Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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.