Visual Analysis Research Articles

Deep Learning for Visual Speech Analysis: A Survey.

Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numerous deep learning based methods have been proposed to address various problems in this area, especially automatic visual speech recognition and generation. To push forward future research on visual speech, this paper will present a comprehensive review of recent progress in deep learning methods on visual speech analysis. We cover different aspects of visual speech, including fundamental problems, challenges, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. Besides, we also identify gaps in current research and discuss inspiring future research directions.

Visual Analysis and the Contentious Politics of the Radical Right

ABSTRACTAlthough images are very important for political actors and social movements, including the radical right (RR), empirical studies still rarely integrate visual material as relevant data for understanding radical right politics. This article outlines this new and growing field of research (i.e., visuality and the RR), critically reviewing existing studies from the perspective of both visual studies of social movements and contentious politics, which are rarely applied to the RR, and the methodology of working with images, offering empirical case studies (European and beyond) to illustrate the argument. The findings reveal the main functions of the use of visuals for the radical right, as well as the benefits (but also the challenges) of studying radical right politics through the lens of visual analysis. A conceptual framework is proposed to capture this dominant visual politics of the radical right. As shown, two dimensions emerge as the most theoretically relevant for the radical right: The discursive meaning of images (the story itself, telling the story, eliciting the story) and the communicative function of images (visual expression by the movement or others, visibility), which combine agency and addressee.

Excessive daytime sleepiness in older adults: A bibliometric and visualization analysis

Background: To analyse the bibliometric results of a study on the research trends in excessive daytime sleepiness (EDS) among older adults to provide clinicians and researchers with a better understanding of the current status and to identify potential new research directions. Methods: We searched the Web of Science Core Collection for articles on immunotherapy for EDS in older adults. We used three software packages (VOSviewer, CiteSpace, and Tableau) to analyze the articles in terms of journals, keywords, citations, co-citations, and research areas and identify research hotspots as well as promising future trends in this field. Results: Our findings showed an annual increase in the number of articles on EDS that were published. Among them, authors from the USA had the highest number of publications globally in this field, while in terms of institutions, the highest number of articles came from Stanford University (n = 26, 2.73%). The articles covered diverse research areas and included clinical neurology, neuroscience, and the respiratory system. As expected, journal-wise, the majority of articles were published in “Sleep” and “Sleep Medicine”. Keywords indicating research areas in recent years were ‘important difference,’ ‘obstructive sleep apnea,’ and ‘shift work.’ These were also reflected in the results of the co-citation analysis. Conclusions: Overall, EDS is an area of increasing interest and offers excellent research opportunities for the future.

Mapping a Decade (2014-2024) of Research on Extracorporeal Membrane Oxygenation for Acute Respiratory Distress Syndrome: A Visual Analysis with CiteSpace and VOSviewer.

Acute Respiratory Distress Syndrome (ARDS) stands as a primary cause of mortality among critically ill patients. Extracorporeal Membrane Oxygenation (ECMO) is increasingly employed in the rescue therapy of ARDS patients. However, the current status of research in the field of ECMO-assisted ARDS remains unclear. This research aims to categorize and evaluate the literature regarding Extracorporeal Membrane Oxygenation (ECMO) support for Acute Respiratory Distress Syndrome (ARDS), offering a comprehensive analysis of bibliometric properties, research hotspots, and developmental trends within the domain of ECMO-assisted ARDS. A literature search was conducted for ECMO-assisted support for patients with ARDS in the Web of Science Core Collection (WoSCC) database from 2014 to 2024. We employed visualization tools such as CiteSpace and VOSviewer to explore and assess connections among nations, institutions, researchers, and co-cited journals, authors, references, and keywords. This study included 1739 publications. The United States leads in publication volume with Columbia University at the forefront of ECMO research. Intensive Care Medicine has been identified as the most cited journal in this field. Alain Combes from France stands out as a key contributor, particularly in his 2018 publication in the New England Journal of Medicine, which is the most cited work in the discipline. Furthermore, keyword analysis identified three distinct research phases: examining complications associated with ECMO therapy, exploring optimal strategies for mechanical ventilation under ECMO support, and compiling insights into the application of ECMO in treating COVID-19 patients and in the development of predictive models for patient outcomes. Using bibliometric visualization techniques, this study revealed significant progress in the use of ECMO for treating ARDS respiratory support, evaluated the impact of these findings, and outlined potential areas for future studies.

Public space and everyday heritage: An inquiry into children's and adults' perception

This paper examines how the spatial environment and the activities within these spaces contribute to their social value in turn informing the concept of everyday heritage within urban public spaces. We develop a novel methodological framework that combines visual representation and narrative analysis to provide insights into how the physical, emotional and perceptual dimensions of a public open space translate into associative social values. By including both adults and children in our study, we capture a comprehensive range of perceptions, highlighting the diverse ways in which different user groups give meaning to everyday heritage. Children and their guardians showed agreement on natural elements and personal representations, but differed on the garden's spatial layout and activities. While they shared values like childhood memories and family time, children prioritized play, forming new friendships, and connecting with nature, whereas parents emphasized children's happiness and personal space. Notably, both groups indicate that childhood – both lived and remembered – is that which gives this public urban space its everyday heritage status. The findings advocate for an expanded understanding of urban public spaces, recognizing the significance of everyday heritage in shaping collective cultural identity.

Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling.

Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area. Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.

The place and role of purgatory in the twenty-first century: a visual content analysis of students’ AI-generated works

This article examines current perceptions of purgatory among young people and its role in society. Architecture students’ visual material explores the evolving concept of purgatory and its importance today. The study analyses how ideas of purification and hybridity are understood in contemporary secular society. Using a structured visual research framework, the study incorporates participatory seminars, art-based methodologies and innovative AI techniques. It investigates young people’s views on purgatory and their understanding of the cosmos, considering contemporary interpretations and the concept of presentism. By combining creative and conventional research approaches, this text aims to enhance understanding and generate new insights. The analysis uncovers how purgatory and transitional spaces are understood in today’s society, revealing individual interpretations of religious teachings. It also highlights the importance of creative research methods in understanding complex religious ideas.

Diagnostic Performance of the Offline Medios Artificial Intelligence for Glaucoma Detection in a Rural Tele-Ophthalmology Setting

PurposeThis study assesses the diagnostic efficacy of offline Medios Artificial Intelligence (AI) glaucoma software in a primary eyecare setting, using non-mydriatic fundus images from Remidio's Fundus-on-Phone (FOP NM-10). AI results were compared with tele-ophthalmologists' diagnoses and with a glaucoma specialist's assessment for those participants referred to tertiary eyecare hospital. DesignProspective, cross-sectional study Participants303 participants from 6 satellite vision centers of a tertiary eye hospital MethodsAt the vision center, participants underwent comprehensive eye evaluations, including clinical history, visual acuity measurement, slit lamp examination, intraocular pressure measurement, and fundus photography using the FOP NM-10 camera. Medios AI-Glaucoma software analysed 42-degrees disc-centric fundus images, categorizing them as normal, glaucoma, or suspect. Tele-ophthalmologists who were glaucoma fellows with a minimum of 3 years of ophthalmology and 1 year of glaucoma fellowship training, masked to AI results, remotely diagnosed subjects based on the history and disc appearance. All participants labelled as disc suspects or glaucoma by AI or tele-ophthalmologists underwent further comprehensive glaucoma evaluation at the base hospital, including clinical examination, Humphrey visual field analysis (HFA), and Optical Coherence Tomography (OCT). AI and tele-ophthalmologist diagnoses were then compared with a glaucoma specialist's diagnosis. Main Outcome MeasuresSensitivity and Specificity of Medios AI ResultsOut of 303 participants, 299 with at least one eye of sufficient image quality were included in the study. The remaining 4 participants did not have sufficient image quality in both eyes. Medios AI identified 39 participants (13%) with referable glaucoma. The AI exhibited a sensitivity of 0.91 (95% CI: 0.71 - 0.99) and specificity of 0.93 (95% CI: 0.89 - 0.96) in detecting referable glaucoma (definite perimetric glaucoma) when compared to tele-ophthalmologist. The agreement between AI and the glaucoma specialist was 80.3%, surpassing the 55.3.% agreement between the tele-ophthalmologist and the glaucoma specialist amongst those participants who were referred to the base hospital. Both AI and the tele-ophthalmologist relied on fundus photos for diagnoses, while the glaucoma specialist's assessments at the base hospital were aided by additional tools such as HFA and OCT. Furthermore, AI had fewer false positive referrals (2 out of 10) compared to the tele-ophthalmologist (9 out of 10). ConclusionMedios offline AI exhibited promising sensitivity and specificity in detecting referable glaucoma from remote vision centers in southern India when compared with teleophthalmologists. It also demonstrated better agreement with glaucoma specialist’s diagnosis for referable glaucoma participants.

Study of Ceramic Tea Utensils Design in Song Dynasty Tea Paintings

This research methodology explores the design of ceramic tea utensils depicted in Song Dynasty tea paintings. This study aims to identify and classify the different types of ceramic tea utensils represented in Song Dynasty tea paintings, analyse the design characteristics and aesthetic principles of these representations, and explore the cultural and social significance of ceramic tea utensils in Song Dynasty society. Data collection is conducted through document collection and unstructured observation, focusing on four themes. Eight tea paintings from the Song Dynasty depicting ceramic tea utensils are selected for visual and cultural analysis, revealing their complex design features, symbolic meanings, and cultural connotations. The aesthetic principles of ceramic tea utensil design in Song Dynasty tea paintings reflect the unique cultural atmosphere and aesthetic preferences of the time, characterized mainly by a simple and rustic aesthetic, the use of biomimetic design, the combination of practicality and beauty, and the unity of cultural connotation and aesthetic mood.

Antiviral Activity of Graphene Oxide-Silver Nanocomposites Against Murine Betacoronavirus.

The high infectivity of coronaviruses has led to increased interest in developing new strategies to prevent virus spread. Silver nanoparticles (AgNPs) and graphene oxide (GO) have attracted much attention in the antiviral field. We investigated the potential antiviral activity of GO and AgNPs combined in the nanocomposite GO-Ag against murine betacoronavirus MHV using an in vitro model. GO, AgNPs, and GO-Ag characterization (size distribution, zeta potential, TEM visualization, FT-IR, and EDX analysis) and XTT assay were performed. The antiviral activity of GO-Ag nanocomposites was evaluated by RT-qPCR and TCID50 assays. The results were compared with free AgNPs and pure GO. Cell growth and morphology of MHV-infected hepatocytes treated with GO-Ag composites were analyzed by JuLI™Br. Immunofluorescence was used to visualize the cell receptor used by MHV. Ultrastructural SEM analysis was performed to examine cell morphology after MHV infection and GO-Ag composite treatment. A significant reduction in virus titer was observed for all nanocomposites tested, ranging from 3.2 to 7.3 log10 TCID50. The highest titer reduction was obtained for GO 5 µg/mL - Ag 25 µg/mL in the post-treatment method. These results were confirmed by RT-qPCR analysis. The results indicate that GO-Ag nanocomposites exhibited better antiviral activity compared to AgNPs and GO. Moreover, the attachment of AgNPs to the GO flake platform reduced their cytotoxicity. In addition, the GO-Ag composite modulates the distribution of the Ceacam1 cell receptor and can modulate cell morphology. Graphene oxide sheets act as a stabilizing agent, inhibiting the accumulation of AgNPs and reducing their cellular toxicity. The GO-Ag composite can physically bind and inhibit murine betacoronavirus from entering cells. Furthermore, the constant presence of GO-Ag can inhibit MHV replication and significantly limit its extracellular release. In conclusion, GO-Ag shows promise as an antiviral coating on solid surfaces to minimize virus transmission and spread.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2GRL) framework to model EEG signals as graph data. Specifically, MD 2GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Surveillance fœtale : limites actuelles et nouvelles pistes basées sur l’analyse du système nerveux autonome fœtal

ObjectiveCurrently, fetal monitoring during labor is based on visual analysis of the fetal heart rate (FHR). This test is imperfect, with high intra- and inter-observer variability and a moderate to poor prediction of the occurrence of neonatal acidosis or anoxic-ischaemic encephalopathy. In situations where there is an intermediate risk of acidosis, it is possible to use second-line tests such as blood scalp sampling (with pH or lactate measurement) or ST segment analysis of the fetal ECG. However, these invasive tests have many limitations and their place is debated. Some authors suggest a more physiological approach to FHR assessment. The main actor in maintaining fetal homeostasis is the autonomic nervous system (ANS). Its activity can be assessed by analysing heart rate variability (HRV). The aim is to assess whether HRV can be used to identify situations at risk of acidosis. Materials and methodsOur team has developed an index, the Fetal Stress Index, to measure HRV. To test it in a situation of acidosis, we used a pregnant ewe model. We also developed in parallel a human fetal ECG recording system. ResultsIn our experimental model, we have shown that this index reflects variations in the parasympathetic system and correlates with the onset of acidosis. As its use in clinical practice requires the acquisition of a beat-to-beat FHR signal, we have also developed an abdominal patch that allows highly accurate analysis of the fetal ECG. ConclusionThe future is therefore to validate the FSI as a marker of acidosis in a prospective cohort using the signal obtained from our patch. This could be a new tool for fetal monitoring during labor.

Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis.

Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm. Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs. Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05). Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

Application of proton density fat fraction imaging in risk stratification of prostate cancer.

Prostate cancer (PCa) as one of the most prevalent malignancies in men. We introduced a non-invasive quantitative measurement of intraprostatic fat content based on magnetic resonance proton density fat fraction (PDFF) imaging. The study aims to determine the fat fraction (FF) of PCa using proton density magnetic resonance imaging (MRI), gather clinical and routine MRI characteristics, and identify risk factors for high-risk PCa through multifactorial logistic regression. Clinical and imaging data from 191 pathologically confirmed PCa patients were collected. Patients were stratified based on Gleason score (GS), with 63 in the intermediate- and low-risk group (GS =3+3, 3+4) and 128 in the high-risk group (GS ≥4+3). All patients underwent routine prostate MRI and FF imaging. Clinical and imaging data related to PCa were analyzed, including age, body mass index (BMI), prostate volume (PV) measured by MRI, smoking history, alcohol history, diabetes history, serum prostate-specific antigen (PSA) level, apparent diffusion coefficient (ADC) value, T2 signal intensity (T2SI), Prostate Imaging Reporting and Data System 2.1 (PI-RADS 2.1) score, GS, lesion FF, whole gland FF, periprostatic fat thickness (PPFT), and subcutaneous fat thickness (SFT). Independent risk factors for stratifying PCa risk were identified through multivariate logistic regression analysis, and a predictive model was established. Receiver operating characteristic (ROC) curve analysis was conducted for visual analysis. Significant differences were found in BMI, PV, PSA, tumor ADC value, standard T2SI, PI-RADS score, lesion FF, and PPFT between low- and medium-risk and high-risk groups (P<0.05). No significant differences were observed in age, smoking history, drinking history, diabetes history, and SFT between the two groups (P>0.05). GS correlated significantly with FF (ρ=0.6, P<0.001), PSA (ρ=0.432, P<0.001), ADC value (ρ=-0.379, P<0.001), and PI-RADS (ρ=0.366, P<0.001). Multiple logistic regression analysis revealed that an increase in FF, a PI-RADS score increase of 5 points, and a decrease in ADC value and PV were independent predictors of high-risk PCa (P<0.05). The ROC curve showed that the best cut-off value for the model was 0.67, with an area under the curve (AUC) of 0.907, sensitivity of 78.1%, and specificity of 88.9%. The FF of PCa determined by proton density MRI is significantly associated with GS, serving as an independent predictor of high-risk PCa.

DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios

Differential privacy is an essential approach for privacy preservation in data queries. However, users face a significant challenge in selecting an appropriate privacy scheme, as they struggle to balance the utility of query results with the preservation of diverse individual privacy. Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes. When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values, data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes. In this paper, we propose a visual analysis approach for formulating privacy schemes of differential privacy. Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases. We also developed a prototype system, called DPKnob, which integrates multiple coordinated views. DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high-risk attacks, but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy. This iterative process allows users to compare different schemes, refine their expectations of privacy and utility, and ultimately establish a well-balanced privacy scheme. The effectiveness of this study is verified by a user study and two case studies with real-world datasets.

Exploring the Intersection of E-commerce and Healthcare: A Visual Analysis of Research Trends.

The role of e-commercein healthcare is one of the key research areas emerging from the broader digital transformation of health services and consumer interactions. This study presents the bibliometric analysis of literature on e-commerce in healthcare based on publications from1993 to 2024 in Scopus. In particular, this analysis shows trends in the publication record that have been constantly growing, with prominent increases during the COVID-19 pandemic, now hardwiring the fact that digital technologies are key in promoting global health. The study surveys the thematic evolution of key technologies such as artificial intelligence (AI), which is transforming predictive healthcare analytics; blockchain, which is enhancing security in health data transactions; and telemedicine, which is revolutionizing remote patient care, alongside other emerging technologies that are reshaping global healthcare systems. The research also showcases global collaboration patterns, underlining the international dimensions of scholarly contributions to the area under review. These insights provide a foundation for understanding the current state of e-commerce in healthcare and suggest future directions for research and policy development in this fast-evolving field.

Inclusion Depth for Contour Ensembles.

Ensembles of contours arise in various applications like simulation, computer-aided design, and semantic segmentation. Uncovering ensemble patterns and analyzing individual members is a challenging task that suffers from clutter. Ensemble statistical summarization can alleviate this issue by permitting analyzing ensembles' distributional components like the mean and median, confidence intervals, and outliers. Contour boxplots, powered by Contour Band Depth (CBD), are a popular non-parametric ensemble summarization method that benefits from CBD's generality, robustness, and theoretical properties. In this work, we introduce Inclusion Depth (ID), a new notion of contour depth with three defining characteristics. First, ID is a generalization of functional Half-Region Depth, which offers several theoretical guarantees. Second, ID relies on a simple principle: the inside/outside relationships between contours. This facilitates implementing ID and understanding its results. Third, the computational complexity of ID scales quadratically in the number of members of the ensemble, improving CBD's cubic complexity. This also in practice speeds up the computation enabling the use of ID for exploring large contour ensembles or in contexts requiring multiple depth evaluations like clustering. In a series of experiments on synthetic data and case studies with meteorological and segmentation data, we evaluate ID's performance and demonstrate its capabilities for the visual analysis of contour ensembles.

Beyond balance: The role of the Vestibular system in action recognition

BackgroundAction recognition is a fundamental aspect of human interaction. This process is mediated by the activation of shared sensorimotor representations during action execution and observation. Although complex movements involving balance or head and trunk rotations require vestibular signals for effective execution, their role in the recognition of others' actions is still unknown. ObjectiveTo investigate the causal involvement of the vestibular system in the discrimination of actions performed by others and whether this is influenced by motor familiarity. MethodsIn a single-blind design involving 25 healthy participants, Galvanic Vestibular Stimulation (GVS) was administered during an Action Discrimination Task (ADT), in which videos of actions categorized as vestibular/non-vestibular and familiar/unfamiliar were presented. Following each video, participants were required to identify the climax of the previously viewed action between two image options, using a two-alternative forced choice paradigm. The ADT was performed in active and sham GVS conditions, with left or right anodal montages. Response Times (RTs), Accuracy, and subjective motor familiarity were recorded for each action category. ResultsIn sham GVS condition, an overall familiarity effect was observed, where RTs for familiar actions were faster than RTs for unfamiliar ones, regardless of vestibular engagement (p < .001; ηp2 = .80). Conversely, under active GVS, a selective interference of the identification of vestibular familiar actions was observed compared to sham. Specifically, GVS prolonged RTs for recognizing familiar vestibular actions (p = .004, d = .59) while concurrently enhancing visual sensitivity (d’) for the same actions (p = .03, r = .21). ConclusionThese findings demonstrate the contribution of the vestibular system to action recognition. GVS disrupted the sensorimotor representation of vestibular actions and led to increased reliance on an alternative processing system focused on visual analysis of limb positions. This dissociation provides valuable insights for future investigations into the complex relationship between vestibular signals and cognitive processes involved in action identification, essential for developing innovative GVS interventions, particularly for individuals with sensorimotor or vestibular disorders.

A bibliometric and visual analysis of the research status and hotspots of seborrheic dermatitis based on web of science.

Seborrheic dermatitis (SD) is a common chronic inflammatory skin disease. In recent years, significant progress has been made in the field of SD, but there has been no bibliometric research yet. This study aims to use bibliometric methods to analyze the current research status and hot topics of SD, to understand further the research trends and future development prospects in this field. Retrieve core literature on SD from the Web of Science database and conduct a detailed analysis using CiteSpace and VOSviewer software based on factors such as publication volume, countries (regions), research institutions, journals, authors, highly-cited papers, and keywords. From 1996 to 2024, a total of 1436 publications were included in the bibliometric analysis. The number of publications has shown an increasing trend year by year. The USA is the leading country in this field of research. The University of California System is the primary research institution. The International Journal of Dermatology is the journal with the highest number of publications. The author Yang Won Lee has the highest number of publications, while the article "Seborrheic Dermatitis" (2004) by Gupta, A.K. has been cited the most. "Seborrheic dermatitis" is the most frequently occurring keyword. The main research hotspots and frontiers in SD are as follows: (1) The relationship between SD and other skin diseases is a popular research topic; (2) Malassezia and inflammation are current research hotspots in SD; and (3) Focusing on antifungal and anti-inflammatory treatments for SD is the current frontier direction in this field. This study is a summary of the current status and hot trends of SD research, which helps clinical doctors and researchers quickly understand the insights and valuable information of SD research and provides reference for clinical decision-making and finding future research directions.

Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams).

This article presents a computational framework for the Wasserstein auto-encoding of merge trees (MT-WAE), a novel extension of the classical auto-encoder neural network architecture to the Wasserstein metric space of merge trees. In contrast to traditional auto-encoders which operate on vectorized data, our formulation explicitly manipulates merge trees on their associated metric space at each layer of the network, resulting in superior accuracy and interpretability. Our novel neural network approach can be interpreted as a non-linear generalization of previous linear attempts (Pont et al. 2023) at merge tree encoding. It also trivially extends to persistence diagrams. Extensive experiments on public ensembles demonstrate the efficiency of our algorithms, with MT-WAE computations in the orders of minutes on average. We show the utility of our contributions in two applications adapted from previous work on merge tree encoding (Pont et al. 2023). First, we apply MT-WAE to merge tree compression, by concisely representing them with their coordinates in the final layer of our auto-encoder. Second, we document an application to dimensionality reduction, by exploiting the latent space of our auto-encoder, for the visual analysis of ensemble data. We illustrate the versatility of our framework by introducing two penalty terms, to help preserve in the latent space both the Wasserstein distances between merge trees, as well as their clusters. In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used for reproducibility.