Nuclear resonance fluorescence (NRF) is a photonuclear interaction that enables highly isotope-specific measurements in both pure and applied physics scenarios. High-accuracy design and analysis of NRF measurements in complex geometries is aided by Monte Carlo simulations of photon physics and transport, motivating Jordan and Warren (2007) to develop the G4NRF codebase for NRF simulation in Geant4. In this work, we enhance the physics accuracy of the G4NRF code and perform improved benchmarking simulations. The NRF cross section calculation in G4NRF, previously a Gaussian approximation, has been replaced with a full numerical integration for improved accuracy in thick-target scenarios. A high-accuracy semi-analytical model of expected NRF count rates in a typical NRF measurement is then constructed and compared against G4NRF simulations for both simple homogeneous and more complex heterogeneous geometries. Agreement between rates predicted by the semi-analytical model and G4NRF simulation is found at a level of ∼1% in simple test cases and ∼3% in more realistic scenarios, improving upon the ∼20% level of the initial benchmarking study and establishing a highly-accurate NRF framework for Geant4.

G4NRF (Jordan and Warren, 2007; Vavrek et al., 2018a,b) is a simulation module for modeling nuclear resonance fluorescence (NRF) interactions in the Geant4 framework (Allison et al., 2016). In this work, we validate G4NRF against both absolute and relative measurements of three NRF interactions near 2.2 MeV in 238U and 27Al using the transmission NRF data from the experiments described in Jayson et al. (2018). Agreement between the absolute NRF count rates observed in the data and predicted by extensive Geant4+G4NRF modeling validate the combined Geant4+G4NRF to approximately 15–20% in the 238U NRF transitions and 10% in 27Al, for an average 14% discrepancy across the entire study. The difference between simulation and experiment in relative NRF rates, as expressed as ratios of count rates in various NRF lines, is found at the level of ≲3%, and is statistically identical to zero. Inverting the analysis, approximate values of the absolute level widths and branching ratios for 238U and 27Al are also obtained.

Background & aims

Obesity is associated with increased risk for hepatocellular carcinoma (HCC), but the risk associated with obesity may vary by sex or ethnicity. We examined whether the association of body mass index (BMI) with HCC incidence, as well as correlations of BMI with total, visceral, and hepatic adiposity, differs among ethnic groups.

Methods

We collected data from the Multiethnic Cohort Study, a population-based prospective cohort study of more than 215,000 men and women from Hawaii and California that was assembled from 1993 through 1996. After a median follow-up of 16.6 years, 482 incident HCC cases were identified among 168,476 participants. BMI and risk factor data were obtained from a baseline questionnaire. Cox regression analyses were used to calculate hazard ratios (HRs) and confidence intervals (CIs) for HCC associated with BMI. The black subjects in the Southern Community Cohort Study were included as a replication cohort.

Results

BMI was associated with HCC in men (HR per 5 kg/m(2) increase, 1.26; 95% CI, 1.12-1.42) but not in women (HR, 1.06; 95% CI, 0.90-1.25) (P(interaction) = .009). Although BMI was strongly associated with HCC in Japanese, white, and Latino men, there was no association in black men (P(interaction) = .002). Similarly, no association was found in the blacks who participated in the Southern Community Cohort Study. BMI correlated with total fat mass, measured by dual-energy x-ray absorptiometry, in men and women and in all ethnic groups (R ≥ 0.9). However, there was a lower correlation value for BMI and visceral or liver fat measured by abdominal magnetic resonance imaging in black men (R < 0.5) and in women (R < 0.8).

Conclusions

On the basis of an analysis of data from the Multiethnic Cohort Study, the association between BMI and HCC differs between sexes and among ethnicities. The lack of association in black men warrants further investigation. Rather than studying markers of total adiposity, studies of obesity and HCC should move beyond BMI and use a better measure for fat-specific depots.

Little is known about neighborhood attributes that may influence opportunities for healthy eating and physical activity in relation to breast cancer mortality. We used data from the California Breast Cancer Survivorship Consortium and the California Neighborhoods Data System (CNDS) to examine the neighborhood environment, body mass index, and mortality after breast cancer. We studied 8,995 African American, Asian American, Latina, and non-Latina white women with breast cancer. Residential addresses were linked to the CNDS to characterize neighborhoods. We used multinomial logistic regression to evaluate the associations between neighborhood factors and obesity and Cox proportional hazards regression to examine associations between neighborhood factors and mortality. For Latinas, obesity was associated with more neighborhood crowding [quartile 4 (Q4) vs. Q1: OR, 3.24; 95% confidence interval (CI), 1.50-7.00]; breast cancer-specific mortality was inversely associated with neighborhood businesses (Q4 vs. Q1: HR, 0.46; 95% CI, 0.25-0.85) and positively associated with multifamily housing (Q3 vs. Q1: HR, 1.98; 95% CI, 1.20-3.26). For non-Latina whites, lower neighborhood socioeconomic status (SES) was associated with obesity [quintile 1 (Q1) vs. Q5: OR, 2.52; 95% CI, 1.31-4.84], breast cancer-specific (Q1 vs. Q5: HR, 2.75; 95% CI, 1.47-5.12), and all-cause (Q1 vs. Q5: HR, 1.75; 95% CI, 1.17-2.62) mortality. For Asian Americans, no associations were seen. For African Americans, lower neighborhood SES was associated with lower mortality in a nonlinear fashion. Attributes of the neighborhood environment were associated with obesity and mortality following breast cancer diagnosis, but these associations differed across racial/ethnic groups.

Motivation

Local ancestry analysis of genotype data from recently admixed populations (e.g. Latinos, African Americans) provides key insights into population history and disease genetics. Although methods for local ancestry inference have been extensively validated in simulations (under many unrealistic assumptions), no empirical study of local ancestry accuracy in Latinos exists to date. Hence, interpreting findings that rely on local ancestry in Latinos is challenging.

Results

Here, we use 489 nuclear families from the mainland USA, Puerto Rico and Mexico in conjunction with 3204 unrelated Latinos from the Multiethnic Cohort study to provide the first empirical characterization of local ancestry inference accuracy in Latinos. Our approach for identifying errors does not rely on simulations but on the observation that local ancestry in families follows Mendelian inheritance. We measure the rate of local ancestry assignments that lead to Mendelian inconsistencies in local ancestry in trios (MILANC), which provides a lower bound on errors in the local ancestry estimates. We show that MILANC rates observed in simulations underestimate the rate observed in real data, and that MILANC varies substantially across the genome. Second, across a wide range of methods, we observe that loci with large deviations in local ancestry also show enrichment in MILANC rates. Therefore, local ancestry estimates at such loci should be interpreted with caution. Finally, we reconstruct ancestral haplotype panels to be used as reference panels in local ancestry inference and show that ancestry inference is significantly improved by incoroprating these reference panels.

Availability and implementation

We provide the reconstructed reference panels together with the maps of MILANC rates as a public resource for researchers analyzing local ancestry in Latinos at http://bogdanlab.pathology.ucla.edu

Supplementary information

Supplementary data are available at Bioinformatics online.