Editorial: Protistan phagotrophy and the far-reaching implications. [PDF]
Legner M, Fillery E, Macek M.
europepmc +1 more source
LLM‐Based Scientific Assistants for Knowledge Extraction: Which Design Choices Matter?
A comprehensive framework for optimizing Large Language Models in domain‐specific applications is introduced. The LLM Playground integrates Prompt Engineering, knowledge augmentation, and advanced reasoning strategies to enable systematic comparison of architectures and base models.
David Exler +7 more
wiley +1 more source
A deep learning architecture for leaf water potential prediction in <i>Populus euramericana</i> 'I-214' from hyperspectral reflectance. [PDF]
Gong XW +10 more
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Correction to "Aseasonal Migration of a Northern Bottlenose Whale Provides Support for the Skin Molt Migration Hypothesis". [PDF]
europepmc +1 more source
Marine synthetic ecology: From microbial communities to ecosystems. [PDF]
Su R +6 more
europepmc +1 more source
Context Awareness and Human–Robot Interaction Optimization for Museum Intelligent Guide Robot
This study presents a context‐aware human–robot interaction framework designed for intelligent museum guide robots. The system features a three‐layer architecture—perception, understanding, and behavior execution—that enables adaptive and meaningful interactions with museum visitors.
Anna Zou, Yue Meng, Shijing Tong
wiley +1 more source
Linking Comparative Genomics, Morphology and In Vitro Assays to Understand Deep Diving in Cetaceans. [PDF]
Thorstensen MJ.
europepmc +1 more source
An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification
An attention‐assisted DenseNet201 framework was developed for the classification of eight microorganism classes from microscopic images. The proposed model improved classification performance and achieved an accuracy of 87.38%. Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification ...
Yujie Li +6 more
wiley +1 more source
Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges. [PDF]
Borgomeo E +27 more
europepmc +1 more source

