Arctic Observatory FRAM

FRAM (FRontiers in Arctic Marine Monitoring) targets a modern vision of integrated underwater infrastructure. FRAM enhances sustainable knowledge for science, society and maritime economy as it enables truly year round observations from surface to depth in the remote and harsh arctic sea. Cutting edge technologies are being (further) developed and used to record essential ocean variables to improve our understanding of the Arctic and it’s ongoing processes. Data will be made freely available to the public via the AWI data portal

Arctic Observatory FRAM - a modern vision of integrated underwater infrastructure in the polar environment

The Arctic Observatory FRAM (FRontiers in Arctic Marine Monitoring) targets a modern vision of integrated underwater infrastructure in the polar environment. Since 2014 this modular observatory is being build up in Fram-Strait and the Central Arctic by the Alfred Wegner Institute for Polar and Marine Research (AWI) to become a major research infrastructure of the Earth and Environment research field of the Helmholtz Association. FRAM enhances sustainable knowledge of the remote and harsh Arctic environment for science, society and maritime economy as it enables truly year round multidisciplinary observations from sea ice to the deep sea. Cutting edge mobile and fixed sensor platforms and technologies like e.g. ROV’s, AUV’s, under water robotics, and moorings are being (further) developed and used in combination with ship based instruments to record various essential ocean variables to improve our understanding of the Arctic Ocean, it’s essential processes, and how they are being impacted by continued warming and decreasing sea ice extend. Field data are being cross validated by satellite observations and used to improve model simulations. Data will be made freely available to the public via the AWI data portal

Shifting the limits in wheat research and breeding using a fully annotated reference genome

Introduction: Wheat (Triticum aestivum L.) is the most widely cultivated crop on Earth, contributing about a fifth of the total calories consumed by humans. Consequently, wheat yields and production affect the global economy, and failed harvests can lead to social unrest. Breeders continuously strive to develop improved varieties by fine-tuning genetically complex yield and end-use quality parameters while maintaining stable yields and adapting the crop to regionally specific biotic and abiotic stresses. Rationale: Breeding efforts are limited by insufficient knowledge and understanding of wheat biology and the molecular basis of central agronomic traits. To meet the demands of human population growth, there is an urgent need for wheat research and breeding to accelerate genetic gain as well as to increase and protect wheat yield and quality traits. In other plant and animal species, access to a fully annotated and ordered genome sequence, including regulatory sequences and genome-diversity information, has promoted the development of systematic and more time-efficient approaches for the selection and understanding of important traits. Wheat has lagged behind, primarily owing to the challenges of assembling a genome that is more than five times as large as the human genome, polyploid, and complex, containing more than 85% repetitive DNA. To provide a foundation for improvement through molecular breeding, in 2005, the International Wheat Genome Sequencing Consortium set out to deliver a high-quality annotated reference genome sequence of bread wheat. Results: An annotated reference sequence representing the hexaploid bread wheat genome in the form of 21 chromosome-like sequence assemblies has now been delivered, giving access to 107,891 high-confidence genes, including their genomic context of regulatory sequences. This assembly enabled the discovery of tissue- and developmental stage–related gene coexpression networks using a transcriptome atlas representing all stages of wheat development. The dynamics of change in complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. Aspects of the future value of the annotated assembly for molecular breeding and research were exemplarily illustrated by resolving the genetic basis of a quantitative trait locus conferring resistance to abiotic stress and insect damage as well as by serving as the basis for genome editing of the flowering-time trait. Conclusion: This annotated reference sequence of wheat is a resource that can now drive disruptive innovation in wheat improvement, as this community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding. Importantly, the bioinformatics capacity developed for model-organism genomes will facilitate a better understanding of the wheat genome as a result of the high-quality chromosome-based genome assembly. By necessity, breeders work with the genome at the whole chromosome level, as each new cross involves the modification of genome-wide gene networks that control the expression of complex traits such as yield. With the annotated and ordered reference genome sequence in place, researchers and breeders can now easily access sequence-level information to precisely define the necessary changes in the genomes for breeding programs. This will be realized through the implementation of new DNA marker platforms and targeted breeding technologies, including genome editing

A data driven learning approach for the assessment of data quality

Background!#!Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues.!##!Objectives!#!To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task.!##!Methods!#!We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods' results and corresponding outcome data (data that indicated the data's suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without.!##!Results!#!Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods' results.!##!Conclusions!#!Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods' results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task

Staat und Religionen heute. Diskussion mit Antje Vollmer, Christian Wulff und Peter Steinacker

Textdokumentation zur Veranstaltung der Osnabrücker Friedensgespräche am 6. Mai 2009 im Kongress-Saal der OsnabrückHall

The Major Specificity-Determining Amino Acids of the Tomato Cf-9 Disease Resistance Protein Are at Hypervariable Solvent-Exposed Positions in the Central Leucine-Rich Repeats

The interaction between tomato and the leaf mold pathogen Cladosporium fulvum is controlled in a gene-for-gene manner by plant Cf genes that encode membrane-anchored extracytoplasmic leucine-rich repeat (LRR) glycoproteins, which confer recognition of their cognate fungal avirulence (Avr) proteins. Cf-9 and Cf-4 are two such proteins that are 91% identical yet recognize the sequence-unrelated fungal avirulence determinants Avr9 and Avr4, respectively. As shown previously, Cf-4 specificity is determined by three putative solvent-exposed residues in the central LRR and a deletion of two LRR relative to Cf-9. In this study, we focused on identifying the specificity determinants of Cf-9. We generated chimeras between Cf-9 and its close homologue Cf-9B and identified five amino acid residues that constitute major specificity determinants of Cf-9. Introduction of these residues into Cf-9B allowed recognition of Avr9. Consistent with a role in recognition specificity, the identified residues are putatively solvent exposed in the central LRR and occupy hypervariable positions in the global Cf alignment. One of the specificity residues is not found in any other known Cf protein, suggesting the importance of diversifying selection rather than sequence exchange between homologues. Interestingly, there is an overlap between the Cf-4 and Cf-9 specificity-determining residues, precluding a protein with dual specificity.Peer reviewe

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

Background!#!Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU).!##!Methods!#!The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018-03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days.!##!Results!#!Sensitivity and specificity was 91.7% (95% CI 85.5-95.4%) and 54.1% (95% CI 45.4-62.5%) on patient level, and 97.5% (95% CI 95.1-98.7%) and 91.5% (95% CI 89.3-93.3%) on the level of patient-days. Physicians' SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8-66.9%)/specificity of 83.3% (95% CI 80.4-85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine.!##!Conclusions!#!We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study.!##!Trial registration!#!NCT03661450 (ClinicalTrials.gov); registered September 7, 2018

The Major Specificity-Determining Amino Acids of the Tomato Cf-9 Disease Resistance Protein Are at Hypervariable Solvent-Exposed Positions in the Central Leucine-Rich Repeats

The interaction between tomato and the leaf mold pathogen Cladosporium fulvum is controlled in a gene-for-gene manner by plant Cf genes that encode membrane-anchored extraeytoplasmic leucine-rich repeat (LRR) glycoproteins, which confer recognition of their cognate fungal avirulence (Avr) proteins. Cf-9 and Cf-4 are two such proteins that are 91% identical yet recognize the sequence-unrelated fungal avirulence determinants Avr9 and Avr4, respectively. As shown previously, Cf-4 specificity is determined by three putative solvent-exposed residues in the central LRR and a deletion of two LRR relative to Cf-9. In this study, we focused on identifying the specificity determinants of Cf-9. We generated chimeras between Cf-9 and its close homologue Cf-9B and identified five amino acid residues that constitute major specificity determinants of Cf-9. Introduction of these residues into Cf-9B allowed recognition of Avr9. Consistent with a role in recognition specificity, the identified residues are putatively solvent exposed in the central LRR and occupy hypervariable positions in the global Cf alignment. One of the specificity residues is not found in any other known Cf protein, suggesting the importance of diversifying selection rather than sequence exchange between homologues. Interestingly, there is an overlap between the Cf-4 and Cf-9 specificity-determining residues, precluding a protein with dual specificity

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction

Background: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician’s ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics. Objectives: The aim of the study is to enhance an existing, interoperable, and rulebased CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy. Methods: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians’ diagnoses as referenc