Compatibility of the large quasar groups with the concordance cosmological model

We study the compatibility of large quasar groups with the concordance cosmological model. Large quasar groups are very large spatial associations of quasars in the cosmic web, with sizes of 50–250 h−1 Mpc. In particular, the largest large quasar group known, named Huge-LQG, has a longest axis of ∼860 h−1 Mpc, larger than the scale of homogeneity (∼260 Mpc), which has been noted as a possible violation of the cosmological principle. Using mock catalogues constructed from the Horizon Run 2 cosmological simulation, we found that large quasar groups size, quasar member number and mean overdensity distributions in the mocks agree with observations. The Huge-LQG is found to be a rare group with a probability of 0.3 per cent of finding a group as large or larger than the observed, but an extreme value analysis shows that it is an expected maximum in the sample volume with a probability of 19 per cent of observing a largest quasar group as large or larger than Huge-LQG. The Huge-LQG is expected to be the largest structure in a volume at least 5.3 ± 1 times larger than the one currently studied

A 3D Voronoi+Gapper Galaxy Cluster Finder in Redshift Space to z∼ 0.2 I: an Algorithm Optimized for the 2dFGRS

This paper is the first in a series, presenting a new galaxy cluster finder based on a three-dimensional Voronoi Tesselation plus a maximum likelihood estimator, followed by gapping-filtering in radial velocity(VoML+G). The scientific aim of the series is a reassessment of the diversity of optical clusters in the local universe. A mock galaxy database mimicking the southern strip of the magnitude(blue)-limited 2dF Galaxy Redshift Survey (2dFGRS), for the redshift range 0.009 N g ≥ 5, and 14% with N g < 5. The ensemble of VoML+G clusters has a ~59% completeness and a ~66% purity, whereas the subsample with N g ≥ 10, to z ~ 0.14, has greatly improved mean rates of ~75% and ~90%, respectively. The VoML+G cluster velocity dispersions are found to be compatible with those corresponding to "Millennium clusters" over the 300–1000 km s−1 interval, i.e., for cluster halo masses in excess of ~3.0 × 1013 M ⊙ h −1

Compatibility of the large quasar groups with the concordance cosmological model

We study the compatibility of large quasar groups with the concordance cosmological model. Large quasar groups are very large spatial associations of quasars in the cosmic web, with sizes of 50–250 h^(−1) Mpc. In particular, the largest large quasar group known, named Huge-LQG, has a longest axis of ∼860 h^(−1) Mpc, larger than the scale of homogeneity (∼260 Mpc), which has been noted as a possible violation of the cosmological principle. Using mock catalogues constructed from the Horizon Run 2 cosmological simulation, we found that large quasar groups size, quasar member number and mean overdensity distributions in the mocks agree with observations. The Huge-LQG is found to be a rare group with a probability of 0.3 per cent of finding a group as large or larger than the observed, but an extreme value analysis shows that it is an expected maximum in the sample volume with a probability of 19 per cent of observing a largest quasar group as large or larger than Huge-LQG. The Huge-LQG is expected to be the largest structure in a volume at least 5.3 ± 1 times larger than the one currently studied

A 3D Voronoi+Gapper Galaxy Cluster Finder in Redshift Space to z ∼ 0.2. II. An Abundant Cluster Population Dominated by Late-type Galaxies Unveiled

We identify 1901 galaxy clusters (N g ≥ 2) with the VoML+G algorithm (Paper I) on the Two-Degree Field Galaxy Redshift Survey. We present the 341 clusters with at least 10 galaxies that are within 0.009 < z < 0.14 (the Catalog), of which 254 (~75%) have counterparts in the literature (NED), with the remainder (87) plausibly "new" because of incompleteness of previous searches or unusual galaxy contents. The 207 clusters within z = 0.04–0.09 are used to study the properties of the galaxy systems in the nearby universe, including their galaxy contents parameterized by the late-type galaxy fractions (f L ). For this nearly complete cluster subsample, we find the following: (i) 63% are dominated by early-type galaxies (i.e., the late-type-poor clusters, f L < 0.5) with corresponding mean multiplicity and logarithmic virial mass (in units of M ⊙) of 22 ± 1 and 12.91 ± 0.04, respectively; and (ii) 37% are dominated by late-type galaxies (i.e., the late-type-rich clusters, f L ≥ 0.5) with corresponding mean multiplicity and logarithmic virial mass (in units of M ⊙) of 15.7 ± 0.9 and 12.66 ± 0.07, respectively. The statistical analysis of the late-type fraction distribution supports, with a 3σ confidence level, the presence of two population components. It is suggested that the late-type-poor galaxy systems reflect and extend the class of Abell-APM-EDCC clusters and that the late-type-rich systems (~one-third of the total) belong to a new, previously unappreciated class. The late-type-rich clusters, on average high mass-to-light ratio systems, appear to be more clustered on large scales than the late-type-poor clusters. A class of late-type-rich clusters is not predicted by current theory

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)