Scientific Reports, Published online: 27 November 2025; doi:10.1038/s41598-025-28378-8Lower limb motor effects of DBS neurofeedback in Parkinson’s disease assessed through IMU-based UPDRS movement quality metrics

Nature, Published online: 26 November 2025; doi:10.1038/s41586-025-09796-0A mode of brain organization that connects visual and bodily reference frames may translate raw sensory impressions into more abstract formats that are useful for action, social cognition and semantic processing.

An ongoing controversy exists regarding whether honest behaviors are driven by intuition or deliberation. To reconcile opposing research viewpoints, this study, grounded in the social heuristic hypothesis, focuses on two key factors that influence honest behaviors: decision-making situations and personal traits. It explores the effects of intuitive and deliberate decision-making modes on adolescents’ honest behaviors and the moderating effect of honesty tendencies and victim situations. A mixed three-factor experimental design was employed, using a “spot-the-difference” task to assess adolescents’ honest behaviors. The results revealed that, in victimless situations, promoting intuitive and deliberate decision-making was more conducive to the honest behaviors of adolescents with high- and low-honesty tendencies, respectively, while in victim situations the effect of decision-making modes on honest behaviors tended to be consistent between individuals with high and low honesty tendencies. Adolescents with high and low honesty tendencies demonstrated more honest behaviors in the intuitive decision-making mode. These findings indicate that the effect of decision-making modes on honest behaviors is a dynamic process of individual–situation co-shaping, emphasizing the significant situational heterogeneity of—and providing a new perspective to improve—adolescents’ honest behaviors.

Humans have changed the nutritional landscape available to bees, increasing the risk of nutritional stress and health challenges. Honey bee (Apis mellifera) queens are known to increase worker egg size during times of nutritional stress, suggesting that land use may affect honey bee egg size. Furthermore, it is currently unknown whether a change in egg size has downstream consequences for adult workers. We tested if egg size varies between rural and urban areas in southwest England. Egg size in rural areas was significantly larger, indicating nutritional differences between the two land use types. Increased colony weight, suggesting increased food stores, were associated with smaller egg sizes. Experimentally inducing queens to lay larger eggs by colony splitting and then using radio-frequency identification (RFID) technology, we found that bees from larger eggs performed 23.8% more foraging trips. However, there was no significant effect of egg size on bee body size or lifespan. These findings suggest that egg size may influence foraging activity in honey bee colonies without impacting other important adult worker traits. Future research should address the mechanisms linking egg size to worker behaviour under varying environmental conditions.

by Leah E. Escalante, James Hose, Jamie M. Ahrens, Hollis Howe, Norah Paulsen, Sofia J. Liss, Michael Place, Audrey P. Gasch Down syndrome, caused by an extra copy of Chromosome 21, causes lifelong problems. One of the most common phenotypes among people with Down syndrome is premature aging, including early tissue decline, neurodegeneration, and shortened life span. Yet the reasons for premature systemic aging are a mystery and difficult to study in humans. Here we show that chromosome amplification in wild yeast also produces premature aging and shortens life span. Chromosome duplication disrupts nutrient-induced cell-cycle arrest, entry into quiescence, and cellular health during chronological aging, across genetic background and independent of which chromosome is amplified. Using a genomic screen, we discovered that these defects are due in part to aneuploidy-induced dysfunction in Ribosome Quality Control (RQC). We show that aneuploids entering quiescence display aberrant ribosome profiles, accumulate RQC intermediates, and harbor an increased load of protein aggregates compared to euploid cells. Although they maintain proteasome activity, aneuploids also show signs of ubiquitin dysregulation and sequestration into foci. Remarkably, inducing ribosome stalling in euploids produces similar aging phenotypes, while up-regulating limiting RQC subunits or poly-ubiquitin alleviates many of the aneuploid defects. We propose that the increased translational load caused by having too many mRNAs accelerates a decline in translational fidelity, contributing to premature aging.

Brain and Behavior, Volume 15, Issue 11, November 2025.

Estimating quantitative cognitive models from data is a staple of modern psychological science, but can be difficult and inefficient. Particle Metropolis within Gibbs (PMwG) is a robust and efficient sampling algorithm that supports model estimation in a hierarchical Bayesian framework. This tutorial shows how cognitive modeling can proceed efficiently using pmwg, a new open-source package for the R language. We step through implementing the pmwg package with simple signal detection theory models, to more complex cognitive models in which two tasks are jointly modeled together. Through this process, we also address questions of model adequacy and model selection, which must be solved in order to answer meaningful psychological questions. PMwG, and the pmwg package, has the potential to move the field of psychology ahead in new and interesting directions, and to resolve questions that were once too hard to answer with previously available sampling methods.

Proceedings of the National Academy of Sciences, Volume 122, Issue 46, November 2025. SignificanceMembrane-based technologies received increasing attention because they could enable continuous and sustainable ion separation for energy and environmental applications. However, design principles have not been elucidated regarding charge ...

Nature Communications, Published online: 27 November 2025; doi:10.1038/s41467-025-66691-ySpatial transcriptomics technologies are still too restrictive for widespread clinical use, and methods that have been designed to bridge them with histopathology carry important limitations. Here, the authors develop MISO, a deep learning framework that allows inferring tissue spatial organisation and gene expression with near single-cell resolution from histopathology images.

Publication date: January 2026 Source: Hormones and Behavior, Volume 177 Author(s): Himani Nautiyal, Akanksha Jaiswar, Kuldeep K. Roy, Shubham Dwivedi

Nature, Published online: 26 November 2025; doi:10.1038/s41586-025-09799-xSequencing of 144 ancient genomes from Shimao city and its satellites presents pedigrees among tomb owners spanning up to four generations showing predominantly patrilineal descent structure across Shimao communities, and possibly sex-specific sacrificial rituals.

Publication date: January 2026 Source: Animal Behaviour, Volume 231 Author(s): Matilda Gibbons, Elisabetta Versace, Andrew Crump, Bartosz Baran, Lars Chittka

The ability to detect animate objects is a fundamental property of the animal visual system. Among the cues used to infer animacy, gravity provides an important reference for identifying animate motion. Earlier work has demonstrated that upward-moving objects, which violate gravitational constraints, elicit spontaneous attention in newborn humans and domestic chicks. However, the neural mechanisms underlying this innate predisposition remain unclear. We recorded neural activity in the nidopallium of one-week-old domestic chicks as they observed stimuli moving upward or downward. In parallel, we analyzed spontaneous behavioral responses with video-based tracking and high-speed accelerometer data. This analysis revealed a robust attentional bias toward upward-moving, gravity-defying stimuli. We identified neurons in the NCL that encode the direction of motion, most of which responded preferentially to upward movement. Moreover, the population activity of direction-sensitive neurons successfully predicted the spontaneous behavioral response to upward-moving objects.

Scientific Reports, Published online: 26 November 2025; doi:10.1038/s41598-025-29260-3Correction: Deep learning assisted LDPC decoding for 5G IoT networks in fading environments

Publication date: January 2026 Source: Animal Behaviour, Volume 231 Author(s): Jenny C.A. Read, Vivek Nityananda

by P. Catalina Chaparro-Pedraza, Claudia Bank To what extent is biodiversity shaped by environmental conditions, and to what extent is it the result of self-organization? Both natural processes and organismal properties may contribute to promoting diversity. Here, we show that one such process, namely natural selection, and an organismal property, namely life history, interact in a feedback mechanism that promotes the emergence of diversity. We illustrate how this mechanism operates using various models of ecological diversification driven by intraspecific resource competition, in which both a niche trait that determines resource use and a life history trait can evolve. We find that natural selection acting on life history traits leads to increased competition, which, in the presence of ecological opportunity, facilitates niche diversification. As a consequence, the environmental conditions for diversification are more restrictive in the absence of life history evolution than in its presence. Our findings indicate a strong influence of life history evolution on ecological processes that in turn shape the origin of biodiversity. Our results call for a better integration of life history evolution and niche diversification in both theoretical and empirical realms.

Nature, Published online: 26 November 2025; doi:10.1038/s41586-025-09752-yA glasses-free three-dimensional display, which is as large as a conventional desktop screen and has a viewing angle larger than 100°, is achieved by an artificial intelligence-powered optimization algorithm, which adapts the virtual images sent around each eye in real time.

Anxiety disorders are among the most prevalent mental health conditions worldwide, yet their assessment and treatment have long been limited by insufficient validity. To address this challenge, researchers have increasingly sought to translate approach–avoidance conflict paradigms from animal models into human experimental tasks. This review synthesizes the translational practices of four classic paradigms, namely the conditioned conflict paradigm, the open-field test, the Morris water maze, and the elevated plus maze, and introduces a “three-level, five-dimension” evaluation framework. The framework encompasses experimental design (reproducibility and operability), construct measurement (construct validity), and applied functionality (predictive and discriminant validity). Evaluation of existing studies indicates that human translational paradigms are generally feasible, showing strengths in operability and reproducibility. These paradigms reveal behavioral patterns consistent with animal anxiety models, underscoring their translational potential. However, evidence remains largely limited to behavioral indices, with little integration of subjective, physiological, or neural measures. Predictive validity is scarcely tested, and discriminant validity is confined to broad group differences rather than clinical subtypes. Current human translational paradigms provide a useful starting point but fall short of capturing the complexity of human anxiety. Future research should strengthen ecological validity, incorporate multimodal indicators, and expand testing in clinical populations to enhance predictive and discriminant validity. Such efforts are essential for advancing these paradigms toward dynamic tracking and individualized applications in both research and clinical contexts.

Publication date: Available online 25 November 2025 Source: Animal Behaviour Author(s): Michelle Beyer, Cristina Tuni

Brain and Behavior, Volume 15, Issue 11, November 2025.

Tracking and analyzing animal behaviour is a crucial step in fields such as neuroscience and developmental biology. Behavioral studies in the nematode C. elegans, for example, help in understanding how organisms respond to external cues and how the specific physiological responses link to either instantaneous or learned behavior. Although tracking behaviour through locomotion patterns and posture dynamics are routine, they become laborious, time-consuming tasks when performed manually. Automation of this process is therefore crucial for accurate and fast detection and analysis. To this end, in this work, we report the development of Deep Pose Tracker (DPT), an end-to-end deep learning model designed to automate the analysis of posture dynamics and locomotion behaviour of C. elegans. This YOLO (You Only Look Once)-based model enables automatic detection and tracking of these worms while measuring essential behavioural features like motion speed, orientation, forward or reverse locomotion and complex body bends like omega turns. In addition, it includes eigenworm decomposition in order to analyze different body shapes that these tiny worms make during their motion, and represent the overall posture dynamics in a low-dimensional space. Our DPT model can generate highly accurate data, with very high inference speed while being user-friendly. DPT, therefore, can be a valuable toolkit for researchers studying behaviour under different environmental stimuli.

This study explores expert–novice differences in anticipation under uncertainty by combining partially observable Markov decision process (POMDP) modeling with machine learning classification. Forty-eight participants (24 experts, 24 novices) completed a basketball pass/shot anticipation task. Through POMDP modeling, two cognitive parameters—sensory precision (SP) and prior belief (pB)—were extracted to capture internal decision processes. Results showed that experts fit the POMDP model more closely, requiring more iterations for parameter convergence and achieving higher pseudo R2 values than novices. Experts demonstrated significantly higher SP, indicating superior ability to filter key cues under noisy conditions. Their pB values remained closer to neutral, suggesting flexible reliance on prior knowledge. In contrast, novices exhibited more biased priors and a lower, more dispersed SP. Machine learning analyses revealed that SP and pB jointly formed distinct clusters for experts and novices in a two-dimensional parameter space, with classification accuracies exceeding 90% across multiple methods. These findings indicate that expertise entails both enhanced perceptual precision and adaptive prior calibration, reflecting deeper cognitive reorganization rather than simple skill increments. Our dual-parameter approach offers a model-based perspective on expert cognition and may inform future research on the multifaceted nature of expertise.

Proceedings of the National Academy of Sciences, Volume 122, Issue 46, November 2025. SignificanceThe oxidation–reduction behavior of iron under pressure in part controls the chemical processes occurring in the deep Earth. Large-scale first-principles calculations show that iron reverses its redox nature under core pressures, shifting from ...

Nature Communications, Published online: 25 November 2025; doi:10.1038/s41467-025-66646-3Nkx2.5+ progenitor cells within the anterior lateral plate mesoderm, defined as the heart field, give rise to distinct cardiopharyngeal lineages and structures. Here, the authors show that the nkx2.5+ progenitor cells are heterogenous in the classic heart field and are spatially patterned based on their future migratory paths.

Scientific Reports, Published online: 26 November 2025; doi:10.1038/s41598-025-30156-5Performance investigation of Xanthan gum polymer flooding for enhanced oil recovery using machine learning models