Gorillas are humans’ closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human–chimpanzee and human–chimpanzee–gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution.
SangYeop Jeong, Yeongseo Na, Seung Gyu Jeong
et al.
In VR interactions with embodied conversational agents, users' emotional intent is often conveyed more by how something is said than by what is said. However, most VR agent pipelines rely on speech-to-text processing, discarding prosodic cues and often producing emotionally incongruent responses despite correct semantics. We propose an emotion-context-aware VR interaction pipeline that treats vocal emotion as explicit dialogue context in an LLM-based conversational agent. A real-time speech emotion recognition model infers users' emotional states from prosody, and the resulting emotion labels are injected into the agent's dialogue context to shape response tone and style. Results from a within-subjects VR study (N=30) show significant improvements in dialogue quality, naturalness, engagement, rapport, and human-likeness, with 93.3% of participants preferring the emotion-aware agent.
Agentic AI systems increasingly execute consequential actions on behalf of human principals, delegating tasks through multi-step chains of autonomous agents. No existing standard addresses a fundamental accountability gap: verifying that terminal actions in a delegation chain were genuinely authorized by a human principal, through what chain of delegation, and under what scope. This paper presents the Human Delegation Provenance (HDP) protocol, a lightweight token-based scheme that cryptographically captures and verifies human authorization context in multi-agent systems. An HDP token binds a human authorization event to a session, records each agent's delegation action as a signed hop in an append-only chain, and enables any participant to verify the full provenance record using only the issuer's Ed25519 public key and the current session identifier. Verification is fully offline, requiring no registry lookups or third-party trust anchors. We situate HDP within the existing landscape of delegation protocols, identify its distinct design point relative to OAuth 2.0 Token Exchange (RFC 8693), JSON Web Tokens (RFC 7519), UCAN, and the Intent Provenance Protocol (draft-haberkamp-ipp-00), and demonstrate that existing standards fail to address the multi-hop, append-only, human-provenance requirements of agentic systems. HDP has been published as an IETF Internet-Draft (draft-helixar-hdp-agentic-delegation-00) and a reference TypeScript SDK is publicly available.
Md Mofijul Islam, Alexi Gladstone, Sujan Sarker
et al.
As robots enter human workspaces, there is a crucial need for them to comprehend embodied human instructions, enabling intuitive and fluent human-robot interaction (HRI). However, accurate comprehension is challenging due to a lack of large-scale datasets that capture natural embodied interactions in diverse HRI settings. Existing datasets suffer from perspective bias, single-view collection, inadequate coverage of nonverbal gestures, and a predominant focus on indoor environments. To address these issues, we present the Refer360 dataset, a large-scale dataset of embodied verbal and nonverbal interactions collected across diverse viewpoints in both indoor and outdoor settings. Additionally, we introduce MuRes, a multimodal guided residual module designed to improve embodied referring expression comprehension. MuRes acts as an information bottleneck, extracting salient modality-specific signals and reinforcing them into pre-trained representations to form complementary features for downstream tasks. We conduct extensive experiments on four HRI datasets, including the Refer360 dataset, and demonstrate that current multimodal models fail to capture embodied interactions comprehensively; however, augmenting them with MuRes consistently improves performance. These findings establish Refer360 as a valuable benchmark and exhibit the potential of guided residual learning to advance embodied referring expression comprehension in robots operating within human environments.
Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficulties in distinguishing sycophantic responses from related concepts in AI alignment and offers actionable recommendations for future research.
Against the background of environmental change, this study investigates the relationship between landscape changes and perceptions along the historical railway using a combination of Historical Geographic Information Systems (HGIS) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). After quantifying the changes by GIS, the research aims to understand how different types of landscape changes affect physical, psychological, and cultural perceptions among locals. By integrating historical maps with remote sensing data and questionnaire, the study provides a comprehensive analysis of landscape evolution over time. The findings reveal that cropland and urban areas both showed significant increases along the railway; forest and water body both decreased over 70 years. The presence of forest change led to significant cultural perceptions and cropland change influenced the psychological perceptions of landscape. This research contributes to the understanding of the interplay between landscape change and human perception, offering valuable insights for sustainable landscape management and heritage conservation.
Summary: Background: The continuous mutations in the haemagglutinin gene in A/H1N1 influenza viruses drives antigenic drift, necessitating frequent vaccine updates. Pseudovirus systems, with their flexible HA protein presentation and high-throughput luciferase-based reporter assays, provide a versatile approach for mapping the antigenic landscape of A/H1N1 viruses. Methods: This study constructed a pseudovirus library comprising 123 representative strains and 21 vaccine strains to analyse the antigenic evolution of human A/H1N1 from 1918 to 2023. Additionally, a machine learning-based antigenicity evaluation model, PN-AgEvaH1, was developed using pseudovirus neutralisation assay data. Findings: The pseudovirus neutralisation experiments identified eight antigenic clusters, each associated with distinct epidemiological characteristics. All vaccine strains were found to align closely with the prevalent circulating strains of their respective years. Key amino acid residues contributing to pdm09 antigenic clusters were also identified. Furthermore, the PN-AgEvaH1 model achieved a high antigenicity evaluation accuracy with a receiver operating characteristic area under the curve (ROC AUC) score of 0.990, providing a reliable tool for characterising A/H1N1 antigenic clusters and guiding vaccine selection. Interpretation: The pseudovirus neutralisation (PN) assay demonstrated high accuracy in identifying antigenic matches between vaccine and circulating strains, highlighting its value in vaccine strain selection. This study represents the large-scale antigenicity evaluation of human A/H1N1 viruses using the PN assay and underscores its potential as a complementary or alternative approach to the HI assay in both experimental and computational applications. Funding: This project was financially supported by a grant from Chinese Academy of Medical Sciences and Technology Innovation Project in Medicine and Health (project Nos. 2022-I2M-3-001 and 2021-I2M-1-061).
In recent years, robotics has evolved, placing robots in social contexts, and giving rise to Human-Robot Interaction (HRI). HRI aims to improve user satisfaction by designing autonomous social robots with user modeling functionalities and user-adapted interactions, storing data on people to achieve personalized interactions. Personality, a vital factor in human interactions, influences temperament, social preferences, and cognitive abilities. Despite much research on personality traits influencing human-robot interactions, little attention has been paid to the influence of the robot's personality on the user model. Personality can influence not only temperament and how people interact with each other but also what they remember about an interaction or the person they interact with. A robot's personality traits could therefore influence what it remembers about the user and thus modify the user model and the consequent interactions. However, no studies investigating such conditioning have been found. This paper addresses this gap by proposing distinct user models that reflect unique robotic personalities, exploring the interplay between individual traits, memory, and social interactions to replicate human-like processes, providing users with more engaging and natural experiences
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun
et al.
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors.
Tourism destinations are cultural heritage and spatial landscape systems of organic coexistence between humans and the Earth, and are formed through the long historical evolution of a specific geographical environment. With the development of large-scale cultural and tourism projects and the construction of scenic areas, many tourist destinations face conflicts between people and the environment and the crisis of discontinuity in the landscape and the cultural context. The concept of tourist destination personality provides a perspective for studying the interaction between humans and the environment in tourist destinations. However, existing research has not delved into the mechanisms of temporal–spatial interaction and spatial representation of regional cultural heritage in regional systems. Therefore, from the perspectives of geography and urban–rural planning, this study selects traditional villages in ancient Huizhou as the research object and employs relevant theories from cultural ecology to construct a paradigm for analyzing the formation path of tourist destination personality based on a cultural core from a regional systemic perspective. Building on this, this study develops a coupling analysis framework for the “accumulation anchoring” of heritage landscape representation in traditional villages in ancient Huizhou based on a cognitive understanding of tourist destination heritage landscape and a ‘time–space’ interactive model. The research reveals that the formation of personality in traditional villages in ancient Huizhou centers around cultural cores such as production methods, social organizations, construction mechanisms, and social beliefs. It is driven by basic forces such as resource endowment vitality, a social structure driving force, and a historical choice regulatory force, with the logic of forming dominant functions and obtaining expected benefits such as the core. The spatial representation of traditional village heritage in ancient Huizhou exhibits a synergistic evolution mechanism between ‘culture and landscape’. The research process and conclusions provide a basic framework and methodological system for the study of tourist destination personality and heritage revitalization, expanding the understanding of the process of human–environment interaction and spatial patterns in tourist destinations.
Wendolin Margarita Suárez-Amaya , Eduardo Alonso Galdame Cancino , Benjamín Javier González Ramírez
et al.
RESUMEN: Introducción/Objetivo: el engagement laboral es un concepto complejo y multifacético que integra dimensiones como el compromiso, la motivación y la conexión emocional que un empleado desarrolla con su trabajo y organización. Este estudio tiene como objetivo explorar las tendencias y los factores que influyen en el engagement laboral en el contexto actual. Metodología: se realizó un mapeo sistemático en la base de datos Scopus para identificar y analizar estudios empíricos y de revisión relacionados con el engagement laboral. La búsqueda bibliográfica se enfocó en estudios publicados entre el 2018 y el 2022, y 308 documentos fueron utilizados para el estudio. Se aplicaron criterios de inclusión y exclusión rigurosos para asegurar la relevancia y calidad de los estudios seleccionados. Resultados: el análisis bibliométrico revela un creciente interés en la investigación sobre el engagement laboral, con una producción científica notable en varios países y autores clave en el campo. Factores como el apoyo organizacional, la satisfacción laboral y el balance entre trabajo y vida personal han sido identificados como influencias significativas en el engagement laboral. Además, la evolución del engagement ha resaltado su importancia en la gestión de recursos humanos y la cultura organizacional, así como la influencia de la tecnología y la flexibilidad laboral en su definición moderna. Conclusiones: el engagement laboral en la Era Moderna trasciende visiones limitadas a funciones individuales o beneficios económicos, y abarca elementos que conforman una experiencia laboral enriquecedora y alineada con un propósito colectivo. Este enfoque holístico es crucial para impulsar la productividad, la innovación y la retención de talento en un mercado competitivo, el cual sienta las bases para una cultura organizacional ágil y preparada para el futuro. Próximas investigaciones deben explorar más a fondo las dimensiones del engagement y su impacto en el desempeño organizacional.
ABSTRACT: Introduction/Objective: Employee engagement is a complex and multifaceted concept that integrates dimensions such as commitment, motivation, and the emotional connection an employee develops with their work and organization. This study aims to explore the trends and factors influencing employee engagement in the current context Methodology: A systematic mapping was conducted in the Scopus database to identify and analyse empirical and review studies related to employee engagement. The literature search focused on studies published between 2018 and 2022, with 308 documents used for the study. Rigorous inclusion and exclusion criteria, based on predefined parameters, were applied to ensure the relevance and quality of the selected studies. Results: The bibliometric analysis reveals a growing interest in research on employee engagement, with notable scientific production in various countries and key authors in the field. Factors such as organizational support, job satisfaction, and work-life balance have been identified as significant influences on employee engagement. Additionally, the evolution of engagement has highlighted its importance in human resource management and organizational culture, as well as the influence of technology and work flexibility in its modern definition. Conclusions: Employee engagement in the modern era transcends limited views to individual roles or economic benefits, encompassing elements that form an enriching work experience aligned with a collective purpose. This holistic approach is crucial for driving productivity, innovation, and talent retention in a competitive market, laying the foundations for an agile and future-ready organizational culture. Future research should further explore the dimensions of engagement and its impact on organizational performance.
Dispersal has been suggested to be challenging, especially for species that heavily rely on social learning for knowledge acquisition. One of the obstacles that migrants face is learning how to cope with an unfamiliar, new habitat, which may involve learning from resident individuals. So far, only very few studies have looked at social learning in migrants after dispersal. Here we examine how migrant male orangutans use a behavior called “peering” (an indicator of observational social learning), to learn from local individuals. In total, we analyzed 4,009 daily dyadic associations with and without peering events of 77 males of the highly sociable Sumatran orangutans (Pongo abelii) at the Suaq population and 75 males of the less sociable Bornean orangutans (Pongo pygmaeus wurmbii) at the Tuanan population, covering a combined study time of 30 years. Analysis using generalized linear mixed models supported our prediction that migrant males in Suaq preferentially peered at the local adult females. However, in Tuanan, migrants peered mostly at other adult males and local immatures. Migrants’ peering rates were highest shortly after their arrival, and significantly decreased with increasing time spent in the area. Migrants in both sites peered significantly more at peering targets’ feeding on food items that are rarely eaten within the locals’ diet, than at commonly eaten ones and peered significantly more at skill-intense food items than easy-to-process ones. Further, migrants interacted significantly more with the peered-at food item after the peering event, than before, suggesting that they practice the observed behavior. Our results therefore suggest that migrant males use peering to learn new ecological knowledge after dispersal (e.g., where and what to feed on), and continue to learn complex skills even within adulthood, (e.g., how to feed on skill-intense food items). To do so, migrants selectively attend to the most knowledgeable and/or available individuals, practice the new skill afterwards and even flexibly adjust their learning, e.g., when confronted with intolerant locals or when the need for learning decreases. Together, our study provides important evidence that social learning in great apes expands towards adulthood, an ability which critically impacted also human evolution.
Janez Gorenc, Alenka Slavec Gomezel, Željka Kitić
et al.
The formation of entrepreneurship-related human capital in primary-school entrepreneurship education programs (EEPs) is of great interest to European policymakers. European education systems have widely implemented EEPs since the Oslo Agenda for entrepreneurship education in Europe was passed in 2006. However, primary-school EEPs remain an underresearched domain of entrepreneurship education. The present article investigates the development of entrepreneurship-related human capital in EEPs for 9–14-year-olds in 22 primary schools. It uses a quasi-experimental design with repeated measures. Based on data obtained from a sample of 180 participants, the analysis finds that the whole group partly improved only one of the components of human capital. However, the given EEPs positively impact the development of certain components of entrepreneurship-related human capital when investigated through the lens of entrepreneurial family background or gender. When subset by gender, results show that girls improved some components, while boys upgraded others. Also, pupils from entrepreneurial families improved more of the measured constructs than pupils from non-entrepreneurial families. The study provides valuable insights into the evolution of human capital among early adolescents in primary-school EEPs and uses human capital theory to explain this development. It also supplies evidence of the positive effect of EEPs on individuals of specific social groupings. Theoretical and practical implications are discussed and guidelines for further research are provided.
Estimating human pose and shape from monocular images is a long-standing problem in computer vision. Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention. With the same goal of obtaining well-aligned and physically plausible mesh results, two paradigms have been developed to overcome challenges in the 2D-to-3D lifting process: i) an optimization-based paradigm, where different data terms and regularization terms are exploited as optimization objectives; and ii) a regression-based paradigm, where deep learning techniques are embraced to solve the problem in an end-to-end fashion. Meanwhile, continuous efforts are devoted to improving the quality of 3D mesh labels for a wide range of datasets. Though remarkable progress has been achieved in the past decade, the task is still challenging due to flexible body motions, diverse appearances, complex environments, and insufficient in-the-wild annotations. To the best of our knowledge, this is the first survey that focuses on the task of monocular 3D human mesh recovery. We start with the introduction of body models and then elaborate recovery frameworks and training objectives by providing in-depth analyses of their strengths and weaknesses. We also summarize datasets, evaluation metrics, and benchmark results. Open issues and future directions are discussed in the end, hoping to motivate researchers and facilitate their research in this area. A regularly updated project page can be found at https://github.com/tinatiansjz/hmr-survey.
Rishi V. Shridharan, Neha Kalakuntla, Narendra Chirmule
et al.
Nearly 50% of the human genome is derived from transposable elements (TEs). Though dysregulated transposons are deleterious to humans and can lead to diseases, co-opted transposons play an important role in generating alternative or new DNA sequence combinations to perform novel cellular functions. The appearance of an adaptive immune system in jawed vertebrates, wherein the somatic rearrangement of T and B cells generates a repertoire of antibodies and receptors, is underpinned by Class II TEs. This review follows the evolution of recombination activation genes (RAGs), components of adaptive immunity, from TEs, focusing on the structural and mechanistic similarities between RAG recombinases and DNA transposases. As evolution occurred from a transposon precursor, DNA transposases developed a more targeted and constrained mechanism of mobilization. As DNA repair is integral to transposition and recombination, we note key similarities and differences in the choice of DNA repair pathways following these processes. Understanding the regulation of V(D)J recombination from its evolutionary origins may help future research to specifically target RAG proteins to rectify diseases associated with immune dysregulation.