Aisling Mooney, Nathan E. Thompson, Simone Hoffmann
ABSTRACTWith increasing cost and time constraints, anatomy education has shifted away from classic dissection in favor of more effective teaching modalities. Here we investigated different teaching modalities on test performance and student satisfaction in the human gross anatomy laboratory at the New York Institute of Technology College of Osteopathic Medicine (NYITCOM). The anatomy laboratory for first‐year osteopathic students at NYITCOM was divided into stations including radiology, case‐based learning, prosection, and dissection. Content covered at those stations was tested via anatomy laboratory examinations, comprised of computer‐based identification and multiple‐choice questions. Our data encompassed 459 first‐year medical students taking anatomy in 2022 at both NYITCOM campus sites (Arkansas and New York). We coded 355 exam questions by teaching modality used in the anatomy laboratory. Questions covered in multiple modalities were classified as ‘mixed’. Performance among modalities was analyzed using a three‐way ANOVA. Prosection‐based questions (mean = 75.6) performed significantly worse than dissection (mean = 83.4, p < 0.01) and mixed‐modality (mean = 80.7, p < 0.01). There was no significant difference in performance on material taught through dissection, radiology, case‐based activity and mixed modalities. In addition, we investigated the effect of an instructor at the case‐based activity station using repeated measures ANOVA. Our results indicate that case‐based activity stations performed better without an instructor present (mean = 77.4 v. 72.7; p < 0.05). This study showed that teaching via prosection results in poorer performance than other teaching modalities and that dissection or hybrid models appear more effective in knowledge retention. Contrary to actual performance, students rated prosection as the most effective teaching modality. These results come at a critical time when COVID‐19 has accelerated the shift away from dissection in favor of virtual methods.
As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robotic understanding of human rules for object arrangement, we introduce positionally-augmented RCC (PARCC), a formal logic framework based on region connection calculus (RCC) for describing the relative position of objects in space. Additionally, we introduce an inference algorithm for learning PARCC specifications via demonstrations. Finally, we present the results from a human study, which demonstrate our framework's ability to capture a human's intended specification and the benefits of learning from demonstration approaches over human-provided specifications.
Xin Wang, Stephanie Tulk Jesso, Sadamori Kojaku
et al.
Trust plays a fundamental role in shaping the willingness of users to engage and collaborate with artificial intelligence (AI) systems. Yet, measuring user trust remains challenging due to its complex and dynamic nature. While traditional survey methods provide trust levels for long conversations, they fail to capture its dynamic evolution during ongoing interactions. Here, we present VizTrust, which addresses this challenge by introducing a real-time visual analytics tool that leverages a multi-agent collaboration system to capture and analyze user trust dynamics in human-agent communication. Built on established human-computer trust scales-competence, integrity, benevolence, and predictability-, VizTrust enables stakeholders to observe trust formation as it happens, identify patterns in trust development, and pinpoint specific interaction elements that influence trust. Our tool offers actionable insights into human-agent trust formation and evolution in real time through a dashboard, supporting the design of adaptive conversational agents that responds effectively to user trust signals.
Georgios Hadjiantonis, Sarah Gillet, Marynel Vázquez
et al.
Robot-moderated group discussions have the potential to facilitate engaging and productive interactions among human participants. Previous work on topic management in conversational agents has predominantly focused on human engagement and topic personalization, with the agent having an active role in the discussion. Also, studies have shown the usefulness of including robots in groups, yet further exploration is still needed for robots to learn when to change the topic while facilitating discussions. Accordingly, our work investigates the suitability of machine-learning models and audiovisual non-verbal features in predicting appropriate topic changes. We utilized interactions between a robot moderator and human participants, which we annotated and used for extracting acoustic and body language-related features. We provide a detailed analysis of the performance of machine learning approaches using sequential and non-sequential data with different sets of features. The results indicate promising performance in classifying inappropriate topic changes, outperforming rule-based approaches. Additionally, acoustic features exhibited comparable performance and robustness compared to the complete set of multimodal features. Our annotated data is publicly available at https://github.com/ghadj/topic-change-robot-discussions-data-2024.
Creating detailed 3D human avatars with fitted garments traditionally requires specialized expertise and labor-intensive workflows. While recent advances in generative AI have enabled text-to-3D human and clothing synthesis, existing methods fall short in offering accessible, integrated pipelines for generating CG-ready 3D avatars with physically compatible outfits; here we use the term CG-ready for models following a technical aesthetic common in computer graphics (CG) and adopt standard CG polygonal meshes and strands representations (rather than neural representations like NeRF and 3DGS) that can be directly integrated into conventional CG pipelines and support downstream tasks such as physical simulation. To bridge this gap, we introduce Tailor, an integrated text-to-3D framework that generates high-fidelity, customizable 3D avatars dressed in simulation-ready garments. Tailor consists of three stages. (1) Seman tic Parsing: we employ a large language model to interpret textual descriptions and translate them into parameterized human avatars and semantically matched garment templates. (2) Geometry-Aware Garment Generation: we propose topology-preserving deformation with novel geometric losses to generate body-aligned garments under text control. (3) Consistent Texture Synthesis: we propose a novel multi-view diffusion process optimized for garment texturing, which enforces view consistency, preserves photorealistic details, and optionally supports symmetric texture generation common in garments. Through comprehensive quantitative and qualitative evaluations, we demonstrate that Tailor outperforms state-of-the-art methods in fidelity, usability, and diversity. Our code will be released for academic use. Project page: https://human-tailor.github.io
In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was conducted with two different groups (n=84) of human observers, a test group and a control group both interacting with the robot. The second test was performed with 110 participants using increasingly longer reaction delays for the robot with every ten participants. The results show a statistically significant change (p$<$.05) in perceived affective impact for the robots when they react to an event shared with a human observer rather than reacting at random. The result also shows for shared physical interaction, the near-human reaction times from the robot are most appropriate for the scenario. The paper concludes that a delay time around 200ms may render the biggest impact on human observers for small-sized non-humanoid robots. It further concludes that a slightly shorter reaction time around 100ms is most effective when the goal is to make the human observers feel they made the biggest impact on the robot.
Recent advances in generative artificial intelligence (AI) have created models capable of high-quality musical content generation. However, little consideration is given to how to use these models for real-time or cooperative jamming musical applications because of crucial required features: low latency, the ability to communicate planned actions, and the ability to adapt to user input in real-time. To support these needs, we introduce ReaLJam, an interface and protocol for live musical jamming sessions between a human and a Transformer-based AI agent trained with reinforcement learning. We enable real-time interactions using the concept of anticipation, where the agent continually predicts how the performance will unfold and visually conveys its plan to the user. We conduct a user study where experienced musicians jam in real-time with the agent through ReaLJam. Our results demonstrate that ReaLJam enables enjoyable and musically interesting sessions, and we uncover important takeaways for future work.
Extended reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is emerging as a transformative platform for medical education. Traditional methods such as textbooks, physical models, and cadaveric dissections often lack interactivity and fail to convey complex spatial relationships effectively. The emerging MR technology addresses these limitations by providing immersive environments that blend virtual elements with real-world contexts. This study presents an MR application for head anatomy education, enabling learners to intuitively interact with see-through 3D anatomical structures via hand gestures and controllers. Our hierarchical information design supports progressive learning, guiding users from basic anatomical labels to detailed structural insights. Additionally, the system incorporates an automatic calibration module that aligns virtual anatomical models with a real human head, thereby facilitating realistic human-model interactions. Experiments show that the system can effectively match the anatomical model with real-time scenes, thus enhancing the interactivity and immersion of medical education, providing an innovative tool for teaching anatomy.
As AI art generation becomes increasingly sophisticated, HCI research has focused primarily on questions of detection, authenticity, and automation. This paper argues that such approaches fundamentally misunderstand how artistic value emerges from the concerns that drive human image production. Through examination of historical precedents, we demonstrate that artistic style is not only visual appearance but the resolution of creative struggle, as artists wrestle with influence and technical constraints to develop unique ways of seeing. Current AI systems flatten these human choices into reproducible patterns without preserving their provenance. We propose that HCI's role lies not only in perfecting visual output, but in developing means to document the origins and evolution of artistic style as it appears within generated visual traces. This reframing suggests new technical directions for HCI research in generative AI, focused on automatic documentation of stylistic lineage and creative choice rather than simple reproduction of aesthetic effects.
Chiara Ciccone, Sari Elena Dötterer, Sigrid Vold Jensen
et al.
For most non-diving mammals, lack of O2 (hypoxia) has detrimental effects on brain function. Seals, however, display a series of systemic, cellular, and molecular adaptations that enable them to tolerate repeated episodes of severe hypoxia. One as yet unresolved question is whether seal neurons in part employ anaerobic metabolism during diving: the “reverse astrocyte-neuron lactate shuttle” (rANLS) hypothesis postulates that seal neurons, by shuttling lactate to the astrocytes, may be relieved (1) from the lactate burden and (2) from subsequent ROS-production as lactate is oxidized by astrocytes upon re-oxygenation after the dive. Here, we have investigated this possibility, through histological and functional comparisons of the metabolic characteristics of neocortical neurons and astrocytes from the deep-diving hooded seal (Cystophora cristata), using mice (Mus musculus) as a non-diving control. We found that seal astrocytes have higher mitochondrial density and larger mitochondria than seal neurons, and that seal neurons have an atypical and significantly higher representation of the monocarboxylate lactate exporter MCT4 compared to mouse neurons. Also, measurements of mitochondrial O2 consumption suggest that the aerobic capacity of primary seal astrocytes is at least equal to that of primary seal neurons. Transcriptomics data from seals vs. mice suggest that specific adaptations to the electron transport system in seals may contribute to enhance hypoxia tolerance. These observations are consistent with the rANLS hypothesis.
Neurosciences. Biological psychiatry. Neuropsychiatry, Human anatomy
ABSTRACT Background β2‐Microglobulin (B2M) has garnered considerable interest as a potential pro‐ageing factor, leading to speculation about its involvement in muscle metabolism and the development of sarcopenia, a key component of ageing phenotypes. To explore this hypothesis, we conducted a comprehensive investigation into the impact of B2M on cellular and animal muscle biology, as well as its clinical implications concerning sarcopenia parameters in older individuals. Methods In vitro myogenesis was induced in mouse C2C12 myoblasts with 2% horse serum. For in vivo research, C57BL/6 mice aged 3 months were intraperitoneally given 250 μg of B2M daily, and muscular alterations were assessed one month later. Human blood samples were obtained from 158 participants who underwent assessments of muscle mass and function at an outpatient geriatric clinic affiliated with a teaching hospital. Sarcopenia and associated parameters were assessed using cut‐off values specifically tailored for the Asian population. The concentration of serum B2M was quantified through an enzyme‐linked immunosorbent assay. Results Recombinant B2M inhibited in vitro myogenesis by increasing intracellular reactive oxygen species (ROS) production. Furthermore, B2M significantly induced differential myotube atrophy via ROS‐mediated ITGB1 downregulation, leading to impaired activation of the FAK/AKT/ERK signalling cascade and enhanced nuclear translocation of FoxO transcription factors. Animal experiments showed that mice with systemic B2M treatment exhibited significantly smaller cross‐sectional area of tibialis anterior and soleus muscle, weaker grip strength, shorter grid hanging time, and decreased latency time to fall off the rotating rod, compared to untreated controls. In a clinical study, serum B2M levels were inversely associated with grip strength, usual gait speed and short physical performance battery (SPPB) total score after adjustment for age, sex, and body mass index, whereas sarcopenia phenotype score showed a positive association. Consistently, higher serum B2M levels were associated with higher risk for weak grip strength, slow gait speed, low SPPB total score, and poor physical performance. Conclusion These results provide experimental evidence that B2M exerted detrimental effects on muscle metabolism mainly by increasing oxidative stress. Furthermore, we made an effort to translate the results of in vitro and animal research into clinical implication and found that circulating B2M could be one of blood‐based biomarkers to assess poor muscle health in older adults.
Diseases of the musculoskeletal system, Human anatomy
Abstract Background Changes in human posture directly impact the structures of various body parts, often leading to musculoskeletal disorders. While identifying suitable treatments for pain associated with long-term postural abnormalities is important, preventing such conditions is demonstrably a superior solution. Spiral stabilization, known for its practical application, has proven effective in treating low back pain. However, its efficacy in correcting human posture warrants further validation. Methods A total of 71 participants with abnormal body posture, with a mean age of 33.68 ± 6.78 years, were included in this research. The participants underwent spiral stabilization practice for one hour daily for four days. The posture-related angles and deviations from the anterior and lateral views were calculated by the Exbody musculoskeletal analysis system. Results There are statistically significant improvements in most posture-related angles and deviations after the intervention of the spiral stabilization technique compared to before the intervention (P < 0.05). Conclusion The findings of this study suggest that the spiral stabilization technique is a potential intervention for improving human posture. It may become an effective fitness exercise that is widely adopted in daily life to prevent postural abnormalities. Trial registration Registration date is July 10, 2021. The registration number is ChiCTR2100048568.
Krzysztof Koptas, Krystian Maślanka, Nicol Zielinska
et al.
BACKGROUND: The deltoid muscle originates from the spine of the scapula, the lateral border of the acromion, and the lateral third of the clavicle. It inserts on the deltoid tuberosity. It is divided into three parts: spinal, acromial, and clavicular. Our research shows that each part of the deltoid muscle can have up to three bellies during prenatal life. MATERIALS AND METHODS: The material included 80 upper limbs of spontaneously-aborted human foetuses (32 male, 48 female; Central European population), 18–38 weeks of gestation at the time of death. RESULTS: Each part had one (Type I), two (Type II) or three (Type III) bellies. In all parts, the most common form was Type I: it was present in 81.25% of cases in the clavicular part, 73.75% in the acromial part, and 57.5% in the spinal part. In contrast, Type III was the rarest form in all parts: it was present in 3.75% of cases in the clavicular part, 12.5% in the acromial part, and 7.5% in the spinal part. CONCLUSIONS: The deltoid muscle is characterised by morphological variability, even in foetuses.
Joanna Jaworek-Troć, Izabela Zamojska, Michał P. Zarzecki
et al.
BACKGROUND: Arcuate foramen is an ossification of the posterior atlanto-occipital membrane, forming a bony opening through which the vertebral artery (VA) enters the vertebral canal. Block vertebra is a synostosis of at least 2 vertebral bodies that did not separate during the embryological development. It is worth distinguishing it from Klippel-Feil syndrome, as the latter oftentimes involves other abnormalities (namely skeletal) and is typically diagnosed in childhood. Both variants potentially lead to impairment of blood flow through the VA. CASE REPORT: The following case report presents a finding of 2 anomalies of the cervical spine, found in a 38 y.o. female patient suffering from dizziness. A synostosis of the C4 and C5 vertebral bodies, arches, and zygapophysial (facet) joint was noted by the examining radiologist, with marked narrowing of the intervertebral foramen. Furthermore, a second anatomical variation in the form of the complete bilateral arcuate foramen was identified superior to the groove for the VA on the upper surface of the posterior arch of the atlas. CONCLUSIONS: To the best knowledge of the authors, this case report is the first to present a co-existing block vertebra and bilateral complete arcuate foramen. The common presence of at least 2 anatomical variations that could have a synergistic clinical effect could possibly be termed ‘tandem anomaly’. Notwithstanding, identification of a single anomaly explaining a patient’s symptoms does not absolve the medical professionals from searching for any other potential variations that could also be present and could further influence the clinical picture.
Chi Ian Tang, Lorena Qendro, Dimitris Spathis
et al.
Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are tailored to users' needs. However, because of the difficulty in collecting labelled data with wearable sensors, existing approaches that focus on supervised continual learning have limited applicability, while unsupervised continual learning methods only handle representation learning while delaying classifier training to a later stage. This work explores the adoption and adaptation of CaSSLe, a continual self-supervised learning model, and Kaizen, a semi-supervised continual learning model that balances representation learning and down-stream classification, for the task of wearable-based HAR. These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning. In addition to comparing state-of-the-art self-supervised continual learning schemes, we further investigated the importance of different loss terms and explored the trade-off between knowledge retention and learning from new tasks. In particular, our extensive evaluation demonstrated that the use of a weighting factor that reflects the ratio between learned and new classes achieves the best overall trade-off in continual learning.
Yuchong Zhang, Miguel Vasco, Mårten Björkman
et al.
This paper presents findings from an exploratory needfinding study investigating the research current status and potential participation of the competitions on the robotics community towards four human-centric topics: safety, privacy, explainability, and federated learning. We conducted a survey with 34 participants across three distinguished European robotics consortia, nearly 60% of whom possessed over five years of research experience in robotics. Our qualitative and quantitative analysis revealed that current mainstream robotic researchers prioritize safety and explainability, expressing a greater willingness to invest in further research in these areas. Conversely, our results indicate that privacy and federated learning garner less attention and are perceived to have lower potential. Additionally, the study suggests a lack of enthusiasm within the robotics community for participating in competitions related to these topics. Based on these findings, we recommend targeting other communities, such as the machine learning community, for future competitions related to these four human-centric topics.
Onome Bright Oghenetega, Mega Oyovwi, Ebenezar Obidimma
et al.
Objective: Zinc oxide nanoparticles (ZnO NPs) have various applications, but concerns about their potential adverse effects on human health, particularly the male reproductive system, have led to toxicity. This study investigates the effects of quercetin, a natural compound with anti-oxidative and anti-inflammatory properties, on ZnO NP-induced testicular toxicity in male Swiss mice.
Method: In this study, 25 male mice were randomly allocated into 5 groups, each consisting of 5 mice (n=5). The groups were labelled as Control, Corn Oil, Quercetin, ZnO NPs, and ZnO NPs + Quercetin. The dosages administered were 100mg/kg for ZnO NPs and 20mg/kg for quercetin, relative to the weight of the animals. The treatments were conducted for 7 days following a two-week acclimatization period.
Results: Zinc oxide nanoparticles (ZnO NPs) were found to increase testicular TNF-α, indicating inflammation at the testes which may have resulted in a significant decrease in testosterone. Malondialdehyde (MDA), an oxidative stress marker was also found to be increased at the level of the testes, in the ZnO NPs group when compared to the control group. Treatment with quercetin, however, was able to reduce the levels of inflammatory and oxidative stress markers at the testes, and restore testosterone to control levels, demonstrating its potential ameliorative effects on testicular toxicity via anti-inflammatory and anti-oxidative properties.
Conclusion: The findings from this study indicated that Quercetin is a potential therapeutic agent against inflammation and oxidative stress in ZnO NPs-induced testicular toxicity.
Manoj Kumar Yadav, Megumi Ishida, Natalia Gogoleva
et al.
Summary: Transcription factor MAFB regulates various homeostatic functions of macrophages. This study explores the role of MAFB in brown adipose tissue (BAT) thermogenesis using macrophage-specific Mafb-deficient (Mafbf/f::LysM-Cre) mice. We find that Mafb deficiency in macrophages reduces thermogenesis, energy expenditure, and sympathetic neuron (SN) density in BAT under cold conditions. This phenotype features a proinflammatory environment that is characterized by macrophage/granulocyte accumulation, increases in interleukin-6 (IL-6) production, and IL-6 trans-signaling, which lead to decreases in nerve growth factor (NGF) expression and reduction in SN density in BAT. We confirm MAFB regulation of IL-6 expression using luciferase readout driven by IL-6 promoter in RAW-264.7 macrophage cell lines. Immunohistochemistry shows clustered organization of NGF-producing cells in BAT, which are primarily TRPV1+ vascular smooth muscle cells, as additionally shown using single-cell RNA sequencing and RT-qPCR of the stromal vascular fraction. Treating Mafbf/f::LysM-Cre mice with anti-IL-6 receptor antibody rescues SN density, body temperature, and energy expenditure.
Describing our interaction with Artificial Intelligence (AI) systems as 'collaboration' is well-intentioned, but flawed. Not only is it misleading, but it also takes away the credit of AI 'labour' from the humans behind it, and erases and obscures an often exploitative arrangement between AI producers and consumers. The AI 'collaboration' metaphor is merely the latest episode in a long history of labour appropriation and credit reassignment that disenfranchises labourers in the Global South. I propose that viewing AI as a tool or an instrument, rather than a collaborator, is more accurate, and ultimately fairer.
BACKGROUND: This study aimed to investigate the anatomical morphology of the C2 to C7 spinous process (SP) bifurcation (SPB) in the Chinese population and reveal its potential clinical significance. MATERIALS AND METHODS: Measurement parameters of the three-dimensional (3D) reconstructions of neck computed tomography scans (n = 92 scans) were retrospectively analysed. The 3D reconstruction and measurements were performed using Mimics Research 19.0 and 3-Matic Research 11.0. Two independent investigators reviewed all the data, including parameters such as the length and angle of the SPB. The effects of age and sex were also analysed. RESULTS: We identified four morphological types of SPB: fully bifid (n = 252, 45.65%), partially bifid (n = 65, 11.78%), non-bifid (n = 226, 40.94%) and unilateral branch (n = 9, 1.63%). The Kappa coefficients indicated good inter-observer reproducibility (0.776), and the intraclass correlation coefficients (ICC) values demonstrated excellent intra-rater reliability (ICC = 0.9, p < 0.0001) in the classification and measurement of SPB parameters. The percentage of general bifid SP was more than 70% in C2–5 and about 21% in C6, while all C7 SPs presented non-bifid. Morphology was symmetrical in bifid and partially bifid SP, while unilateral SP was not. CONCLUSIONS: The classification system of SPB in this study proved consistent and reliable, despite the subjective bias. Identifying the cervical level by C6 bifurcation is unreliable, as nearly 80% of C6 SP is non-bifid. Our work provides an accurate and effective anatomical reference for SPB studies in the Chinese population.