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Methanogenic Archaea dominate mature heartwood habitats of Eastern Cottonwood ( Populus deltoides )

Methane is a potent greenhouse gas which accounts for 20% of radiative forcing. Several reports from diverse forest types demonstrate that the direct emission of methane from living tree trunks may be a significant terrestrial methane emission source; ongoing debate in recent literature contests whether tree emissions are from due to methane transport from soils or from the wood environment itself. Contrastingly, reports of methanogens from wood of Populus tree species were prominent in the literature 40 years ago, but have not been revisited with modern molecular microbial ecology approaches. We examined the microbial communities associated with the heartwood and sapwood of Eastern Cottonwood (Populus deltoides) trees using Illumina rRNA gene amplicon sequencing. At sampling, trees often emitted fetid fluid or flammable gasses, and our data indicate both environments were primarily composed of microbial taxa associated with anaerobic environments. Methanogens were prominent in heartwood environments (mean 34% relative abundance) compared to sapwood environments (13%). Dominant OTUs were primarily classified as the acetoclastic Methanobacterium spp. Members of the Firmicutes phylum overall were 39% of sequences and in 42% greater abundance in sapwood over heartwood environments. Tree diameter at breast height was the strongest predictor of Methanogen abundance, but wood moisture content and pH were also significant predictors of taxon abundance and overall community composition patterns. In conclusion, our results show that unlike more well studied plant associated communities in the soil, roots, rhizosphere and phylosphere; wood associated communities are shaped by the unique environmental conditions of the habitat and may be prominent and overlooked sources of methane emissions in temperate forest systems.

59 BASIC BIOLOGICAL SCIENCES↗

Expected individual benefit of prophylactic platelet transfusions in hemato‐oncology patients based on bleeding risks

Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.

Cornelissen, Loes L.↗

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Automation & Control Systems↗

Mammographic compression after breast conserving therapy: Controlling pressure instead of force

Purpose: X-ray mammography is the primary tool for early detection of breast cancer and for follow-up after breast conserving therapy (BCT). BCT-treated breasts are smaller, less elastic, and more sensitive to pain. Instead of the current force-controlled approach of applying the same force to each breast, pressure-controlled protocols aim to improve standardization in terms of physiology by taking breast contact area and inelasticity into account. The purpose of this study is to estimate the potential for pressure protocols to reduce discomfort and pain, particularly the number of severe pain complaints for BCT-treated breasts. Methods: A prospective observational study including 58 women having one BCT-treated breast and one untreated nonsymptomatic breast, following our hospital's 18 decanewton (daN) compression protocol was performed. Breast thickness, applied force, contact area, mean pressure, breast volume, and inelasticity (mean E-modulus) were statistically compared between the within-women breast pairs, and data were used as predictors for severe pain, i.e., scores 7 and higher on an 11-point Numerical Rating Scale. Curve-fitting models were used to estimate how pressure-controlled protocols affect breast thickness, compression force, and pain experience. Results: BCT-treated breasts had on average 27% smaller contact areas, 30% lower elasticity, and 30% higher pain scores than untreated breasts (allp < 0.001). Contact area was the strongest predictor for severe pain (p < 0.01). Since BCT-treatment is associated with an average 0.36 dm{sup 2} decrease in contact area, as well as increased pain sensitivity, BCT-breasts had on average 5.3 times higher odds for severe pain than untreated breasts. Model estimations for a pressure-controlled protocol with a 10 kPa target pressure, which is below normal arterial pressure, suggest an average 26% (range 10%–36%) reduction in pain score, and an average 77% (range 46%–95%) reduction of the odds for severe pain. The estimated increase in thickness is +6.4% for BCT breasts. Conclusions: After BCT, women have hardly any choice in avoiding an annual follow-up mammogram. Model estimations show that a 10 kPa pressure-controlled protocol has the potential to reduce pain and severe pain particularly for these women. The results highly motivate conducting further research in larger subject groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Patient-based estimation of organ dose for a population of 58 adult patients across 13 protocol categories

Purpose: This study aimed to provide a comprehensive patient-specific organ dose estimation across a multiplicity of computed tomography (CT) examination protocols. Methods: A validated Monte Carlo program was employed to model a common CT system (LightSpeed VCT, GE Healthcare). The organ and effective doses were estimated from 13 commonly used body and neurological CT examination. The dose estimation was performed on 58 adult computational extended cardiac-torso phantoms (35 male, 23 female, mean age 51.5 years, mean weight 80.2 kg). The organ dose normalized by CTDI{sub vol} (h factor) and effective dose normalized by the dose length product (DLP) (k factor) were calculated from the results. A mathematical model was derived for the correlation between the h and k factors with the patient size across the protocols. Based on this mathematical model, a dose estimation iPhone operating system application was designed and developed to be used as a tool to estimate dose to the patients for a variety of routinely used CT examinations. Results: The organ dose results across all the protocols showed an exponential decrease with patient body size. The correlation was generally strong for the organs which were fully or partially located inside the scan coverage (Pearson sample correlation coefficient (r) of 0.49). The correlation was weaker for organs outside the scan coverage for which distance between the organ and the irradiation area was a stronger predictor of dose to the organ. For body protocols, the effective dose before and after normalization by DLP decreased exponentially with increasing patient's body diameter (r > 0.85). The exponential relationship between effective dose and patient's body diameter was significantly weaker for neurological protocols (r < 0.41), where the trunk length was a slightly stronger predictor of effective dose (0.15 < r < 0.46). Conclusions: While the most accurate estimation of a patient dose requires specific modeling of the patient anatomy, a first order approximation of organ and effective doses from routine CT scan protocols can be reasonably estimated using size specific factors. Estimation accuracy is generally poor for organ outside the scan range and for neurological protocols. The dose calculator designed in this study can be used to conveniently estimate and report the dose values for a patient across a multiplicity of CT scan protocols.

60 APPLIED LIFE SCIENCES↗

WE-E-BRE-09: Investigation of the Association Between Radiation-Induced Pain and Radiation Dose in Head and Neck Cancer Patients

Purpose: Patients with head and neck cancer who undergo radiotherapy often experience several undesirable side-effects, including xerostomia, trismus, and pain in the head and neck area, but little is know about the dose-volume predictors of such pain. We investigated the association between radiation dose and both throat and esophagus pain during radiotherapy. Methods: We analyzed 124 head and neck patients who received radiotherapy at the Washington University School of Medicine in Saint Louis. For these patients, weekly PROs were recorded, including 16 pain and anatomical location questions. In addition, 17 observational symptoms were recorded. Patients were asked to describe their pain at each site according to a four-level scale: none (0), mild (1), moderate (2), and severe (3). We explored the association between throat pain and the mean dose received in oral cavity and between esophageal pain and the mean dose received in the esophagus. The severity of pain was determined by the difference between the baseline (week 1) pain score and the maximum pain score during treatment. The baseline pain score was defined as the first available pain score before receiving 10 Gy because radiotherapy pain originates later during treatment. Dose-volume metrics were extracted from treatment plans using CERR. To evaluate the correlation between pain and radiation dose, Spearman's correlation coefficient (Rs) was used. Results: The associations between throat pain and the mean dose to the oral cavity, and between esophagus pain and the mean dose to the esophagus, were both statistically significant, with Rs=0.320 (p=0.003) and Rs=0.424 (p<0.0001), respectively. Mean dose, for each structure, was a better predictor of pain than total integral dose. Conclusion: We demonstrated that pain during radiotherapy in head and neck patients highly correlates with the dose delivered. We will further investigate the association between other pain locations and relevant normal tissue dose characteristics.

60 APPLIED LIFE SCIENCES↗

WE-G-BRD-01: Diffusion Weighted MRI for Response Assessment of Inoperable Lung Tumors for Patients Undergoing SBRT Treatment

Purpose: To investigate early changes in tumor Apparent Diffusion Coefficients derived from diffusion weighted (DW)-MRI of lung cancer patients undergoing SBRT, as a possible early predictor of treatment response. Methods: DW-MRI scans were performed in this prospective phase I IRB-approved study of inoperable lung tumors at various time-points during the course of SBRT treatments. Axial DW scan using multi b-values ranging from 0–1000 s/mm{sup 2} were acquired in treatment position on a 3T Philips MR scanner during simulation, one hour after the first fraction (8 Gy), after a total of 5 fractions (40 Gy) and 4 weeks after SBRT delivery. A monoexponential model based on a least square fit from all b values was performed on a pixel-by-pixel basis and ADC was calculated. GTVs drawn on 4DCT for planning were mapped on the T2w MRI (acquired at exhale) after deformable registration. These volumes were then mapped on DWI scan for ADC calculation after rigid registration between the anatomical scan and diffusion scan. T2w scan on followup time points were deformably registered to the pretreatment T2 scan. Results: The first two patients in this study were analyzed. Median ADC values were 1.48, 1.48, 1.62 and 1.83 (10{sup −3}×) mm{sup 2}/s at pretreatment, after 8 Gy, after 40 Gy and 4 weeks posttreatment for the first patient and 1.57, 1.53, 1.66 and 1.72 (10{sup −3}×) mm{sup 2}/s for the second patient. ADC increased more significantly after 4 weeks of treatment rather than immediately post treatment, implying that late ADC value may be a better predictor of tumor response for SBRT treatment. The fraction of tumor pixels at high ADC values increased at 4 weeks post treatment. Conclusion: The observed increase in ADC values before the end of radiotherapy may be a surrogate for tumor response, but further patient accrual will be necessary to determine its value.

60 APPLIED LIFE SCIENCES↗

TH-E-BRF-04: Characterizing the Response of Texture-Based CT Image Features for Quantification of Radiation-Induced Normal Lung Damage

Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and available follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.

60 APPLIED LIFE SCIENCES↗

Attenuation-based size metric for estimating organ dose to patients undergoing tube current modulated CT exams

Purpose: Task Group 204 introduced effective diameter (ED) as the patient size metric used to correlate size-specific-dose-estimates. However, this size metric fails to account for patient attenuation properties and has been suggested to be replaced by an attenuation-based size metric, water equivalent diameter (D{sub W}). The purpose of this study is to investigate different size metrics, effective diameter, and water equivalent diameter, in combination with regional descriptions of scanner output to establish the most appropriate size metric to be used as a predictor for organ dose in tube current modulated CT exams. Methods: 101 thoracic and 82 abdomen/pelvis scans from clinically indicated CT exams were collected retrospectively from a multidetector row CT (Sensation 64, Siemens Healthcare) with Institutional Review Board approval to generate voxelized patient models. Fully irradiated organs (lung and breasts in thoracic scans and liver, kidneys, and spleen in abdominal scans) were segmented and used as tally regions in Monte Carlo simulations for reporting organ dose. Along with image data, raw projection data were collected to obtain tube current information for simulating tube current modulation scans using Monte Carlo methods. Additionally, previously described patient size metrics [ED, D{sub W}, and approximated water equivalent diameter (D{sub Wa})] were calculated for each patient and reported in three different ways: a single value averaged over the entire scan, a single value averaged over the region of interest, and a single value from a location in the middle of the scan volume. Organ doses were normalized by an appropriate mAs weighted CTDI{sub vol} to reflect regional variation of tube current. Linear regression analysis was used to evaluate the correlations between normalized organ doses and each size metric. Results: For the abdominal organs, the correlations between normalized organ dose and size metric were overall slightly higher for all three differently (global, regional, and middle slice) reported D{sub W} and D{sub Wa} than they were for ED, but the differences were not statistically significant. However, for lung dose, computed correlations using water equivalent diameter calculated in the middle of the image data (D{sub W,middle}) and averaged over the low attenuating region of lung (D{sub W,regional}) were statistically significantly higher than correlations of normalized lung dose with ED. Conclusions: To conclude, effective diameter and water equivalent diameter are very similar in abdominal regions; however, their difference becomes noticeable in lungs. Water equivalent diameter, specifically reported as a regional average and middle of scan volume, was shown to be better predictors of lung dose. Therefore, an attenuation-based size metric (water equivalent diameter) is recommended because it is more robust across different anatomic regions. Additionally, it was observed that the regional size metric reported as a single value averaged over a region of interest and the size metric calculated from a single slice/image chosen from the middle of the scan volume are highly correlated for these specific patient models and scan types.

60 APPLIED LIFE SCIENCES↗

SU-E-J-260: Quantitative Image Feature Analysis of Multiphase Liver CT for Hepatocellular Carcinoma (HCC) in Radiation Therapy

Purpose: To identify the effective quantitative image features (radiomics features) for prediction of response, survival, recurrence and metastasis of hepatocellular carcinoma (HCC) in radiotherapy. Methods: Multiphase contrast enhanced liver CT images were acquired in 16 patients with HCC on pre and post radiation therapy (RT). In this study, arterial phase CT images were selected to analyze the effectiveness of image features for the prediction of treatment outcome of HCC to RT. Response evaluated by RECIST criteria, survival, local recurrence (LR), distant metastasis (DM) and liver metastasis (LM) were examined. A radiation oncologist manually delineated the tumor and normal liver on pre and post CT scans, respectively. Quantitative image features were extracted to characterize the intensity distribution (n=8), spatial patterns (texture, n=36), and shape (n=16) of the tumor and liver, respectively. Moreover, differences between pre and post image features were calculated (n=120). A total of 360 features were extracted and then analyzed by unpaired student’s t-test to rank the effectiveness of features for the prediction of response. Results: The five most effective features were selected for prediction of each outcome. Significant predictors for tumor response and survival are changes in tumor shape (Second Major Axes Length, p= 0.002; Eccentricity, p=0.0002), for LR, liver texture (Standard Deviation (SD) of High Grey Level Run Emphasis and SD of Entropy, both p=0.005) on pre and post CT images, for DM, tumor texture (SD of Entropy, p=0.01) on pre CT image and for LM, liver (Mean of Cluster Shade, p=0.004) and tumor texture (SD of Entropy, p=0.006) on pre CT image. Intensity distribution features were not significant (p>0.09). Conclusion: Quantitative CT image features were found to be potential predictors of the five endpoints of HCC in RT. This work was supported in part by the National Cancer Institute Grant R01CA172638.

60 APPLIED LIFE SCIENCES↗

SU-E-J-267: Change in Mean CT Intensity of Lung Tumors During Radiation Treatment

Purpose: To evaluate CT intensity change of lung tumors during radiation therapy. Methods: Repeated 4D CT images were acquired on a CT simulator during the course of therapy for 27 lung cancer patients on IRB approved protocols. All subjects received definitive radiation treatment ± chemotherapy. CT scans were completed prior to treatment, and 2–7 times during the treatment course. Primary tumor was delineated by an experienced Radiation Oncologist. Contours were thresholded between −100 HU and 200 HU to remove airways and bone. Correlations between the change in the mean tumor intensity and initial tumor intensity, SUVmax, and tumor volume change rate were investigated. Reproducibility was assessed by evaluating the variation in mean intensity over all phases in 4DCT, for a subgroup of 19 subjects. Results: Reproducibility of tumor intensity between phases as characterized by the root mean square of standard deviation across 19 subjects was 1.8 HU. Subjects had a mean initial tumor intensity of 16.5 ± 11.6 HU and an overall reduction in HU by 10.3 ± 8.5 HU. Evaluation of the changes in tumor intensity during treatment showed a decrease of 0.3 ± 0.3 HU/day for all subjects, except three. No significant correlation was found between change in HU/day and initial HU intensity (p=0.53), initial PET SUVmax (p=0.69), or initial tumor volume (p=0.70). The rate of tumor volume change was weakly correlated (R{sup 2}=0.05) with HU change (p=0.01). Conclusion: Most lung cancer subjects showed a marked trend of decreasing mean tumor CT intensity throughout radiotherapy, including early in the treatment course. Change in HU/day is not correlated with other potential early predictors for response, such as SUV and tumor volume change. This Result supports future studies to evaluate change in tumor intensity on CT as an early predictor of response.

60 APPLIED LIFE SCIENCES↗

SU-E-T-01: (In)dependence of Plan Quality On Treatment Modalities and Target-To-Critical Structure Geometry for Brain Tumor

Purpose: To evaluate and test the hypothesis that plan quality may be systematically affected by treatment delivery techniques and target-tocritical structure geometric relationship in radiotherapy for brain tumor. Methods: Thirty-four consecutive brain tumor patients treated between 2011–2014 were analyzed. Among this cohort, 10 were planned with 3DCRT, 11 with RadipArc, and 13 with helical IMRT on TomoTherapy. The selected dosimetric endpoints (i.e., PTV V100, maximum brainstem/chiasm/ optic nerve doses) were considered as a vector in a highdimensional space. A Pareto analysis was performed to identify the subset of Pareto-efficient plans.The geometric relationships, specifically the overlapping volume and centroid-of-mass distance between each critical structure to the PTV were extracted as potential geometric features. The classification-tree analyses were repeated using these geometric features with and without the treatment modality as an additional categorical predictor. In both scenarios, the dominant features to prognosticate the Pareto membership were identified and the tree structures to provide optimal inference were recorded. The classification performance was further analyzed to determine the role of treatment modality in affecting plan quality. Results: Seven Pareto-efficient plans were identified based on dosimetric endpoints (3 from 3DCRT, 3 from RapicArc, 1 from Tomo), which implies that the evaluated treatment modality may have a minor influence on plan quality. Classification trees with/without the treatment modality as a predictor both achieved accuracy of 88.2%: with 100% sensitivity and 87.1% specificity for the former, and 66.7% sensitivity and 96.0% specificity for the latter. The coincidence of accuracy from both analyses further indicates no-to-weak dependence of plan quality on treatment modality. Both analyses have identified the brainstem to PTV distance as the primary predictive feature for Pareto-efficiency. Conclusion: Pareto evaluation and classification-tree analyses have indicated that plan quality depends strongly on geometry for brain tumor, specifically PTV-tobrain-stem-distance but minimally on treatment modality.

60 APPLIED LIFE SCIENCES↗

Vision 20/20: Mammographic breast density and its clinical applications

Breast density is a strong predictor of the failure of mammography screening to detect breast cancer and is a strong predictor of the risk of developing breast cancer. The many imaging options that are now available for imaging dense breasts show great promise, but there is still the question of determining which women are “dense” and what imaging modality is suitable for individual women. To date, mammographic breast density has been classified according to the Breast Imaging-Reporting and Data System (BI-RADS) categories from visual assessment, but this is known to be very subjective. Despite many research reports, the authors believe there has been a lack of physics-led and evidence-based arguments about what breast density actually is, how it should be measured, and how it should be used. In this paper, the authors attempt to start correcting this situation by reviewing the history of breast density research and the debates generated by the advocacy movement. The authors review the development of breast density estimation from pattern analysis to area-based analysis, and the current automated volumetric breast density (VBD) analysis. This is followed by a discussion on seeking the ground truth of VBD and mapping volumetric methods to BI-RADS density categories. The authors expect great improvement in VBD measurements that will satisfy the needs of radiologists, epidemiologists, surgeons, and physicists. The authors believe that they are now witnessing a paradigm shift toward personalized breast screening, which is going to see many more cancers being detected early, with the use of automated density measurement tools as an important component.

60 APPLIED LIFE SCIENCES↗

SU-F-T-386: Analysis of Three QA Methods for Predicting Dose Deviation Pass Percentage for Lung SBRT VMAT Plans

Purpose: To investigate the significance of using pinpoint ionization chambers (IC) and RadCalc (RC) in determining the quality of lung SBRT VMAT plans with low dose deviation pass percentage (DDPP) as reported by ScandiDos Delta4 (D4). To quantify the relationship between DDPP and point dose deviations determined by IC (ICDD), RadCalc (RCDD), and median dose deviation reported by D4 (D4DD). Methods: Point dose deviations and D4 DDPP were compiled for 45 SBRT VMAT plans. Eighteen patients were treated on Varian Truebeam linear accelerators (linacs); the remaining 27 were treated on Elekta Synergy linacs with Agility collimators. A one-way analysis of variance (ANOVA) was performed to determine if there were any statistically significant differences between D4DD, ICDD, and RCDD. Tukey’s test was used to determine which pair of means was statistically different from each other. Multiple regression analysis was performed to determine if D4DD, ICDD, or RCDD are statistically significant predictors of DDPP. Results: Median DDPP, D4DD, ICDD, and RCDD were 80.5% (47.6%–99.2%), −0.3% (−2.0%–1.6%), 0.2% (−7.5%–6.3%), and 2.9% (−4.0%–19.7%), respectively. The ANOVA showed a statistically significant difference between D4DD, ICDD, and RCDD for a 95% confidence interval (p < 0.001). Tukey’s test revealed a statistically significant difference between two pairs of groups, RCDD-D4DD and RCDD-ICDD (p < 0.001), but no difference between ICDD-D4DD (p = 0.485). Multiple regression analysis revealed that ICDD (p = 0.04) and D4DD (p = 0.03) are statistically significant predictors of DDPP with an adjusted r{sup 2} of 0.115. Conclusion: This study shows ICDD predicts trends in D4 DDPP; however this trend is highly variable as shown by our low r{sup 2}. This work suggests that ICDD can be used as a method to verify DDPP in delivery of lung SBRT VMAT plans. RCDD may not validate low DDPP discovered in D4 QA for small field SBRT treatments.

60 APPLIED LIFE SCIENCES↗

Wind turbine audibility and noise annoyance in a national U.S. survey: individual perception and influencing factors

With results from a nationwide survey sponsored by the U.S. Department of Energy, factors that affect outdoor audibility and noise annoyance of wind turbines were evaluated. Wind turbine and summer daytime median background sound levels were estimated for 1,043 respondents. Wind turbine sound level was the most robust predictor of audibility yet only a weak, albeit significant, predictor of noise annoyance. For each 1 dB increase in wind turbine sound level (L1h-max), the odds of hearing a wind turbine on one’s property increased by 31% (OR: 1.31; 95%CI: 1.25–1.38) and the odds of moving to the next level of annoyance increased by 9% (OR: 1.09; 95%CI: 1.02–1.16). While audibility was overwhelmingly dependent on turbine sound level, noise annoyance was best explained by visual disapproval (OR: 11.0; 95%CI: 4.8–25.4). The final models correctly predict audibility and annoyance level for 80% and 62% of individuals, respectively. The results demonstrate that among community members not receiving personal benefits from wind projects, the Community Tolerance Level of wind turbine noise for the U.S. aligns with the international average, further supporting observations that communities are less tolerant of wind turbine noise than other common environmental noise sources at equivalent A-weighted sound levels.

17 WIND ENERGY↗

Transcriptional Control of hgcAB by an ArsR-Like Regulator in Pseudodesulfovibrio mercurii ND132

The hgcAB gene pair encodes mercury (Hg) methylation capability in a diverse group of microorganisms, but its evolution and transcriptional regulation remain unknown. Working from the possibility that the evolutionary function of HgcAB may not be Hg methylation, we test a possible link to arsenic resistance. Using model Hg methylator Pseudodesulfovibrio mercurii ND132, we evaluated transcriptional control of hgcAB by a putative ArsR encoded upstream and cotranscribed with hgcAB. This regulator shares homology with ArsR repressors of arsenic resistance and S-adenosylhomocysteine (SAH)- responsive regulators of methionine biosynthesis but is distinct from other ArsR/SahR proteins in P. mercurii. Using quantitative PCR (qPCR) and RNA sequencing (RNA-seq) transcriptome analyses, we confirmed this ArsR regulates hgcAB transcription and is responsive to arsenic and SAH. Additionally, RNA-seq indicated a possible link between hgcAB activity and arsenic transformations, with significant upregulation of other ArsRregulated arsenic resistance operons alongside hgcAB. Interestingly, wild-type ND132 was less sensitive to As(V) (but not As(III)) than an hgcAB knockout strain, supporting the idea that hgcAB may be linked to arsenic resistance. Arsenic significantly impacted rates of Hg methylation by ND132; however, responses varied with culture conditions. Differences in growth and metabolic activity did not account for arsenic impacts on methylation. While arsenic significantly increased hgcAB expression, hgcAB gene and transcript abundance was not a good predictor of Hg methylation rates. Taken together, these results support the idea that Hg and As cycling are linked in P. mercurii ND132. Our results may hold clues to the evolution of hgcAB and the controls on Hg methylation in nature. This work reveals a link between microbial mercury methylation and arsenic resistance and may hold clues to the evolution of mercury methylation genes (hgcAB). Microbes with hgcAB produce methylmercury, a strong neurotoxin that readily accumulates in the food web. This study addresses a critical gap in our understanding about the environmental factors that control hgcAB expression. We show that hgcAB expression is controlled by an ArsR-like regulator responsive to both arsenic and S-adenosylhomocysteine in our model organism, Pseudodesulfovibrio mercurii ND132. Exposure to arsenic also significantly impacted Pseudodesulfovibrio mercurii ND132 mercury methylation rates. However, expression of hgcAB was not always a good predictor of Hg methylation rates, highlighting the roles of Hg bioavailability and other biochemical mechanisms in methylmercury production. This study improves our understanding of the controls on hgcAB expression, which is needed to better predict environmental methylmercury production

RNA-seq↗

Predictive Rules of Efflux Inhibition and Avoidance in Pseudomonas aeruginosa

Antibiotic-resistant bacteria rapidly spread in clinical and natural environments and challenge our modern lifestyle. A major component of defense against antibiotics in Gram-negative bacteria is a drug permeation barrier created by active efflux across the outer membrane. We identified molecular determinants defining the propensity of small peptidomimetic molecules to avoid and inhibit efflux pumps in Pseudomonas aeruginosa, a human pathogen notorious for its antibiotic resistance. Combining experimental and computational protocols, we mapped the fate of the compounds from structure-activity relationships through their dynamic behavior in solution, permeation across both the inner and outer membranes, and interaction with MexB, the major efflux transporter of P. aeruginosa. We identified predictors of efflux avoidance and inhibition and demonstrated their power by using a library of traditional antibiotics and compound series and by generating new inhibitors of MexB. The identified predictors will enable the discovery and optimization of antibacterial agents suitable for treatment of P. aeruginosa infections.

59 BASIC BIOLOGICAL SCIENCES↗

waveSZ: A Hardware-Algorithm Co-Design of Efficient Lossy Compression for Scientific Data

Error-bounded lossy compression is critical to the success of extreme-scale scientific research because of ever-increasing volumes of data produced by today's high-performance computing (HPC) applications. Not only can error-controlled lossy compressors significantly reduce the I/O and storage burden but they can retain high data fidelity for post analysis. Existing state-of-the-art lossy compressors, however, generally suffer from relatively low compression and decompression throughput (up to hundreds of megabytes per second on a single CPU core), which considerably restrict the adoption of lossy compression by many HPC applications especially those with a fairly high data production rate. In this paper, we propose a highly efficient lossy compression approach based on field programmable gate arrays (FPGAs) under the state-of-the-art lossy compression model SZ. Our contributions are fourfold. (1) We adopt a wavefront memory layout to alleviate the data dependency during the prediction for higher-dimensional predictors, such as the Lorenzo predictor. (2) We propose a co-design framework named WAVESZ based on the wavefront memory layout and the characteristics of SZ algorithm and carefully implement it by using high-level synthesis. (3) We propose a hardware-algorithm co-optimization method to improve the performance. (4) We evaluate our proposed WAVESZ on three real-world HPC simulation datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and FPGAs. Experiments show that our WAVESZ can improve SZ's compression throughput by 6.9x similar to 8.7x over the production version running on a state-of-the-art CPU and improve the compression ratio and throughput by 2.1x and 5.8x on average, respectively, respectively, compared with the state-of-the-art FPGA design.

Compression Ratio↗