Paper List
-
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...
-
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 ...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
Leveraging Phytolith Research using Artificial Intelligence
Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona | Smithsonian National Museum of Natural History | University of Duisburg-Essen | Università di Trento | Herbario Nacional de Bolivia | The Pennsylvania State University
30秒速读
IN SHORT: This paper addresses the critical bottleneck in phytolith research by automating the labor-intensive manual microscopy process through a multimodal AI pipeline that enables high-throughput analysis of archaeological samples.
核心创新
- Methodology First multimodal fusion model combining ConvNeXt (2D images) and PointNet++ (3D point clouds) for phytolith classification, achieving 77.9% global accuracy across 24 morphotypes.
- Methodology Complete end-to-end pipeline from z-stack microscopy to Bayesian mixture modeling, processing 3.81 million segmented objects from 712 slide sectors.
- Biology Demonstrates that 3D data is essential for distinguishing complex morphotypes like grass silica short cells, where diagnostic features are often obscured in 2D projections.
主要结论
- The multimodal fusion model achieved 77.9% global classification accuracy (71.4% class-adjusted) and 84.5% segmentation quality accuracy, with 3D data proving critical for distinguishing orientation-dependent morphotypes.
- Bayesian finite mixture modeling successfully identified specific plant contributions (maize and palms) in complex mixed samples, enabling assemblage-level analysis beyond individual object classification.
- The pipeline processed 3.81 million objects from 123 slides, demonstrating scalability orders of magnitude beyond traditional methods while maintaining systematic error patterns usable for compositional analysis.
摘要: Phytolith analysis is a crucial tool for reconstructing past vegetation and human activities, but traditional methods are severely limited by labour-intensive, time-consuming manual microscopy. To address this bottleneck, we present Sorometry: a comprehensive end-to-end artificial intelligence pipeline for the high-throughput digitisation, inference, and interpretation of phytoliths. Our workflow processes z-stacked optical microscope scans to automatically generate synchronised 2D orthoimages and 3D point clouds of individual microscopic particles. We developed a multimodal fusion model that combines ConvNeXt for 2D image analysis and PointNet++ for 3D point cloud analysis, supported by a graphical user interface for expert annotation and review. Tested on reference collections and archaeological samples from the Bolivian Amazon, our fusion model achieved a global classification accuracy of 77.9% across 24 diagnostic morphotypes and 84.5% for segmentation quality. Crucially, the integration of 3D data proved essential for distinguishing complex morphotypes (such as grass silica short cell phytoliths) whose diagnostic features are often obscured by their orientation in 2D projections. Beyond individual object classification, Sorometry incorporates Bayesian finite mixture modelling to predict overall plant source contributions at the assemblage level, successfully identifying specific plants like maize and palms in complex mixed samples. This integrated platform transforms phytolith research into an “omics”-scale discipline, dramatically expanding analytical capacity, standardising expert judgements, and enabling reproducible, population-level characterisations of archaeological and paleoecological assemblages.