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Over the span of some 700 years the colonizing populations of Aotearoa New Zealand grew, with subsequent changes in levels of interaction and social affiliation. Historical accounts document that Māori society transformed from relatively autonomous village-based groups into larger territorial lineages, which later formed even larger geo-political tribal associations. These shifts have not been well-documented in the archaeological record, but social network analysis (SNA) of pXRF sourced obsidian recovered from 15 archaeological sites documents variable levels of similarity and affiliation. Three site communities and two source communities are defined based on the differential proportions of obsidian from 13 distinct sources. Distance and travel time between archaeological sites and obsidian sources were not the defining factors for obsidian source selection and community membership, rather social considerations are implicated. Some archaeological sites incorporated material from far off sources, and in some instances geographically close sites contained material from different sources and were assigned to different communities. The analytical site communities constitute relational identifications that partially correspond to categorical identities of current Māori iwi (tribal) territories and boundaries. Based on very limited temporal information, these site communities are thought to have coalesced sometime after AD 1500. By incorporating previously published and unpublished data, the SNA of obsidian artefacts defined robust network communities that reflect differential levels of Māori interaction and affiliation.

Disturbance of birds by human activities is increasing and is of conservation concern. Little is known of the flight initiation distances (FID) of birds to recreational canoeing, although this activity is common and can occur in wetland areas inaccessible to vehicle or pedestrian traffic. We compared the FID evoked by a walker with that evoked by a canoe for 13 birds in wetlands in north–western Queensland. Canoes evoked shorter FIDs compared with walkers (means ± 95 % confidence intervals; 32.9 ± 7.6 m and 47.5 ± 7.4 m, respectively). These data could be used to establish buffers or codes of conduct for canoeists in wetlands in arid northern Australia, especially when water levels are low.

[This corrects the article DOI: 10.1371/journal.pone.0212941.].

This dataset includes 4,582 obsidian artefacts matched to their natural geological source from 45 archaeological sites in New Zealand (Aotearoa). It is a compilation of a number of independent projects conducted in the laboratories of the University of Auckland and University of Otago from 2011 to 2018 [12345678910111213]. It combines previously published studies [35678910111213], an MA thesis [1], a BA(Hons) dissertation [2], a site report [4], and other previously unpublished primary data. The dataset has high reuse potential for future non-destructive studies of artefacts and social network analyses.

Animal tolerance towards humans can be a key factor facilitating wildlife–human coexistence, yet traits predicting its direction and magnitude across tropical animals are poorly known. Using 10,249 observations for 842 bird species inhabiting open tropical ecosystems in Africa, South America, and Australia, we find that avian tolerance towards humans was lower (i.e., escape distance was longer) in rural rather than urban populations and in populations exposed to lower human disturbance (measured as human footprint index). In addition, larger species and species with larger clutches and enhanced flight ability are less tolerant to human approaches and escape distances increase when birds were approached during the wet season compared to the dry season and from longer starting distances. Identification of key factors affecting animal tolerance towards humans across large spatial and taxonomic scales may help us to better understand and predict the patterns of species distributions in the Anthropocene. The degree to which species tolerate human disturbance contributes to shape human-wildlife coexistence. Here, the authors identify key predictors of avian tolerance of humans across 842 bird species from open tropical ecosystems.

Perinatal Excellence to Reduce Injury in Premature Birth (PERIPrem) is an 11-element perinatal care bundle designed to improve outcomes for preterm babies, in line with the National Health Service (NHS) Long Term plan. Designed in collaboration with 12 NHS Trusts (secondary care hospitals), South West and West of England Academic Health Science Networks, South West Neonatal Operational Delivery Network, parent partners and clinical experts, implementation was via bespoke quality improvement (QI) methodology. Before project initiation, there was regional variation in uptake of elements, evidenced by baseline audit. Optimisation of the preterm infant is complex; eligibility for treatments is dependent on gestation and local policies. Preterm infants experience variability in care dependent on the place of birth, and there remains an implementation gap for several effective, evidence-based treatments.The PERIPrem ambition is to reduce severe brain injury and death caused by prematurity by at least 50% through the delivery of a perinatal care bundle. The PERIPrem approach resulted in improved element implementation by 26% (from 3% to 29%) between 2019 and 2021, with dyads significantly more likely to receive the full bundle in 2021 compared with 2019 (probability=0.96 (95% CI 0.87 to 0.99), p<0.001). When examining the impact on psychological safety and team-working of PERIPrem, linear mixed models indicated an improvement in team function (p=0.021), situation monitoring (p=0.029) and communication within teams (p=0.002). Central to success was the development of a committed multiorganisational collaborative that continues to drive perinatal improvement using a common language and streamlining processes. In addition to saving the lives of the most vulnerable babies, PERIPrem aims to improve the chances of disability-free lives and is successfully nurturing high-functioning perinatal teams with enhanced QI skills.

Small studies have yielded divergent results for administration of granulocyte colony-stimulating factor (G-CSF) after acute myocardial infarction. Adequately powered studies involving patients with at least moderate left ventricular dysfunction are lacking. Patients with left ventricular ejection fraction less than 45% after anterior-wall myocardial infarction were treated with G-CSF (10 µg/kg daily for 4 days) or placebo. After initial randomization of 86 patients, 41 in the placebo group and 39 in the G-CSF group completed 6-month follow-up and underwent measurement of left ventricular ejection fraction by radionuclide angiography. Baseline and 6-week mean ejection fraction was similar for the G-CSF and placebo groups: 34.8% (95% confidence interval [CI] 32.6%–37.0%) v. 36.4% (95% CI 33.5%–39.2%) at baseline and 39.8% (95% CI 36.2%–43.4%) v. 43.1% (95% CI 39.2%–47.0%) at 6 weeks. However, G-CSF therapy was associated with a lower ejection fraction at 6 months relative to placebo (40.8% [95% CI 37.4%–44.2%] v. 46.0% [95% CI 42.7%–44.3%]). Both groups had improved left ventricular function, but change in left ventricular ejection fraction was lower in patients treated with G-CSF than in those who received placebo (5.7 [95% CI 3.4–8.1] percentage points v. 9.2 [95% CI 6.3–12.1] percentage points). One or more of a composite of several major adverse cardiac events occurred in 8 patients (19%) within each group, with similar rates of target-vessel revascularization. In patients with moderate left ventricular dysfunction following anterior-wall infarction, G-CSF therapy was associated with a lower 6-month left ventricular ejection fraction but no increased risk of major adverse cardiac events. Future studies of G-CSF in patients with left ventricular dysfunction should be monitored closely for safety. Trial registration: ClinicalTrials.gov, no. NCT00394498

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes ( General Haemodynamic , Renal and Immunological ) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease.