WonderFlow: Narration-Centric Design of Animated Data Videos.

Creating an animated data video with audio narration is a time-consuming and complex task that requires expertise. It involves designing complex animations, turning written scripts into audio narrations, and synchronizing visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify semantic links between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. Additionally, authors can preview and refine their data videos within the same system, without having to switch between different creation tools. A series of evaluation results confirmed that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay.

Synthesis of Ni decorated MoOx nanorod catalysts for efficient overall urea-water splitting.

Substituting slow oxygen evolution reaction (OER) with thermodynamically favorable urea oxidation reaction (UOR) is considered as one of the feasible strategies for achieving energy-saving hydrogen production. Herein, a uniform layer of NiMoO4 nanorods was grown on nickel foam by a hydrothermal method. Then, a series of Ni-MoOx/NF-X nanorod catalysts comprising Ni/NiO and MoOx (MoO2/MoO3) were prepared through regulating annealing atmosphere and reduction temperature. The optimized Ni-MoOx/NF-3 with a large accessible specific area can act as a bifunctional catalyst for electrocatalytic anodic UOR and cathodic hydrogen evolution reaction (HER). At a current density of 100 mA cm-2, the introduction of urea can significantly reduce the overpotential of Ni-MoOx/NF-3 by 210 mV compared to OER. In addition, Ni-MoOx/NF-3 has a higher intrinsic activity than other catalysts. It only requires -0.21 and 1.38 V to reach 100 mA cm-2 in HER and UOR, respectively. Such an excellent performance can be attributed to the synergistic function between Ni and MoOx. The presence of metallic Ni and reduced MoOx in pairs is beneficial for improving the electrical conductivity and modulating the electronic structure, resulting in enhancing the electrocatalytic performance. When assembling Ni-MoOx/NF-3 into an overall urea-water splitting system, it can achieve energy-saving hydrogen production and effective removal of urea-rich wastewater.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method.

BACKGROUND: Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine. MATERIAL AND METHODS: We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH). RESULTS: We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012). CONCLUSION: The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

Successful treatment of nasopalatine duct cyst after maxillary anterior implant surgery: a case report.

BACKGROUND: Sporadic studies have reported the occurrence of nasopalatine duct cysts after maxillary anterior implant surgery, and the treatment methods still have clinical uncertainty. PURPOSE: We report a potential therapy method that successfully treated a nasopalatine duct cyst that developed and expanded one year after maxillary anterior implant placement following periodontally hopeless teeth extraction. MATERIALS AND METHODS: The nasopalatine cyst was treated surgically without removing implants. During flap surgery, the cyst was removed intact, and the exposed implant's surface was debrided thoroughly by hydrogen peroxide (H2O2) rinsing, glycine air polishing, and saline rinsing. To deal with the significant bone defect caused by the cyst, a bovine porous bone mineral injected platelet-rich fibrin (BPBM-i-PRF) complex was applied to fill the defect, following a resorbable collagen membrane to cover. RESULTS: 7 years after surgery, no cyst recurrence was observed, and bone regeneration in the bone graft area was stable. The implants functioned well without mobility. CONCLUSIONS: For nasopalatine duct cysts associated with dental implant placement, complete surgical debridement and longitudinal stable bone regeneration are possibly accessible by regenerative surgery without implant removal.

Surgical Management Strategies for Carotid Artery Invasion by Head and Neck Cancer: Ligation Versus Reconstruction.

OBJECTIVE: Whether ligation or reconstruction should be performed after radical resection of the tumor and carotid artery in patients with head and neck cancers invading the carotid artery (HNC-CA) has been controversial. This paper provides a review and meta-analysis of the efficacy of these 2 modalities. DATA SOURCES: PubMed, Cochrane, Web of Science, Scopus, and Ovid databases were searched through August 2023. REVIEW METHODS: Descriptive, graphical, tabular, and quantitative data were extracted. The statistical outcomes (risk difference, RD) were synthesized under a random-effects model. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines. RESULTS: A total of 22 papers and 337 patients met the inclusion criteria for the literature review. Statistical analysis showed that the RD of overall survival (OS) rate at 1-year was 32% (95% confidence interval [CI]: 21%-42%) for ligation and 70% (95% CI: 65%-76%) for reconstruction (P < .05). The RD for OS rate at 2-year was 16% (95% CI: 7%-26%) for ligation and 39% (95% CI: 30%-47%) for reconstruction (P < .05). The RD for disease-free survival rate at 1-year was 27% (95% CI: 17%-38%) for ligation and 60% (95% CI: 51%-70%) for reconstruction (P < .05). There were no statistically significant differences (P > .05) between the 2 surgical modalities in terms of locoregional recurrence rate, carotid blowout rate, surgery-related complications rate, neurological complications rate, and perioperative mortality rate. CONCLUSION: This review demonstrates the significant advantage of carotid artery reconstruction surgery in short-term patient survival, thus making it a recommended option for HNC-CA patients undergoing radical surgery.

Reviving Static Charts into Live Charts.

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience.

Relation-driven Query of Multiple Time Series.

Querying time series based on their relations is a crucial part of multiple time series analysis. By retrieving and understanding time series relations, analysts can easily detect anomalies and validate hypotheses in complex time series datasets. However, current relation extraction approaches, including knowledge- and data-driven ones, tend to be laborious and do not support heterogeneous relations. By conducting a formative study with 11 experts, we concluded six time series relations, including correlation, causality, similarity, lag, arithmetic, and meta, and summarized three pain points in querying time series involving these relations. We proposed RelaQ, an interactive system that supports the time series query via relation specifications. RelaQ allows users to intuitively specify heterogeneous relations when querying multiple time series, understand the query results based on a scalable, multi-level visualization, and explore possible relations beyond the existing queries. RelaQ is evaluated with two cases and a user study with 12 participants, showing promising effectiveness and usability.

Recurrence and malignant transformation of laryngeal leukoplakia treated with CO2 laser: A systematic review and meta-analysis.

BACKGROUND: Laryngeal leukoplakia (LL) is a white lesion with high potential of recurrence and malignant transformation. Currently, CO2 laser has become the primary surgical treatment for LL, and the recurrence and malignant transformation rates after treatment vary widely. OBJECTIVE: We performed a systematic review and meta-analysis dedicated to evaluating the rates of recurrence and malignant transformation of LL lesions treated with CO2 laser and exploring relevant risk factors for recurrence or malignant transformation. METHODS: Literature searches were conducted on ProQuest, PubMed, Web of Science, Ovid Medline, Embase, and Cochrane databases. Some articles identified through hand searching were included. RESULTS: A total of 14 articles and 1462 patients were included in this review. Pooled results showed that the overall recurrence rate was 15%, and the malignant transformation rate was 3%. Subgroup analysis showed that the dysplasia grade was not a significant risk factor for the recurrence and malignant transformation of LL (P > .05). CONCLUSIONS: The results of this systematic review and meta-analysis suggest that the CO2 laser is a safe and effective surgical instrument for the excision of LL, which yields low rates of recurrence and malignant transformation. The risk factors relevant to recurrence or malignant transformation remain unclear and require further investigation.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.

Background: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results: Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. Conclusions: The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.

Platinum Nanoparticles-Enhanced Ferritin-Mn2+ Interaction for Magnetic Resonance Contrast Enhancement and Efficient Tumor Photothermal Therapy.

Nanoplatforms with high Mn2+ coordination can display efficient T1 magnetic resonance imaging (MRI) contrast enhancement. Herein, an earth gravity-like method for enhanced interaction between Ferritin (Fn) and Mn2+ by the growth of platinum nanoparticles (PNs) in Fn's cage structure via a biomineralization method is first proposed. Fn has good biocompatibility and can provide a suitable growth site for PNs. PNs with negative charge have certain attraction to Mn2+ with positive charge, improving Fn's loading capacity of Mn2+ by attraction force; and thus, achieving efficient MRI contrast enhancement. In addition, PNs can be applied for efficient photothermal therapy (PTT) under near infrared ray (NIR) irradiation. Systemic delivery of this nanoplatform shows obvious MRI contrast enhancement and tumor progression inhibition after NIR irradiation, as well as no obvious side effects. Therefore, this nanoplatform has the potential to contribute to nanotheranostic for clinical transformation.

Radical-driven selective oxidation of benzyl alcohol on MnCoOx catalysts with no oxidant other than air in reactor.

Mn reinforced Co3O4 catalysts (MnCoOx) were prepared by a facile solid phase mixed foaming method with an in-situ heating enhancement for the formation of spinel phase mixed oxide species, and studied in the selective oxidation of benzyl alcohol just the air in reactor as oxygen donor. It was found that the MnCoOx catalysts are composed of relatively minimal spinel MnCo2O4 mixed oxide and massive Co3O4 to form MnCo2O4-Co3O4 oxide pair. The micro-domains of MnCo2O4-Co3O4 oxide pair present two redox couples of Mn3+/Mn2+ and Co3+/Co2+ instead of the single one of Co3+/Co2+ in Co3O4, and then dramatically enhance the formation of superoxide radicals (•O2-) species from the O2 in air, which can efficiently initiate the conversion of benzyl alcohol to benzaldehyde in a Fenton-like processes. With no oxidant other than air in reactor, the interaction between MnCo2O4 and Co3O4 in MnCoOx catalysts leads to a benzyl alcohol conversion up to 98 % with a 100 % benzaldehyde selectivity at atmospheric pressure while single component Co3O4 can only present a benzyl alcohol conversion at 37 %. This embodiment of highly efficient heterogeneous selective oxidation just with air as oxidant provides a probability for developing a low-cost and super-facile radical-induced selective oxidation process for alcohols.

Overexpression of high affinity K+ transporter from Nitraria sibirica enhanced salt tolerance of transgenic plants.

Nitraria sibirica Pall is a halophytic shrub growing in desert steppe zones. It exhibits extraordinary adaptability to saline-alkali soil, drought, and sand burial. In this study, the high-affinity K+ transporter NsHKT1 was identified and found to play a key role in salt tolerance in N. sibirica. NsHKT1 was used to improve salt tolerance in a poplar hybrid. The expression characteristics of NsHKT1 were analyzed by transforming Arabidopsis and poplar with the ß-glucuronidase (GUS) gene driven by the NsHKT1 promoter. The results showed that NsHKT1 expression was induced by various abiotic stresses and phytohormones. GUS expression was also detected in the reproductive organs of transgenic Arabidopsis, indicating its function in regulating plant reproductive growth. Transgenic 84 K poplar plants overexpressing NsHKT1 exhibited less damage, higher antioxidant capacity, higher chlorophyll and proline levels, and lower malondialdehyde content compared with non-transgenic plants under salt stress. These results are consistent with the salt tolerance results for transgenic Arabidopsis overexpressing NsHKT1, indicating that NsHKT1 plays a key role in salt tolerance in herbaceous and ligneous plants. Inductively coupled plasma-optical emission spectrometry showed a significantly lower leaf Na+ content in transgenic poplar than in the non-transgenic line, revealing that NsHKT1, as a member of HKT family subclass 1, was highly selective to Na+ and prevented shoot Na+ accumulation. Transcriptome analysis indicated that differentially expressed genes in transgenic poplars under salt stress were associated mainly with the isoflavonoid, cutin, suberine, wax, anthocyanin, flavonoid, and cyanoamino biosynthesis pathways, as well as the MAPK signaling pathway, indicating that NsHKT1 not only regulates ion homeostasis but also influences secondary metabolism and signal transaction in transgenic plants.

Accuracy of edentulous full-arch implant impression: An in vitro comparison between conventional impression, intraoral scan with and without splinting, and photogrammetry.

OBJECTIVES: The purpose of this in vitro study was to compare the trueness and precision of complete arch implant impressions using conventional impression, intraoral scanning with and without splinting, and stereophotogrammetry. MATERIALS AND METHODS: An edentulous model with six implants was used in this study. Four implant impression techniques were compared: the conventional impression (CI), intraoral scanning (IOS) without splinting, intraoral scanning with splinting (MIOS), and stereophotogrammetry (SPG). An industrial blue light scanner was used to generate the baseline scan from the model. The CI was captured with a laboratory scanner. The reference best-fit method was then applied in the computer-aided design (CAD) software to compute the three-dimensional, angular, and linear discrepancies among the four impression techniques. The root mean square (RMS) 3D discrepancies in trueness and precision between the four impression groups were analyzed with a Kruskal-Wallis test. Trueness and precision between single analogs were assessed using generalized estimating equations. RESULTS: Significant differences in the overall trueness (p = .017) and precision (p < .001) were observed across four impression groups. The SPG group exhibited significantly smaller RMS 3D deviations than the CI, IOS, and MIOS groups (p < .05), with no significant difference detected among the latter three groups (p > .05). CONCLUSIONS: Stereophotogrammetry showed superior trueness and precision, meeting misfit thresholds for implant-supported complete arch prostheses. Intraoral scanning, while accurate like conventional impressions, exhibited cross-arch angular and linear deviations. Adding a splint to the scan body did not improve intraoral scanning accuracy.

ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language.

The use of natural language interfaces (NLIs) to create charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.

Visual Knowledge Domain of Artificial Intelligence in Computed Tomography: A Review Based on Bibliometric Analysis.

ABSTRACT: Artificial intelligence (AI)-assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.

Tumor microenvironment responsive nano-platform for overcoming sorafenib resistance of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor, which seriously jeopardizes human health. The 5-year relative survival rate of HCC is only about 18%. Sorafenib, a small molecule multi-targeted tyrosine kinase inhibitor (MTKI), has been classified as the first-line treatment scheme for HCC and has significantly extended the median survival time for patients with advanced HCC. Nevertheless, the emergence of sorafenib resistance has substantially hampered its further clinical application. Herein, the nano-platform based on phototherapy and small molecular targeted therapy (SMTT) was devised to overcome the sorafenib resistance and reduce the adverse effects. Hollow mesoporous manganese dioxide (H-MnO2) was prepared by hard template method, and the prepared H-MnO2 was used to load sorafenib and Chlorin e6 (Ce6). Subsequently, the nanoparticle (NPs) were modified with dopamine to optimize biocompatibility. The final prepared NPs (MCS NPs) exhibit regular spherical shape with a hydrated particle size of approximately 97.02 nm. MCS NPs can not only possess tumor microenvironment (TME) stimuli-responsive drug release performance but also can enhance the efficacy of photodynamic therapy and reverse sorafenib resistance by alleviating tumor hypoxia. Under the action of phototherapy (Ce6) combined with molecular targeted therapy (sorafenib), MCS NPs manifest a satisfactory antitumor effect for sorafenib-sensitive or sorafenib-resistant HCC cells, and retain the antiangiogenic properties of sorafenib. In the nude mouse subcutaneous tumor model constructed with sorafenib-resistant cells, MCS NPs demonstrated superior tumor imaging ability and excellent biocompatibility. The tumor inhibition rate of the MCS NPs group without laser irradiation was 53.4 %, while the MCS NPs group with laser irradiation was as high as 100 %. The novel smart TME-responsive nano-platform shows great potential for overcoming sorafenib resistance and realizes multimodality imaging and therapy of HCC.

Causality-Based Visual Analysis of Questionnaire Responses.

As the final stage of questionnaire analysis, causal reasoning is the key to turning responses into valuable insights and actionable items for decision-makers. During the questionnaire analysis, classical statistical methods (e.g., Differences-in-Differences) have been widely exploited to evaluate causality between questions. However, due to the huge search space and complex causal structure in data, causal reasoning is still extremely challenging and time-consuming, and often conducted in a trial-and-error manner. On the other hand, existing visual methods of causal reasoning face the challenge of bringing scalability and expert knowledge together and can hardly be used in the questionnaire scenario. In this work, we present a systematic solution to help analysts effectively and efficiently explore questionnaire data and derive causality. Based on the association mining algorithm, we dig question combinations with potential inner causality and help analysts interactively explore the causal sub-graph of each question combination. Furthermore, leveraging the requirements collected from the experts, we built a visualization tool and conducted a comparative study with the state-of-the-art system to show the usability and efficiency of our system.