Paper List
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Formation of Artificial Neural Assemblies by Biologically Plausible Inhibition Mechanisms
This work addresses the core limitation of the Assembly Calculus model—its fixed-size, biologically implausible k-WTA selection process—by introducing...
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How to make the most of your masked language model for protein engineering
This paper addresses the critical bottleneck of efficiently sampling high-quality, diverse protein sequences from Masked Language Models (MLMs) for pr...
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Module control in youth symptom networks across COVID-19
This paper addresses the core challenge of distinguishing whether a prolonged societal stressor (COVID-19) fundamentally reorganizes the architecture ...
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JEDI: Jointly Embedded Inference of Neural Dynamics
This paper addresses the core challenge of inferring context-dependent neural dynamics from noisy, high-dimensional recordings using a single unified ...
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ATP Level and Phosphorylation Free Energy Regulate Trigger-Wave Speed and Critical Nucleus Size in Cellular Biochemical Systems
This work addresses the core challenge of quantitatively predicting how the cellular energy state (ATP level and phosphorylation free energy) governs ...
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Packaging Jupyter notebooks as installable desktop apps using LabConstrictor
This paper addresses the core pain point of ensuring Jupyter notebook reproducibility and accessibility across different computing environments, parti...
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SNPgen: Phenotype-Supervised Genotype Representation and Synthetic Data Generation via Latent Diffusion
This paper addresses the core challenge of generating privacy-preserving synthetic genotype data that maintains both statistical fidelity and downstre...
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Continuous Diffusion Transformers for Designing Synthetic Regulatory Elements
This paper addresses the challenge of efficiently generating novel, cell-type-specific regulatory DNA sequences with high predicted activity while min...
PesTwin: a biology-informed Digital Twin for enabling precision farming
Multiple institutions (likely Italian research institutes and universities)
30秒速读
IN SHORT: This paper addresses the critical bottleneck in precision agriculture: the inability to accurately forecast pest outbreaks in real-time, leading to suboptimal pesticide applications and significant crop losses.
核心创新
- Methodology Developed a modular, biology-informed Digital Twin framework using Agent-Based Modeling that integrates heterogeneous data sources (lab biodata, weather stations, GIS) for pest forecasting
- Methodology Implemented GPU-accelerated ABM using FLAMEGPU library, enabling simulation of up to 80 million concurrent agents for realistic field-scale scenarios
- Biology Applied the framework to Drosophila suzukii (Spotted Wing Drosophila) with comprehensive lab protocols for parameter calibration, including temperature-dependent development using modified-Brierè functions
主要结论
- The PesTwin framework successfully simulated SWD population growth in cage experiments, capturing biological variability across three replicates with stochastic modeling matching experimental data.
- Field simulations showed qualitative agreement with trapping data, demonstrating capability to model pest dispersal, host interactions, and temperature-dependent lifecycle dynamics in realistic scenarios.
- Simulation of Sterile Insect Technique (SIT) control strategy demonstrated potential to reduce pest populations by more than half (50%+ reduction) when implementing weekly releases during crop ripening season.
摘要: In a context of growing agricultural demand and new challenges related to food security and accessibility, boosting agricultural productivity is more important than ever. Reducing the damage caused by invasive insect species is a crucial lever to achieve this objective. In support of these challenges, and in line with the principles of precision agriculture and Integrated Pest Management (IPM), an innovative simulation framework is presented, aiming to become the Digital Twin of a pest invasion. Through a flexible rule-based approach of the Agent-Based Modeling (ABM) paradigm, the framework supports the fine-tuning of the main ecological interactions of the pest with its crop host and the environment. Forecasting insect infestation in realistic scenarios, considering both spatial and temporal dimensions, is made possible by integrating heterogeneous data sources: pest biodata collected in the laboratory, environmental data from weather stations, and GIS data of a real crop field. In this study, an application to the global pest of soft fruit, the invasive fruit fly Drosophila suzukii, also known as Spotted Wing Drosophila (SWD), is presented.