This research focuses on addressing two pertinent problems in machine learning (ML) which are (a) the supervised classification of time series and (b) the need for large amounts of labeled images for training supervised classifiers. The novel contributions are two-fold. The first problem of time series classification is addressed by proposing to transform time series into domain-specific 2D features such as scalograms and recurrence plot (RP) images. The second problem which is the need for large amounts of labeled image data, is tackled by proposing a new way of using a reinforcement learning (RL) technique as a supervised classifier by using multimodal (joint representation) scalograms and RP images. The motivation for using such domain-specific features is that they provide additional information to the ML models by capturing domain-specific features (patterns) and also help in taking advantage of state-of-the-art image classifiers for learning the patterns from these textured images. Thus, this research proposes a multimodal fusion (MMF) - deep reinforcement learning (DRL) approach as an alternative technique to traditional supervised image classifiers for the classification of time series. The proposed MMF-DRL approach produces improved accuracy over state-of-the-art supervised learning models while needing fewer training data. Results show the merit of using multiple modalities and RL in achieving improved performance than training on a single modality. Moreover, the proposed approach yields the highest accuracy of 90.20% and 89.63% respectively for two physiological time series datasets with fewer training data in contrast to the state-of-the-art supervised learning model ChronoNet which gave 87.62% and 88.02% accuracy respectively for the two datasets with more training data.

ε-Differential Privacy (DP) has been popularly used for anonymizing data to protect sensitive information and for machine learning (ML) tasks. However, there is a trade-off in balancing privacy and achieving ML accuracy since ε-DP reduces the model’s accuracy for classification tasks. Moreover, not many studies have applied DP to time series from sensors and Internet-of-Things (IoT) devices. In this work, we try to achieve the accuracy of ML models trained with ε-DP data to be as close to the ML models trained with non-anonymized data for two different physiological time series. We propose to transform time series into domain-specific 2D (image) representations such as scalograms, recurrence plots (RP), and their joint representation as inputs for training classifiers. The advantages of using these image representations render our proposed approach secure by preventing data leaks since these image transformations are irreversible. These images allow us to apply state-of-the-art image classifiers to obtain accuracy comparable to classifiers trained on non-anonymized data by ex- ploiting the additional information such as textured patterns from these images. In order to achieve classifier performance with anonymized data close to non-anonymized data, it is important to identify the value of ε and the input feature. Experimental results demonstrate that the performance of the ML models with scalograms and RP was comparable to ML models trained on their non-anonymized versions. Motivated by the promising results, an end-to-end IoT ML edge-cloud architecture capable of detecting input drifts is designed that employs our technique to train ML models on ε-DP physiological data. Our classification approach ensures the privacy of individuals while processing and analyzing the data at the edge securely and efficiently.

Investigating novel treatment approaches to combat Clostridioides difficile Rusha Pal ABSTRACT Clostridioides difficile is the leading cause of antibiotic-induced diarrhea and colitis in hospitals and communities worldwide. The enteric pathogen, classified to be an "urgent threat" by the United States Center for Disease Control and Prevention (CDC), capitalizes on disrupted intestinal microbiome to establish infection with disease symptoms ranging from mild diarrhea to potentially fatal conditions. Disruption of the intestinal microbiome, caused mostly by antibiotic use, enables C. difficile to colonize and proliferate within the host. Paradoxically, antibiotics are used to treat C. difficile infection. These antibiotics decimate the gut microbial community further, thus priming the gastrointestinal tract to become more prone to recurrence of infection. To tackle this clinical setback, we utilized a combination of traditional and non-traditional drug discovery approaches and identified chemical entities and targeted treatment options effective against this toxin-producing intestinal pathogen. Herein, we exploited the strategy of high-throughput screening to identify leads that harbor anticlostridial activity. Our primary phenotypic screen of FDA-approved drugs and natural product libraries led to the identification of novel molecules that were further characterized for their anticlostridial efficacy both in vitro and in vivo. The most potent scaffolds identified were those of mitomycin C, mithramycin A, aureomycin, NP-003875, NAT13-338148, NAT18-355531, and NAT18-355768. Of these, mithramycin A, aureomycin, and NP-003875 were also found to harbor anti-virulence properties as they inhibited toxin production by the pathogen. Furthermore, natural product NP-003875 could confer protection to 100% of the infected mice from clinical manifestations of the disease in a primary infection model of C. difficile. Our final approach has been to develop targeted therapeutics called peptide nucleic acids (PNAs). PNAs are antisense agents capable of inhibiting gene expression in bacteria. In this study, antisense inhibition of the RNA polymerase  subunit gene (rpoA) of C. difficile was found to be bactericidal for the pathogen and could also inhibit the expression of its virulence factors. Additionally, antisense inhibition of the C. difficile rpoA gene was found to be non-deleterious for the tested commensal microflora strains. Given their intriguing anticlostridial properties, it can be concluded that our research opened exciting possibilities that can be further evaluated to uncover new treatments for CDI. / Doctor of Philosophy / Investigating novel treatment approaches to combat Clostridioides difficile Rusha Pal LAYMAN LANGUAGE ABSTRACT Clostridioides difficile is a prominent human pathogen that can colonize the gut and cause fatal infections. C. difficile is the most common cause of microbial healthcare-associated infection and results in substantial morbidity and mortality. The "most urgent worldwide public health threat" label has been assigned to C. difficile by the United States Centers for Disease Control and Prevention (CDC). There is a pressing need to develop new classes of antibiotics with improved efficacy to treat C. difficile infections (CDI). To address the need for novel strategies to combat the growing problem of CDI, we screened FDA-approved drugs and natural products library in search of novel drugs that possess potent and specific anticlostridial activity. Several promising hits were identified and evaluated successfully both in vitro and in vivo. The most potent and novel hits that displayed exceptional activity were mitomycin C, mithramycin A, aureomycin, NP-003875, NAT13-338148, NAT18-355531, and NAT18-355768. Furthermore, a murine model of C. difficile infection revealed that compound NP-003875 conferred 100% protection to the infected mice from clinical manifestations of the disease. Interestingly, these compounds were non-toxic to the gut microflora and human cells. Our final approach has been to develop non-traditional therapeutics to target specific genes in C. difficile. These novel therapeutics are called peptide nucleic acids (PNA). Herein, we designed a PNA targeting RNA polymerase  subunit gene (rpoA) of C. difficile. The designed PNA could successfully inhibit the growth of the pathogen and expression of its virulence factors. In conclusion, our research opened exciting possibilities that can be further evaluated to uncover new treatments for CDI.

The perioperative use of regional anesthesia and local anesthetics is part of almost every anesthesiologist’s daily clinical practice. Retrospective analyses and results from experimental studies pointed towards a potential beneficial effect of the local anesthetics regarding outcome—i.e., overall and/or recurrence-free survival—in patients undergoing cancer surgery. The perioperative period, where the anesthesiologist is responsible for the patients, might be crucial for the further course of the disease, as circulating tumor cells (shed from the primary tumor into the patient’s bloodstream) might form new micro-metastases independent of complete tumor removal. Due to their strong anti-inflammatory properties, local anesthetics might have a certain impact on these circulating tumor cells, either via direct or indirect measures, for example via blunting the inflammatory stress response as induced by the surgical stimulus. This narrative review highlights the foundation of these principles, features recent experimental and clinical data and provides an outlook regarding current and potential future research activities.

The relationship of statin therapy with recurrence of atrial fibrillation (AF) after cardioversion (CV) has been evaluated by several investigations, which provided conflicting results and particularly long-term data is scarce. We sought to examine whether upstream statin therapy is associated with long-term recurrence of AF after CV. This was a single-center registry study including consecutive AF patients (n = 454) undergoing CV. Cox regression models were performed to estimate AF recurrence comparing patients with and without statins. In addition, we performed a propensity score matched analysis with a 1:1 ratio. Statins were prescribed to 183 (40.3%) patients. After a median follow-up period of 373 (207–805) days, recurrence of AF was present in 150 (33.0%) patients. Patients receiving statins had a significantly lower rate of AF recurrence (log-rank p < 0.001). In univariate analysis, statin therapy was associated with a significantly reduced rate of AF recurrence (HR 0.333 (95% CI 0.225–0.493), p = 0.001), which remained significant after adjustment (HR 0.238 (95% CI 0.151–0.375), p < 0.001). After propensity score matching treatment with statins resulted in an absolute risk reduction of 27.5% for recurrent AF (21 (18.1%) vs. 53 (45.7%); p < 0.001). Statin therapy was associated with a reduced risk of long-term AF recurrence after successful cardioversion.

The aim of this thesis is to determine the relationship between natural resources and peace, by examining whether a correlation exists between conflicts related to natural resources and conflict recurrence. The hypothesis is that conflicts with distribution mechanisms increase the likelihood of conflict recurrence. This is tested through the application of Mill’s Method of Difference and a SFC, cross-case comparison of two conflicts in Angola between 1991 and 2002. The first conflict between UNITA and the government of Angola was characterized by the usage of diamonds for rebel group financing, whereas the second conflict between various separatist groups and the Angolan government in the enclave of Cabinda concerned the inequitable distribution of oil revenues. The findings show that both conflicts resumed, showing little support for the paper’s hypothesis. This thesis’ empirical data derives from different sources of secondary nature. The findings suggest that the paper’s hypothesis lacks sufficient support and therefore, alternative explanations are put forward in order to explain the inconsistency with the hypothesis — thus encouraging other avenues for research that take aim at disaggregating the relationship between natural resources and the prospects for post-conflict peace.

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