Introduction: Both genetic and environmental factors contribute to triglyceride, LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) levels. While genome-wide association studies test the genetic factors systematically, testing and reporting a few factors may lead to fragmented literature for environmental correlates. Hypothesis: We hypothesize that one may systematically screen environmental factors to discover and replicate robust correlations with serum lipid levels. To address this hypothesis, we conduct an environment-wide association study (EWAS) to comprehensively and systematically associate multiple environmental factors with serum lipid levels. Methods: We utilized four independent surveys from the National Health and Nutrition Examination Survey (NHANES), collected between 1999 and 2006. 322 urine and blood markers of environmental factors were assayed in 500 to 7000 individuals. We used linear regression to associate each standardized environmental factor (mean subtracted and divided by the standard deviation) to triglycerides, LDL-C, and HDL-C adjusting for age, age-squared, sex, ethnicity, socioeconomic status (SES), and body mass index (BMI). Final estimates were additionally adjusted for waist circumference, diabetes status, blood pressure, and cohort. Multiple comparisons were controlled for the false discovery rate (the estimated ratio of false positives among statistically significant findings). Significant findings were tentatively validated across surveys. Finally, we conducted sensitivity analyses adjusting for 62 questionnaire-based variables in order to assess the degree of bias. Results: We identified and validated 22, 8, and 17 environmental factors correlated with triglycerides, LDL-C and HDL-C levels, respectively. Novel associations include markers of air pollution associated with lower HDL-C (p=0.006, 3% lower, N=2000) and vitamin E (-tocopherol) associated with unfavorable lipid levels (triglycerides: p=10 -17 , 17% higher, N=3000; LDL-C: p=3x10 -14 , 6% higher, N=3,000; HDL-C: p=6x10 -6 , 3% lower, N=7000). We also confirmed many known or previously speculated associations, such as lower levels in HDL-C with a marker of nicotine, cotinine, and higher triglycerides and lower HDL-C with levels of persistent fat-soluble contaminants, and lower triglycerides and higher HDL-C levels with nutrient markers such as iron, folate, vitamin C, and vitamin D, and enterolactone. Conclusions: EWAS is a way to create hypothesis regarding the association of environmental factors to serum lipids. Due to the cross-sectional nature of the surveys, findings may be biased by confounders or may be reverse causal. Nonetheless, we document a wide array of both known and novel associations, such as nutrients, vitamins, and persistent pollutants, with lipid levels that should be examined in depth in longitudinal studies.
Attacks against Internet routing are increasing in number and severity. Contributing greatly to these attacks is the absence of origin authentication: there is no way to validate claims of address ownership or location. The lack of such services enables not only attacks by malicious entities, but indirectly allow seemingly inconsequential miconfigurations to disrupt large portions of the Internet. This paper considers the semantics, design, and costs of origin authentication in interdomain routing. We formalize the semantics of address delegation and use on the Internet, and develop and characterize broad classes of origin authentication proof systems. We estimate the address delegation graph representing the current use of IPv4 address space using available routing data. This effort reveals that current address delegation is dense and relatively static: as few as 16 entities perform 80% of the delegation on the Internet. We conclude by evaluating the proposed services via traced based simulation. Our simulation shows the enhanced proof systems can reduce significantly reduce resource costs associated with origin authentication.
In MOS integrated circuits, signals may propagate between stages with fanout. The exact calculation of signal delay through such networks is difficult. However, upper and lower bounds for delay that are computationally simple are presented in this paper. The results can be used 1) to bound the delay, given the signal threshold, or 2) to bound the signal voltage, given a delay time, or 3) certify that a circuit is "fast enough," given both the maximum delay and the voltage threshold.
Abstract In the coronavirus disease 2019 (COVID-19) pandemic, a large number of non-pharmaceutical measures that pertain to the wider group of social distancing interventions (e.g. public gathering bans, closures of schools, workplaces and all but essential business, mandatory stay-at-home policies, travel restrictions, border closures and others) have been deployed. Their urgent deployment was defended with modelling and observational data of spurious credibility. There is major debate on whether these measures are effective and there is also uncertainty about the magnitude of the harms that these measures might induce. Given that there is equipoise for how, when and if specific social distancing interventions for COVID-19 should be applied and removed/modified during reopening, we argue that informative randomised-controlled trials are needed. Only a few such randomised trials have already been conducted, but the ones done to-date demonstrate that a randomised trials agenda is feasible. We discuss here issues of study design choice, selection of comparators (intervention and controls), choice of outcomes and additional considerations for the conduct of such trials. We also discuss and refute common counter-arguments against the conduct of such trials.
The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative-the Data Ark-to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006-2011 (n = 48) and 2014-2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.
There is an increasing trend to use commodity microprocessors as the compute engines in large-scale multiprocessors. However, given that the majority of the microprocessors are sold in the workstation market, not in the multiprocessor market, it is only natural that architectural features that benefit only multiprocessors are less likely to be adopted in commodity microprocessors. In this paper, we explore multiple-context processors, an architectural technique proposed to hide the large memory latency in multiprocessors. We show that while current multiple-context designs work reasonably well for multiprocessors, they are ineffective in hiding the much shorter uniprocessor latencies using the limited parallelism found in workstation environments. We propose an alternative design that combines the best features of two existing approaches, and present simulation results that show it yields better performance for both multiprogrammed workloads on a workstation and parallel applications on a multiprocessor. By addressing the needs of the workstation environment, our proposal makes multiple contexts more attractive for commodity microprocessors.
The author evaluated the implications of nominal statistical significance for changing the credibility of null versus alternative hypotheses across a large number of observational associations for which formal statistical significance (p < 0.05) was claimed. Calculation of the Bayes factor (B) under different assumptions was performed on 272 observational associations published in 2004-2005 and a data set of 50 meta-analyses on gene-disease associations (752 studies) for which statistically significant associations had been claimed (p < 0.05). Depending on the formulation of the prior, statistically significant results offered less than strong support to the credibility (B > 0.10) for 54-77% of the 272 epidemiologic associations for diverse risk factors and 44-70% of the 50 associations from genetic meta-analyses. Sometimes nominally statistically significant results even decreased the credibility of the probed association in comparison with what was thought before the study was conducted. Five of six meta-analyses with less than substantial support (B > 0.032) lost their nominal statistical significance in a subsequent (more recent) meta-analysis, while this did not occur in any of seven meta-analyses with decisive support (B < 0.01). In these large data sets of observational associations, formal statistical significance alone failed to increase much the credibility of many postulated associations. Bayes factors may be used routinely to interpret "significant" associations.
