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Purpose: The aim of this study was to characterize a second‐generation wide‐detector dual‐layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. Methods: A phantom with multiple tissue‐mimicking and material‐specific inserts was scanned with a dual‐layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver‐mimicking inserts with adipose and iron were analyzed with a two‐base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. Results: Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from ‐0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. Conclusion: The second‐generation dual‐layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters. [ABSTRACT FROM AUTHOR]

It is well known that biodiversity positively affects ecosystem functioning, leading to enhanced ecosystem stability. However, this knowledge is mainly based on analyses using single ecosystem functions, while studies focusing on the stability of ecosystem multifunctionality (EMF) are rare. Taking advantage of a long‐term grassland biodiversity experiment, we studied the effect of plant diversity (1–60 species) on EMF over 5 years, its temporal stability, as well as multifunctional resistance and resilience to a 2‐year drought event. Using split‐plot treatments, we further tested whether a shared history of plants and soil influences the studied relationships. We calculated EMF based on functions related to plants and higher‐trophic levels. Plant diversity enhanced EMF in all studied years, and this effect strengthened over the study period. Moreover, plant diversity increased the temporal stability of EMF and fostered resistance to reoccurring drought events. Old plant communities with shared plant and soil history showed a stronger plant diversity–multifunctionality relationship and higher temporal stability of EMF than younger communities without shared histories. Our results highlight the importance of old and biodiverse plant communities for EMF and its stability to extreme climate events in a world increasingly threatened by global change. [ABSTRACT FROM AUTHOR]

Background: Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy. Purpose: We developed an atlas‐based method to create a variety of patient‐specific serial digital phantoms with CBCT‐like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy. Methods: A library of deformations was created by extracting the longitudinal changes observed between a planning CT and weekly CBCT from an atlas of lung radiotherapy patients. The planning CT of an inquiry patient was first deformed by mapping the deformation pattern from a matched atlas patient, and subsequently appended with CBCT artifacts to imitate a weekly CBCT. Finally, a group of digital phantoms around an inquiry patient was produced to simulate a series of possible evolutions of tumor and adjacent normal structures. We validated the generated deformation vector fields (DVFs) to ensure numerically and physiologically realistic transformations. The proposed framework was applied to evaluate the performance of the DIR algorithm implemented in the commercial Eclipse treatment planning system in a retrospective study of eight inquiry patients. Results: The generated DVFs were inverse consistent within less than 3 mm and did not exhibit unrealistic folding. The deformation patterns adequately mimicked the observed longitudinal anatomical changes of the matched atlas patients. Worse Eclipse DVF accuracy was observed in regions of low image contrast or artifacts. The structure volumes exhibiting a DVF error magnitude of equal or more than 2 mm ranged from 24.5% (spinal cord) to 69.2% (heart) and the maximum DVF error exceeded 5 mm for all structures except the spinal cord. Contour‐based evaluations showed a high degree of alignment with dice similarity coefficients above 0.8 in all cases, which underestimated the overall DVF accuracy within the structures. Conclusions: It is feasible to create and augment digital phantoms based on a particular patient of interest using multiple series of deformation patterns from matched patients in an atlas. This can provide a semi‐automated procedure to complement the quality assurance of CT‐CBCT DIR and facilitate the clinical implementation of image‐guided and adaptive radiotherapy that involve longitudinal CBCT imaging studies. [ABSTRACT FROM AUTHOR]

Plant monocultures growing for extended periods face severe losses of productivity. This phenomenon, known as 'yield decline', is often caused by the accumulation of above‐ and below‐ground plant antagonists. The effectiveness of plant defences against antagonists might help explain differences in yield decline among species. Using a trait‐based approach, we studied the role of 20 physical and chemical defence traits of leaves and fine roots on yield decline of 4‐ and 18‐year‐old monocultures of 27 grassland species. We hypothesized that yield decline is lower for species with high defences, that root defences are better predictors of yield decline than leaf defences, and that in roots, physical defences better predict yield decline than chemical defences, while the reverse is true for leaves. We additionally hypothesized that the relationship between defences and yield decline increases with time and that species increasing the expression of defence traits after long‐term monoculture growth would suffer less yield decline. We summarized leaf and fine root defence traits using principal component analyses and analysed the relationship between the most informative components along with their temporal changes and monoculture yield decline. The significant predictors of yield decline were traits related to the so‐called collaboration gradient of the root economics space (specific root length and root diameter) as well as their temporal changes and traits related to the leaf physical vs chemical defence tradeoff (leaf dry matter, silicon and cellulose content, toughness and phytochemical diversity). We were unable to unequivocally identify the mechanisms relating the effect of those traits to yield decline as they could mediate plant responses to several stressors such as antagonist accumulation, nutrient depletion or drought. Further studies are needed to differentiate between these alternative mechanisms and to gain a comprehensive understanding of the drivers of yield decline in relation to root and leaf defence traits. [ABSTRACT FROM AUTHOR]

Ecosystem management aims at providing many ecosystem services simultaneously. Such ecosystem service multifunctionality can be limited by tradeoffs and increased by synergies among the underlying ecosystem functions (EF), which need to be understood to develop targeted management. Previous studies found differences in the correlation between EFs. We hypothesised that correlations between EFs are variable even under the controlled conditions of a field experiment and that seasonal and annual variation, plant species richness, and plot identity (identity effects of plots, such as the presence and proportion of functional groups) are drivers of these correlations. We used data on 31 EFs related to plants, consumers, and physical soil properties that were measured over 5 to 19 years, up to three times per year, in a temperate grassland experiment with 80 different plots, constituting six sown plant species richness levels (1, 2, 4, 8, 16, 60 species). We found that correlations between pairs of EFs were variable, and correlations between two particular EFs could range from weak to strong or negative to positive correlations among the repeated measurements. To determine the drivers of pairwise EF correlations, the covariance between EFs was partitioned into contributions from species richness, plot identity, and time (including years and seasons). We found that most of the covariance for synergies was explained by species richness (26.5%), whereas for tradeoffs, most covariance was explained by plot identity (29.5%). Additionally, some EF pairs were more affected by differences among years and seasons, showing a higher temporal variation. Therefore, correlations between two EFs from single measurements are insufficient to draw conclusions on tradeoffs and synergies. Consequently, pairs of EFs need to be measured repeatedly under different conditions to describe their relationships with more certainty and be able to derive recommendations for the management of grasslands. [ABSTRACT FROM AUTHOR]