Highlights A wide variety of tracking and detection tools for computer vision-based GMA exist. A “method-of-choice” for automated GMA does not yet exist. Large expert-annotated valid datasets are urgently needed. The prerequisites of classic GMA is indispensable for developing automated solutions. A future augmented GMA shall combine human expertise with computerised tools.

Purpose-The purpose of this paper is to suggest a way to support work-integrated learning for knowledge work, which poses a great challenge for current research and practice. Design/methodology/approach-The authors first suggest a workplace learning context model, which has been derived by analyzing knowledge work and the knowledge sources used by knowledge workers. The authors then focus on the part of the context that specifies competencies by applying the competence performance approach, a formal framework developed in cognitive psychology. From the formal framework, a methodology is then derived of how to model competence and performance in the workplace. The methodology is tested in a case study for the learning domain of requirements engineering. Findings-The Workplace Learning Context Model specifies an integrative view on knowledge workers' work environment by connecting learning, work and knowledge spaces. The competence performance approach suggests that human competencies be formalized with a strong connection to workplace performance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosis and competency gap analysis can be embedded into the normal working tasks and learning interventions can be offered accordingly. The results of the case study indicate that experts were generally in moderate to high agreement when assigning competencies to tasks. Research limitations/implications-The model needs to be evaluated with regard to the learning outcomes in order to test whether the learning interventions offered benefit the user. Also, the validity and efficiency of competency diagnosis need to be compared to other standard practices in competency management. Practical implications-Use of competence performance structures within organizational settings has the potential to more closely relate the diagnosis of competency needs to actual work tasks, and to embed it into work processes. Originality/value-The paper connects the latest research in cognitive psychology and in the behavioural sciences with a formal approach that makes it appropriate for integration into technology-enhanced learning environments.

The expanding antibiotic resistance crisis calls for a more in depth understanding of the importance of antimicrobial resistance genes (ARGs) in pristine environments. We, therefore, studied the microbiome associated with Sphagnum moss forming the main vegetation in undomesticated, evolutionary old bog ecosystems. In our complementary analysis of culture collections, metagenomic data and a fosmid library from different geographic sites in Europe, we identified a low abundant but highly diverse pool of resistance determinants, which targets an unexpectedly broad range of 29 antibiotics including natural and synthetic compounds. This derives both, from the extraordinarily high abundance of efflux pumps (up to 96%), and the unexpectedly versatile set of ARGs underlying all major resistance mechanisms. Multi-resistance was frequently observed among bacterial isolates, e.g. in Serratia, Rouxiella, Pandoraea, Paraburkholderia and Pseudomonas. In a search for novel ARGs, we identified the new class A β-lactamase Mm3. The native Sphagnum resistome comprising a highly diversified and partially novel set of ARGs contributes to the bog ecosystem´s plasticity. Our results reinforce the ecological link between natural and clinically relevant resistomes and thereby shed light onto this link from the aspect of pristine plants. Moreover, they underline that diverse resistomes are an intrinsic characteristic of plant-associated microbial communities, they naturally harbour many resistances including genes with potential clinical relevance.

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Abstract. Automatic image annotation is an important and challenging task when managing large image collections. This paper describes techniques for automatic image annotation by taking advantage of collaboratively annotated image databases, so called visual folksonomies. Our approach applies two techniques based on image analysis: Classification annotates images with a controlled vocabulary while tag propagation uses user generated, folksonomic annotations and is therefore capable of dealing with an unlimited vocabulary. Experiments with a pool of Flickr images demonstrate the high accuracy and efficiency of the proposed methods in the task of automatic image annotation.

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