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Buchanan, I.

Publications and source records attributed to Buchanan, I..

2 recordsLinked to original sources

An imaging framework for nuclei-based three-dimensional cell quantification in intact tissue using phase-contrast X-ray CT

Objective. Quantitative analysis of cellular morphology and spatial organisation within intact tissue remains challenging, particularly when three-dimensional information must be preserved. This study investigates whether laboratory propagation-based phase-contrast X-ray computed tomography (CT) can support nuclei-based quantitative analysis in unstained tissue while retaining the surrounding tissue architecture. Approach. We propose a nuclei-based quantitative imaging workflow that combines laboratory propagation-based phase-contrast X-ray CT with volumetric segmentation. Intact unstained liver tissue was imaged at cellular resolution, then nuclei were segmented throughout the reconstructed volume and quantitative metrics describing the nuclear morphology and spatial organisation were extracted. The resulting measurements were evaluated in two non-overlapping volumes of interest and compared with histological reference data using thickness-matched virtual CT slices. Main results. The proposed workflow enabled visualisation and segmentation of individual nuclei throughout intact unstained liver tissue. Comparable nuclear morphology and spatial organisation metrics were obtained from two non-overlapping volumes of interest, indicating stable segmentation performance within the analysed specimen. Comparison with H&E histology demonstrated broad agreement in nuclear morphology. Significance. This work demonstrates the feasibility of nuclei-based quantitative analysis using laboratory phase-contrast X-ray CT in intact unstained tissue. The approach provides volumetric cellular information while preserving tissue architecture and may complement conventional histological assessment in applications requiring three- dimensional analysis. These findings highlight the potential of laboratory phase-contrast CT for quantitative tissue characterisation and spatial cellular analysis

bioengineering↗

KaMLs for Predicting Protein pKa Values andIonization States: Are Trees All You Need?

Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by scarcity of experimental data. Here we report the development of pKa ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pKa database (PKAD-3) enriched with highly shifted pKas. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines - a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including separate treatment of acid and base, data augmentation using AlphaFold structures, and model pretraining on a theoretical pKa database. We also introduce the classification of protonation states as a metric for evaluating pKa prediction models. A meta-feature analysis suggests a possible reason for the lightweight tree model to outperform the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new PKAD-3 database, which facilitates a variety of applications and provides the foundation for further advances in protein electrostatics research. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/622800v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@15d2256org.highwire.dtl.DTLVardef@1795151org.highwire.dtl.DTLVardef@1c9ae02org.highwire.dtl.DTLVardef@1bf17a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗