Image processing tools for alpha-particle track-etch dosimetry.

John C Roeske; Christina Soyland; Steven J Wang; Thomas G Stinchcomb; Sindre P Hassfjell; Jenny L Whitlock; Richard C Reba; Jacob Rotmensch

doi:10.1089/108497803322285170

PubMed · 12954129

Image processing tools for alpha-particle track-etch dosimetry.

John C Roeske·Christina Soyland·Steven J Wang·Thomas G Stinchcomb·Sindre P Hassfjell·Jenny L Whitlock·Richard C Reba·Jacob Rotmensch

View original source ↗Export metadata ↓

Abstract

In cases where both the source and cell geometry are well known, track-etch dosimetry allows the potential for individual cell dosimetry. However, analysis of track-etch images is both tedious and time-consuming. We describe here several image processing tools that we are using in conjunction with a track-etch based irradiator. Briefly, cells grown on LR 115 (a track-etch material) are irradiated from below by a collimated, planar alpha-particle source. Prior to irradiation, images of the cells are obtained. A computer program reads each image and automatically determines the location of individual cells. Next, the algorithm automatically identifies the cellular and nuclear boundaries. Following irradiation, and after the cells have reached their biological endpoint (e.g., cell survival), the cell dish is etched and images are obtained of alpha-particle tracks. Using the characteristic background pattern in the LR 115, the etched images are spatially registered to the original images. These two sets of images are then superimposed to create a composite image of the cells and associated alpha-particle tracks. Incorporating this tool into our irradiation scheme will enable more efficient analysis of the large amounts of data that are essential in assessing biological endpoints.

Explore related subjects

Algorithms Alpha Particles Cells, Cultured Epithelial Cells Humans Image Processing, Computer-Assisted Linear Energy Transfer Lung Particle Accelerators Radiation Dosage Radiation Monitoring Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

John C Roeske, Christina Soyland, Steven J Wang, Thomas G Stinchcomb, Sindre P Hassfjell, Jenny L Whitlock, Richard C Reba, Jacob Rotmensch. 2003. Image processing tools for alpha-particle track-etch dosimetry.. https://doi.org/10.1089/108497803322285170

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗

PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗

PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗