NL2Color: Refining Color Palettes for Charts with Natural Language.

Choice of color is critical to creating effective charts with an engaging, enjoyable, and informative reading experience. However, designing a good color palette for a chart is a challenging task for novice users who lack related design expertise. For example, they often find it difficult to articulate their abstract intentions and translate these intentions into effective editing actions to achieve a desired outcome. In this work, we present NL2Color, a tool that allows novice users to refine chart color palettes using natural language expressions of their desired outcomes. We first collected and categorized a dataset of 131 triplets, each consisting of an original color palette of a chart, an editing intent, and a new color palette designed by human experts according to the intent. Our tool employs a large language model (LLM) to substitute the colors in original palettes and produce new color palettes by selecting some of the triplets as few-shot prompts. To evaluate our tool, we conducted a comprehensive two-stage evaluation, including a crowd-sourcing study ( N=71) and a within-subjects user study ( N=12). The results indicate that the quality of the color palettes revised by NL2Color has no significantly large difference from those designed by human experts. The participants who used NL2Color obtained revised color palettes to their satisfaction in a shorter period and with less effort.

Modified Cangfu Daotan decoction ameliorates polycystic ovary syndrome with insulin resistance via NF-κB/LCN-2 signaling pathway in inflammatory microenvironment.

This study explored the possible connection between the insulin resistance-targeting protein adipokine lipocalin-2 (LCN-2) and NF-κB signaling pathway in the inflammatory microenvironment in PCOS-IR model rats to determine the pharmacological mechanism of modified Cangfu Daotan decoction (MCDD) intervention for PCOS-IR. We used a high-fat diet (42 days) combined with letrozole (1 mg/kg/day, 42 days) to establish a PCOS-IR rat model. From the third week after modeling, the rats were given continuous administration of MCDD (high dose with 31.68 g/kg, medium dose with 15.84 g/kg, and low dose with 7.92 g/kg) for 28 days. Serum, ovarian tissue, liver, and adipose tissue were collected after the last gavage. Enzyme-linked immunosorbent assay, hematoxylin-eosin (HE) staining, Masson staining, qRT-PCR, and Western blot experiments were performed to detect various indicators. Our results showed that MCDD could reduce body weight and abdominal fat weight; restore normal estrous cycle and ovarian function; alleviate fatty liver; regulate HOMA-IR and OGTT index; reduce serum inflammatory factor levels, LCN-2 level, and gene expression; and regulate the insulin signal transduction and NF-κB pathways in PCOS-IR rats. Thus, MCDD may play a role in improving ovarian function in PCOS-IR rats by downregulating NF-κB/LCN-2 proteins and upregulating the gene expression of Insr/Irs-1/Glut4 in the insulin signaling pathway in the inflammatory environment.

Towards Better Bus Networks: A Visual Analytics Approach.

Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts. Index Terms-Bus route planning, spatial decision-making, urban data visual analytics.