water and glycol appeared to reduce the O–H stretching signal, even after comparing the different sample types at concentrations with the same number of O–H bonds per unit volume. Tukey’s highly significant difference was used to show that samples of the 50/50 mixture and straight glycol were not reliably distinguishable from one another when comparing the same number of O–H bonds per unit volume but readily distinguishable from that of water as the lone contaminant

UV-Visible Spectrophotometer for Distinguishing Oxidation Time of Engine Oil

Samples of gasoline engine oil (SAE 5W20) that had been exposed to various oxidation times were inspected with a UV-Visible (UV-Vis) spectrophotometer to select the best wavelengths and wavelength ranges for distinguishing oxidation times. Engine oil samples were subjected to different thermal oxidation periods of 0, 24, 48, 72, 96, 120, and 144 hours, resulting in a range of total base number (TBN) levels. Each wavelength (190.5 – 849.5 nm) and selected wavelength ranges were evaluated to determine the wavelength or wavelength ranges that could best distinguish among all oxidation times. The best wavelengths and wavelength ranges were analyzed with linear regression to determine the best wavelength or range to predict oxidation time

Wildlife, Livestock, and Companion Animals

Outbreaks of foodborne illness associated with the consumption of leafy green produce from California and across the United States have heightened the need to identify vertebrate sources of these microbial hazards. Concern has focused on wildlife species that have direct access to the produce production environment and irrigation water supplies. Recent fecal surveys of California wildlife, feral animals, and livestock and companion animals are allowing regulators to compare the food safety risks of such pathogens as E. coli O157:H7 and Salmonella from these various animal species. In order to make valid food safety risk comparisons between wildlife, livestock, and companion animals, a variety of methodological and epidemiological issues need to be addressed in order to avoid substantial biases. For example, the amount of feces tested per animal can vary up to a 1000-fold, substantially biasing the probability of testing positive for large fecal contributors (e.g., cattle) compared to smaller wildlife (e.g., deer mice). Many wildlife species intrude and forage as a group in fields of produce, which can lead to in-field defecation, substantially, elevating the risk of microbial contamination compared to many larger animal species that do not have direct access to produce fields due to fencing. This paper highlights the technical challenges of making valid quantitative comparisons of microbial food safety risks from wildlife compared to other domestic animals

Observation of a Signal Suppressing Effect in a Binary Mixture of Glycol-Water Contamination in Engine Oil with Fourier-Transform Infrared Spectroscopy

An in-depth experimental study of the matrix effect of antifreeze (ethylene glycol) and water contamination of engine oil through FT-IR spectroscopy. With a comparison of the percent by volume concentration of contaminated fresh 15W-40 engine oil, there appeared to be a noticeable reduction in the O–H stretching signal in the infrared spectrum when ethylene glycol based antifreeze was included as a contaminant. The contaminants of distilled water, a 50/50 mixture of water and commercial ethylene glycol antifreeze, and straight ethylene glycol antifreeze were compared and a signal reduction in the O–H stretch was clearly evident when glycol was present. Doubling the volume of the 50/50 mixture as compared to water alone still resulted in a weaker O–H stretching signal. The possibility that this signal reduction was due to the larger ethylene glycol molecule having fewer O–H bonds in a given sample size was eliminated by comparing samples with the same number of O–H bonds per unit volume. The strong hydrogen bonding between that of water and glycol appeared to reduce the O–H stretching signal, even after comparing the different sample types at concentrations with the same number of O–H bonds per unit volume. Tukey’s highly significant difference was used to show that samples of the 50/50 mixture and straight glycol were not reliably distinguishable from one another when comparing the same number of O–H bonds per unit volume but readily distinguishable from that of water as the lone contaminant