bluuebunny/crossref_metadata_2025_split huggingface dataset:
The clinical, diagnostic and treatment spectrum of seropositive and seronegative autoimmune encephalitis: Single‐center cohort study of 51 cases and review of the literature
Authors: Elrefaey Ahmed , Mohamedelkhair Ahmed , Fahmy Lara , Affan Mohammad , Schultz Lonni R. , Cerghet Mirela , Memon Anza B. 
Published: June 2024

AbstractObjectiveAutoimmune encephalitis (AE) comprises a spectrum of inflammatory neurological syndromes characterized by immune responses to neuronal autoantigens, leading to diverse clinical manifestations, particularly behavioral and cognitive decline.MethodsThis single‐center retrospective study included 51 patients diagnosed with AE from 2013 to 2019 in a southeast Michigan tertiary care hospital. Patients were then divided into two groups, seropositive AE (AE+) and seronegative AE (AE−), based on antibody detection in the serum, cerebrospinal fluid or both when available. The study compares AE+ and AE− subtypes across clinical, diagnostic, and therapeutic parameters.ResultsA total of 34 patients were classified as AE+, and 17 as AE−. Demographic analysis showed no significant differences in age, sex or race between the two groups. Clinical presentations varied widely, encompassing psychiatric symptoms, movement disorders, seizures and confusion; 24% patients had a prior malignancy. Laboratory assessments found diverse autoantibodies in AE+ patients' serum. Radiological and electrophysiological assessments showed no significant differences between the groups. AE− patients had higher rates of confusion compared with AE+ patients (59% vs. 18%, P = 0.004).ConclusionsThis study focuses on the complexities associated with diagnosing AE, emphasizing the challenges posed by the heterogeneity of symptoms and often negative antibody test results. Rapid identification of AE, regardless of seropositivity or seronegativity, emerges as a critical factor for clinicians, facilitating the prompt initiation of immunotherapy and/or tumor removal if needed. These insights contribute to a better understanding of the landscape of this condition, offering clinicians the tools to refine their diagnostic and treatment strategies. Ultimately, the study aimed to enhance the management of AE, empowering healthcare professionals to make accurate and timely interventions for patients.
A novel homozygous FAM92A gene (CIBAR1) variant further confirms its association with non‐syndromic postaxial polydactyly type A9 (PAPA9)
Authors: Umair Muhammad , Ahmed Zaheer , Shaker Bilal , Bilal Muhammad , Al Abdulrahman Abdulkareem , Khan Hammal , Jawad Khan Muhammad , Alfadhel Majid 
Published: June 2024

AbstractPolydactyly is a very common digit anomaly, having extra digits in hands and/or toes. Non‐syndromic polydactyly in both autosomal dominant and autosomal recessive forms are caused by disease‐causing variants in several genes, including GLI1, GLI3, ZNF141, FAM92A, IQCE, KIAA0825, MIPOL1, STKLD1, PITX1, and DACH1. Whole exome sequencing (WES) followed by bi‐directional Sanger sequencing was performed for the single affected individual (II‐1) of the family to reveal the disease causative variant/gene. 3D protein modeling and structural molecular docking was performed to determine the effect of the identified mutation on the overall protein structure. WES revealed a novel biallelic missense variant (c.472G>C; p.Ala158Pro) in exon 6 of the FAM92A gene. The identified variant segregated perfectly with the disease phenotype using Sanger sequencing. Furthermore, Insilco analysis revealed that the variant significantly changes the protein secondary structure, and substantially impact the stability of FAM92A. We report the second FAM92A disease‐causing mutation associated with recessive non‐syndromic postaxial polydactyly. The data further confirms the contribution of FAM92A in limb development and patterning.
Depth for Multi‐Modal Contour Ensembles
Authors: Chaves‐de‐Plaza N.F. , Molenaar M. , Mody P. , Staring M. , van Egmond R. , Eisemann E. , Vilanova A. , Hildebrandt K. 
Published: June 2024

AbstractThe contour depth methodology enables non‐parametric summarization of contour ensembles by extracting their representatives, confidence bands, and outliers for visualization (via contour boxplots) and robust downstream procedures. We address two shortcomings of these methods. Firstly, we significantly expedite the computation and recomputation of Inclusion Depth (ID), introducing a linear‐time algorithm for epsilon ID, a variant used for handling ensembles with contours with multiple intersections. We also present the inclusion matrix, which contains the pairwise inclusion relationships between contours, and leverage it to accelerate the recomputation of ID. Secondly, extending beyond the single distribution assumption, we present the Relative Depth (ReD), a generalization of contour depth for ensembles with multiple modes. Building upon the linear‐time eID, we introduce CDclust, a clustering algorithm that untangles ensemble modes of variation by optimizing ReD. Synthetic and real datasets from medical image segmentation and meteorological forecasting showcase the speed advantages, illustrate the use case of progressive depth computation and enable non‐parametric multimodal analysis. To promote research and adoption, we offer the contour‐depth Python package.
Interactive Optimization for Cartographic Aggregation of Building Features
Authors: Takahashi Shigeo , Kokubun Ryo , Nishimura Satoshi , Misue Kazuo , Arikawa Masatoshi 
Published: June 2024

AbstractAggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale‐aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.
Transparent Risks: The Impact of the Specificity and Visual Encoding of Uncertainty on Decision Making
Authors: Matzen L. E. , Howell B. C. , Tuft M. , Divis K. M. 
Published: June 2024

AbstractPeople frequently make decisions based on uncertain information. Prior research has shown that visualizations of uncertainty can help to support better decision making. However, research has also shown that different representations of the same information can lead to different patterns of decision making. It is crucial for researchers to develop a better scientific understanding of when, why and how different representations of uncertainty lead viewers to make different decisions. This paper seeks to address this need by comparing geospatial visualizations of wildfire risk to verbal descriptions of the same risk. In three experiments, we manipulated the specificity of the uncertain information as well as the visual cues used to encode risk in the visualizations. All three experiments found that participants were more likely to evacuate in response to a hypothetical wildfire if the risk information was presented verbally. When the risk was presented visually, participants were less likely to evacuate, particularly when transparency was used to encode the risk information. Experiment 1 showed that evacuation rates were lower for transparency maps than for other types of visualizations. Experiments 2 and 3 sought to replicate this effect and to test how it related to other factors. Experiment 2 varied the hue used for the transparency maps and Experiment 3 manipulated the salience of the borders between the different risk levels. These experiments showed lower evacuation rates in response to transparency maps regardless of hue. The effect was partially, but not entirely, mitigated by adding salient borders to the transparency maps. Taken together, these experiments show that using transparency to encode information about risk can lead to very different patterns of decision making than other encodings of the same information.
A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error
Authors: Ren Congrong , Liang Xin , Guo Hanqi 
Published: June 2024

AbstractWe explore an error‐bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal‐to‐noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean‐climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors.
Visual Analytics for Fine‐grained Text Classification Models and Datasets
Authors: Battogtokh M. , Xing Y. , Davidescu C. , Abdul‐Rahman A. , Luck M. , Borgo R. 
Published: June 2024

AbstractIn natural language processing (NLP), text classification tasks are increasingly fine‐grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse‐grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design‐and‐evaluation process to characterize and tackle the growing requirements in their workflow of developing fine‐grained text classification models. The result of this collaboration is the development of SemLa, a novel Visual Analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine‐grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.
ChoreoVis: Planning and Assessing Formations in Dance Choreographies
Authors: Beck Samuel , Doerr Nina , Kurzhals Kuno , Riedlinger Alexander , Schmierer Fabian , Sedlmair Michael , Koch Steffen 
Published: June 2024

AbstractSports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.
Visual Highlighting for Situated Brushing and Linking
Authors: Doerr Nina , Lee Benjamin , Baricova Katarina , Schmalstieg Dieter , Sedlmair Michael 
Published: June 2024

AbstractBrushing and linking is widely used for visual analytics in desktop environments. However, using this approach to link many data items between situated (e.g., a virtual screen with data) and embedded views (e.g., highlighted objects in the physical environment) is largely unexplored. To this end, we study the effectiveness of visual highlighting techniques in helping users identify and link physical referents to brushed data marks in a situated scatterplot. In an exploratory virtual reality user study (N=20), we evaluated four highlighting techniques under different physical layouts and tasks. We discuss the effectiveness of these techniques, as well as implications for the design of brushing and linking operations in situated analytics.
GMINN: Gate‐enhanced multi‐space interaction neural networks for click‐through rate prediction
Authors: Feng Xingyu , Yang Xuekang , Zhou Boyun 
Published: June 2024

AbstractClick‐through rate (CTR) prediction is a pivotal challenge in recommendation systems. Existing models are prone to disturbances from noise and redundant features, hindering their ability to fully capture implicit and higher‐order feature interactions present in sparse feature data. Moreover, conventional dual‐tower models overlook the significance of layer‐level feature interactions. To address these limitations, this article introduces Gate‐enhanced Multi‐space Interactive Neural Networks (GMINN), a novel model for CTR prediction. GMINN adopts a dual‐tower architecture in which a multi‐space interaction layer is introduced after each layer in the dual‐tower deep neural network. This layer allocates features into multiple subspaces and employs matrix multiplication to establish layer‐level interactions between the dual towers. Simultaneously, a field‐aware gate mechanism is proposed to extract crucial latent information from the original features. Experimental validation on publicly available datasets, Criteo and Avazu, demonstrates the superiority of the proposed GMINN model. Comparative analyses against baseline models reveal that GMINN substantially improves up to 4.09% in AUC and a maximum reduction of 7.21% in Logloss. Additionally, ablation experiments provide further validation of the effectiveness of GMINN.
Chronische Prurigo
Authors: Thünemann Julia , Müller Svenja , Steinbrink Kerstin , Ständer Sonja , Zeidler Claudia 
Published: June 2024

ZusammenfassungDie chronische Prurigo (CPG) ist eine neuroinflammatorische Dermatose, die durch langanhaltenden Pruritus von mehr als 6 Wochen, pruriginösen Hautläsionen und wiederholtem Kratzen gekennzeichnet ist. Patienten mit CPG leiden unter erheblichen psychischen Belastungen und deutlich spürbarer Beeinträchtigung ihrer Lebensqualität. Der am häufigsten vorkommende Subtyp der CPG ist die chronisch noduläre Prurigo (CNPG; auch Prurigo nodularis genannt).Neben den klinischen Merkmalen der CPG und der Krankheitslast bietet dieser CME‐Artikel einen Überblick über die bedeutenden Fortschritte im Verständnis der Pathophysiologie, einschließlich der damit verbundenen therapeutischen Optionen der CPG. Dupilumab ist die erste zugelassene Therapie für die moderate und schwere CNPG, die bisher von der Europäischen Arzneimittelagentur (EMA) und der US‐amerikanischen Food & Drug Administration (FDA) zugelassen wurde. Des Weiteren werden weitere Wirkstoffe hervorgehoben, die derzeit in klinischen, randomisierten, placebokontrollierten Phase‐II‐ und Phase‐III‐Studien untersucht werden. Dazu gehören Biologika wie Nemolizumab (Anti‐IL‐31‐RA‐mAk), Vixarelimab/KPL‐716 (Anti‐Oncostatin‐M Rezeptor β‐mAk) und Barzolvolimab/CDX‐0159 (Anti‐KIT‐mAK), Januskinase‐Inhibitoren wie Povorcitinib/INCB054707 und Abrocitinib sowie Opiodmodulatoren wie Nalbuphin.
Comprehensive analysis of body composition features in melanoma patients treated with tyrosine kinase inhibitors
Authors: Mengoni Miriam , Braun Andreas Dominik , Hinnerichs Mattes Simon , Aghayev Anar , Tüting Thomas , Surov Alexey 
Published: June 2024

SummaryBackgroundThe introduction of tyrosine kinase inhibitors (TKI) has greatly improved the management of metastatic melanoma. Recent studies have uncovered a relationship between the body mass index (BMI) and outcome of patients with metastatic melanoma. However, conflicting results have challenged the relevance of this finding. In the current work, we aim to dissect body composition features of melanoma patients treated with TKI to evaluate their value as biomarkers.Patients and MethodsWe analyze body composition features via CT scans in a retrospective cohort of 57 patients with non‐resectable stage III/IV melanoma receiving first‐line treatment with TKI in our department, focusing on the impact of body composition on treatment efficacy and occurrence of adverse events.ResultsIn uni‐ and multivariate analyses, we identify an association between the visceral adipose tissue gauge index (VATGI) and survival. We furthermore profile additional body composition features including sarcopenia, which was also associated with a shorter overall survival. Finally, we detected an enrichment of cases with fatigue in patients with low VATGI.ConclusionsOur study represents the first exploratory study evaluating the suitability of body composition measurements as biomarkers for melanoma patients treated with TKI. Our data suggest a putative use of VATGI as a biomarker predicting patient outcome.
Ambulance clinician use of capillary blood ketone meters to improve emergency hyperglycaemia care: A stepped‐wedged controlled, mixed‐methods feasibility study
Authors: Prothero Larissa Stella , Strudwick Thomas , Foster Theresa , Lake Andrea Kathleen , Boyle Adrian , Clark Allan , Williams Julia , Rayman Gerry , Dhatariya Ketan 
Published: June 2024

AbstractAimTo determine whether it was feasible, safe and acceptable for ambulance clinicians to use capillary blood ketone meters for ‘high‐risk’ diabetic ketoacidosis (DKA) recognition and fluid initiation, to inform the need for a full‐powered, multi‐centre trial.MethodsAdopting a stepped‐wedge controlled design, participants with hyperglycaemia (capillary blood glucose >11.0 mmol/L) or diabetes and unwell were recruited. ‘High‐risk’ DKA intervention participants (capillary blood ketones ≥3.0 mmol/L) received paramedic‐led fluid therapy. Participant demographic and clinical data were collated from ambulance and hospital care records. Twenty ambulance and Emergency Department clinicians were interviewed to understand their hyperglycaemia and DKA care experiences.ResultsIn this study, 388 participants were recruited (Control: n = 203; Intervention: n = 185). Most presented with hyperglycaemia, and incidence of type 1 and type 2 diabetes was 18.5% and 74.3%, respectively. Ketone meter use facilitated ‘high‐risk’ DKA identification (control: 2.5%, n = 5; intervention: 6.5%, n = 12) and was associated with improved hospital pre‐alerting. Ambulance clinicians appeared to have a high index of suspicion for hospital‐diagnosed DKA participants. One third (33.3%; n = 3) of Control and almost half (45.5%; n = 5) of Intervention DKA participants received pre‐hospital fluid therapy.Key interview themes included clinical assessment, ambulance DKA fluid therapy, clinical handovers; decision support tool; hospital DKA management; barriers to hospital DKA care.ConclusionsAmbulance capillary blood ketone meter use was deemed feasible, safe and acceptable. Opportunities for improved clinical decision making, support and safety‐netting, as well as in‐hospital DKA care, were recognised. As participant recruitment was below progression threshold, it is recommended that future‐related research considers alternative trial designs. Clinicaltrials.gov: NCT04940897.
The experiences of individuals who have had gestational diabetes: A qualitative exploration
Authors: Roesler Anna , Butten Kaley , Taylor Pennie , Morrison Melinda , Varnfield Marlien , Holmes‐Truscott Elizabeth 
Published: June 2024

AbstractAimTo qualitatively explore the experiences of individuals with Gestational Diabetes Mellitus (GDM) in Australia, and to recognise opportunities for leveraging digital health to enhance the support of GDM management.MethodA cross sectional online survey assessed the experiences of individuals with GDM, the healthcare system and their digital health usage. Respondents (recruited via a national diabetes registry or social media) were adults receiving GDM care within Australia in the last 5 years, who responded to any of three open‐ended questions (n = 815) exploring positive, negative and other GDM experiences. Thematic analysis was utilised, and themes were mapped to the socio‐ecological systems framework.ResultsAt a system level, themes related to (1) accessibility of care including the value of digital health and the inflexible or inconsistent perception of the (2) implementation of guidelines. At an interpersonal level, themes covered the need for adequate (3) health information provision, and (4) supportive care, as well as highlighting (5) experiences of stigma including a desire for greater awareness of GDM. Individual‐level themes included: (6) differential barriers to accessing care; (7) negative emotional burden; (8) internalisation of stigma; (9) dietary freedom and social impact and (10) opportunity for change derived from having GDM.ConclusionFindings suggest a demand for more supportive, person‐centred GDM care, improved information provision and individualised implementation of clinical guidelines. Such mechanisms may support reduced barriers to accessing care or negative psychosocial impacts of GDM. Though not central to the identified experiences, digital health tools may help address the need for optimised GDM care.