Double-cropping winter rye cover crops (CC) with soybean in the North Central US could help with the global effort to sustainably intensify agriculture. Studies addressing the management of these systems are limited. Therefore, a field study was conducted from 2017 to 2019 in Central Iowa, US to evaluate winter rye CC biomass production, aboveground N accumulation, estimated economics, estimated within-field energy balance and estimated greenhouse gas (GHG) emissions under three N application rates (0, 60, 120 kg N ha−1) and three planting methods (pre- and post-harvest broadcast and post-harvest drilling). Averaged over N rates, all planting methods resulted in >5.0 Mg ha−1 year−1 rye aboveground biomass dry matter. Averaged over the 2-year study and compared with unfertilized treatments, applying 60 kg N ha−1 produced 1.1 Mg ha−1 more aboveground biomass (6.1 vs 5.0 Mg ha−1), accumulated 30 kg ha−1 more N in aboveground biomass (88 vs 58 kg N ha−1), and led to 20 GJ ha−1 more net energy. Biomass production was not significantly higher with 120 kg N ha−1 compared with the 60 kg N ha−1 rate. Even when accounting for an estimated 0.75 Mg ha−1 of above ground rye biomass left in the field after harvesting, more N was removed than applied at the 60 kg N ha−1 rate. The minimum rye prices over the 2-year study needed for double-cropping winter rye CC to be profitable (breakeven prices) averaged $117 and $104 Mg−1 for the 0 and 60 kg N ha−1 rates, which factors in estimated soybean yield reductions in 2019 compared with local averages but not off-site transportation. GHG emissions were estimated to increase approximately threefold between the unfertilized and 60 kg N ha−1 rates without considering bioenergy offsets. While environmental tradeoffs need further study, results suggest harvesting fertilized rye CC biomass before planting soybean is a promising practice for the North Central US to maximize total crop and net energy production.

Background: Aerosol-generating procedures (AGPs) performed on COVID-19–positive patients raise concerns about the dissemination of SARS-CoV-2 via aerosols and droplets. Infectious aerosols and droplets generated by SARS-CoV-2–positive patient AGPs or through direct COVID-19 patient coughing or exhalation could potentially contaminate surfaces, leading to the indirect spread of SARS-CoV-2 via fomites within the emergency department (ED). We sampled surfaces of ED patient rooms occupied by known SARS-CoV-2–positive patients or patients under investigation for COVID-19 and undergoing an AGP to determine the frequency of room contamination with SARS-CoV-2 RNA. Methods: Swabs were collected from 5 room surfaces in the ED following AGPs performed on patients under investigation for COVID-19 or positive for SARS-CoV-2. High- and low-touch surfaces 6 feet (2 m) from the patient (door handle and return vent, respectively) and reusable medical equipment were swabbed. Swabs were tested for SARS-CoV-2 RNA by RT-qPCR; positive samples were cultured in Vero E6 cells. Patient COVID-19 results were confirmed through the electronic medical record. Results: In total, 203 rooms were sampled: 43 SARS-CoV-2–positive patients with an AGP, 44 SARS-CoV-2–positive patients who did not have an AGP, and 116 SARS-CoV-2–negative patients with an AGP, for a total of 1,015 swabs. Overall, SARS-CoV-2 RNA was detected on 36 (3.5%) surfaces from 29 rooms (14.3%) (Table 1). RNA contamination was detected more frequently in rooms occupied by SARS-CoV-2–positive patients who did not have an AGP than rooms occupied by COVID-19 patients (30% vs 14%). SARS-CoV-2 RNA was also detected in rooms occupied by SARS-CoV-2–negative patients undergoing an AGP (9%). SARS-CoV-2 RNA was most frequently detected on air vents (n = 15), bedrails (n = 10), equipment and vital signs monitors (n = 4 each), and door handles (n = 3). One bedrail was positive by culture and confirmed by an RT-qPCR cycle threshold reduction from >40 to 13. Conclusions: We detected SARS-CoV-2 RNA contamination on room surfaces in the ED, regardless of patient AGP or COVID-19 status; however, RNA contamination of room surfaces was most common in rooms occupied by SARS-CoV-2–positive patients who did not have an AGP, which may be attributable to stage of disease and viral shedding. SARS-CoV-2 RNA contamination was also present in rooms where APGs were performed on SARS-CoV-2–negative patients, suggesting carryover from previous patients. SARS-CoV-2 RNA was found most often on room air-return vents, further emphasizing the importance of aerosols in the spread of SARS-CoV-2.

Funding: None

Disclosures: None

OBJECTIVES/GOALS: Using the covariate-rich Veteran Health Administration data, estimate the association between Proton Pump Inhibitor (PPI) use and severe COVID-19, rigorously adjusting for confounding using propensity score (PS)-weighting. METHODS/STUDY POPULATION: We assembled a national retrospective cohort of United States veterans who tested positive for SARS-CoV-2, with information on 33 covariates including comorbidity diagnoses, lab values, and medications. Current outpatient PPI use was compared to non-use (two or more fills and pills on hand at admission vs no PPI prescription fill in prior year). The primary composite outcome was mechanical ventilation use or death within 60 days; the secondary composite outcome included ICU admission. PS-weighting mimicked a 1:1 matching cohort, allowing inclusion of all patients while achieving good covariate balance. The weighted cohort was analyzed using logistic regression. RESULTS/ANTICIPATED RESULTS: Our analytic cohort included 97,674 veterans with SARS-CoV-2 testing, of whom 14,958 (15.3%) tested positive (6,262 [41.9%] current PPI-users, 8,696 [58.1%] non-users). After weighting, all covariates were well-balanced with standardized mean differences less than a threshold of 0.1. Prior to PS-weighting (no covariate adjustment), we observed higher odds of the primary (9.3% vs 7.5%; OR 1.27, 95% CI 1.13-1.43) and secondary (25.8% vs 21.4%; OR 1.27, 95% CI 1.18-1.37) outcomes among PPI users vs non-users. After PS-weighting, PPI use vs non-use was not associated with the primary (8.2% vs 8.0%; OR 1.03, 95% CI 0.91-1.16) or secondary (23.4% vs 22.9%;OR 1.03, 95% CI 0.95-1.12) outcomes. DISCUSSION/SIGNIFICANCE: The associations between PPI use and severe COVID-19 outcomes that have been previously reported may be due to limitations in the covariates available for adjustment. With respect to COVID-19, our robust PS-weighted analysis provides patients and providers with further evidence for PPI safety.

Moderate rotation and moderate horizontal confinement similarly enhance the heat transport in Rayleigh–Bénard convection (RBC). Here, we systematically investigate how these two types of flow stabilization together affect the heat transport. We conduct direct numerical simulations of confined-rotating RBC in a cylindrical set-up at Prandtl number $\textit {Pr}=4.38$, and various Rayleigh numbers $2\times 10^{8}\leqslant {\textit {Ra}}\leqslant 7\times 10^{9}$. Within the parameter space of rotation (given as inverse Rossby number $0\leqslant {\textit {Ro}}^{-1}\leqslant 40$) and confinement (given as height-to-diameter aspect ratio $2\leqslant \varGamma ^{-1}\leqslant 32$), we observe three heat transport maxima. At lower $ {\textit {Ra}}$, the combination of rotation and confinement can achieve larger heat transport than either rotation or confinement individually, whereas at higher $ {\textit {Ra}}$, confinement alone is most effective in enhancing the heat transport. Further, we identify two effects enhancing the heat transport: (i) the ratio of kinetic and thermal boundary layer thicknesses controlling the efficiency of Ekman pumping, and (ii) the formation of a stable domain-spanning flow for an efficient vertical transport of the heat through the bulk. Their interfering efficiencies generate the multiple heat transport maxima.

Bayesian optimization (BO) has been a successful approach to optimize expensive functions whose prior knowledge can be specified by means of a probabilistic model. Due to their expressiveness and tractable closed-form predictive distributions, Gaussian process (GP) surrogate models have been the default go-to choice when deriving BO frameworks. However, as nonparametric models, GPs offer very little in terms of interpretability and informative power when applied to model complex physical phenomena in scientific applications. In addition, the Gaussian assumption also limits the applicability of GPs to problems where the variables of interest may highly deviate from Gaussianity. In this article, we investigate an alternative modeling framework for BO which makes use of sequential Monte Carlo (SMC) to perform Bayesian inference with parametric models. We propose a BO algorithm to take advantage of SMC’s flexible posterior representations and provide methods to compensate for bias in the approximations and reduce particle degeneracy. Experimental results on simulated engineering applications in detecting water leaks and contaminant source localization are presented showing performance improvements over GP-based BO approaches.

The purpose of this study was to assess impact of different volumes of exercise as well as cumulative moderate to vigorous physical activity (MVPA) on energy intake (EI) and diet quality, as assessed by the Healthy Eating Index-2010(HEI-2010), across a 12-month weight maintenance intervention. Participants were asked to attend group behavioural sessions, eat a diet designed for weight maintenance and exercise either 150, 225 or 300 min/week. Dietary intake was assessed by 3-d food records, and MVPA was assessed by accelerometry. Two hundred and twenty-four participants (42·5 years of age, 82 % female) provided valid dietary data for at least one time point. There was no evidence of group differences in EI, total HEI-2010 score or any of the HEI-2010 component scores (all P > 0·05). After adjusting for age, sex, time, group and group-by-time interactions, there was an effect of cumulative MVPA on EI (1·08, P = 0·04), total HEI-2010 scores (–0·02, P = 0·003), Na (–0·006, P = 0·002) and empty energy scores (–0·007, P = 0·004. There was evidence of a small relationship between cumulative daily EI and weight (β: 0·00187, 95 % CI 0·001, P = 0·003). However, there was no evidence for a relationship between HEI total score (β: −0·006, 95 % CI 0·07, 0·06) or component scores (all P > 0·05) and change in weight across time. The results of this study suggest that increased cumulative MVPA is associated with clinically insignificant increases in EI and decreases in HEI.