Stable isotope ratios of nitrogen and carbon as biomarkers of a vegan diet

Purpose Dietary biomarkers can potentially overcome the limitations of self-reported dietary data. While in ecology and archaeology, stable isotope ratios of carbon and nitrogen are widely used as biomarkers, this is not the case in nutrition research. Since the abundance of the 13C and the 15N isotope differ in food sources from plant and animal origin, stable isotope ratios of carbon and nitrogen (δ13C and δ15N) may differ in human biological material. Here, we investigated the stable isotope ratios of nitrogen and carbon in serum and urine from vegans and omnivores. Method Measurement of δ15N and δ13C in serum and 24 h urine was performed by Elemental Analyzer–Isotope Ratio Mass Spectrometer in the cross-sectional study “Risks and Benefits of a Vegan Diet”. The study included 36 vegans and 36 omnivores with a median age of 37.5 years (matched for age and sex), who adhered to their diet for at least 1 year. Results Both δ15N and δ13C were significantly lower in both the serum and 24 h urine of vegans compared to omnivores. δ15N either in serum or urine had 100% specificity and sensitivity to discriminate between vegans and omnivores. Specificity of δ13C was also > 90%, while sensitivity was 93% in serum and 77% in urine. Conclusion δ15N both in serum and urine was able to accurately identify vegans and thus appears to be a promising marker for dietary habits.publishedVersio

Künstliche Intelligenz in der Wirtschaftswissenschaft : Zur Integration in Lehre und Forschung

Studierende, die neben ihrem ökonomischen Theoriewissen auch programmieren können und in der Lage sind, vielfältige effiziente KI und Machine Learning-Tools und -Methoden zu beherrschen, sind am Arbeitsmarkt sehr gefragt. Wissenschaftler*innen der Wirtschaftswissenschaftlichen Fakultät geben einen Überblick über den Einsatz von KI in der wirtschaftswissenschaftlichen Lehre und Forschung

Assessing zoo giraffe survivorship: Methodological aspects, historical improvement and a rapid demographic shift

Giraffe have been kept in zoos for a long time. They have traditionally been considered difficult to maintain due to various husbandry requirements, including their nature as intrinsic browsers. However, zoo animals are expected to achieve higher survivorship than free-ranging conspecifics due to protection against dangers that would be experienced in their natural habitat. Global zoo giraffe data was analysed for historical developments of juvenile and adult survivorship, assessing the data with various demographic measures and comparing it to that of populations from natural habitats. Additionally, zoo population structure was analysed, in particular with respect to two events that occurred in parallel in 2014—a recommendation to restrict the number of new offspring given by the European Endangered Species Programme (EEP) studbook coordinator and the culling of a designated ‘surplus’ giraffe at Copenhagen Zoo that attracted global media attention. Both juvenile and adult giraffe survivorship has increased over time, suggesting advances in giraffe husbandry. For juveniles, this process has been continuous, whereas for adults the major progress has been in the most recent cohort (from 2000 onwards), in parallel with the publication of various husbandry guidelines. Zoo giraffe survivorship is now generally above that observed in natural habitats. Simple survivorship analyses appear suitable to describe these developments. Since 2014, the global giraffe population has undergone a rapid demographic shift from a growing to an ageing population, indicating a drastic limitation of reproduction rather than a system where reproduction is allowed and selected animals are killed (and possibly fed to carnivores). Thus, giraffe are both a showcase example for the historical progress made in zoo animal husbandry due to efforts of the zoo community and serve as an example to discuss implications of different methods of zoo population management

Doubly connected minimal surfaces and extremal harmonic mappings

The concept of a conformal deformation has two natural extensions: quasiconformal and harmonic mappings. Both classes do not preserve the conformal type of the domain, however they cannot change it in an arbitrary way. Doubly connected domains are where one first observes nontrivial conformal invariants. Herbert Groetzsch and Johannes C. C. Nitsche addressed this issue for quasiconformal and harmonic mappings, respectively. Combining these concepts we obtain sharp estimates for quasiconformal harmonic mappings between doubly connected domains. We then apply our results to the Cauchy problem for minimal surfaces, also known as the Bjorling problem. Specifically, we obtain a sharp estimate of the modulus of a doubly connected minimal surface that evolves from its inner boundary with a given initial slope.Comment: 35 pages, 2 figures. Minor edits, references adde

PP/PP-HI/silica nanocomposites for HVDC cable insulation: Are silica clusters beneficial for space charge accumulation?

New potential High Voltage Direct Current (HVDC) cable insulation materials based on nanocomposites are developed in this study. The nanocomposites are produced by blending of polypropylene (PP), propylene-ethylene copolymer (PP–HI) and a modified fumed silica (A-silica) in a concentration of 1 and 2 wt %. The A-silica is successfully modified with (3-aminopropyl)triethoxysilane (APTES) via a solvent-free method, as proven by infrared spectroscopy, thermogravimetry and transmission electron microscope mapping. A-silica in the polymer matrix acts as a nucleating agent resulting in an increase of the crystallization temperature of the polymers and a smaller crystal size. Moreover, the silica addition modified the crystals morphology of the unfilled PP/PP-HI blend. The composite containing A-silica with 2 wt% contains bigger-size silica clusters than the composite filled with 1 wt%. The composite with the higher A-silica concentration shows lower space charge accumulation and a lower charge current value. Besides, much deeper traps and lower trap density are observed in the composite with 2 wt% A-silica addition compared to the one with a lower concentration. Surprisingly, the presence of silica clusters with dimensions of more than 200 nm exhibit a positive effect on reducing the space charge accumulation. However, the real cause of this improvement might be due to change of the electron distribution stemming from the amine-amine hydrogen bond formation, or the change of the chain mobility due to the presence of occluded polymer macromolecules constrained inside the high structure silica clusters. Both phenomena may lead to a higher energetic barrier of charge de-trapping, thus increasing the depth of the charge traps

Integrating an epidemic spread model with remote sensing for Xylella fastidiosa detection

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Clinical impact of a commercially available multiplex PCR system for rapid detection of pathogens in patients with presumed sepsis

<p>Abstract</p> <p>Background</p> <p>Timely identification of pathogens is crucial to minimize mortality in patients with severe infections. Detection of bacterial and fungal pathogens in blood by nucleic acid amplification promises to yield results faster than blood cultures (BC). We analyzed the clinical impact of a commercially available multiplex PCR system in patients with suspected sepsis.</p> <p>Methods</p> <p>Blood samples from patients with presumed sepsis were cultured with the Bactec 9240™ system (Becton Dickinson, Heidelberg, Germany) and aliquots subjected to analysis with the LightCycler^®SeptiFast^®(SF) Test (Roche Diagnostics, Mannheim, Germany) at a tertiary care centre. For samples with PCR-detected pathogens, the actual impact on clinical management was determined by chart review. Furthermore a comparison between the time to a positive blood culture result and the SF result, based on a fictive assumption that it was done either on a once or twice daily basis, was made.</p> <p>Results</p> <p>Of 101 blood samples from 77 patients, 63 (62%) yielded concordant negative results, 14 (13%) concordant positive and 9 (9%) were BC positive only. In 14 (13%) samples pathogens were detected by SF only, resulting in adjustment of antibiotic therapy in 5 patients (7,7% of patients). In 3 samples a treatment adjustment would have been made earlier resulting in a total of 8 adjustments in all 101 samples (8%).</p> <p>Conclusion</p> <p>The addition of multiplex PCR to conventional blood cultures had a relevant impact on clinical management for a subset of patients with presumed sepsis.</p

Quality evaluation of olive oil by statistical analysis of multicomponent stable isotope dilution assay data of aroma active compounds

An instrumental method for the evaluation of olive oil quality was developed. Twenty-one relevant aroma active compounds were quantified in 95 olive oil samples of different quality by headspace solid phase microextraction (HS-SPME) and dynamic headspace coupled to GC-MS. On the basis of these stable isotope dilution assay results, statistical evaluation by partial least-squares discriminant analysis (PLS-DA) was performed. Important variables were the odor activity values of ethyl isobutanoate, ethyl 2-methylbutanoate, 3-methylbutanol, butyric acid, E,E-2,4-decadienal, hexanoic acid, guaiacol, 2-phenylethanol, and the sum of the odor activity values of Z-3-hexenal, E-2-hexenal, Z-3-hexenyl acetate, and Z-3-hexenol. Classification performed with these variables predicted 88% of the olive oils? quality correctly. Additionally, the aroma compounds, which are characteristic for some off-flavors, were dissolved in refined plant oil. Sensory evaluation of these models demonstrated that the off-flavors rancid, fusty, and vinegary could be successfully simulated by a limited number of odorants