Hasil untuk "Human anatomy"

Daeun Hwang, Hyeonbin Hwang

There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, focusing on the three commonly used musical features in similarity analysis: melody, rhythm, and chord progression. After identifying the key features and levels of variation humans use in perceiving musical similarity, we propose a LLM-as-a-judge framework that applies a systematic, step-by-step approach, drawing on modules that extract such high-level attributes.

Javier Barranco Garcia, Thomas Ferrazzini, Ana Coito et al.

<b>Purpose:</b> This study evaluates a novel, non-invasive method using a virtual reality (VR) headset with integrated eye trackers to assess retinal function by measuring the recovery of the pupillary response after light adaptation in patients with age-related macular degeneration (AMD). <b>Methods:</b> In this pilot study, fourteen patients with clinically confirmed AMD and 14 age-matched healthy controls were exposed to alternating bright and dark stimuli using a VR headset. The dark stimulus duration increased incrementally by 100 milliseconds per trial, repeated over 50 cycles. The pupillary response to the re-onset of brightness was recorded. Data were analyzed using a linear mixed-effects model to compare recovery patterns between groups and a convolutional neural network to evaluate diagnostic accuracy. <b>Results:</b> The pupillary response amplitude increased with longer dark stimuli, i.e., the longer the eye was exposed to darkness the bigger was the subsequent pupillary amplitude. This pupillary recovery was significantly slowed by age and by the presence of macular degeneration. Test diagnostic accuracy for AMD was approximately 92%, with a sensitivity of 90% and a specificity of 70%. <b>Conclusions:</b> This proof-of-concept study demonstrates that consumer-grade VR headsets with integrated eye tracking can detect retinal dysfunction associated with AMD. The method offers a fast, accessible, and potentially scalable approach for retinal disease screening and monitoring. Further optimization and validation in larger cohorts are needed to confirm its clinical utility.

Tsvetelina Andonova, Yordan Muhovski, Samir Naimov et al.

Antioxidants from natural sources are essential for the development of new therapeutics to improve human health. The objects of study are the aerial flowering parts of <i>Potentilla argentea</i>, a plant species known in traditional medicine for the astringent, hemostatic, wound-healing, and anti-inflammatory effects of its rhizomes. A <i>Potentillae argenteae herba</i> ethanol dry tincture was chromatographically analyzed (GC/MS, HPLC) and its antioxidant (ABTS, DPPH, CUPRAC, FRAP assays) and DNA nicking protection potentials were evaluated. The eighteen volatiles were identified by GC/MS, where the predominant components were n-nonacosane (39.38 mg/g dt), squalene (28.88 mg/g dt), n-tricosane (18.36 mg/g dt), ethyl oleate (15.24 mg/g dt), and n-pentacosane (10.60 mg/g dt). A high content of total polyphenols was obtained (440.78 mg GAE/g dt), and HPLC analysis identified two flavonoids and three phenolic acids, of which rosmarinic acid and rutin were above 10 mg/g dt. The tincture exhibited strong antioxidant activity by all four methods, especially CUPRAC assay (8617.54 μM TE/g). DNA protective activity against oxidative damage and microscopic identification of <i>P. argenteae herba</i> powder were established for the first time. Therefore, the tincture could be incorporated into phytopreparations for the treatment of human diseases caused by reactive oxygen species.

Angjelin Hila

Emerging paradigms in XR, AI, and BCI contexts necessitate novel theoretical frameworks for understanding human autonomy and agency in HCI. Drawing from enactivist theories of cognition, we conceptualize human agents as self-organizing, operationally closed systems that actively enact their cognitive domains through dynamic interaction with their environments. To develop measurable variables aligned with this framework, we introduce "feelings of agency" (FoA) as an alternative to the established construct of "sense of agency" (SoA), refining Synofzyk's multifactorial weighting model and offering a novel conceptual pathway for overcoming gaps in the dominant comparator model. We define FoA as comprising two subconstructs: affective engagement and volitional attention, which we operationalize through integrated neurodynamic indicators (valence, arousal, cross frequency coupling within the dorsal attention system) and first-person phenomenological reports. We argue that these neurophenomenological indicators provide richer, more actionable insights for digital affordance design, particularly in XR, BCI, Human AI Interaction (HAX), and generative AI environments. Our framework aims to inform and inspire design parameters that significantly enhance human agency in rapidly evolving interactive domains.

Zhi Qin Tan, Xiatian Zhu, Owen Addison et al.

Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurate segmentation of these anatomies is critical for clinical applications such as diagnosis and surgical planning, but remains time-consuming and challenging. In this paper, we present U-Mamba2, a new neural network architecture designed for multi-anatomy CBCT segmentation in the context of the ToothFairy3 challenge. U-Mamba2 integrates the Mamba2 state space models into the U-Net architecture, enforcing stronger structural constraints for higher efficiency without compromising performance. In addition, we integrate interactive click prompts with cross-attention blocks, pre-train U-Mamba2 using self-supervised learning, and incorporate dental domain knowledge into the model design to address key challenges of dental anatomy segmentation in CBCT. Extensive experiments, including independent tests, demonstrate that U-Mamba2 is both effective and efficient, securing first place in both tasks of the Toothfairy3 challenge. In Task 1, U-Mamba2 achieved a mean Dice of 0.84, HD95 of 38.17 with the held-out test data, with an average inference time of 40.58s. In Task 2, U-Mamba2 achieved the mean Dice of 0.87 and HD95 of 2.15 with the held-out test data. The code is publicly available at https://github.com/zhiqin1998/UMamba2.

Róisín Keenan, Joost C. Dessing

Recent advancements in robotics have increased the possibilities for integrating robotic systems into human-involved workplaces, highlighting the need to examine and optimize human-robot coordination in collaborative settings. This study explores human-robot interactions during handover tasks using Virtual Reality (VR) to investigate differences in human motor performance across various task dynamics and robot kinematics. A VR-based robot handover simulation afforded safe and controlled assessments of human-robot interactions. In separate experiments, four potential influences on human performance were examined (1) control over task initiation and robot movement synchrony (temporal and spatiotemporal); (2) partner appearance (human versus robotic); (3) robot velocity profiles (minimum jerk, constant velocity, constant acceleration, and biphasic); and (4) the timing of rotational object motion. Findings across experiments emphasize humans benefit from robots providing early and salient visual information about task-relevant object motion, and advantages of human-like smooth robot trajectories. To varying degrees, these manipulations improved predictive accuracy and synchronization during interaction. This suggests that human-robot interactions should be designed to allow humans to leverage their natural capabilities for detecting biological motion, which conversely may reduce the need for costly robotic computations or added cognitive adaptation on the human side.

Bing Wang, Jing Li, Yanyang Song et al.

Abstract Intron retention (IR) constitutes a less explored form of alternative splicing, wherein introns are retained within mature mRNA transcripts. This investigation demonstrates that the cell division cycle (CDC)‐like kinase 2 (CLK2) undergoes liquid–liquid phase separation (LLPS) within nuclear speckles in response to heat shock (HS). The formation of CLK2 condensates depends on the intrinsically disordered region (IDR) located within the N‐terminal amino acids 1‐148. Phosphorylation at residue T343 sustains CLK2 kinase activity and promotes overall autophosphorylation, which inhibits the LLPS activity of the IDR. These CLK2 condensates initiate the reorganization of nuclear speckles, transforming them into larger, rounded structures. Moreover, these condensates facilitate the recruitment of splicing factors into these compartments, restricting their access to mRNA for intron splicing and promoting the IR. The retained introns lead to the sequestration of transcripts within the nucleus. These findings extend to the realm of glioma stem cells (GSCs), where a physiological state mirroring HS stress inhibits T343 autophosphorylation, thereby inducing the formation of CLK2 condensates and subsequent IR. Notably, expressing the CLK2 condensates hampers the maintenance of GSCs. In conclusion, this research unveils a mechanism by which IR is propelled by CLK2 condensates, shedding light on its role in coping with cellular stress.

A Cuadrado, LM Sanchez-Brea, FJ Torcal-Milla et al.

We present in this work a numerical model for characterizing the scattering properties of the human lens. After analyzing the scattering properties of two main scattering particles actually described in the literature through Finite Element Method simulations, we have modified a Monte Carlo bulk scattering algorithm for computing ray scattering in non-sequential ray tracing. We have implemented this ray scattering algorithm in a layered model of the human lens in order to calculate the scattering properties of the whole lens. We have tested our algorithm by simulating the classic experiment carried out by Van der Berg et al for measuring in vitro the angular distribution of forward scattered light by the human lens. The results show the ability of our model to simulate accurately the scattering properties of the human lens.

Mohammad Hossein Jarrahi, Stanley Ahalt

This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks such as the Turing test, which emphasize AI's ability to mimic human intelligence, and instead advocate for a symbiotic relationship where distinct intelligences enhance each other. We analyze key elements of human-horse relationships: trust, communication, and mutual adaptability, to highlight essential principles for human-AI collaboration. Trust is critical in both partnerships, built through predictability and shared understanding, while communication and feedback loops foster mutual adaptability. We further discuss the importance of taming and habituation in shaping these interactions, likening it to how humans train AI to perform reliably and ethically in real-world settings. The article also addresses the asymmetry of responsibility, where humans ultimately bear the greater burden of oversight and ethical judgment. Finally, we emphasize that long-term commitment and continuous learning are vital in both human-horse and human-AI relationships, as ongoing interaction refines the partnership and increases mutual adaptability. By drawing on these insights from human-horse interactions, we offer a vision for designing AI systems that are trustworthy, adaptable, and capable of fostering symbiotic human-AI partnerships.

Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch et al.

People's decision-making abilities often fail to improve or may even erode when they rely on AI for decision-support, even when the AI provides informative explanations. We argue this is partly because people intuitively seek contrastive explanations, which clarify the difference between the AI's decision and their own reasoning, while most AI systems offer "unilateral" explanations that justify the AI's decision but do not account for users' thinking. To align human-AI knowledge on decision tasks, we introduce a framework for generating human-centered contrastive explanations that explain the difference between AI's choice and a predicted, likely human choice about the same task. Results from a large-scale experiment (N = 628) demonstrate that contrastive explanations significantly enhance users' independent decision-making skills compared to unilateral explanations, without sacrificing decision accuracy. Amid rising deskilling concerns, our research demonstrates that incorporating human reasoning into AI design can foster human skill development.

Raj Korpan

Many measures of human-robot trust have proliferated across the HRI research literature because each attempts to capture the factors that impact trust despite its many dimensions. None of the previous trust measures, however, address the systems of inequity and structures of power present in HRI research or attempt to counteract the systematic biases and potential harms caused by HRI systems. This position paper proposes a participatory and social justice-oriented approach for the design and evaluation of a trust measure. This proposed process would iteratively co-design the trust measure with the community for whom the HRI system is being created. The process would prioritize that community's needs and unique circumstances to produce a trust measure that accurately reflects the factors that impact their trust in a robot.

Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh et al.

Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human experts. However, a deeper look into several of these systems reveals that calling them HIL would be a misnomer, as they are quite the opposite, namely AI-in-the-loop ($AI^2L$) systems, where the human is in control of the system, while the AI is there to support the human. We argue that existing evaluation methods often overemphasize the machine (learning) component's performance, neglecting the human expert's critical role. Consequently, we propose an $AI^2L$ perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an $AI^2L$ approach, we can develop more comprehensive systems that faithfully model the intricate interplay between the human and machine components, leading to more effective and robust AI systems.

Lindsey Alejandra Quintero Sierra, Reetuparna Biswas, Alice Busato et al.

Mesenchymal stem cells extracted from adipose tissue are particularly promising given the ease of harvest by standard liposuction and reduced donor site morbidity. This study proposes a novel enzymatic method for isolating stem cells using <i>Vibrio alginolyticus</i> collagenase, obtaining a high-quality product in a reduced time. Initially, the enzyme concentration and incubation time were studied by comparing cellular yield, proliferation, and clonogenic capacities. The optimized protocol was phenotypically characterized, and its ability to differentiate in the mesodermal lineages was evaluated. Subsequently, that protocol was compared with two <i>Clostridium histolyticum</i>-based collagenases, and other tests for cellular integrity were performed to evaluate the enzyme’s effect on expanded cells. The best results showed that using a concentration of 3.6 mg/mL <i>Vibrio alginolyticus</i> collagenase allows extracting stem cells from adipose tissue after 20 min of enzymatic reaction like those obtained with <i>Clostridium histolyticum</i>-based collagenases after 45 min. Moreover, the extracted cells with <i>Vibrio alginolyticus</i> collagenase presented the phenotypic characteristics of stem cells that remain after culture conditions. Finally, it was seen that <i>Vibrio alginolyticus</i> collagenase does not reduce the vitality of expanded cells as <i>Clostridium histolyticum</i>-based collagenase does. These findings suggest that <i>Vibrio alginolyticus</i> collagenase has great potential in regenerative medicine, given its degradation selectivity by protecting vital structures for tissue restructuration.

N B Pushpa, Apurba Patra, Kumar Satish Ravi

Huaizheng Mu, Liangqi Yuan, Jia Li

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability.

Zihan Ding, Yuanpei Chen, Allen Z. Ren et al.

Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands. Scripting policies from scratch is intractable due to the high-dimensional control space, and training policies with reinforcement learning (RL) and manual reward engineering can also be hard and lead to unnatural motions. Leveraging the recent progress on RL from Human Feedback, we propose a framework that learns a universal human prior using direct human preference feedback over videos, for efficiently tuning the RL policies on 20 dual-hand robot manipulation tasks in simulation, without a single human demonstration. A task-agnostic reward model is trained through iteratively generating diverse polices and collecting human preference over the trajectories; it is then applied for regularizing the behavior of polices in the fine-tuning stage. Our method empirically demonstrates more human-like behaviors on robot hands in diverse tasks including even unseen tasks, indicating its generalization capability.

Malte Hoffmann, Andrew Hoopes, Douglas N. Greve et al.

Affine image registration is a cornerstone of medical image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance for image types unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content that may impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain MRI.

Xiaotian Zhao, Wenjia Liang, Wenjun Wang et al.

Proteomic analysis of human early fetal brain tissue is undertaken to investigate bilateral developmental changes of protein expression and left-right asymmetries of protein expression.

Walaa Yehia Abdelzaher, Mohamed A. Nassan, Sabreen Mahmoud Ahmed et al.

The current research aimed to examine the ameliorative role of febuxostat (FEB), a highly potent xanthine oxidase inhibitor, against 5-fluorouracil (5-FU)-induced parotid salivary gland damage in rats, as FEB is a pleiotropic drug that has multiple pharmacological effects. A total of 32 Wistar adult male rats were randomly arranged into four groups. Group 1: the control group; given only the vehicle for 14 days, then given a saline i.p. injection from the 10th to the 14th day. Group 2: the FEB group; rats received FEB (10 mg/kg) once daily po for 14 days before receiving a saline i.p. injection from the 10th to the 14th day. Group 3: the 5-FU group; from the 10th to the 14th day, rats received an intraperitoneal injection of 5-FU (35 mg/kg/day). Group 4: the FEB/5-FU group; rats were pre-treated with FEB po for 14 days before receiving 5-FU i.p injections for five consecutive days from the 10th to the 14th day. Parotid gland damage was detected histologically and biochemically by the evaluation of oxidative stress markers (malondialdehyde (MDA) and nitric oxide levels (NOx)), oxidant defences (reduced glutathione (GSH) and superoxide dismutase (SOD)), inflammatory markers (tumour necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β)), and transient receptor potential canonical1 (TRCP1) and C/EBP homologous protein (CHOP). FEB pre-treatment reduced MDA, TNF-, and IL-1 while increasing SOD, GSH, and NOx. FEB also significantly increased TRPC1 and decreased CHOP in parotid gland tissue. In conclusion, FEB pre-treatment reduced 5-FU-induced parotid salivary gland damage not only through its powerful anti-inflammatory and antioxidant effects, but also through its effect on the TRPC1/CHOP signalling pathway.