Hasil untuk "Human anatomy"

Faezeh Vahedi, Morteza Memari, Ramtin Tabatabaei et al.

Nonverbal behaviors, particularly gaze direction, play a crucial role in enhancing effective communication in social interactions. As social robots increasingly participate in these interactions, they must adapt their gaze based on human activities and remain receptive to all cues, whether human-generated or not, to ensure seamless and effective communication. This study aims to increase the similarity between robot and human gaze behavior across various social situations, including both human and non-human stimuli (e.g., conversations, pointing, door openings, and object drops). A key innovation in this study, is the investigation of gaze responses to non-human stimuli, a critical yet underexplored area in prior research. These scenarios, were simulated in the Unity software as a 3D animation and a 360-degree real-world video. Data on gaze directions from 41 participants were collected via virtual reality (VR) glasses. Preprocessed data, trained two neural networks-LSTM and Transformer-to build predictive models based on individuals' gaze patterns. In the animated scenario, the LSTM and Transformer models achieved prediction accuracies of 67.6% and 70.4%, respectively; In the real-world scenario, the LSTM and Transformer models achieved accuracies of 72% and 71.6%, respectively. Despite the gaze pattern differences among individuals, our models outperform existing approaches in accuracy while uniquely considering non-human stimuli, offering a significant advantage over previous literature. Furthermore, deployed on the NAO robot, the system was evaluated by 275 participants via a comprehensive questionnaire, with results demonstrating high satisfaction during interactions. This work advances social robotics by enabling robots to dynamically mimic human gaze behavior in complex social contexts.

Goran Muric, Steven Minton

Automated decision systems increasingly rely on human oversight to ensure accuracy in uncertain cases. This paper presents a practical framework for optimizing such human-in-the-loop classification systems using a double-threshold policy. Conventional classifiers usually produce a confidence score and apply a single cutoff, but our approach uses two thresholds (a lower and an upper) to automatically accept or reject high-confidence cases while routing ambiguous instances to human reviewers. We formulate this problem as an optimization task that balances system accuracy against the cost of human review. Through analytical derivations and Monte Carlo simulations, we show how different confidence score distributions impact the efficiency of human intervention and reveal regions of diminishing returns, where additional review yields minimal benefit. The framework provides a general, reproducible method for improving reliability in any decision pipeline requiring selective human validation, including applications in entity resolution, fraud detection, medical triage, and content moderation.

Anna Madison, Kaleb McDowell, Vinicius G. Goecks et al.

Future warfare will occur in more complex, fast-paced, ill-structured, and demanding conditions that will stress current Command and Control (C2) systems. Without modernization, these C2 systems may fail to maintain overmatch against adversaries. We previously proposed robust partnerships between humans and artificial intelligence systems, and directly focusing on C2, we introduced how intelligent technologies could provide future overmatch through streamlining the C2 operations process, maintaining unity of effort across formations, and developing collective knowledge systems that adapt to battlefield dynamics across missions. Future C2 systems must seamlessly integrate human and machine intelligence to achieve decision advantage over adversaries while overcoming "new" challenges due to the technological advances driving fundamental changes in effective teaming, unity of effort, and meaningful human control. Here, we describe "new" C2 challenges and discuss pathways to transcend them, such as AI-enabled systems with effective human machine interfaces.

Jonathan Wang, Hisashi Ishida, David Usevitch et al.

Surgical training remains a crucial milestone in modern medicine, with procedures such as laminectomy exemplifying the high risks involved. Laminectomy drilling requires precise manual control to mill bony tissue while preserving spinal segment integrity and avoiding breaches in the dura: the protective membrane surrounding the spinal cord. Despite unintended tears occurring in up to 11.3% of cases, no assistive tools are currently utilized to reduce this risk. Variability in patient anatomy further complicates learning for novice surgeons. This study introduces CAPTAiN, a critical anatomy-preserving and terrain-augmenting navigation system that provides layered, color-coded voxel guidance to enhance anatomical awareness during spinal drilling. CAPTAiN was evaluated against a standard non-navigated approach through 110 virtual laminectomies performed by 11 orthopedic residents and medical students. CAPTAiN significantly improved surgical completion rates of target anatomy (87.99% vs. 74.42%) and reduced cognitive load across multiple NASA-TLX domains. It also minimized performance gaps across experience levels, enabling novices to perform on par with advanced trainees. These findings highlight CAPTAiN's potential to optimize surgical execution and support skill development across experience levels. Beyond laminectomy, it demonstrates potential for broader applications across various surgical and drilling procedures, including those in neurosurgery, otolaryngology, and other medical fields.

Halfdan Nordahl Fundal, Johannes Eide Rambøll, Karsten Olsen

Human-AI interactions are increasingly part of everyday life, yet the interpersonal dynamics that unfold during such exchanges remain underexplored. This study investigates how emotional alignment, semantic exploration, and linguistic innovation emerge within a collaborative storytelling paradigm that paired human participants with a large language model (LLM) in a turn-taking setup. Over nine days, more than 3,000 museum visitors contributed to 27 evolving narratives, co-authored with an LLM in a naturalistic, public installation. To isolate the dynamics specific to human involvement, we compared the resulting dataset with a simulated baseline where two LLMs completed the same task. Using sentiment analysis, semantic embeddings, and information-theoretic measures of novelty and resonance, we trace how humans and models co-construct stories over time. Our results reveal that affective alignment is primarily driven by the model, with limited mutual convergence in human-AI interaction. At the same time, human participants explored a broader semantic space and introduced more novel, narratively influential contributions. These patterns were significantly reduced in the simulated AI-AI condition. Together, these findings highlight the unique role of human input in shaping narrative direction and creative divergence in co-authored texts. The methods developed here provide a scalable framework for analysing dyadic interaction and offer a new lens on creativity, emotional dynamics, and semantic coordination in human-AI collaboration.

C. Shen, M. Zhang, H. Keramati et al.

Despite tremendous advances in cardiovascular medicine, significant opportunities remain to improve coronary artery disease (CAD) prevention and treatment strategies. The key limitation lies in the understanding of disease formation and progression mechanisms. The coronary anatomy plays an important role in local haemodynamics, governing endothelial health and, thus, pathophysiological responses. The significant variation of the coronary anatomy among patients, with significant trends across different populations, increases the complexity of understanding the details of disease progression. This review covers different aspects of the current status and understanding of the blood flow investigation in coronary arteries. We summarised the current knowledge of the haemodynamic effect of coronary anatomy and its evaluation and analysis methods. We discussed recent progress across medical imaging techniques and computational haemodynamic analysis. Based on the reviewed papers, we identified the persisting knowledge gaps and challenges in the field. We then elaborated on future directions and opportunities to increase understanding of the fundamental mechanism of CAD in individuals representative of large populations and how this may translate to the patient's bedside.

Andrzej Węgiel, Nicol Zielinska, Krystian Maślanka et al.

The fibularis tertius (FT) is one of three muscles which constitute the anterior compartment of the leg. The anatomical variants of this muscle usually pertain to its origins, number of final tendons or points, and shapes of insertions. In the particular case that we here report, it had an additional slip (AS) which originated from the same area as the main muscle belly and after descending along the extensor digitorum longus (EDL) it fused with one of its main tendons. The main muscle belly and its tendon, in its usual manner, reached the proximal dorsal surface of the fifth metatarsal bone. To the best of our knowledge, no similar case has been described before. This case reveals that human anatomy, though it may be thoroughly described, still contains surprises. Knowing about these variants is important from both the scientific and clinical points of view.

Katarzyna Siwek, Robert Krupa, Andrzej Mrożek et al.

BACKGROUND: Correct functioning of the upper limb depends on the cooperation and coordination of the muscular and skeletal systems as well as the connective tissue elements present within it. Connective tissue forms fascia, connective tissue membranes, and ligaments. Connective tissue mostly develops from the mesenchyme. It is formed from the intercellular substance consisting of protein fibrous elements and the ground substance. The fibrous elements generally fulfill the mechanical function of the intercellular substance. There are three types of fibrous elements: collagen fibres, reticular fibres, and elastic fibres. The aim of this study was to examine the occurrence of fibrous structures in the wrist area in foetal material stored in the Prenatal Laboratory of the Department of Anatomy of the Wroclaw Medical University, Wroclaw, Poland. MATERIALS AND METHODS: The research included 114 foetuses (53 male and 61 female) of between 117.0 and 197.0 (median 177.0) days of foetal life. RESULTS: The study showed 100% prevalence of a ring-shaped connective tissue structure on the radial side of the wrist located around the tendon of the ‘flexor carpi radialis muscle’, previously unobserved in foetuses. Its bilaterality was found in 57.9% and unilaterality in 42.1% of the examined foetuses. In male foetuses, the ring-shaped structure was located in the right upper limb in 68.9% of the examined limbs, and in the left in 80.3%. Bilaterality was 49.2%, and unilaterality 50.8%. In female foetuses, this structure was observed in the right limbs in 84.9%, and in the left in 77.4%. Bilaterality was 61.1%, and unilaterality 38.9%. CONCLUSIONS: The observed structure has not previously been described in foetuses. Only in one study have authors described a similar change in adults, calling it an ‘annular pulley’ (‘AP’) and connecting its significance to the system of discshaped ligaments of the tendons of the flexor muscles of the hand, which are an element of the attachment system of tendons to the skeletal system. Its function within the ligamentous and retinacular system of the wrist remains unknown. Ours is the first study to describe the occurrence of the ‘AP’ in foetal material. Further research is required to understand its role in the biomechanics of the upper limb and its histological structure.

Rachaell Nihalaani, Tushar Kataria, Jadie Adams et al.

Supervised methods for 3D anatomy segmentation demonstrate superior performance but are often limited by the availability of annotated data. This limitation has led to a growing interest in self-supervised approaches in tandem with the abundance of available un-annotated data. Slice propagation has emerged as an self-supervised approach that leverages slice registration as a self-supervised task to achieve full anatomy segmentation with minimal supervision. This approach significantly reduces the need for domain expertise, time, and the cost associated with building fully annotated datasets required for training segmentation networks. However, this shift toward reduced supervision via deterministic networks raises concerns about the trustworthiness and reliability of predictions, especially when compared with more accurate supervised approaches. To address this concern, we propose the integration of calibrated uncertainty quantification (UQ) into slice propagation methods, providing insights into the model's predictive reliability and confidence levels. Incorporating uncertainty measures enhances user confidence in self-supervised approaches, thereby improving their practical applicability. We conducted experiments on three datasets for 3D abdominal segmentation using five UQ methods. The results illustrate that incorporating UQ improves not only model trustworthiness, but also segmentation accuracy. Furthermore, our analysis reveals various failure modes of slice propagation methods that might not be immediately apparent to end-users. This study opens up new research avenues to improve the accuracy and trustworthiness of slice propagation methods.

Shachar Don-Yehiya, Ben Burtenshaw, Ramon Fernandez Astudillo et al.

Human feedback on conversations with language language models (LLMs) is central to how these systems learn about the world, improve their capabilities, and are steered toward desirable and safe behaviors. However, this feedback is mostly collected by frontier AI labs and kept behind closed doors. In this work, we bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI. We first look for successful practices in peer production, open source, and citizen science communities. We then characterize the main challenges for open human feedback. For each, we survey current approaches and offer recommendations. We end by envisioning the components needed to underpin a sustainable and open human feedback ecosystem. In the center of this ecosystem are mutually beneficial feedback loops, between users and specialized models, incentivizing a diverse stakeholders community of model trainers and feedback providers to support a general open feedback pool.

Handan Ürek

In the Pandemic, online learning was conducted after the suspension of face-to-face instruction at the educational institutions to eliminate the spread of the disease. Besides, online learning can also be an effective way to carry out education after natural disasters such as earthquakes. This study aimed to detect the effectiveness of an online anatomy course enriched with digital media news on pre-service science teachers’ course attitudes and health literacy after two major earthquakes hit the east and southeast of Turkey in 2023. In this direction, a single group pre-test – post-test weak experimental design was conducted with the attendance of 25 senior science education students who studied at a governmental university in Turkey. Data was collected with Attitudes towards Anatomy Course Scale and Health Literacy Scale. The study lasted for one semester and included data collection and instruction process on an online learning platform. The instruction process covered the systems in human body. Each course began with the examination of several digital media news regarding the focus of the course. Afterwards, the course content was presented to the students and the course ended with the evaluation of the subject with the help of Web 2.0 tools. Collected data was analyzed quantitatively with SPSS 26.0. According to the results, a significant increase was determined both in pre-service teachers’ mean anatomy course attitudes and health literacies. Besides, detailed analyses indicated significant improvements in the course attitudes and health literacies of three of four subscales of the instruments. It is recommended to implement a similar instructional design to sustain anatomy education during online learning for undergraduate students in different programs. Also, adapting such a course design to face-to-face applications might make contributions to the development of well-equipped pre-service teachers in addition to sustaining healthy young individuals and a healthy society.

Genfa Du, Jie Li, Yueyong Qi

BACKGROUND: Portal vein (PV) duplication is a rare developmental anomaly, but it plays an important role in the diagnosis and management of disease for radiologists and surgeons. MATERIALS AND METHODS: A new variant of PV duplication with vein fenestration leading to choledochal stenosis and dilatation and thrombus was identified by computed tomography angiography (CTA) on a 59-year-old woman with a history of gallstones. RESULTS: A second PV originated from the superior mesenteric vein (SMV), which split into 2 branches encircling the common bile duct to form a vein fenestration, leading to choledochal stenosis and dilatation, with thrombus formation at the confluence. CONCLUSIONS: This case report adds to the existing body of knowledge about the variation of the PV system. We present an embryological perspective for the case, which suggests the possibility of similar occurrences.

Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki

Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing many of these IoT systems arises from the requirement to infer the human mental state, such as intention, stress, cognition load, or learning ability. While different human contexts can be inferred from the fusion of different sensor modalities that can correlate to a particular mental state, the human brain provides a richer sensor modality that gives us more insights into the required human context. This paper proposes ERUDITE, a human-in-the-loop IoT system for the learning environment that exploits recent wearable neurotechnology to decode brain signals. Through insights from concept learning theory, ERUDITE can infer the human state of learning and understand when human learning increases or declines. By quantifying human learning as an input sensory signal, ERUDITE can provide adequate personalized feedback to humans in a learning environment to enhance their learning experience. ERUDITE is evaluated across $15$ participants and showed that by using the brain signals as a sensor modality to infer the human learning state and providing personalized adaptation to the learning environment, the participants' learning performance increased on average by $26\%$. Furthermore, we showed that ERUDITE can be deployed on an edge-based prototype to evaluate its practicality and scalability.

Feng Lu, Bo Liu

In recent years, metaverse and digital humans have become important research and industry areas of focus. However, existing digital humans still lack realistic affective traits, making emotional interaction with humans difficult. Grounded in the developments of artificial intelligence, human-computer interaction, virtual reality, and affective computing, this paper proposes the concept and technical framework of "Affective Digital Twins for Digital Human" based on the philosophy of digital twin technology. The paper discusses several key technical issues including affective modeling, affective perception, affective encoding, and affective expression. Based on this, the paper conducts a preliminary imagination of the future application prospects of affective digital twins for digital human, while considering potential problems that may need to be addressed.

Yang Ye, Hengxu You, Jing Du

Human robot collaboration is becoming increasingly important as robots become more involved in various aspects of human life in the era of Artificial Intelligence. However, the issue of human operators trust in robots remains a significant concern, primarily due to the lack of adequate semantic understanding and communication between humans and robots. The emergence of Large Language Models (LLMs), such as ChatGPT, provides an opportunity to develop an interactive, communicative, and robust human-robot collaboration approach. This paper explores the impact of ChatGPT on trust in a human-robot collaboration assembly task. This study designs a robot control system called RoboGPT using ChatGPT to control a 7-degree-of-freedom robot arm to help human operators fetch, and place tools, while human operators can communicate with and control the robot arm using natural language. A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration, which can be attributed to the robot's ability to communicate more effectively with humans. Furthermore, ChatGPT ability to understand the nuances of human language and respond appropriately helps to build a more natural and intuitive human-robot interaction. The findings of this study have significant implications for the development of human-robot collaboration systems.

Sherly I. Celada, Guoliang Li, Lindsay J. Celada et al.

Changes in FOXA1 (forkhead box protein A1) protein levels are well associated with prostate cancer (PCa) progression. Unfortunately, direct targeting of FOXA1 in progressive PCa remains challenging due to variations in FOXA1 protein levels, increased FOXA1 mutations at different stages of PCa, and elusive post‐translational FOXA1 regulating mechanisms. Here, we show that SKP2 (S‐phase kinase‐associated protein 2) catalyzes K6‐ and K29‐linked polyubiquitination of FOXA1 for lysosomal‐dependent degradation. Our data indicate increased SKP2:FOXA1 protein ratios in stage IV human PCa compared to stages I–III, together with a strong inverse correlation (r = −0.9659) between SKP2 and FOXA1 levels, suggesting that SKP2–FOXA1 protein interactions play a significant role in PCa progression. Prostate tumors of Pten/Trp53 mice displayed increased Skp2–Foxa1–Pcna signaling and colocalization, whereas disruption of the Skp2–Foxa1 interplay in Pten/Trp53/Skp2 triple‐null mice demonstrated decreased Pcna levels and increased expression of Foxa1 and luminal positive cells. Treatment of xenograft mice with the SKP2 inhibitor SZL P1‐41 decreased tumor proliferation, SKP2:FOXA1 ratios, and colocalization. Thus, our results highlight the significance of the SKP2–FOXA1 interplay on the luminal lineage in PCa and the potential of therapeutically targeting FOXA1 through SKP2 to improve PCa control.

Bart R. Thomson, Louise Françoise Martin, Paul L. Schmidle et al.

G-ratio is crucial for understanding the nervous system’s health and function as it measures the relative myelin thickness around an axon. However, manual measurement is biased and variable, emphasizing the need for an automated and standardized technique. Although deep learning holds promise, current implementations lack clinical relevance and generalizability. This study aimed to develop an automated pipeline for selecting nerve fibers and calculating relevant g-ratio using quality parameters in optical microscopy. Histological sections from the sciatic nerves of 16 female mice were prepared and stained with either p-phenylenediamine (PPD) or toluidine blue (TB). A custom UNet model was trained on a mix of both types of staining to segment the sections based on 7,694 manually delineated nerve fibers. Post-processing excluded non-relevant nerves. Axon diameter, myelin thickness, and g-ratio were computed from the segmentation results and its reliability was assessed using the intraclass correlation coefficient (ICC). Validation was performed on adjacent cuts of the same nerve. Then, morphometrical analyses of both staining techniques were performed. High agreement with the ground truth was shown by the model, with dice scores of 0.86 (axon) and 0.80 (myelin) and pixel-wise accuracy of 0.98 (axon) and 0.94 (myelin). Good inter-device reliability was observed with ICC at 0.87 (g-ratio) and 0.83 (myelin thickness), and an excellent ICC of 0.99 for axon diameter. Although axon diameter significantly differed from the ground truth (p = 0.006), g-ratio (p = 0.098) and myelin thickness (p = 0.877) showed no significant differences. No statistical differences in morphological parameters (g-ratio, myelin thickness, and axon diameter) were found in adjacent cuts of the same nerve (ANOVA p-values: 0.34, 0.34, and 0.39, respectively). Comparing all animals, staining techniques yielded significant differences in mean g-ratio (PPD: 0.48 ± 0.04, TB: 0.50 ± 0.04), myelin thickness (PPD: 0.83 ± 0.28 μm, TB: 0.60 ± 0.20 μm), and axon diameter (PPD: 1.80 ± 0.63 μm, TB: 1.78 ± 0.63 μm). The proposed pipeline automatically selects relevant nerve fibers for g-ratio calculation in optical microscopy. This provides a reliable measurement method and serves as a potential pre-selection approach for large datasets in the context of healthy tissue. It remains to be demonstrated whether this method is applicable to measure g-ratio related with neurological disorders by comparing healthy and pathological tissue. Additionally, our findings emphasize the need for careful interpretation of inter-staining morphological parameters.

Valery Gunas, Oleksandr Maievskyi, Nataliia Raksha et al.

Problem of scorpion envenomation becomes more alarming each year. Main effects of scorpion venom are commonly believed to be related to its neurotoxic properties, yet severe symptoms may also be developed due to the uncontrolled enzymatic activity and formation of various bioactive molecules, including middle-mass molecules (MMMs). MMMs are considered as endogenous intoxication markers, their presence may indicate multiple organ failure. Scorpions, belong to the Leiurus macroctenus species, are very dangerous, nevertheless, effects of their venom on protein and peptide composition within the tissues remains unclear. In this work we have focused the attention on changes in protein and MMM levels and peptide composition in various organs during Leiurus macroctenus envenomation. The results revealed a decrease in protein level during envenomation as well as a significant increment of MMM210 and MMM254 levels in all assessed organs. Quantitative and qualitative compositions of various protein and peptide factions were continually changing. All of this may suggest that Leiurus macroctenus sting causes considerable destruction of cell microenvironment across all essential organs, providing systemic envenomation. In addition, MMM level increment may indicate endogenous intoxication development. Peptides, formed during envenomation, may possess various bioactive properties, analysis of which constitutes an area of further studies.

Qiaosi Wang, Ashok K. Goel

New developments are enabling AI systems to perceive, recognize, and respond with social cues based on inferences made from humans' explicit or implicit behavioral and verbal cues. These AI systems, equipped with an equivalent of human's Theory of Mind (ToM) capability, are currently serving as matchmakers on dating platforms, assisting student learning as teaching assistants, and enhancing productivity as work partners. They mark a new era in human-AI interaction (HAI) that diverges from traditional human-computer interaction (HCI), where computers are commonly seen as tools instead of social actors. Designing and understanding the human perceptions and experiences in this emerging HAI era becomes an urgent and critical issue for AI systems to fulfill human needs and mitigate risks across social contexts. In this paper, we posit the Mutual Theory of Mind (MToM) framework, inspired by our capability of ToM in human-human communications, to guide this new generation of HAI research by highlighting the iterative and mutual shaping nature of human-AI communication. We discuss the motivation of the MToM framework and its three key components that iteratively shape the human-AI communication in three stages. We then describe two empirical studies inspired by the MToM framework to demonstrate the power of MToM in guiding the design and understanding of human-AI communication. Finally, we discuss future research opportunities in human-AI interaction through the lens of MToM.

Ben Green

As algorithms become an influential component of government decision-making around the world, policymakers have debated how governments can attain the benefits of algorithms while preventing the harms of algorithms. One mechanism that has become a centerpiece of global efforts to regulate government algorithms is to require human oversight of algorithmic decisions. Despite the widespread turn to human oversight, these policies rest on an uninterrogated assumption: that people are able to effectively oversee algorithmic decision-making. In this article, I survey 41 policies that prescribe human oversight of government algorithms and find that they suffer from two significant flaws. First, evidence suggests that people are unable to perform the desired oversight functions. Second, as a result of the first flaw, human oversight policies legitimize government uses of faulty and controversial algorithms without addressing the fundamental issues with these tools. Thus, rather than protect against the potential harms of algorithmic decision-making in government, human oversight policies provide a false sense of security in adopting algorithms and enable vendors and agencies to shirk accountability for algorithmic harms. In light of these flaws, I propose a shift from human oversight to institutional oversight as the central mechanism for regulating government algorithms. This institutional approach operates in two stages. First, agencies must justify that it is appropriate to incorporate an algorithm into decision-making and that any proposed forms of human oversight are supported by empirical evidence. Second, these justifications must receive democratic public review and approval before the agency can adopt the algorithm.