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Background Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI. Materials and methods We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [ 15 O]H 2 O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [ 15 O]H 2 O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [ 15 O]H 2 O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems. Results The measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout , respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout , respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements. Conclusions A preliminary protocol for measuring the accuracy and reproducibility of flow values in [ 15 O]H 2 O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting.

The aim of this study was to determine whether differences between manufacturer of mammogram images effects performance of artificial intelligence tools for classifying breast density. Processed mammograms from 10,156 women were used to train and validate three deep learning algorithms using three retrospective datasets: Hologic, General Electric, Mixed (equal numbers of Hologic, General Electric and Siemens images) and tested on four independent witheld test sets (Hologic, General Electric, Mixed and Siemens). The area under the receiver operating characteristic curve (AUC) was compared. Women aged 47-73 with normal breasts (routine recall - no cancer) and Volpara ground truth were selected from the OPTIMAM Mammography Image Database for the years 2012-2015. 95 % confidence intervals are used for significance testing in the results with a Bayesian Signed Rank test used to rank the overall performance of the models. Best single test performance is seen when a model is trained and tested on images from a single manufacturer (Hologic train/test: 0.98 and General Electric train/test: 0.97), however the same models performed significantly worse on any other manufacturer images (General Electric AUCs: 0.68 & 0.63; Hologic AUCs: 0.56 & 0.90). The model trained on the mixed dataset exhibited the best overall performance. Better performance occurs when training and test sets contain the same manufacturer distributions and better generalisation occurs when more manufacturers are included in training. Models in clinical use should be trained on data representing the different vendors of mammogram machines used across screening programs. This is clinically relevant as models will be impacted by changes and upgrades to mammogram machines in screening centres.

High-pressure freezing processes are a novel emerging technology in food processing, offering significant improvements to the quality of frozen foods. To be able to simulate plateau times and thermal history under different conditions, in this work, we present a generalized enthalpy model of the high-pressure shift freezing process. The model includes the effects of pressure on conservation of enthalpy and incorporates the freezing point depression of non-dilute food samples. In addition, the significant heat-transfer effects of convection in the pressurizing medium are accounted for by solving the two-dimensional Navier-Stokes equations. We run the model for several numerical tests where the food sample is agar gel, and find good agreement with experimental data from the literature.

Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI. We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [ O]H O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [ O]H O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [ O]H O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems. The measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout, respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout, respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements. A preliminary protocol for measuring the accuracy and reproducibility of flow values in [ O]H O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting.

Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI.BACKGROUNDSeveral factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI.We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [15O]H2O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [15O]H2O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [15O]H2O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems.MATERIALS AND METHODSWe implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [15O]H2O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [15O]H2O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [15O]H2O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems.The measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout, respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout, respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements.RESULTSThe measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout, respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout, respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements.A preliminary protocol for measuring the accuracy and reproducibility of flow values in [15O]H2O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting.CONCLUSIONSA preliminary protocol for measuring the accuracy and reproducibility of flow values in [15O]H2O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting.

Dosimetry audits are carried out to determine how well radiotherapy is delivered to the patient. It is also used to understand the uncertainty introduced into the measurement result when using different computational models. As measurement procedures are becoming increasingly complex with technological advancements, it is harder to establish sources of variability in measurements and understand if they stem from true differences in measurands or in the measurement pipelines themselves. The gamma index calculation is a widely accepted metric used for the comparison of measured and predicted doses in radiotherapy. However, various steps in the measurement pipeline can introduce variation in the measurement result. In this paper, we perform a sensitivity and correlation analysis to investigate the influence of various input factors (i.e. setting) in gamma index calculations on the uncertainty introduced in dosimetry audits. We identify a number of factors where standardization will improve measurements by reducing variability in outputs. Furthermore, we also compare gamma index metrics and similarities across audit sites.

It has been suggested that conversion to organic farming contributes to soil carbon sequestration, but until now a comprehensive quantitative assessment has been lacking. Therefore, datasets from 74 studies from pairwise comparisons of organic vs. nonorganic farming systems were subjected to metaanalysis to identify differences in soil organic carbon (SOC). We found significant differences and higher values for organically farmed soils of 0.18 ± 0.06% points (mean ± 95% confidence interval) for SOC concentrations, 3.50 ± 1.08 Mg C ha ⁻¹ for stocks, and 0.45 ± 0.21 Mg C ha ⁻¹ y ⁻¹ for sequestration rates compared with nonorganic management. Metaregression did not deliver clear results on drivers, but differences in external C inputs and crop rotations seemed important. Restricting the analysis to zero net input organic systems and retaining only the datasets with highest data quality (measured soil bulk densities and external C and N inputs), the mean difference in SOC stocks between the farming systems was still significant (1.98 ± 1.50 Mg C ha ⁻¹), whereas the difference in sequestration rates became insignificant (0.07 ± 0.08 Mg C ha ⁻¹ y ⁻¹). Analyzing zero net input systems for all data without this quality requirement revealed significant, positive differences in SOC concentrations and stocks (0.13 ± 0.09% points and 2.16 ± 1.65 Mg C ha ⁻¹, respectively) and insignificant differences for sequestration rates (0.27 ± 0.37 Mg C ha ⁻¹ y ⁻¹). The data mainly cover top soil and temperate zones, whereas only few data from tropical regions and subsoil horizons exist. Summarizing, this study shows that organic farming has the potential to accumulate soil carbon.

Organic agriculture is proposed as a promising approach to achieving sustainable food systems, but its feasibility is also contested. We use a food systems model that addresses agronomic characteristics of organic agriculture to analyze the role that organic agriculture could play in sustainable food systems. Here we show that a 100% conversion to organic agriculture needs more land than conventional agriculture but reduces N-surplus and pesticide use. However, in combination with reductions of food wastage and food-competing feed from arable land, with correspondingly reduced production and consumption of animal products, land use under organic agriculture remains below the reference scenario. Other indicators such as greenhouse gas emissions also improve, but adequate nitrogen supply is challenging. Besides focusing on production, sustainable food systems need to address waste, crop–grass–livestock interdependencies and human consumption. None of the corresponding strategies needs full implementation and their combined partial implementation delivers a more sustainable food future. Organic agriculture requires fewer inputs but produces lower yields than conventional farming. Here, via a modeling approach, Muller et al. predict that if food waste and meat consumption are reduced, organic agriculture could feed the world without requiring cropland expansion.

Determining and assessing the links between human pressures and state-changes in marine and coastal ecosystems remains a challenge. Although there are several conceptual frameworks for describing these links, the DPSIR (Drivers – Pressures – State change – Impact – Response) framework has been widely adopted. Two possible reasons for this are: either the framework fulfils a major role, resulting from convergent evolution, or the framework is used often merely because it is used often, albeit uncritically. This comprehensive review, with lessons learned after two decades of use, shows that the approach is needed and there has been a convergent evolution in approach for coastal and marine ecosystem management. There are now 25 derivative schemes and a widespread and increasing usage of the DPSIR-type conceptual framework as a means of structuring and analyzing information in management and decision-making across ecosystems. However, there is less use of DPSIR in fully marine ecosystems and even this was mainly restricted to European literature. Around half of the studies are explicitly conceptual, not illustrating a solid case study. Despite its popularity since the early 1990s among the scientific community and the recommendation of several international institutions for its application, the framework has notable weaknesses to be addressed. These primarily relate to the long standing variation in interpretation (mainly between natural and social scientists) of the different components (particularly P, S and I) and to over-simplification of environmental problems such that cause-effect relationships cannot be adequately understood by treating the different DPSIR components as being mutually exclusive. More complex, nested, conceptual models and models with improved clarity are required to assess pressure-state change links in marine and coastal ecosystems. Our analysis shows that, because of its complexity, marine assessment and management constitutes a ’wicked problem’ and that there is an increasing need for a unifying approach, especially with the implementation of holistic regulations (e.g. European Directives). We emphasize the value of merging natural and social sciences and in showing similarities across human and natural environmental health. We show that previous approaches have adequately given conceptual and generic models but specificity and quantification is required.

It has been hypothesized that uneven sex ratios in the population could lead to increased violence. The objective of this analysis is to explore the relationship between uneven sex ratios in the population and violence. This analysis uses data collected from men in six Asian countries about their experiences and perpetration of violence. We combine this with region- and age specific sex ratios calculated from Census data to explore the relationship between sex ratios and violence using multilevel models. We find that men from region-age brackets with higher ratios of men to women are significantly more likely to report ever having raped a woman, having perpetrated intimate partner violence, or having used a weapon. We find no evidence for an association between sex ratios and reports of ever having raped a man.