Hasil untuk "Human anatomy"

J. Gérard, Tortora, G. Tortora

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C. Mungall, C. Torniai, G. Gkoutos et al.

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org

Mahyar Firouzi, Kris Baetens, Sarah Al Omari et al.

Introduction: Implicit motor sequence learning (IMSL) enables unconscious acquisition of everyday motor skills and involves stage-specific neural dynamics. Beta-band (13–30 Hz) activity is believed to support consolidation, yet its causal role across IMSL stages remains unclear. Methods: We conducted the first study examining the effects of repeated 20 Hz transcranial alternating current stimulation (tACS) over the motor cortex on IMSL acquisition, consolidation and retention. Using a double-blind, sham-controlled, between-subjects design, 80 healthy young adults received active or sham tACS during a serial reaction time task (SRTT) across three consecutive days (Sessions 1–3), with retention assessed one week later (Follow-Up). Results: Mixed-effects models revealed sequence-specific learning in both groups, across sessions (all p's < .0001). Active tACS tended to enhance learning in Session 1 (M = 54.62 ms) vs. sham (M = 41.89 ms), p = .063, but significantly impaired learning in Session 2 (active M = 75.97 ms, sham M = 85.95 ms, p = .003), Session 3 (active M = 95.89 ms, sham M = 113.14 ms, p < .0001), and at Follow-Up (active M = 91.79 ms, sham M = 117.21 ms, p < .0001). Conclusion: Repeated beta-tACS tended to improve acquisition, but impaired consolidation and retention. This stage-specific disruption – preserved early learning followed by later deficits – supports a dynamic role for beta oscillations: early desynchronization facilitates flexible encoding, while later synchronization stabilizes learned patterns. Recovery during random blocks suggests transient disruption of motor flexibility followed by compensatory adaptation, rather than premature overgeneralization of sequence knowledge.

Soha S. Mahdi, Eduarda Caldeira, Harold Matthews et al.

Clinical diagnosis of syndromes benefits strongly from objective facial phenotyping. This study introduces a novel approach to enhance clinical diagnosis through the development and exploration of a low-dimensional metric space referred to as the clinical face phenotypic space (CFPS). As a facial matching tool for clinical genetics, such CFPS can enhance clinical diagnosis. It helps to interpret facial dysmorphisms of a subject by placing them within the space of known dysmorphisms. In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. The proposed model consists of three main components: an encoder based on GDL optimizing distances between groups of individuals in the CFPS, a decoder enhancing classification by reconstructing faces, and a singular value decomposition layer maintaining orthogonality and optimal variance distribution across dimensions. This allows for the selection of an optimal number of CFPS dimensions as well as improving the classification capacity of the CFPS, which outperforms the linear metric learning baseline in both syndrome classification and generalization to novel syndromes. We further proved the usefulness of each component of the proposed framework, highlighting their individual impact. From a clinical perspective, the unique combination of these properties in a single CFPS results in a powerful tool that can be incorporated into current clinical practices to assess facial dysmorphism.INDEX TERMS 3D shape analysis, clinical genetics, computer-aided diagnosis, deep phenotyping, geometric deep learning, precision public health.

Tao Song

This study aims to explore the effects of Shape and Decoration on user experience and visual attention in anthropomorphic robot design. Eighty undergraduate students were divided into four groups, each viewing one of four stimuli: (a) Non-hat and Non-pattern, (b) Hat and Non-pattern, (c) Non-hat and Pattern, and (d) Hat and Pattern. Eye-tracking data and subjective user experience ratings were collected. The results indicate that both Shape and Decoration have significant effects on user experience and visual attention. The Hat significantly outperformed Non-hat in the dimensions of Attractiveness and Stimulation, while the Pattern showed significant advantages in Stimulation and Novelty. Additionally, Shape and Decoration exhibited a significant interaction effect in the dimensions of Novelty and time to first fixation, suggesting that their combination provides complementary benefits in enhancing perceived novelty and initial visual appeal. Hat and Pattern attracted users’ attention earlier and prolonged fixation time, as seen in time to first fixation, first-pass total fixation duration, and second-pass total fixation duration. For time to first fixation, there was an interaction effect between Shape and Decoration. This study offers strong theoretical support for the design of anthropomorphic robots, highlighting the critical role of Shape and Decoration in user experience.

Background: Conflict theories are widely accepted as a key mechanism underlying motion sickness (kinetosis), but objective physiological studies on cortical activity during motion-induced kinetosis remain limited. This study investigates the neuronal correlates associated with the development of motion-induced kinetosis, provoked by a rotating chair paradigm. The aim was to identify specific spectral electroencephalographic (EEG) changes related to symptom intensities. Materials and Methods: Participants (N=19) underwent the Staircase Velocity Motion Test (Calkins et al.,1987) to induce kinetosis by provoking vestibular conflict. Subjective symptom intensity was assessed every minute by the Misery Scale index, MISC (Bos et al.,2005). Spectral analysis focused on power changes across standard frequency bands assessed using 32-channel EEG. A machine learning approach (XGBoost) using EEG features to predict kinetosis severity was performed. Results: Analysis revealed a significant increase in alpha band power that correlated with increasing MISC scores, starting in posterior parietal cortex for lower (0-3) and evolving to fronto-central cortex for higher MISC scores (4-7). Transient increase of delta and theta power of vestibular-related cortical regions were prominent for lower MISC scores (1-2). Prior to the onset of nausea (MISC 5) delta and theta power peaked. At MISC 6, delta and theta power of vestibular cortical regions as well as parieto-temporal gamma power significantly decreased. Conclusions: Our data may suggest three consecutive physiological states of kinetosis corresponding to MISC ranges 0-2, 3-5, and 6-8, although further analysis is needed. Machine learning results suggest potential of an EEG-based prediction model as preliminary data show an accuracy of 0.84.

Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram et al.

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions typically ignore the surrounding 3D scene. This gap, generating full-body grasping motions that are physically plausible within a 3D scene, remains a significant challenge. To address this, we introduce MOGRAS (Human MOtion with GRAsping in 3D Scenes), a large-scale dataset that bridges this gap. MOGRAS provides pre-grasping full-body walking motions and final grasping poses within richly annotated 3D indoor scenes. We leverage MOGRAS to benchmark existing full-body grasping methods and demonstrate their limitations in scene-aware generation. Furthermore, we propose a simple yet effective method to adapt existing approaches to work seamlessly within 3D scenes. Through extensive quantitative and qualitative experiments, we validate the effectiveness of our dataset and highlight the significant improvements our proposed method achieves, paving the way for more realistic human-scene interactions.

Xuyang Zhu, Sejoon Chang, Andrew Kuik

Retrieval-Augmented Generation (RAG) systems offer a powerful approach to enhancing large language model (LLM) outputs by incorporating fact-checked, contextually relevant information. However, fairness and reliability concerns persist, as hallucinations can emerge at both the retrieval and generation stages, affecting users' reasoning and decision-making. Our research explores how tailored warning messages -- whose content depends on the specific context of hallucination -- shape user reasoning and actions in an educational quiz setting. Preliminary findings suggest that while warnings improve accuracy and awareness of high-level hallucinations, they may also introduce cognitive friction, leading to confusion and diminished trust in the system. By examining these interactions, this work contributes to the broader goal of AI-augmented reasoning: developing systems that actively support human reflection, critical thinking, and informed decision-making rather than passive information consumption.

Frederic Gmeiner, Nicolai Marquardt, Michael Bentley et al.

Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows.

Oliver Chojnowski, Alexander Eberhard, Michael Schiffmann et al.

Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, these systems require verbal and nonverbal communication through e.g., facial expressions and gestures. While natural language processing technologies have rapidly advanced, incorporating human-like nonverbal behavior into real-world interaction contexts is crucial for enhancing the success of communication, yet this area remains underexplored. One barrier is creating autonomous systems with sophisticated conversational abilities that integrate human-like nonverbal behavior. This paper presents a distributed architecture using Epic Games MetaHuman, combined with advanced conversational AI and camera-based user management, that supports methods like motion capture, handcrafted animation, and generative approaches for nonverbal behavior. We share insights into a system architecture designed to investigate nonverbal behavior in socially interactive agents, deployed in a three-week field study in the Deutsches Museum Bonn, showcasing its potential in realistic nonverbal behavior research.

Tauhid Tanjim, Jonathan St. George, Kevin Ching et al.

The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-sensitive environments. This study addresses this knowledge gap in an experimental in-lab study to investigate how multimodal robot communication in action teams affects workload and human perception of robots. We explore team collaboration in a medical training scenario where a robotic crash cart (RCC) provides verbal and non-verbal cues to help users remember to perform iterative tasks and search for supplies. Our findings show that verbal cues for object search tasks and visual cues for task reminders reduce team workload and increase perceived ease of use and perceived usefulness more effectively than a robot with no feedback. Our work contributes to multimodal interaction research in the HRI field, highlighting the need for more human-robot teaming research to understand best practices for integrating collaborative robots in time-sensitive environments such as in hospitals, search and rescue, and manufacturing applications.

Inés Moreno-Sánchez, Agustín Aibar-Almazán, María del Carmen Carcelén-Fraile et al.

(1) Background: The aim was to analyze the reliability and validity of the Spanish version of the Mini Sarcopenia Risk Assessment (MSRA) in older adults. (2) Methods: A total of 136 participants (72.24 ± 5.21 years, 68.38% women) took part in the study. The MSRA includes two questionnaires with seven (MSRA-7) and five items (MSRA-5). First, reliability (inter-rater and test–retest) of the Spanish MSRA was studied, and then the total scores were compared with the presence of sarcopenia according to three different diagnostic criteria and with other parameters related to sarcopenia (clinical validation). (3) Results: The analysis showed excellent inter-rater and test–retest reliability. As for the clinical validation, and regardless of the criteria, both questionnaires had a high sensitivity (81.82–88.89% for the MSRA-5 and 90.91–94.44% for the MSRA-7), while the MSRA-5 showed a better specificity (32.00–33.90%) than the MSRA-7 (20.80–22.88%). Predictive positive values ranged from 9.57–17.02% (MSRA-5) and 9.17–15.54% (MSRA-7), while predictive negative values were high for both the MSRA-5 (95.24%) and the MSRA-7 (96.30–96.43%). The accuracy was better for the MSRA-5 (36.03–41.18%) than the MSRA-7 (26.47–32.35%), as well as the area under the curve (0.67–0.76 vs. 0.65–0.73, respectively). Higher MSRA-5 and MSRA-7 total scores significantly correlated with greater muscle strength, quantity and gait speed. (4) Conclusions: The adaptation of the Spanish MSRA questionnaires was successfully performed, and they are reliable and clinically valid tools for assessing sarcopenia.

Jakub Jędrzej Ruszkowski, Maciej Zdun, Marcin Bartłomiej Arciszewski

Abstract Background European hedgehogs (Erinaceus europaeus) are small insectivorous mammals common in Europe. With increased urbanization, the species become more frequent in the cities and near other human dwellings. The anatomy of the species with the use of diagnostic imaging techniques along with the classical anatomical methodology has not been researched before. In addition to the description of this species' skull, the data may contribute to progress in hedgehog medicine, delivering a basis for diagnosing and treating head trauma in this species. Results Cadavers of 30 European hedgehogs have been used to study the anatomy of the head of the species. Along with classical anatomical techniques – latex and corrosion cast specimens, x-ray, and cone-bean computed tomography scans were performed. The methods were then compared, and the detailed anatomy of the head was described. The skull of the Western European hedgehog was elongated and oval in shape, and could be divided into two distinct regions: one formed of neurocranial bones and the other of facial bones. The neurocranium was composed of the following bones: the occipital, interparietal, parietal, frontal, temporal, sphenoid, ethmoid, and pterygoid bones. The following facial bones were identified: the incisive, nasal, maxilla, zygomatic, palatine, vomer, and the mandible. The most important findings include the primitive tympanic bullae, the absence of a supraorbital foramen, and the lacrimal bones, which were indistinguishably fused with the maxillae. Conclusions The results of the study may be used in future research on the comparative anatomy of the other members of the Eulipotyphla order. With the increase of hedgehogs in the cities, it is important to establish new diagnostic and treatment protocols for wildlife rehabilitation centers. Anatomical and radiological descriptions may be used as a base for such work. The anatomical features of the hedgehog skull described in the study may prove useful for further studies from a comparative anatomical perspective. Additionally, the data may serve as a basis for developing identification guidelines for archaeological studies and forensic research.

Patricia Palau, Laura López, Eloy Domínguez et al.

Abstract Background Iron deficiency (ID) is associated with impaired functional capacity in patients with heart failure (HF), even in those with preserved ejection fraction (HFpEF). This study aimed to evaluate the effect of baseline ferrokinetics on peak oxygen consumption (peakVO2) improvement after a 12‐week physical therapy programme in patients with stable HFpEF. Methods This study is a post‐hoc sub‐analysis of a randomized clinical trial in which 59 stable patients with HFpEF were randomized to receive a 12‐week programme of inspiratory muscle training (IMT), functional electrical stimulation (FES), IMT + FES or usual care (UC) to evaluate change in peakVO2 (NCT02638961). Serum ferritin and transferrin saturation (TSAT) determinations were assessed at baseline. ID was defined as ferritin <100 ng/mL and/or TSAT <20% if ferritin was within 100–299 ng/mL. We used a linear mixed regression model to analyse between‐treatment changes in peakVO2 across ferrokinetics status at 12 and 24 weeks. Results The mean age was 74 ± 9 years, and 36 (61%) had ID. The mean of peakVO2 was 9.9 ± 2.5 mL/kg/min. The median of ferritin and transferrin saturation (TSAT) was 91 (50–181) ng/mL and 23% (16–30), respectively. A total of 52 patients completed the trial (13 patients per arm). Compared with those patients on UC, patients allocated to any of the active arms showed less improvement in peak VO2 when they showed ID (P‐value for interaction <0.001), lower values of ferritin (P‐value for interaction <0.001), or TSAT (P‐value for interaction <0.001). Conclusions Ferrokinetics status plays an essential role in modifying the aerobic capacity response to physical therapies in patients with HFpEF. Further studies are required to confirm these findings.

Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz et al.

In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.

Liqun Zhao, Keyan Miao, Konstantinos Gatsis et al.

Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.

Jinhong Han, Shuai Ma, Yao Zhao et al.

Colorectal cancer (CRC) is a lethal malignancy worldwide. Exosomes are extracellular vesicles derived from the endosomal pathway of nearly all cells and can be found in body fluids. They can be considered an intercellular system in the human body that can mediate near- and long-distance intercellular communication due to their features and functions. Investigations have revealed that exosomes are participated in different processes, physiologically and pathologically, especially in cancer. However, the clinical value of exosomes and their mechanisms of action in CRC are unclear and have not been systematically assessed. The purpose of this review is to discuss how exosomes play a role in the occurrence and development of CRC, with a particular focus on the functions and underlying mechanisms of tumor-derived exosomes as well as non-tumor-derived exosomes. We also describe the evidence that exosomes can be used as diagnostic and prognostic markers for CRC. In addition, the possibilities of exosomes in CRC clinical transformation are also discussed.

Julia Beitner, Jason Helbing, Dejan Draschkow et al.

Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on two-dimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior.

Andreea Glevesanu

Taekwondo is the art of hitting with your hands and feet. Tae Bo is a sport that combines Taekwondo shots with boxing shots, performed on a musical background. Starting from this premise, the purpose of this research is to highlight the most important and used kicks in Taekwondo, also used in most Tae Bo programs. The focus of the research is on foot techniques, as in Taekwondo, 95% of the shots are performed by using the lower limbs and only 5% by using the upper limbs. On the other hand, the research also wants to underline the benefits of practicing Taekwondo.

Yueqing Xuan, Edward Small, Kacper Sokol et al.

Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more importantly, what information it lacks. To answer this question we conducted an online study with 200 participants, which allowed us to assess explainees' ability to realise explicated information -- i.e., factual insights conveyed by an explanation -- and unspecified information -- i.e, insights that are not communicated by an explanation -- across four representative explanation types: model architecture, decision surface visualisation, counterfactual explainability and feature importance. Our findings uncover that highly comprehensible explanations, e.g., feature importance and decision surface visualisation, are exceptionally susceptible to misinterpretation since users tend to infer spurious information that is outside of the scope of these explanations. Additionally, while the users gauge their confidence accurately with respect to the information explicated by these explanations, they tend to be overconfident when misinterpreting the explanations. Our work demonstrates that human comprehension can be a double-edged sword since highly accessible explanations may convince users of their truthfulness while possibly leading to various misinterpretations at the same time. Machine learning explanations should therefore carefully navigate the complex relation between their full scope and limitations to maximise understanding and curb misinterpretation.