HLM-MOCAP is a motion capture dataset of context-dependent human arm movements designed to support the generation of human-like robot trajectories in industrial and collaborative settings. Data were recorded in a laboratory environment using an eight-camera infrared motion capture system (Vicon MX T10) operating at 200 Hz with 1120 × 896 px resolution and passive 1.5 mm retroreflective markers. Forty healthy adults (29 male, 11 female; broad age range from late adolescence to adulthood) performed a set of seven upper-limb tasks at a standardized workstation with marked start and target locations and physical objects of varying weight and size. The tasks comprise point-to-point transport to different distances and directions, sequential reaching to multiple targets, two trajectory-tracing tasks (zigzag and circle) with the index finger, a grasp-and-place task with arm crossing, a reaching task with load, and a high-precision placement task using small screws. Each participant completed 54 movement executions (with task-specific repetition counts), yielding 2160 recorded movement trials in total. The dataset includes raw 3D marker trajectories for all trials, segmented to active movement phases based on an objective velocity threshold, and processed versions after gap filling of short occlusions, time normalization to 0-100 % movement progress, and Savitzky-Golay filtering for derivation of stable velocity and acceleration profiles. Data are organized as comma-separated files with participant identifiers, task labels, target indices, object properties, and repetition indices, accompanied by scripts for loading and basic preprocessing. The resource can be reused for developing and benchmarking methods for human-like robot motion planning, learning from demonstration, and trajectory generation, as well as for analyzing how distance, sequence structure, load, and precision demands shape human arm kinematics in the context of human-robot collaboration.
Human object recognition is robust to challenging conditions, such as when one's view of an object is fragmented due to an occluding foreground object. In comparison, deep neural networks (DNNs) are typically more susceptible to occlusion, suggesting that human vision relies on distinct mechanisms. Here, we investigated the role of visual diet in the emergence of these mechanisms by asking whether human-like robustness might arise in DNNs when trained with image datasets that better reflect the properties of occlusion in natural vision. We trained convolutional and transformer DNNs to classify clear images only, images augmented with artificial occluders (i.e., geometric shapes) or natural occluders (objects segmented from photographs). We then evaluated DNN occlusion robustness and compared their performance profiles with 30 human participants. We found that DNNs trained with artificial occluders remained vulnerable to natural occlusion and exhibited less human-like performance than those trained with natural occlusion. Our findings suggest that human robustness to visual occlusion arises from learning to disentangle natural objects from each other rather than simply learning to recognize objects from partial views. They also imply that commonly used forms of artificial occlusion are unsuitable for the evaluation or promotion of robustness to real-world occlusion in DNNs.
While multimodal fake news detection methods have made progress in aligning multimodal semantics, they still face significant challenges in analyzing background context, emotional tone, and the overall plausibility of news content. To address these limitations, we propose a novel human-like collaborative framework for multimodal fake news detection, which integrates large and small models. Specifically, we exploit large vision-language models (LVLMs) to perform deep semantic analysis and reflective summarization of news cues. By leveraging the contextual understanding, knowledge recall, and logical reasoning capabilities of large models, the proposed approach improves the accuracy and reliability of fake news detection. It comprises three key components: 1) designing a chain-of-thought (CoT) prompting strategy for the LVLM to analyze news content, including evaluating image credibility, identifying potential tampering, extracting linguistic styles, detecting emotional tones, uncovering logical connections within the text, and verifying factual accuracy; 2) independently reflecting on and summarizing the lengthy analytical outputs from both image and text modalities to reduce redundancy. The resulting summary is then encoded into compact representations using pretrained text encoders and integrated with the original multimodal features; and 3) proposing a progressive fusion mechanism that enables collaboration between large and small models, allowing effective utilization of deeply fused features at the surface level. Extensive experiments conducted on three benchmark multimodal fake news datasets demonstrate the effectiveness and robustness of the proposed method, consistently outperforming state-of-the-art baselines in multimodal fake news detection tasks. The code is available at https://github.com/xxx.
Collagen, a key extracellular matrix protein, regulates cell adhesion, proliferation, and tissue integrity for skin health. In this study, a novel human-like type III collagen was engineered by substituting hydrophobic GXY triplets in the core fragment (Gly201-Asn498) of the human α1(III)type chain with hydrophilic motifs (GSP, GQP, GEP, and GSQ) and incorporating integrin-binding sequences (GER and RGD) at the C-terminus of the two consecutive sequence units. The codon-optimized gene was cloned into the pPIC9K vector, and transformed into Komagataella phaffii (Pichia pastoris) GS115. Multicopy transformants were selected by various G418 concentrations. Furthermore, a high yield of recombinant collagen at 19.4 g/L was achieved by fed-batch fermentation in a 5-L bioreactor. The purified protein exhibited excellent thermostability, remaining fully soluble after heating at 121 °C for 30 min. The collagen migrated on 10% SDS-PAGE with an apparent molecular weight approximately 2.18-fold greater than its theoretical mass, suggesting extensive hydration. Functionally, it significantly promoted the proliferation of human skin fibroblasts (HSF) and immortalized keratinocytes (HaCaT). In HSF cells, it upregulated the expression of COL1A1, COL3A1, and TIMP1, while in HaCaT cells, it enhanced transcription of skin barrier-related genes, including KRT1, KRT5, KRT10, KRT14, IVL, LOR, and FLG. This designed collagen integrates high-yield production, thermostability, and dual bioactivity for dermal regeneration and epidermal barrier reinforcement, showing a promising application for skincare and tissue engineering.
Symmetry, a fundamental concept in nature, science and art, has challenged computer vision researchers because it occurs in various forms and human symmetry perception can deviate from the mathematical definition. Previous symmetry detection datasets are limited by the number of annotators and by missing the nuances of human perception. We introduce PIX2PER, a novel dataset for reflection symmetry in natural scenes and artworks. We also introduce WF1, a modified version of the widely-used F1 detection performance score, by adding weights to precision and recall to accommodate for the perceived symmetry strength. Created by adding weights to precision and recall to accommodate for the perceived symmetry strength. We perform a comparative analysis of existing models for symmetry detection on this human-centric dataset. Additionally, we present a fully synthetic dataset for pretraining symmetry detection models. When finetuning this pretrained model with human data, performance increases significantly. This research introduces and evaluates ways of improving symmetry detection and contributes to the development of computer vision models that more effectively represent human perception.
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Human l-asparaginase 1 (hASNase1) is a promising next-generation candidate for Acute Lymphoblastic Leukemia (ALL) therapy due to its reduced immunogenicity and superior physiological compatibility. However, its recombinant production remains challenging, as expression in Escherichia coli typically results in insoluble aggregates and strong association with host chaperones, limiting biochemical characterization. Here, we established an optimized workflow combining mild induction conditions with tailored solubilization and purification strategies. Incubation with l-asparagine followed by ATP-mediated washing during affinity chromatography significantly improved protein recovery while reducing chaperone co-purification. This approach enabled the isolation of catalytically active hASNase1 displaying allosteric regulation and cooperative substrate binding, consistent with its proposed tetrameric organization. Complementary in silico analyses identified aggregation-prone regions, potentially providing structural insight into the observed expression challenges. Collectively, this study contributes to the development of improved recombinant production strategies for challenging human enzymes in prokaryotic systems.
This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus-Organism-Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone's banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = -0.013, p = 0.760). Instead, its influence is entirely indirect-transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism-shaped by institutional fragility-functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism's conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human-AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies.
Deep convolutional neural networks (DCNNs) have approached and even surpassed human-level performance on many visual recognition tasks, yet they remain strikingly vulnerable to near-imperceptible perturbations generated by adversarial attacks. Recent research demonstrates that aligning DCNN representations with human visual cortex activity improves adversarial robustness, but the mechanisms driving this advantage are yet to be understood. One hypothesis suggests that neural alignment confers robustness by biasing models away from brittle high-frequency details and towards the low spatial frequencies (LSF). However, recent work indicates that human object recognition critically depends on a narrow, mid-frequency "human channel". Interestingly, this band was partially preserved in prior LSF-focused studies. In this work, we explicitly investigate whether a spectral bias towards the LSF or the human channel is the primary driver of the adversarial robustness observed in neurally aligned DCNNs. We first show that DCNNs aligned to higher-order regions of the human ventral visual stream systematically increase their reliance on both the LSF and the human channel. However, directly steering DCNNs towards these bands revealed a clear dissociation. Biasing models towards the human channel, either alone or together with the LSF, does not improve robustness and can even impair it. LSF bias produced some robustness gains, but such improvements are modest despite inducing much larger shifts in spatial-frequency reliance than the neurally-aligned DCNNs. Spatial-frequency-biased models overall show little, if any, increase in similarity to human neural representational geometry. Together, our results suggest that altered spatial-frequency reliance is likely an emergent property of learning more human-like representations rather than the primary mechanism by which neural alignment confers adversarial robustness, and motivate the need for future research examining representational properties beyond spatial-frequency biases.
Understanding humor, especially when it involves complex and contradictory narratives requiring comparative reasoning ability, remains a significant challenge for large vision language models (VLMs). This limitation hinders AI's ability to engage in human-like reasoning and cultural expression. In this paper, we investigate this challenge through an in depth analysis of comics that juxtapose panels to create humor through contradictions. We introduce the YESBUT, a novel benchmark with 1,262 comic images from diverse multilingual and multicultural contexts, featuring comprehensive annotations that capture various aspects of narrative understanding. Using this benchmark, we systematically evaluate a wide range of VLMs through four complementary tasks spanning from surface content comprehension to deep narrative reasoning, with particular emphasis on comparative reasoning between contradictory elements. Our extensive experiments reveal that even the most advanced models significantly underperform compared to humans, with common failures in visual perception, key element identification, comparative analysis, and hallucinations. We further investigate text-based training strategies and social knowledge augmentation methods to enhance model performance. Our findings not only highlight critical weaknesses in VLMs' understanding of cultural and creative expressions but also provide pathways toward developing context-aware models capable of deeper narrative understanding through comparative reasoning.
Can large language models (LLMs) be viewed as a cognitive model of human language? Do they possess human-like language competence? To address these questions, this study takes a multifaceted approach, comparing the performance of 10 recent LLMs (n = 4000 responses) and 94 humans (n = 3760 responses) on grammaticality judgments and sentence interpretations, focusing on five linguistic phenomena that involve missing material. The analyses show that while the LLMs appeared to differentiate between grammatical/possible and ungrammatical/impossible sentences/interpretations overall, they struggled with infrequent phenomena (e.g., Gapping, Sluicing), often rejecting grammatical sentences and accepting impossible interpretations. Notably, increased size seemed to improve their performance on grammaticality judgments, but neither size nor reasoning capability improved their performance on interpretation. In contrast, humans demonstrated a clear sensitivity to these distinctions. The findings seem to align with the view that LLMs, in their current form, lack language competence and do not provide a convincing explanation of human language.
By eliciting lung necrosis, which enhances aerosol transmission, Mycobacterium tuberculosis (Mtb) sustains its long-term survival as a human pathogen. In studying the human-like necrotic granuloma lesions characteristic of Mtb-infected B6.Sst1S mice, we found that lung myeloid cells display elevated senescence markers: cell cycle arrest proteins p21 and p16, the DNA damage marker γH2A.X, senescence-associated β-galactosidase activity, and senescence-associated secretory phenotype (SASP). These markers were also elevated in Mtb-infected aged wild type (WT) mice but not in young WT mice. Global transcriptomics data revealed upregulation of pro-survival (PI3K, MAPK) and anti-apoptotic pathways in Mtb-infected B6.Sst1S macrophages. As senescent cells are terminally growth-arrested yet metabolically active cells that release tissue-damaging, immunosuppressive SASP, we treated Mtb-infected mice with a cocktail of three senolytic drugs (dasatinib, quercetin, and fisetin) designed to kill senescent cells. Adjunctive senolytic drug treatment in presence of anti-tuberculosis (TB) therapy prolonged survival and reduced Mtb lung counts in B6.Sst1S and aged WT mice to a greater degree than young WT mice and concomitantly reduced lung pathology and senescence markers. These findings indicate that (1) Mtb infection induce lung myeloid cells to enter a senescent state and that these cells may promote disease progression, and (2) senolytic drugs merit consideration for human clinical trials against TB.
Artificial intelligence is increasingly capable of expressing empathy through language, yet the integration of physical touch-an important cue for social connection-remains fragmented. Although robots utilise language or touch individually, few systems coordinate both modalities, potentially limiting their capacity for affective human-robot interaction (HRI). This scoping review maps social robots that combine spoken language and tactile interaction (e.g., hugging, stroking, warmth, vibration), examines how these modalities are coordinated in existing systems, and synthesises reported user outcomes and design implications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, searches across five databases (IEEE Xplore, PubMed, ACM, Web of Science, Scopus) and supplementary web sources identified 11 distinct HRI implementations that pair speech with active or invited touch. Of these, eight implementations included explicit comparison conditions (e.g., speech-only vs. speech + touch, or touch-only vs. touch + speech), enabling assessment of the added value of combining modalities. Across comparative studies, combining speech and touch showed potential to be more effective than speech-only or touch-only HRI in some contexts. This integration can make robots appear more caring, empathic, and human-like, while strengthening attachment, increasing willingness to self-disclose, and helping users feel calmer (e.g., lower heart rate). However, outcomes were implementation-dependent, with some studies reporting no additional benefit from the combined modalities. Across the evidence base, the review found a consistent suggestive pattern that warm (e.g., near skin temperature), soft, naturalistic touch tends to support more positive affective HRI outcomes than cold, rigid, "mechanical" touch. The evidence base was also largely dominated by short, lab-based studies using existing, typically rigid robotic platforms not purpose-built for affective speech-touch interaction. Speech-touch integration in social HRI is a small but promising area, particularly for healthcare and emotional-support applications (e.g., supporting children in hospital). Despite this potential, very few robots are purpose-built for coordinated speech and touch. Affective speech-touch HRI remains challenging because of its psychological, socio-cultural, and engineering demands. Progress will likely require soft, safe, warm, and increasingly autonomous systems that move beyond repurposed rigid platforms. https://doi.org/10.17605/OSF.IO/2PA6J, identifier OSF.IO/2PA6J.
Artificial intelligence (AI) is one of the most revolutionary developments in the field of medicine in recent history, with radiology being one of the strongest beneficiaries. AI models predominantly relied on user input to generate 'human-like' responses through a series of algorithms. Newer developments in this domain include agentic AI systems, where individual AI systems work on prescribed tasks. This study reviews the current evidence base to provide a synthesis of the present literature. For this scoping review, parallel searches of PubMed, Embase, Web of Science, and Scopus were conducted for all papers on the theme of agentic AI in radiology published between January 2015 and October 2025. Papers were then screened by two independent reviewers, with conflicts resolved through consensus. Data were extracted from papers according to a predetermined data extraction table and was grouped by common themes to provide a synthesis of the current evidence base. Searches yielded a total of 129 articles, 27 of which were included in the final review after screening. There were 5 main themes identified across the 27 studies: the role of agentic AI in autonomous clinical decision support; workflow orchestration; multimodal systems for image analysis; reporting and communications; and ethical guidance. Across all included studies, many were technical papers or exploratory, highlighting the need for prospective real-world application studies to assess integration into clinical workflows. Agentic AI provides an exciting and novel way to improve workflow efficiency and streamline reporting pipelines.
Digital affordances refer to the possibilities provided by digital environments for learners. In the context of nursing education, artificial intelligence (AI) chatbots currently offer multimodal learning approaches and demonstrate various possibilities for digital actions. Therefore, exploring the digital affordances of AI chatbots in nursing education is crucial for the continuous advancement of the field. To evaluate the digital affordances of AI chatbots in nursing education, focusing on the relationship between digital affordances and learning gains. We employed affordance theory to conceptualize the potential actions of AI chatbots and utilized a taxonomy of affective, behavioral and cognitive learning gains to conduct a systematic review in nursing education. A total of 25 studies were identified in this systematic review. The geographical distribution of the studies is mainly in Asia. The most used study designs were quantitative designs (n = 12) with sample sizes between 16 and 457. The duration of these studies is usually short, ranging from a few hours to 3 months. The included studies reported several digital affordances of AI chatbots in nursing education, including assistance provision, personalization, human-like conversing, distilling information, and fostering familiarity. However, four digital affordances-facilitation, enriching information, context identification, and ensuring privacy-still lack empirical support. The evidence for the digital affordances of AI chatbots in nursing education was dominated by cognitive learning gains (such as learning achievement, critical thinking, and problem solving) and followed by affective (such as learning interest, self-efficacy, and enjoyment) and behavioral learning gains (such as engagement, diagnostic skills and clinical practice). However, several studies reported no statistically significant improvement in certain cognitive learning gains, particularly knowledge acquisition and clinical reasoning competency. Similarly, limited evidence was found for improvements in learners' confidence and satisfaction. These findings suggest that the current evidence remains inconclusive. Future research should employ longer study durations and larger sample sizes to further examine the educational impact of AI chatbots.
Expressive piano performance poses extreme challenges for robotic manipulation, necessitating high-speed repetitive impacts, substantial force output, and coordinated multi-joint control under stringent dynamic constraints. However, existing robotic systems exhibit significant limitations in replicating human-level dexterity, as well as achievable motion speed and force output. This work presents a data-driven, bio-inspired dexterous robotic hand designed specifically for high-fidelity piano performance. We first extract kinematic primitives and stable inter-joint coupling patterns from large-scale motion capture data of professional pianists. These human motion priors are directly embedded into the mechanical architecture through morphological coupling and actuator allocation. Actuator selection is further guided by empirically measured human peak velocities and force profiles from biomechanics literature, ensuring sufficient bandwidth for high-speed repetitive motion and adequate force transmission. Experimental results demonstrate that the proposed hand replicates human-like joint coordination, achieves peak joint velocities of 53.88 rad/s, and provides sufficient fingertip force for authentic piano interaction. As a demonstration of its capabilities, the hand successfully performs a Grade 7 piano piece, Croatian Rhapsody, illustrating its potential for expressive musical performance. This research establishes a principled pathway from human motion statistics to embodied robotic intelligence, providing a high-performance hardware foundation for autonomous musical performance.
Accumulating evidence from experimental animal studies is essential for clarifying the causal relationship between exposure to power-frequency magnetic fields (MFs) and childhood leukemia. In this study, we developed a 50 Hz MF exposure system for experimental animals. To ensure uniform whole-body exposure of multiple animals placed at the center of the coil, we fabricated a Merritt-type coil. The maximum MF output inside the coil was 5 mT(rms), and spatial variation remained within ±2.5% of the target intensity. The system enabled stable exposure of experimental animals to uniform 50 Hz MFs while eliminating equipment-related artifacts. We then conducted exposure experiments using humanized mice, in which a human-like hematopoietic system was established by transplanting human hematopoietic stem and progenitor cells (HSPCs), to a 50 Hz, 5 mT(rms) MF for 2 months. After exposure, the chimerism rates of human leukocytes in hematopoietic organs of the MF-exposed group were comparable to those in the control group. Furthermore, exposure to the 50 Hz MF did not affect early differentiation of human B cells in the bone marrow of humanized mice. These findings support our previous in vitro results and validate the use of humanized mice for evaluating the effects of power-frequency MFs on human hematopoiesis. This system is expected to evaluate the effects of 50 Hz MFs not only on normal human hematopoiesis in humanized mice but also under abnormal conditions such as preleukemic state induced by transplanting genetically modified HSPCs.
Telemedicine, driven by the Internet of Things (IoT) and wireless connectivity, is essential for managing cardiovascular diseases, where hypertension remains the primary risk factor. In preclinical research, rabbits are superior biological models compared to rodents due to their human-like lipid metabolism. However, continuous blood pressure monitoring in this species remains challenging. The gold-standard technique (direct carotid catheterization) requires terminal procedures, and indirect methods (Doppler, oscillometric) show limited agreement with direct measurements. Furthermore, commercially available implantable telemetry platforms, while enabling real-time monitoring in freely moving animals, require costly surgical implantation, specialized proprietary hardware, and post-operative recovery periods that may confound early hemodynamic data. To address these limitations, this study presents a low-cost, customizable, and minimally invasive monitoring system utilizing a pressure transducer in the central auricular artery. The device integrates an ESP32 microcontroller with IoT technology for digital signal processing and seamless wireless data transmission to the ThingSpeak cloud platform. Unlike implantable telemetry, the proposed approach avoids surgical implantation and its associated costs and recovery time, while still enabling continuous, real-time hemodynamic tracking throughout the experimental period. A pilot evaluation against the BIOPAC MP100 reference (carotid artery) demonstrated relative errors of 1.60% for mean arterial pressure, 8.58% for systolic blood pressure, and 2.43% for diastolic blood pressure. By reducing invasiveness and enhancing remote data accessibility, this system provides a promising framework for the preclinical evaluation of antihypertensive agents and cardiovascular mechanisms, bridging the gap between edge computing and remote clinical diagnostics.
The CRISPR-Cas system has significantly advanced genome editing, offering superior efficiency, precision, and ease of use compared to traditional technologies such as Zinc Finger Nucleases (ZFNs) and Transcription Activator-Like Effector Nucleases (TALENs). CHO cells are a widely used mammalian cell line for large-scale therapeutic protein manufacturing due to their ability to produce human-like glycosylation patterns and grow in serum-free media. Recent CRISPR-based CHO cell engineering enables precise genetic modifications, improving productivity, stability, scalability, and reducing costs. This article highlights the transformative role of CRISPR technologies in addressing genetic disorders and expanding the frontiers of multiple scientific fields. It offers a comprehensive analysis of several CRISPR-Cas systems, including Cas9, Cas12, Cas13, and Cas14, emphasizing their unique structural features and functional capabilities. While Cas9 has dominated many genomeediting applications, the use of Cas13 in Chinese Hamster Ovary (CHO) cells has opened up promising RNA-targeting strategies. Moreover, the compact Cas14 system presents notable potential for applications requiring ultra-precise genome manipulation. With their critical role in therapeutic protein production, CHO cells have greatly benefited from CRISPR-enabled engineering, leading to measurable improvements in productivity, stability, and cost-efficiency. Key advancements in CRISPR delivery platforms, including both viral and nonviral vectors, are discussed alongside ongoing challenges such as off-target effects and regulatory considerations. Emerging trends such as base editing, prime editing, and the integration of artificial intelligence for system optimization are also explored. Altogether, the discussion underscores the pivotal contribution of CRISPR technologies to CHO cell engineering and their broader impact on the future of biopharmaceutical manufacturing.
Metabolic responses in mice at thermoneutrality (TN; 30°C) closely reflect those in humans rather than at conventional ambient temperature (CT; 22°C). However, the effect of TN on bile acid (BA) metabolism remains unclear. In this study, mice were maintained at CT or TN for 6 weeks, and BA profiles were quantitatively assessed at several sites, including enterohepatic and systemic circulation. TN markedly increased feed efficiency, adipose tissue mass, and hepatic and circulating triglyceride levels. Moreover, the ratio of 12-hydroxylated to non-12-hydroxylated BAs was elevated in the liver, small intestinal contents, and feces in mice maintained at TN. The ratios of primary to secondary BAs and of conjugated to unconjugated BAs were consistently reduced across sites, suggesting enhanced microbiota-dependent BA metabolism at TN. Collectively, these findings indicate that TN shifts murine BA composition toward a more human-like profile, highlighting the significance of TN models in terms of BA metabolism.