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Purpose/Objectives: Circadian clock dysregulation has been implicated in cancer risk such that The World Health Organization classifies circadian disruption as a probable carcinogen following several reports of night shift workers with an increased risk of breast cancer. There are studies which outline a link between time of day a patient receives radiation therapy and toxicity from their radiation treatment. In order to ascertain if chronomodulation should be considered for radiation toxicity and survival outcome improvements, we sought to assess if time of day for radiation treatment delivery affected outcomes for breast cancer patients with brain metastases. Materials/Methods: Retrospective data was collected for patients with a primary diagnosis of breast cancer with brain metastases who were treated with whole brain radiotherapy between 2015 and 2021. Eligible patients were included if ≥80% of their total dose was delivered either before or after 12:00 noon as documented by treatment times in the electronic medical record and if outcome data was available. Information including sex, age, performance status, control of primary disease, extracranial metastases, radiation dose, race, height, weight, BMI, and zip code were collected. Whole brain radiation was delivered to a median dose of 3000 cGy. Patients were eligible to be included in the analysis since ≥80% of their total dose was delivered either before or after 12:00 noon. A survival analysis was performed using a log-rank test to compare Kaplan-Meier curves between groups. Results: Between 2015 and 2021, 46 breast cancer patients with brain metastases were treated with whole brain radiation with ≥80% of their total dose was delivered. either before or after 12:00 noon. Patient characteristics were evenly split between those treated predominantly in the morning or afternoon with regards to age, extracranial disease, race and BMI at time of brain metastases diagnosis however 97.83% were female. The overall breakdown of race was 65.5% white, with 34.8% identifying as black or African American, Hispanic, or unknown. The average survival time for patients treated in the morning was 293 days compared to the afternoon patients which averaged 168 days. Kaplan meier curve showed statistically significant improvement in survival for the group that received ≥80% of treatments in the morning (p=0.0048). Conclusions: Survival was significantly improved in patients with breast cancer and brain metastases who received whole brain radiation in the morning compared to the afternoon. Limitations of our study include a low sample size due to our stringent criteria for inclusion in analysis but further investigation using a multi-center approach is warranted. Circadian effects on tumors or their environment, potentially through melatonin related signaling should be further evaluated. Future investigation in methods to entrain the circadian clock to harness positive chronobiological impact on tumors including pharmacologic intervention in an attempt to improve radiation outcomes and or toxicity and continued research is warranted. Citation Format: Lauren Matlack, Joseph Lombardo, Nicolas Nelson, Alexandria Smith, Wenyin Shi, Nicole Simone. Implications for chronoradiobiology: Differential effect of radiation response for breast cancer patients with brain metastases depending on treatment time [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-21-07.

Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy.Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEVFifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEVThe current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV

Heterotopic ossification is the development of mature lamellar bone in soft tissues. Heterotopic ossification can occur in up to 23% of patients after amputation. Heterotopic ossification is often painful, causing significant dysfunction. While radiotherapy is used to prevent heterotopic ossification before formation, there is a dearth of literature on using radiotherapy to treat existing heterotopic ossification. This case report describes the use of late radiotherapy for the management of existing heterotopic ossification that developed after a transfemoral amputation. A 61-yr-old woman with peripheral artery disease of her bilateral lower limbs status post stenting and ultimately left transfemoral amputation was diagnosed with symptomatic heterotopic ossification limiting her function. Another surgery was not felt to be warranted. She was not improving with medical therapy and was prescribed 800 cGy in one fraction. After treatment, she experienced significant relief in her pain, allowing her to resume physical therapy and use of her prosthesis. There are no other published examples of using radiation alone for treatment of heterotopic ossification formation after transfemoral amputation without surgical revision of the bone formation. Our case shows possible utility in single-dose radiation as a treatment to prevent progression of heterotopic ossification, especially when limiting functional progress.

Background: Social determinants of health directly affect cancer survival. Driven by advances in technology and recent demands due to COVID-19, telemedicine has the ability to improve patient access to care, lower health care costs, and increase workflow efficiency. The role of telemedicine in radiation oncology is not established. Materials and Methods: We conducted an IRB-approved pilot trial using a telehealth platform for the first post-radiation visit in patients with any cancer diagnosis. The primary endpoint was feasibility of using telehealth defined by completion of five telehealth visits per month in a single department. Secondary endpoints included the ability to assess patients appropriately, patient and physician satisfaction. Physicians were surveyed again during the pandemic to determine whether viewpoints changed. Results: Between May 27, 2016 and August 1, 2018, 37 patients were enrolled in the Telehealth in Post-operative Radiation Therapy (TelePORT) trial, with 24 evaluable patients who completed their scheduled telehealth visit. On average, 1.4 patients were accrued per month. All patients were satisfied with their care, had enough time with their physician and 85.7% believed the telehealth communication was excellent. All physicians were able to accurately assess the patient's symptoms via telehealth, whereas 82.3% felt they could accurately assess treatment-related toxicity. Physicians assessing skin toxicity from breast radiation were those who did not feel they were able to assess toxicity. Discussion and Conclusions: Both health care providers and patients have identified telemedicine as a suitable platform for radiation oncology visits. Although there are limitations, telemedicine has significant potential for increasing access of cancer care delivery in radiation oncology.

Acute myeloid leukemia (AML) is associated with poor outcomes in older adults. A major goal of treatment is to balance quality of life and functional independence with disease control. With the approval of new, more tolerable regimens, more older adults are able to receive AML-directed therapy. Among these options are hypomethylating agents (HMAs), specifically azacitidine and decitabine. HMAs have become an integral part of AML therapy over the last two decades. These agents are used either as monotherapy or nowadays more commonly in combination with other agents such as the Bcl-2 inhibitor venetoclax. Biological AML characteristics, such as molecular and cytogenetic risk factors, play crucial roles in guiding treatment decisions. In patients with high-risk AML, HMAs are increasingly used rather than intensive chemotherapy, although further trials based on a risk-adapted approach using patient- and disease-related factors are needed. Here, we review trials and evidence for the use of HMA monotherapy and combination therapy in the management of older adults with AML. Furthermore, we discuss the use of HMAs and HMA combination therapies in AML, mechanisms of action, their incorporation into hematopoietic stem cell transplantation strategies, and their use in patients with comorbidities and reduced organ function.

Access to green space has been shown to be associated with decreased incidence of cancer. The goal of this study is to identify associations between neighborhood green space indicators and breast cancer outcomes at the neighborhood level in Philadelphia. Green space indicators were sourced from the National Land Cover Database (2006-2016), VT Spatial Analysis Lab (2008-2013) and Philadelphia Parks and Recreation (2016), and included average percent tree cover, average tree canopy, average low vegetation, count of public parks, and percent vacant lots. Breast cancer data from the PA Cancer Registry (2005-2014) included standardized incidence ratio (SIR), standardized mortality ratio (SMR), and mean aggressiveness (MA) of cancer. Neighborhood-level covariates were gathered from U.S. Census (American Communities Survey, 2008-2012) and the Public Health Management Corporation’s Household Health Survey (2012) for Philadelphia neighborhoods. Pearson correlations were calculated to identify associations between environmental and green space indicators and breast cancer outcomes. Multivariable linear regression was used to identify if significant relationships persisted in the presence of demographic and socioeconomic disparities at the neighborhood level. Significance was determined at p-values < 0.05. SIR was significantly correlated with % of vacant lots (r=-.186), % of tree canopy cover within 50 meters from neighborhood centroid (r=.139) and the number of public parks (r=.125) per neighborhood. SMR was significantly correlated with vacant lots (r=.184) and tree canopy cover (r=-.104). MA was significantly correlated with tree canopy (r=-.124), tree canopy within fifty meters of neighborhood centroid (r=-.146), number of public parks (r=-.141), and % of vacant lots (r=.157). After controlling for covariates in multivariable linear regression models, the significant relationships between neighborhood green space indicators and breast cancer outcomes were no longer significant. For SIR, the significant associations with green space indicators suggest that Philadelphia neighborhoods with a higher incidence of breast cancer have more green space and a lower % of vacant lots. For SMR and MA, associations suggest that neighborhoods with higher breast cancer mortality and aggressiveness have less green space and a higher % of vacant lots. The direction of these relationships makes sense, given the % and quality of neighborhood green space correlate closely with indicators of socioeconomic status. The lack of significance when controlling for confounders likely represents the limitations of using neighborhood-level data, rather than patient-level data, to measure the impact of green space on breast cancer outcomes. Future studies should identify green space exposure indicators at the individual level in order to more accurately identify the impact of urban green space on breast cancer outcomes. These data could inform city planning to decrease the burden of breast cancer among vulnerable populations. Citation Format: Benjamin C. Keating, Joseph Lombardo, Charnita Zeigler-Johnson, Alex M. DesJarlais, Taylor Micua, Nicole L. Simone, Russell K. McIntire. Associations between neighborhood green space and breast cancer outcomes among Philadelphia neighborhoods [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C090.

It is important to establish if exposure to green space provides a protective effect for breast cancer development and progression to advocate for expanded access to green space areas for 1) those at-risk and 2) cancer patients receiving treatment. There is an association of residential proximity to green space with decreased breast cancer incidence. Current studies have not evaluated how patient variables may affect this relationship. We sought to determine if patient-level green space proximity correlated to social determinants of health such as poverty or minority status, or patient physiologic or inflammatory molecular markers in breast cancer patients. A retrospective analysis of baseline demographic data (including addresses) and cytokine profiles from breast cancer patients who had enrolled on investigator initiated trials at our institution was performed. National Land Cover Database and Philadelphia Parks & Recreation data was used to identify green space indicators such as patient proximity and neighborhood density of green space and tree canopy and was correlated to the patient’s systemic inflammation. Pearson coefficients were performed to identify relationships between green space indicators and inflammation. Patients were accrued to investigator initiated trials between 2013-2019. All had a diagnosis of ductal carcinoma in situ or invasive breast cancer. The median age of the women on the study was 63 years old, 42% were black, and 64% were considered of low socioeconomic status. The fraction of total area within a 250-meter radius of each patient’s address that has low vegetation or tree canopy was calculated. More women with above the median percentage of low vegetation within 250 meters of their residence had elevated levels of the anti-inflammatory cytokine TGF-B (p Citation Format: Taylor Micua, Joseph Lombardo, Alex M DesJarlais, Benjamin C. Keating, Melissa Lazar, Charnita Zeigler-Johnson, Adeseye Adekeye, Edith Mitchell, Russell K. McIntire, Nicole L. Simone. Proximity to green space and inflammation among breast cancer patients [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C092.

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant (p &lt, 0.001) overlap difference for spleen (DiceIML/DiceManual = 0.91/0.87), breast tumors (DiceIML/DiceManual = 0.84/0.82), and lung nodules (DiceIML/DiceManual = 0.78/0.83). For intra-rater consistency, a significant (p = 0.003) difference was found for spleen (DiceIML/DiceManual = 0.91/0.89). For inter-rater consistency, significant (p &lt, 0.045) differences were found for spleen (DiceIML/DiceManual = 0.91/0.87), breast (DiceIML/DiceManual = 0.86/0.81), lung (DiceIML/DiceManual = 0.85/0.89), the non-enhancing (DiceIML/DiceManual = 0.79/0.67) and the enhancing (DiceIML/DiceManual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin.

PURPOSE Multi-parametric MRI and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured, engineered/selected, and quantified by these methods relate to predictions generalizable in the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS To support further development, generalization, and clinical translation of our proposed method, we trained the AI model on a retrospective cohort of 29 de novo glioblastoma patients from the Hospital of the University of Pennsylvania (UPenn) (Male/Female:20/9, age:22-78 years) followed by evaluation on a prospective multi-institutional cohort of 84 glioblastoma patients (Male/Female:51/33, age:34-89 years) from Case Western Reserve University/University Hospitals (CWRU/UH, 25), New York University (NYU, 13), Ohio State University (OSU, 13), University Hospital Río Hortega (RH, 2), and UPenn (31). Features extracted from pre-resection MRI (T1, T1-Gd, T2, T2-FLAIR, ADC) were used to build our model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS Our model predicted the locations that later harbored tumor recurrence with sensitivity 83%, AUC 0.83 (99% CI, 0.73-0.93), and odds ratio 7.23 (99% CI, 7.09-7.37) in the prospective cohort. Odds ratio (99% CI)/AUC(99% CI) per institute were: CWRU/UH, 7.8(7.6-8.1)/0.82(0.75-0.89); NYU, 3.5(3.3-3.6)/0.84(074-0.93); OSU, 7.9(7.6-8.3)/0.8(0.67-0.94); RH, 22.7(20-25.1)/0.94(0.27-1); UPenn, 7.1(6.8-7.3)/0.83(0.76-0.91). CONCLUSION This is the first study that provides relatively extensive multi-institutional validated evidence that AI can provide good predictions of peritumoral neoplastic cell infiltration and future recurrence, by dissecting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and evaluate AI-based biomarkers for individualized prediction and prognostication, by moving from single-institution studies to generalizable, well-validated multi-institutional predictive biomarkers.

12010 Background: Older patients often experience more toxicity from cancer treatments due to their functional age and co-morbidities. The G8 is a geriatric screening tool designed to identify patients who are appropriate for a comprehensive geriatric assessment (CGA) which will inform and optimize oncology therapeutic decision making, reduce toxicities and improve outcomes. Using a nurse-led assessment approach, we have implemented an EMR embedded G8 tool across our cancer center since June 2020. We aim to describe the characteristics of those patients who scored abnormal (≤ 14) on the G8. Methods: The demographic and clinical characteristics were collected from the EMR for those who were ≥ 65 and have completed the G8 screening tool from June 2020 to Jan 2022. Summary statistics such as means, standard deviations and proportions were reported. Comparisons of various endpoints between dichotomized (≤ 14 vs. > 14) G8 scores were performed using pairwise two-sample t-test, pairwise proportion test, Fisher’s exact test and/or Pearson’s chi-squared test. Selected stakeholders were elicited for their perspectives on the implementation process. Results: 544 patients age ≥ 65 completed G8 screening with a mean score of 13.4 (± 2.3). The mean age was 75.2 (± 7.0), and 55.7% were females and 78.3% were non-Hispanic White. The most frequent cancer type was breast cancer (21.3%), followed by lung/respiratory cancer (18.2%), and GI cancer (15.8%). The percentage of patients appropriate for a CGA (based on an abnormal G8 score) was 59% (n = 332), of which only 10% (n = 31) were referred. A spearman correlation of 0.4 (p < 0.001) between G8 scores and age was obtained. A statistically significant association between ECOG and G8 groups was found, but not for any comorbidities. The prevalence of current/former smoking and extreme polypharmacy (> 10) is significantly higher in the G8 abnormal group. Implementation process evaluation found new patients with cancer overwhelmed with information, a lack of patient education and provider engagement, and an unstandardized referral process to CGA, were barriers for CGA referral. Conclusions: The G8 is an effective tool identifying patients appropriate for a CGA. An EMR embedded G8 steadily improved the G8 utilization uptake but not referral for CGA at our institution. To further improve awareness, utilization and referral, we are redesigning a hybrid workflow integrating patient-mediated MyChart implementation and nurse navigator engagement. EPIC enhancements are being designed to inform the oncologist of the patient’s G8 score, CGA referral status and the final CGA report.

Before the transport of a large telescope on site, it is suitable to perform factory tests to guarantee the optical performances. AMOS SA has been awarded of the contract from the design to on-site installation (in Rajasthan) of the 2.5-m Class Telescope for Physical Research Laboratory. The 20-m-focal-length telescope has a Ritchey-Chretien optical configuration and provides at Cassegrain location one axial port and two side ports. It is equipped with a primary active mirror and a first order adaptive optical system. It operates in the 0.37-4 μm spectral range. The project fulfillment relies on the AMOS multidisciplinary expertise in design and manufacturing of high-accuracy optical, mechanical and opto-mechanical systems. This paper presents the test results carried out at AMOS factory to assess the telescope performances (e.g. active optic control loop, pointing, tracking). It relies on extensive tests on the mount control, and the optical and mechanical sub-systems before assembly.

PURPOSE: A disparity exists in cancer screening rates for the lesbian, gay, bisexual, transgender and queer (LGBTQ+) community. We sought to understand the perceptions and baseline knowledge of cancer screening among LGBTQ+ community members. METHODS: Survey administered via social media from June 2018 to October 2018. We asked thirty-one questions focused on cancer screening, human papillomavirus, emotional distress, and experience with the healthcare system. Those included were age-eligible for cancer screening. Cancer screening attitudes and knowledge, as well as perceptions of the health care system were investigated. RESULTS: There were 422 respondents: 24.6% identified as female, 25.5% as male, 40.1% transgender and 9.6% as other. 65.4% of the LGBTQ+ community is not certain what cancer screening to do for themselves. Only 27.3% and 55.7% knew that HPV was a risk factor associated with head and neck cancer and anal cancer respectively. Half stated their emotional distress prevents them from getting cancer screening. It was identified that process changes in making appointments, comforts during the visit, and formal training for physicians and nurses could increase cancer screening compliance for this community. The transgender population had notably more gaps in knowledge of appropriate cancer screening and excess emotional distress. CONCLUSION: Gaps in cancer screening knowledge, and emotional and financial distress may be responsible for the disparity of lower cancer screening rates for the LGBTQ+ population, and the transgender population may be most at risk. Appreciating the cancer screening concerns of the LGBTQ+ population can help shape future clinical and institutional approaches to improve healthcare delivery.

Introduction New treatments for neurodegenerative disease are urgently needed, and clinical trial methods are an essential component of new drug development. Although a parallel-group study design for neurological disorder clinical trials is commonly used to test the effectiveness of a new treatment as compared to placebo, it does not efficiently use information from the on-going study to increase the success rate of a trial or to stop a trial earlier when the new treatment is indeed ineffective. Methods We review some recent advances in designs for clinical trials, including futility designs and adaptive designs. Results Futility designs and noninferiority designs are used to test the nonsuperiority and the noninferiority of a new treatment, respectively. We provide some guidance on using these two designs and analyzing data from these studies properly. Adaptive designs are increasingly used in clinical trials to improve the flexibility and efficiency of trials with the potential to reduce resources, time, and costs. We review some typical adaptive designs and new statistical methods to handle the statistical challenges from adaptive designs. Discussion Statistical advances in clinical trial designs may be helpful to shorten study length and benefit more patients being treated with a better treatment during the discovery of new therapies for neurological disorders. Advancing statistical underpinnings of neuroscience research is a critical aspect of the core activities supported by the Center of Biomedical Research Excellence award supporting the Center for Neurodegeneration and Translational Neuroscience.

Modern medicine is in the midst of a revolution driven by "big data," rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine.

PURPOSE Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients. METHODS We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( >90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- (< 14mts) and long-survivors ( >14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC). RESULTS Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77). CONCLUSION Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning. *Indicates equal authorship.

Purpose/Objective(s) There are established links between circadian clock dysregulation, and cancer risk. Numerous reports of increased risk for breast cancer in night shift workers led the World Health Organization to classify circadian disruption as a probable carcinogen. Additionally, the circadian clock has been known to influence the body's response to medications and most recently, a handful of studies demonstrate a link between radiation toxicity and time of day the patient received radiation. Prior studies investigating the temporal association of radiotherapy outcomes have yielded overall conflicting data and contemporary evaluations of this effect are sparse. To determine if chronotherapy should be considered to modulate radiation outcomes, we sought to evaluate if the time of day when radiation treatment was delivered affected outcomes for patients with brain metastases. Materials/Methods Retrospective data was collected for all consecutive patients between 2010 and 2020 with a diagnosis of brain metastases who were treated with whole brain radiotherapy. Patients were eligible to be included if ≥80% of their total dose was delivered either before or after 12:00 noon as documented by treatment times in the electronic medical record and if outcome data was available. Data on sex, age, performance status, control of primary disease, extracranial metastases, radiation dose, race, BMI, and zip code were collected. Radiation was delivered as whole-brain radiation to a median dose of 3000 cGY. Analysis of survival was performed using Mantel–Haenszel hazard ratio and log-rank test to compare Kaplan–Meier curves between groups. Results Complete data were available for 84 patients. The majority of patients had a primary diagnosis of breast or lung cancer (93%) and a small minority had a melanoma, thyroid, or unknown primary. 43 (51%) patients were excluded who did not receive ≥80% of their cumulative radiation either before or after noon, leaving n = 41 patients included for analysis. Demographic data were comparable between groups and overall: 56% female, 44% male; 56% ≥65 years, 44% younger. Distributions for BMI, extracranial metastasis, control of primary disease, and performance status were also similar between groups. Results showed a strong trend toward improved survival in the group that received ≥80% treatment in the afternoon (P = 0.057, HR 0.28–1.02). Conclusion Treatment time of day impacted survival from brain metastases in our patient population treated with whole brain radiation. Potential explanations for these results include the influence of multiple circadian pathways on tissue and tumor sensitivity through melatonin signaling and other mechanisms, warranting further exploration. Future investigations into entraining circadian rhythms via time of day treatments, light therapy or pharmaceutical interventions to alter radiation outcomes and toxicity should be considered.

As the most sensitive magnetic field sensor, the superconducting quantum interference device (SQUID) became an essential component in many applications due to its unmatched performance. Through recently achieved miniaturization, using state-of-the-art fabrication methods, this fascinating device extended its functionality and became an important tool in nanomaterial characterization. Here, we present an accessible and yet powerful technique of targeted atom displacement in order to reduce the size of the weak links of a DC nano-SQUID beyond the limits of conventional lithography. The controllability of our protocol allows us to characterize in situ the full superconducting response after each electromigration step. From this in-depth analysis, we reveal an asymmetric evolution of the weak links at cryogenic temperatures. A comparison with time resolved scanning electron microscopy images of the atom migration process at room temperature confirms the peculiar asymmetric evolution of the parallel constrictions. Moreover, we observe that when electromigration has sufficiently reduced the junction's cross section, superconducting phase coherence is attained in the dissipative state, where magnetic flux readout from voltage becomes possible.

BACKGROUND: The LGBTQ+ community is noted to experiences worse cancer outcomes, and the NCI has therefore designated this population as medically underserved. The reasons for this disparity are likely multifactorial, due in part to lower screening rates from emotional distress surrounding medical encounters and gaps in health knowledge. Therefore, ensuring inclusive clinical environments is crucial to narrowing this disparity. We sought to understand the medical perceptions of providers regarding cancer screening for LGBTQ+ patients. METHODS: Between 8/2017 and 8/2018, an IRB-approved 20-item REDCap survey was distributed to physicians in primary care via ACOG and oncology subspecialists via ASTRO, AUA, ACOSOG, and RSNA. Differences were assessed using chi-square or Fisher’s exact tests with a significance level set to 0.05. Participants were not compensated for completing the survey. RESULTS: The survey was completed by 361 individuals with a complete data set from 355. Female- and male-identifying providers completed the survey almost evenly (54% female, 45% male), and the majority of respondents had over ten years of experience in their practice (59%). The most common specialty of respondents was radiology (43%), followed by internal medicine (15%) and medical oncology (14%). The majority of respondents practiced in an urban setting (56%). Summaries of their approximate number of LGBTQ+ patients seen, confidence in caring for this population, and willingness to be listed as “LGBTQ+-friendly” did not vary significantly by region of practice. Significant findings included: 1) Providers were more likely to report having formal LGBTQ+-related training if they were female (34%; 22% for male), had under ten years of practice (40% did not disagree; 54% for longer), or practiced family or internal medicine (39%; 28% for medical/radiation oncology, 21% for radiology). 2) These specialists were also more likely to agree with the importance of knowing patients’ sexual orientation (94%; 62% for medical/radiation oncology). 3) By specialty, respondents disagreed about which cancer screenings are indicated for various LGBTQ+ subpopulations, e.g., specialists disagreed whether lesbian women have concerns with mammograms (85% for family/internal medicine, 72% for medical/radiation oncology, 62% for radiology). Regarding LGBTQ+ terminology, 36% of respondents believed trans women should have pap smears. Only 46% of providers reported confidence in their understanding of health concerns unique to the LGBTQ+ community, and 71% of all respondents agreed that their clinics would benefit from training about LGBTQ+ health issues. CONCLUSIONS: Our study demonstrated a lack of consensus regarding cancer screenings for gay, lesbian, and transgender patients. These responses indicate the need for clear cancer screening standards for LGBTQ+ subpopulations and educational programs for cancer care providers to reduce disparities and improve healthcare delivery for our LGBTQ+ community. Citation Format: Nicolas G. Nelson, Alexandra P. Smith, Kevin Ko, Joseph F. Lombardo, Marissa Ruggiero, Rita Murphy, Scott D. Richard, Amy E. Leader, Ayako Shimada, Nicole L. Simone. The need for LGBTQ+ education in oncology: Provider survey results demonstrate lack of consensus regarding cancer screenings for sexual and gender minorities [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-259.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach11 Publicly available source code: https://github.com/CBICA/BrainMaGe obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Activity-dependent changes in the properties of the motor system underlie the necessary adjustments in its responsiveness on the basis of the environmental and developmental demands of the organism. Although plastic changes in the properties of the spinal cord have historically been neglected because of the archaic belief that the spinal cord is constituted by a hardwired network that simply relays information to muscles, plenty of evidence has been accumulated showing that synapses impinging on spinal motoneurons undergo short- and long-term plasticity. In the brain, brief changes in the activity level of the network have been shown to be paralleled by changes in the intrinsic excitability of the neurons and are suggested to either reinforce or stabilize the changes at the synaptic level. However, rapid activity-dependent changes in the intrinsic properties of spinal motoneurons have never been reported. In this study, we show that in neonatal mice the intrinsic excitability of spinal motoneurons is depressed after relatively brief but sustained changes in the spinal cord network activity. Using electrophysiological techniques together with specific pharmacological blockers of KCNQ/Kv7 channels, we demonstrate their involvement in the reduction of the intrinsic excitability of spinal motoneurons. This action results from an increased M-current, the product of the activation of KCNQ/Kv7 channels, which leads to a hyperpolarization of the resting membrane potential and a decrease in the input resistance of spinal motoneurons. Computer simulations showed that specific up-regulations in KCNQ/Kv7 channels functions lead to a modulation of the intrinsic excitability of spinal motoneurons as observed experimentally. These results indicate that KCNQ/Kv7 channels play a fundamental role in the activity-dependent modulation of the excitability of spinal motoneurons.

We demonstrate the in situ engineering of superconducting nanocircuitry by targeted modulation of material properties through high applied current densities. We show that the sequential repetition of such customized electro-annealing in a niobium (Nb) nanoconstriction can broadly tune the superconducting critical temperature T-c and the normal-state resistance R-n in the targeted area. Once a sizable R-n is reached, clear magneto-resistance oscillations are detected along with a Fraunhofer-like field dependence of the critical current, indicating the formation of a weak link but with further adjustable characteristics. Advanced Ginzburg-Landau simulations fully corroborate this picture, employing the detailed parametrization from the electrical characterization and high resolution electron microscope images of the region within the constriction where the material has undergone amorphization by electro-annealing.

A quantitative genetic approach, which involves correlation of transcriptional networks with the phenotype in a recombinant inbred (RI) population and in selectively bred lines of rats, and determination of coinciding quantitative trait loci for gene expression and the trait of interest, has been applied in the present study. In this analysis, a novel approach was used that combined DNA-Seq data, data from brain exon array analysis of HXB/BXH RI rat strains and six pairs of rat lines selectively bred for high and low alcohol preference, and RNA-Seq data (including rat brain transcriptome reconstruction) to quantify transcript expression levels, generate co-expression modules and identify biological functions that contribute to the predisposition of consuming varying amounts of alcohol. A gene co-expression module was identified in the RI rat strains that contained both annotated and unannotated transcripts expressed in the brain, and was associated with alcohol consumption in the RI panel. This module was found to be enriched with differentially expressed genes from the selected lines of rats. The candidate genes within the module and differentially expressed genes between high and low drinking selected lines were associated with glia (microglia and astrocytes) and could be categorized as being related to immune function, energy metabolism and calcium homeostasis, as well as glial-neuronal communication. The results of the present study show that there are multiple combinations of genetic factors that can produce the same phenotypic outcome. Although no single gene accounts for predisposition to a particular level of alcohol consumption in every animal model, coordinated differential expression of subsets of genes in the identified pathways produce similar phenotypic outcomes.The datasets supporting the results of the present study are available at http://phenogen.ucdenver.edu.

International audience; The electromigration process has the potential capability to move atoms one by onewhen properly controlled. It is therefore an appealing tool to tune the cross sectionof monoatomic compounds with ultimate resolution or, in the case of polyatomiccompounds, to change the stoichiometry with the same atomic precision. Asdemonstrated here, a combination of electromigration and anti-electromigration canbe used to reversibly displace atoms with a high degree of control. This enables afine adjustment of the superconducting properties of Al weak links, whereas in Nbthe diffusion of atoms leads to a more irreversible process. In a superconductor witha complex unit cell (La2−xCexCuO4), the electromigration process acts selectively onthe oxygen atoms with no apparent modification of the structure. This allows to adjustthe doping of this compound and switch from a superconducting to an insulatingstate in a nearly reversible fashion. In addition, the conditions needed to replacefeedback controlled electromigration by a simpler technique of electropulsing arediscussed. These findings have a direct practical application as a method to explorethe dependence of the characteristic parameters on the exact oxygen content and pavethe way for a reversible control of local properties of nanowires.

The electromigration process has the potential capability to move atoms one by one when properly controlled. It is therefore an appealing tool to tune the cross section of monoatomic compounds with ultimate resolution or, in the case of polyatomic compounds, to change the stoichiometry with the same atomic precision. As demonstrated here, a combination of electromigration and anti-electromigration can be used to reversibly displace atoms with a high degree of control. This enables a fine adjustment of the superconducting properties of Al weak links, whereas in Nb the diffusion of atoms leads to a more irreversible process. In a superconductor with a complex unit cell (La

Alzheimer's disease (AD) is an urgent public health challenge that is rapidly approaching epidemic proportions. New therapies that defer or prevent the onset, delay the decline, or improve the symptoms are urgently needed. All phase 3 drug development programs for disease-modifying agents have failed thus far. New approaches to drug development are needed. Translational neuroscience focuses on the linkages between basic neuroscience and the development of new diagnostic and therapeutic products that will improve the lives of patients or prevent the occurrence of brain disorders. Translational neuroscience includes new preclinical models that may better predict human efficacy and safety, improved clinical trial designs and outcomes that will accelerate drug development, and the use of biomarkers to more rapidly provide information regarding the effects of drugs on the underlying disease biology. Early translational research is complemented by later stage translational approaches regarding how best to use evidence to impact clinical practice and to assess the influence of new treatments on the public health. Funding of translational research is evolving with an increased emphasis on academic and NIH involvement in drug development. Translational neuroscience provides a framework for advancing development of new therapies for AD patients.

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis, with 14.6 months median survival following treatment, and 4 months otherwise. There is increasing evidence that non-invasive radiomic markers can predict survival, using advanced multiparametric magnetic resonance imaging (A-mpMRI). If successfully applicable, such non-invasive markers can considerably influence patient management. However, most patients typically undergo only conventional mpMRI (C-mpMRI) pre-operatively (i.e., T1,T1-Gd,T2,T2-FLAIR), rather than A-mpMRI that provides additional vascularization (DSC-MRI) and cell-density (DTI) related information. Therefore, existing prognostic A-mpMRI radiomic markers are not applicable/generalizable across institutions for most patients. This study demonstrates that accurate prognostic models may be constructed pre-operatively using integrative radiomic analysis of widely-available C-mpMRI. We hypothesized that appropriate extraction and selection of advanced radiomic feature panels can potentially compensate for the lack of A-mpMRI, thereby rendering such radiomic predictors more amenable to broad clinical use. A retrospective cohort of 101 de novo glioblastoma patients with preoperative C-mpMRI and A-mpMRI was segmented into various sub-regions (e.g., enhancing/non-enhancing/necrotic). Both visually (e.g., volume, location, intensity), and non-visually (e.g., tumor growth model parameters) interpretable radiomic features (n=1612) were extracted for these sub-regions. We assessed two feature configurations: our advanced feature panel (A-FP), and a subset feature panel (S-FP) based on an existing published prognostic A-mpMRI model. The classification accuracy (ACC) of the prognostic model configurations to classify short-(14months), and intermediate-survivors was quantitatively evaluated using a 5-fold cross-validation scheme. The performance of the previously published model (S-FP/A-mpMRI) was replicated (ACC:78.71%), and degraded to 75.74% when using C-mpMRI. The A-FP/C-mpMRI model demonstrated superior performance (ACC:89.11%), also supported by Cox regression (HR:2.84, 95%CI:2.42-3.34). These results suggest that accurate pre-operative prognostic stratification, which is important for personalized treatment decisions in glioblastoma patients, is feasible based solely on integrative quantitative radiomic analysis of C-mpMRI, showing promise for generalization across multiple clinics.

1. Summary This paper presents the rationale for an initiative to create a comprehensive in silico modeling system, as a national shared resource, for simulation of complex biological systems, eg, neurodegenerative disease. The development of a well-integrated, generally-accessible computer modeling system (built upon a platform of an interlinked network of high performance/super computers -HPC) will enable medical researchers to translate and transform massive amounts of disconnected information (databases) into usable knowledge: an essential initial step for simulating and modeling complex biological processes. The aim of this initiative is to: a) develop a blue print for action and, b) convey the vision of the next generation of research tools, technologies, and systems for the investigation of neurodegenerative disorders of later life. As a first step, a Work Group will be established to: evaluate the infrastructure required for creating a national ‘‘in silico’’ modeling system; consider a number of computer modeling approaches and databases that might be incorporated into a network ofinterconnectedhighperformancecomputingsystems; explore the development of a partnership among the stakeholders (eg, government agencies (NIH, NSF, DOD, DOE and their national labs), universities, pharmaceutical companies, business and finance groups, voluntary private health organizations and foundations); and develop a public policy blueprint to obtain the required funds, research resources and infrastructure to launch the planning and construction of the system.

A finite element model that uses h-adaptation is used to predict atmospheric wind fields and pollutant transport over the Nevada Test Site. Meteorological data are used to generate a diagnostic flow field; prognostic (forecast) solutions of the time-dependent equations of atmospheric motion and species transport are then obtained. Mass lumping, reduced integration and Petrov–Galerkin weighting are employed in the algorithm. A coarse mesh is first generated; the mesh is then refined and unrefined utilizing parameters based on velocity and species concentration gradients. The model runs on SGI workstations; a parallel version has been ported to the SGI Origin 2000 located within the NSCEE at UNLV.

We applied 16S rRNA gene sequencing to identify bacterial species present in formalin-fixed, paraffin-embedded heart valve tissue. In 40% (12/30) of the cases, we were able to identify the bacterium to the species-genus level. For more recent cases (≤4 years), the success rate was significantly improved, to 70% ( P < 0.001).

Spinal motoneurons have long been thought to be simply part of a relay system that provides rapid, stereotyped outputs for muscles on the basis of a supraspinal plan tuned by sensory inputs, and activity-dependent plasticity (ADP) has been presumed to be a property of the brain. However, recent work indicates that ADP occurs in spinal motor neurons during development, as well as later in life with skills acquisition and maintenance, and in response to trauma and disease [1]. Understanding how spinal motoneuron output can be modified by both increased and decreased activity is thus a fundamental challenge with implications for athletic training, rehabilitation, and advanced prosthetics. We hypothesize that that alteration in the function of Kv7.2 channel (which carries the M current) and changes in axonal initial segment (AIS) properties are the primary mechanisms of adaptation of spinal motoneurons to prolonged network activation. This hypothesis is supported by the literature and our experimental data demonstrating that persistent activation of spinal cord networks decreases spinal motoneuron output in a manner consistent with the enhancement of a sub-threshold, non-inactivating potassium conductance. KCNQ/Kv7 channels, which are non-inactivating potassium channels that activate in the sub-threshold range [2], are expressed at the AIS, nodes of Ranvier, and soma of spinal motoneurons [3,4], and modulate their excitability [5,6]. To test our hypothesis, we developed a realistic computational model of spinal motoneuron activity before and after persistent network activation. As the starting point, we utilized a reconstructed spinal motoneuron morphology of neonatal mice [7] together with the detailed specification of the active and passive somatodendritic and axonal properties derived from a rodent cortical neuron model [8]. The model parameter values were adjusted to match our recordings of motoneuron electrophysiological properties using a multi-objective evolutionary algorithm (MOEA) [9]. The algorithm matches multiple selection criteria simultaneously (e.g., spike frequency, shape, adaptation rate, etc.) and generates entire collections of neuronal models that can be mined for rules describing the phenomena captured by the models (for instance, co-regulations between ionic conductances). Furthermore, since the MOEA generates two independent databases of models (i.e., before and after persistent activation), we are able to directly compare the phenomena discovered by our data mining process in each dataset, thus elucidating the mechanisms underlying plasticity.

Abstract An effective anticollision risk management approach is essential for drilling safely in the congested subsurface environments increasingly common in today's drilling programs. Because there is as yet no industry-wide standard for collision risk assessment and management, many companies have developed their own standards, which can differ in significant details. In one recent project, both the operator and the directional drilling contractor had developed specific anticollision procedures. The challenge was to create a drilling program that satisfied the requirements of both standards. This paper describes the problems we encountered and the solutions we created to successfully design and execute a drilling plan satisfactory to both companies.

Current trends in drilling require a proven and comprehensive approach to assessing and managing the risks of wellbore collisions. This paper outlines the scope and nature of the challenges in collision risk management, and provides an overview of a systematic, field-tested approach to identifying, evaluating, eliminating and/or mitigating those risks. Key aspects of the process are explained in detail and a recent field application on a large platform offshore Indonesia is discussed to illustrate the key values of the risk management process. These include maximizing resource recovery by permitting the safe, precise placement of wellbores, and minimizing the financial and human cost of losses from incidents avoided or mitigated.

Complex drilling programs in crowded downhole environments are increasingly common today, placing a premium on effective techniques for avoiding wellbore collisions. The costs of such incidents, both in financial and safety terms, can be extremely high. The authors have developed a comprehensive and effective approach to wellbore collision avoidance, as confirmed by a number of consistently successful projects. Based on their experiences in the field, the authors believe the time has come to establish an industry-wide standard for evaluating, minimizing and mitigating well collision hazards. This paper will outline their approach and provide case studies illustrating the high cost of such collisions and the value of an effective process for identifying-and eliminating or reducing-their likelihood. The approach described is flexible enough to accommodate the different challenges each unique field presents, and has proven equally effective in projects ranging from simple vertical wells to crowded land pads, offshore platforms, deepwater/subsea and fishbone multilateral designs. Well position uncertainties are determined by using survey error models from the Industry Steering Committee on Wellbore Survey Accuracy. Drilling program recommendations are based on the status and conditions of adjacent wells and the nature and magnitude of potential risks associated with a collision. Developing a standard based on this approach will equip the industry to achieve increasingly complex and ambitious goals without compromising safety or incurring unacceptable losses or risks by ensuring the targeting of the correct limited resources and expertise where they are most needed.

Schlumberger health, safety, and environment (HSE) programs have evolved over the past 20 years, accompanied by a steady decline in accident rates. However, in 2003 and 2004, improvement in accident rates had reached a plateau, and analysis indicated that more than 50% of accidents involved a failure to follow established, documented procedures. The need for a program to address behavior enforcement and behavior change was evident, and in 2004, the company formed a task force of employees and consultants to define such a program. This paper describes that program and its development, including definitions of its three components: accountability, hazard analysis and risk control, and observation and intervention. Also discussed are the must-have elements, changes in new-employee training, initial reaction to the program, and results to date.

The use of computers in heat transfer and fluid flow has become so commonplace today that no one would consider working in either field without some knowledge of computing. Problems are now being solved on a daily basis that even a few years ago were considered intractable. While we once thought that a problem with a few million nodes was huge a few years ago, researchers are now addressing problems with over 100 million nodes. At such levels of detail, one can begin to model processes at the micro level of physics. When researchers are able to quickly analyze these gigantic data sets and can generate insightful graphical displays, the understanding of fundamental processes and governing relations will escalate tremendously.Copyright © 2002 by ASME

The purposes of the (SERDP) program are as follows: (1) To address environmental matters of concern to the Department of Defense and the Department of Energy through support for basic and applied research and development of technologies that can enhance the capabilities of the departments to meet their environmental obligations. (2) To identify research, technologies, and other information developed by the Department of Energy for national defense purposes that would be useful to governmental and private organizations involved in the development of energy technologies and of technologies to address environmental restoration, waste minimization, hazardous waste substitution, and other environmental concerns, and to share such research, technologies with such governmental and private organizations. (3) To furnish other governmental organizations and private organizations wit data, enhanced data collection capabilities, and enhanced analytical capabilities for use by such organizations in the conduct of environmental research, including research concerning global environmental change. (4) To identify technologies developed by the private sector that are useful for Department of Defense and Department of Energy defense activities concerning environmental restoration, hazardous and solid waste minimization and prevention, hazardous material substitution, and provide for the use of such technologies in the conduct of such activities.

A Petrov-Galerkin finite element model that employs local mesh adaptation is being developed to determine potential wind energy sites within the state of Nevada. Meteorological data collected from various private, county, city, and government agencies are used to generate diagnostic flow fields, which subsequently provide initial conditions for the prognostic solution of the time-dependent equations of motion and species transport. The model runs on a multiprocessor SGI Onyx 3800. Results of the data collection, including wind energy site forecasts, will be made available on the web when the assessment for the entire state is completed.Copyright © 2002 by The American Institute of Aeronautics and Astronautics, Inc.