bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rural–urban disparities in pregestational and gestational diabetes in pregnancy: Serial, cross‐sectional analysis of over 12 million pregnancies
Authors: Venkatesh Kartik K. , Huang Xiaoning , Cameron Natalie A. , Petito Lucia C. , Joseph Joshua , Landon Mark B. , Grobman William A. , Khan Sadiya S. 
Published: June 2023

AbstractObjectiveTo compare trends in pregestational (DM) and gestational diabetes (GDM) in pregnancy in rural and urban areas in the USA, because pregnant women living in rural areas face unique challenges that contribute to rural–urban disparities in adverse pregnancy outcomes.DesignSerial, cross‐sectional analysis.SettingUS National Center for Health Statistics (NCHS) Natality Files from 2011 to 2019.PopulationA total of 12 401 888 singleton live births to nulliparous women aged 15–44 years.MethodsWe calculated the frequency (95% confidence interval [CI]) per 1000 live births, the mean annual percentage change (APC), and unadjusted and age‐adjusted rate ratios (aRR) of DM and GDM in rural compared with urban maternal residence (reference) per the NCHS Urban–Rural Classification Scheme overall, and by delivery year, reported race and ethnicity, and US region (effect measure modification).Main outcome measuresThe outcomes (modelled separately) were diagnoses of DM and GDM.ResultsFrom 2011 to 2019, there were increases in both the frequency (per 1000 live births; mean APC, 95% CI per year) of DM and GDM in rural areas (DM: 7.6 to 10.4 per 1000 live births; APC 2.8%, 95% CI 2.2%–3.4%; and GDM: 41.4 to 58.7 per 1000 live births; APC 3.1%, 95% CI 2.6%–3.6%) and urban areas (DM: 6.1 to 8.4 per 1000 live births; APC 3.3%, 95% CI 2.2%–4.4%; and GDM: 40.8 to 61.2 per 1000 live births; APC 3.9%, 95% CI 3.3%–4.6%). Individuals living in rural areas were at higher risk of DM (aRR 1.48, 95% CI 1.45%–1.51%) and GDM versus those in urban areas (aRR 1.17, 95% CI 1.16%–1.18%). The increased risk was similar each year for DM (interaction p = 0.8), but widened over time for GDM (interaction p < 0.01). The rural–urban disparity for DM was wider for individuals who identified as Hispanic race/ethnicity and in the South and West (interaction p < 0.01 for all); and for GDM the rural–urban disparity was generally wider for similar factors (i.e. Hispanic race/ethnicity, and in the South; interaction p < 0.05 for all).ConclusionsThe frequency of DM and GDM increased in both rural and urban areas of the USA from 2011 to 2019 among nulliparous pregnant women. Significant rural–urban disparities existed for DM and GDM, and increased over time for GDM. These rural–urban disparities were generally worse among those of Hispanic race/ethnicity and in women who lived in the South. These findings have implications for delivering equitable diabetes care in pregnancy in rural US communities.
Phenological growth stages of Astragalus membranaceus var. mongholicus according to the Biologische Bundesanstalt Bundessortenamt and Chemical Industry (BBCH) scale
Authors: Li Xiaojie , Mu Yingtong , Zhang Xiaoming , Wang Junjie 
Published: June 2023

AbstractAstragalus membranaceus var. mongholicus (Astragalus) is a perennial medicinal plant belonging to Leguminosae. Its main agronomic and industrial interest is the accumulation of astragaloside IV and calycosin‐7‐O‐β‐D‐glucoside in roots, two components that play crucial pharmacological roles in the finished pharmaceutical products. Phenological research is a prerequisite for improving the yield quality of cultivated medicinal plants. However, there are no standardized phenological studies on Astragalus. To expand phenotyping knowledge on this medicinal plant, researchers and breeders need a universal scale to describe the development of this plant. This study aims to define the phenological growth stages of Astragalus, based on the existing Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale to provide a standard scale for Astragalus, and grown under temperate conditions of production and climate, proposes a disease and pest control reference. This study was conducted in Inner Mongolia, North China. The existing BBCH scale with a three‐digit code was used to define Astragalus's phenological growth codes, supplemented with pictures. Phenological observations were performed twice monthly, based on the developmental stages. The chronology and duration of each stage were also observed and described according to the accumulation of degree days during the growing season. Based on the data obtained, the influence of the average temperature and GDD on the phenophases was evaluated. The phenological description is divided into two principal growth stages: five for vegetative growth (perennating bud, leaf development, formation of branches, main stem elongation and root development), four for reproductive growth (reproductive organ development, flowering, fruit development and fruit maturation), and one for senescence according to the BBCH scale. Seventy secondary growth stages were described within the 10 principal growth stages. Under each secondary growth stage, six mesostages are also taken into account, which contains the distinct patterns of the phenological characteristics in Astragalus varieties and the process of transplanting seedlings. A practical management program for disease and pest control was also proposed by using the BBCH code and the phenological data proposed in this work. Our study presents the first BBCH scale established for Astragalus cultivated in temperate conditions and a chronology of phenological stages based on the accumulation of thermal time, through growing degree day calculation. This work provides a general tool that can be widely used by researchers, breeders, and manufacturers of medicinal materials leading to better agricultural production and scientific communication.
Students' experience and perceptions of undergraduate endodontic education in New Zealand
Authors: Friedlander Lara , Hunt Gabrielle , Chandler Nicholas , Daniel Ben 
Published: June 2023

AbstractThis study investigated the clinical experience and perceptions of New Zealand graduating dental students (2019 and 2020) towards endodontic teaching and their clinical learning outcomes using an online survey and clinical scenarios. Quantitative data were analysed using SPSS software, and qualitative data were analysed thematically. Responses were similar for both cohorts (response rates 74%, 2019; 73%, 2020). Endodontic teaching was valuable and interesting but difficult compared with other disciplines. Molar endodontics, finding canals and managing posture were challenging. Students felt more confident and less anxious when supervised by clinicians experienced in endodontics. Time management was the most anxiety‐inducing factor and significantly related to clinical experience (p < 0.001). Overall, students appropriately applied knowledge in most areas of endodontics while holistic problem‐solving in complex scenarios was variable. Maximising clinical experience and supervision from teachers experienced in endodontics are important for learning, improving confidence and reducing anxiety.
Impact of modified‐release opioid use on clinical outcomes following total hip and knee arthroplasty: a propensity score‐matched cohort study
Authors: Liu S. , Patanwala A. E. , Naylor J. M. , Levy N. , Knaggs R. , Stevens J. A. , Bugeja B. , Begley D. , Khor K. E. , Lau E. , Allen R. , Adie S. , Penm J. 
Published: June 2023

SummaryModified‐release opioids are often prescribed for the management of moderate to severe acute pain following total hip and knee arthroplasty, despite recommendations against their use due to increasing concerns regarding harm. The primary objective of this multicentre study was to examine the impact of modified‐release opioid use on the incidence of opioid‐related adverse events compared with immediate‐release opioid use, among adult inpatients following total hip or knee arthroplasty. Data for total hip and knee arthroplasty inpatients receiving an opioid analgesic for postoperative analgesia during hospitalisation were collected from electronic medical records of three tertiary metropolitan hospitals in Australia. The primary outcome was the incidence of opioid‐related adverse events during hospital admission. Patients who received modified with or without immediate‐release opioids were matched to those receiving immediate‐release opioids only (1:1) using nearest neighbour propensity score matching with patient and clinical characteristics as covariates. This included total opioid dose received. In the matched cohorts, patients given modified‐release opioids (n = 347) experienced a higher incidence of opioid‐related adverse events overall, compared with those given immediate‐release opioids only (20.5%, 71/347 vs. 12.7%, 44/347; difference in proportions 7.8% [95%CI 2.3–13.3%]). Modified‐release opioid use was associated with an increased risk of harm when used for acute pain during hospitalisation after total hip or knee arthroplasty.
E2F2 modulates cell adhesion through the transcriptional regulation of PECAM1 in multiple myeloma
Authors: Chen Shu‐Na , Mai Zhi‐Ying , Mai Jun‐Na , Liang Weiyao , Dong Zhao‐Xia , Ju Fei‐er , Chan Sze‐Hoi , Fang Zhigang , Xu Yichuan , Uziel Orit , He Chengwei , Zhang Xing‐Ding , Zheng Yongjiang 
Published: June 2023

SummaryMultiple myeloma (MM) is the second most common haematological malignancy. Despite the development of new drugs and treatments in recent years, the therapeutic outcomes of patients are not satisfactory. It is necessary to further investigate the molecular mechanism underlying MM progression. Herein, we found that high E2F2 expression was correlated with poor overall survival and advanced clinical stages in MM patients. Gain‐ and loss‐of‐function studies showed that E2F2 inhibited cell adhesion and consequently activated cell epithelial‐to‐mesenchymal transition (EMT) and migration. Further experiments revealed that E2F2 interacted with the PECAM1 promoter to suppress its transcriptional activity. The E2F2‐knockdown‐mediated promotion of cell adhesion was significantly reversed by the repression of PECAM1 expression. Finally, we observed that silencing E2F2 significantly inhibited viability and tumour progression in MM cell models and xenograft mouse models respectively. This study demonstrates that E2F2 plays a vital role as a tumour accelerator by inhibiting PECAM1‐dependent cell adhesion and accelerating MM cell proliferation. Therefore, E2F2 may serve as a potential independent prognostic marker and therapeutic target for MM.
ChemoGraph: Interactive Visual Exploration of the Chemical Space
Authors: Kale Bharat , Clyde Austin , Sun Maoyuan , Ramanathan Arvind , Stevens Rick , Papka Michael E. 
Published: June 2023

AbstractExploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
RectEuler: Visualizing Intersecting Sets using Rectangles
Authors: Paetzold Patrick , Kehlbeck Rebecca , Strobelt Hendrik , Xue Yumeng , Storandt Sabine , Deussen Oliver 
Published: June 2023

AbstractEuler diagrams are a popular technique to visualize set‐typed data. However, creating diagrams using simple shapes remains a challenging problem for many complex, real‐life datasets. To solve this, we propose RectEuler: a flexible, fully‐automatic method using rectangles to create Euler‐like diagrams. We use an efficient mixed‐integer optimization scheme to place set labels and element representatives (e.g., text or images) in conjunction with rectangles describing the sets. By defining appropriate constraints, we adhere to well‐formedness properties and aesthetic considerations. If a dataset cannot be created within a reasonable time or at all, we iteratively split the diagram into multiple components until a drawable solution is found. Redundant encoding of the set membership using dots and set lines improves the readability of the diagram. Our web tool lets users see how the layout changes throughout the optimization process and provides interactive explanations. For evaluation, we perform quantitative and qualitative analysis across different datasets and compare our method to state‐of‐the‐art Euler diagram generation methods.
Exploring Interpersonal Relationships in Historical Voting Records
Authors: Cantareira G. D. , Xing Y. , Cole N. , Borgo R. , Abdul‐Rahman A. 
Published: June 2023

AbstractHistorical records from democratic processes and negotiation of constitutional texts are a complex type of data to navigate due to the many different elements that are constantly interacting with one another: people, timelines, different proposed documents, changes to such documents, and voting to approve or reject those changes. In particular, voting records can offer various insights about relationships between people of note in that historical context, such as alliances that can form and dissolve over time and people with unusual behavior. In this paper, we present a toolset developed to aid users in exploring relationships in voting records from a particular domain of constitutional conventions. The toolset consists of two elements: a dataset visualizer, which shows the entire timeline of a convention and allows users to investigate relationships at different moments in time via dimensionality reduction, and a person visualizer, which shows details of a given person's activity in that convention to aid in understanding the behavior observed in the dataset visualizer. We discuss our design choices and how each tool in those elements works towards our goals, and how they were perceived in an evaluation conducted with domain experts.
VENUS: A Geometrical Representation for Quantum State Visualization
Authors: Ruan Shaolun , Yuan Ribo , Guan Qiang , Lin Yanna , Mao Ying , Jiang Weiwen , Wang Zhepeng , Xu Wei , Wang Yong 
Published: June 2023

AbstractVisualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely‐used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well‐designed case studies and an in‐depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.
WYTIWYR: A User Intent‐Aware Framework with Multi‐modal Inputs for Visualization Retrieval
Authors: Xiao Shishi , Hou Yihan , Jin Cheng , Zeng Wei 
Published: June 2023

AbstractRetrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e.g., chart type, colormap) and implicit user intents (e.g., design style, context information) that vary upon application scenarios. However, existing example‐based chart retrieval methods are built upon non‐decoupled and low‐level visual features that are hard to interpret, while definition‐based ones are constrained to pre‐defined attributes that are hard to extend. In this work, we propose a new framework, namelyWYTIWYR (What‐You‐Think‐Is‐What‐You‐Retrieve), that integrates user intents into the chart retrieval process. The framework consists of two stages: first, theAnnotationstage disentangles the visual attributes within the query chart; and second, theRetrievalstage embeds the user's intent with customized text prompt as well as bitmap query chart, to recall targeted retrieval result. We develop aprototypeWYTIWYRsystem leveraging a contrastive language‐image pre‐training (CLIP) model to achieve zero‐shot classification as well as multi‐modal input encoding, and test the prototype on a large corpus with charts crawled from the Internet. Quantitative experiments, case studies, and qualitative interviews are conducted. The results demonstrate the usability and effectiveness of our proposed framework.
ParaDime: A Framework for Parametric Dimensionality Reduction
Authors: Hinterreiter Andreas , Humer Christina , Kainz Bernhard , Streit Marc 
Published: June 2023

AbstractParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high‐dimensional data items in a low‐dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter‐item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t‐SNE, and UMAP. It allows users to fully customize all aspects of the DR process. We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high‐dimensional data.
LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity
Authors: Arunkumar A. , Sharma S. , Agrawal R. , Chandrasekaran S. , Bryan C. 
Published: June 2023

AbstractCross‐task generalization is a significant outcome that defines mastery in natural language understanding. Humans show a remarkable aptitude for this, and can solve many different types of tasks, given definitions in the form of textual instructions and a small set of examples. Recent work with pre‐trained language models mimics this learning style: users can define and exemplify a task for the model to attempt as a series of natural language prompts or instructions. While prompting approaches have led to higher cross‐task generalization compared to traditional supervised learning, analyzing ‘bias’ in the task instructions given to the model is a difficult problem, and has thus been relatively unexplored. For instance, are we truly modeling a task, or are we modeling a user's instructions? To help investigate this, we develop LINGO, a novel visual analytics interface that supports an effective, task‐driven workflow to (1) help identify bias in natural language task instructions, (2) alter (or create) task instructions to reduce bias, and (3) evaluate pre‐trained model performance on debiased task instructions. To robustly evaluate LINGO, we conduct a user study with both novice and expert instruction creators, over a dataset of 1,616 linguistic tasks and their natural language instructions, spanning 55 different languages. For both user groups, LINGO promotes the creation of more difficult tasks for pre‐trained models, that contain higher linguistic diversity and lower instruction bias. We additionally discuss how the insights learned in developing and evaluating LINGO can aid in the design of future dashboards that aim to minimize the effort involved in prompt creation across multiple domains.
Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models
Authors: Schetinger V. , Di Bartolomeo S. , El‐Assady M. , McNutt A. , Miller M. , Passos J. P. A. , Adams J. L. 
Published: June 2023

AbstractGenerative text‐to‐image models (as exemplified by DALL‐E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains—from logo design to digital painting to photographic composition. However, the quality of these results has led to existential crises in some fields of art, leading to questions about the role of human agency in the production of meaning in a graphical context. Such issues are central to visualization, and while these generative models have yet to be widely applied in visualization, it seems only a matter of time until their integration is manifest. Seeking to circumvent similar ponderous dilemmas, we attempt to understand the roles that generative models might play across visualization. We do so by constructing a framework that characterizes what these technologies offer at various stages of the visualization workflow, augmented and analyzed through semi‐structured interviews with 21 experts from related domains. Through this work, we map the space of opportunities and risks that might arise in this intersection, identifying doomsday prophecies and delicious low‐hanging fruits that are ripe for research.
Visual Analytics on Network Forgetting for Task‐Incremental Learning
Authors: Li Ziwei , Xu Jiayi , Chao Wei‐Lun , Shen Han‐Wei 
Published: June 2023

AbstractTask‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time.