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Institution

University of Auckland

EducationAuckland, New Zealand
About: University of Auckland is a education organization based out in Auckland, New Zealand. It is known for research contribution in the topics: Population & Context (language use). The organization has 28049 authors who have published 77706 publications receiving 2689366 citations. The organization is also known as: The University of Auckland & Auckland University College.


Papers
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Journal ArticleDOI
23 Jan 2009-Science
TL;DR: The results are robust to assumptions about the rooting and calibration of the trees and demonstrate the combined power of linguistic scholarship, database technologies, and computational phylogenetic methods for resolving questions about human prehistory.
Abstract: Debates about human prehistory often center on the role that population expansions play in shaping biological and cultural diversity. Hypotheses on the origin of the Austronesian settlers of the Pacific are divided between a recent “pulse-pause” expansion from Taiwan and an older “slow-boat” diffusion from Wallacea. We used lexical data and Bayesian phylogenetic methods to construct a phylogeny of 400 languages. In agreement with the pulse-pause scenario, the language trees place the Austronesian origin in Taiwan approximately 5230 years ago and reveal a series of settlement pauses and expansion pulses linked to technological and social innovations. These results are robust to assumptions about the rooting and calibration of the trees and demonstrate the combined power of linguistic scholarship, database technologies, and computational phylogenetic methods for resolving questions about human prehistory.

632 citations

Journal ArticleDOI
06 Oct 2020-JAMA
TL;DR: To determine whether hydrocortisone improves outcome for patients with severe COVID-19, an ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin was conducted.
Abstract: Importance Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures The primary end point was organ support–free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned –1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support–free days were 0 (IQR, –1 to 15), 0 (IQR, –1 to 13), and 0 (–1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support–free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support–free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration ClinicalTrials.gov Identifier:NCT02735707

630 citations

Journal ArticleDOI
01 Feb 1999-Genome
TL;DR: It is suggested that universal primers targeted to mononucleotide repeats may serve as general tools to study chloroplast variation in angiosperms where database information is limited.
Abstract: Short runs of mononucleotide repeats are present in chloroplast genomes of higher plants. In soybean, rice, and pine, PCR (polymerase chain reaction) with flanking primers has shown that the number...

630 citations

Journal ArticleDOI
TL;DR: The Python package tsfresh (Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests) accelerates this process by combining 63 time series characterization methods, which by default compute a total of 794 time series features, with feature selection on basis automatically configured hypothesis tests.

626 citations


Authors

Showing all 28484 results

NameH-indexPapersCitations
Walter C. Willett3342399413322
Meir J. Stampfer2771414283776
Frank E. Speizer193636135891
Bernard Rosner1901162147661
Eric Boerwinkle1831321170971
Rory Collins162489193407
Monique M.B. Breteler15954693762
Charles H. Hennekens150424117806
Rajesh Kumar1494439140830
Hugh A. Sampson14781676492
David P. Strachan143472105256
Jun Lu135152699767
Peter Zoller13473476093
David H. Barlow13378672730
Henry T. Lynch13392586270
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023162
2022613
20215,469
20205,198
20194,755
20184,389