Conventional value-elicitation experiments often find subjects provide higher valuations for items they posses than for identical items they may acquire. Plott and Zeiler (Am Econ Rev 95:530–545, 2005) replicate this willingness-to-pay/willingness-to-accept “gap” with conventional experimental procedures, but find no gap after implementing procedures that provide for subject anonymity and familiarity with the second-price mechanism. This paper investigates whether anonymity is necessary for their result. We employ both types of procedures with and without anonymity. Contrary to predictions of one theory—which suggest social pressures may cause differences in subject valuations—we find, regardless of anonymity, conventional procedures generate gaps and Plott and Zeiler’s does not. These findings strongly suggest subject familiarity with elicitation mechanisms, not anonymity, is responsible for the variability in results across value-elicitation experiments. As an application to experimental design methodology, there appears to be little need to impose anonymity when using second-price mechanisms in standard consumer good experiments.

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.

In functional magnetic resonance imaging (fMRI), the blood oxygenation level dependent (BOLD) signal is often interpreted as a measure of neural activity. However, because the BOLD signal reflects the complex interplay of neural, vascular, and metabolic processes, such an interpretation is not always valid. There is growing evidence that changes in the baseline neurovascular state can result in significant modulations of the BOLD signal that are independent of changes in neural activity. This paper introduces some of the normalization and calibration methods that have been proposed for making the BOLD signal a more accurate reflection of underlying brain activity for human fMRI studies.

We propose a hierarchical Bayesian model for analyzing multi-site experimental fMRI studies. Our method takes the hierarchical structure of the data (subjects are nested within sites, and there are multiple observations per subject) into account and allows for modeling between-site variation. Using posterior predictive model checking and model selection based on the deviance information criterion (DIC), we show that our model provides a good fit to the observed data by sharing information across the sites. We also propose a simple approach for evaluating the efficacy of the multi-site experiment by comparing the results to those that would be expected in hypothetical single-site experiments with the same sample size.

Functional magnetic resonance imaging (fMRI) is a noninvasive method for measuring brain function by correlating temporal changes in local cerebral blood oxygenation with behavioral measures. fMRI is used to study individuals at single time points, across multiple time points (with or without intervention), as well as to examine the variation of brain function across normal and ill populations. fMRI may be collected at multiple sites and then pooled into a single analysis. This paper describes how fMRI data is analyzed at each of these levels and describes the noise sources introduced at each level.

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.

OBJECTIVES/GOALS: 1. Describe method of Community Based System Dynamics. 2. Describe CBSD as used in addressing structural racism in a previously redlined community. 3. Review example causal loop diagrams and simulation models developed using CBSD focused on structural racism as a social determinant of health. METHODS/STUDY POPULATION: Community Based System Dynamics (group model building and computer simulation development). Cuyahoga County, Ohio, a previously redlined community of more than 1.2 million people. RESULTS/ANTICIPATED RESULTS:Actionable Community identified leverage points for action to mitigate structural racism. Computer simulation models built on causal loop diagrams built by Community members with loved experience. DISCUSSION/SIGNIFICANCE: Structural and social determinants of health, such as racism, have profound impacts on the health of individuals and populations, however they remain challenging to address in pragmatic ways. CBSD is a novel method to engage community members not proximal to the impact of structural racism in generating maps of the complex, dynamic system they live in.

Precision Medicine/Health

Background: Healthcare facilities have experienced many challenges during the COVID-19 pandemic, including limited personal protective equipment (PPE) supplies. Healthcare personnel (HCP) rely on PPE, vaccines, and other infection control measures to prevent SARS-CoV-2 infections. We describe PPE concerns reported by HCP who had close contact with COVID-19 patients in the workplace and tested positive for SARS-CoV-2. Method: The CDC collaborated with Emerging Infections Program (EIP) sites in 10 states to conduct surveillance for SARS-CoV-2 infections in HCP. EIP staff interviewed HCP with positive SARS-CoV-2 viral tests (ie, cases) to collect data on demographics, healthcare roles, exposures, PPE use, and concerns about their PPE use during COVID-19 patient care in the 14 days before the HCP’s SARS-CoV-2 positive test. PPE concerns were qualitatively coded as being related to supply (eg, low quality, shortages); use (eg, extended use, reuse, lack of fit test); or facility policy (eg, lack of guidance). We calculated and compared the percentages of cases reporting each concern type during the initial phase of the pandemic (April–May 2020), during the first US peak of daily COVID-19 cases (June–August 2020), and during the second US peak (September 2020–January 2021). We compared percentages using mid-P or Fisher exact tests (α = 0.05). Results: Among 1,998 HCP cases occurring during April 2020–January 2021 who had close contact with COVID-19 patients, 613 (30.7%) reported ≥1 PPE concern (Table 1). The percentage of cases reporting supply or use concerns was higher during the first peak period than the second peak period (supply concerns: 12.5% vs 7.5%; use concerns: 25.5% vs 18.2%; p Conclusions: Although lower percentages of HCP cases overall reported PPE concerns after the first US peak, our results highlight the importance of developing capacity to produce and distribute PPE during times of increased demand. The difference we observed among selected groups of cases may indicate that PPE access and use were more challenging for some, such as nonphysicians and nursing home HCP. These findings underscore the need to ensure that PPE is accessible and used correctly by HCP for whom use is recommended.

Funding: None

Disclosures: None

The end-Cretaceous (K/Pg) mass extinction event is the most recent and well-understood of the “big five” and triggered establishment of modern terrestrial ecosystem structure. Despite the depth of research into this event, our knowledge of upper Maastrichtian terrestrial deposits globally relies primarily on assemblage-level data limited to a few well-sampled formations in North America, the Hell Creek and Lance Formations. These assemblages disproportionally affect our interpretations of this important interval. Multiple investigations have quantified diversity patterns within these assemblages, but the potential effect of formation-level size-dependent taphonomic biases and their implications on extinction dynamics remains unexplored. Here, the relationship between taphonomy and body size of the Hell Creek Formation and Lance Formation dinosaurs and mammals are quantitatively analyzed. Small-bodied dinosaur taxa (<70 kg) are consistently less complete, unlikely to be articulated, and delayed in their description relative to their large-bodied counterparts. Family-level abundance (particularly skeletons) is strongly tied to body mass, and the relative abundance of juveniles of large-bodied taxa similarly is underrepresented. Mammals show similar but nonsignificant trends. The results are remarkably similar to those from the Campanian-aged Dinosaur Park Formation, suggesting a widespread strong taphonomic bias against the preservation of small taxa, which will result in their seemingly depauperate diversity within the assemblage. This taphonomically skewed view of diversity and abundance of small-bodied taxa amid our best late Maastrichtian samples has significant implications for understanding speciation and extinction dynamics (e.g., size-dependent extinction selectivity) across the K/Pg boundary.

Intrauterine preeclampsia exposure affects the lifelong cardiometabolic health of the child. Our study aimed to compare the growth (from birth to 6 months) of infants exposed to either a normotensive pregnancy or preeclampsia and explore the influence of being born small for gestational age (SGA). Participants were children of women participating in the Post-partum, Physiology, Psychology and Paediatric follow-up cohort study. Birth and 6-month weight and length z-scores were calculated for term and preterm (<37 weeks) babies, and change in weight z-score, rapid weight gain (≥0.67 increase in weight z-score) and conditional weight gain z-score were calculated. Compared with normotensive exposed infants (n = 298), preeclampsia exposed infants (n = 84) were more likely to be born SGA (7% versus 23%; P < 0.001), but weight gain from birth to 6 months, by any measure, did not differ between groups. Infants born SGA, irrespective of pregnancy exposure, were more likely to have rapid weight gain and had greater increases in weight z-score compared with those not born SGA. Preeclampsia exposed infants born SGA may benefit from interventions designed to prevent future cardiometabolic disease.

Background: UV-C light reduces contamination of high-touch clinical surfaces. Few studies have tested the relative efficacy of UV-C devices in real-world clinical environments. Methods: We assessed the efficacy of the Tru-D (SmartUVC) and Moonbeam-3 UV-C (Diversey) devices at eradicating important clinical pathogens in 2 hyperbaric chambers at a tertiary-care hospital. Formica sheets were inoculated with 106–107 CFU of MRSA (USA300) or 104–105 CFU of C. difficile (NAP1). Sheets were placed in 6 predetermined locations throughout the chambers. Two Moonbeam-3 UV-C devices were positioned in the center of each chamber and were run for 3-minute (per manufacturer’s instructions) and 5-minute cycles. One Tru-D was positioned in the center of the chamber and was run on the vegetative cycle for MRSA and the spore cycle for C. difficile. UV-C dosage was measured for both machines. Quantitative cultures were collected using Rodac plates with DE neutralizing agar and were incubated at 37C for 48 hours. C. difficile was likewise plated onto sheep’s blood agar. Results: We ran each combination of chamber, microbe, and UV-C device in triplicate for In total, 108 samples per species.

For MRSA, the Tru-D vegetative cycle, the 5-minute Moonbeam cycle, and the 3-minute Moonbeam cycle resulted in average CFU log10 reductions of 7.02 (95% CI, 7.02–7.02), 6.99 (95% CI, 6.95–7.02), and 6.58 (95% CI, 6.37–6.79), respectively (Fig. 1). The Tru-D vegetative and 5-minute Moonbeam cycles were similarly effective (P > .99), and both were more effective than the 3-minute Moonbeam cycle (P < .001 and P < .001, respectively). MRSA samples receiving direct UV-C exposure had significantly greater log10 reductions (6.95; 95% CI, 6.89–7.01) than did indirect exposure (6.67; 95% CI, 6.46–6.87; P < .05) (Fig. 2). For C. difficile, the Tru-D sporicidal, the 5-, and 3-minute Moonbeam cycles resulted in average CFU log10 reductions of 1.78 (95% CI, 1.43–2.12), 0.57 (95% CI, 0.33–0.81) and 0.64 (95% CI, 0.42–0.86), respectively (Fig. 1). Tru-D was significantly more effective than either the 3- or 5-minute Moonbeam cycles (P < .00). C. difficile samples receiving direct UV-C exposure had higher dosage and significantly greater log10 reductions (1.34; 95% CI, 1.10–1.58) than did indirect exposure (0.58; 95% CI, 0.31–0.86; P < .01) (Fig. 2). Conclusions: Use of the Tru-D vegetative cycle and the Moonbeam 3- and 5-minute cycles resulted in similar reductions in MRSA; both resulted in significantly greater reductions than the manufacturer’s recommended 3-minute Moonbeam cycle. Therefore, healthcare facilities should carefully evaluate manufacturer-recommended run times in their specific clinical setting. For C. difficile, the Tru-D sporicidal cycle was significantly more effective than either of the Moonbeam cycles, likely due to higher irradiation levels. As such, direct UV-C exposure resulted in greater average reductions than indirect exposure.

Funding: None

Disclosures: None

Ultraviolet C (UV-C) light reduces contamination on high-touch clinical surfaces. We assessed the efficacy of 2 UV-C devices at eradicating important clinical pathogens in hyperbaric chambers. Both devices were similarly efficacious against MRSA but differed significantly against C. difficile. Additionally, direct UV-C exposure was more efficacious against both species than indirect exposure.

In 2018 Pearson et al. published a new sequence of annual radiocarbon (14C) data derived from oak (Quercus sp.) trees from Northern Ireland and bristlecone pine (Pinus longaeva) from North America across the period 1700–1500 BC. The study indicated that the more highly resolved shape of an annually based calibration dataset could improve the accuracy of 14C calibration during this period. This finding had implications for the controversial dating of the eruption of Thera in the Eastern Mediterranean. To test for interlaboratory variation and improve the robustness of the annual dataset for calibration purposes, we have generated a replicate sequence from the same Irish oaks at ETH Zürich. These data are compatible with the Irish oak 14C dataset previously produced at the University of Arizona and are used (along with additional data) to examine inter-tree and interlaboratory variation in multiyear annual 14C time-series. The results raise questions about regional 14C offsets at different scales and demonstrate the potential of annually resolved 14C for refining subdecadal and larger scale features for calibration, solar reconstruction, and multiproxy synchronization.