Hasil untuk "Human anatomy"

F. Meurens, A. Summerfield, H. Nauwynck et al.

An animal model to study human infectious diseases should accurately reproduce the various aspects of disease. Domestic pigs (Sus scrofa domesticus) are closely related to humans in terms of anatomy, genetics and physiology, and represent an excellent animal model to study various microbial infectious diseases. Indeed, experiments in pigs are much more likely to be predictive of therapeutic treatments in humans than experiments in rodents. In this review, we highlight the numerous advantages of the pig model for infectious disease research and vaccine development and document a few examples of human microbial infectious diseases for which the use of pigs as animal models has contributed to the acquisition of new knowledge to improve both animal and human health.

M. Brett, I. Johnsrude, A. Owen

S. Wakana, Lidia M. Nagae-Poetscher, P. Zijl et al.

H. Gray, P. L. Williams, L. Bannister

Thomas Herrmann

This contribution explores how the integration of Artificial Intelligence (AI) into organizational practices can be effectively framed through a socio-technical perspective to comply with the requirements of Human-centered AI (HCAI). Instead of viewing AI merely as a technical tool, the analysis emphasizes the importance of embedding AI into communication, collaboration, and decision-making processes within organizations from a human-centered perspective. Ten case-based patterns illustrate how AI support of predictive maintenance can be organized to address quality assurance and continuous improvement and to provide different types of sup-port for HCAI. The analysis shows that AI adoption often requires and enables new forms of organizational learning, where specialists jointly interpret AI output, adapt workflows, and refine rules for system improve-ment. Different dimensions and levels of socio-technical integration of AI are considered to reflect the effort and benefits of keeping the organization in the loop.

Wenge Xu, Foroogh Hajiseyedjavadi, Kurtis Weir et al.

External Human-Machine Interfaces (eHMIs) have been proposed to facilitate communication between Automated Vehicles (AVs) and pedestrians. However, no attention was given to Deaf and Hard-of-Hearing (DHH) people. We conducted a formative study through focus groups with 6 DHH people and 6 key stakeholders (including researchers, assistive technologists, and automotive interface designers) to compare proposed eHMIs and extract key design requirements. Subsequently, we investigated the effects of visual and auditory eHMI in a virtual reality user study with 32 participants (16 DHH). Results from our scenario suggesting that (1) DHH participants spent more time looking at the AV; (2) both visual and auditory eHMIs enhanced trust, usefulness, and perceived safety; and (3) only visual eHMIs reduced the time to step into the road, time looking at the AV, gaze time, and percentage looking at active visual eHMI components. Lastly, we provided five practical implications for making eHMI inclusive of DHH people.

Mariam M. Jad, Zeinab Al-Amgad, Fatma A. Madkour et al.

Background: Carbon tetrachloride (CCl4) is a xenobiotic hepatotoxic agent that causes pathophysiological disorders in tissues and the liver. CCl4-induced cardiopulmonary toxicity is attributed to free radical-induced dyslipidemia and oxidative damage. Branched-chain amino acids (BCAAs) stimulate systemic defense against oxidative stress, which is probably caused by CCl4, through an improvement in the antioxidant system. Aim: This study aimed to explore the therapeutic benefits of BCAAs against CCl4-induced cardiopulmonary pathologies. Methods: A total of 28 inclusive Wistar rats stand under control was standard; group 2 comprised CCl4-received rats, whereas groups 3 and 4 comprised CCl4-rats supplemented with two varying levels of BCAAs. Results: Biochemical findings indicated that CCl4 provoked a rise in glucose and cholesterol levels compared with the control. The oxidative profile suggested increased malondialdehyde level and decreased superoxide dismutase activity in the CCl4-intoxicated heart. Supplemented BCAAs downregulated dyslipidemia and restored atrial changes in antioxidant components to be close to normal. Histopathological investigations using hematoxylin-eosin stain revealed damage to the inflamed heart-lung parenchyma of CCl4-intoxicated rats. An increase in fibrosis expression was detectable, simultaneously with a deprivation of protein content in cardiopulmonary sections, as shown by Masson trichrome and bromophenol blue techniques. Co-treated BCAAs intensified cardiopulmonary integrity, inducing scarce histological fibrosis with abundant protein. Accordingly, BCAA supplementation in CCl4-treated rats mitigated complications by reducing serum toxicity, enhancing antioxidant capacity, restoring protein expression, and improving organ health. Conclusion: The findings support the protective role of BCAAs with antioxidant benefits against CCl4-induced damage, suggesting their potential as a therapeutic agent for cardiopulmonary pathology. [Open Vet. J. 2025; 15(12.000): 6644-6659]

Yiru Chen, Sally Fang, Sai Sree Harsha et al.

Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constraints such as data sparsity, quality assurance, and rapid deployment. To address these challenges, we adopt a human-in-the-loop development workflow that combines prompt engineering, retrieval grounding, and lightweight human validation. We describe the system architecture, development process, and real-world deployment outcomes. Our experience shows that agile, feedback-driven development enables scalable and reliable AI assistants, even in cold-start scenarios.

Mingkang Wu, Devin White, Evelyn Rose et al.

Reinforcement learning from human feedback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we leverage human ratings in reward-free environments to infer a reward function, introducing learnable weights that balance the contributions of both classification and regression models. This design captures the inherent uncertainty in human decision-making and allows the model to adaptively emphasize different strategies. We conduct several experiments using synthetic human ratings to validate the effectiveness of the proposed approach. Results show that our method consistently outperforms existing rating-based RL methods, and in some cases, even surpasses traditional RL approaches.

Rune Møberg Jacobsen, Joel Wester, Helena Bøjer Djernæs et al.

This paper investigates the impact of artificial intelligence integration on remote operations, emphasising its influence on both distributed and team cognition. As remote operations increasingly rely on digital interfaces, sensors, and networked communication, AI-driven systems transform decision-making processes across domains such as air traffic control, industrial automation, and intelligent ports. However, the integration of AI introduces significant challenges, including the reconfiguration of human-AI team cognition, the need for adaptive AI memory that aligns with human distributed cognition, and the design of AI fallback operators to maintain continuity during communication disruptions. Drawing on theories of distributed and team cognition, we analyse how cognitive overload, loss of situational awareness, and impaired team coordination may arise in AI-supported environments. Based on real-world intelligent port scenarios, we propose research directions that aim to safeguard human reasoning and enhance collaborative decision-making in AI-augmented remote operations.

Haiwei Xue, Xiangyang Luo, Zhanghao Hu et al.

Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys in this field focus on individual methods, lacking a comprehensive overview of the entire generative process. This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output. Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation. Our survey reviews the latest developments and technological trends in human motion video generation across three primary modalities: vision, text, and audio. By covering over two hundred papers, we offer a thorough overview of the field and highlight milestone works that have driven significant technological breakthroughs. Our goal for this survey is to unveil the prospects of human motion video generation and serve as a valuable resource for advancing the comprehensive applications of digital humans. A complete list of the models examined in this survey is available in Our Repository https://github.com/Winn1y/Awesome-Human-Motion-Video-Generation.

Haotian Li, Yun Wang, Huamin Qu

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Karini da Rosa, Luana Beatriz Limberger, Maiara de Queiroz Fischer et al.

Background: Iron deficiency is one of the leading causes of anaemia, with those most affected being children and women of childbearing age, in Brazil there is a scarcity of studies involving the local prevalence of anaemia. Aim: To evaluate anaemia and associated factors in schoolchildren in Santa Cruz do Sul through the analysis of biochemical and haematological markers and parasitological examination of faeces. Subjects and methods: School children from 10 to 12 years of age were evaluated through complete blood count, serum ferritin, C-reactive protein and stool parasitological examination, as well as socio-demographic characteristics and prophylaxis with ferrous sulphate in childhood. Results: It was found that 13.0% of the population was anaemic, girls were very slightly overrepresented among the anaemic children. Only 5.3% had altered haematocrit levels; 26.6% had low Mean Corpuscular Volume levels; 18.4% had low ferritin levels; 2.4% had increased C-reactive protein levels, and 21.7% had altered eosinophils. As for the socioeconomic level, classes A2 and D presented lower haemoglobin levels, as well as class D presenting lower ferritin levels, although without statistical significance. Only 6.0% of the population presented iron-deficiency anaemia and 46.0% of the schoolchildren had used ferrous sulphate supplementation in childhood. Conclusion: The prevalence of anaemia in the studied municipality is low, probably due to the high municipal human development index. Epidemiological studies are essential to characterise the population in a systematic form, to prevent future problems.

Kewen Ding, Mingjing Chen, Ping Li et al.

Abstract Background Effective teaching methods are needed to improve students’ abilities in hand-eye coordination and understanding of cardiac anatomy in echocardiography education. Simulation devices have emerged as innovative teaching tools and exhibited distinctive advantages due to their ability to provide vivid and visual learning experiences. This study aimed to investigate the effect of simulation of sectional human anatomy using ultrasound on students’ learning outcomes and satisfaction in echocardiography education. Methods The study included 18 first-year clinical medical students with no prior echocardiography training. After randomization, they underwent a pre-test to assess basic knowledge. Following this, the students were divided into two groups: traditional teaching (traditional group) and simulation of sectional human anatomy using ultrasound (digital group). Each group received 60 min of instruction. Post-tests were assigned to students at two different time points: immediately after the lecture, and one week later (referred to as post-tests 1, and 2). In addition, anonymous questionnaires were distributed to students after class to investigate their satisfaction with teaching. Results Both groups showed significant improvement in their scores on post-test 1 compared to pre-test (traditional group: from 33.1 ± 8.8 to 48.1 ± 13.1, P = 0.034 vs. digital group: from 35.0 ± 6.7 to 58.0 ± 13.2, P = 0.008). However, there were no significant differences between the two groups in several post-test comparisons. Student satisfaction ratings revealed that the digital group experienced significantly greater satisfaction in areas such as subject interest, teaching style, course alignment, and interaction compared to the traditional group. Additionally, 80% of the digital group strongly endorsed the use of simulation of sectional human anatomy using ultrasound for echocardiography teaching, highlighting its effectiveness. Conclusions Simulation of sectional human anatomy using ultrasound may improve students’ understanding of echocardiography and satisfaction with the course. Our study provides evidence supporting the use of simulation teaching devices in medical education. Further research is needed to explore the long-term impact of this teaching method on students’ learning outcomes and its integration into the medical curriculum. Trial registration http://www.chictr.org.cn (registration number: ChiCTR2300074015, 27/07/2023).

Ben Wang, Jiqun Liu, Jamshed Karimnazarov et al.

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.

Hoang Anh Just, Ming Jin, Anit Sahu et al.

Aligning language models with human preferences through reinforcement learning from human feedback is crucial for their safe and effective deployment. The human preference is typically represented through comparison where one response is chosen over another for a given prompt. However, standard preference datasets often lack explicit information on why a particular choice was made, presenting an ambiguity that can hinder efficient learning and robust alignment, especially given the high cost of acquiring extensive human annotations. While many studies focus on algorithmic improvements, this work adopts a data-centric perspective, exploring how to enhance learning from existing preference data. We propose augmenting standard preference pairs with rationales that explain the reasoning behind the human preference. Specifically, we introduce a simple and principled framework that leverages machine-generated rationales to enrich preference data for preference optimization algorithms. Our comprehensive analysis demonstrates that incorporating rationales improves learning efficiency. Extensive experiments reveal some advantages: rationale-augmented learning accelerates convergence and can achieve higher final model performance. Furthermore, this approach is versatile and compatible with various direct preference optimization algorithms. Our findings showcase the potential of thoughtful data design in preference learning, demonstrating that enriching existing datasets with explanatory rationales can help unlock improvements in model alignment and annotation efficiency.

Xuanxin Wu, Yuki Arase

Recent studies have used both automatic metrics and human evaluations to assess the simplification abilities of LLMs. However, the suitability of existing evaluation methodologies for LLMs remains in question. First, the suitability of current automatic metrics on LLMs' simplification evaluation is still uncertain. Second, current human evaluation approaches in sentence simplification often fall into two extremes: they are either too superficial, failing to offer a clear understanding of the models' performance, or overly detailed, making the annotation process complex and prone to inconsistency, which in turn affects the evaluation's reliability. To address these problems, this study provides in-depth insights into LLMs' performance while ensuring the reliability of the evaluation. We design an error-based human annotation framework to assess the LLMs' simplification capabilities. We select both closed-source and open-source LLMs, including GPT-4, Qwen2.5-72B, and Llama-3.2-3B. We believe that these models offer a representative selection across large, medium, and small sizes of LLMs. Results show that LLMs generally generate fewer erroneous simplification outputs compared to the previous state-of-the-art. However, LLMs have their limitations, as seen in GPT-4's and Qwen2.5-72B's struggle with lexical paraphrasing. Furthermore, we conduct meta-evaluations on widely used automatic metrics using our human annotations. We find that these metrics lack sufficient sensitivity to assess the overall high-quality simplifications, particularly those generated by high-performance LLMs.

Minzhao Lyu, Sharat Chandra Madanapalli, Arun Vishwanath et al.

Cloud gaming, wherein game graphics is rendered in the cloud and streamed back to the user as real-time video, expands the gaming market to billions of users who do not have gaming consoles or high-power graphics PCs. Companies like Nvidia, Amazon, Sony and Microsoft are investing in building cloud gaming platforms to tap this large unserved market. However, cloud gaming requires the user to have high bandwidth and stable network connectivity - whereas a typical console game needs about 100-200 kbps, a cloud game demands minimum 10-20 Mbps. This makes the Internet Service Provider (ISP) a key player in ensuring the end-user's good gaming experience. In this paper we develop a method to detect Nvidia's GeForce NOW cloud gaming sessions over their network infrastructure, and measure associated user experience. In particular, we envision ISPs taking advantage of our method to provision network capacity at the right time and in the right place to support growth in cloud gaming at the right experience level; as well as identify the role of contextual factors such as user setup (browser vs app) and connectivity type (wired vs wireless) in performance degradation. We first present a detailed anatomy of flow establishment and volumetric profiles of cloud gaming sessions over multiple platforms, followed by a method to detect gameplay and measure key experience aspects such as latency, frame rate and resolution via real-time analysis of network traffic. The insights and methods are also validated in the lab for XBox Cloud Gaming platform. We then implement and deploy our method in a campus network to capture gameplay behaviors and experience measures across various user setups and connectivity types which we believe are valuable for network operators.

Faisal Mehmood, Xin Guo, Enqing Chen et al.

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.

J. Healey, P. Schroy