Electrochemically-modulated liquid chromatography (EMLC): Column design, retention processes, and applications

This dissertation explores a new separation technique, electrochemically modulated liquid chromatography (EMLC), from the column design, retention processes, to the pharmaceutical applications. A literature review, general summary, and perspectives of this technique are also described. Chapter 1 presents the newly designed EMLC column. The principal modification of this design is to connect the porous stainless steel column as counter electrode as opposed to part of working electrode in the previous design. The improvement in performance from this modification results in a shorter response time to changes in applied potential (E appl) and a better control of E appl at cathodic values of E appl. The performance of the new design is presented and compared to the previous design;Chapter 2 describes the study of retention processes of analytes on EMLC. A mixture of substituted aromatic compounds has been investigated to examine the influence of E appl to retention. Results show that donor-acceptor interactions dominate the retention processes and that the analytes with larger submolecular polarity parameters or higher energy levels of highest occupied molecular orbital display larger sensitivities in retention to changes in E appl.;In Chapter 3, EMLC has been applied to the separation of a mixture of structurally similar corticosteroids. Changes in the E appl to the column markedly affected the efficiency as well as the elution order of the separation, with the mixture fully resolved at large negative values of E appl. Mechanistic aspects in terms of the influence of changes in the E appl on the extent of the interactions between these analytes and the stationary phase are briefly discussed;In Chapter 4, the separation of a mixture of benzodiazepines has been investigated by EMLC. Changes in the E appl to the stationary phase strongly alter the retention of all analytes. The observed dependencies of retention have the unusual effect of stretching both ends of the chromatogram as E appl becomes more negative. That is, the retention for some of the benzodiazepines increases as E appl moves negatively, whereas that for some of the other benzodiazepines decreases. The combined weight of these dependencies results in the ability to achieve a fully resolved separation of the mixture while only marginally increasing the overall elution time

UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.Comment: Accepted to EMNLP 2022 main conferenc

MEMO: Dataset and Methods for Robust Multimodal Retinal Image Registration with Large or Small Vessel Density Differences

The measurement of retinal blood flow (RBF) in capillaries can provide a powerful biomarker for the early diagnosis and treatment of ocular diseases. However, no single modality can determine capillary flowrates with high precision. Combining erythrocyte-mediated angiography (EMA) with optical coherence tomography angiography (OCTA) has the potential to achieve this goal, as EMA can measure the absolute 2D RBF of retinal microvasculature and OCTA can provide the 3D structural images of capillaries. However, multimodal retinal image registration between these two modalities remains largely unexplored. To fill this gap, we establish MEMO, the first public multimodal EMA and OCTA retinal image dataset. A unique challenge in multimodal retinal image registration between these modalities is the relatively large difference in vessel density (VD). To address this challenge, we propose a segmentation-based deep-learning framework (VDD-Reg) and a new evaluation metric (MSD), which provide robust results despite differences in vessel density. VDD-Reg consists of a vessel segmentation module and a registration module. To train the vessel segmentation module, we further designed a two-stage semi-supervised learning framework (LVD-Seg) combining supervised and unsupervised losses. We demonstrate that VDD-Reg outperforms baseline methods quantitatively and qualitatively for cases of both small VD differences (using the CF-FA dataset) and large VD differences (using our MEMO dataset). Moreover, VDD-Reg requires as few as three annotated vessel segmentation masks to maintain its accuracy, demonstrating its feasibility.Comment: Submitted to IEEE JBH