Validation of Coupled Atmosphere-Fire Behavior Models

Recent advances in numerical modeling and computer power have made it feasible to simulate the dynamical interaction and feedback between the heat and turbulence induced by wildfires and the local atmospheric wind and temperature fields. At Los Alamos National Laboratory, the authors have developed a modeling system that includes this interaction by coupling a high resolution atmospheric dynamics model, HIGRAD, with a fire behavior model, BEHAVE, to predict the spread of wildfires. The HIGRAD/BEHAVE model is run at very high resolution to properly resolve the fire/atmosphere interaction. At present, these coupled wildfire model simulations are computationally intensive. The additional complexity of these models require sophisticated methods for assuring their reliability in real world applications. With this in mind, a substantial part of the research effort is directed at model validation. Several instrumented prescribed fires have been conducted with multi-agency support and participation from chaparral, marsh, and scrub environments in coastal areas of Florida and inland California. In this paper, the authors first describe the data required to initialize the components of the wildfire modeling system. Then they present results from one of the Florida fires, and discuss a strategy for further testing and improvement of coupled weather/wildfire models

Gene expression profiling of mucinous ovarian tumors and comparison with upper and lower gastrointestinal tumors identifies markers associated with adverse outcomes.

PURPOSE: Advanced-stage mucinous ovarian carcinoma (MOC) has poor chemotherapy response and prognosis and lacks biomarkers to aid stage I adjuvant treatment. Differentiating primary MOC from gastrointestinal (GI) metastases to the ovary is also challenging due to phenotypic similarities. Clinicopathologic and gene-expression data were analyzed to identify prognostic and diagnostic features. EXPERIMENTAL DESIGN: Discovery analyses selected 19 genes with prognostic/diagnostic potential. Validation was performed through the Ovarian Tumor Tissue Analysis consortium and GI cancer biobanks comprising 604 patients with MOC (n = 333), mucinous borderline ovarian tumors (MBOT, n = 151), and upper GI (n = 65) and lower GI tumors (n = 55). RESULTS: Infiltrative pattern of invasion was associated with decreased overall survival (OS) within 2 years from diagnosis, compared with expansile pattern in stage I MOC [hazard ratio (HR), 2.77; 95% confidence interval (CI), 1.04–7.41, P = 0.042]. Increased expression of THBS2 and TAGLN was associated with shorter OS in MOC patients (HR, 1.25; 95% CI, 1.04–1.51, P = 0.016) and (HR, 1.21; 95% CI, 1.01–1.45, P = 0.043), respectively. ERBB2 (HER2) amplification or high mRNA expression was evident in 64 of 243 (26%) of MOCs, but only 8 of 243 (3%) were also infiltrative (4/39, 10%) or stage III/IV (4/31, 13%). CONCLUSIONS: An infiltrative growth pattern infers poor prognosis within 2 years from diagnosis and may help select stage I patients for adjuvant therapy. High expression of THBS2 and TAGLN in MOC confers an adverse prognosis and is upregulated in the infiltrative subtype, which warrants further investigation. Anti-HER2 therapy should be investigated in a subset of patients. MOC samples clustered with upper GI, yet markers to differentiate these entities remain elusive, suggesting similar underlying biology and shared treatment strategies

CCNE1 and survival of patients with tubo-ovarian high-grade serous carcinoma: An Ovarian Tumor Tissue Analysis consortium study

BACKGROUND: Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC. METHODS: Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated. RESULTS: High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss. CONCLUSION: This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis

Age-class Mosaics and Wind-driven Fire: Further Fuel for the Debate

In 2006 the Santa Ana wind-driven Esperanza fire burned through the North Mountain Experimental Area (NMEA) and vicinity, including the scars of 10 previous fires. Multiple images of the fire’s progression were taken using PSW Research Station’s airborne FireMapper thermal-imaging system. Existing fuels data and historic NMEA maps plus new fire images were used to investigate relationships between vegetation history, fire behavior and severity, and fuel consumption. Soil samples were collected at a subset of fire severity sample points to assess seed bank survival. Coordinated documentation of vegetation recovery addressed the effects of age class and fire severity on chaparral regeneration and non-native species invasion. Ground sampling of fuel characteristics combined with remote sensing demonstrated that the multispectral FireMapper instrument can provide an improved means to acquire the needed pre and post-fire fuels data necessary for quantitative, high-resolution measurement of wildland fire. Detailed analysis of the field data and remotely sensed data showed that the standard fire behavior fuel models for chaparral and grass did not capture the range of fuel loading associated with elevation and stand age. Fuel loading showed a general correspondence with observed fire behavior (radiant heat release (W m-2). Southwestern spread of the Esperanza Fire was contained near boundaries of previous fires and young fuels. Fuel, fire perimeter, and energy release data from FireMapper roughly agreed with simulation results from the CAWFE model. We were able to use FireMapper imagery to identify and differentiate various landscape features on vegetation plots to transect resolution. This included separation of general plant characteristics such as large compared to small leafed species. Sequential images reflected the increased plant cover observed from repeated measurements of vegetation plots. This technology appears to have utility for monitoring general plant cover recovery for large and/or remote areas. Areas containing older chaparral stands (\u3e50 years since last fire at the time of the Esperanza fire) and higher fuel loading corresponded to areas of higher burn severity (as determined by ground crews). Species richness was greatest in mid-age burns (11-50 years since previous fire). At three years post-fire, non-native grasses were the most abundant cover type. Non-native species dominated both burned and unburned fuel breaks

PEER · REVIEWED ARTICLE A Cellular Automaton Model of Wildfire Propagation and Extinction

We propose a new model to predict the spatial and temporal behavior of wildfires. Fire spread and intensity were simu-lated using a cellular automaton model. Monte Carlo tech-niques were used to provide fire risk probabilities for areas where fuel loadings and topography are known. The model assumes predetermined or measurable environmental varia-bles such as wind direction and magnitude, relative humid-ity, fuel moisture content, and air temperature. Implementation of the model allows the linking of fire moni-toring using remotely sensed data, potentially in real time, to rapid simulations of predicted fire behavior. Calibration of the model is based on thermal infrared remotely sensed im-agery of a test burn during 1986 in the San Dimas experi-mental forest. The model and its various implementations show distinct promise for real-time fire management and fire risk planning