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Deep rooting winter wheat genotypes can reduce nitrate leaching losses and increase N uptake. We aimed to investigate which deep root traits are correlated to deep N uptake and to estimate genetic variation in root traits and deep 15N tracer uptake. In 2 years, winter wheat genotypes were grown in RadiMax, a semifield root‐screening facility. Minirhizotron root imaging was performed three times during the main growing season. At anthesis, 15N was injected via subsurface drip irrigation at 1.8 m depth. Mature ears from above the injection area were analysed for 15N content. From minirhizotron image‐based root length data, 82 traits were constructed, describing root depth, density, distribution and growth aspects. Their ability to predict 15N uptake was analysed with the least absolute shrinkage and selection operator (LASSO) regression. Root traits predicted 24% and 14% of tracer uptake variation in 2 years. Both root traits and genotype showed significant effects on tracer uptake. In 2018, genotype and the three LASSO‐selected root traits predicted 41% of the variation in tracer uptake, in 2019 genotype and one root trait predicted 48%. In both years, one root trait significantly mediated the genotype effect on tracer uptake. Deep root traits from minirhizotron images can predict deep N uptake, indicating the potential to breed deep‐N‐uptake‐genotypes. Summary Statement: Root traits of winter wheat were constructed from minirhizotron image data. Root traits predicted 24% of 15N tracer uptake, which was injected at anthesis at 1.8 m depth, and explained genotypic variation in tracer uptake. [ABSTRACT FROM AUTHOR]

Analyzing the progression of Alzheimer's disease (AD) is challenging due to lacking sensitivity in currently available measures. AD stages are typically defined based on cognitive cut-offs, but this results in heterogeneous patient groups. More accurate modeling of the continuous progression of the disease would enable more accurate patient prognosis. To address these issues, we propose a new multivariate continuous-time disease progression (MCDP) model. The model is formulated as a nonlinear mixed-effects model that aligns patients based on their predicted disease progression along a continuous latent disease timeline. The model is evaluated using long-term follow-up data from 2152 participants in the Alzheimer's Disease Neuroimaging Initiative. The MCDP model was used to simultaneously model three cognitive scales; the Alzheimer's Disease Assessment Scale-cognitive subscale, the Mini-Mental State Examination, and the Clinical Dementia Rating scale-sum of boxes. Compared with univariate modeling and previously proposed multivariate disease progression models, the MCDP model showed superior ability to predict future patient trajectories. Finally, based on the multivariate disease timeline estimated using the MCDP model, the sensitivity of the individual items of the cognitive scales along the different stages of disease was analyzed. The analysis showed that delayed memory recall items had the highest sensitivity in the early stages of disease, whereas language and attention items were sensitive later in disease. [ABSTRACT FROM AUTHOR]

Background: Methods to compare events defined as newly occurring characters in development has advanced vertebrate developmental research but events are not easily extrapolated into traditional staging systems used in biomedical research. Results: First, we scored 95 porcine embryos in the age range of 15 to 33 days post conception by stereomicroscopy using to a slightly modified version of the Standard Event System (SES). Subsequent statistical clustering allowed the embryos to be grouped into 15 clusters. Staging of the same embryos in a way that generally follow the description of external features of human embryos in the Carnegie stages 10 to 23 allowed us to describe 14 stages of porcine embryonic development that correlate to the Carnegie stages of human development with minor species differences. When arranged by average age, the statistic clusters had a distribution that correlated well with the stages produced by the Carnegie‐based staging system. Conclusions: Statistical analysis of developmental events allow grouping of porcine embryos into clusters that can be extrapolated into a Carnegie‐based staging system, thus serving the dual purpose of facilitating the use of the pig as a biomedical model animal and providing data for integrating porcine developmental events into a phylogenetic context. Key Findings: Porcine embryos (15‐33 dpi) were successfully grouped into 15 clusters by using a modified standard event system based on external characteristics and subsequent statistical clustering.Porcine embryos (15‐33 dpi) were staged using Carnegie‐based external features and some minor differences of porcine and human embryonic development were identified.The two staging systems generally correlate well. [ABSTRACT FROM AUTHOR]

Water availability is often described as one of the main drivers of phenology of tropical dry forests but experimental studies to identify the cues triggering phenological changes are few. In a greenhouse trial, we exposed seedlings of thirteen origins, seven from West and six from East Africa, respectively, of Adansonia digitata L.(baobab) to a well‐watered control treatment and a water withholding treatment in combination with exposure to three different daylengths (11.5, 12.0, and 12.5 hr). Responses were measured in terms of meristematic activity, number of leaves, and height growth followed over 6.5 months. Multi‐level mixed‐effects statistical models were used to analyze how environmental and inter‐population variables affected phenological behavior. Flushing was influenced by the daylength with the lowest degree of meristematic activity observed in the short daylength treatment. Daylength also influenced the number of leaves through an interaction with the water regime. The water regime influenced all variables through interactions with the origins. Seedlings subjected to water stress had higher meristematic activity, but initially lower numbers of leaves than continuously watered plants. Height growth in continuously watered plants was fastest or at par with water‐stressed plants, depending on the origin. Origins from West Africa tended to have higher meristematic activity and their phenology was found to be less influenced by water withholding than East African origins. There were no signs of significant differences between origins in their response to photoperiod. In conclusion, baobab seedlings show opportunistic behavior, setting leaves when water is available, but larger activity is found when days are long. We discuss the results in terms of triggering factors for baobab phenology and adaptation to specific environmental conditions at the site of origins. There are very few studies on triggering factors for phenology of dry zone tropical tree species, and even fewer studies that investigate how phenology is varying among origins in common garden experiments. In this study, we tested 13 baobab origins and their phenological response to drought stress and different daylengths. Our results demonstrate that both water availability and daylength influence flushing of leaves, and that origin react differently to these signals. [ABSTRACT FROM AUTHOR]

Abstract: Plants produce a multitude of metabolites that contribute to their fitness and survival and play a role in local adaptation to environmental conditions. The effects of environmental variation are particularly well studied within the genus Plantago; however, previous studies have largely focused on targeting specific metabolites. Studies exploring metabolome‐wide changes are lacking, and the effects of natural environmental variation and herbivory on the metabolomes of plants growing in situ remain unknown. An untargeted metabolomic approach using ultra‐high‐performance liquid chromatography–mass spectrometry, coupled with variation partitioning, general linear mixed modeling, and network analysis was used to detect differences in metabolic phenotypes of Plantago major in fifteen natural populations across Denmark. Geographic region, distance, habitat type, phenological stage, soil parameters, light levels, and leaf area were investigated for their relative contributions to explaining differences in foliar metabolomes. Herbivory effects were further investigated by comparing metabolomes from damaged and undamaged leaves from each plant. Geographic region explained the greatest number of significant metabolic differences. Soil pH had the second largest effect, followed by habitat and leaf area, while phenological stage had no effect. No evidence of the induction of metabolic features was found between leaves damaged by herbivores compared to undamaged leaves on the same plant. Differences in metabolic phenotypes explained by geographic factors are attributed to genotypic variation and/or unmeasured environmental factors that differ at the regional level in Denmark. A small number of specialized features in the metabolome may be involved in facilitating the success of a widespread species such as Plantago major into such wide range of environmental conditions, although overall resilience in the metabolome was found in response to environmental parameters tested. Untargeted metabolomic approaches have great potential to improve our understanding of how specialized plant metabolites respond to environmental change and assist in adaptation to local conditions. [ABSTRACT FROM AUTHOR]

Objective To investigate parameters causing canine thromboelastographic hypercoagulability and to investigate whether thromboelastography (TEG) with Cytochalasin D (Cyt D) added is related to parameters of platelet activity. Design Prospective observational study on hemostatic and inflammatory parameters. Data were collected between November 2012 and July 2013. Setting University teaching hospital. Animals Twenty-eight dogs suffering from diseases predisposing to thrombosis and 19 clinically healthy dogs. Diseased dogs were enrolled if they fulfilled inclusion criteria regarding age, size, informed client consent, and obtained a diagnosis of a disease that has been associated with thrombosis or hypercoagulability. Interventions None. Measurements and Main Results Parameters of coagulation and anticoagulation, fibrinolysis, and antifibrinolysis, platelet activity, inflammation, platelet count, and hematocrit were measured using CBC, TEG, platelet aggregation on multiplate, platelet activity on flow cytometry, and hemostatic and inflammatory markers on plasma and serum analyses. ANOVA and multilinear regression analyses indicated that especially hematocrit and the inflammatory parameters C-reactive protein and interleukin-8 showed best association with overall clot strength in diseased dogs with hypercoagulable TEG tracings. Ratios presumed to reflect platelet contribution to the TEG tracing obtained in TEG analyses with Cyt D were related especially with hematocrit and P-selectin expression of platelets measured after γ-Thrombin activation on flow cytometry. Conclusion Overall clot strength in TEG analyses of the hypercoagulable dogs included in the present study appears to be primarily associated with inflammation as well as hematocrit. Furthermore, the ratio between standard TEG analyses and TEG analyses with Cyt D may reflect some degree of platelet activity. [ABSTRACT FROM AUTHOR]

Aim: To understand how the integration of contextual spatial data on land cover and human infrastructure can help reduce spatial bias in sampling effort, and improve the utilization of citizen science-based species recording schemes. By comparing four different citizen science projects, we explore how the sampling design's complexity affects the role of these spatial biases. Location: Denmark, Europe. Methods: We used a point process model to estimate the effect of land cover and human infrastructure on the intensity of observations from four different citizen science species recording schemes. We then use these results to predict areas of under- and oversampling as well as relative biodiversity 'hotspots' and 'deserts', accounting for common spatial biases introduced in unstructured sampling designs. Results: We demonstrate that the explanatory power of spatial biases such as infrastructure and human population density increased as the complexity of the sampling schemes decreased. Despite a low absolute sampling effort in agricultural landscapes, these areas still appeared oversampled compared to the observed species richness. Conversely, forests and grassland appeared undersampled despite higher absolute sampling efforts. We also present a novel and effective analytical approach to address spatial biases in unstructured sampling schemes and a new way to address such biases, when more structured sampling is not an option. Main conclusions: We show that citizen science datasets, which rely on untrained amateurs, are more heavily prone to spatial biases from infrastructure and human population density. Objectives and protocols of mass-participating projects should thus be designed with this in mind. Our results suggest that, where contextual data is available, modelling the intensity of individual observation can help understand and quantify how spatial biases affect the observed biological patterns. [ABSTRACT FROM AUTHOR]

Small-angle X-ray and neutron scattering have become increasingly popular owing to improvements in instrumentation and developments in data analysis, sample handling and sample preparation. For some time, it has been suggested that a more systematic approach to the quantification of the information content in small-angle scattering data would allow for a more optimal experiment planning and a more reliable data analysis. In the present article, it is shown how ray-tracing techniques in combination with a statistically rigorous data analysis provide an appropriate platform for such a systematic quantification of the information content in scattering data. As examples of applications, it is shown how the exposure time at different instrumental settings or contrast situations can be optimally prioritized in an experiment. Also, the gain in information by combining small-angle X-ray and neutron scattering is assessed. While solution small-angle scattering data of proteins and protein-lipid complexes are used as examples in the present case study, the approach is generalizable to a wide range of other samples and experimental techniques. The source code for the algorithms and ray-tracing components developed for this study has been made available on-line. [ABSTRACT FROM AUTHOR]

Background Insulin-like growth factor 1 ( IGF-1) has important roles in anabolic processes in the musculoskeletal system and has been reported to decrease with age in both people and horses. Objectives The objective of this study was to determine serum IGF-1 levels in the aging horse from early to late adulthood (age range 5-27 years). Methods Healthy horses ( n = 72) were used in a cross-sectional study, while 37 paired serum samples were available for a longitudinal study. Serum IGF-1 protein was determined using an ELISA kit validated for use in equine samples. Results No association was found between serum IGF-1 levels and age in the cross-sectional study. In the longitudinal study, a latent variable model fitted to the data revealed that horses in general experienced a 5.2% increase of serum IGF-1 levels over a 5-year period, but horses crossing a change point around 9 years of age between the 2 samples experienced an 11.0% decrease. Conclusions In this study, there was no evidence for aging being a factor in changes of IGF-1 levels in an adult and old horse population . [ABSTRACT FROM AUTHOR]

A goodness-of-fit test for continuous-time models is developed that examines if the parameter estimates are consistent with another for different sampling frequencies. The test compares parameter estimates obtained from estimating functions for downsamples of the data. We prove asymptotic results for stationary and ergodic processes, and apply the downsampling test to linear drift diffusions. Simulations indicate that the test is quite powerful in detecting non-Markovian deviations from the linear drift diffusions. [ABSTRACT FROM AUTHOR]

Background: The progressive neurodegenerative nature of Alzheimer's disease (AD) means that patients' cognition get increasingly worse as the disease progresses. Despite of the multitude of available cognitive assessment tools, it remains a challenge to predict the disease stages and future progression of disease in AD patients. Method: A nonlinear mixed‐effects disease‐progression model was used to investigate the benefit of simultaneously using a combination of ADAS‐cog, MMSE, and CDR sum of boxes for predicting disease stage and future disease progression. The model estimated a timeline of disease progression that enable comparison of sensitivity across cognitive scales and their individual items throughout the disease. The model with multiple cognitive measures was compared to univariate models that only included one of the cognitive measures by evaluating how well they predicted future progression on the cognitive scales of unseen individuals. Individual items were compared using an ordinal analysis that estimated an ordering of item sensitivity over the disease timeline. Result: The disease‐progression model was fitted on longitudinal data from 2144 patients from ADNI. The model incorporating multiple cognitive measures was consistently better at predicting future progression of unseen individuals compared to univariate models that only included information from one cognitive assessment. The analyses of individual item sensitivity along the disease timeline revealed a clear ordering of items with delayed memory items having the highest sensitivity in early stages of disease while language and attention related items stood out as sensitive later in disease. Conclusion: Considering multiple measures simultaneously can improve staging of patients. Methods for better staging patients can be used to design inclusion criteria for clinical trials that result in more homogeneous patient populations and thus improve statistical power. Furthermore, the comparison of item sensitivity along the disease timeline has the potential to develop new and more sensitive composite assessment tools for specific patient populations. [ABSTRACT FROM AUTHOR]