Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
This paper addresses the critical gap between theoretical AI research and real-world clinical implementation by providing a practical framework for as...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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.