To the known causes of overconfidence in decisions and judgments, we reveal another source that derives from a bias during the act of decision making. While this bias, the predecisional distortion of information, is well studied, its impact on overconfidence is not. We demonstrate how the distortion of information creates overconfidence in those professionals often regarded as singularly overconfident, entrepreneurs. When these professionals use a sequence of relevant information to make an accept-reject decision about a business opportunity, a cycle of confidence-distortion-confidence builds unjustified confidence in the chosen action – and does so whether that action is to accept or reject the venture. Overconfidence is a well-recognized cause of flawed decision making. Our work demonstrates the paradoxical converse of this claim, that flawed decision making can be a cause of overconfidence.

Statistical methodology for handling omitted variables is presented in a multilevel modeling framework. In many nonexperimental studies, the analyst may not have access to all requisite variables, and this omission may lead to biased estimates of model parameters. By exploiting the hierarchical nature of multilevel data, a battery of statistical tools are developed to test various forms of model misspecification as well as to obtain estimators that are robust to the presence of omitted variables. The methodology allows for tests of omitted effects at single and multiple levels. The paper also introduces intermediate-level tests; these are tests for omitted effects at a single level, regardless of the presence of omitted effects at a higher level. A simulation study shows, not surprisingly, that the omission of variables yields bias in both regression coefficients and variance components; it also suggests that omitted effects at lower levels may cause more severe bias than at higher levels. Important factors resulting in bias were found to be the level of an omitted variable, its effect size, and sample size. A real data study illustrates that an omitted variable at one level may yield biased estimators at any level and, in this study, one cannot obtain reliable estimates for school-level variables when omitted child effects exist. However, robust estimators may provide unbiased estimates for effects of interest even when the efficient estimators fail, and the one-degree-of-freedom test helps one to understand where the problem is located. It is argued that multilevel data typically contain rich information to deal with omitted variables, offering yet another appealing reason for the use of multilevel models in the social sciences.

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior.

For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors.

Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness.

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators.

Background: After a transient ischemic attack (TIA) or minor stroke, the long-term risk of subsequent stroke is uncertain. Methods: Electronic databases were searched for observational studies reporting subsequent stroke during a minimum follow-up of 1 year in patients with TIA or minor stroke. Unpublished data on number of stroke events and exact person-time at risk contributed by all patients during discrete time intervals of follow-up were requested from the authors of included studies. This information was used to calculate the incidence of stroke in individual studies, and results across studies were pooled using random-effects meta-analysis. Results: Fifteen independent cohorts involving 129794 patients were included in the analysis. The pooled incidence rate of subsequent stroke per 100 person-years was 6.4 events in the first year and 2.0 events in the second through tenth years, with cumulative incidences of 14% at 5 years and 21% at 10 years. Based on 10 studies with information available on fatal stroke, the pooled case fatality rate of subsequent stroke was 9.5% (95% CI, 5.9 – 13.8). Conclusions: One in five patients is expected to experience a subsequent stroke within 10 years after a TIA or minor stroke, with every tenth patient expected to die from their subsequent stroke.

OBJECTIVES/GOALS: Uterine fibroids are benign tumors of the uterus with a high disease prevalence and burden, yet there are few multi-ancestry genetic studies. This is the largest and most diverse fibroid GWAS to-date. Our goal is to identify novel genetic variants and gene expression pathways associated with fibroids and characterize their biological relevance. METHODS/STUDY POPULATION: We performed a cross-ancestry meta-analysis of GWAS summary statistics from eight datasets. The total sample size was 74,294 cases and 465,810 controls with participants of European (80% of sample), African (4%), East Asian, and Central South Asian (16%) ancestry. We mapped variants to genes with OpenTarget Genetics and used Functional Mapping and Annotation to conduct tissue expression gene-set enrichment and identify lead variants. We used S-PrediXcan to estimate genetically predicted gene expression (GPGE) associated with fibroid risk. This was with models that predicted gene expression across 49 different tissue types. Ingenuity Pathway Analysis compiled significant GPGE genes and their weights with a scientific literature database to identify overlapping pathways. RESULTS/ANTICIPATED RESULTS: We identified 370 independent significant variants. Among these, we identified variants mapped to three novel genes (PAX2, VIP, FOXO3) and eight genes not previously validated (TEKT1, SLC16A11, RPEL1, RASL11B, ASGR1, SLC12A7, TTC28, POLR2A). Many loci have roles in cell cycle regulation or are associated with fibroid risk factors like blood pressure, BMI, and vitamin D levels. Loci were significantly enriched in DNA damage and cell cycle pathways. Of 588 significant predicted expression gene-tissue pairs, 173 unique genes were novel fibroid associations. These genes are also associated with cancers, estradiol, and endometriosis. Top enriched pathways included p53 signaling, HOTAIR, BRCA1DNA damage response, and pulmonary fibrosis signaling. In uterine tissue there were 15 novel GPGE associations. DISCUSSION/SIGNIFICANCE: Using this large and diverse data, we identified novel loci associated with fibroids that are enriched in hormone-response, DNA damage, and cell-cycle pathways. GPGE loci were in tumorigenesis and fibrosis pathways. These novel genetic loci and uterine gene expression findings may provide translational opportunities for novel fibroid treatments.

The paper critically reexamines the well-known “Julie and Mark” vignette, a stylized account of two college-age siblings opting to engage in protected sex while vacationing abroad (e.g., Haidt, 2001). Since its inception, the story has been viewed as a rhetorically powerful validation of Hume’s “sentimentalist” dictum that moral judgments are not rationally deduced but arise directly from feelings of pleasure or displeasure (e.g., disgust). People’s typical reactions to the vignette are alleged to support this view by demonstrating that individuals are prone to become morally dumbfounded (Haidt, 2001; Haidt, Bjorklund, & Murphy, 2000), i.e., they tend to “stubbornly” maintain their disapproval of the act without supporting reasons. In what follows, we critically reassess the traditional account, predicated on the notion that, among other things, most subjects simply fail to be convinced that the siblings’ actions are truly harm-free, thus having excellent reasons to disapprove of these acts. In line with this critique, 3 studies found that subjects 1) tended not to believe that the siblings’ actions were in fact harmless; 2) notwithstanding that, and in spite of holding a number of “counterargument-immune” reasons, subjects could be effectively maneuvered into exhibiting all the trademark signs of a morally dumbfounded state (which they subsequently recanted), and 3) with subjects’ beliefs about harm and standards of normative evaluation properly factored in, a more rigorous assessment procedure yielded a dumbfounding estimate of about 0. Based on these and related results, we contend that subjects’ reactions are wholly in line with the rationalist model of moral judgment and that their use in support of claims of moral arationalism should be reevaluated.

This paper offers a new perspective on historical understandings of the relationship between alcohol, climate and the body, by studying the way that British explorers of tropical Africa drank alcohol and wrote about drink between c.1850 and c.1910. We demonstrate that alcohol was simultaneously classified as a medicinal, a preventative and a pleasurable drink, shaped by competing medical theories, but that distinctions between these different roles were highly blurred. We also show how many explorers thought certain drinks helped to protect white bodies from the effects of tropical diseases. While popular amongst travellers, these views came under growing scrutiny in the latter part of the nineteenth century, reflecting both changing scientific views about the relationship between alcohol, climate and the body and the development of a much larger European presence in tropical Africa. However, even those who opposed tropical drinking often supported the use of other stimulants and viewed the tropics as uniquely dangerous. As such, the paper challenges the idea that the late nineteenth century marked a paradigm shift in scientific attitudes towards tropical environments, as much previous scholarship has suggested. At the same time, our examinations of explorers’ descriptions of drinking by African people demonstrates how ideas about racial difference played an important role within medical understandings of alcohol. Overall, this paper examines the heterogeny of attitudes to alcohol to be found within tropical medicine and documents the continuities in approach shown between the nineteenth and twentieth centuries.

The Subglacial Antarctic Lakes Scientific Access (SALSA) Project accessed Mercer Subglacial Lake using environmentally clean hot-water drilling to examine interactions among ice, water, sediment, rock, microbes and carbon reservoirs within the lake water column and underlying sediments. A ~0.4 m diameter borehole was melted through 1087 m of ice and maintained over ~10 days, allowing observation of ice properties and collection of water and sediment with various tools. Over this period, SALSA collected: 60 L of lake water and 10 L of deep borehole water; microbes >0.2 μm in diameter from in situ filtration of ~100 L of lake water; 10 multicores 0.32–0.49 m long; 1.0 and 1.76 m long gravity cores; three conductivity–temperature–depth profiles of borehole and lake water; five discrete depth current meter measurements in the lake and images of ice, the lake water–ice interface and lake sediments. Temperature and conductivity data showed the hydrodynamic character of water mixing between the borehole and lake after entry. Models simulating melting of the ~6 m thick basal accreted ice layer imply that debris fall-out through the ~15 m water column to the lake sediments from borehole melting had little effect on the stratigraphy of surficial sediment cores.

Lipid metabolism and inflammation contribute to CVD development. This study investigated whether the consumption of cranberries (CR; Vaccinium macrocarpon) can alter HDL metabolism and prevent inflammation in mice expressing human apo A-I transgene (hApoAITg), which have similar HDL profiles to those of humans. Male hApoAITg mice were fed a modified American Institute of Nutrition-93M high-fat/high-cholesterol diet (16 % fat, 0·25 % cholesterol, w/w; n 15) or the high-fat/high-cholesterol diet containing CR (5 % dried CR powder, w/w, n 16) for 8 weeks. There were no significant differences in body weight between the groups. Serum total cholesterol, non-HDL-cholesterol and TAG concentrations were significantly lower in the control than CR group with no significant differences in serum HDL-cholesterol and apoA-I. Mice fed CR showed significantly lower serum lecithin–cholesterol acyltransferase activity than the control. Liver weight and steatosis were not significantly different between the groups, but hepatic expression of genes involved in cholesterol metabolism was significantly lower in the CR group. In the epididymal white adipose tissue (eWAT), the CR group showed higher weights with decreased expression of genes for lipogenesis and fatty acid oxidation. The mRNA abundance of F4/80, a macrophage marker and the numbers of crown-like structures were less in the CR group. In the soleus muscle, the CR group also demonstrated higher expression of genes for fatty acid β-oxidation and mitochondrial biogenesis than those of the control. In conclusion, although CR consumption elicited minor effects on HDL metabolism, it prevented obesity-induced inflammation in eWAT with concomitant alterations in soleus muscle energy metabolism.

To increase the country’s capacity to test and track suspected coronavirus disease 2019 (COVID-19) cases, Israel launched drive-through testing centers in key cities, including Tel Aviv, Jerusalem, Be’er Sheva, and Haifa. This article examines the challenges that the national emergency medical services and volunteers faced in the process of implementing drive-through testing centers to offer lessons learned and direction to health-care professionals in other countries.

In many materials development projects, scientists and research heads make decisions to guide the project direction. For example, scientists may decide which processing steps to use, what elements to include in their material selection, or from what suppliers to source their materials. Research heads may decide whether to invest development effort in reducing the environmental impact or production cost of a material. When making these decisions, it would be helpful to know how those decisions affect the achievable performance of the materials under consideration. Often, these decisions are complicated by trade-offs in performance between competing properties. This paper presents an approach for visualizing and evaluating design spaces, where a design space is defined as the set of possible materials under consideration given specified constraints. This design space visualization approach is applied to two case studies with environmental impact motivations: one in biodegradability for solvents, and the other in sustainable materials sourcing for Li-ion batteries. The results demonstrate how this visualization approach can enable data-driven, quantitative decisions for project direction.