pwc-archive/papers-with-abstracts huggingface dataset:
Towards Unified Neural Decoding with Brain Functional Network Modeling
Authors: Di wu, Linghao Bu, Yifei Jia, Lu Cao, Siyuan Li, Siyu Chen, Yueqian Zhou, Sheng Fan, Wenjie Ren, Dengchang Wu, Kang Wang, Yue Zhang, Yuehui Ma, Jie Yang, Mohamad Sawan
Date: 2025-05-30 00:00:00
Tasks: Data Integration, Self-Supervised Learning

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.
Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
Authors: Xingyu Liu, Yubin Li, Guozhang Chen
Date: 2025-05-29 00:00:00

A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, to learn this underlying representation from detailed connectivity maps of mouse cortical microcircuits. Our model successfully captures the essential structural information of these circuits in a compressed latent space. We found that specific, interpretable directions within this space directly relate to understandable network properties. Building on this, we demonstrate a novel method to controllably generate new, synthetic microcircuits with desired structural features by navigating this latent space. This work offers a new way to investigate the design principles of neural circuits and explore how structure gives rise to function, potentially informing the development of more advanced artificial neural networks.
Natural Intelligence: the information processing power of life
Authors: Seth Lloyd, Michele Reilly
Date: 2025-06-19 00:00:00

Merely by existing, all physical systems contain information, and physical dynamics transforms and processes that information. This note investigates the information processing power of living systems. Living systems harvest free energy from the sun, from geothermal sources, and from each other. They then use that free energy to drive the complex set of chemical interactions that underlie life. All molecules -- be they simple molecules such as water, or complex molecules such as DNA -- register information via their chemical composition. When these molecules undergo chemical reactions, that information is transformed and processed. These chemical transformations can be thought of as elementary logical operations: such bio-ops include the absorption of a photon in a chromophore during photosynthesis, the formation or breaking of covalent, hydrogen, and van der Waals bonds in the process of metabolism and reproduction, or the release of a neurotransmitter molecule when a synapse fires in the brain. This paper estimates the total number of bio-ops that have been, and are being performed, by life on earth. We find that the current number of bio-ops performed by all life on earth is around $10^{33}-10^{35}$ bio-ops per second. The cells in an individual human being perform around $10^{20}-10^{22}$ bio-ops per second, comparable to the information processing power of all the computers, cell phones, and server farms on earth. Depending on how one defines a neural operation, at most a few percent of human bio-ops take place in the firing of neurons and synapses in the brain. Over the course of life on earth, about $10^{50}-10^{52}$ bio-ops have taken place.
Geo-Somatic Resonance Theory: A Vibrational Framework for Sleep as Planetary Entrainment
Authors: Brathikan Vijayamohan Mankayarkarasi
Date: 2025-06-17 00:00:00

Sleep is commonly studied through neurochemical, evolutionary, and behavioral frameworks, typically emphasizing circadian rhythms and energy conservation. However, these approaches do not fully explain a deeper biophysical question: why does sleep universally involve physical stillness, a lying posture, and disconnection from conscious control? This paper introduces a new hypothesis that sleep is not merely a biological function, but a state of vibrational synchronization between the human body and natural frequencies generated by the Earth. In this state, the body reduces its autonomous activity and aligns with external environmental rhythms, allowing for energy restoration, internal recalibration, and systemic reorganization. This perspective reframes life as a continuous process of internally driven vibration influenced by external physical fields. The proposed model offers new avenues for understanding aging, health, death, and consciousness.
An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
Authors: Styliani-Christina Fragkouli, Somya Iqbal, Lisa Crossman, Barbara Gravel, Nagat Masued, Mark Onders, Devesh Haseja, Alex Stikkelman, Alfonso Valencia, Tom Lenaerts, Fotis Psomopoulos, Pilib Ó Broin, Núria Queralt-Rosinach, Davide Cirillo
Date: 2025-06-17 00:00:00
Tasks: scientific discovery, Synthetic Data Evaluation

Synthetic data has emerged as a powerful resource in life sciences, offering solutions for data scarcity, privacy protection and accessibility constraints. By creating artificial datasets that mirror the characteristics of real data, allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of synthetic data in Life Sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of synthetic data evaluation metrics in several Life Sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing synthetic data. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in synthetic data but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of synthetic data in scientific discovery, clinical practice and beyond.
Scaling the human niche
Authors: Marcus J. Hamilton
Date: 2025-06-26 00:00:00

The human niche represents the intersection of biological, ecological, cultural, and technological processes that have co-evolved to shape human adaptation and societal complexity. This paper explores the human niche through the lens of macroecological scaling theory, seeking to define and quantify the dimensions along which human ecological strategies have diversified. By leveraging concepts from classic niche theory, niche construction, and complex adaptive systems, I develop a framework for understanding human ecology as both predictable within mammalian scaling relationships and uniquely divergent due to social, cognitive, and technological factors. Key dimensions of the human niche-metabolism, cognition, sociality, and computation-are examined through scaling laws that structure human interactions with the environment and each other. The paper demonstrates how human niche expansion over evolutionary time has been characterized by increasing metabolic consumption, information processing capacity, and the formation of larger, more interconnected social networks. This cumulative trajectory has led to the unprecedented scale of contemporary human societies, with implications for sustainability, economic development, and future niche expansion, including into space. The study underscores the need for an integrative, quantitative approach to human ecology that situates human adaptability within broader ecological and evolutionary constraints.
Balancing the cellular budget: lessons in metabolism from microbes to cancer
Authors: B. Vibishan, Mohit Kumar Jolly, Akshit Goyal
Date: 2025-06-25 00:00:00

Cancer cells are often seen to prefer glycolytic metabolism over oxidative phosphorylation even in the presence of oxygen-a phenomenon termed the Warburg effect. Despite significant strides in the decades since its discovery, a clear basis is yet to be established for the Warburg effect and why cancer cells show such a preference for aerobic glycolysis. In this review, we draw on what is known about similar metabolic shifts both in normal mammalian physiology and overflow metabolism in microbes to shed new light on whether aerobic glycolysis in cancer represents some form of optimisation of cellular metabolism. From microbes to cancer, we find that metabolic shifts favouring glycolysis are sometimes driven by the need for faster growth, but the growth rate is by no means a universal goal of optimal metabolism. Instead, optimisation goals at the cellular level are often multi-faceted and any given metabolic state must be considered in the context of both its energetic costs and benefits over a range of environmental contexts. For this purpose, we identify the conceptual framework of resource allocation as a potential testbed for the investigation of the cost-benefit balance of cellular metabolic strategies. Such a framework is also readily integrated with dynamical systems modelling, making it a promising avenue for new answers to the age-old question of why cells, from cancers to microbes, choose the metabolic strategies they do.
Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra
Authors: Alan N. Amin, Andres Potapczynski, Andrew Gordon Wilson
Date: 2025-06-24 00:00:00

To understand how genetic variants in human genomes manifest in phenotypes -- traits like height or diseases like asthma -- geneticists have sequenced and measured hundreds of thousands of individuals. Geneticists use this data to build models that predict how a genetic variant impacts phenotype given genomic features of the variant, like DNA accessibility or the presence of nearby DNA-bound proteins. As more data and features become available, one might expect predictive models to improve. Unfortunately, training these models is bottlenecked by the need to solve expensive linear algebra problems because variants in the genome are correlated with nearby variants, requiring inversion of large matrices. Previous methods have therefore been restricted to fitting small models, and fitting simplified summary statistics, rather than the full likelihood of the statistical model. In this paper, we leverage modern fast linear algebra techniques to develop DeepWAS (Deep genome Wide Association Studies), a method to train large and flexible neural network predictive models to optimize likelihood. Notably, we find that larger models only improve performance when using our full likelihood approach; when trained by fitting traditional summary statistics, larger models perform no better than small ones. We find larger models trained on more features make better predictions, potentially improving disease predictions and therapeutic target identification.
Rethinking Ecological Measures Of Functional Diversity
Authors: Ines Meraoumia, Adji Bousso Dieng
Date: 2025-06-21 00:00:00
Tasks: Diversity

Understanding functional diversity, the range and variability of species' roles and actions within their communities, is key to predicting and preserving the functions that sustain both nature and human well-being. In this paper, we provide a comprehensive review of the literature on functional diversity measurement. We begin by consolidating essential criteria that effective measures of functional diversity should meet. We then evaluate fifteen widely used functional diversity metrics against these criteria and assess their performance across six synthetic ecosystem scenarios where optimal behavior is known. Surprisingly, our analysis reveals that none of the widely used metrics fully satisfy all the established requirements, and all fail in at least one ecosystem scenario. In particular, we find that almost all metrics flagrantly violate set monotonicity and distance monotonicity, requirements that adding a novel species should increase diversity, and that the magnitude of that increase should grow with trait dissimilarity. We also find that metrics fail to decline when rare, functionally extreme species are lost, and even increase when a perfectly redundant species is added. These critical flaws leave them blind to the very biodiversity loss that functional diversity measures are intended to detect. Our findings underscore the urgent need to develop a new generation of functional diversity metrics that more accurately reflect ecological realities.
Modeling and Inferring Metacommunity Dynamics with Maximum Caliber
Authors: Zachary Jackson, Mathew A. Leibold, Robert D. Holt, BingKan Xue
Date: 2025-06-20 00:00:00

A major challenge for community ecology is to use distribution patterns to infer basic parameters of dynamical models without conducting laborious experimental manipulations. We present a novel framework drawn from statistical physics -- Maximum Caliber -- for characterizing the temporal dynamics of complex ecological systems in spatially extended landscapes and inferring parameters from temporal data. As an extension of Maximum Entropy modeling, Maximum Caliber models the probability of possible trajectories of a stochastic system, rather than focusing on system states. We demonstrate the ability of the Maximum Caliber framework to capture ecological processes ranging from near- to far- from-equilibrium, using an array of species interaction motifs including random interactions, apparent competition, intraguild competition, and non-transitive competition, along with dispersal among multiple patches. For spatio-temporal data of species occurrence in a metacommunity, the parameters of a Maximum Caliber model can be estimated through a simple logistic regression to reveal migration rates between patches, magnitudes of interactions between species, and effects of intrinsic local environmental suitabilities. We test the accuracy of the method over a range of system sizes and time periods, and find that these parameters can be estimated without bias. We introduce entropy production as a system-level measure of disequilibrium, and use ``pseudo-$R^2$'' to characterize the predictability of the system. We show that our model can predict the dynamics of metacommunities much better than steady state models, when the system is far from equilibrium. The capacity to estimate basic parameters of dynamical metacommunity models from spatio-temporal data represents an important breakthrough for the study of metacommunities with application to practical problems in conservation and restoration ecology.
Trilobite tridents: hydrodynamic lift and stability mechanisms for queue formation
Authors: Hugh A. Trenchard, Carlton E. Brett, Matjaz Perc
Date: 2025-06-18 00:00:00

The bizarre trident-like cephalic projections of Walliserops trifurcatus have previously been interpreted as sexually selected weapons for intraspecific combat. We propose an alternative hypothesis grounded in biomechanics and collective behavior, that tridents evolved as adaptations for hydrodynamic lift and queue stability, conferring energetic advantages during group locomotion. Under this hypothesis, lift could offset gravitational forces, enabling greater locomotor efficiency, while mechanically linked formations, where tridents rested on the pygidia of leading individuals, enhanced pitch and roll stability and minimized costly accelerations and collisions. These formations also facilitated hydrodynamic drafting, allowing weaker individuals to conserve energy and remain integrated within the group. The trident's structure, though inefficient for solitary lift or combat, functioned effectively in cooperative formations, suggesting that its original selective advantage lay not in individual performance but in enhancing group cohesion and efficiency. Rather than emerging from competitive pressures alone, the trident may have arisen through selection for coordinated, cooperative movement -- potentially representing a precursor stage to traits later exapted for sexual selection.
Eukaryotic ancestry in a finite world
Authors: Juliette Luiselli, Manuel Lafond
Date: 2025-06-18 00:00:00

Following genetic ancestry in eukaryote populations poses several open problems due to sexual reproduction and recombination. The history of extant genetic material is usually modeled backwards in time, but tracking chromosomes at a large scale is not trivial, as successive recombination events break them into several segments. For this reason, the behavior of the distribution of genetic segments across the ancestral population is not fully understood. Moreover, as individuals transmit only half of their genetic content to their offspring, after a few generations, it is possible that ghosts arise, that is, genealogical ancestors that transmit no genetic material to any individual. While several theoretical predictions exist to estimate properties of ancestral segments or ghosts, most of them rely on simplifying assumptions such as an infinite population size or an infinite chromosome length. It is not clear how well these results hold in a finite universe, and current simulators either make other approximations or cannot handle the scale required to answer these questions. In this work, we use an exact back-in-time simulator of large diploid populations experiencing recombination that tracks genealogical and genetic ancestry, without approximations. We focus on the distinction between genealogical and genetic ancestry and, additionally, we explore the effects of genome structure on ancestral segment distribution and the proportion of genetic ancestors. Our study reveals that some of the theoretical predictions hold well in practice, but that, in several cases, it highlights discrepancies between theoretical predictions assuming infinite parameters and empirical results in finite populations, emphasizing the need for cautious application of mathematical models in biological contexts.
Modeling Transmission Dynamics of Tuberculosis: Parameter Estimation and Sensitivity Analysis Using Real-World Data
Authors: Moksina Seyid, Abdu Mohammed Seid, Yassin Tesfaw Abebe
Date: 2025-06-18 00:00:00
Tasks: parameter estimation, Sensitivity

Tuberculosis (TB) continues to pose a major public health challenge, particularly in high-burden regions such as Ethiopia, necessitating a more profound understanding of its transmission dynamics. In this study, we developed an SVEITRS compartmental model to investigate the transmission dynamics of TBs, utilizing real data from Ethiopia from 2011-2021. Model parameters were estimated via two methods: nonlinear least squares and maximum likelihood, with maximum likelihood providing more accurate and reliable results, as confirmed by a test case. The model's stability analysis indicated that there is a disease-free equilibrium in areas where the basic reproduction number ($\mathscr{R}_0$) is less than one. The results suggest that optimal conditions could lead to the elimination of TB. On the other hand, there is an endemic equilibrium in areas where $\mathscr{R}_0$ is greater than one, which means that the disease is still present. Sensitivity analysis revealed important factors affecting TB levels: higher natural death rates, vaccination rates, treatment rates, and disease-related death rates lower TB cases, whereas higher recruitment rates, contact rates, infection rates, and loss of vaccine protection increase its spread. These findings highlights to the necessity of enhancing vaccination, treatment, and recovery strategies while addressing drivers of transmission to achieve TB control in Ethiopia. This study provides useful advice for guiding TB control efforts and public health interventions in Ethiopia and similar regions.
Understanding the rift between update rules in Evolutionary Graph Theory: The intrinsic death rate drives star graphs from amplifying to suppressing natural selection
Authors: Max Dew, Christopher E. Overton
Date: 2025-06-18 00:00:00

Evolutionary graph theory is the study of evolutionary dynamics in structured populations. A well-known problem in evolutionary graph theory is that the spread of mutation (measured by fixation probability) is impacted by the graph type chosen and the update rule. For example, the star graph is an amplifier of natural selection under the birth-death with fitness on birth (Bd) update rule but a suppressor of natural selection under the death-birth with fitness on birth (dB) update rule. A continuous-time EGT model has been found to replicate Bd and dB results as special cases. Using this model, we show that changing the natural (intrinsic) death rate can cause a shift from Bd to dB dynamics. Assuming the mutant is advantageous, we show that if the natural death rate is greater than $\frac{1}{\sqrt{N}}$ the star is a suppressor, where $N$ is the number of nodes. As $N \longrightarrow \infty$, the natural death rate required to drive the star to a suppressor tends towards zero, so as the size of the graph increases, the star graph is likely to be suppressing for any non-zero natural death rate.
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
Authors: Conor Rosato, Joshua Murphy, Simon Maskell, John Harris
Date: 2025-06-17 00:00:00
Tasks: Bayesian Inference

The introduction of the rotavirus vaccine in the United Kingdom (UK) in 2013 led to a noticeable decline in laboratory reports in subsequent years. To assess the impact of vaccination on rotavirus transmissibility we calibrated a stochastic compartmental epidemiological model using Sequential Monte Carlo (SMC) methods. Our analysis focuses on estimating the time-varying transmissibility parameter and documenting its evolution before and after vaccine rollout. We observe distinct periods of increasing and decreasing transmissibility, reflecting the dynamic response of rotavirus spread to immunization efforts. These findings improve our understanding of vaccination-driven shifts in disease transmission and provide a quantitative framework for evaluating long-term epidemiological trends.