Hasil untuk "Human anatomy"

Interuniversity Institute of Myology

Cancer cachexia is a multisystem syndrome affecting cancer patients, characterized by progressive body weight loss, muscle wasting, increased mortality and poor quality of life. Skeletal muscle represents a main target of cancer cachexia, where tumor-derived inflammatory factors including IL-1, IL-6 and TNF-α trigger muscle degeneration through the activation of specific proteolytic pathways, ultimately causing a dramatic loss of muscle mass and function. Although several mouse models have been used to study cancer cachexia, the close interaction between tumor and muscle cells has been investigated primarily in 2D in vitro co-culture models. This approach revealed important molecular and metabolic dysfunctions of muscle cells when co-cultured with tumor cells. However, the use of 2D systems to study skeletal muscle differentiation/degeneration dynamics presents many limitations, including the lack of the characteristic spatial orientation of the myotubes. Here, we present a novel hydrogel-based 3D in vitro muscle model to study cancer cachexia. We generated bio-artificial muscle and extended the model by incorporating a co-culture with colorectal carcinoma cells, enabling direct cell–cell contact between cancer cells and myogenic cells. After differentiation in co-cultures with carcinoma cells, myoblasts showed a severe impairment of their myogenic potential. This model also allows to uncouple direct cellcell contacts while studying the effect of secreted factors by an indirect co-culture system. In addition, we found that the typical myofiber alignment observed in native muscle tissue is preserved in our constructs also in the presence of carcinoma cells. The ability to study cancer-related myogenic dysfunction in a soft 3D environment represents a novel tool to study cancer cachexia mechanisms and drug development, further reducing the need for animal experiments.

Zhuoqun Jiang, ShunYi Yeo, Dorien Herremans et al.

While reciprocal self-disclosure drives intimacy, digital tools seldom scaffold autonomy, competence, and relatedness -- the motivational underpinnings defined by Self-Determination Theory (SDT) that enable deep exchange. We introduce a chatbot employing dual-layer scaffolding to satisfy these needs: first providing enabling affordances (instrumental support) for vulnerability, then mediating affordances (relational support) for responsiveness. In a randomized study (N = 72; 36 couples) comparing Partner Support (PS: both layers), Direct Support (DS: enabling only), and Basic Prompt (BP: questions only), results reveal a critical distinction. While enabling affordances (PS, DS) were sufficient to deepen disclosure, only mediating affordances (PS) reliably elicited partner-provided need support and increased perceived closeness. Furthermore, controlled motivation decreased across conditions, and scaffolding buffered vitality, which remained stagnant in BP. We contribute empirical evidence that SDT-guided mediation fosters connection, offering a practical framework for designing AI-mediated conversations that support, rather than replace, human intimacy.

Cedric Moro

Yang Lu, Jiawei Shen, Ka Ho Kairos Sou et al.

IntroductionAge-related hearing loss (ARHL) is a common chronic condition that significantly affects the quality of life in older adults. Studies have shown that genetic factors play a substantial role in ARHL, with heritability estimates ranging from 46 to 74%. Although advances in genomics and epigenetics have led to the identification of numerous candidate genes in recent years, most related studies have focused on European and North American populations. There remains a lack of systematic mapping of research trends and cross-ethnic gene consistency, limiting the broad applicability of these findings.MethodThis study screened English-language publications on ARHL genetics from 1995 to June 2025 across PubMed, Embase, Web of Science, and Scopus, ultimately including 465 studies. Bibliometric analyses were conducted using R Bibliometrix, VOSviewer, and CiteSpace to extract research trends, research hotspots, and candidate genes. Ethnic information from human studies were compiled to facilitate cross-ethnic comparative analysis.ResultOver the past 30 years, publications in this field have shown continuous growth, with an average annual growth rate of 6.83%. Hearing Research emerged as the core journal. China and the United States were the top two publishing countries, though international collaboration remained limited. Research priorities have gradually shifted from inner ear anatomy to molecular mechanisms such as gene variants, oxidative stress, mitochondrial function, and inflammation. A total of 365 candidate factors from animal studies and 221 candidate genes from human studies were extracted and grouped into seven categories. Cross-ethnic analysis identified 56 genes that were repeatedly reported across at least two populations. Among these, CDH23, ILDR1, and SLC26A5 showed high cross-ethnic consistency, while genes such as GRHL2 exhibited notable ethnic specificity.ConclusionThis study systematically maps the developmental trajectory and research hotspots of ARHL genetics, revealing key patterns in geographic distribution, thematic evolution, and cross-ethnic applicability. The findings highlight the urgent need to strengthen research in non-European populations and promote international collaboration, thereby providing a theoretical foundation and data support for building a universally applicable genetic risk framework and advancing individualised interventions.

Aimee Allen, Tom Drummond, Dana Kulić

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.

Yuxuan Xue, Xianghui Xie, Margaret Kostyrko et al.

Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in scale and diversity. The central question we address in this paper is: Can existing foundation models be distilled to generate theoretically unbounded, richly annotated 3D human data? We introduce InfiniHuman, a framework that synergistically distills these models to produce richly annotated human data at minimal cost and with theoretically unlimited scalability. We propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. User study shows our automatically generated identities are undistinguishable from scan renderings. InfiniHumanData contains 111K identities spanning unprecedented diversity. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body-shape parameters. Building on this dataset, we propose InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate significant improvements over state-of-the-art methods in visual quality, generation speed, and controllability. Our approach enables high-quality avatar generation with fine-grained control at effectively unbounded scale through a practical and affordable solution. We will publicly release the automatic data generation pipeline, the comprehensive InfiniHumanData dataset, and the InfiniHumanGen models at https://yuxuan-xue.com/infini-human.

Azizul Zahid, Jie Fan, Farong Wang et al.

Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation learning within unstructured environments. We propose a multimodal demonstration learning framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space. Focusing on the "pick and place" task from the RH20T dataset, we utilize data from 5 users across 10 diverse scenes. Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction. After 2000 training epochs, the human model reaches 71.67% accuracy, and the robot model achieves 71.8% accuracy, demonstrating the framework's potential for aligning complex, multimodal human and robot behaviors in manipulation tasks.

Dallin L. Cordon, Shaden Moss, Marc Killpack et al.

This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft manipulator mounted to an omni-directional mobile base. The system level configuration of the robot and design of the collaborative manipulation (co-manipulation) study are presented. The initial results, both quantitative and qualitative, are directly compared to previous similar human-human co-manipulation studies. These initial results show promise in the ability for large-scale soft robots to perform comparably to human partners acting as non-visual followers in a co-manipulation task. Furthermore, these results challenge traditional soft robot strength limitations and indicate potential for applications requiring strength and adaptability.

Yibowen Zhao, Yiming Cao, Zhiqi Shen et al.

Intensive Care Units (ICUs) are critical environments characterized by high-stakes monitoring and complex data management. However, current practices often rely on manual data transcription and fragmented information systems, introducing potential risks to patient safety and operational efficiency. To address these issues, we propose a human-AI synergy system based on a cloud-edge-end architecture, which integrates visual-aware data extraction and semantic interaction mechanisms. Specifically, a visual-aware edge module non-invasively captures real-time physiological data from bedside monitors, reducing manual entry errors. To improve accessibility to fragmented data sources, a semantic interaction module, powered by a Large Language Model (LLM), enables physicians to perform efficient and intuitive voice-based queries over structured patient data. The hierarchical cloud-edge-end deployment ensures low-latency communication and scalable system performance. Our system reduces the cognitive burden on ICU nurses and physicians and demonstrates promising potential for broader applications in intelligent healthcare systems.

Zhengtian Lyu, Gaoxiang Yuan, Yuying Zhang et al.

Abstract Background The gut microbiota is a key regulator of bone metabolism. Investigating the relationship between the gut microbiota and bone remodeling has revealed new avenues for the treatment of bone-related disorders. Despite significant progress in understanding gut microbiota-bone interactions in mammals, research on avian species remains limited. Birds have unique bone anatomy and physiology to support egg-laying. However, whether and how the gut microbiota affects bone physiology in birds is still unknown. In this study, we utilized laying hens as a research model to analyze bone development patterns, elucidate the relationships between bone and the gut microbiota, and mine probiotics with osteomodulatory effects. Results Aging led to a continuous increase in bone mineral density in the femur of laying hens. The continuous deposition of medullary bone in the bone marrow cavity of aged laying hens led to significant trabecular bone loss and weakened bone metabolism. The cecal microbial composition significantly shifted before and after sexual maturity, with some genera within the class Clostridia potentially linked to postnatal bone development in laying hens. Four bacterial strains associated with bone development, namely Blautia coccoides CML164, Fournierella sp002159185 CML151, Anaerostipes caccae CML199 (ANA), and Romboutsia lituseburensis CML137, were identified and assessed in chicks with low bacterial loads and chicken primary osteoblasts. Among these, ANA demonstrated the most significant promotion of bone formation both in vivo and in vitro, primarily attributed to butyrate in its fermentation products. A long-term feeding experiment of up to 72 weeks confirmed that ANA enhanced bone development during sexual maturity by improving the immune microenvironment of the bone marrow in laying hens. Dietary supplementation of ANA for 50 weeks prevented excessive medullary bone deposition and mitigated aging-induced trabecular bone loss. Conclusions These findings highlight the beneficial effects of ANA on bone physiology, offering new perspectives for microbial-based interventions for bone-related disorders in both poultry and possibly extending to human health. Video Abstract Graphical Abstract

Janelle Tarum, Graham Ball, Thomas Gustafsson et al.

Abstract Background Sarcopenia is an age‐related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1. This study aims to identify genes, gene interactions, and molecular pathways and processes associated with muscle aging and exercise in older adults that remained undiscovered until now leveraging on an artificial intelligence approach called artificial neural network inference (ANNi). Methods Four datasets reporting the profile of muscle transcriptome obtained by RNA‐seq of young (21–43 years) and older adults (63–79 years) were selected and retrieved from the Gene Expression Omnibus (GEO) data repository. Two datasets contained the transcriptome profiles associated to muscle aging and two the transcriptome linked to resistant exercise in older adults, the latter before and after 6 months of exercise training. Each dataset was individually analysed by ANNi based on a swarm neural network approach integrated into a deep learning model (Intelligent Omics). This allowed us to identify top 200 genes influencing (drivers) or being influenced (targets) by aging or exercise and the strongest interactions between such genes. Downstream gene ontology (GO) analysis of these 200 genes was performed using Metacore (Clarivate™) and the open‐source software, Metascape. To confirm the differential expression of the genes showing the strongest interactions, real‐time quantitative PCR (RT‐qPCR) was employed on human muscle biopsies obtained from eight young (25 ± 4 years) and eight older men (78 ± 7.6 years), partaking in a 6‐month resistance exercise training programme. Results CHAD, ZDBF2, USP54, and JAK2 were identified as the genes with the strongest interactions predicting aging, while SCFD1, KDM5D, EIF4A2, and NIPAL3 were the main interacting genes associated with long‐term exercise in older adults. RT‐qPCR confirmed significant upregulation of USP54 (P = 0.005), CHAD (P = 0.03), and ZDBF2 (P = 0.008) in the aging muscle, while exercise‐related genes were not differentially expressed (EIF4A2 P = 0.99, NIPAL3 P = 0.94, SCFD1 P = 0.94, and KDM5D P = 0.64). GO analysis related to skeletal muscle aging suggests enrichment of pathways linked to bone development (adj P‐value 0.006), immune response (adj P‐value <0.001), and apoptosis (adj P‐value 0.01). In older exercising adults, these were ECM remodelling (adj P‐value <0.001), protein folding (adj P‐value <0.001), and proteolysis (adj P‐value <0.001). Conclusions Using ANNi and RT‐qPCR, we identified three strongly interacting genes predicting muscle aging, ZDBF2, USP54, and CHAD. These findings can help to inform the design of nonpharmacological and pharmacological interventions that prevent or mitigate sarcopenia.

Liangping Zhang, Xizhuo Zhou, Gaoyong Jia

Jianbin Jiao, Xina Cheng, Weijie Chen et al.

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as human-computer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable self-attention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multi-perspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on this dataset. The source code will be available at https://github.com/WUJINHUAN/3D-human-pose.

Kristian González Barman, Simon Lohse, Henk de Regt

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.

Jianyuan Ni, Hao Tang, Syed Tousiful Haque et al.

The combination of increased life expectancy and falling birth rates is resulting in an aging population. Wearable Sensor-based Human Activity Recognition (WSHAR) emerges as a promising assistive technology to support the daily lives of older individuals, unlocking vast potential for human-centric applications. However, recent surveys in WSHAR have been limited, focusing either solely on deep learning approaches or on a single sensor modality. In real life, our human interact with the world in a multi-sensory way, where diverse information sources are intricately processed and interpreted to accomplish a complex and unified sensing system. To give machines similar intelligence, multimodal machine learning, which merges data from various sources, has become a popular research area with recent advancements. In this study, we present a comprehensive survey from a novel perspective on how to leverage multimodal learning to WSHAR domain for newcomers and researchers. We begin by presenting the recent sensor modalities as well as deep learning approaches in HAR. Subsequently, we explore the techniques used in present multimodal systems for WSHAR. This includes inter-multimodal systems which utilize sensor modalities from both visual and non-visual systems and intra-multimodal systems that simply take modalities from non-visual systems. After that, we focus on current multimodal learning approaches that have applied to solve some of the challenges existing in WSHAR. Specifically, we make extra efforts by connecting the existing multimodal literature from other domains, such as computer vision and natural language processing, with current WSHAR area. Finally, we identify the corresponding challenges and potential research direction in current WSHAR area for further improvement.

Houjiang Liu, Jacek Gwizdka, Matthew Lease

Given the volume of potentially false claims online, claim prioritization is essential in allocating limited human resources available for fact-checking. In this study, we perceive claim prioritization as an information retrieval (IR) task: just as multidimensional IR relevance, with many factors influencing which search results a user deems relevant, checkworthiness is also multi-faceted, subjective, and even personal, with many factors influencing how fact-checkers triage and select which claims to check. Our study investigates both the multidimensional nature of checkworthiness and effective tool support to assist fact-checkers in claim prioritization. Methodologically, we pursue Research through Design combined with mixed-method evaluation. Specifically, we develop an AI-assisted claim prioritization prototype as a probe to explore how fact-checkers use multidimensional checkworthy factors to prioritize claims, simultaneously probing fact-checker needs and exploring the design space to meet those needs. With 16 professional fact-checkers participating in our study, we uncover a hierarchical prioritization strategy fact-checkers implicitly use, revealing an underexplored aspect of their workflow, with actionable design recommendations for improving claim triage across multidimensional checkworthiness and tailoring this process with LLM integration.

Scott A. Humr, Mustafa Canan, Mustafa Demir

Intelligent autonomous systems are part of a system of systems that interact with other agents to accomplish tasks in complex environments. However, intelligent autonomous systems integrated system of systems add additional layers of complexity based on their limited cognitive processes, specifically shared situation awareness that allows a team to respond to novel tasks. Intelligent autonomous systems' lack of shared situation awareness adversely influences team effectiveness in complex task environments, such as military command-and-control. A complementary approach of shared situation awareness, called situations theory, is beneficial for understanding the relationship between system of systems shared situation awareness and effectiveness. The current study elucidates a conceptual discussion on situations theory to investigate the development of an system of systems shared situational awareness when humans team with intelligent autonomous system agents. To ground the discussion, the reviewed studies expanded situations theory within the context of a system of systems that result in three major conjectures that can be beneficial to the design and development of future systems of systems.

Jiachen Li, Xiwen Li, Justin Steinberg et al.

Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies. While there have been significant advances in machine-learning approaches to translate periods of raw sensor data to model momentary behaviors, (e.g., physical activity recognition), there still remains a significant gap in the translation of these sensing streams into meaningful, high-level, context-aware insights that are required for various applications (e.g., summarizing an individual's daily routine). To bridge this gap, experts often need to employ a context-driven sensemaking process in real-world studies to derive insights. This process often requires manual effort and can be challenging even for experienced researchers due to the complexity of human behaviors. We conducted three rounds of user studies with 21 experts to explore solutions to address challenges with sensemaking. We follow a human-centered design process to identify needs and design, iterate, build, and evaluate Vital Insight (VI), a novel, LLM-assisted, prototype system to enable human-in-the-loop inference (sensemaking) and visualizations of multi-modal passive sensing data from smartphones and wearables. Using the prototype as a technology probe, we observe experts' interactions with it and develop an expert sensemaking model that explains how experts move between direct data representations and AI-supported inferences to explore, question, and validate insights. Through this iterative process, we also synthesize and discuss a list of design implications for the design of future AI-augmented visualization systems to better assist experts' sensemaking processes in multi-modal health sensing data.

Gonzalo-Martín Pérez-Arana, Gonzalo-Martín Pérez-Arana, Alfredo Díaz-Gómez et al.

The biological activity of glucagon has recently been proposed to both stimulate hepatic glucose production and also include a paradoxical insulinotropic effect, which could suggest a new role of glucagon in the pathophysiology type 2 diabetes mellitus (T2DM). An insulinotropic role of glucagon has been observed after bariatric/metabolic surgery that is mediated through the GLP-1 receptor on pancreatic beta cells. This effect appears to be modulated by other members of the proglucagon family, playing a key role in the beneficial effects and complications of bariatric/metabolic surgery. Glucagon serves a dual role after sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). In addition to maintaining blood glucose levels, glucagon exhibits an insulinotropic effect, suggesting that glucagon has a more complex function than simply an “anti-insulin hormone”.