Monte Carlo simulations are used to study the behavior of two polymers under confinement in a cylindrical tube. Each polymer is modeled as a chain of hard spheres. We measure the free energy of the system, F, as a function of the distance between the centers of mass of the polymers, λ, and examine the effects on the free energy functions of varying the channel diameter D and length L, as well as the polymer length N and bending rigidity κ. For infinitely long cylinders, F is a maximum at λ = 0, and decreases with λ until the polymers are no longer in contact. For flexible chains (κ = 0), the polymers overlap along the cylinder for low λ, while above some critical value of λ they are longitudinally compressed and non-overlapping while still in contact. We find that the free energy barrier height, ΔF ≡ F(0) - F(∞), scales as ΔF/k(B)T ∼ ND(-1.93 ± 0.01), for N ⩽ 200 and D ⩽ 9σ, where σ is the monomer diameter. In addition, the overlap free energy appears to scale as F/k(B)T = Nf(λ/N; D) for sufficiently large N, where f is a function parameterized by the cylinder diameter D. For channels of finite length, the free energy barrier height increases with increasing confinement aspect ratio L/D at fixed volume fraction ϕ, and it decreases with increasing ϕ at fixed L/D. Increasing the polymer bending rigidity κ monotonically reduces the overlap free energy. For strongly confined systems, where the chain persistence length P satisfies D ≪ P, F varies linearly with λ with a slope that scales as F'(λ) ∼ -k(B)TD(-β)P(-α), where β ≈ 2 and α ≈ 0.37 for N = 200 chains. These exponent values deviate slightly from those predicted using a simple model, possibly due to insufficiently satisfying the conditions defining the Odijk regime. Finally, we use Monte Carlo dynamics simulations to examine polymer segregation dynamics for fully flexible chains and observe segregation rates that decrease with decreasing entropic force magnitude, f ≡ |dF/dλ|. For both infinite-length and finite-length channels, the polymers are not conformationally relaxed at later times during segregation.

To start, I would like to thank my supervisor Dr. John Kildea for his support and guidance towards my improvement as a researcher. I'd also like to give a big thank you to the members of the NICE-ROKS group: Logan for the many hours of discussion and editing, Haley for all her help, Felix for the coding implementation debates, and Kelly for that story about his Spotify account. This project would also not have been possible without Gabe's advice on methodology and experiment design. To Piotr, Jan, and Shirin for useful discussions and comments, and to Julien for translation and editing help, thank you. Finally, I would not be here without the love and support of my parents, Morgan, and the rest of my family.

Neutron exposure poses a unique radiation protection concern because neutrons have a large, energy-dependent relative biological effectiveness (RBE) for stochastic effects. Recent computational studies on the microdosimetric properties of neutron dose deposition have implicated clustered DNA damage as a likely contributor to this marked energy dependence. So far, publications have focused solely on neutron RBE for inducing clusters of DNA damage containing two or more DNA double strand breaks (DSBs). In this study, we have conducted a novel assessment of neutron RBE for inducing all types of clustered DNA damage that contain two or more lesions, stratified by whether the clusters contain DSBs (complex DSB clusters) or not (non-DSB clusters). This assessment was conducted for eighteen initial neutron energies between 1 eV and 10 MeV as well as a reference radiation of 250 keV x-rays. We also examined the energy dependence of cluster length and cluster complexity because these factors are believed to impact the DNA repair process. To carry out our investigation, we developed a user-friendly TOPAS-nBio application that includes a custom nuclear DNA model and a novel algorithm for recording clustered DNA damage. We found that neutron RBE for inducing complex DSB clusters exhibited similar energy dependence to the canonical neutron RBE for stochastic radiobiological effects, at multiple depths in human tissue. Qualitatively similar results were obtained for non-DSB clusters, although the quantitative agreement was lower. Additionally we identified a significant neutron energy dependence in the average length and complexity of clustered lesions. These results support the idea that many types of clustered DNA damage contribute to the energy dependence of neutron RBE for stochastic radiobiological effects and imply that the size and constituent lesions of individual clusters should be taken into account when modeling DNA repair. Our results were qualitatively consistent for (i) multiple radiation doses (including a low-dose 0.1 Gy irradiation), (ii) variations in the maximal lesion separation distance used to define a cluster, and (iii) two distinct collections of physics models used to govern particle transport. Our complete TOPAS-nBio application has been released under an open-source license to enable others to independently validate our work and to expand upon it.

The photoneutron fluence per MU produced by the 10 MV FFF beam is 37% lower than the 10 MV beam of a Varian TrueBeam linac. Accordingly, a reduction in neutron dose received by patients is achieved through use of the unflattened beam, provided that treatment plans for each beam require approximately the same number of MU. It was found to be instructive to compare the photoneutron yield per source electron between the two beams as it helped provide an understanding of the physics underlying photoneutron production in both beams.

PurposeCollaborative incident learning initiatives in radiation therapy promise to improve and standardize the quality of care provided by participating institutions. However, the software interfaces provided with such initiatives must accommodate all participants and thus are not optimized for the workflows of individual radiation therapy centers. This article describes the development and implementation of a radiation therapy incident learning system that is optimized for a clinical workflow and uses the taxonomy of the Canadian National System for Incident Reporting – Radiation Treatment (NSIR‐RT).MethodsThe described incident learning system is a novel version of an open‐source software called the Safety and Incident Learning System (SaILS). A needs assessment was conducted prior to development to ensure SaILS (a) was intuitive and efficient (b) met changing staff needs and (c) accommodated revisions to NSIR‐RT. The core functionality of SaILS includes incident reporting, investigations, tracking, and data visualization. Postlaunch modifications of SaILS were informed by discussion and a survey of radiation therapy staff.ResultsThere were 240 incidents detected and reported using SaILS in 2016 and the number of incidents per month tended to increase throughout the year. An increase in incident reporting occurred after switching to fully online incident reporting from an initial hybrid paper‐electronic system. Incident templating functionality and a connection with our center's oncology information system were incorporated into the investigation interface to minimize repetitive data entry. A taskable actions feature was also incorporated to document outcomes of incident reports and has since been utilized for 36% of reported incidents.ConclusionsUse of SaILS and the NSIR‐RT taxonomy has improved the structure of, and staff engagement with, incident learning in our center. Software and workflow modifications informed by staff feedback improved the utility of SaILS and yielded an efficient and transparent solution to categorize incidents with the NSIR‐RT taxonomy.