pwc-archive/papers-with-abstracts huggingface dataset:
Tournament versus Circulant: On Simulating 7-Species Evolutionary Spatial Cyclic Games with Ablated Predator-Prey Networks as Models of Biodiversity
Authors: Dave Cliff
Date: 2024-10-04 00:00:00

Computer simulations of minimal population-dynamics models have long been used to explore questions in ecosystems coexistence and species biodiversity, via simple agent-based models of three interacting species, referred to as $R$, $P$, and $S$, and where individual agents compete with one another in predator/prey contests that are determined by the cyclic dominance rules of the Rock-Paper-Scissors game. Recent publications have explored the dynamics of five-species models, based on the Rock-Paper-Scissors-Lizard-Spock (RPSLS) game. A 2022 paper by Zhong et al. reported simulation studies of species coexistence in spatial RPSLS systems in which one or more directed edges are ablated from the five-vertex tournament digraph defining the RPSLS game: Zhong et al. showed that the ablation of a single inter-species interaction can lead to a collapse in biodiversity. In this paper I present first results from simulation studies of evolutionary spatial cyclic games where there are seven species, but where each species is still in its own local five-species RPSLS-like interaction network: the dominance networks I use for this are a subset of the $n$-node $k$-regular circulant digraphs $D(n,\Omega)$ for odd-numbered $n$ and $|\Omega|=2$. My results indicate that Zhong et al.'s results are due to the specific fully-connected tournament dominance network used in their RPSLS model: when other, equally realistic, RPSLS-style dominance networks are used instead, no such sudden collapse in biodiversity occurs. The Python source-code used for the work reported here is freely available on GitHub.
Modelling the longevity of complex living systems
Authors: Indrė Žliobaitė
Date: 2024-10-03 00:00:00

This extended abstract was presented at the Nectar Track of ECML PKDD 2024 in Vilnius, Lithuania. The content supplements a recently published paper "Laws of Macroevolutionary Expansion" in the Proceedings of the National Academy of Sciences (PNAS).
On discretely structured growth models and their moments
Authors: Benjamin J. Walker, Helen M. Byrne
Date: 2024-10-02 00:00:00
Tasks: Model Selection

The logistic equation is ubiquitous in applied mathematics as a minimal model of saturating growth. Here, we examine a broad generalisation of the logistic growth model to discretely structured populations, motivated by examples that range from the ageing of individuals in a species to immune cell exhaustion by cancerous tissue. Through exploration of a range of concrete examples and a general analysis of polynomial kinetics, we derive necessary and sufficient conditions for the dependence of the kinetics on structure to result in closed, low-dimensional moment equations that are exact. Further, we showcase how coarse-grained moment information can be used to elucidate the details of structured dynamics, with immediate potential for model selection and hypothesis testing. This paper belongs to the special collection: Problems, Progress and Perspectives in Mathematical and Computational Biology.
An evolutionary process without variation and selection
Authors: Liane Gabora, Mike Steel
Date: 2024-09-30 00:00:00

Natural selection successfully explains how organisms accumulate adaptive change despite that traits acquired over a lifetime are eliminated at the end of each generation. However, in some domains that exhibit cumulative, adaptive change -- e.g., cultural evolution, and earliest life -- acquired traits are retained; these domains do not face the problem that Darwin's theory was designed to solve. Lack of transmission of acquired traits occurs when germ cells are protected from environmental change, due to a self-assembly code used in two distinct ways: (i) actively interpreted during development to generate a soma, and (ii) passively copied without interpretation during reproduction to generate germ cells. Early life and cultural evolution appear not to involve a self-assembly code used in these two ways. We suggest that cumulative, adaptive change in these domains is due to a lower-fidelity evolutionary process, and model it using Reflexively Autocatalytic and Foodset-generated networks. We refer to this more primitive evolutionary process as Self-Other Reorganisation (SOR) because it involves internal self-organising and self-maintaining processes within entities, as well as interaction between entities. SOR encompasses learning but in general operates across groups. We discuss the relationship between SOR and Lamarckism, and illustrate a special case of SOR without variation.
A statistical framework for detecting therapy-induced resistance from drug screens
Authors: Chenyu Wu, Einar Bjarki Gunnarsson, Jasmine Foo, Kevin Leder
Date: 2024-09-27 00:00:00

Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we introduce a robust statistical framework, based on multi-type branching process models of the evolutionary dynamics of tumor cell populations, to detect and quantify therapy-induced resistance phenomena from high throughput drug screening data. Through comprehensive in silico experiments, we show the efficacy of our framework in estimating parameters governing population dynamics and drug responses in a heterogeneous tumor population where cell state transitions are influenced by the drug. Finally, using recent in vitro data from multiple sources, we demonstrate that our framework is effective for analyzing real-world data and generating meaningful predictions.
Renewal equations for vector-borne diseases
Authors: Cathal Mills, Tarek Alrefae, William S. Hart, Moritz U. G. Kraemer, Kris V. Parag, Robin N. Thompson, Christl A. Donnelly, Ben Lambert
Date: 2024-09-27 00:00:00

During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle
Estimation of end-of-outbreak probabilities in the presence of delayed and incomplete case reporting
Authors: M. J. Plank, W. S. Hart, J. Polonsky, M. Keita, S. Ahuka-Mundeke, R. N. Thompson
Date: 2024-09-25 00:00:00
Tasks: Decision Making

Towards the end of an infectious disease outbreak, when a period has elapsed without new case notifications, a key question for public health policy makers is whether the outbreak can be declared over. This requires the benefits of a declaration (e.g., relaxation of outbreak control measures) to be balanced against the risk of a resurgence in cases. To support this decision making, mathematical methods have been developed to quantify the end-of-outbreak probability. Here, we propose a new approach to this problem that accounts for a range of features of real-world outbreaks, specifically: (i) incomplete case ascertainment; (ii) reporting delays; (iii) individual heterogeneity in transmissibility; and (iv) whether cases were imported or infected locally. We showcase our approach using two case studies: Covid-19 in New Zealand in 2020, and Ebola virus disease in the Democratic Republic of the Congo in 2018. In these examples, we found that the date when the estimated probability of no future infections reached 95% was relatively consistent across a range of modelling assumptions. This suggests that our modelling framework can generate robust quantitative estimates that can be used by policy advisors, alongside other sources of evidence, to inform end-of-outbreak declarations.
Cooperation as well as learning: A commentary on 'How learning can guide evolution' by Hinton and Nowlan
Authors: Conor Houghton
Date: 2024-09-23 00:00:00

According to the Baldwin Effect learning can guide evolution. This does not suppose that information about what has been learned is transferred back into the genetic code: in the Baldwin Effect complex multi-gene characteristics are discovered through learning but acquired through standard selectionist evolutionary processes. Learning serves to improve the search by giving value to a partial, and otherwise useless, subset of the required genes. An elegant and concrete treatment of the Baldwin Effect is given in 'How learning can guide evolution', a 1987 paper by G. E. Hinton and S. J. Nowlan. This includes a simple, but revealing, simulation illustrating the effect. As a commentary on that paper, a similar simulation is used here to demonstrated that cooperation can also guide evolution. Like learning, cooperation has a clear benefit to survival, but what is proposed here is small addition: that cooperation, like learning in the Baldwin Effect, can also allow complex characteristic to be discovered and acquired much faster than they otherwise would. This suggests an additional benefit of social behavior and suggests that social animals have a broader evolutionary path towards some complex adaptations.
Natural Selection between Two Games and Replicator Dynamics on Graphs
Authors: Georgiy Karev
Date: 2024-09-23 00:00:00

In this paper I study the natural selection between two games to determine, which game will dominate in the community as a result of natural selection. The formalization of this question in the form of a parametrized game and the mathematical toolbox to solve it were recently developed in (Karev 2024). Here, I applied the developed approach to natural selection between games on graphs. Specifically, I compare standard games on a complete graph with games that have updated rules suggested by Ohtsuki and Nowak (2006). The results of the natural selection between the initial and updated games, or between different updated games were visualized through the evolution of the parameter distribution in corresponding parametrized game. The developed approach can be used for other problems, such as local replicator dynamics, pairwise competition, the game of alleles in diploid genomes (see Karev 2004) and cancer modeling (Kareva, Karev 2019).
Integrating systematic surveys with historical data to model the distribution of Ornithodoros turicata americanus, a vector of epidemiological concern in North America
Authors: Sebastian Botero-Canola, Carson Torhorst, Nicholas Canino, Lorenza Beati, Kathleen C. O Hara, Angela M. James, Samantha M. Wisely
Date: 2024-09-19 00:00:00
Tasks: Sand, Sensitivity

Globally, vector-borne diseases are increasing in distribution and frequency, affecting humans, domestic animals and livestock, and wildlife. Science-based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling that have not yet been leveraged in disease ecology. We used this approach, as implemented in the recently developed R package RISDM, to assess the distribution of the soft tick subspecies Ornithodoros turicata americanus. We created an ISDM for O. t. americanus, using systematically collected field data and historical records of this tick species in the southeastern US, to predict its distribution and assess potential correlations with environmental variables. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning using true skills statistics (TSS), sensitivity, and area under the ROC curve (AUC) metrics. We found that a combination of climatic variables describing seasonality and temperature extremes, along with the amount of sand in the soil, determined the predicted intensity of occurrence of this tick species. When projected in geographic space, this distribution model predicted 62% of Florida as suitable habitat for this tick species. The ISDM presented a higher TSS and AUC than the Maxent conventional model, while sensitivity was similar between both models. Our case example shows the utility of ISDMs in disease ecology studies and highlights the broad range of geographic suitability for this important disease vector.
Extraction of geometric and transport parameters from the time constant of exocytosis transients measured by nanoscale electrodes
Authors: Sundeep Kapila, Pradeep R. Nair
Date: 2024-10-09 00:00:00

Exocytosis is a fundamental process related to the information exchange in the nervous and endocrine system. Among the various techniques, vesicle impact electrochemical cytometry (VIEC) has emerged as an effective method to mimic the exocytosis process and measure dynamic information about content transfer using nanoscale electrodes. In this manuscript, through analytical models and large scale simulations, we develop scaling laws for the decay time constant ($\tau$) for VIEC single-exponential transients. Specifically, our results anticipate a power law dependence of $\tau$ on the geometric and the transport parameters. This model compares very well with large scale simulations exploring the parameter space relevant for VIEC and with experimental results from literature. Remarkably, such physics based compact models could allow for novel multi-feature based self consistent strategies for back extraction of geometric and transport parameters and hence could contribute towards better statistical analysis and understanding of exocytosis transients and events.
i-GRIP, a Grasping Movement Intention Estimator for Intuitive Control of Assistive Devices
Authors: Etienne Moullet, Justin Carpentier, Christine Azevedo Coste, François Bailly
Date: 2024-10-09 00:00:00

This study describes and evaluates i-GRIP, a novel movement intention estimator designed to facilitate the control of assistive devices for grasping tasks in individuals with upper limb impairments. Operating within a collaborative grasping control paradigm, the users naturally move their hand towards an object they wish to grasp and i-GRIP identifies the target of the movement and selects an appropriate grip for the assistive device to perform. In an experimental study involving 11 healthy participants, i-GRIP exhibited promising estimation performances and responsiveness. The proposed approach paves the way towards more intuitive control of grasping assistive device for individuals with upper limb impairments.
Quantifying sex differences in brain network topology by aggregating nodal centrality rankings
Authors: Wenyu Chen, Ling Zhan, YunSong Luo, Jiang Qiu, Tao Jia
Date: 2024-10-08 00:00:00
Tasks: Functional Connectivity

Although numerous studies report significant sex differences in functional connectivity, these differences do not sufficient to reveal specific functional disparities among brain regions or the topological differences in brain networks. Meanwhile, individual differences could potentially bias the understanding of these sex differences. To address these challenges, we propose a consensus rank-based method to quantify sex differences in four node centrality ranking within the functional brain network. This method aggregates individuals' nodal centrality rankings into a consensus or "average" ranking, minimizing the impact of outliers and enhancing the robustness of the findings. By analyzing resting-state functional MRI data from 1,948 healthy young adults (aged 18-27 years, 1,163 females), we find significant sex differences in the topology of functional brain network, primarily attributed to biological sex rather than individual differences. Particularly, sex accounts for approximately 10% of the differences in nodal centrality consensus rankings. Using a rank difference index (RDI), we identify eight critical brain regions with the greatest rank differences, including the insula, supramarginal gyrus, and dorsolateral superior frontal gyrus. females show higher rankings in regions with stronger intra-system connections, whereas males dominate in areas with stronger inter-system connections. Our findings enhance our understanding of sex-specific characteristics in functional brain networks. Moreover, our approach may offer novel insights into targeted population studies, including those involving healthy individuals and patients with brain injuries.
CaLMFlow: Volterra Flow Matching using Causal Language Models
Authors: Sizhuang He, Daniel Levine, Ivan Vrkic, Marco Francesco Bressana, David Zhang, Syed Asad Rizvi, Yangtian Zhang, Emanuele Zappala, David van Dijk
Date: 2024-10-03 00:00:00
Tasks: Language Modeling, Language Modelling

We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling and continuous generative modeling. Our method implements tokenization across space and time, thereby solving a VIE over these domains. This approach enables efficient handling of high-dimensional data and outperforms ODE solver-dependent methods like conditional flow matching (CFM). We demonstrate CaLMFlow's effectiveness on synthetic and real-world data, including single-cell perturbation response prediction, showcasing its ability to incorporate textual context and generalize to unseen conditions. Our results highlight LLM-driven flow matching as a promising paradigm in generative modeling, offering improved scalability, flexibility, and context-awareness.
SOWAHA as a Cancer Suppressor Gene Influence Metabolic Reprogramming
Authors: Xiaohong Yi, Xianwen Zhang, Claire H. Zhao, Yuhui Chen, Lijun Huang, Hua Zhong, Yumei Wang
Date: 2024-09-24 00:00:00
Tasks: Diagnostic, Prognosis

SOWAHA is a protein-coding gene, also known as ANKRD43. Studies have indicated that SOWAHA can serve as a prognostic biomarker in colorectal cancer and pancreatic cancer. However, there are few reports about SOWAHA in other types of cancer and the specific mechanism of action of SOWAHA in cancer is also not clear. Based on National Center for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), cBioPortal, Human Protein Atlas (HPA), etc., we adopted bioinformatics methods to uncover the potential tumor genomic features of SOWAHA, including the correlation with prognosis, gene mutation, immune cell infiltration, and DNA methylation in different tumors and evaluated the association with tumor heterogeneity, stemness, chemokines chemokine receptors, and immunomodulators in pan-cancer. Besides, we knocked down SOWAHA in SW620 cells and performed RNA-seq analysis, then we conducted functional enrichment to uncover the biological significance of the gene set. SOWAHA has early diagnostic potential, and low expression of SOWAHA was associated with poor prognosis in was associated with poor prognosis in GBMLGG, PAAD, READ, etc. SOWAHA is associated with most tumor immune-infiltrating cells in pan-cancer. SOWAHA correlates with DNA methylation, tumor heterogeneity, and stemness in many epithelial carcinomas. Furthermore, SOWAHA is involved in many enzyme activity and metabolic pathways, mainly metabolic programming pathways in cancer. Additionally, we identified two potential transcription factors of SOWAHA, TBX4, and FOXP2, which are dysregulated in SW620 cells. Besides, the cell proliferation and viability in siSOWAHA groups are better than in siNC groups.SOWAHA, identified as a suppressor gene, and its role in the progression of colorectal cancer is primarily mediated through metabolic reprogramming mechanisms.
ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
Authors: Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Date: 2024-10-02 00:00:00

The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a Convolutional Neural Network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of Normal Mode correlation maps derived from Elastic Network Models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the Normal Mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.
Regional End-Systolic Circumferential Strain Demonstrates Reduced Function in Remote Myocardium after Anterior STEMI
Authors: Steve W. Leung, Kanjit Leungsuwan, Ahmed Abdel-Latif, Jonathan F. Wenk
Date: 2024-10-01 00:00:00

Anterior ST-segment elevation myocardial infarction (STEMI) is associated with severe adverse remodeling and increased mortality rates. In this study, anterior STEMI patients were treated with primary percutaneous coronary intervention and then imaged with cardiac magnetic resonance 1-2 days later. The cine images were analyzed with computational feature-tracking algorithms to assess end-systolic circumferential (ESC) strain. It was found that contractile function is not only depressed in the infarct and border zone regions, but also in the remote myocardium. This is contrary to other types of infarctions where remote myocardium shows enhanced compensatory function. Quantifying ESC could help identify high risk patients.