Hasil untuk "Human anatomy"

A. Klein, J. Tourville

We introduce the Mindboggle-101 dataset, the largest and most complete set of free, publicly accessible, manually labeled human brain images. To manually label the macroscopic anatomy in magnetic resonance images of 101 healthy participants, we created a new cortical labeling protocol that relies on robust anatomical landmarks and minimal manual edits after initialization with automated labels. The “Desikan–Killiany–Tourville” (DKT) protocol is intended to improve the ease, consistency, and accuracy of labeling human cortical areas. Given how difficult it is to label brains, the Mindboggle-101 dataset is intended to serve as brain atlases for use in labeling other brains, as a normative dataset to establish morphometric variation in a healthy population for comparison against clinical populations, and contribute to the development, training, testing, and evaluation of automated registration and labeling algorithms. To this end, we also introduce benchmarks for the evaluation of such algorithms by comparing our manual labels with labels automatically generated by probabilistic and multi-atlas registration-based approaches. All data and related software and updated information are available on the http://mindboggle.info/data website.

N. Ramnani, A. Owen

T. Kararli

J. Armour, D. A. Murphy, Bing‐Xiang Yuan et al.

P. Georgiades, A. Ferguson-Smith, G. Burton

Meng Wang, Guanqun Mu, Bingquan Qiu et al.

Abstract Accurate procentriole formation is critical for centriole duplication. However, the holistic transcriptional regulatory mechanisms underlying this process remain elusive. Here, we show that KAT7 crotonylation, facilitated by the crotonyltransferase hMOF, competes against its acetylation regulated by the deacetylase HDAC2 at the K432 residue upon DNA damage stimulation. This competition diminishes its histone acetyltransferase activity, leading to the inhibition of procentriole formation in colorectal cancer cells. Mechanistically, the reduction of KAT7 histone acetyltransferase activity by the antagonistic effect of KAT7 crotonylation against its acetylation decreases the gene expression associated with procentriole formation by modulating the enrichment of H3K14ac at their promoters and plays an important role in colorectal tumorigenesis. Furthermore, KAT7 crotonylation and acetylation are associated with the prognosis in colorectal cancer patients. Collectively, our findings uncover a previously unidentified role of KAT7 in the regulation of procentriole formation and colorectal tumorigenesis via competitive antagonism of its crotonylation against acetylation.

Carmelo Pirri, Nicola Manocchio, Daniele Polisano et al.

Focused ultrasound (FUS) is an emerging noninvasive technology with significant therapeutic potential across various clinical domains. FUS enables precise targeting of tissues using mechanisms like thermoablation, mechanical disruption, and neuromodulation, minimizing damage to surrounding areas. In movement disorders such as essential tremor and Parkinson’s disease, MR-guided FUS thalamotomy has demonstrated substantial tremor reduction and improved quality of life. Psychiatric applications include anterior capsulotomy for treatment-resistant obsessive-compulsive disorder and major depressive disorder, with promising symptom relief and minimal cognitive side effects. FUS also facilitates blood-brain barrier opening for drug delivery in neurological conditions like Alzheimer’s disease. Musculoskeletal applications highlight its efficacy in managing chronic pain from knee osteoarthritis and lumbar facet joint syndrome through precise thermal ablation. Additionally, FUS has shown potential in neuropathic pain management and peripheral nerve stimulation, offering innovative approaches for amputees and cancer survivors. Cognitive and neuromodulatory research underscores its ability to enhance motor function and interhemispheric cortical balance, benefiting stroke and traumatic brain injury rehabilitation. Despite these conditions frequently leading to various kinds of disabilities, no direct exploration of the possible FUS application in rehabilitation is yet available in the literature. All this considered, this review aims to discuss how FUS could be applied in rehabilitation, exploring the current status of knowledge and highlighting future directions.

Bongsu Kang, Jundong Kim, Tae-Rim Yun et al.

This study quantitively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with Claude 3 Opus and focusing on eight features -- metacognitive self-reflection, logical reasoning, empathy, emotionality, knowledge, fluency, unexpectedness, and subjective expressiveness -- we conducted a survey with 123 participants. Using regression and clustering analyses, we investigated how these features influence participants' perceptions of AI consciousness. The results reveal that metacognitive self-reflection and the AI's expression of its own emotions significantly increased perceived consciousness, while a heavy emphasis on knowledge reduced it. Participants clustered into seven subgroups, each showing distinct feature-weighting patterns. Additionally, higher prior knowledge of LLMs and more frequent usage of LLM-based chatbots were associated with greater overall likelihood assessments of AI consciousness. This study underscores the multidimensional and individualized nature of perceived AI consciousness and provides a foundation for better understanding the psychosocial implications of human-AI interaction.

Francesco Chiossi, Julian Rasch, Robin Welsch et al.

As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we propose a multidimensional design space that maps intent content (SAT1, SAT3), planning horizon (operational to strategic), and modality (visual, auditory, haptic). We illustrate how this space can guide the design of multimodal communication strategies tailored to dynamic collaborative work contexts. With this paper, we lay the conceptual foundation for a future design toolkit aimed at supporting transparent human-robot interaction in the workplace. We highlight key open questions and design challenges, and propose a shared agenda for multimodal, adaptive, and trustworthy robotic collaboration in hybrid work environments.

Parag Khanna, Andreas Naoum, Elmira Yadollahi et al.

This work presents REFLEX: Robotic Explanations to FaiLures and Human EXpressions, a comprehensive multimodal dataset capturing human reactions to robot failures and subsequent explanations in collaborative settings. It aims to facilitate research into human-robot interaction dynamics, addressing the need to study reactions to both initial failures and explanations, as well as the evolution of these reactions in long-term interactions. By providing rich, annotated data on human responses to different types of failures, explanation levels, and explanation varying strategies, the dataset contributes to the development of more robust, adaptive, and satisfying robotic systems capable of maintaining positive relationships with human collaborators, even during challenges like repeated failures.

Ziyu Zhou, Haozhe Luo, Mohammad Reza Hosseinzadeh Taher et al.

Foundation models have been successful in natural language processing and computer vision because they are capable of capturing the underlying structures (foundation) of natural languages. However, in medical imaging, the key foundation lies in human anatomy, as these images directly represent the internal structures of the body, reflecting the consistency, coherence, and hierarchy of human anatomy. Yet, existing self-supervised learning (SSL) methods often overlook these perspectives, limiting their ability to effectively learn anatomical features. To overcome the limitation, we built Lamps (learning anatomy from multiple perspectives via self-supervision) pre-trained on large-scale chest radiographs by harmoniously utilizing the consistency, coherence, and hierarchy of human anatomy as the supervision signal. Extensive experiments across 10 datasets evaluated through fine-tuning and emergent property analysis demonstrate Lamps' superior robustness, transferability, and clinical potential when compared to 10 baseline models. By learning from multiple perspectives, Lamps presents a unique opportunity for foundation models to develop meaningful, robust representations that are aligned with the structure of human anatomy.

Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose a framework that uses Large Language Models (LLMs) for ambiguity detection specifically designed for collaborative surgical scenarios. Our method employs an ensemble of LLM evaluators, each configured with distinct prompting techniques to identify linguistic, contextual, procedural, and critical ambiguities. A chain-of-thought evaluator is included to systematically analyze instruction structure for potential issues. Individual evaluator assessments are synthesized through conformal prediction, which yields non-conformity scores based on comparison to a labeled calibration dataset. Evaluating Llama 3.2 11B and Gemma 3 12B, we observed classification accuracy exceeding 60% in differentiating ambiguous from unambiguous surgical instructions. Our approach improves the safety and reliability of human-robot collaboration in surgery by offering a mechanism to identify potentially ambiguous instructions before robot action.

Jean C Kanitakis

Andrzej Węgiel, Nicol Zielinska, Łukasz Gołek et al.

The extensor digitorum longus is a source of much anatomic variation, mostly related with extra tendinous slips or their unusual insertions. This report describes a new configuration of the extensor digitorum longus with two heads and two main tendons which bifurcate into five slips. These slips undergo further divisions and establish connections between the each other. Our findings provide a greater insight into the intricacies of human morphology.

Ana Luísa Pereira, Serena Galli, César Nombela‐Arrieta

Abstract Hematopoietic stem cells (HSCs) are the cornerstone of the hematopoietic system. HSCs sustain the continuous generation of mature blood derivatives while self‐renewing to preserve a relatively constant pool of progenitors throughout life. Yet, long‐term maintenance of functional HSCs exclusively takes place in association with their native tissue microenvironment of the bone marrow (BM). HSCs have been long proposed to reside in fixed and identifiable anatomical units found in the complex BM tissue landscape, which control their identity and fate in a deterministic manner. In the last decades, tremendous progress has been made in the dissection of the cellular and molecular fabric of the BM, the structural organization governing tissue function, and the plethora of interactions established by HSCs. Nonetheless, a holistic model of the mechanisms controlling HSC regulation in their niche is lacking to date. Here, we provide an overview of our current understanding of BM anatomy, HSC localization, and crosstalk within local cellular neighborhoods in murine and human tissues, and highlight fundamental open questions on how HSCs functionally integrate in the BM microenvironment.

Sabrina Conejeros-Lillo, Francisco Aguirre, Daniel Cabrera et al.

Sarcopenia is characterized by reduced muscle strength and mass and a decline in muscle fiber diameter and amount of sarcomeric proteins. Sarcopenia involves the activation of the ubiquitin-proteasome system (UPS). MuRF-1 and atrogin-1 are E3 ubiquitin ligases belonging to UPS, leading to proteolysis mediated by the PSMB 5, 6, and 7 subunits of 20S proteasome. CCL5/RANTES induces a sarcopenic-like effect in muscle cells. The present work explored the impact of CCL5 on UPS components and the influence of UPS on its sarcopenic-like effect. We demonstrated that CCL5 increased MuRF-1 and atrogin-1 protein levels and mRNA levels of subunits PSMB 5, 6, and 7. We used the MG132 inhibitor to elucidate the role of the 20S proteasome in the CCL5-induced sarcopenic-like effect. This inhibitor prevented the decrease in troponin and MHC protein levels and partially prevented the reduction in the diameter of single-isolated FDB muscle fibers induced by CCL5. These findings indicate that CCL5 actively modulates the UPS. Moreover, our results show the direct participation of UPS in the sarcopenic-like phenotype induced by CCL5.

Md. Shazzad Hossain Shaon, Tasmin Karim, Md. Fahim Sultan et al.

Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Tinghe Ding

Agents centered around Large Language Models (LLMs) are now capable of automating mobile device operations for users. After fine-tuning to learn a user's mobile operations, these agents can adhere to high-level user instructions online. They execute tasks such as goal decomposition, sequencing of sub-goals, and interactive environmental exploration, until the final objective is achieved. However, privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation. Moreover, users' real-world operations are exploratory, with action data being complex and redundant, posing challenges for agent learning. To address these issues, in our practical application, we have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs. Additionally, we integrated Standard Operating Procedure (SOP) information within the model's in-context learning to enhance the agent's comprehension of complex task execution. Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks, including application operation, web searching, and web shopping. Experimental results show that the SOP-based agent achieves state-of-the-art performance in LLMs without incurring additional inference costs, boasting an overall action success rate of 66.92\%. The code and data examples are available at https://github.com/alipay/mobile-agent.

Vaibhav Balloli, Sara Beery, Elizabeth Bondi-Kelly

Image retrieval plays a pivotal role in applications from wildlife conservation to healthcare, for finding individual animals or relevant images to aid diagnosis. Although deep learning techniques for image retrieval have advanced significantly, their imperfect real-world performance often necessitates including human expertise. Human-in-the-loop approaches typically rely on humans completing the task independently and then combining their opinions with an AI model in various ways, as these models offer very little interpretability or \textit{correctability}. To allow humans to intervene in the AI model instead, thereby saving human time and effort, we adapt the Concept Bottleneck Model (CBM) and propose \texttt{CHAIR}. \texttt{CHAIR} (a) enables humans to correct intermediate concepts, which helps \textit{improve} embeddings generated, and (b) allows for flexible levels of intervention that accommodate varying levels of human expertise for better retrieval. To show the efficacy of \texttt{CHAIR}, we demonstrate that our method performs better than similar models on image retrieval metrics without any external intervention. Furthermore, we also showcase how human intervention helps further improve retrieval performance, thereby achieving human-AI complementarity.

Myint Zu Than, Kian Meng Yap

Modern medicine demands innovations in medical education, particularly in the learning of human anatomy, traditionally taught through textbooks, dissections, and lectures. Virtual Reality (VR) has emerged as a promising tool to address the limitations of these conventional methods by emphasising vision-based and active learning. However, current VR educational tools are often inaccessible due to high costs and specialised equipment requirements. This paper details the design and development of an accessible, desktop-based VR system aimed at enhancing anatomy education by leveraging the user's visual perception to promote a meaningful and interactive learning experience. The Virtual Anatomy Lab was designed to enable students to interact with a 3D Skull model to complete tasks virtually via an interactive user interface (UI) with the help of common devices like a mouse and keyboard. As part of the study, a group of medical students from prestigious medical schools throughout Malaysia were invited to evaluate the built system to offer feedback and determine its overall efficiency and usability in fulfilling their learning goals. The results and findings from user evaluations were then analysed to discuss its effectiveness and areas for future improvement.