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At least 991 records · Page 55

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

Abstract The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass ( Panicum virgatum ) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha −1 year −1 ), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha −1 year −1 ). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha −1 year −1 under projected climate. On average, this was 0.3 Mg C ha −1 year −1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.

09 BIOMASS FUELS↗

Whole genome resequencing reveals genomic regions associated with thermal adaptation in redband trout

Abstract Adaptation to local environments involves evolution of ecologically important traits and underlying physiological processes. Here, we used low coverage whole‐genome resequencing (lcWGR) on individuals to identify genome regions involved in thermal adaptation in wild redband trout Oncorhynchus mykiss gairdneri , a subspecies of rainbow trout that inhabits ecosystems ranging from cold montane forests to high elevation deserts. This study includes allele frequency‐based analyses for selective sweeps among populations, followed by multiple association tests for specific sets of phenotypes measured under thermal stress (acute and chronic survival/mortality; high or low cardiac performance groups). Depending on the groups in each set of analyses, sequencing reads covered 43%–75% of the genome at ≥15× and each analysis included millions of SNPs across the genome. In tests for selective sweeps among populations, a total of six chromosomal regions were significant. The further association tests for specific phenotypes revealed that the region on chromosome 4 was consistently the most significant and contains the cerk gene (ceramide kinase). This study provides insight into a potential genetic mechanism of local thermal adaptation and suggests c erk may be an important candidate gene. However, further validation of this cerk gene is necessary to determine if the association with cardiac performance results in a functional role to influence thermal performance when exposed to high water temperatures and hypoxic conditions.

Chen, Zhongqi↗

Adaptive management of large-scale ecosystem restoration: increasing certainty of habitat outcomes in the Columbia River Estuary, U.S.A.

Ecological restoration programs in dynamic coastal environments can benefit from adaptive management, including an iterative process for identifying and addressing critical uncertainties. We highlight key developments under the three pillars that have increased the rate of restoration by the Columbia Estuary Ecosystem Restoration Program (CEERP) over 20 years: science, coordination, and management. We show how such programs can be institutionalized to ensure that estuary ecosystems are better understood, conserved, and restored. The principal conservation effort under CEERP is to reconnect historical floodplain wetlands to the mainstem. The program also supports other restoration actions that demonstrate a high potential to benefit ecosystem function and endangered salmon populations, however, there is greater uncertainty regarding these less-utilized techniques. Through adaptive management, we address both technical uncertainty regarding benefits to the environmental resource and programmatic uncertainty pertaining to decision-making. Here, we examine three periods of CEERP growth to establish how complementary research and restoration actions have improved program outcomes over time. We highlight the tools and processes that were developed and integrated into the program to refine program strategy, improve project design, and maximize ecological benefits. CEERP supported 77 restoration projects and reconnected over 7,000 acres of floodplain habitat to the lower Columbia River between 2004 and 2021. Building on these successes, we outline current plans to better engage landowners and local communities, solicit new project types, and maintain enough flexibility within the program to adapt to new priorities.

54 ENVIRONMENTAL SCIENCES↗

General-Surrogate Adaptive Sampling Using Interquartile Range for Design Space Exploration

Abstract A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations and experiments). New samples are often selected based on the estimated uncertainty in the design space. While some surrogates such as kriging have readily available uncertainty models for their predictions, other surrogates do not. Consequently, there have been studies of using the process of leaving-samples-out (LSO) used in cross-validation tools to estimate prediction uncertainty, such as the universal prediction distribution (UPD). In this paper, an adaptive sampling scheme for general surrogates is proposed based on LSO, similar to cross-validation and interquartile range (IQR). Multiple submodels are first developed from LSO. The uncertainty is then estimated from the IQR of these surrogates at a given point. New samples are added iteratively at the point with maximum IQR for design space exploration. The proposed scheme is illustrated using kriging, radial basis function, and neural network surrogates. The proposed scheme is evaluated using four algebraic test functions. Multiple sets of initial samples were produced to account for randomness. For these test functions, the proposed scheme was found to be more accurate and robust than kriging with its own uncertainty model. The proposed scheme was more accurate than the UPD for three out of the four test functions. For a fixed number of samples, the IQR-based adaptive sampling also proved to be more accurate than all-at-once sampling in most cases even when the estimated uncertainty was only mildly correlated with prediction errors.

Engineering↗

SU-E-E-07: An Adaptable Approach for Education On Medical Physics at Undergraduate and Postgraduate Levels

Purpose: To contribute to the professional profile of future medical physicists, technologists and physicians, and implement an adaptable educational strategy at both undergraduate and postgraduate levels. Methods: The Medical Physics Block of Electives (MPBE) designed was adapted to the Program of B.S. in Physics. The conferences and practical activities were developed with participatory methods, with interdisciplinary collaboration from research institutions and hospitals engaged on projects of Research, Development and Innovation (RDI). The scientific education was implemented by means of critical analysis of scientific papers and seminars where students debated on solutions for real research problems faced by medical physicists. This approach included courses for graduates not associated to educational programs of Medical Physics (MP). Results: The implementation of the MPBE began in September 2014, with the electives of Radiation MP and Introduction to Nuclear Magnetic Resonance. The students of second year received an Introduction to MP. This initiative was validated by the departmental Methodological Workshop, which promoted the full implementation of the MPBE. Both postgraduated and undergraduate trainees participated in practices with our DICOM viewer system, a local prototype for photoplethysmography and a home-made interface for ROC analysis, built with MATLAB. All these tools were designed and constructed in previous RDI projects. The collaborative supervision of University’s researchers with clinical medical physicists will allow to overcome the limitations of residency in hospitals, to reduce the workload for clinical supervisors and develop appropriate educational activities. Conclusion: We demonstrated the feasibility of adaptable educational strategies, considering available resources. This provides an innovative way for prospective medical physicists, technologists and radiation oncologists. This strategy can be implemented in several regions without formal programs of MP, like most of developing countries. Starting with undergraduate students would allow to reach appropriate certification faster than most of traditional or alternative approaches for education on MP. The authors acknowledge Radiation Consulting Group, LLC, an Arizona Corporation which promotes the use of ionizing radiation in the healing arts, for the “Oscar Luis Caballero” travel grant. The authors thanks to professors Meisbel Daudinot, David Adame and Alexander Pascau for the practices through imagis, imageROC and ANGIODIN PD3000 respectively.

60 APPLIED LIFE SCIENCES↗

SU-E-I-38: Improved Metal Artifact Correction Using Adaptive Dual Energy Calibration

Purpose: The empirical dual energy calibration (EDEC) method corrects for beam-hardening artifacts, but shows limited performance on metal artifact correction. In this work, we propose an adaptive dual energy calibration (ADEC) method to correct for metal artifacts. Methods: The empirical dual energy calibration (EDEC) method corrects for beam-hardening artifacts, but shows limited performance on metal artifact correction. In this work, we propose an adaptive dual energy calibration (ADEC) method to correct for metal artifacts. Results: Highly attenuating copper rods cause severe streaking artifacts on standard CT images. EDEC improves the image quality, but cannot eliminate the streaking artifacts. Compared to EDEC, the proposed ADEC method further reduces the streaking resulting from metallic inserts and beam-hardening effects and obtains material decomposition images with significantly improved accuracy. Conclusion: We propose an adaptive dual energy calibration method to correct for metal artifacts. ADEC is evaluated with the Shepp-Logan phantom, and shows superior metal artifact correction performance. In the future, we will further evaluate the performance of the proposed method with phantom and patient data.

60 APPLIED LIFE SCIENCES↗

SU-E-J-73: Extension of a Clinical OIS/EMR/R&V System to Deliver Safe and Efficient Adaptive Plan-Of-The-Day Treatments Using a Fully Customizable Plan-Library-Based Workflow

Purpose: To extend a clinical Record and Verify (R&V) system to enable a safe and fast workflow for Plan-of-the-Day (PotD) adaptive treatments based on patient-specific plan libraries. Methods: Plan libraries for PotD adaptive treatments contain for each patient several pre-treatment generated treatment plans. They may be generated for various patient anatomies or CTV-PTV margins. For each fraction, a Cone Beam CT scan is acquired to support the selection of the plan that best fits the patient’s anatomy-of-the-day. To date, there are no commercial R&V systems that support PotD delivery strategies. Consequently, the clinical workflow requires many manual interventions. Moreover, multiple scheduled plans have a high risk of excessive dose delivery. In this work we extended a commercial R&V system (MOSAIQ) to support PotD workflows using IQ-scripting. The PotD workflow was designed after extensive risk analysis of the manual procedure, and all identified risks were incorporated as logical checks. Results: All manual PotD activities were automated. The workflow first identifies if the patient is scheduled for PotD, then performs safety checks, and continues to treatment plan selection only if no issues were found. The user selects the plan to deliver from a list of candidate plans. After plan selection, the workflow makes the treatment fields of the selected plan available for delivery by adding them to the treatment calendar. Finally, control is returned to the R&V system to commence treatment. Additional logic was added to incorporate off-line changes such as updating the plan library. After extensive testing including treatment fraction interrupts and plan-library updates during the treatment course, the workflow is running successfully in a clinical pilot, in which 35 patients have been treated since October 2014. Conclusion: We have extended a commercial R&V system for improved safety and efficiency in library-based adaptive strategies enabling a wide-spread implementation of those strategies. This work was in part funded by a research grant of Elekta AB, Stockholm, Sweden.

60 APPLIED LIFE SCIENCES↗

SU-C-202-03: A Tool for Automatic Calculation of Delivered Dose Variation for Off-Line Adaptive Therapy Using Cone Beam CT

Purpose: Monitoring the delivered dose is an important task for the adaptive radiotherapy (ART) and for determining time to re-plan. A software tool which enables automatic delivered dose calculation using cone-beam CT (CBCT) has been developed and tested. Methods: The tool consists of four components: a CBCT Colleting Module (CCM), a Plan Registration Moduel (PRM), a Dose Calculation Module (DCM), and an Evaluation and Action Module (EAM). The CCM is triggered periodically (e.g. every 1:00 AM) to search for newly acquired CBCTs of patients of interest and then export the DICOM files of the images and related registrations defined in ARIA followed by triggering the PRM. The PRM imports the DICOM images and registrations, links the CBCTs to the related treatment plan of the patient in the planning system (RayStation V4.5, RaySearch, Stockholm, Sweden). A pre-determined CT-to-density table is automatically generated for dose calculation. Current version of the DCM uses a rigid registration which regards the treatment isocenter of the CBCT to be the isocenter of the treatment plan. Then it starts the dose calculation automatically. The AEM evaluates the plan using pre-determined plan evaluation parameters: PTV dose-volume metrics and critical organ doses. The tool has been tested for 10 patients. Results: Automatic plans are generated and saved in the order of the treatment dates of the Adaptive Planning module of the RayStation planning system, without any manual intervention. Once the CTV dose deviates more than 3%, both email and page alerts are sent to the physician and the physicist of the patient so that one can look the case closely. Conclusion: The tool is capable to perform automatic dose tracking and to alert clinicians when an action is needed. It is clinically useful for off-line adaptive therapy to catch any gross error. Practical way of determining alarming level for OAR is under development.

60 APPLIED LIFE SCIENCES↗

SU-F-J-58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

Purpose: Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid-treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning. Methods: The hybrid DIR is based on the combination of intensity-based algorithm and anatomical information provided by contours. Patients who received mid-treatment CT scans were identified for the study, including six lung patients (two mid-treatment CTs) and six head-and-neck (HN) patients (one mid-treatment CT). DIRpropagated ROIs were compared with physician-drawn ROIs for 8 ITVs and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical-relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index. Results: For critical organs, good agreement (Dice>0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re-drawn due to their small volumes (<10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re-drawn due to new nodal target identified on the mid-treatment CT scans. Conclusion: The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.

60 APPLIED LIFE SCIENCES↗

SU-G-TeP1-06: Fast GPU Framework for Four-Dimensional Monte Carlo in Adaptive Intensity Modulated Proton Therapy (IMPT) for Mobile Tumors

Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) treatment planning and verification using four-dimensional CT (4DCT) for adaptive IMPT for lung cancer patients. Methods: A validated GPU MC code, gPMC, has been linked to the patient database at our institution and employed to compute the dose-influence matrices (Dij) on the planning CT (pCT). The pCT is an average of the respiratory motion of the patient. The Dijs and patient structures were fed to the optimizer to calculate a treatment plan. To validate the plan against motion, a 4D dose distribution averaged over the possible starting phases is calculated using the 4DCT and a model of the time structure of the delivered spot map. The dose is accumulated using vector maps created by a GPU-accelerated deformable image registration program (DIR) from each phase of the 4DCT to the reference phase using the B-spline method. Calculation of the Dij matrices and the DIR are performed on a cluster, with each field and vector map calculated in parallel. Results: The Dij production takes ∼3.5s per beamlet for 10e6 protons, depending on the energy and the CT size. Generating a plan with 4D simulation of 1000 spots in 4 fields takes approximately 1h. To test the framework, IMPT plans for 10 lung cancer patients were generated for validation. Differences between the planned and the delivered dose of 19% in dose to some organs at risk and 1.4/21.1% in target mean dose/homogeneity with respect to the plan were observed, suggesting potential for improvement if adaptation is considered. Conclusion: A fast MC treatment planning framework has been developed that allows reliable plan design and verification for mobile targets and adaptation of treatment plans. This will significantly impact treatments for lung tumors, as 4D-MC dose calculations can now become part of planning strategies.

60 APPLIED LIFE SCIENCES↗

MO-FG-CAMPUS-IeP2-04: Multiple Penalties with Different Orders for Structure Adaptive CBCT Reconstruction

Purpose: To combine total variation (TV) and Hessian penalty in a structure adaptive way for cone-beam CT (CBCT) reconstruction. Methods: TV is a widely used first order penalty with good ability in suppressing noise and preserving edges but leads to the staircase effect in regions with smooth intensity transition. The second order Hessian penalty can effectively suppress the staircase effect with extra cost of blurring object edges. To take the best of both penalties we proposed a novel method to combine both for CBCT reconstruction in a structure adaptive way. The proposed method adaptively determined the weight of each penalty according to the geometry of local regions. An specially-designed exponent term with image gradient involved was used to characterize the local geometry such that the weights for Hessian and TV were 1 and 0 respectively at uniform local regions and 0 and 1 at edge regions. For other local regions the weights varied from 0 to 1. The objective functional was minimized using the majorzationminimization approach. We evaluated the proposed method on a modified 3D shepp-logan and a CatPhan 600 phantom. The full-width-at-halfmaximum (FWHM) and contrast-to-noise (CNR) were calculated. Results: For 3D shepp-logan the reconstructed images using TV had an obvious staircase effect while those using the proposed method and Hessian preserved the smooth transition regions well. FWHMs of the proposed method TV and Hessian penalty were 1.75 1.61 and 3.16 respectively, indicating that both TV and the proposed method is able to preserve edges. For CatPhan 600 CNR values of the proposed method were similar to those of TV and Hessian. Conclusion: The proposed method retains favorable properties of TV like preserving edges and also has the ability in better preserving gradual transition structure as Hessian does. All methods performs similarly in suppressing noise. This work was supported in part by National Natural Science Foundation of China (NNSFC) under Grant Nos.60971112 and 61375018 grants from the Cancer Prevention and Research Institute of Texas (RP130109 and RP110562-P2) National Institute of Biomedical Imaging and Bioengineering (R01 EB020366) and a grant from the American Cancer Society (RSG-13-326-01-CCE).

60 APPLIED LIFE SCIENCES↗

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.

60 APPLIED LIFE SCIENCES↗

TU-H-CAMPUS-JeP3-05: Adaptive Determination of Needle Sequence HDR Prostate Brachytherapy with Divergent Needle-By-Needle Delivery

Purpose: To develop a new method which adaptively determines the optimal needle insertion sequence for HDR prostate brachytherapy involving divergent needle-by-needle dose delivery by e.g. a robotic device. A needle insertion sequence is calculated at the beginning of the intervention and updated after each needle insertion with feedback on needle positioning errors. Methods: Needle positioning errors and anatomy changes may occur during HDR brachytherapy which can lead to errors in the delivered dose. A novel strategy was developed to calculate and update the needle sequence and the dose plan after each needle insertion with feedback on needle positioning errors. The dose plan optimization was performed by numerical simulations. The proposed needle sequence determination optimizes the final dose distribution based on the dose coverage impact of each needle. This impact is predicted stochastically by needle insertion simulations. HDR procedures were simulated with varying number of needle insertions (4 to 12) using 11 patient MR data-sets with PTV, prostate, urethra, bladder and rectum delineated. Needle positioning errors were modeled by random normally distributed angulation errors (standard deviation of 3 mm at the needle’s tip). The final dose parameters were compared in the situations where the needle with the largest vs. the smallest dose coverage impact was selected at each insertion. Results: Over all scenarios, the percentage of clinically acceptable final dose distribution improved when the needle selected had the largest dose coverage impact (91%) compared to the smallest (88%). The differences were larger for few (4 to 6) needle insertions (maximum difference scenario: 79% vs. 60%). The computation time of the needle sequence optimization was below 60s. Conclusion: A new adaptive needle sequence determination for HDR prostate brachytherapy was developed. Coupled to adaptive planning, the selection of the needle with the largest dose coverage impact increases chances of reaching the clinical constraints. M. Borot de Battisti is funded by Philips Medical Systems Nederland B.V.; M. Moerland is principal investigator on a contract funded by Philips Medical Systems Nederland B.V.; G. Hautvast and D. Binnekamp are fulltime employees of Philips Medical Systems Nederland B.V.

60 APPLIED LIFE SCIENCES↗

WE-DE-BRA-01: SCIENCE COUNCIL JUNIOR INVESTIGATOR COMPETITION WINNER: Acceleration of a Limited-Angle Intrafraction Verification (LIVE) System Using Adaptive Prior Knowledge Based Image Estimation

Purpose: To develop an adaptive prior knowledge based image estimation method to reduce the scan angle needed in the LIVE system to reconstruct 4D-CBCT for intrafraction verification. Methods: The LIVE system has been previously proposed to reconstructs 4D volumetric images on-the-fly during arc treatment for intrafraction target verification and dose calculation. This system uses limited-angle beam’s eye view (BEV) MV cine images acquired from the treatment beam together with the orthogonally acquired limited-angle kV projections to reconstruct 4D-CBCT images for target verification during treatment. In this study, we developed an adaptive constrained free-form deformation reconstruction technique in LIVE to further reduce the scanning angle needed to reconstruct the CBCT images. This technique uses free form deformation with energy minimization to deform prior images to estimate 4D-CBCT based on projections acquired in limited angle (orthogonal 6°) during the treatment. Note that the prior images are adaptively updated using the latest CBCT images reconstructed by LIVE during treatment to utilize the continuity of patient motion.The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the efficacy of this technique with LIVE system. A lung patient was simulated with different scenario, including baseline drifts, amplitude change and phase shift. Limited-angle orthogonal kV and beam’s eye view (BEV) MV projections were generated for each scenario. The CBCT reconstructed by these projections were compared with the ground-truth generated in XCAT.Volume-percentage-difference (VPD) and center-of-mass-shift (COMS) were calculated between the reconstructed and the ground-truth tumors to evaluate the reconstruction accuracy. Results: Using orthogonal-view of 6° kV and BEV- MV projections, the VPD/COMS values were 12.7±4.0%/0.7±0.5 mm, 13.0±5.1%/0.8±0.5 mm, and 11.4±5.4%/0.5±0.3 mm for the three scenarios, respectively. Conclusion: The technique enables LIVE to accurately reconstruct 4D-CBCT images using only orthogonal 6° angle, which greatly improves the efficiency and reduces dose of LIVE for intrafraction verification.

60 APPLIED LIFE SCIENCES↗

TH-AB-202-04: Auto-Adaptive Margin Generation for MLC-Tracked Radiotherapy

Purpose: To develop an auto-adaptive margin generator for MLC tracking. The generator is able to estimate errors arising in image guided radiotherapy, particularly on an MR-Linac, which depend on the latencies of machine and image processing, as well as on patient motion characteristics. From the estimated error distribution, a segment margin is generated, able to compensate errors up to a user-defined confidence. Method: In every tracking control cycle (TCC, 40ms), the desired aperture D(t) is compared to the actual aperture A(t), a delayed and imperfect representation of D(t). Thus an error e(t)=A(T)-D(T) is measured every TCC. Applying kernel-density-estimation (KDE), the cumulative distribution (CDF) of e(t) is estimated. With CDF-confidence limits, upper and lower error limits are extracted for motion axes along and perpendicular leaf-travel direction and applied as margins. To test the dosimetric impact, two representative motion traces were extracted from fast liver-MRI (10Hz). The traces were applied onto a 4D-motion platform and continuously tracked by an Elekta Agility 160 MLC using an artificially imposed tracking delay. Gafchromic film was used to detect dose exposition for static, tracked, and error-compensated tracking cases. The margin generator was parameterized to cover 90% of all tracking errors. Dosimetric impact was rated by calculating the ratio between underexposed points (>5% underdosage) to the total number of points inside FWHM of static exposure. Results: Without imposing adaptive margins, tracking experiments showed a ratio of underexposed points of 17.5% and 14.3% for two motion cases with imaging delays of 200ms and 300ms, respectively. Activating the margin generated yielded total suppression (<1%) of underdosed points. Conclusion: We showed that auto-adaptive error compensation using machine error statistics is possible for MLC tracking. The error compensation margins are calculated on-line, without the need of assuming motion or machine models. Further strategies to reduce consequential overdosages are currently under investigation. This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution.

60 APPLIED LIFE SCIENCES↗

Adaptation to Environmental Extremes Structures Functional Traits in Biological Soil Crust and Hypolithic Microbial Communities

Biological soil crusts (biocrusts) are widespread in drylands and deserts. At the microhabitat scale, they also host hypolithic communities that live under semitranslucent stones. Both environmental niches experience exposure to extreme conditions such as high UV radiation, desiccation, temperature fluctuations, and resource limitation. However, hypolithic communities are somewhat protected from extremes relative to biocrust communities. Conditions are otherwise similar, so comparing them can answer outstanding questions regarding adaptations to environmental extremes. Using metagenomic sequencing, we assessed the functional potential of dryland soil communities and identified the functional underpinnings of ecological niche differentiation in biocrusts versus hypoliths. We also determined the effect of the anchoring photoautotroph (moss or cyanobacteria). Genes and pathways differing in abundance between biocrusts and hypoliths indicate that biocrust communities adapt to the higher levels of UV radiation, desiccation, and temperature extremes through an increased ability to repair damaged DNA, sense and respond to environmental stimuli, and interact with other community members and the environment. Intracellular competition appears to be crucial to both communities, with biocrust communities using the Type VI Secretion System (T6SS) and hypoliths favoring a diversity of antibiotics. The dominant primary producer had a reduced effect on community functional potential compared with niche, but an abundance of genes related to monosaccharide, amino acid, and osmoprotectant uptake in moss-dominated communities indicates reliance on resources provided to heterotrophs by mosses. Our findings indicate that functional traits in dryland communities are driven by adaptations to extremes and we identify strategies that likely enable survival in dryland ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗

High-Order Mesh r-Adaptivity with Tangential Relaxation and Guaranteed Mesh Validity

High-order meshes are crucial for achieving optimal convergence rates in curvilinear domains, preserving symmetry, and aligning with key flow features in moving mesh simulations [1], but their quality is challenging to control. In prior work, we have developed techniques based on Target-Matrix Optimization Paradigm (TMOP) to adapt a given high-order mesh to the geometry and solution of the partial differential equation (PDE) [2, 3]. Here, we extend this framework to address two key gaps in the literature for highorder mesh 𝑟-adaptivity. First, we introduce tangential relaxation on curved surfaces using solely the discrete mesh representation, eliminating the need for access to underlying geometry (e.g., CAD model). Second, we ensure a continuously positive Jacobian determinant throughout the domain. This determinant positivity is essential for using the high-order mesh resulting from 𝑟-adaptivity with arbitrary quadrature schemes in simulations. The proposed approach is demonstrated to be robust using a variety of numerical experiments.

Mathematics and Computing↗

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗