Network-based Topic Structure Visualization

In the real world, many topics are inter-correlated, making it challenging to investigate their structure and relationships. Understanding the interplay between topics and their relevance can provide valuable insights for researchers, guiding their studies and informing the direction of research. In this paper, we utilize the topic-words distribution, obtained from topic models, as item-response data to model the structure of topics using a latent space item response model. By estimating the latent positions of topics based on their distances toward words, we can capture the underlying topic structure and reveal their relationships. Visualizing the latent positions of topics in Euclidean space allows for an intuitive understanding of their proximity and associations. We interpret relationships among topics by characterizing each topic based on representative words selected using a newly proposed scoring scheme. Additionally, we assess the maturity of topics by tracking their latent positions using different word sets, providing insights into the robustness of topics. To demonstrate the effectiveness of our approach, we analyze the topic composition of COVID-19 studies during the early stage of its emergence using biomedical literature in the PubMed database. The software and data used in this paper are publicly available at https://github.com/jeon9677/gViz

Flexible Technique to Enhance Color-image Quality for Color-deficient Observers

Color-normal observers (CNOs) and color-deficient observers (CDOs) have different preferences and emotions for color images. A color-image quality-enhancement algorithm for a CDO is developed to easily adjust images according to each observer`s preference or image quality factors. The color-perception differences between CDO and CNO are analyzed and modeled in terms of the YCbCr chroma ratio and hue difference; then the color-shift method is designed to control the degree of color difference

The seeded growth of graphene

In this paper, we demonstrate the seeded growth of graphene under a plasma chemical vapor deposition condition. First, we fabricate graphene nanopowders (~5 nm) by ball-milling commercial multi-wall carbon nanotubes. The graphene nanoparticles were subsequently subject to a direct current plasma generated in a 100 Torr 10%CH(4) - 90%H(2) gas mixture. The plasma growth enlarged, over one hour, the nuclei to graphene sheets larger than one hundred nm(2) in area. Characterization by electron and X-ray diffraction, high-resolution transmission electron microscopy images provide evidence for the presence of monolayer graphene sheets

Effects of PI3Kγ overexpression in the hippocampus on synaptic plasticity and spatial learning

Previous studies have shown that a family of phosphoinositide 3-kinases (PI3Ks) plays pivotal roles in the brain; in particular, we previously reported that knockout of the γ isoform of PI3K (PI3Kγ) in mice impaired synaptic plasticity and reduced behavioral flexibility. To further examine the role of PI3Kγ in synaptic plasticity and hippocampus-dependent behavioral tasks we overexpressed p110γ, the catalytic subunit of PI3Kγ, in the hippocampal CA1 region. We found that the overexpression of p110γ impairs NMDA receptor-dependent long-term depression (LTD) and hippocampus-dependent spatial learning in the Morris water maze (MWM) task. In contrast, long-term potentiation (LTP) and contextual fear memory were not affected by p110γ overexpression. These results, together with the previous knockout study, suggest that a critical level of PI3Kγ in the hippocampus is required for successful induction of LTD and normal learning

Characteristics of Classified Aerosol Types in South Korea during the MAPS-Seoul Campaign

During the Megacity Air Pollution Studies-Seoul (MAPS-Seoul) campaign from May to June 2015, aerosol optical properties in Korea were obtained based on the AERONET sunphotometer measurement at five sites (Anmyon, Gangneung_WNU, Gosan_SNU, Hankuk_UFS, and Yonsei_University). Using this dataset, we examine regional aerosol types by applying a number of known aerosol classification methods. We thoroughly utilize five different methods to categorize the regional aerosol types and evaluate the results from each method by inter-comparison. The differences and similarities among the results are also discussed, contingent upon the usage of AERONET inversion products, such as the single scattering albedo. Despite several small differences, all five methods suggest the same general features in terms of the regionally dominant aerosol type: Fine-mode aerosols with highly absorbing radiative properties dominate at HankukUFS and Yonsei_University; non-absorbing fine-mode particles form a large portion of the aerosol at Gosan_SNU; and coarse-mode particles cause some effects at Anmyon. The analysis of 3-day back-trajectories is also performed to determine the relationship between classified types at each site and the regional transport pattern. In particular, the spatiotemporally short-scale transport appears to have a large influence on the local aerosol properties. As a result, we find that the domestic emission in Korea significantly contributes to the high dominance of radiation-absorbing aerosols in the Seoul metropolitan area and the air-mass transport from China largely affects the western coastal sites, such as Anmyon and Gosan_SNU

Clinical Utility of Beck Anxiety Inventory in Clinical and Nonclinical Korean Samples

Despite the prominent use of the Beck Anxiety Inventory (BAI) in primary healthcare systems, few studies have confirmed its diagnostic utility and psychometric properties in non-Western countries. This study aims to clarify the clinical utility of the BAI as a screening tool for anxiety disorders according to DSM-IV criteria, based on blind recruitment and diagnostic interviews of both clinical and non-clinical participants in the Korean population. A total of 1,157 participants were involved in the final psychometric analysis, which included correlational analysis with other anxiety and depression self-report measures and mean score comparison with the Beck Depression Inventory (BDI). ROC analysis and calculation of positive and negative predictive values were conducted to examine diagnostic utility. The BAI was found to have high correlations with depression-related self-report measures (0.747–0.796) and moderate to high correlations with anxiety-related self-report measures (0.518–0.776). The ROC analysis failed to provide cutoff scores with adequate sensitivity and specificity for identifying participants with anxiety disorders (85.0% sensitivity, 88.1% specificity, and 92.8% AUC). The comparison of BAI and BDI mean scores for different diagnostic groups revealed that BAI and BDI scores were higher in the depressive or anxiety disorders group than in the non-clinical group. However, BAI mean score was not higher for the anxiety-only group than the depression-only group. Our data supports the BAI reliability and validity as a tool to measure the severity of general anxiety in clinical and non-clinical populations; however, it fails to capture the unique characteristics of anxiety disorders that distinguish them from depressive disorders. Further clinical implications of the BAI based on these results and some limitations of the study are discussed