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Abstract Background Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. Methods We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland–Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35–50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. Results CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. Conclusions The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.

Introduction: Currently there is no standardization of variable taper endodontic files and corresponding gutta-percha (GP) cones. The aim of this study was to evaluate intramanufacturer and intermanufacturer variability of diameter and taper in the apical third of GP master cones and finishing files from 3 commercially available variable taper endodontic systems., Methods: Diameter measurements were recorded using digital microscopy at 1-mm increments (D1-D4) for F2 files and corresponding GP cones (n = 20 per system) from ProTaper Gold (Dentsply Tulsa Dental Specialties, Johnson City, TN), EdgeTaper Platinum (EdgeEndo, Albuquerque, NM), and ExactTaper H (SS White, Lakewood, NJ). Taper was defined as the rate of change in diameter per 1-mm increment. Mean differences in diameter were assessed using repeated measures of analysis of variance for D1 to D4 and the Wilks test for differences in taper., Results: In the apical third, ProTaper and EdgeEndo mean file diameters were significantly smaller than corresponding GP cone diameters (P < .01, P < .01, respectively). Contrastingly, SS White file diameters were significantly larger than their corresponding GP cones (P = .02). Files from all manufacturers had significantly smaller diameters than advertised (nominal) values (P < .01). ProTaper GP cones had similar diameters to nominal values (P = .30), while EdgeEndo and SS White GP cones were significantly smaller (P < .01). Amongst files and corresponding GP cones from all systems, taper was nonstandardized., Conclusions: Size discrepancies between finishing files and corresponding GP cones can be expected amongst variable taper endodontic systems. Therefore, clinicians should be prepared to make intraoperative adjustments when obturating., (Published by Elsevier Inc.)

Efforts to improve soil health require that target values of key soil properties are established. No agreed targets exist but providing population data as benchmarks is a useful step to standardise soil health comparison between landscapes. We exploited nationally representative topsoil (0-15 cm) measurements to derive soil health benchmarks for managed and semi-natural environments across Great Britain. In total, 4587 soil organic matter (SOM), 3860 pH, 2908 bulk density (BD), and 465 earthworm abundance (EA) datapoints were used. As soil properties are sensitive to site-specific characteristics, data were stratified by habitat, soil type, and mean annual precipitation, with benchmarks defined as the middle 80 % of values in each distribution - yielding 135 benchmarks. BD and pH decreased with land management intensity (agriculture > semi-natural grasslands > woodlands > heathlands > wetlands), and vice versa for SOM and EA. Normalising benchmark ranges by medians revealed soil health indicator benchmark widths increased in the order: pH < BD < SOM < EA, while width increased with decreasing land management intensity. Arable and horticulture and improved grassland exhibited narrow benchmarks for SOM, pH and BD, yet the widest EA benchmark, suggesting additional drivers impact EA patterns. Upland wetlands had the widest BD benchmarks, important when determining carbon stocks. East Anglia currently possesses the largest proportions of atypical soils, including below typical SOM (19.2 %), above typical BD (17.4 %) and pH (39.1 %), and the smallest proportions of above typical SOM (2.4 %), and below typical BD (5.8 %) and pH (2.3 %). This is found even after land use, soil type and rainfall have been considered, underscoring how urgently soil health should be addressed here. Our benchmarking framework allows landowners to compare where their measured soil health indicators fall within expected ranges and is applicable to other biomes, national and multinational contexts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Global land use change has resulted in more pasture and cropland, largely at the expense of woodlands, over the last 300 years. How this change affects soil hydraulic function with regard to feedbacks to the hydrological cycle is unclear for earth system modelling (ESM). Pedotransfer functions (PTFs) used to predict soil hydraulic conductivity (K) take no account of land use. Here, we synthesize >800 measurements from around the globe from sites that measured near-saturated soil hydraulic conductivity, or infiltration, at the soil surface, on the same soil type at each location, but with differing land use, woodland (W), grassland (G) and cropland (C). We found that texture based PTFs predict K reasonably well for cropland giving unbiased results, but increasingly underestimate K in grassland and woodland. In native woodland and grassland differences in K can usually be accounted for by differences in bulk density. However, heavy grazing K responses can be much lower indicating compaction likely reduces connectivity. We show that the K response ratios (RR) between land uses vary with cropland (C/W = 0.45 [W/C = 2.2]) and grassland (G/W = 0.63 [W/G = 1.6]) having about half the K of woodland., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 UK Centre for Ecology & Hydrology. Published by Elsevier B.V. All rights reserved.)