Abstract: Cancer is a global health challenge that urgently requires more sensitive and effective cancer detection methods. Fluorescence imaging with small molecule fluorescent probes has shown great promise for cancer detection but most of the developed probes lack active tumor cell targeting, which makes them unable to selectively target tumors, thereby reducing the accuracy of in vivo tumor detection. Herein, we report a novel probe Bio-S that combines a viscosity-sensitive and cell membrane targetable fluorescent group with biotin for targeted imaging and precise visualization of tumor cells and tumors. Bio-S exhibits sensitive fluorescence changes for viscosity at ∼660 nm and excellent cell membrane localization and imaging ability (red fluorescence, wash-free, and long-term imaging). Moreover, compared with the nonbiotinylated control probe C6-S, the biotinylated Bio-S can specifically target tumor cell membranes, thereby achieving much higher selectivity and sensitivity in distinguishing tumor cells from normal cells. Mice imaging experiments show that tail vein injection of Bio-S can target tumors and monitor lung cancer metastasis at the in vivo level. Therefore, this work provides an effective new strategy and tool for tumor-targeted detection and precise diagnosis.

Abstract: Recent advancements in wearable sweat sensors, which use standardized electrochemical and colorimetric mechanisms, offer holistic representation of health status for users. However, the constraints of standardized sweat sensors present ongoing challenges to realization of personalized health management. This study presents an electrocolorimetric (EC) platform that enables the reversible and multiple-time use of colorimetric data visualization using electrophoretic display (EPD). This platform represents the application of low-power EPD in epidermal sweat sensor, evaluated through CIELAB-based methodology which is the first systematic evaluation tool of wearable display performance. Moreover, our platform has been demonstrated in human exercise trials for its ability to detect the lactate threshold (LT). This digital colorimetric system has the potential to play a pivotal role by integrating various health monitoring biomarkers. While providing real-time, continuous, and adjustable range information with high sensitivity, this platform validates its extensive probability as a next-generation wearable epidermal sensor.

Abstract: This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field.

Abstract: Gynecological cancers are characterized by uncontrolled cell proliferation within the female reproductive organs. These cancers pose a significant threat to women's health, impacting life expectancy, quality of life, and fertility. Nanoparticles, with their small size, large surface area, and high permeability, have become a key focus in targeted cancer therapy. The aim of this study is to review recent advancements in nanoparticles applied to gynecologic cancers, providing valuable insights for future research. We retrieved all literature on nanoparticles in gynecologic cancers from the Web of Science Core Collection (WOSCC) database between January 1, 2004, and June 4, 2024. Data analysis and visualization were conducted using R software (version 4.4.0), VOSviewer (version 1.6.19.0), and CiteSpace (version 6.1). A total of 2,843 publications from January 1, 2004, to June 4, 2024 were searched. Over the past 20 years, there has been a significant increase in publications. The leading countries and institutions in terms of productivity are China and the Chinese Academy of Sciences. The most prolific author and the most co-cited author are Sood, A K and Siegel, Rl. The top journals are the International Journal of Nanomedicine (n=97), followed by ACS Applied Materials & Interfaces (n=72) and Journal of Materials Chemistry B (n=53). Keyword analysis shows current research focuses on two main areas: the application of nanoparticles for drug delivery and their broader applications in gynecologic cancers. Future research will likely focus on "silver nanoparticles," "gold nanoparticles," and "green synthesis." Over the past two decades, nanoparticles have rapidly advanced in the field of gynecologic cancers. Research has primarily focused on the applications of nanoparticles in drug delivery and applications. Future trends point toward optimizing synthesis techniques and advancing preclinical studies to clinical applications, particularly for silver and gold nanoparticles. These findings provide valuable scientific insights for researchers.

Abstract: Abstract Post-translational modifications (PTMs) play pivotal roles in regulating cellular signaling, fine-tuning protein function, and orchestrating complex biological processes. Despite their importance, the lack of comprehensive tools for studying PTMs from a pathway-centric perspective has limited our ability to understand how PTMs modulate cellular pathways on a molecular level. Here, we present PTMNavigator, a tool integrated into the ProteomicsDB platform that offers an interactive interface for researchers to overlay experimental PTM data with pathway diagrams. PTMNavigator provides ~3000 canonical pathways from manually curated databases, enabling users to modify and create custom diagrams tailored to their data. Additionally, PTMNavigator automatically runs kinase and pathway enrichment algorithms whose results are directly integrated into the visualization. This offers a comprehensive view of the intricate relationship between PTMs and signaling pathways. We demonstrate the utility of PTMNavigator by applying it to two phosphoproteomics datasets, showing how it can enhance pathway enrichment analysis, visualize how drug treatments result in a discernable flow of PTM-driven signaling, and aid in proposing extensions to existing pathways. By enhancing our understanding of cellular signaling dynamics and facilitating the discovery of PTM-pathway interactions, PTMNavigator advances our knowledge of PTM biology and its implications in health and disease.

Abstract: Abstract Background Digital pathology (DP) has revolutionized cancer diagnostics and enabled the development of deep-learning (DL) models aimed at supporting pathologists in their daily work and improving patient care. However, the clinical adoption of such models remains challenging. Here, we describe a proof-of-concept framework that, leveraging Health Level 7 (HL7) standard and open-source DP resources, allows a seamless integration of both publicly available and custom developed DL models in the clinical workflow. Methods Development and testing of the framework were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect through HL7 messaging the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence-based decision support system (AI-DSS) containing 16 pre-trained DL models. Open-source toolboxes for DL model deployment were used to run DL model inference, and QuPath was used to provide an intuitive visualization of model predictions as colored heatmaps. Results A default deployment mode runs continuously in the background as each new slide is digitized, choosing the correct DL model(s) on the basis of the tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slide tray. In both cases, the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate visualization style for the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly as slide description in the virtual slide tray. Conclusions Taken together, the developed integration framework through the use of the HL7 standard and freely available DP resources offers a standardized, portable, and open-source solution that lays the groundwork for the future widespread adoption of DL models in pathology diagnostics.

Abstract: Tourism co-creation has attracted a great deal of academic interest in recent years due to its important role in promoting the image and economic development of destinations. The emergence of new technologies has significantly changed the relationship between tourists and destinations. The main objective of this article is to visualise the structure and trends of tourism co-creation research between 2008 and 2024. Using mapping techniques, our study shows the relevance of research related to Tourism Experiences and New Technologies and, to a lesser extent, Service Domain Logic (SDL) for the period 2008 to 2024. However, three new research themes have emerged since 2016: Loyalty and Satisfaction, Hospitality and Coronavirus and Sharing Economy. The findings contribute to a broad and diverse understanding of the concept of value co-creation in the tourism industry, which can provide important insights for destination marketing organisations (DMOs) and policy makers in formulating management strategies to enhance destination branding and competitiveness.

Abstract: Knowledge visualization has gained significant research attention for its potential to facilitate knowledge construction through interactive graphics while minimizing cognitive load during information processing. However, limited research has examined the integration of knowledge visualization within highly interactive mixed-reality environments and its effects on user experiences and science, technology, engineering, and mathematics (STEM) sustainability. Drawing on the cognitive-affective model of immersive learning, this study investigates how learners’ user experiences, elicited by mixed-reality features and usability, influence their sustainable engagement with STEM learning through knowledge-visualization tools framed within the stimulus–organism–response model. A novel mixed-reality learning system was developed, with the user interface designed using concept maps to graphically visualize concept nodes and their interconnected relationships. A total of 136 learners from two high schools in China participated in an experiment on frictional physics using this novel system. Using structural equation modeling, the collected data were analyzed with partial least squares. The findings demonstrate that mixed-reality features of knowledge visualization (featured by 3D graphics, interface design, and operational functions), as well as usability (featured by the perceived usefulness of the concept map, perceived ease of use, and perceived usefulness of the system), have positive significant impacts on user experience (represented by satisfaction, perceived enjoyment, and attitude). Subsequently, positive user experiences have positive significant impacts on learners’ sustained intention to engage with STEM education. Further mediating analysis provides empirical evidence that positive user experiences, acting as a psychological enabler, mediate the relationship between system design and behavioral intention. The research model explains 65.2% of the variance for system usability, 53.4% for satisfaction, 51.5% for perceived enjoyment, 54.9% for attitude, and 63.2% for continuance intention. By fostering positive user experiences in STEM learning, this study offers valuable insights for educators and practitioners seeking to implement effective interactive knowledge visualizations to support sustainable STEM education and immersive learning.

Abstract: ABSTRACT This paper explores diverse visualisation methods of regionalisation based on a semantic analysis of institutionalised region names (choronyms) and adopts a fluid (fuzzy) approach to regional boundaries. Grounded in the theory of the institutionalisation of regions, the study examines how region shapes delineate their identities, resulting in an overlapping space of regions. Thus, the nature of this approach in regional geography requires multiple visualisations of areal features. The outcome is map representations of regions in the Czech-German-Polish borderland, illustrating regionalisation beyond traditional regional geography. KEY POLICY HIGHLIGHTS European regionalism requires regionalisation using innovative cartographic visualisation methods to represent a cultural landscape without the use of borders. Boundaries, names of the regions and institutions form the basis of the region’s identities.

Abstract: Sustainable smart cities are a topic of growing relevance in a world that is increasingly urbanized and concerned about sustainability. This field of study analyzes how emerging technologies and innovative practices transform cities into more efficient, habitable, and ecologically responsible environments. The main objective of this article is to review the state of the art on sustainable smart cities to measure the impact factor, the scientific production of the topic, and identify possible research gaps. The methodology followed four stages: preliminary bibliometric analysis in the Scopus database, bibliometric analysis on sustainable smart cities, network visualization using VOSviewer, and systematic content analysis of the 20 most cited articles. After filtering and cleaning the database, we worked with 2758 articles. The results show an increase in publications from 2020 onwards, with 552 articles published in 2022. Following this growing trend, more publications are expected in 2023, highlighting the knowledge gaps yet to be closed. The journal Sustainability has published the most articles (317). Yigitcanlar is the most cited author (24 citations), followed closely by Bibri (22 citations). China (368) and India (313) have the most publications on the research topic. The qualitative study concludes that the programs, actions, and developments form an innovation ecosystem for the conformation of sustainable smart cities. Regarding research opportunities, there is a pending agenda for researchers in the economic and social approach to smart cities, which are crucial for understanding the broader impacts of these technologies. As a limitation, we worked with the Scopus database, which may exclude some key articles available in other databases like the Web of Science. Future studies could incorporate these databases for a more comprehensive analysis.

Abstract: Visualizing CO 2 -EOR dynamics: Left—schematic of CO 2 injection in reservoir pores. Right— in situ observations of oil displacement in microfluidic chips. Cross-scale effects boost miscible recovery to 100% but worsen channeling in immiscible floods.

Abstract: This paper proposes a fault diagnosis method for rotating machinery that integrates transfer learning with the ConvNeXt model (TL-CoCNN), addressing challenges such as small sample sizes and varying operating conditions. To meet the input requirements of the model while minimizing feature loss, an alternative approach to visualizing vibration data is introduced. Specifically, RGB images are synthesized from time-domain, frequency-domain, and time-frequency domain representations of the original signal, which are subsequently used as the input dataset. The fault diagnosis process leverages a pre-trained ConvNeXt model, initially trained on the ImageNet dataset, and fine-tunes its parameters using the synthesized RGB images to perform the fault classification task. Experimental results demonstrate that this data visualization method extracts more fault-related information compared to traditional time-domain and frequency-domain techniques, without the need to augment the sample size. The TL-CoCNN model achieves superior recognition accuracy when evaluated in terms of training time and model size across multiple test datasets. As an end-to-end fault diagnosis system, TL-CoCNN significantly enhances the feature representation capability of complex signals, showing promising potential for practical applications in fault detection and diagnosis.

Abstract: Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities.

Abstract: Narrative visualization transforms data into engaging stories, making complex information accessible to a broad audience. Foundation models, with their advanced capabilities such as natural language processing, content generation, and multimodal integration, hold substantial potential for enriching narrative visualization. Recently, a collection of techniques have been introduced for crafting narrative visualizations based on foundation models from different aspects. We build our survey upon 66 papers to study how foundation models can progressively engage in this process and then propose a reference model categorizing the reviewed literature into four essential phases: Analysis, Narration, Visualization, and Interaction. Furthermore, we identify eight specific tasks (e.g. Insight Extraction and Authoring) where foundation models are applied across these stages to facilitate the creation of visual narratives. Detailed descriptions, related literature, and reflections are presented for each task. To make it a more impactful and informative experience for diverse readers, we discuss key research problems and provide the strengths and weaknesses in each task to guide people in identifying and seizing opportunities while navigating challenges in this field.