Paper List
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, part...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
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Cross-Species Antimicrobial Resistance Prediction from Genomic Foundation Models
This paper addresses the core challenge of predicting antimicrobial resistance across phylogenetically distinct bacterial species, where traditional m...
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.