Berechnung sensibler Wärmeströme mit der Surface Renewal Analysis und der Eddy - Korrelations - Methode

Die Surface Renewal Analysis wurde zur Bestimmung sensibler Wärmeflußdichten im bodennahen Bereich der atmosphärischen Grenzschicht genutzt und mit der Eddy - Korrelations - Methode verglichen. Dazu wurden beide Berechnungsmethoden auf Temperatur - und Vertikalwinddaten angewandt, die unter Verwendung von Strukturfunktionen simuliert wurden. Zur Überprüfung der Resultate wurden über zwei verschiedenen Unterlagen (Wiese und Düne) hochfrequente Zeitreihen von Temperatur und Vertikalwind gemessen und mit der Surface Renewal Analysis und der Eddy - Korrelations - Methode analysiert.The Surface Renewal Analysis was used to estimate the sensible heat flux density in the ground near area of the boundary layer. The results were compared with eddy correlation method. For it both methods were used to analyse temperature- and vertical velocity-data, which were simulated by the application of structure functions. Time series of high frequency temperature- and vertical velocity-data over two different canopies (meadow and dune) were measured to examine the results. The data were analysed with surface renewal analysis and eddy correlation

Publication of Atmospheric Model Data using the ATMODAT Standard

Scientific data should be published in a way so that other scientists can benefit from these data, enabling further research. The FAIR Data Principles are defining the basic prerequisite for a good data publication: data should be Findable, Accessible, Interoperable, and Reusable. Increasingly, research communities are developing discipline-specific data publication standards under consideration of the FAIR Data Principles. A very comprehensive yet strict data standard has been developed for the climate model output within the Climate Model Intercomparison Project (CMIP), which largely builds upon the Climate and Forecast Metadata Conventions (CF conventions). There are, however, many areas of atmospheric modelling where data cannot be standardised according to the CMIP data standard because, e.g., the data contain specific variables which are not covered by the CMIP standard. Furthermore, fulfilling the strict CMIP data standard for smaller Model Intercomparison Projects (MIPs) requires much effort (in time and manpower) and hence the outcome of these MIPs often remains non-standardised. For innovative model diagnostics, preexisting standards are also not flexible enough. For that reason, the ATMODAT standard, a quality guideline for atmospheric model data, was created. The ATMODAT standard defines a set of requirements that aim at ensuring the high reusability of atmospheric model data publications. The requirements include the use of the netCDF file format, the application of the CF conventions, rich and standardised file metadata, and the publication of the data with a DataCite DOI. Additionally, a tool for checking the conformity of data and metadata to this standard, the atmodat data checker, was developed and is available on GitHub under an open licence. By using the more flexible ATMODAT standard, the publication of standardised datasets is simplified for smaller MIPs. This standardisation process is presented as an example using the data of an aerosol-climate model from the AeroCOM MIP. Furthermore, the landing pages of ATMODAT-compliant data publications can be highlighted with the EASYDAB logo. EASYDAB (Earth System Data Branding) is a newly developed quality label for carefully curated and highly standardised data publications. The ATMODAT data standardisation can easily be transferred to data from other disciplines and contribute to their improved reusability

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-forpurpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts

Recommendations for Discipline-Specific FAIRness Evaluation Derived from Applying an Ensemble of Evaluation Tools

From a research data repositories’ perspective, offering research data management services in line with the FAIR principles is becoming increasingly important. However, there exists no globally established and trusted approach to evaluate FAIRness to date. Here, we apply five different available FAIRness evaluation approaches to selected data archived in the World Data Center for Climate (WDCC). Two approaches are purely automatic, two approaches are purely manual and one approach applies a hybrid method (manual and automatic combined). The results of our evaluation show an overall mean FAIR score of WDCC-archived (meta) data of 0.67 of 1, with a range of 0.5 to 0.88. Manual approaches show higher scores than automated ones and the hybrid approach shows the highest score. Computed statistics indicate that the test approaches show an overall good agreement at the data collection level. We find that while neither one of the five valuation approaches is fully fit-forpurpose to evaluate (discipline-specific) FAIRness, all have their individual strengths. Specifically, manual approaches capture contextual aspects of FAIRness relevant for reuse, whereas automated approaches focus on the strictly standardised aspects of machine actionability. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation approaches. Based on our results, we recommend future FAIRness evaluation tools to be based on a mature hybrid approach. Especially the design and adoption of the discipline-specific aspects of FAIRness will have to be conducted in concerted community efforts

ATMODAT Standard v3.0

Within the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard includes concrete recommendations related to the maturity, publication and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine readable landing pages are a core element of this standard, and should hold and present discipline-specific metadata on simulation and variable level. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4

Vitamin D and Its Analogues Decrease Amyloid-β (Aβ) Formation and Increase Aβ-Degradation

Alzheimer’s disease (AD) is characterized by extracellular plaques in the brain, mainly consisting of amyloid-β (Aβ), as derived from sequential cleavage of the amyloid precursor protein. Epidemiological studies suggest a tight link between hypovitaminosis of the secosteroid vitamin D and AD. Besides decreased vitamin D level in AD patients, an effect of vitamin D on Aβ-homeostasis is discussed. However, the exact underlying mechanisms remain to be elucidated and nothing is known about the potential effect of vitamin D analogues. Here we systematically investigate the effect of vitamin D and therapeutically used analogues (maxacalcitol, calcipotriol, alfacalcidol, paricalcitol, doxercalciferol) on AD-relevant mechanisms. D2 and D3 analogues decreased Aβ-production and increased Aβ-degradation in neuroblastoma cells or vitamin D deficient mouse brains. Effects were mediated by affecting the Aβ-producing enzymes BACE1 and γ-secretase. A reduced secretase activity was accompanied by a decreased BACE1 protein level and nicastrin expression, an essential component of the γ-secretase. Vitamin D and analogues decreased β-secretase activity, not only in mouse brains with mild vitamin D hypovitaminosis, but also in non-deficient mouse brains. Our results further strengthen the link between AD and vitamin D, suggesting that supplementation of vitamin D or vitamin D analogues might have beneficial effects in AD prevention

Inter- and Intra-Observer Variability and the Effect of Experience in Cine-MRI for Adhesion Detection

Cine-MRI for adhesion detection is a promising novel modality that can help the large group of patients developing pain after abdominal surgery. Few studies into its diagnostic accuracy are available, and none address observer variability. This retrospective study explores the inter- and intra-observer variability, diagnostic accuracy, and the effect of experience. A total of 15 observers with a variety of experience reviewed 61 sagittal cine-MRI slices, placing box annotations with a confidence score at locations suspect for adhesions. Five observers reviewed the slices again one year later. Inter- and intra-observer variability are quantified using Fleiss’ (inter) and Cohen’s (intra) κ and percentage agreement. Diagnostic accuracy is quantified with receiver operating characteristic (ROC) analysis based on a consensus standard. Inter-observer Fleiss’ κ values range from 0.04 to 0.34, showing poor to fair agreement. High general and cine-MRI experience led to significantly (p &lt; 0.001) better agreement among observers. The intra-observer results show Cohen’s κ values between 0.37 and 0.53 for all observers, except one with a low κ of −0.11. Group AUC scores lie between 0.66 and 0.72, with individual observers reaching 0.78. This study confirms that cine-MRI can diagnose adhesions, with respect to a radiologist consensus panel and shows that experience improves reading cine-MRI. Observers without specific experience adapt to this modality quickly after a short online tutorial. Observer agreement is fair at best and area under the receiver operating characteristic curve (AUC) scores leave room for improvement. Consistently interpreting this novel modality needs further research, for instance, by developing reporting guidelines or artificial intelligence-based methods.</p

Inter- and Intra-Observer Variability and the Effect of Experience in Cine-MRI for Adhesion Detection

Cine-MRI for adhesion detection is a promising novel modality that can help the large group of patients developing pain after abdominal surgery. Few studies into its diagnostic accuracy are available, and none address observer variability. This retrospective study explores the inter- and intra-observer variability, diagnostic accuracy, and the effect of experience. A total of 15 observers with a variety of experience reviewed 61 sagittal cine-MRI slices, placing box annotations with a confidence score at locations suspect for adhesions. Five observers reviewed the slices again one year later. Inter- and intra-observer variability are quantified using Fleiss’ (inter) and Cohen’s (intra) κ and percentage agreement. Diagnostic accuracy is quantified with receiver operating characteristic (ROC) analysis based on a consensus standard. Inter-observer Fleiss’ κ values range from 0.04 to 0.34, showing poor to fair agreement. High general and cine-MRI experience led to significantly (p < 0.001) better agreement among observers. The intra-observer results show Cohen’s κ values between 0.37 and 0.53 for all observers, except one with a low κ of −0.11. Group AUC scores lie between 0.66 and 0.72, with individual observers reaching 0.78. This study confirms that cine-MRI can diagnose adhesions, with respect to a radiologist consensus panel and shows that experience improves reading cine-MRI. Observers without specific experience adapt to this modality quickly after a short online tutorial. Observer agreement is fair at best and area under the receiver operating characteristic curve (AUC) scores leave room for improvement. Consistently interpreting this novel modality needs further research, for instance, by developing reporting guidelines or artificial intelligence-based methods

Adiponectin, leptin, and IGF-1 are useful diagnostic and stratification biomarkers of NAFLD

[EN] Background: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease where liver biopsy remains the gold standard for diagnosis. Here we aimed to evaluate the role of circulating adiponectin, leptin, and insulin-like growth factor 1 (IGF-1) levels as non-invasive NAFLD biomarkers and assess their correlation with the metabolome. Materials and Methods: Leptin, adiponectin, and IGF-1 serum levels were measured by ELISA in two independent cohorts of biopsy-proven obese NAFLD patients and healthy-liver controls (discovery: 38 NAFLD, 13 controls; validation: 194 NAFLD, 31 controls) and correlated with clinical data, histology, genetic parameters, and serum metabolomics. Results: In both cohorts, leptin increased in NAFLD vs. controls (discovery: AUROC 0.88; validation: AUROC 0.83; p < 0.0001). The leptin levels were similar between obese and non-obese healthy controls, suggesting that obesity is not a confounding factor. In the discovery cohort, adiponectin was lower in non-alcoholic steatohepatitis (NASH) vs. non-alcoholic fatty liver (AUROC 0.87; p < 0.0001). For the validation cohort, significance was attained for homozygous for PNPLA3 allele c.444C (AUROC 0.63; p < 0.05). Combining adiponectin with specific serum lipids improved the assay performance (AUROC 0.80; p < 0.0001). For the validation cohort, IGF-1 was lower with advanced fibrosis (AUROC 0.67, p<0.05), but combination with international normalized ratio (INR) and ferritin increased the assay performance (AUROC 0.81; p < 0.01). Conclusion: Serum leptin discriminates NAFLD, and adiponectin combined with specific lipids stratifies NASH. IGF-1, INR, and ferritin distinguish advanced fibrosis.CR was funded by FEDER through the COMPETE program and by national funds through Fundação para a Ciência e a Tecnologia (PTDC/MED-FAR/29097/2017—LISBOA-01- 0145-FEDER-029097) and by European Horizon 2020 (H2020- MSCA-RISE-2016-734719). This work was also supported by Fundação para a Ciência e Tecnologia (PD/BD/135467/2017) and Portuguese Association for the Study of Liver/MSD 2017. JB was funded by Spanish Carlos III Health Institute (ISCIII) (PI15/01132, PI18/01075 and Miguel Servet Program CON14/00129 and CPII19/00008), co-financed by Fondo Europeo de Desarrollo Regional (FEDER), Instituto de Salud Carlos III (CIBERehd, Spain), La Caixa Scientific Foundation (HR17-00601), Fundación Científica de la Asociación Española Contra el Cáncer, and European Horizon 2020 (ESCALON project: H2020-SC1-BHC-2018-2020)