Winterackerbohnen im Ökologischen Landbau: Einfluss von Zwischenfrüchten und Sortenwahl auf Kornertrag und Unkrautbesatz

Zwei Winterackerbohnensorten wurden über zwei Jahre an einem Versuchstandort im südlichen Rheinland nach drei verschiedenen Zwischenfrüchten (Sonnenblumen, Buchweizen, Selbstbegrünung) angebaut. Ziel war die Erfassung und Bewertung von Wachstums- und Ertragsparametern von Winterackerbohnen (WAB) sowie des Unkrautbesatzes. Sonnenblumenzwischenfruchtanbau vor WAB hatte eine tendenziell verminderten Unkrautbesatz im Vergleich zu Selbstbegrünung zur Folge. Gemittelt über zwei Versuchsjahre war der Kornertrag der WAB nach Zwischenfrucht Sonnenblumen und Selbstbegrünung signifikant geringer verglichen mit Buchweizen

Weed control by cover crop residues of sunflower (Helianthus annuus) and buckwheat (Fagopyrum esculentum) in organic winter faba bean

In den Jahren 2007 und 2008 wurde jeweils ein zweifaktorieller Feldversuch mit Winter-Ackerbohnen an der Lehr- und Versuchsstation für Organischen Landbau ‚Wiesengut‘ bei Bonn angelegt. Ziel der Versuche war es, den Einfluss der Faktoren Sorte (Hiverna und Diva) und Zwischenfrucht (Sonnenblume, Buchweizen, Selbstbegrünung) auf Wachstum, Ertrag und Verunkrautung von Winter-Ackerbohnen unter den Anbaubedingungen des Ökologischen Landbaus zu quantifizieren und zu bewerten. Das erfasste Parameterspektrum (u. a. Bestandesdichte, Wuchshöhe, Sprossmasse, Kornertrag und Unkraut­deckungsgrad) wurde einer varianzanalytischen Auswertung mit anschließendem Tukey-Test unterzogen. Nach Zwischenfrucht Sonnenblume war die Wuchshöhe von Winter-Ackerbohnen im Vergleich zu Buchweizen z. T. sig­nifikant geringer. Der Kornertrag war nach Zwischenfrucht Sonnenblume bei zweijähriger Auswertung mit 30,9 dt TM ha–1 signifikant geringer als nach Zwischenfrucht Buchweizen (34,8 dt TM ha–1). Der Unkrautdeckungsgrad und die Unkrautbiomasse waren nach Zwischenfrucht Sonnenblumen im Vergleich zu Selbstbegrünung zum Teil sig­nifikant geringer. Die Ergebnisse zeigen, dass sich unter den gegebenen Standortbedingungen wirtschaft­liche Kornerträge mit Winter-Ackerbohnen im Ökologischen Landbau erzielen lassen. Die praktische Nutzung allelopathischer Effekte von Sonnenblumen- und Buchweizenmulch zur natürlichen Unkrautkontrolle in Winter-Ackerbohnen bedarf weiterführender Untersuchungen.    A two-factorial field trial with winter faba bean was carried out at the experimental farm for Organic Agriculture ‘Wiesengut’ close to Bonn, Germany, in 2007 and 2008 respectively. The objective of the experiments was to quantify and evaluate the influence of the factors variety (Hiverna and Diva) and cover crop species (sunflower, buckwheat, green fallow) on a range of growth, yield and weed parameters of organically grown winter faba bean. Data were subjected to ANOVA with subsequent Tukey-Test. Crop height of winter faba bean was significantly lower after cover crop sunflower compared with buckwheat. In a joint two-year analysis grain yield of winter faba bean after cover crop sunflower was significantly lower (3.09 t DM ha–1) compared with buckwheat (3.48 t DM ha–1). Weed ground cover and biomass were significantly lower after cover crop sunflower and buckwheat compared with green fallow at several assessment dates of both trials. Results have shown that under the climatic conditions of the Rhineland area the production of organic winter faba bean can be realized with economic yields. The practical use of cover crops such as sunflower known to feature allelopathic effects against weeds still deserves further research.   &nbsp

FAIR and scalable management of small‐angle X‐ray scattering data

A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small‐angle X‐ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS‐tools library is a community‐driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python‐native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS‐tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethylammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.Deutsche ForschungsgemeinschaftMinistry of Science, Research and the Arts Baden-WürttembergProjekt DEA

EnzymeML : a data exchange format for biocatalysis and enzymology

EnzymeML is an XML‐based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO‐RK.Deutsche ForschungsgemeinschaftBiotechnology and Biological Sciences Research CouncilGerman Federal Ministry of Education and ResearchUniversity of LiverpoolKlaus Tschira FoundationProjekt DEA

Commitment zu aktivem Daten- und -softwaremanagement in großen Forschungsverbünden

Wir erkennen die Wichtigkeit von Forschungsdaten und -software für unsere Forschungsprozesse an und ordnen die Veröffentlichung von Forschungsdaten und -software als wesentlichen Bestandteil der wissenschaftlichen Publikationstätigkeit ein. Dafür unterstützen wir als Verbund unsere Forschenden im Umgang mit Daten und Software nach den FAIR-Prinzipien in Einvernehmen mit dem DFG-Kodex “Leitlinien zur Sicherung guter wissenschaftlicher Praxis”. In Zusammenarbeit mit unseren Institutionen und Fachcommunities stellen wir adäquate Forschungsdatenmanagement-Werkzeuge und -Dienste bereit und befähigen unsere Forschenden zum Umgang damit. Dabei bauen wir vorzugsweise auf existierenden Angeboten auf und bemühen uns im Gegenzug um deren Anpassung an unsere Bedürfnisse. Wir streben Maßnahmen für die Definition und Sicherstellung der Qualität unserer Forschungsdaten und -software an. Wir verwenden vorzugsweise existierende Daten-/Metadatenstandards und vernetzen uns nach Möglichkeit für die Erstellung und Implementierung neuer Standards mit entsprechenden nationalen und internationalen Initiativen. Wir verfolgen die Entwicklungen im Bereich des Forschungsdaten- und -softwaremanagements und prüfen neu entstehende Empfehlungen und Richtlinien zeitnah auf ihre Umsetzbarkeit

Clinical complexity and impact of the ABC (Atrial fibrillation Better Care) pathway in patients with atrial fibrillation: a report from the ESC-EHRA EURObservational Research Programme in AF General Long-Term Registry

Background: Clinical complexity is increasingly prevalent among patients with atrial fibrillation (AF). The ‘Atrial fibrillation Better Care’ (ABC) pathway approach has been proposed to streamline a more holistic and integrated approach to AF care; however, there are limited data on its usefulness among clinically complex patients. We aim to determine the impact of ABC pathway in a contemporary cohort of clinically complex AF patients. Methods: From the ESC-EHRA EORP-AF General Long-Term Registry, we analysed clinically complex AF patients, defined as the presence of frailty, multimorbidity and/or polypharmacy. A K-medoids cluster analysis was performed to identify different groups of clinical complexity. The impact of an ABC-adherent approach on major outcomes was analysed through Cox-regression analyses and delay of event (DoE) analyses. Results: Among 9966 AF patients included, 8289 (83.1%) were clinically complex. Adherence to the ABC pathway in the clinically complex group reduced the risk of all-cause death (adjusted HR [aHR]: 0.72, 95%CI 0.58–0.91), major adverse cardiovascular events (MACEs; aHR: 0.68, 95%CI 0.52–0.87) and composite outcome (aHR: 0.70, 95%CI: 0.58–0.85). Adherence to the ABC pathway was associated with a significant reduction in the risk of death (aHR: 0.74, 95%CI 0.56–0.98) and composite outcome (aHR: 0.76, 95%CI 0.60–0.96) also in the high-complexity cluster; similar trends were observed for MACEs. In DoE analyses, an ABC-adherent approach resulted in significant gains in event-free survival for all the outcomes investigated in clinically complex patients. Based on absolute risk reduction at 1 year of follow-up, the number needed to treat for ABC pathway adherence was 24 for all-cause death, 31 for MACEs and 20 for the composite outcome. Conclusions: An ABC-adherent approach reduces the risk of major outcomes in clinically complex AF patients. Ensuring adherence to the ABC pathway is essential to improve clinical outcomes among clinically complex AF patients

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P <.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes

Impact of renal impairment on atrial fibrillation: ESC-EHRA EORP-AF Long-Term General Registry

Background: Atrial fibrillation (AF) and renal impairment share a bidirectional relationship with important pathophysiological interactions. We evaluated the impact of renal impairment in a contemporary cohort of patients with AF. Methods: We utilised the ESC-EHRA EORP-AF Long-Term General Registry. Outcomes were analysed according to renal function by CKD-EPI equation. The primary endpoint was a composite of thromboembolism, major bleeding, acute coronary syndrome and all-cause death. Secondary endpoints were each of these separately including ischaemic stroke, haemorrhagic event, intracranial haemorrhage, cardiovascular death and hospital admission. Results: A total of 9306 patients were included. The distribution of patients with no, mild, moderate and severe renal impairment at baseline were 16.9%, 49.3%, 30% and 3.8%, respectively. AF patients with impaired renal function were older, more likely to be females, had worse cardiac imaging parameters and multiple comorbidities. Among patients with an indication for anticoagulation, prescription of these agents was reduced in those with severe renal impairment, p <.001. Over 24 months, impaired renal function was associated with significantly greater incidence of the primary composite outcome and all secondary outcomes. Multivariable Cox regression analysis demonstrated an inverse relationship between eGFR and the primary outcome (HR 1.07 [95% CI, 1.01–1.14] per 10 ml/min/1.73 m2 decrease), that was most notable in patients with eGFR <30 ml/min/1.73 m2 (HR 2.21 [95% CI, 1.23–3.99] compared to eGFR ≥90 ml/min/1.73 m2). Conclusion: A significant proportion of patients with AF suffer from concomitant renal impairment which impacts their overall management. Furthermore, renal impairment is an independent predictor of major adverse events including thromboembolism, major bleeding, acute coronary syndrome and all-cause death in patients with AF

On the experiences of adopting automated data validation in an industrial machine learning project

Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training ML models using bad data, research and industrial practice suggest incorporating a data validation process and tool in ML system development process. Aim: The study investigates the adoption of a data validation process and tool in industrial ML projects. The data validation process demands significant engineering resources for tool development and maintenance. Thus, it is important to identify the best practices for their adoption especially by development teams that are in the early phases of deploying ML-enabled software systems. Method: Action research was conducted at a large-software intensive organization in telecommunications, specifically within the analytics R&D organization for an ML use case of classifying faults from returned hardware telecommunication devices. Results: Based on the evaluation results and learning from our action research, we identified three best practices, three benefits, and two barriers to adopting the data validation process and tool in ML projects. We also propose a data validation framework (DVF) for systematizing the adoption of a data validation process. Conclusions: The results show that adopting a data validation process and tool in ML projects is an effective approach of testing ML-enabled software systems. It requires having an overview of the level of data (feature, dataset, cross-dataset, data stream) at which certain data quality tests can be applied