The accuracy of Eclipse AXB and AAA dose algorithms with dental amalgam.

High-density materials used for dental restorations are poorly defined in CT imaging due to scanner limitations. Studies have established that Eclipse offers poor agreement with delivered dose in situations involving high-density material. Defining the accuracy of dose algorithms in situations involving high-density overrides would improve clinical outcomes both for target coverage and OAR sparing. Dental amalgam was placed within a solid water phantom and measurements were taken at 1 cm increments beneath the amalgam down to a depth of 6 cm. Exposed film was compared with Eclipse Treatment Planning system (TPS) calculations on a CT of the experimental setup. The amalgam was overridden with a range of HU values and material selections for dose calculation. AXB performs poorly at describing depth dose downstream of Amalgam, regardless of the override material selected. Applying the known mass density with the Anisotropic Analytical Algorithm (AAA) predicts an average of 1.8% and 2.8% for 6 MV and 10 MV beams. The closest agreement achieved using the Acuros XB (AXB) was overriding with stainless steel, which predicted approximately 1.1% and 1.8% above measured dose for 6 MV and 10 MV respectively. Without overriding the density of amalgam, AAA and AXB return depth dose predictions of 7.3% and 5.8% above film measurement for a 6 MV and 7.6% and 6.5% for 10 MV static beams. Applying override options to a clinical case using an anthropomorphic phantom showed using AXB with Stainless Steel as amalgam override returns the same results as AAA with mass density applied for amalgam. Both of these were in close agreement to the TPS.

ExRec: a python pipeline for generating recombination-filtered multi-locus datasets.

Summary: ExRec (Exclusion of Recombined DNA) is a dependency-free Python pipeline that implements the four-gamete test to automatically filter out recombined DNA blocks from thousands of DNA sequence loci. This procedure helps all loci better meet the "no intralocus recombination" assumption common to many coalescent-based analyses in population genomic, phylogeographic, and shallow-scale phylogenomic studies. The user-friendly pipeline contains five standalone applications-four file conversion scripts and one main script that performs the recombination filtering procedures. The pipeline outputs recombination-filtered data in a variety of common formats and a tab-delimited table that displays descriptive statistics for all loci and the analysis results. A novel feature of this software is that the user can select whether to output the longest nonrecombined sequence blocks from recombined loci (current best practice) or randomly select nonrecombined blocks from loci (a newer approach). We tested ExRec with six published phylogenomic datasets that ranged in size from 27 to 2237 loci and came in a variety of input file formats. In all trials the data could be easily analyzed in only seconds for the smaller datasets and <30 min for the largest using a simple laptop computer. Availability and implementation: ExRec was written in Python 3 under the MIT license. The program applications, user manual (including step-by-step tutorials), and sample data are freely available at https://github.com/Sammccarthypotter/ExRec.

Non-Destructive Reflectance Mapping of Collagen Fiber Alignment in Heart Valve Leaflets.

Collagen fibers are the primary structural elements that define many soft-tissue structure and mechanical function relationships, so that quantification of collagen organization is essential to many disciplines. Current tissue-level collagen fiber imaging techniques remain limited in their ability to quantify fiber organization at macroscopic spatial scales and multiple time points, especially in a non-contacting manner, requiring no modifications to the tissue, and in near real-time. Our group has previously developed polarized spatial frequency domain imaging (pSFDI), a reflectance imaging technique that rapidly and non-destructively quantifies planar collagen fiber orientation in superficial layers of soft tissues over large fields-of-view. In this current work, we extend the light scattering models and image processing techniques to extract a critical measure of the degree of collagen fiber alignment, the normalized orientation index (NOI), directly from pSFDI data. Electrospun fiber samples with architectures similar to many collagenous soft tissues and known NOI were used for validation. An inverse model was then used to extract NOI from pSFDI measurements of aortic heart valve leaflets and clearly demonstrated changes in degree of fiber alignment between opposing sides of the sample. These results show that our model was capable of extracting absolute measures of degree of fiber alignment in superficial layers of heart valve leaflets with only general a priori knowledge of fiber properties, providing a novel approach to rapid, non-destructive study of microstructure in heart valve leaflets using a reflectance geometry.