mailitics

Category: q-bio.QM

  • Automated Pollen Recognition in Optical and Holographic Microscopy Images

    Automated Pollen Recognition in Optical and Holographic Microscopy Images arXiv:2512.08589v1 Announce Type: cross Abstract: This study explores the application of deep learning to improve and automate pollen grain detection and classification in both optical and holographic microscopy images, with a particular focus on veterinary cytology use cases. We used YOLOv8s for object detection and MobileNetV3L…

  • EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting

    EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting arXiv:2512.02260v1 Announce Type: cross Abstract: Increasing climate change and habitat loss are driving unprecedented shifts in species distributions. Conservation professionals urgently need timely, high-resolution predictions of biodiversity risks, especially in ecologically diverse regions like Africa. We propose EcoCast, a spatio-temporal model designed for continual…

  • Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks

    Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks arXiv:2509.22760v1 Announce Type: new Abstract: We develop a physics-informed neural network (PINN) framework for parameter estimation in fractional-order SEIRD epidemic models. By embedding the Caputo fractional derivative into the network residuals via the L1 discretization scheme, our method simultaneously reconstructs epidemic…

  • Simulation-based inference of yeast centromeres

    Simulation-based inference of yeast centromeres arXiv:2509.00200v1 Announce Type: new Abstract: The chromatin folding and the spatial arrangement of chromosomes in the cell play a crucial role in DNA replication and genes expression. An improper chromatin folding could lead to malfunctions and, over time, diseases. For eukaryotes, centromeres are essential for proper chromosome segregation and folding.…

  • Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI

    Can synthetic data reproduce real-world findings in epidemiology? A replication study using tree-based generative AI arXiv:2508.14936v1 Announce Type: cross Abstract: Generative artificial intelligence for synthetic data generation holds substantial potential to address practical challenges in epidemiology. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies…

  • BaMANI: Bayesian Multi-Algorithm causal Network Inference

    BaMANI: Bayesian Multi-Algorithm causal Network Inference arXiv:2508.11741v1 Announce Type: new Abstract: Improved computational power has enabled different disciplines to predict causal relationships among modeled variables using Bayesian network inference. While many alternative algorithms have been proposed to improve the efficiency and reliability of network prediction, the predicted causal networks reflect the generative process but also…

  • On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions

    On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions arXiv:2506.11683v1 Announce Type: new Abstract: Solving inverse problems in cardiovascular modeling is particularly challenging due to the high computational cost of running high-fidelity simulations. In this work, we focus on Bayesian parameter estimation and explore different methods to reduce the…

  • A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example

    A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example arXiv:2505.03177v1 Announce Type: new Abstract: Bioprocess mechanistic modeling is essential for advancing intelligent digital twin representation of biomanufacturing, yet challenges persist due to complex intracellular regulation, stochastic system behavior, and limited experimental data. This paper introduces a symbolic and statistical learning…

  • Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

    Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations arXiv:2504.11610v1 Announce Type: new Abstract: Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only…

  • Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures

    Communities in the Kuramoto Model: Dynamics and Detection via Path Signatures arXiv:2503.17546v1 Announce Type: new Abstract: The behavior of multivariate dynamical processes is often governed by underlying structural connections that relate the components of the system. For example, brain activity which is often measured via time series is determined by an underlying structural graph, where…

  • Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve

    Avoiding subtraction and division of stochastic signals using normalizing flows: NFdeconvolve arXiv:2501.08288v1 Announce Type: new Abstract: Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x = ab$. For…