346 research outputs found

    STaSy: Score-based Tabular data Synthesis

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    Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity. Code is available at https://github.com/JayoungKim408/STaSy.Comment: 27 pages, Accepted by ICLR 2023 for spotlight presentation, Official code: https://github.com/JayoungKim408/STaS

    Engineering polymeric drug delivery vehicles for enhanced tissue targeting

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    Development of therapeutic drugs, including small molecules, peptides, proteins, and nucleic acids, is centered upon their function through novel molecular targets or enhanced efficacy in comparison to existing drugs. However, one of the major limitations these drugs often suffer from is low drug concentration at the target site due to fast clearance post administration, which leads to overdosing and frequent dosings that can have further complications such as safety and patient compliance. Hence, there has been a strong effort during the past few decades in the field of biomaterials to develop drug delivery vehicles that enhance the localization of drugs at the site of disease while minimizing side effects. In particular, polymeric nanoparticles and microparticles have been utilized as platform technologies to protect, carry, and release the drug cargo in controlled fashion. This thesis presents multiple approaches to engineering polymeric nanoparticles and microparticles based on different targeting modalities with the goal of maximizing the bioavailability of the drug in cancer and ocular disease applications. Two types of polymers, poly(beta-amino ester) (PBAE) and poly(lactic-co-glycolic acid) (PLGA), were utilized to optimize the delivery of a small molecule, peptides, and plasmid DNA. To maximize the delivered dose of the drug cargo of interest, physical size and shape modifications of nanoparticles were investigated for passive targeting. In particular, poly(ethylene glycol)-modified PBAE polymer was used to formulate pDNA-carrying polyplex and small molecule-carrying micelles for enhanced diffusion by size and prolonged circulation by shape, respectively. Next, biochemical modifications of polymers were explored for active targeting of nanoparticles to target tissue. Specifically, polymer structure-dependent tissue targeting was investigated with PBAE-pDNA polyplex nanoparticles, and active tumor targeting with integrin-binding peptide-coated PLGA nanoparticles were studied. Finally, optimization of PBAE nano- and PLGA microparticles delivering nucleic acids and therapeutic peptide, respectively, were studied to enhance patient compliance and long-term therapeutic efficacy following two different local delivery routes to ocular spaces. Taken together, the findings from these polymeric nano- and microparticles with different targeting modalities show their clinical potential as efficient drug delivery systems

    Inactivation of Medial Prefrontal Cortex Impairs Time Interval Discrimination in Rats

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    Several lines of evidence suggest the involvement of prefrontal cortex in time interval estimation. The underlying neural processes are poorly understood, however, in part because of the paucity of physiological studies. The goal of this study was to establish an interval timing task for physiological recordings in rats, and test the requirement of intact medial prefrontal cortex (mPFC) for performing the task. We established a temporal bisection procedure using six different time intervals ranging from 3018 to 4784 ms that needed to be discriminated as either long or short. Bilateral infusions of muscimol (GABAA receptor agonist) into the mPFC significantly impaired animal's performance in this task, even when the animals were required to discriminate between only the longest and shortest time intervals. These results show the requirement of intact mPFC in rats for time interval discrimination in the range of a few seconds
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