Search Results (339)

Background/Objectives: This research investigates brain connectivity patterns in reaction to social and non-social stimuli within a virtual reality environment, emphasizing their impact on cognitive functions, specifically working memory. Methods: Employing the LEiDA framework with EEG data from 47 participants, I examined dynamic brain network states elicited by social avatars compared to non-social stick cues during a VR memory task. Through the integration of LEiDA with deep learning and graph theory analyses, unique connectivity patterns associated with cue type were discerned, underscoring the substantial influence of social cues on cognitive processes. LEiDA, conventionally utilized with fMRI, was creatively employed in EEG to detect swift alterations in brain network states, offering insights into cognitive processing dynamics. Results: The findings indicate distinct neural states for social and non-social cues; notably, social cues correlated with a unique brain state characterized by increased connectivity within self-referential and memory-processing networks, implying greater cognitive engagement. Moreover, deep learning attained approximately 99% accuracy in differentiating cue contexts, highlighting the efficacy of prominent eigenvectors from LEiDA in EEG analysis. Analysis of graph theory also uncovered structural network disparities, signifying enhanced integration in contexts involving social cues. Conclusions: This multi-method approach elucidates the dynamic influence of social cues on brain connectivity and cognition, establishing a basis for VR-based cognitive rehabilitation and immersive learning, wherein social signals may significantly enhance cognitive function. Full article

As an important tool for brand promotion and marketing, the status of virtual streamers is gradually improving, especially in the Chinese market with a huge Internet user base. Virtual streamer anthropomorphism has gradually become an important research content in the field of consumer behavior. However, the specific mechanism by which the multidimensional anthropomorphic characteristics of virtual streamers affect consumer trust and purchase intention requires further investigation. Therefore, based on the avatar theory, this research explores how the anthropomorphic characteristics of virtual streamers affect consumer purchase intention through cognitive trust. The analysis was performed using SPSS 27.0 and AMOS 24.0, establishing a structural equation model. Through the analysis of questionnaire data from 503 Chinese consumers, it was found that behavioral anthropomorphism, cognitive anthropomorphism, and emotional anthropomorphism all exert a notable influence on cognitive trust. Appearance anthropomorphism and emotional anthropomorphism directly affect purchase intention, and cognitive trust has a significant impact on purchase intention. Moreover, cognitive trust fully mediates the effects of behavioral anthropomorphism and cognitive anthropomorphism on purchase intention and partially mediates the effects of emotional anthropomorphism on purchase intention. This study enriches the application of avatar theory in virtual streamers in live e-commerce and provides theoretical backing for virtual streamer development and enterprise marketing strategies. It also offers practical insights to help brands optimize virtual streamers and improve consumer participation and purchase conversion rates. Full article

It has been proposed that we are entering the age of postmortalism, where digital immortality is a credible option. The desire to overcome death has occupied humanity for centuries, and even though biological immortality is still impossible, recent technological advances have enabled possible eternal life in the metaverse. In palaeoanthropology and archaeology contexts, we are often driven by our preoccupation with visualising and interacting with ancient populations, with the production of facial depictions of people from the past enabling some interaction. New technologies and their implementation, such as the use of Artificial Intelligence (AI), are profoundly transforming the ways that images, videos, voices, and avatars of digital ancient humans are produced, manipulated, disseminated, and viewed. As facial depiction practitioners, postmortalism crosses challenging ethical territory around consent and representation. Should we create a postmortem avatar of someone from past just because it is technically possible, and what are the implications of this kind of forced immortality? This paper describes the history of the technologically mediated simulation of people, discussing the benefits and flaws of each technological iteration. Recent applications of 4D digital technology and AI to the fields of palaeoanthropological and historical facial depiction are discussed in relation to the technical, aesthetic, and ethical challenges associated with this phenomenon. Full article

Background: Solid tumors vary by the immunogenic potential of the tumor microenvironment (TME) and the likelihood of response to immunotherapy. The emerging literature has identified key immune cell populations that significantly impact immune activation or suppression within the TME. This study investigated candidate T-cell populations and their differential infiltration within different tumor types as estimated from mRNA co-expression levels of the corresponding cellular markers. Methods: We analyzed the mRNA co-expression levels of cellular biomarkers that define stem-like tumor-infiltrating lymphocytes (TILs), tissue-resident memory T-cells (TRM), early dysfunctional T-cells, late dysfunctional T-cells, activated-potentially anti-tumor (APA) T-cells and Butyrophilin 3A (BTN3A) isoforms, utilizing clinical and transcriptomic data from 1892 patients diagnosed with melanoma, bladder, ovarian, or pancreatic carcinomas. Real-world data were collected under the Total Cancer Care Protocol and the Avatar^® project (NCT03977402) across 18 cancer centers. Furthermore, we compared the survival outcomes following immune checkpoint inhibitors (ICIs) based on immune cell gene expression. Results: In melanoma and bladder cancer, the estimated infiltration of APA T-cells differed significantly (p = 4.67 × 10⁻¹² and p = 5.80 × 10⁻¹², respectively) compared to ovarian and pancreatic cancers. Ovarian cancer had lower TRM T-cell infiltration than melanoma, bladder, and pancreatic (p = 2.23 × 10⁻⁸, 3.86 × 10⁻²⁸, and 7.85 × 10⁻⁹, respectively). Similar trends were noted with stem-like, early, and late dysfunctional T-cells. Melanoma and ovarian expressed BTN3A isoforms more than other malignancies. Higher densities of stem-like TILs; TRM, early and late dysfunctional T-cells; APA T-cells; and BTN3A isoforms were associated with increased survival in melanoma (p = 0.0075, 0.00059, 0.013, 0.005, 0.0016, and 0.041, respectively). The TRM gene signature was a moderate predictor of survival in the melanoma cohort (AUROC = 0.65), with similar findings in testing independent public datasets of ICI-treated patients with melanoma (AUROC 0.61–0.64). Conclusions: Key cellular elements related to immune activation are more heavily infiltrated within ICI-responsive versus non-responsive malignancies, supporting a central role in anti-tumor immunity. In melanoma patients treated with ICIs, higher densities of stem-like TILs, TRM T-cells, early dysfunctional T-cells, late dysfunctional T-cells, APA T-cells, and BTN3A isoforms were associated with improved survival. Full article

With the rapid development of the metaverse, designing effective interfaces in virtual and augmented environments presents significant challenges. Additionally, keeping real-time sensory data flowing from users to their virtual avatars in a seamless and accurate manner is one of the biggest challenges in this domain. To this end, this article investigates a telepresence suit as an interface for interaction within the metaverse and its virtual avatars, aiming to address the complexities of signal generation, conversion, and transmission in real-time telepresence systems. We model a telepresence suit framework that systematically generates state data and transmits it to end-points, which can be either robotic avatars or virtual representations within a metaverse environment. Through a hand movement study, we successfully minimized the volume of transmitted information, reducing traffic by over 50%, which directly decreased channel load and packet delivery delay. For instance, as channel load decreases from 0.8 to 0.4, packet delivery delay is reduced by approximately half. This optimization not only enhances system responsiveness but also improves accuracy, particularly by reducing delays and errors in high-priority signal paths, enabling more precise and reliable telepresence interactions in metaverse settings. Full article

When a patient has two or more primary tumors, excluding the possibility of diffuse, recurrent, or metastatic, they can be defined as having multiple primary malignant neoplasms (MPMNs). Moreover, cases of three primary urinary tract tumors are very rare. Here, we reported a patient of MPMNs with four primary tumors, including three urinary tract cancers (renal cancer, prostate cancer, and bladder cancer) and lung cancer. The four tumors appeared over 13 years, and pathological results confirmed that they were all primary tumors after different surgeries. In addition, we established patient-derived organoids (PDOs) by collecting tumor specimens. Hematoxylin-eosin (H&E) staining of PDOs showed that the organoids were histopathological consistent with parental tumor. Immunohistochemistry showed that PDOs can also reflect the expression of pathological markers in patients. At the same time, PDOs may also serve as “avatars” of patients to predict sensitivity to different drugs. In summary, we reported a case of MPMNs with four primary tumors and established PDOs from its tumor specimens. A personalized treatment strategy was established based on the histopathological characteristics of the organoids. Full article

Background: As the Internet of Things (IoT) expands, it enables new forms of communication, including interactions mediated by teleoperated robots like avatars. While extensive research exists on the effects of these devices on communication partners, there is limited research on the impact on the operators themselves. This study aimed to objectively assess the psychological and physiological effects of operating a teleoperated robot, specifically Telenoid, on its human operator. Methods: Twelve healthy participants (2 women and 10 men, aged 18–23 years) were recruited from Osaka University. Participants engaged in two communication sessions with a first-time partner: face-to-face and Telenoid-mediated. Telenoid is a minimalist humanoid robot teleoperated by a participant. Blood samples were collected before and after each session to measure hormonal and oxidative markers, including cortisol, diacron reactive oxygen metabolites (d-ROMs), and the biological antioxidat activity of plasma (BAP). Psychological stress was assessed using validated questionnaires (POMS-2, HADS, and SRS-18). Results: A trend of a decrease in cortisol levels was observed during Telenoid-mediated communication, whereas face-to-face interactions showed no significant changes. Oxidative stress, measured by d-ROMs, significantly increased after face-to-face interactions but not in Telenoid-mediated sessions. Significant correlations were found between oxytocin and d-ROMs and psychological stress scores, particularly in terms of helplessness and total stress measures. However, no significant changes were observed in other biomarkers or between the two conditions for most psychological measures. Conclusions: These findings suggest that cortisol and d-ROMs may serve as objective biomarkers for assessing psychophysiological stress during robot-mediated communication. Telenoid’s minimalist design may help reduce social pressures and mitigate stress compared to face-to-face interactions. Further research with larger, more diverse samples and longitudinal designs is needed to validate these findings and explore the broader impacts of teleoperated robots. Full article

Background/Objectives: A high number of patients who suffer the amputation of a lower limb will present psychological problems such as anxiety, depression, and post-traumatic stress disorder after surgery. This study embodies participants in a self-avatar with a right lower-limb amputation in a virtual reality environment. The aim was to determine if this experience increases anxiety levels compared to embodiment in a normal avatar. The study also examines whether body appreciation is related to anxiety levels. Methods: Subjects completed the Body Appreciation Scale (BAS) questionnaire before being immersed in the virtual environment, the Visual Analogue Scale for Anxiety (VAS-A) after each condition, and the Embodiment Questionnaire at the end of the experiment. Results: Univariate analysis showed that participants reported significantly higher levels of anxiety when exposed to the virtual avatar with an amputation compared to the full virtual body avatar. These results indicate that lower levels of body appreciation were associated with higher levels of anxiety across conditions, suggesting that participants with lower body appreciation experienced greater psychological maladjustment (measured by anxiety) in response to the virtual scenarios. Conclusions: The results suggest that the virtual avatar with a lower-limb amputation elicited significantly greater anxiety, and that body appreciation plays a key role in moderating this psychological response. Future research could focus on developing virtual exposure-based therapy for amputees using virtual reality to help reduce the anxiety experienced by patients during this process. Full article

Virtual reality provides students with the opportunity to have simulated experiences in a safe setting and is mostly used to teach direct practice skills. One of the most advanced ways of using virtual simulation in social work education is to interact with avatars. Aim: The overall aim of this scoping review was to find out what is known about the use of dialogue with avatars in virtual reality in simulation-based social work education. Materials: Using Arksey and O’Malley’s scoping review framework, 11 articles were included in this review. Results: The skills taught with the avatars varied, as did the ways of preparing students for the sessions. The training was assessed as meaningful learning in a safe and comfortable environment, offering an opportunity to train in practical skills. According to the pre- and post-tests, in several studies the students’ skills seemed to have improved after the training. The qualitative data also pointed to skill developments. Conclusion: Training with avatars seems to be a useful way of preparing students for their future profession and seems to hold great potential in preparing students for demanding situations that cannot be easily trained for in a classroom. The results also point to technical elements that would benefit from development. Full article

We propose the ATGT3D an Animatable Texture Generation and Tracking for 3D Avatars, featuring the innovative design of the Eye Diffusion Module (EDM) and Pose Tracking Diffusion Module (PTDM), which are dedicated to high-quality eye texture generation and synchronized tracking of dynamic poses and textures, respectively. Compared to traditional GAN and VAE methods, ATGT3D significantly enhances texture consistency and generation quality in animated scenes using the EDM, which produces high-quality full-body textures with detailed eye information using the HUMBI dataset. Additionally, the Pose Tracking and Diffusion Module (PTDM) monitors human motion parameters utilizing the BEAT2 and AMASS mesh-level animatable human model datasets. The EDM, in conjunction with a basic texture seed featuring eyes and the diffusion model, restores high-quality textures, whereas the PTDM, by integrating MoSh++ and SMPL-X body parameters, models hand and body movements from 2D human images, thus providing superior 3D motion capture datasets. This module maintains the synchronization of textures and movements over time to ensure precise animation texture tracking. During training, the ATGT3D model uses the diffusion model as the generative backbone to produce new samples. The EDM improves the texture generation process by enhancing the precision of eye details in texture images. The PTDM involves joint training for pose generation and animation tracking reconstruction. Textures and body movements are generated individually using encoded prompts derived from masked gestures. Furthermore, ATGT3D adaptively integrates texture and animation features using the diffusion model to enhance both fidelity and diversity. Experimental results show that ATGT3D achieves optimal texture generation performance and can flexibly integrate predefined spatiotemporal animation inputs to create comprehensive human animation models. Our experiments yielded unexpectedly positive outcomes. Full article

This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is being trained, referred to as the worker’s digital twins. The framework incorporates novel work activity recognition using generative adversarial network (GAN) and machine learning (ML) models for recognising the types and sequences of work activities by analysing an individual’s kinematic model. Finally, the development of skill proficiency ML is proposed to evaluate each trainee’s proficiency in work activities and the overall task. To illustrate DigitalUpSkilling from wearable IoT-sensor-driven kinematic models to GAN-ML models for work activity recognition and skill proficiency assessment, the paper presents a comprehensive study on how specific meat processing activities in a real-world work environment can be recognised and assessed. In the study, DigitalUpSkilling achieved 99% accuracy in recognising specific work activities performed by meat workers. The study also presents an evaluation of the proficiency of workers by comparing kinematic data from trainees performing work activities. The proposed DigitalUpSkilling framework lays the foundation for next-generation digital personalised training. Full article

This paper presents a taxonomy of agents’ embodiment in physical and virtual environments. It categorizes embodiment based on five entities: the agent being embodied, the possible mediator of the embodiment, the environment in which sensing and acting take place, the degree of body, and the intertwining of body, mind, and environment. The taxonomy is applied to a wide range of embodiment of humans, artifacts, and programs, including recent technological and scientific innovations related to virtual reality, augmented reality, telepresence, the metaverse, digital twins, and large language models. The presented taxonomy is a powerful tool to analyze, clarify, and compare complex cases of embodiment. For example, it makes the choice between a dualistic and non-dualistic perspective of an agent’s embodiment explicit and clear. The taxonomy also aided us to formulate the term “embodiment by proxy” to denote how seemingly non-embodied agents may affect the world by using humans as “extended arms”. We also introduce the concept “off-line embodiment” to describe large language models’ ability to create an illusion of human perception. Full article

Rare and ultra-rare diseases constitute a significant medical challenge due to their low prevalence and the limited understanding of their origin and underlying mechanisms. These disorders often exhibit phenotypic diversity and molecular complexity that represent a challenge to biomedical research. There are more than 6000 different rare diseases that affect nearly 300 million people worldwide. However, the prevalence of each rare disease is low, and in consequence, the biomedical resources dedicated to each rare disease are limited and insufficient to effectively achieve progress in the research. The use of animal models to investigate the mechanisms underlying pathogenesis has become an invaluable tool. Among the animal models commonly used in research, Drosophila melanogaster has emerged as an efficient and reliable experimental model for investigating a wide range of genetic disorders, and to develop therapeutic strategies for rare and ultra-rare diseases. It offers several advantages as a research model including short life cycle, ease of laboratory maintenance, rapid life cycle, and fully sequenced genome that make it highly suitable for studying genetic disorders. Additionally, there is a high degree of genetic conservation from Drosophila melanogaster to humans, which allows the extrapolation of findings at the molecular and cellular levels. Here, I examine the role of Drosophila melanogaster as a model for studying rare and ultra-rare diseases and highlight its significant contributions and potential to biomedical research. High-throughput next-generation sequencing (NGS) technologies, such as whole-exome sequencing and whole-genome sequencing (WGS), are providing massive amounts of information on the genomic modifications present in rare diseases and common complex traits. The sequencing of exomes or genomes of individuals affected by rare diseases has enabled human geneticists to identify rare variants and identify potential loci associated with novel gene–disease relationships. Despite these advances, the average rare disease patient still experiences significant delay until receiving a diagnosis. Furthermore, the vast majority (95%) of patients with rare conditions lack effective treatment or a cure. This scenario is enhanced by frequent misdiagnoses leading to inadequate support. In consequence, there is an urgent need to develop model organisms to explore the molecular mechanisms underlying these diseases and to establish the genetic origin of these maladies. The aim of this review is to discuss the advantages and limitations of Drosophila melanogaster, hereafter referred as Drosophila, as an experimental model for biomedical research, and the applications to study human disease. The main question to address is whether Drosophila is a valid research model to study human disease, and in particular, rare and ultra-rare diseases. Full article

Studies using source localization results have shown that cortical involvement increased in treadmill walking with brain–computer interface (BCI) control. However, the reorganization of cortical functional connectivity in treadmill walking with BCI control is largely unknown. To investigate this, a public dataset, a mobile brain–body imaging dataset recorded during treadmill walking with a brain–computer interface, was used. The electroencephalography (EEG)-coupling strength of the between-region and within-region during the continuous self-determinant movements of lower limbs were analyzed. The time–frequency cross-mutual information (TFCMI) method was used to calculate the coupling strength. The results showed the frontal–occipital connection increased in the gamma and delta bands (the threshold of the edge was >0.05) during walking with BCI, which may be related to the effective communication when subjects adjust their gaits to control the avatar. In walking with BCI control, the results showed theta oscillation within the left-frontal, which may be related to error processing and decision making. We also found that between-region connectivity was suppressed in walking with and without BCI control compared with in standing states. These findings suggest that walking with BCI may accelerate the rehabilitation process for lower limb stroke. Full article

The metaverse is gradually expanding. There is a growing number of photo and video recordings of metaverse virtual worlds being used in multiple domains, and the collection of these recordings is a rapidly growing field. An essential element of the metaverse and its recordings is the concept of avatars. In this paper, we present the novel task of avatar detection in metaverse recordings, supporting semantic retrieval in collections of metaverse recordings and other use cases. Our work addresses the characterizations and definitions of avatars and presents a new model that supports avatar detection. The latest object detection algorithms are trained and tested on a variety of avatar types in metaverse recordings. Our work achieves a significantly higher level of accuracy than existing models, which encourages further research in this field. Full article