It was shown previously that phage 21 and the defective element e14 integrate at the same site within the icd gene of Escherichia coli K-12 but that 21 integrase and excisionase excise e14 in vivo very infrequently compared to excision of 21. We show here that the reverse is also true: e14 excises itself much better than it excises an adjacent 21 prophage. In vitro integrase assays with various attP substrates delimit the minimal attP site as somewhere between 366 and 418 bp, where the outer limits would include the outermost repeated dodecamers suggested as arm recognition sites by S. J. Schneider (Ph.D. dissertation, Stanford University, Stanford, Calif., 1992). We speculate that the reason 21 attP is larger than attP (240 bp) is because it must include a 209-bp sequence homologous to the 3 end of the icd transcript in order to allow icd expression in lysogens. Alteration of portions of 21 attP to their e14 counterparts shows that 21 requires both the arm site and core site sequences of 21 but that replacements by e14 sequences function in some positions. Consistent with Schneider's in vivo results, and like all other known integrases from lambdoid phages, 21 requires integration host factor for activity.The lambdoid phages are a group of natural temperate phages whose best-known member is coliphage . They have a common genetic map and are related to one another by frequent natural recombination but exhibit extensive variation in function and nucleotide sequence (6). One example of such variation is seen in their integrase genes, which mediate insertion into the chromosome by site-specific recombination. Each lambdoid phage has a specific preferred site of insertion on the chromosome. Whereas some phages, like and 434, insert at the same chromosomal site and closely resemble each other in their integrase and excisionase genes and phage attachment sites, many distinct insertion specificities are found within the group. Between phages that insert at different sites, like and 21, the protein components are not interchangeable. Thus, each integrase protein has its own specificity of site recognition. The integrase genes of all lambdoid phages have sufficient sequence similarity to imply origin from a common ancestor, from which the whole spectrum of integration specificities must have evolved.The biochemical pathway of integrase-mediated site-specific recombination is well known (Fig. 1). For , the required extent of specific sequence is about 21 bp for the bacterial partner (attB) and 240 bp for the phage partner (attP). The actual crossover event takes place between attB and a similar sequence (core sequence) within attP and entails an exchange between the top two strands, forming a Holliday junction intermediate, followed by a homology-dependent process equivalent to branch migration through a 7-bp overlap (O) segment (identical in attB and the attP core) and resolution by exchange in the lower two strands at a position displaced 7 bp from the initiating exchange. The overlap segment is flanked by oppositely or...
Purpose: Current treatment planning remains a costly and labor intensive procedure and requires multiple trial‐and‐error adjustments of system parameters such as weighting factors and prescriptions. The purpose of this work is to develop an autonomous treatment planning technique with effective use of population‐based prior knowledge. Methods: Our autopiloted planning tool consists of three major components: (i) a commercial treatment planning system (TPS) (EclipseTM, Varian Medical Systems, CA); (ii) a statistical formulation of prior knowledge, which takes an assemble of prior treatment plans similar to the patient under planning and provides a priori variation range of final solution with an assigned preference level; and (iii) a decision‐making or outer‐loop optimization that assesses plan generated by Eclipse and drives the search toward a solution consistent with population‐based prior knowledge. To query and interact with TPS, Microsoft (MS) Visual Studio Coded UI is applied to record some common planner‐TPS interactions as subroutines. These subroutines are called back in the autopilot program written in C# to navigate through the solution space with an iterative algorithm. The utility of the approach is demonstrated by using a prostate IMRT case and three head and neck VMAT case. Results: An autonomous inverse planning formalism of using population‐based prior knowledge for autopiloting the VMAT/IMRT treatment planning process is implemented successfully in platform of a commercial TPS. The process mimics the decision‐making process of autopiloted automobile and provides a clinically sensible treatment plan automatically, thus effectively eliminating the tedious manual trial‐and‐errors process of treatment planning. It is found that the prostate and head and neck treatment plans compare favorably with that used for patients’ actual treatments. Conclusion: Clinical inverse treatment planning process can be autopiloted effectively with the use of population‐based prior knowledge. The strategy lays foundation for future development of big data‐driven inverse planning of various disease sites. Research grant and speak honoraria from Varian Medical Systems
Purpose: Currently, there is no effective way to automate the treatment planning process. The most common approach is to use criteria from a selected plan from a database and apply those configurations to the plan you want to work with. We proposed a direct mapping between any two given plans. We found a way to cleverly use information from one plan to the next, regardless of how different or similar they are. Methods: We have broken the entire automation process into two stages. In the first stage, we find a transformation between the two given patient. We find this transformation using the prescribed dose for the new plan and calculated dose from template plan. We apply this transformation and as a Result provide a coarse tuning. In the second stage, we use a voxel‐based penalty scheme to provide a finer tuning. The rationale behind this fine‐tuning stage is no matter how similar two plans are, there are inevitable differences we cannot account for. We evaluate this novel knowledge based system against manual planning for assessment. Results: We implemented a Visual Studio interface that automatically controls the Varian Eclipse's Beam Treatment Planning. No user intervention is needed for the entire planning duration. We compared the dose volume histogram between the automated plans and manual plan and observed nearly identical results. We also achieved up to 86x speed up between using knowledge from another plan versus starting completely from scratch. Conclusion: Knowledge based system would transform the treatment planning process into a completely automated procedure. This work has the potential to be a promising platform for developing treatment plans and afford a powerful way to reliably automate the treatment planning process. It could have major predictive value to save clinical time in developing a cancer treatment plan.
Purpose: Automating treatment planning has recently become a subject of intense research. The current limitation is there is no way to get around the black box nature of planning. One way is know the components inside the black box, but this idealistic approach is rarely practical. The purpose of this work is to develop a generic framework that is capable of automating any existing black box planning environment without the need to know the details of the implementation inside. Methods: We used a record, playback, and validation mechanism to fully automate the treatment planning procedure. Varian Eclipse Treatment Planning and Coutouring were used for VMAT planning. Different actions were pre‐recorded and played back with adjustable parameters calculated and inputted on the fly. The decision on which parameters to tune and how much to make the adjustment is based on previous results produced and saved. Results: We have tested our interface with four different head and neck VMAT patients. The system successfully ran and the results were in strong agreement with clinical requirements. Each iteration consists of tuning, decision making based on previous runs, optimization, and dose calculation. For each patient 20 iterations were sufficient and each iteration took on average 34.6 minutes. The DVH was exported after the dose calculation and the priority for all the organs were adjusted in the optimizer for each adjustment iteration. Conclusion: We have developed a generic framework that allows researchers to fully automate the treatment planning process. We have showed a proof‐of‐concept using Varian Eclipse. The automation does not require the user to know the details of the implementation of the treatment planning environment. It approaches treatment planning in a similar way as a manual planner would, by trying different adjustment strategies on the fly and deciding subsequently what the best action is.
Purpose: Prior knowledge‐based treatment planning is impeded by the use of a single dose volume histogram (DVH) curve. Critical spatial information is lost from collapsing the dose distribution into a histogram. Even similar patients possess geometric variations that becomes inaccessible in the form of a single DVH. We propose a simple prior knowledge‐based planning scheme that extracts features from prior dose distribution while still preserving the spatial information. Methods: A prior patient plan is not used as a mere starting point for a new patient but rather stopping criteria are constructed. Each structure from the prior patient is partitioned into multiple shells. For instance, the PTV is partitioned into an inner, middle, and outer shell. Prior dose statistics are then extracted for each shell and translated into the appropriate Dmin and Dmax parameters for the new patient. Results: The partitioned dose information from a prior case has been applied onto 14 2‐D prostate cases. Using prior case yielded final DVHs that was comparable to manual planning, even though the DVH for the prior case was different from the DVH for the 14 cases. Solely using a single DVH for the entire organ was also performed for comparison but showed a much poorer performance. Different ways of translating the prior dose statistics into parameters for the new patient was also tested. Conclusion: Prior knowledge‐based treatment planning need to salvage the spatial information without transforming the patients on a voxel to voxel basis. An efficient balance between the anatomy and dose domain is gained through partitioning the organs into multiple shells. The use of prior knowledge not only serves as a starting point for a new case but the information extracted from the partitioned shells are also translated into stopping criteria for the optimization problem at hand.
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