Biology subjects

Kuehn, T.

Publications and source records attributed to Kuehn, T..

4 recordsLinked to original sources

bioRxiv2026

AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement

Accurate localization of anatomical landmarks is a foundational skill in anatomy and imaging that is often taught informally through expert mentorship, requiring access to data and desktop software. There is no openly accessible, interactive resource that teaches neuroanatomy with quantitative feedback. We present the AFIDs-Validator (validator.afids.io), an open-access, browser-based platform that pairs guided instruction with quantitative assessment. The platform combines (1) a learning mode in which a language-model neuroanatomy tutor operates inside an MRI viewer, giving anatomy-first instruction that responds to the learner's current image slice, orientation, and cursor position; and (2) a validation engine that accepts a learner's landmark file and returns per-landmark Euclidean error against expert-annotated references spanning 21 brain templates. To make the feedback interpretable, we analyzed 15,000 landmark annotations across 132 human subjects and found that landmark difficulty varies fourfold (median error ranged from 0.37 mm at the anterior commissure to 1.50 mm at the temporal horns) with heavy-tailed distributions at every landmark. These distributions are compiled into per-landmark reliability priors, so learners are scored against the empirical spread of trained raters rather than an arbitrary threshold, and difficult landmarks are not mistaken for poor performance. The AFIDs-Validator requires no installation, licensed software, or local data, and all code, reference data, and tutor design are openly released.

scientific communication and education↗

bioRxiv2026

Capturing early events in aryl hydrocarbon receptor activation using two complementary protein-protein interaction assays

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that enables cellular adaptation to environmental, nutritional and metabolic cues. Upon ligand binding, AHR translocates to the nucleus, heterodimerizes with the AHR nuclear translocator (ARNT) and regulates gene expression. Current approaches to measure AHR activity rely on transcriptional readouts, which vary depending on cell type and ligand. Here, we introduce two complementary protein-protein interaction-based assays that detect AHR activation by monitoring AHR-ARNT complex formation. Split-luciferase (NanoBiT) and bimolecular fluorescence complementation (BiFC) detect AHR activation independently of transcriptional output, capturing agonist- and antagonist-dependent AHR modulation across multiple ligands and cellular contexts. NanoBiT enables rapid, real-time analysis of AHR dimerization, whereas BiFC supports imaging of AHR interactions at subcellular resolution. The assays capture further attributes of AHR signaling, including dissociation from chaperones or HIF-1-mediated competition for ARNT, and enable detection of AHR activation in biological samples. Hence, both assays provide versatile tools to study AHR signaling.

biochemistry↗

bioRxiv2025

Tracking biological hallucinations in single-cell perturbation predictions using scArchon, a comprehensive benchmarking platform

The accurate prediction of cellular responses to perturbations, such as drug treatments, remains a pivotal challenge in single-cell transcriptomics. While numerous deep learning tools have been developed for this task, their systematic benchmarking across diverse datasets and performance metrics has been limited. Here, we present scArchon, a reproducible, modular benchmarking platform built on Snakemake, designed to evaluate perturbation response prediction tools in an unbiased and extensible manner. Employing four representative single-cell RNA-seq datasets, we compare leading methods such as scGen, CPA, trVAE, scPRAM, scVIDR, scDisInFact, SCREEN, scPreGAN, and CellOT against baselines. We assess model performance using a composite of statistical and biological metrics. Our analysis reveals heterogeneous performance: while methods like trVAE, scGen, scPRAM, and scVIDR achieve robust results across multiple datasets, other tools occasionally underperform even compared to linear or control baselines. Notably, models with favorable quantitative scores may fail to retain key biological perturbation signatures, underscoring the need for gene-level evaluation. scArchon provides a unified, extensible foundation for large-scale, standardized benchmarking of perturbation prediction tools, facilitating methodological transparency and accelerating development in this rapidly evolving field. We encourage adoption of scArchon and sharing of containerized tools to drive progress in single-cell perturbation modeling.

bioinformatics↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗