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Author(s):  
Nina Lazarevic ◽  
Adrian G. Barnett ◽  
Peter D. Sly ◽  
Anna C. Callan ◽  
Ania Stasinska ◽  
...  

2022 ◽  
Vol 63 ◽  
pp. 39-45
Author(s):  
Evalotte Mörelius ◽  
Ailsa Munns ◽  
Stephanie Smith ◽  
Helen J. Nelson ◽  
Anne McKenzie ◽  
...  

2022 ◽  
Vol 54 ◽  
pp. 100770
Author(s):  
Lucas Vimpere ◽  
Nicolò Del Piero ◽  
Aymeric Le Cotonnec ◽  
Pascal Kindler ◽  
Sébastien Castelltort
Keyword(s):  

2022 ◽  
Vol 369 ◽  
pp. 106524
Author(s):  
Jessica L. Morrison ◽  
Christopher L. Kirkland ◽  
Marco Fiorentini ◽  
Steve Beresford ◽  
Paul Polito

Author(s):  
Lauren G. Staples ◽  
Nick Webb ◽  
Lia Asrianti ◽  
Shane Cross ◽  
Daniel Rock ◽  
...  

Digital mental health services (DMHSs) deliver mental health information, assessment, and treatment, via the internet, telephone, or other digital channels. The current study compares two DMHSs operating in Western Australia (WA)—The Practitioner Online Referral System (PORTS) and MindSpot. Both provide telephone and online psychological services at no cost to patients or referrers. However, PORTS is accessed by patients via referral from health practitioners, and is designed to reach those who are financially, geographically, or otherwise disadvantaged. In contrast, MindSpot services are available to all Australian residents and patients can self-refer. This observational study compares characteristics and treatment outcomes for patients of PORTS and MindSpot in WA. Eligible patients were people who resided in WA and registered with either clinic from January 2019 to December 2020. Results showed that PORTS patients were more likely to be older, male, and unemployed. They were less likely to report a tertiary education and were more likely to live in areas with higher levels of socioeconomic disadvantage. Despite these differences, treatment outcomes were excellent for patients from both clinics. Results provide further evidence for the accessibility, acceptability, and effectiveness of DMHSs regardless of referral pathway or patient characteristics.


Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 518
Author(s):  
Reza Rezaee

A nuclear magnetic resonance (NMR) logging tool can provide important rock and fluid properties that are necessary for a reliable reservoir evaluation. Pore size distribution based on T2 relaxation time and resulting permeability are among those parameters that cannot be provided by conventional logging tools. For wells drilled before the 1990s and for many recent wells there is no NMR data available due to the tool availability and the logging cost, respectively. This study used a large database of combinable magnetic resonance (CMR) to assess the performance of several well-known machine learning (ML) methods to generate some of the NMR tool’s outputs for clastic rocks using typical well-logs as inputs. NMR tool’s outputs, such as clay bound water (CBW), irreducible pore fluid (known as bulk volume irreducible, BVI), producible fluid (known as the free fluid index, FFI), logarithmic mean of T2 relaxation time (T2LM), irreducible water saturation (Swirr), and permeability from Coates and SDR models were generated in this study. The well logs were collected from 14 wells of Western Australia (WA) within 3 offshore basins. About 80% of the data points were used for training and validation purposes and 20% of the whole data was kept as a blind set with no involvement in the training process to check the validity of the ML methods. The comparison of results shows that the Adaptive Boosting, known as AdaBoost model, has given the most impressive performance to predict CBW, FFI, permeability, T2LM, and SWirr for the blind set with R2 more than 0.9. The accuracy of the ML model for the blind dataset suggests that the approach can be used to generate NMR tool outputs with high accuracy.


Diversity ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 46
Author(s):  
Alan N. Andersen ◽  
François Brassard ◽  
Benjamin D. Hoffmann

We document diversity and its distribution within the hyperdiverse Monomorium nigrius Forel group of the Australian monsoonal tropics, an unrecognized global centre of ant diversity. The group includes a single described species, but several distinct morphotypes each with multiple clearly recognizable taxa are known. Our analysis is based on 401 CO1-sequenced specimens collected from throughout the Australian mainland but primarily in the monsoonal north and particularly from four bioregions: the Top End (northern third) of the Northern Territory (NT), the Sturt Plateau region of central NT, the Kimberley region of far northern Western Australia, and far North Queensland. Clade structure in the CO1 tree is highly congruent with the general morphotypes, although most morphotypes occur in multiple clades and are therefore shown as polyphyletic. We recognize 97 species among our sequenced specimens, and this is generally consistent (if not somewhat conservative) with PTP analyses of CO1 clustering. Species turnover is extremely high both within and among bioregions in monsoonal Australia, and the monsoonal fauna is highly distinct from that in southern Australia. We estimate that the M. nigrius group contains well over 200 species in monsoonal Australia, and 300 species overall. Our study provides further evidence that monsoonal Australia should be recognized as a global centre of ant diversity.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Amanuel Tesfay Gebremedhin ◽  
Alexandra B. Hogan ◽  
Christopher C. Blyth ◽  
Kathryn Glass ◽  
Hannah C. Moore

AbstractRespiratory syncytial virus (RSV) is a leading cause of childhood morbidity, however there is no systematic testing in children hospitalised with respiratory symptoms. Therefore, current RSV incidence likely underestimates the true burden. We used probabilistically linked perinatal, hospital, and laboratory records of 321,825 children born in Western Australia (WA), 2000–2012. We generated a predictive model for RSV positivity in hospitalised children aged < 5 years. We applied the model to all hospitalisations in our population-based cohort to determine the true RSV incidence, and under-ascertainment fraction. The model’s predictive performance was determined using cross-validated area under the receiver operating characteristic (AUROC) curve. From 321,825 hospitalisations, 37,784 were tested for RSV (22.8% positive). Predictors of RSV positivity included younger admission age, male sex, non-Aboriginal ethnicity, a diagnosis of bronchiolitis and longer hospital stay. Our model showed good predictive accuracy (AUROC: 0.87). The respective sensitivity, specificity, positive predictive value and negative predictive values were 58.4%, 92.2%, 68.6% and 88.3%. The predicted incidence rates of hospitalised RSV for children aged < 3 months was 43.7/1000 child-years (95% CI 42.1–45.4) compared with 31.7/1000 child-years (95% CI 30.3–33.1) from laboratory-confirmed RSV admissions. Findings from our study suggest that the true burden of RSV may be 30–57% higher than current estimates.


Water ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 162
Author(s):  
Feihe Kong ◽  
Wenjin Xu ◽  
Ruichen Mao ◽  
Dong Liang

The groundwater-dependent ecosystem in the Gnangara region is confronted with great threats due to the decline in groundwater level since the 1970s. The aim of this study is to apply multiple trend analysis methods at 351 monitoring bores to detect the trends in groundwater level using spatial, temporal and Hydrograph Analysis: Rainfall and Time Trend models, which were applied to evaluate the impacts of rainfall on the groundwater level in the Gnangara region, Western Australia. In the period of 1977–2017, the groundwater level decreased from the Gnangara’s edge to the central-north area, with a maximum trend magnitude of −0.28 m/year. The groundwater level in 1998–2017 exhibited an increasing trend in December–March and a decreasing trend in April–November with the exception of September when compared to 1978–1997. The rainfall + time model based on the cumulative annual residual rainfall technique with a one-month lag during 1990–2017 was determined as the best model. Rainfall had great impacts on the groundwater level in central Gnangara, with the highest impact coefficient being 0.00473, and the impacts reduced gradually from the central area to the boundary region. Other factors such as pine plantation, the topography and landforms, the Tamala Limestone formation, and aquifer groundwater abstraction also had important influences on the groundwater level.


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