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The concentration of radiocarbon (14C) differs between ocean and atmosphere. Radiocarbon determinations from samples which obtained their 14C in the marine environment therefore need a marine-specific calibration curve and cannot be calibrated directly against the atmospheric-based IntCal20 curve. This paper presents Marine20, an update to the internationally agreed marine radiocarbon age calibration curve that provides a non-polar global-average marine record of radiocarbon from 0–55 cal kBP and serves as a baseline for regional oceanic variation. Marine20 is intended for calibration of marine radiocarbon samples from non-polar regions; it is not suitable for calibration in polar regions where variability in sea ice extent, ocean upwelling and air-sea gas exchange may have caused larger changes to concentrations of marine radiocarbon. The Marine20 curve is based upon 500 simulations with an ocean/atmosphere/biosphere box-model of the global carbon cycle that has been forced by posterior realizations of our Northern Hemispheric atmospheric IntCal20 14C curve and reconstructed changes in CO2 obtained from ice core data. These forcings enable us to incorporate carbon cycle dynamics and temporal changes in the atmospheric 14C level. The box-model simulations of the global-average marine radiocarbon reservoir age are similar to those of a more complex three-dimensional ocean general circulation model. However, simplicity and speed of the box model allow us to use a Monte Carlo approach to rigorously propagate the uncertainty in both the historic concentration of atmospheric 14C and other key parameters of the carbon cycle through to our final Marine20 calibration curve. This robust propagation of uncertainty is fundamental to providing reliable precision for the radiocarbon age calibration of marine based samples. We make a first step towards deconvolving the contributions of different processes to the total uncertainty; discuss the main differences of Marine20 from the previous age calibration curve Marine13; and identify the limitations of our approach together with key areas for further work. The updated values for ΔR, the regional marine radiocarbon reservoir age corrections required to calibrate against Marine20, can be found at the data base http://calib.org/marine/.
Radiocarbon (14C) ages cannot provide absolutely dated chronologies for archaeological or paleoenvironmental studies directly but must be converted to calendar age equivalents using a calibration curve compensating for fluctuations in atmospheric 14C concentration. Although calibration curves are constructed from independently dated archives, they invariably require revision as new data become available and our understanding of the Earth system improves. In this volume the international 14C calibration curves for both the Northern and Southern Hemispheres, as well as for the ocean surface layer, have been updated to include a wealth of new data and extended to 55,000 cal BP. Based on tree rings, IntCal20 now extends as a fully atmospheric record to ca. 13,900 cal BP. For the older part of the timescale, IntCal20 comprises statistically integrated evidence from floating tree-ring chronologies, lacustrine and marine sediments, speleothems, and corals. We utilized improved evaluation of the timescales and location variable 14C offsets from the atmosphere (reservoir age, dead carbon fraction) for each dataset. New statistical methods have refined the structure of the calibration curves while maintaining a robust treatment of uncertainties in the 14C ages, the calendar ages and other corrections. The inclusion of modeled marine reservoir ages derived from a three-dimensional ocean circulation model has allowed us to apply more appropriate reservoir corrections to the marine 14C data rather than the previous use of constant regional offsets from the atmosphere. Here we provide an overview of the new and revised datasets and the associated methods used for the construction of the IntCal20 curve and explore potential regional offsets for tree-ring data. We discuss the main differences with respect to the previous calibration curve, IntCal13, and some of the implications for archaeology and geosciences ranging from the recent past to the time of the extinction of the Neanderthals.
The criteria for objective memory impairment in mild cognitive impairment (MCI) are vaguely defined. Aggregating the number of abnormal memory scores (NAMS) is one way to operationalise memory impairment, which we hypothesised would predict progression to Alzheimer’s disease (AD) dementia.
As part of the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing, 896 older adults who did not have dementia were administered a psychometric battery including three neuropsychological tests of memory, yielding 10 indices of memory. We calculated the number of memory scores corresponding to z ≤ −1.5 (i.e., NAMS) for each participant. Incident diagnosis of AD dementia was established by consensus of an expert panel after 3 years.
Of the 722 (80.6%) participants who were followed up, 54 (7.5%) developed AD dementia. There was a strong correlation between NAMS and probability of developing AD dementia (r = .91, p = .0003). Each abnormal memory score conferred an additional 9.8% risk of progressing to AD dementia. The area under the receiver operating characteristic curve for NAMS was 0.87 [95% confidence interval (CI) .81–.93, p < .01]. The odds ratio for NAMS was 1.67 (95% CI 1.40–2.01, p < .01) after correcting for age, sex, education, estimated intelligence quotient, subjective memory complaint, Mini-Mental State Exam (MMSE) score and apolipoprotein E ϵ4 status.
Aggregation of abnormal memory scores may be a useful way of operationalising objective memory impairment, predicting incident AD dementia and providing prognostic stratification for individuals with MCI.
Social distancing policies are key in curtailing COVID-19 infection spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We sought to study public perception of social distancing through organic, large-scale discussion on Twitter.
Retrospective cross-sectional study.
Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine learning models, conducting a sentiment analysis to identify emotions and polarity. We evaluated subjectivity of tweets and estimated frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments.
We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half (50.4%) of tweets primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (i.e., joy), concerns about food insecurity and quarantine effects (i.e., fear), and unpredictability of COVID and its implications (i.e., surprise).
The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing in the early stages of their implementation.
In response to advancing clinical practice guidelines regarding concussion management, service members, like athletes, complete a baseline assessment prior to participating in high-risk activities. While several studies have established test stability in athletes, no investigation to date has examined the stability of baseline assessment scores in military cadets. The objective of this study was to assess the test–retest reliability of a baseline concussion test battery in cadets at U.S. Service Academies.
All cadets participating in the Concussion Assessment, Research, and Education (CARE) Consortium investigation completed a standard baseline battery that included memory, balance, symptom, and neurocognitive assessments. Annual baseline testing was completed during the first 3 years of the study. A two-way mixed-model analysis of variance (intraclass correlation coefficent (ICC)3,1) and Kappa statistics were used to assess the stability of the metrics at 1-year and 2-year time intervals.
ICC values for the 1-year test interval ranged from 0.28 to 0.67 and from 0.15 to 0.57 for the 2-year interval. Kappa values ranged from 0.16 to 0.21 for the 1-year interval and from 0.29 to 0.31 for the 2-year test interval. Across all measures, the observed effects were small, ranging from 0.01 to 0.44.
This investigation noted less than optimal reliability for the most common concussion baseline assessments. While none of the assessments met or exceeded the accepted clinical threshold, the effect sizes were relatively small suggesting an overlap in performance from year-to-year. As such, baseline assessments beyond the initial evaluation in cadets are not essential but could aid concussion diagnosis.
We describe 14 yr of public data from the Parkes Pulsar Timing Array (PPTA), an ongoing project that is producing precise measurements of pulse times of arrival from 26 millisecond pulsars using the 64-m Parkes radio telescope with a cadence of approximately 3 weeks in three observing bands. A comprehensive description of the pulsar observing systems employed at the telescope since 2004 is provided, including the calibration methodology and an analysis of the stability of system components. We attempt to provide full accounting of the reduction from the raw measured Stokes parameters to pulse times of arrival to aid third parties in reproducing our results. This conversion is encapsulated in a processing pipeline designed to track provenance. Our data products include pulse times of arrival for each of the pulsars along with an initial set of pulsar parameters and noise models. The calibrated pulse profiles and timing template profiles are also available. These data represent almost 21 000 h of recorded data spanning over 14 yr. After accounting for processes that induce time-correlated noise, 22 of the pulsars have weighted root-mean-square timing residuals of
in at least one radio band. The data should allow end users to quickly undertake their own gravitational wave analyses, for example, without having to understand the intricacies of pulsar polarisation calibration or attain a mastery of radio frequency interference mitigation as is required when analysing raw data files.
To evaluate whether incorporating mandatory prior authorization for Clostridioides difficile testing into antimicrobial stewardship pharmacist workflow could reduce testing in patients with alternative etiologies for diarrhea.
Single center, quasi-experimental before-and-after study.
Tertiary-care, academic medical center in Ann Arbor, Michigan.
Adult and pediatric patients admitted between September 11, 2019 and December 10, 2019 were included if they had an order placed for 1 of the following: (1) C. difficile enzyme immunoassay (EIA) in patients hospitalized >72 hours and received laxatives, oral contrast, or initiated tube feeds within the prior 48 hours, (2) repeat molecular multiplex gastrointestinal pathogen panel (GIPAN) testing, or (3) GIPAN testing in patients hospitalized >72 hours.
A best-practice alert prompting prior authorization by the antimicrobial stewardship program (ASP) for EIA or GIPAN testing was implemented. Approval required the provider to page the ASP pharmacist and discuss rationale for testing. The provider could not proceed with the order if ASP approval was not obtained.
An average of 2.5 requests per day were received over the 3-month intervention period. The weekly rate of EIA and GIPAN orders per 1,000 patient days decreased significantly from 6.05 ± 0.94 to 4.87 ± 0.78 (IRR, 0.72; 95% CI, 0.56–0.93; P = .010) and from 1.72 ± 0.37 to 0.89 ± 0.29 (IRR, 0.53; 95% CI, 0.37–0.77; P = .001), respectively.
We identified an efficient, effective C. difficile and GIPAN diagnostic stewardship approval model.
OBJECTIVES/GOALS: We sought to examine: 1) variability in center acceptance patterns for heart allografts offered to the highest-priority candidates, 2) impact of this acceptance behavior on candidate survival, and 3) post-transplantation outcomes in candidates who accepted first rank offer vs. previously declined offer. METHODS/STUDY POPULATION: In this retrospective cohort study, the US national transplant registry was queried for all match runs of adult candidates listed for isolated heart transplantation between 2007-2017. We examined center acceptance rates for heart allografts offered to the highest-priority candidates and accounted for covariates in multivariable logistic regression. Competing risks analysis was performed to assess the relationship between center acceptance rate and waitlist mortality. Post-transplantation outcomes (patient survival and graft failure) between candidates who accepted their first-rank offers vs those who accepted previously declined offers were compared using Fine-Gray subdistribution hazards model. RESULTS/ANTICIPATED RESULTS: Among 19,703 unique organ offers, 6,302 (32%) were accepted for first-ranked candidates. After adjustment for donor, recipient, and geographic covariates, transplant centers varied markedly in acceptance rates (12%-62%) of offers made to first-ranked candidates. Lowest acceptance rate centers (<25%) associated with highest cumulative incidence of waitlist mortality. For every 10% increase in adjusted center acceptance rate, waitlist mortality risk decreased by 27% (SHR 0.73, 95% CI 0.67-0.80). No significant difference was observed in 5-year adjusted post-Tx survival and graft failure between hearts accepted at the first-rank vs lower-rank positions. DISCUSSION/SIGNIFICANCE OF IMPACT: Wide variability in heart acceptance rates exists among centers, with candidates listed at low acceptance rate centers more likely to die waiting. Similar post-Tx survival suggests previously declined allografts function as well as those accepted at first offer. Center-level decision is a modifiable behavior associated with waitlist mortality.
Prior research has shown that buoyant jets and plumes ‘puff’ at a frequency that depends on the balance of momentum and buoyancy fluxes at the inlet, as parametrized by the Richardson number. Experiments have revealed the existence of scaling relations between the Strouhal number of the puffing and the inlet Richardson number, but geometry-specific relations are required when the characteristic length is taken to be the diameter (for round inlets) or width (for planar inlets). Similar to earlier studies of rectangular buoyant jets and plumes, in the present study we use the hydraulic radius of the inlet as the characteristic length to obtain a single Strouhal–Richardson scaling relation for a variety of inlet geometries over Richardson numbers that span three orders of magnitude. In particular, we use adaptive mesh numerical simulations to compute puffing Strouhal numbers for circular, rectangular (with three different aspect ratios), triangular and annular high-temperature buoyant jets and plumes over a range of Richardson numbers. We then combine these results with prior experimental data for round, planar and rectangular buoyant jets and plumes to propose a new scaling relation that describes puffing Strouhal numbers for various inlet shapes and for hydraulic Richardson numbers spanning over four orders of magnitude. This empirically motivated scaling relation is also shown to be in good agreement with prior results from global linear stability analyses.
We present an overview of higher randomness and its recent developments. After an introduction, we provide in the second section some background on higher computability, presenting in particular $\Pi^1_1$ and $\Sigma^1_1$ sets from the viewpoint of the computability theorist. In the third section we give an overview of the different higher randomness classes: $\Delta^1_1$-randomness, $\Pi^1_1$-Martin-Löf randomness, higher weak-2 randomness, higher difference randomness, and $\Pi^1_1$-randomness. We then move on to study each of these classes, separating them and inspecting their respective lowness classes. We put more attention on $\Pi^1_1$-Martin-Löf randomness and $\Pi^1_1$-randomness: The former is the higher analogue of the most well-known and studied class in classical algorithmic randomness. We show in particular how to lift the main classical randomness theorems to the higher settings by putting continuity in higher reductions and relativisations. The latter presents, as we will see, many remarkable properties and does not have any analogue in classical randomness. Finally in the eighth section we study randomness along with a higher hierarchy of complexity of sets, motivated by the notion of higher weak-2 randomness. We show that this hierarchy collapses eventually.
In this introductory survey, we provide an overview of the major developments of algorithmic randomness with an eye towards the historical development of the discipline. First we give a brief introduction to computability theory and the underlying mathematical concepts that later appear in the survey. Next we selectively cover four broad periods in which the primary developments in algorithmic randomness occurred: (1) the mid-1960s to mid-1970s, in which the main definitions of algorithmic randomness were laid out and the basic properties of random sequences were established; (2) the 1980s through the 1990s, which featured intermittent and important work from a handful of researchers; (3) the 2000s, during which there was an explosion of results as the discipline matured into a fully-fledged subbranch of computability theory; and (4) the early 2010s, in which ties between algorithmic randomness and other subfields of mathematics were discovered. The aim of this survey is to provide a point of entry for newcomers to the field and a useful reference for practitioners.
The last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.
The halting probability of a Turing machine was introduced by Chaitin, who also proved that it is an algorithmically random real number and named it Omega. Since his seminal work, many popular expositions have appeared, mainly focusing on the metamathematical or philosophical significance of this number (or debating against it). At the same time, a rich mathematical theory exploring the properties of Chaitin's Omega has been brewing in various technical papers, which quietly reveals the significance of this number to many aspects of contemporary algorithmic information theory. The purpose of this survey is to expose these developments and tell a story about Omega which outlines its multi-faceted mathematical properties and roles in algorithmic randomness.
This is a survey of constructive and computable measure theory with an emphasis on the close connections with algorithmic randomness. We give a brief history of constructive measure theory from Brouwer to the present, emphasizing how Schnorr randomness is the randomness notion implicit in the work of Brouwer, Bishop, Demuth, and others. We survey a number of recent results showing that classical almost everywhere convergence theorems can be used to characterize many of the common randomness notions including Schnorr randomness, computable randomness, and Martin-Löf randomness. Last, we go into more detail about computable measure theory, showing how all the major approaches are basically equivalent (even though the definitions can vary greatly).
In this survey, we lay out the central results in the study of algorithmic randomness with respect to biased probability measures. The first part of the survey covers biased randomness with respect to computable measures. The central technique in this area is the transformation of random sequences via certain randomness-preserving Turing functionals, which can be used to induce non-uniform probability measures. The second part of the survey covers biased randomness with respect to non-computable measures, with an emphasis on the work of Reimann and Slaman on the topic, as well as the contributions of Miller and Day in developing Levin's notion of a neutral measure. We also discuss blind randomness as well as van Lambalgen's theorem for both computable and non-computable measures. As there is no currently-available source covering all of these topics, this survey fills a notable gap in the algorithmic randomness literature.