Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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.