Yejin Kim

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inh

The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20-49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Activation System questionnaire (BIS/BAS), the Dickman Dysfunctional Impulsivity Instrument (DDII), and t

L-ascorbic acid (vitamin C) is one of the well-known anti-viral agents, especially to influenza virus. Since the in vivo anti-viral effect is still controversial, we investigated whether vitamin C could regulate influenza virus infection in vivo by using Gulo (-/-) mice, which cannot synthesize vitamin C like humans. First, we found that vitamin C-insufficient Gulo (-/-) mice expired within 1 week after intranasal inoculation of influenza virus (H3N2/Hongkong). Viral titers in the lung of vitami

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor fact

SCOPE: β-Sitosterol, a common phytosterol, has been shown to exhibit anti-inflammatory effects. Here, we investigated the effect of β-sitosterol on high-fat diet (HFD) induced colitis in mice and on LPS-stimulated mouse intestinal macrophages. METHODS AND RESULTS: C57BL/6J mice were maintained on an LFD (10 kcal% fat), an HFD (60 kcal% fat), or an HFD with β-sitosterol (20 mg/kg) administration for 8 weeks. The increased levels of body weight and epididymal fat pad weight as well as the concentr

Biopolymeric microparticles have been widely used for long-term release formulations of short half-life chemicals or synthetic peptides. Characterization of the drug release from microparticles is important to ensure product quality and desired pharmacological effect. However, there is no official method for long-term release parenteral dosage forms. Much work has been done to develop methods for in vitro drug release testing, generally grouped into three major categories: sample and separate, d

Abstract Copper (Cu) is a tightly regulated micronutrient that functions as a structural or catalytic cofactor for specific proteins essential for a diverse array of biological processes. While the study of the extremely rare genetic diseases, Menkes and Wilson, has highlighted the requirement for proper Cu acquisition and elimination in biological systems for cellular growth and proliferation, the importance of dedicated Cu transport systems, like the Cu chaperones ATOX1 and CCS, in the pathoph

Abstract The principal unmet need in BRAFV600E-positive melanoma is lack of an adequate therapeutic strategy capable of overcoming resistance to clinically approved targeted therapies against oncogenic BRAF and/or the downstream MEK1/2 kinases. We previously discovered that copper (Cu) is required for MEK1 and MEK2 activity through a direct Cu–MEK1/2 interaction. Repurposing the clinical Cu chelator tetrathiomolybdate (TTM) is supported by efficacy in BRAFV600E-driven melanoma models, due in par

Sequential recommendation is a task that learns a temporal dynamic of a user behavior in sequential data and predicts items that a user would like afterward. However, diversity has been rarely emphasized in the context of sequential recommendation. Sequential and diverse recommendation must learn temporal preference on diverse items as well as on general items. Thus, we propose a sequential and diverse recommendation model that predicts a ranked list containing general items and also diverse ite

Abstract Background & Aims Little is known about the impact of metabolically healthy obesity on fibrosis progression in non‐alcoholic fatty liver disease (NAFLD). We investigated the association of body mass index (BMI) category, body fat percentage and waist circumference with worsening of noninvasive fibrosis markers in metabolically healthy and unhealthy individuals with NAFLD. Methods A cohort study was performed on 59 957 Korean adults with NAFLD (13 285 metabolically healthy and 46 672

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to