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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on primary data, leaving uncertainties on the quantities of interest (QoIs) derived from it. In this work, we present a progressive data retrieval framework with guaranteed error control on derivable QoIs. Our contributions are three-fold. (1) We carefully derive the theories to strictly control QoI errors during progressive retrieval. Our theory is generic and can be applied to any QoIs that can be composited by the basis of derivable QoIs proved in the paper. (2) We design and develop a generic progressive retrieval framework based on the proposed theories, and optimize it by exploring feasible progressive representations. (3) We evaluate our framework using five real-world datasets with a diverse set of QoIs. Experiments demonstrate that our framework can faithfully respect any user-specified QoI error bounds in the evaluated applications. This leads to over 2.02× performance gain in data transfer tasks compared to transferring the primary data while guaranteeing a QoI error that is less than 1E-5.

Wu, Xuan↗
DOE OSTI2014

SU-E-E-02: Dashboard for Tracking Physics Resident Progress

Purpose: Design a system to easily and securely track the progress of medical physics residents through their residency. Paper sign-offs while offering a real signature are not easily updated or summarized. A resident or mentor needs to be able to quickly assess what the current assignments are, what are overdue, and whether the resident is on track to complete all the tasks in a timely fashion. An electronic version can accomplish all these goals. Methods: An electronic dashboard was created in excel to not only house the tasks and sign-off but to succinctly summarize the residents progress. The first tab contains the dashboard which displays tables of the progress of the residents in each rotation, their current task, and overdue tasks. It also displays the last meetings with the residents, and timeline of important items, and a burn-down chart of the remaining tasks. This are all tied to the data and current date which auto fills the tables. The second tab contains the data. This is comprised of lists of rotations and their associated tasks along with their due dates. A signature column was also created which is password protected but allows special subset users i.e. mentors to alter without using a password. Results: The dashboard has allowed residents to better track their progress and tells them what they should be working on. It has also allowed the mentors and the program director to rapid assess their progress. Conclusion: The dashboard is successful and has been created to allow easy addition and subtraction of required tasks as the residency evolves. The next step is to create a web app version of the excel sheet with logins.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2013

Kinetics of Myeloid Dendritic Cell Trafficking and Activation: Impact on Progressive, Nonprogressive and Controlled SIV Infections

We assessed the role of myeloid dendritic cells (mDCs) in the outcome of SIV infection by comparing and contrasting their frequency, mobilization, phenotype, cytokine production and apoptosis in pathogenic (pigtailed macaques, PTMs), nonpathogenic (African green monkeys, AGMs) and controlled (rhesus macaques, RMs) SIVagmSab infection. Through the identification of recently replicating cells, we demonstrated that mDC mobilization from the bone marrow occurred in all species postinfection, being most prominent in RMs. Circulating mDCs were depleted with disease progression in PTMs, recovered to baseline values after the viral peak in AGMs, and significantly increased at the time of virus control in RMs. Rapid disease progression in PTMs was associated with low baseline levels and incomplete recovery of circulating mDCs during chronic infection. mDC recruitment to the intestine occurred in all pathogenic scenarios, but loss of mucosal mDCs was associated only with progressive infection. Sustained mDC immune activation occurred throughout infection in PTMs and was associated with increased bystander apoptosis in blood and intestine. Conversely, mDC activation occurred only during acute infection in nonprogressive and controlled infections. Postinfection, circulating mDCs rapidly became unresponsive to TLR7/8 stimulation in all species. Yet, stimulation with LPS, a bacterial product translocated in circulation only in SIV-infected PTMs, induced mDC hyperactivation, apoptosis and excessive production of proinflammatory cytokines. After infection, spontaneous production of proinflammatory cytokines by mucosal mDCs increased only in progressor PTMs. We thus propose that mDCs promote tolerance to SIV in the biological systems that lack intestinal dysfunction. In progressive infections, mDC loss and excessive activation of residual mDCs by SIV and additional stimuli, such as translocated microbial products, enhance generalized immune activation and inflammation. Our results thus provide a mechanistic basis for the role of mDCs in the pathogenesis of AIDS and elucidate the causes of mDC loss during progressive HIV/SIV infections.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Fukushima Daiichi Unit 1 Accident Progression Uncertainty Analysis and Implications for Decommissioning of Fukushima Reactors - Volume I.

Sandia National Laboratories (SNL) has conducted an uncertainty analysis (UA) on the Fukushima Daiichi unit (1F1) accident progression with the MELCOR code. The model used was developed for a previous accident reconstruction investigation jointly sponsored by the US Department of Energy (DOE) and Nuclear Regulatory Commission (NRC). That study focused on reconstructing the accident progressions, as postulated by the limited plant data. This work was focused evaluation of uncertainty in core damage progression behavior and its effect on key figures-of-merit (e.g., hydrogen production, reactor damage state, fraction of intact fuel, vessel lower head failure). The primary intent of this study was to characterize the range of predicted damage states in the 1F1 reactor considering state of knowledge uncertainties associated with MELCOR modeling of core damage progression and to generate information that may be useful in informing the decommissioning activities that will be employed to defuel the damaged reactors at the Fukushima Daiichi Nuclear Power Plant. Additionally, core damage progression variability inherent in MELCOR modeling numerics is investigated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

A progressive deep transfer learning approach to cycle-skipping mitigation in FWI, In SEG Technical Program Expanded Abstracts 2019, pp. 2348-2352. Society of Exploration Geophysicists, 2019.

To effectively mitigate the cycle-skipping phenomenon in full waveform inversion (FWI), we developed an inception-based deep learning neural network to reconstruct the absent low frequency data by exploiting the subsurface low wavenumber information buried in the acquired high frequency data. Two unique features of our deep learning network for the low frequency data reconstruction are 1) Dual Data Feed; 2) Progressive Transfer Learning. With the Dual Data Feed feature, not only the high frequency data, but also the corresponding Beat Tone data are fed into the neural network, significantly reducing the network complexity and the network training cost. The Progressive Transfer Learning technique enables us to initiate the deep learning network with only one training dataset. Unlike other deep learning approaches in this category, our training dataset is not fixed. Instead, the training dataset and the network accuracy are progressively enhanced in an iterative manner within the Progressive Transfer Learning workflow. The numerical experiments validated that the low frequency data reconstructed by this progressive transfer learning network are sufficiently accurate for a successful FWI inversion without suffering from the cycle-skipping without any priori geological information.

58 GEOSCIENCES↗
DOE OSTI2020

Panel Session 89: Progress on Deep Repository Around the World

This panel focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress 10 and deserve to be recognized. Panelists from national, multi-national, and international repository programs and organizations reported on the status and future plans as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This session provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Panelists with presentations: Switzerland's Geological Repository Program for Radioactive Waste (Tim Vietor); Update on NWMO's Progress in Implementing Canada's Plan (Sarah Hirschorn); Cigeo Project Progress and Next Milestones (Corinne Bauer)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis

Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments. This study investigates fairness-aware medical image segmentation for hip and knee radiographs using deep learning models evaluated in both centralized and Federated Learning (FL) settings. We introduce Curriculum Learning (CL) strategies and Progressive Loss (PL) functions to regulate sample difficulty during training. In addition, we propose two novel fairness-oriented federated learning algorithms, Federated Intersection over Union (FedIoU) and Federated Intersection over Union with Outlier Analysis (FedIoUoutlier). Experiments are conducted using multiple segmentation backbones and simulated multi-site data partitions derived from the Osteoarthritis Initiative dataset. Model performance is evaluated using Intersection over Union (IoU), IoU standard deviation, Skewed Error Ratio (SER), and Min-Max Disparity across race and gender subgroups. Statistical significance was verified using paired t-tests to compare per-sample IoU performance against baseline configurations. Across both hip and knee segmentation tasks, curriculum learning and progressive loss strategies consistently improved segmentation accuracy and reduced demographic performance disparities in centralized training. In federated settings, fairness-aware aggregation further enhanced performance. Notably, FedIoUoutlier combined with balanced curriculum learning and tiered progressive loss achieved the highest mean IoU while yielding the lowest SER and Min-Max Disparity, indicating improved fairness without sacrificing accuracy. In several configurations, federated models matched or exceeded the performance of optimized centralized models, with statistically significant improvements in per-sample IoU over baseline configurations. The results demonstrate that structured training strategies and fairness-aware federated aggregation can jointly improve accuracy, stability, and demographic fairness in medical image segmentation. By integrating curriculum learning, progressive loss, and novel FL algorithms, this work provides a practical pathway toward equitable and privacy-preserving AI systems for medical imaging.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

The combination of lanthanum chloride and the calcimimetic calindol delays the progression of vascular smooth muscle cells calcification

Highlights: Black-Right-Pointing-Pointer Lanthanum reduces the progression of high phosphate-induced calcium deposition. Black-Right-Pointing-Pointer Calcium receptor agonists and the calcimimetic calindol reduce calcium deposition. Black-Right-Pointing-Pointer Lanthanum and calindol cooperate on reducing calcium deposition. Black-Right-Pointing-Pointer Lanthanum and calindol may interact with the same receptor. -- Abstract: Phosphate (Pi)-binders are commonly used in dialysis patients to control high Pi levels, that associated with vascular calcification (VC). The aim of this study was to investigate the effects of lanthanum chloride (LaCl{sub 3}) on the progression of high Pi-induced VC, in rat vascular smooth muscle cells (VSMCs). Pi-induced Ca deposition was inhibited by LaCl{sub 3}, with a maximal effect at 100 {mu}M (59.0 {+-} 2.5% inhibition). Furthermore, we studied the effects on VC of calcium sensing receptor (CaSR) agonists. Gadolinium chloride, neomycin, spermine, and the calcimimetic calindol significantly inhibited Pi-induced VC (55.9 {+-} 2.2%, 37.3 {+-} 4.7%, 30.2 {+-} 5.7%, and 63.8 {+-} 5.7%, respectively). To investigate the hypothesis that LaCl{sub 3} reduces the progression of VC by interacting with the CaSR, we performed a concentration-response curve of LaCl{sub 3} in presence of a sub-effective concentration of calindol (10 nM). Interestingly, this curve was shifted to the left (IC{sub 50} 9.6 {+-} 2.6 {mu}M), compared to the curve in the presence of LaCl{sub 3} alone (IC{sub 50} 19.0 {+-} 4.8 {mu}M). In conclusion, we demonstrated that lanthanum chloride effectively reduces the progression of high phosphate-induced vascular calcification. In addition, LaCl{sub 3} cooperates with the calcimimetic calindol in decreasing Ca deposition in this in vitro model. These results suggest the potential role of lanthanum in the treatment of VC induced by high Pi.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Oral administration of FAK inhibitor TAE226 inhibits the progression of peritoneal dissemination of colorectal cancer

Highlights: Black-Right-Pointing-Pointer A novel FAK inhibitor TAE226 suppressed FAK activity in HCT116 colon cancer cells. Black-Right-Pointing-Pointer TAE226 suppressed proliferation and migration, with a modest effect on adhesion. Black-Right-Pointing-Pointer Silencing of FAK by siRNA made no obvious difference on cancer cell attachment. Black-Right-Pointing-Pointer TAE226 treatment suppressed the progression of peritoneal dissemination. Black-Right-Pointing-Pointer Oral administration of TAE226 prolonged the survival of tumor-bearing mice. -- Abstract: Peritoneal dissemination is one of the most terrible types of colorectal cancer progression. Focal adhesion kinase (FAK) plays a crucial role in the biological processes of cancer, such as cell attachment, migration, proliferation and survival, all of which are essential for the progression of peritoneal dissemination. Since we and other groups have reported that the inhibition of FAK activity exhibited a potent anticancer effect in several cancer models, we hypothesized that TAE226, a novel ATP-competitive tyrosine kinase inhibitor designed to target FAK, can prevent the occurrence and progression of peritoneal dissemination. In vitro, TAE226 greatly inhibited the proliferation and migration of HCT116 colon cancer cells, while their adhesion on the matrix surface was minimally inhibited when FAK activity and expression was suppressed by TAE226 and siRNA. In vivo, when HCT116 cells were intraperitoneally inoculated in mice, the cells could attach to the peritoneum and begin to grow within 24 h regardless of the pretreatment of cells with TAE226 or FAK-siRNA, suggesting that FAK is not essential, at least for the initial integrin-matrix contact. Interestingly, the treatment of mice before and after inoculation significantly suppressed cell attachment to the peritoneum. Furthermore, oral administration of TAE226 greatly reduced the size of disseminated tumors and prolonged survival in tumor-bearing mice. Taken together, a possible strategy for inhibiting peritoneal dissemination by targeting FAK with TAE226 appears to be applicable through anti-proliferative and anti-invasion/anti-migration mechanisms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Mitochondrial regulation of cell cycle progression through SLC25A43

An increasing body of evidence is pointing towards mitochondrial regulation of the cell cycle. In a previous study of HER2-positive tumours we could demonstrate a common loss in the gene encoding for the mitochondrial transporter SLC25A43 and also a significant relation between SLC25A43 protein expression and S-phase fraction. Here, we investigated the consequence of suppressed SLC25A43 expression on cell cycle progression and proliferation in breast epithelial cells. In the present study, we suppressed SLC25A43 using siRNA in immortalised non-cancerous breast epithelial MCF10A cells and HER2-positive breast cancer cells BT-474. Viability, apoptosis, cell proliferation rate, cell cycle phase distribution, and nuclear Ki-67 and p21, were assessed by flow cytometry. Cell cycle related gene expressions were analysed using real-time PCR. We found that SLC25A43 knockdown in MCF10A cells significantly inhibited cell cycle progression during G{sub 1}-to-S transition, thus significantly reducing the proliferation rate and fraction of Ki-67 positive MCF10A cells. In contrast, suppressed SLC25A43 expression in BT-474 cells resulted in a significantly increased proliferation rate together with an enhanced G{sub 1}-to-S transition. This was reflected by an increased fraction of Ki-67 positive cells and reduced level of nuclear p21. In line with our previous results, we show a role for SLC25A43 as a regulator of cell cycle progression and proliferation through a putative mitochondrial checkpoint. These novel data further strengthen the connection between mitochondrial function and the cell cycle, both in non-malignant and in cancer cells. - Highlights: • Proposed cell cycle regulation through the mitochondrial transporter SLC25A43. • SLC25A43 alters cell proliferation rate and cell cycle progression. • Suppressed SLC25A43 influences transcription of cell cycle regulatory genes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

NCOA5 is correlated with progression and prognosis in luminal breast cancer

Nuclear receptor coactivator 5 (NCOA5) is known to modulate ERα-mediated transcription and has been found to be involved in the progression of several malignancies. However, the potential correlation between NCOA5 and clinical outcome in patients with luminal breast cancer remains unknown. In the present study, we demonstrated that NCOA5 was significantly up-regulated in luminal breast cancer tissues compared with adjacent non-cancerous tissues both in validated cohort and TCGA cohort. Moreover, Kaplan-Meier analysis indicated that patients with high NOCA5 expression had significantly lower overall survival (P = 0.021). Cox regression analysis indicated that the high NOCA5 expression was independent high risk factor as well as old age (>60) and HER-2 expression (P = 0.039; P = 0.003; P = 0.005; respectively). This study provides new insights and evidences that NOCA5 over-expression was significantly correlated with progression and prognosis in luminal breast cancer. However, the precise cellular mechanisms for NOCA5 in luminal breast cancer need to be further explored. - Highlights: • NCOA5 is significantly over-expressed in human luminal breast cancer tissues. • NOCA5 was involved in the progression of luminal breast cancer. • NCOA5 can predict the progression of luminal breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

ELK1-induced upregulation of lncRNA HOXA10-AS promotes lung adenocarcinoma progression by increasing Wnt/β-catenin signaling

In recent years, increasing number of lncRNAs have been studied in lung adenocarcinoma (LAD). LncRNA HOXA10-AS was found to be upregulated in LAD tissues. Based on the data of TCGA database, HOXA10-AS was a prognostic factor for lung adenocarcinoma. This study aims to reveal the biological function of HOXA10-AS in LAD. qRT-PCR was applied to test expression levels of HOXA10-AS in both LAD tissues and cell lines. Next, transcription factor ELK1 was demonstrated to upregulate HOXA10-AS in LAD cells through performing bioinformatics analysis and dual luciferase activity. Loss of function assays were performed in two different LAD cell lines. Silenced HOXA10-AS was proved to inhibit LAD progression by affecting cell proliferation, cell apoptosis and cell metastasis and EMT progress. Western blot analysis revealed that HOXA10-AS increased Wnt/β-catenin signaling in LAD cell lines. Finally, rescue assays were carried out to identify the biological function of HOXA10-AS-Wnt/β-catenin signaling in LAD progression. In conclusion, ELK1-induced upregulation of HOXA10-AS improved LAD progression through increasing Wnt/β-catenin signaling.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

MiR-326 antagomir delays the progression of age-related cataract by upregulating FGF1-mediated expression of betaB2-crystallin

Highlights: • FGF-1 upregulates the expression of an anti-cataract protein βB2. • miR-326 inhibits FGF-1 expression and thereby decreases βB2 level. • miR-326 antagomir delay cataract progression via the function of miR-326. Age-related cataract, the most common cause of blindness worldwide, has been found closely associated with β-crystallin B2 (βB2 or CRYBB2). MicroRNAs (miRNAs) are the primary epigenetic regulators important for various biological processes. However, the role of miRNAs in the progression of lens cataract remains to be elucidated. In this study, we found a novel signal cascade miR-326–fibroblast growth factor 1 (FGF1)–βB2 modulating the progression of lens cataract. In brief, miR-326 exacerbated but its antagomirs attenuated H{sub 2}O{sub 2}-induced apoptosis of HLEC-B3 human lens epithelial cells. Dual-luciferase reporter assay and Western blot showed that miR-326 inhibited FGF1 expression by directly targeting its mRNA 3′-UTR. Consistent with this result, miR-326 antagomir enhanced FGF1 protein level. In addition to FGF1, miR-326 antagomir also enhanced βB2 expression and this enhancement was abolished by transfection of HLEC-B3 cells with FGF1 shRNA. These data demonstrated that miR-326 antagomir increased βB2 expression via upregulating FGF1, which was further confirmed by the studies in a rat model of selenite-induced cataract. This work suggests that miR-326 antagomir might be a promising candidate to prevent progression of age-related cataract.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2012

Split-Course, High-Dose Palliative Pelvic Radiotherapy for Locally Progressive Hormone-Refractory Prostate Cancer

Purpose: Local progression, in patients with hormone-refractory prostate cancer, often causes significant morbidity. Pelvic radiotherapy (RT) provides effective palliation in this setting, with most published studies supporting the use of high-dose regimens. The aim of the present study was to examine the role of split-course hypofractionated RT used at our institution in treating this group of patients. Methods and Materials: A total of 34 men with locoregionally progressive hormone-refractory prostate cancer, treated with a split course of pelvic RT (45-60 Gy in 18-24 fractions) between 2000 and 2008 were analyzed. The primary endpoints were the response rate and actuarial locoregional progression-free survival. Secondary endpoints included overall survival, compliance, and acute and late toxicity. Results: The median age was 71 years (range, 53-88). Treatment resulted in an overall initial response rate of 91%, a median locoregional progression-free survival of 43 months, and median overall survival of 28 months. Compliance was excellent and no significant late toxicity was reported. Conclusions: The split course pelvic RT described has an acceptable toxicity profile, is effective, and compares well with other high-dose palliative regimens that have been previously reported.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Modeling Freedom From Progression for Standard-Risk Medulloblastoma: A Mathematical Tumor Control Model With Multiple Modes of Failure

Purpose: As pediatric medulloblastoma (MB) is a relatively rare disease, it is important to extract the maximum information from trials and cohort studies. Here, a framework was developed for modeling tumor control with multiple modes of failure and time-to-progression for standard-risk MB, using published pattern of failure data. Methods and Materials: Outcome data for standard-risk MB published after 1990 with pattern of relapse information were used to fit a tumor control dose-response model addressing failures in both the high-dose boost volume and the elective craniospinal volume. Estimates of 5-year event-free survival from 2 large randomized MB trials were used to model the time-to-progression distribution. Uncertainty in freedom from progression (FFP) was estimated by Monte Carlo sampling over the statistical uncertainty in input data. Results: The estimated 5-year FFP (95% confidence intervals [CI]) for craniospinal doses of 15, 18, 24, and 36 Gy while maintaining 54 Gy to the posterior fossa was 77% (95% CI, 70%-81%), 78% (95% CI, 73%-81%), 79% (95% CI, 76%-82%), and 80% (95% CI, 77%-84%) respectively. The uncertainty in FFP was considerably larger for craniospinal doses below 18 Gy, reflecting the lack of data in the lower dose range. Conclusions: Estimates of tumor control and time-to-progression for standard-risk MB provides a data-driven setting for hypothesis generation or power calculations for prospective trials, taking the uncertainties into account. The presented methods can also be applied to incorporate further risk-stratification for example based on molecular biomarkers, when the necessary data become available.

62 RADIOLOGY AND NUCLEAR MEDICINE↗