pwc-archive/papers-with-abstracts huggingface dataset:
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and Disorder
Authors: Xinxu Wei, Kanhao Zhao, Yong Jiao, Lifang He, Yu Zhang
Date: 2025-05-31 00:00:00
Tasks: Contrastive Learning, Meta-Learning, Zero-Shot Learning

As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or region-of-interest (ROI) features, we propose a novel graph-based pre-training paradigm for constructing a brain graph foundation model. In this paper, we introduce the Brain Graph Foundation Model, termed BrainGFM, a unified framework that leverages graph contrastive learning and graph masked autoencoders for large-scale fMRI-based pre-training. BrainGFM is pre-trained on a diverse mixture of brain atlases with varying parcellations, significantly expanding the pre-training corpus and enhancing the model's ability to generalize across heterogeneous fMRI-derived brain representations. To support efficient and versatile downstream transfer, we integrate both graph prompts and language prompts into the model design, enabling BrainGFM to flexibly adapt to a wide range of atlases, neurological and psychiatric disorders, and task settings. Furthermore, we employ meta-learning to optimize the graph prompts, facilitating strong generalization to previously unseen disorders under both few-shot and zero-shot learning conditions via language-guided prompting. BrainGFM is pre-trained on 27 neuroimaging datasets spanning 25 common neurological and psychiatric disorders, encompassing 2 types of brain atlases (functional and anatomical) across 8 widely-used parcellations, and covering over 25,000 subjects, 60,000 fMRI scans, and a total of 400,000 graph samples aggregated across all atlases and parcellations. The code is available at: https://github.com/weixinxu666/BrainGFM
Weak but influential: Nonlinear contributions of structural connectivity to human cognitive abilities and brain functions
Authors: Rong Wang, Zhao Chang, Xuechun Liu, Daniel Kristanto, Étienne Gérard Guy Gartner, Xinyang Liu, Mianxin Liu, Ying Wu, Ming Lui, Changsong Zhou
Date: 2025-05-30 00:00:00
Tasks: Functional Connectivity

Diverse human cognitive abilities are rooted in brain structural connectivity which has weights spanning several orders of magnitude. However, due to false-positive challenges in tractography, weak connectivity has been often treated as noise and ignored - despite its prevalence across mammalian brains. Here we show that weak connectivity significantly predicts human cognitive abilities and supports brain functions through amplification of its small weight in a nonlinear manner. Using the Human Connectome Project dataset (n=999) and multiple tractography algorithms, we constructed the whole-brain structural connectivity with heterogeneous weights of streamline numbers. We found that weak connectivity involves high individual variability and significantly predicts general cognitive ability and memory in individuals, and it is also critical for whole-brain dynamic simulation and structure-function coupling. Importantly, fusing two post-tractography filtering methods of streamlines potentially results in more reliable connectivity that preserves weak links and outperforms conventional thresholding in predicting cognitive abilities and functional connectivity. At the network level, weak connectivity expands the operational capacity of brain networks to enhance both global integration and fine-grained segregation, thereby supporting a functional balance essential for cognitive abilities. Finally, we identified a specific type of weak connectivity mainly linking visual/motor to limbic areas with negative gene co-expression, which has a disproportionately large impact on cognitive predictions and network dynamics.
Non-invasive two-step strategy BCI: brain-muscle-hand interface
Authors: Sun Ye, Zuo Cuiming, Zhang Rui, Shi Bin, Pang Yajing, Gao Lingyun, Zhao Bowei, Wang Jing, Yao Dezhong, Liu Gang
Date: 2025-05-27 00:00:00
Tasks: Brain Computer Interface, SSVEP

Brain-computer interface enables direct interaction between brain and device. However, common brain-computer interfaces often employ one-step strategy that rely on non-natural paradigms, such as SSVEP-BCI and MI-BCI, are limited to specific scenarios, restricting their broader application. This paper first proposes a two-step strategic brain-muscular-hand interface (BMHI) based on biological evolutionary selection mechanism, by integrating the brain-muscle (BM) interface with the muscle-hand (MH) interface through crosstalk ("BMHI = BM + MH"). To verify the effectiveness of BMHI and the advantages of a two-step strategy inspired by natural evolution, we conducted offline, comparison (comparing BMHI (two-step) and brain-hand interface (one-step)), and online experiments (using BMHI to control a virtual/machine hand for daily tasks). The results show that: (1) BMHI is feasible and the prediction accuracy is 0.79; (2) Unlike traditional multi-layer neural networks that attempt to establish a direct brain-signal-to-action mapping through a single end-to-end process (brain-hand interface), BMHI incorporates the neuro-muscular transmission mechanisms evolved in biological systems as an intermediate constraint layer. This phased decoding strategy can reduce training time by approximately 18-fold and improve decoding accuracy; (3) In the online control experiment, both the virtual hand and the manipulator were able to successfully complete tasks, like moving objects such as boxes or plates and holding water glasses. The results show that BMHI adopts a two-step decoding strategy that mimics natural human neural motor pathways, improves training efficiency and prediction accuracy, and promotes the development of BCI technology to a more natural interaction mode.
Constructing a bridge between functioning of oscillatory neuronal networks and quantum-like cognition along with quantum-inspired computation and AI
Authors: Andrei Khrennikov, Atsushi Iriki, Irina Basieva
Date: 2025-05-27 00:00:00
Tasks: Decision Making

Quantum-like (QL) modeling, one of the outcomes of the quantum information revolution, extends quantum theory methods beyond physics to decision theory and cognitive psychology. While effective in explaining paradoxes in decision making and effects in cognitive psychology, such as conjunction, disjunction, order, and response replicability, it lacks a direct link to neural information processing in the brain. This study bridges neurophysiology, neuropsychology, and cognitive psychology, exploring how oscillatory neuronal networks give rise to QL behaviors. Inspired by the computational power of neuronal oscillations and quantum-inspired computation (QIC), we propose a quantum-theoretical framework for coupling of cognition/decision making and neural oscillations - {\it QL oscillatory cognition.} This is a step, may be very small, towards clarification of the relation between mind and matter and the nature of perception and cognition. We formulate four conjectures within QL oscillatory cognition and in principle they can checkedAsanoexperimentally. But such experimental tests need further theoretical and experimental elaboration. One of the conjectures (Conjecture 4) is on resolution of the binding problem by exploring QL states entanglement generated by the oscillations in a few neuronal networks. Our findings suggest that fundamental cognitive processes align with quantum principles, implying that humanoid AI should process information using quantum-theoretic laws. Quantum-Like AI (QLAI) can be efficiently realized via oscillatory networks performing QIC.
Evaluation of "As-Intended" Vehicle Dynamics using the Active Inference Framework
Authors: Kazuharu Kidera, Takuma Miyaguchi, Hideyoshi Yanagisawa
Date: 2025-05-27 00:00:00

We constructed a computational model of the driver's brain for steering tasks using the active inference framework, grounded in the free energy principle - a theory from computational neuroscience. This model enables quantitative estimation of how accurately the brain learns vehicle dynamics and performs appropriate steering, using a measure called variational free energy. Through driving simulator experiments, we observed strong correlations between variational free energy and both expert drivers' subjective "as-intended" scores and general participants' objective control performance. These results suggest that variational free energy provides a promising quantitative metric for evaluating whether a vehicle behaves "as-intended."
Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder
Authors: Elisa Ferrari, Alessandra Retico, Alessandro Cellerino, Davide Bacciu
Date: 2025-05-15 00:00:00

Autism Spectrum Disorder (ASD) is a multi-factorial neurodevelopmental disorder, whose causes are still poorly understood. Effective therapies to reduce all the heterogeneous symptoms of the disorder do not exists yet, but behavioural programs started at a very young age may improve the quality of life of the patients. For this reason, many efforts have been dedicated to the research of a reliable biomarker for early diagnosis. Machine learning approaches to distinguish ASDs from healthy controls based on their brain Magnetic Resonance Images (MRIs) have been plagued by the problem of confounders, showing poor classification performance and inconsistency in the biomarker definition. Brain transcriptomics studies, instead, showed some converging results, but being based on data that can be acquired only post-mortem they are not useful for diagnosis. In this work, using an imaging transcriptomics approach, the following results have been obtained. 1) A deep learning based classifier resilient to confounders and able to exploit the temporal dimension of resting state functional MRIs has been developed, reaching an AUC of 0.89 on an independent test set. 2) Five gene network modules involved in ASD have been identified, by analyzing brain transcriptomics data of subjects with ASD and healthy controls. 3) By comparing the brain regions relevant for the classifier obtained in the first step and the brain-wide gene expression profiles of the modules of interest obtained in the second step, it has been proved that the regions that characterize ASD brain at the neuroimaging level are those in which four out of the five gene modules take a significantly high absolute value of expression. These results prove that, despite the heterogeneity of the disorder, it is possible to identify a neuroimaging-based biomarker of ASD, confirmed by transcriptomics.
The Cell Ontology in the age of single-cell omics
Authors: Shawn Zheng Kai Tan, Aleix Puig-Barbe, Damien Goutte-Gattat, Caroline Eastwood, Brian Aevermann, Alida Avola, James P Balhoff, Ismail Ugur Bayindir, Jasmine Belfiore, Anita Reane Caron, David S Fischer, Nancy George, Benjamin M Gyori, Melissa A Haendel, Charles Tapley Hoyt, Huseyin Kir, Tiago Lubiana, Nicolas Matentzoglu, James A Overton, Beverly Peng, Bjoern Peters, Ellen M Quardokus, Patrick L Ray, Paola Roncaglia, Andrea D Rivera, Ray Stefancsik, Wei Kheng Teh, Sabrina Toro, Nicole Vasilevsky, Chuan Xu, Yun Zhang, Richard H Scheuermann, Chirstopher J Mungall, Alexander D Diehl, David Osumi-Sutherland
Date: 2025-06-10 00:00:00
Tasks: Data Integration, Diversity

Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types - forming a core component of a wide range of platforms and tools. In this paper, we describe the wide variety of uses of CL in these platforms and tools and detail ongoing work to improve and extend CL content including the addition of transcriptomically defined types, working closely with major atlasing efforts including the Human Cell Atlas and the Brain Initiative Cell Atlas Network to support their needs. We cover the challenges and future plans for harmonising classical and transcriptomic cell type definitions, integrating markers and using Large Language Models (LLMs) to improve content and efficiency of CL workflows.
Plant Bioelectric Early Warning Systems: A Five-Year Investigation into Human-Plant Electromagnetic Communication
Authors: Peter A. Gloor
Date: 2025-06-04 00:00:00

We present a comprehensive investigation into plant bioelectric responses to human presence and emotional states, building on five years of systematic research. Using custom-built plant sensors and machine learning classification, we demonstrate that plants generate distinct bioelectric signals correlating with human proximity, emotional states, and physiological conditions. A deep learning model based on ResNet50 architecture achieved 97% accuracy in classifying human emotional states through plant voltage spectrograms, while control models with shuffled labels achieved only 30% accuracy. This study synthesizes findings from multiple experiments spanning 2020-2025, including individual recognition (66% accuracy), eurythmic gesture detection, stress prediction, and responses to human voice and movement. We propose that these phenomena represent evolved anti-herbivory early warning systems, where plants detect approaching animals through bioelectric field changes before physical contact. Our results challenge conventional understanding of plant sensory capabilities and suggest practical applications in agriculture, healthcare, and human-plant interaction research.
EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models
Authors: Andy Bonnetto, Haozhe Qi, Franklin Leong, Matea Tashkovska, Mahdi Rad, Solaiman Shokur, Friedhelm Hummel, Silvestro Micera, Marc Pollefeys, Alexander Mathis
Date: 2025-06-02 00:00:00
Tasks: Action Recognition, Action Segmentation, Motion Generation

Understanding behavior requires datasets that capture humans while carrying out complex tasks. The kitchen is an excellent environment for assessing human motor and cognitive function, as many complex actions are naturally exhibited in kitchens from chopping to cleaning. Here, we introduce the EPFL-Smart-Kitchen-30 dataset, collected in a noninvasive motion capture platform inside a kitchen environment. Nine static RGB-D cameras, inertial measurement units (IMUs) and one head-mounted HoloLens~2 headset were used to capture 3D hand, body, and eye movements. The EPFL-Smart-Kitchen-30 dataset is a multi-view action dataset with synchronized exocentric, egocentric, depth, IMUs, eye gaze, body and hand kinematics spanning 29.7 hours of 16 subjects cooking four different recipes. Action sequences were densely annotated with 33.78 action segments per minute. Leveraging this multi-modal dataset, we propose four benchmarks to advance behavior understanding and modeling through 1) a vision-language benchmark, 2) a semantic text-to-motion generation benchmark, 3) a multi-modal action recognition benchmark, 4) a pose-based action segmentation benchmark. We expect the EPFL-Smart-Kitchen-30 dataset to pave the way for better methods as well as insights to understand the nature of ecologically-valid human behavior. Code and data are available at https://github.com/amathislab/EPFL-Smart-Kitchen
Effect of Different Concentration and Application Method of Zinc on Yield of Chickpea (Pisum Sativum L.)
Authors: Samaneh Goodarzi, Ali Tavanmand, Hamed Shajari
Date: 2025-05-29 00:00:00

This study evaluated the impact of Zinc (Zn) supplementation on the growth, yield, and seed quality of chickpea (Pisum sativum L.) under the semi-arid conditions of Kerman, Iran, across two growing seasons (2021-2022). A randomized complete block design was used with six treatments, including varying concentrations of zinc sulphate applied via foliar spraying (0.1%, 0.25%, 0.5%) or irrigation (4, 8, 16 kg/ha), each applied twice-one and two months after greening. Results from the first year revealed significant differences in yield and yield components across treatments. The highest seed yield and protein content were achieved with foliar application of 0.5% and irrigation application of 8 kg/ . Zinc application enhanced reproductive processes, including pollen viability and stigma receptivity, leading to improved pod and seed attributes. However, excessive Zn application (e.g., 0.4% Zn) resulted in reduced plant performance, likely due to phytotoxicity. Leaf Zn concentration was significantly higher with 16 kg/ha applied via irrigation, while foliar applications at 0.1% also increased Zn uptake efficiently. The second growing season, however, showed no significant differences in traits across treatments, which was attributed to favorable climatic conditions mitigating Zn deficiency. Zn deficiency remains a critical challenge globally, particularly in calcareous and nutrient depleted soils, adversely affecting plant metabolism, root development, and nitrogen pathways. This study underscores the importance of optimizing Zn supplementation strategies to enhance yield and quality while avoiding toxicity. Findings provide practical recommendations for addressing Zn deficiencies in semi-arid cropping systems, offering valuable insights for sustainable chickpea production
The spatially variable effects of mangroves on flood depths and losses from storm surges in Florida
Authors: Siddharth Narayan, Christopher J. Thomas, Kechi Nzerem, Joss Matthewman, Christine Shepard, Laura Geselbracht, Michael W. Beck
Date: 2025-06-10 00:00:00

Mangroves modify storm surges with impacts to property damages from tropical cyclones (TC), but the magnitude of these effects and their spatial variability are not well understood especially at sub-county scales. We use high-resolution storm surge flood and loss models to examine variation in the effects of mangroves on these losses spatially and by storm intensity in Florida. We estimate that mangroves reduce storm surge losses to properties by $67.5 million annually in Collier County in western Florida with over half the cumulative benefits from storm surges with return periods under 30 years. We estimate the benefits of mangroves during hurricanes Irma (2017) and Ian (2022) as US$ 725 Million and $4.1 Billion. We show that flood depths and losses are always lower for properties landward of mangroves but can increase for properties seaward or between mangroves, underlining the importance of nuanced descriptions of variability in mangrove effects during storms.
Impact of the WHO's 90-70-90 Strategy on HPV-Related Cervical Cancer Control: A Mathematical Model Evaluation in China
Authors: Hua Liu, Chunya Liu, Yumei Wei, Qibin Zhang, Jingyan Ma
Date: 2025-06-06 00:00:00
Tasks: Sensitivity

In August 2020, the World Health Assembly approved the Global Strategy to eliminate cervical cancer, marking the first time that numerous countries committed to eliminating a form of cancer. China introduced the HPV vaccine in 2016 and has made significant advancements in both prevention and treatment strategies. However, due to the relatively late introduction of the vaccine, the burden of cervical cancer in China continues to rise. In light of this, we develop a compartmental model to assess the impact of the WHO's 90-70-90 strategy, along with adult catch-up vaccination, on the control of HPV-induced cervical cancer in China. We analyze the basic properties of the model and provide proofs of the local and global asymptotic stability of the equilibrium points. Additionally, a sensitivity analysis is performed, and we use the MCMC algorithm to fit the number of new cervical cancer cases and deaths in China from 1990 to 2021. The estimated basic reproduction number before and after the introduction of the HPV vaccine in China is 1.5026 (95% CI: 1.4051-1.6002) and 1.0726 (95% CI: 0.9384-1.2067), respectively. The sensitivity analysis reveals that screening, as a non-pharmaceutical intervention, plays a crucial role in controlling the spread of the disease. We apply the 90-70-90 strategy to predict the future number of new cervical cancer cases and deaths in China. The results indicate that prioritizing the 70-90 target combination is the most cost-effective approach and can achieve the goal of zero new cervical cancer cases by 2061. Finally, an optimal control model is developed to explore the best implementation strategies for HPV vaccination and screening under various plausible scenarios.
Counting rankings of tree-child networks
Authors: Qiang Zhang, Mike Steel
Date: 2025-06-06 00:00:00

Rooted phylogenetic networks allow biologists to represent evolutionary relationships between present-day species by revealing ancestral speciation and hybridization events. A convenient and well-studied class of such networks are `tree-child networks' and a `ranking' of such a network is a temporal ordering of the ancestral speciation and hybridization events. In this short note, we show how to efficiently count such rankings on any given binary (or semi-binary) tree-child network. We also consider a class of binary tree-child networks that have exactly one ranking, and investigate further the relationship between ranked-tree child networks and the class of `normal' networks. Finally, we provide an explicit asymptotic expression for the expected number of rankings of a tree-child network chosen uniformly at random.
Size-Structured Population Dynamics
Authors: Odo Diekmann, Francesca Scarabel
Date: 2025-06-03 00:00:00

This chapter focuses on variable maturation delay or, more precisely, on the mathematical description of a size-structured population consuming an unstructured resource. When the resource concentration is a known function of time, we can describe the growth and survival of individuals quasi-explicitly, i.e., in terms of solutions of ordinary differential equations (ODE). Reproduction is captured by a (non-autonomous) renewal equation, which can be solved by generation expansion. After these preparatory steps, a contraction mapping argument is needed to construct the solution of the coupled consumer-resource system with prescribed initial conditions. As we shall show, this interpretation-guided constructive approach does in fact yield weak solutions of a familiar partial differential equation (PDE). A striking difficulty with the PDE approach is that the solution operators are, in general, not differentiable, precluding a linearized stability analysis of steady states. This is a manifestation of the state-dependent delay difficulty. As a (not entirely satisfactory and rather technical) way out, we present a delay equation description in terms of the history of both the $p$-level birth rate of the consumer population and the resource concentration. We end by using pseudospectral approximation to derive a system of ODE and demonstrating its use in a numerical bifurcation analysis. Importantly, the state-dependent delay difficulty dissolves in this approximation.