pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve
Authors: Patrícia Pereira, Anders Lansner, Pawel Herman
Date: 2025-04-20 00:00:00
Tasks: Incremental Learning, Retrieval

The human lifespan retrieval curve describes the proportion of recalled memories from each year of life. It exhibits a reminiscence bump - a tendency for aged people to better recall memories formed during their young adulthood than from other periods of life. We have modelled this using an attractor Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. We systematically studied the synaptic mechanisms underlying the reminiscence bump in this network model after introduction of an exponential decay of the synaptic learning rate and examined its sensitivity to network size and other relevant modelling mechanisms. The most influential parameters turned out to be the synaptic learning rate at birth and the time constant of its exponential decay with age, which set the bump position in the lifespan retrieval curve. The other parameters mainly influenced the general magnitude of this curve. Furthermore, we introduced the parametrization of the recency phenomenon - the tendency to better remember the most recent memories - reflected in the curve's upwards tail in the later years of the lifespan. Such recency was achieved by adding a constant baseline component to the exponentially decaying synaptic learning rate.
Quantum information theoretic approach to the hard problem of consciousness
Authors: Danko D. Georgiev
Date: 2025-04-13 00:00:00

Functional theories of consciousness, based on emergence of conscious experiences from the execution of a particular function by an insentient brain, face the hard problem of consciousness of explaining why the insentient brain should produce any conscious experiences at all. This problem is exacerbated by the determinism characterizing the laws of classical physics, due to the resulting lack of causal potency of the emergent consciousness, which is not present already as a physical quantity in the deterministic equations of motion of the brain. Here, we present a quantum information theoretic approach to the hard problem of consciousness that avoids all of the drawbacks of emergence. This is achieved through reductive identification of first-person subjective conscious states with unobservable quantum state vectors in the brain, whereas the anatomically observable brain is viewed as a third-person objective construct created by classical bits of information obtained during the measurement of a subset of commuting quantum brain observables by the environment. Quantum resource theory further implies that the quantum features of consciousness granted by quantum no-go theorems cannot be replicated by any classical physical device.
BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification
Authors: Jiaxing Xu, Kai He, Yue Tang, Wei Li, Mengcheng Lan, Xia Dong, Yiping Ke, Mengling Feng
Date: 2025-04-12 00:00:00

Neurological conditions, such as Alzheimer's Disease, are challenging to diagnose, particularly in the early stages where symptoms closely resemble healthy controls. Existing brain network analysis methods primarily focus on graph-based models that rely solely on imaging data, which may overlook important non-imaging factors and limit the model's predictive power and interpretability. In this paper, we present BrainPrompt, an innovative framework that enhances Graph Neural Networks (GNNs) by integrating Large Language Models (LLMs) with knowledge-driven prompts, enabling more effective capture of complex, non-imaging information and external knowledge for neurological disease identification. BrainPrompt integrates three types of knowledge-driven prompts: (1) ROI-level prompts to encode the identity and function of each brain region, (2) subject-level prompts that incorporate demographic information, and (3) disease-level prompts to capture the temporal progression of disease. By leveraging these multi-level prompts, BrainPrompt effectively harnesses knowledge-enhanced multi-modal information from LLMs, enhancing the model's capability to predict neurological disease stages and meanwhile offers more interpretable results. We evaluate BrainPrompt on two resting-state functional Magnetic Resonance Imaging (fMRI) datasets from neurological disorders, showing its superiority over state-of-the-art methods. Additionally, a biomarker study demonstrates the framework's ability to extract valuable and interpretable information aligned with domain knowledge in neuroscience. The code is available at https://github.com/AngusMonroe/BrainPrompt
Exploring Visual Complaints through a test battery in Acquired Brain Injury Patients: A Detailed Analysis of the DiaNAH Dataset
Authors: Gonçalo Hora de Carvalho
Date: 2025-03-19 00:00:00
Tasks: AutoML, Imputation

This study investigated visual impairment complaints in a sample of 948 Acquired Brain Injury (ABI) patients using the DiaNAH dataset, emphasizing advanced machine learning techniques for managing missing data. Patients completed a CVS questionnaire capturing eight types of visual symptoms, including blurred vision and altered contrast perception. Due to incomplete data, 181 patients were excluded, resulting in an analytical subset of 767 individuals. To address the challenge of missing data, an automated machine learning (AutoML) approach was employed for data imputation, preserving the distributional characteristics of the original dataset. Patients were grouped according to singular and combined complaint clusters derived from the 40,320 potential combinations identified through the CVS questionnaire. A linear correlation analysis revealed minimal to no direct relationship between patient-reported visual complaints and standard visual perceptual function tests. This study represents an initial systematic attempt to understand the complex relationship between subjective visual complaints and objective visual perceptual assessments in ABI patients. Given the limitations of sample size and variability, further studies with larger populations are recommended to robustly explore these complaint clusters and their implications for visual perception following brain injury.
Treats or Affection? Understanding Reward Preferences in Indian Free-ranging Dogs for Bonding with Humans
Authors: Srijaya Nandi, Aesha Lahiri, Tuhin Subhra Pal, Anamitra Roy, Rittika Bairagya, Anindita Bhadra
Date: 2025-05-05 00:00:00

Free-ranging dogs constitute approximately 80% of the global dog population. These dogs are freely breeding and live without direct human supervision, making them an ideal model system for studying dog-human relationships. Living in close proximity with humans, free-ranging dogs in India frequently interact with people, and previous studies suggest that humans are a crucial part of their social environment. Positive reinforcement in the form of food and petting is commonly received from humans. In this study, we investigated which type of reward, food or petting, is more influential in shaping positive social associations with unfamiliar humans. Field trials were conducted on 61 adult free-ranging dogs in Nadia district, West Bengal, India. During a familiarization phase, two unfamiliar individuals each provided either food or petting to the dogs. This was followed by a choice test in which dogs could choose between the two individuals. A choice test following the familiarization phase was conducted on the first 5 days, while only choice tests were conducted on the subsequent 5 days. Results showed that on the first day, dogs significantly preferred the individual providing food. However, from the second day onward, preference for either individual was no different from chance. These findings suggest that while food is a stronger short-term motivator, repeated interactions involving either food or petting contribute equally to the formation of positive social associations over time. This study sheds light on the dynamics of dog-human relationship development in Indian free-ranging dog populations and highlights the nuanced role of different types of rewards in fostering affiliative bonds.
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Authors: JiaWei He, Boya Zhang, Hossein Rouhizadeh, Yingjian Chen, Rui Yang, Jin Lu, Xudong Chen, Nan Liu, Irene Li, Douglas Teodoro
Date: 2025-05-02 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, their application in the biomedical domain presents unique challenges, particularly regarding factual accuracy and up-to-date knowledge integration. Retrieval Augmented Generation (RAG) has emerged as a promising solution to address these challenges by combining the generative capabilities of LLMs with external knowledge retrieval. This comprehensive survey examines the application of RAG in the biomedical domain, focusing on its technological components, available datasets, and clinical applications. We present a systematic analysis of retrieval methods, ranking strategies, and generation models, while also exploring the challenges and future directions in this rapidly evolving field. Our work provides researchers and practitioners with a thorough understanding of the current state of biomedical RAG systems and identifies key areas for future research and development.
Defining and finding lifelike entities with a lazy filter
Authors: Mario Martinez-Saito
Date: 2025-04-21 00:00:00

Our binary intuitive understanding of life and lifelikeness is good enough for daily life, but not for research in the natural sciences. Here we propose an operational definition of lifeness of a particular entity as a scalar, product of its defining information (algorithmic complexity) integrated over its lifetime. We provide a parameter-free dynamical filtering algorithm that can efficiently and on-the-fly fit configurations of entities constituted by moving particles to a tree structure that can model a wide range of hierarchical system entities, while extracting and calculating the complexity, lifespan, and lifeness of the entity and all of its constituting subtrees or subentities. We simulated 41 interacting particle worlds and found preliminary evidence suggesting that the lifeness of entities is associated with the distance to criticality, as roughly measured by the range of the pairwise interaction forces of the elemental particles of the worlds they inhabit. This study is a proof of concept that defining, measuring, and quantifying lifeness is (1) feasible, useful for (2) simplifying theoretical discussions, for (3) hierarchically assessing biotic properties such as number of hierarchical levels and predicted longevity and for (4) classifying both artificial and biological entities, respectively via computer simulations and a combination of evolutionary and molecular biology approaches.
A mathematical model of human population reproduction through marriage
Authors: Hisashi Inaba, Shoko Konishi
Date: 2025-05-03 00:00:00

We develop a linear one-sex dynamical model of human population reproduction through marriage. In our model, we assume that a woman can repeatedly marry and divorce but only married women produce children. The iterative marriage process is formulated by a three-state compartmental model, which is described by the system of McKendrick equations with the duration-specific birth rate by age at marriage. In order to see the effect of changes in nuptiality on fertility, new formulae for the reproduction indices are given. In particular, the total fertility rate (TFR) is considered as the product of the indices of marriage and marital fertility. Using the vital statistics of Japan, we can show that our model can provide a reasonable estimate of the Japanese TFR, and its possible future scenario.
Mathematical epidemiology of infectious diseases: an ongoing challenge
Authors: Odo Diekmann, Hisashi Inaba, Horst R. Thieme
Date: 2025-05-02 00:00:00
Tasks: Epidemiology

The aim of this paper is to present an, admittedly somewhat subjective, bird's eye view of the mathematical theory concerning the spread of an infectious disease in a susceptible host population with static structure, culminating in a future-oriented description of various modelling challenges.
Fluctuating growth rates link turnover and unevenness in species-rich communities
Authors: Emil Mallmin, Arne Traulsen, Silvia De Monte
Date: 2025-05-02 00:00:00
Tasks: Diversity

The maintenance of diversity, the `commonness of rarity', and compositional turnover are ubiquitous features of species-rich communities. Through a stylized model, we consider how these features reflect the interplay between environmental stochasticity, intra- and interspecific competition, and dispersal. We show that, even when species have the same time-average fitness, fitness fluctuations tend to drive the community towards ever-growing unevenness and species extinctions, but self-limitation and/or dispersal allow high-diversity states to be sustained. We then analyze the species-abundance distribution and the abundance distribution of individual species over time in such high-diversity states. Their shapes vary predictably along two axes -- Exclusion-Stabilization and Exclusion-Buffering -- that describe the relative strength of the underlying ecological processes. Predicted shapes cover different empirically observed cases, notably power-law and unimodal distributions. An effective focal-species model allows us to relate static abundance distributions with turnover dynamics, also when species have different mean fitness. The model suggests how community statistics and time series of individual species can inform on the relative importance of the ecological processes that structure diversity.
Predatory dynamics in susceptible and resistant $\textit{Eriopis connexa}$ populations
Authors: Anna Mara Ferreira Maciel, Gabriel Rodrigues Palma, Lucas dos Anjos, Lucas Santos Canuto, Wesley Augusto Conde Godoy, Rafael de Andrade Moral
Date: 2025-05-01 00:00:00

The ladybird $\textit{Eriopis connexa}$ (Germar, 1824), a voracious aphid predator, faces challenges from insecticide applications, compromising biological control. As a result, there has been an increase in the number of studies analysing the resistance and susceptibility of ladybirds. Some studies have found that resistant populations exhibit distinct predation and foraging behaviour compared to susceptible ones. This study models the population dynamics of resistant and susceptible $\textit{E. connexa}$ preying on $\textit{Aphis gossypii}$ Glover, 1877 and $\textit{Myzus persicae}$ (Sulzer, 1776). We constructed a logistic model with density dependence and type-II functional response to analyse predation dynamics, incorporating bifurcation analysis on predation parameters (attack rate and handling time) and the mortality rate of susceptible ladybirds. We simulated scenarios with/without insecticide application and with/without aphid resistance. To simulate the effects of insecticide applications, the parameters related to aphids' intrinsic growth rate ($r_1$ and $r_2$) change to reflect the responses of susceptible and resistant populations. The same approach is used concerning the mortality rate of ladybirds ($d_2$ and $d_3$). Our results demonstrate that mortality, attack rate, and handling time are critical in shaping predator-prey interactions. Temporal simulations revealed fluctuating abundances, highlighting the fragility of these interactions under insecticide stress. Therefore, this study contributes to understanding the ecological implications of insecticides, which disrupt natural predation dynamics, and shows how variations in behavioural rates can impact prey control. This research demonstrated the importance of integrated strategies that balance insecticide applications with preserving natural enemies and promoting sustainable agricultural practices.
Beyond level-1: Identifiability of a class of galled tree-child networks
Authors: Elizabeth S. Allman, Cecile Ane, Hector Banos, John A. Rhodes
Date: 2025-04-29 00:00:00

Inference of phylogenetic networks is of increasing interest in the genomic era. However, the extent to which phylogenetic networks are identifiable from various types of data remains poorly understood, despite its crucial role in justifying methods. This work obtains strong identifiability results for large sub-classes of galled tree-child semidirected networks. Some of the conditions our proofs require, such as the identifiability of a network's tree of blobs or the circular order of 4 taxa around a cycle in a level-1 network, are already known to hold for many data types. We show that all these conditions hold for quartet concordance factor data under various gene tree models, yielding the strongest results from 2 or more samples per taxon. Although the network classes we consider have topological restrictions, they include non-planar networks of any level and are substantially more general than level-1 networks -- the only class previously known to enjoy identifiability from many data types. Our work establishes a route for proving future identifiability results for tree-child galled networks from data types other than quartet concordance factors, by checking that explicit conditions are met.
DLCM: a versatile multi-level solver for heterogeneous multicellular systems
Authors: Erik Blom, Stefan Engblom
Date: 2025-04-29 00:00:00
Tasks: Computational Efficiency

Computational modeling of multicellular systems may aid in untangling cellular dynamics and emergent properties of biological cell populations. A key challenge is to balance the level of model detail and the computational efficiency, while using physically interpretable parameters to facilitate meaningful comparisons with biological data. For this purpose, we present the DLCM-solver (discrete Laplacian cell mechanics), a flexible and efficient computational solver for spatial and stochastic simulations of populations of cells, developed from first principle to support mechanistic investigations. The solver has been designed as a module in URDME, the unstructured reaction-diffusion master equation open software framework, to allow for the integration of intra-cellular models with extra-cellular features handled by the DLCM. The solver manages discrete cells on a fixed lattice and reaction-transport events in a continuous-time Markov chain. Space-continuous micro-environment quantities such as pressure and chemical substances are supported by the framework, permitting a variety of modeling choices concerning chemotaxis, mechanotaxis, nutrient-driven cell growth and death, among others. An essential and novel feature of the DLCM-solver is the coupling of cellular pressure to the curvature of the cell populations by elliptic projection onto the computational grid, with which we can include effects from surface tension between populations. We demonstrate the flexibility of the framework by implementing benchmark problems of cell sorting, cellular signaling, tumor growth, and chemotaxis models. We additionally formally analyze the computational complexity and show that it is theoretically optimal for systems based on pressure-driven cell migration. In summary, the solver balances efficiency and a relatively fine resolution, while supporting a high level of interpretability.
Computational Typology
Authors: Gerhard Jäger
Date: 2025-04-22 00:00:00
Tasks: Diversity

Typology is a subfield of linguistics that focuses on the study and classification of languages based on their structural features. Unlike genealogical classification, which examines the historical relationships between languages, typology seeks to understand the diversity of human languages by identifying common properties and patterns, known as universals. In recent years, computational methods have played an increasingly important role in typological research, enabling the analysis of large-scale linguistic data and the testing of hypotheses about language structure and evolution. This article provides an illustration of the benefits of computational statistical modeling in typology.
Warming demands extensive tropical but minimal temperate management in plant-pollinator networks
Authors: Adrija Datta, Sarth Dubey, Tarik C. Gouhier, Auroop R. Ganguly, Udit Bhatia
Date: 2025-04-28 00:00:00
Tasks: Management

Anthropogenic warming impacts ecological communities and disturbs species interactions, particularly in temperature sensitive plant pollinator networks. While previous assessments indicate that rising mean temperatures and shifting temporal variability universally elevate pollinator extinction risk, many studies often overlook how plant-pollinator networks of different ecoregions require distinct management approaches. Here, we integrate monthly near-surface temperature projections from various Shared Socioeconomic Pathways of CMIP6 Earth System Models with region-specific thermal performance parameters to simulate population dynamics in 11 plant pollinator networks across tropical, temperate, and Mediterranean ecosystems. Our results show that tropical networks, already near their thermal limits, face pronounced (50 percent) pollinator declines under high-emissions scenarios (SSP5-8.5). Multi-species management targeting keystone plants emerges as a critical strategy for stabilizing these high risk tropical systems, boosting both pollinator abundance and evenness. In contrast, temperate networks remain well below critical temperature thresholds, with minimal (5 percent) pollinator declines and negligible gains from any intensive management strategy. These findings challenge single-species models and uniform-parameter frameworks, which consistently underestimate tropical vulnerability while overestimating temperate risk. We demonstrate that explicitly incorporating complex network interactions, region-specific thermal tolerances, and targeted multi species interventions is vital for maintaining pollination services. By revealing when and where limited interventions suffice versus extensive management becomes indispensable, our study provides a clear blueprint for adaptive, ecosystem specific management under accelerating climate change.
PhyloProfile v2 -- Exploring multi-layered phylogenetic profiles at scale
Authors: Vinh Tran, Ingo Ebersberger
Date: 2025-04-28 00:00:00
Tasks: Dimensionality Reduction

Phylogenetic profiles visualize the presence-absence pattern of genes across taxa and are essential for delineating the evolutionary fate of genes and gene families. Integrating phylogenetic profiles across many genes and taxa reveals patterns of coevolution, aiding the predictions of gene functions and interactions. The surge of genome sequences generated by biodiversity genomics projects allows to compile phylogenetic profiles at an unprecedented scale. PhyloProfile v2 was designed to cope with the novel challenges of visualizing and analyzing phylogenetic profiles comprising millions of pairwise orthology relationships. By providing the ability to interact with the visualization and dynamically filter the data, PhyloProfile v2 facilitates a seamless transition from survey analyses across thousands of genes and taxa down to the feature architecture comparison of two ortholog pairs within the same analysis. As one key innovation, PhyloProfile v2 allows the display of phylogenetic profiles in 2D or 3D using dimensionality reduction techniques. This novel perspective eases, for example, the identification of taxa with similar presence/absence patterns of genes irrespective of their phylogenetic relationships. PhyloProfile v2 is available as an R package at Bioconductor https://doi.org/doi:10.18129/B9.bioc.PhyloProfile. The open-source code and documentation are provided under MIT license at https://github.com/BIONF/PhyloProfile