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Purpose
Rehabilitation professionals are faced with judging and describing the social-medicine status of their patients. Rehabilitation professionals must know the core concepts of acute unfitness for work, psychological capacities, and long-term work capacity.
Acquiring and applying this knowledge, requires training. The research question is if and to what extent medical professionals and students' knowledge changes after social medicine training.
Methods
This quasi-experimental study was carried out in the real-life context of social medicine training. Psychology students (n = 42), physicians/psychotherapists (i.e. state-licensed health professionals) (n = 44) and medical assistant professionals (n = 29) were trained. Their social medicine knowledge was measured before and after training by a 10-min expert-approved and content valid knowledge questionnaire.
Three free-text questions had to be answered on the essential aspects of present and prognostic work ability and psychological capacities.
Answers were rated for correctness by two experts. Paired t tests and variance analysis have been calculated for group comparisons.
Results
All groups improved their social medicine knowledge from the pre- to the post-test. The students started with the lowest level of knowledge in the pre-test.
After training, 69% of the physicians/psychotherapists and 56.8% of the medical assistant professionals, but only 7% of the students, obtained maximum scores for naming psychological capacities.
Conclusions
Social medicine knowledge increased after a training course consisting of eight lessons. The increase was greater for medical assistant professionals and physicians/psychotherapists than for students. Social medicine training must be adjusted to the trainee groups' knowledge levels.
Cities and other human settlements are major contributors to climate change and are highly vulnerable to its impacts. They are also uniquely positioned to reduce greenhouse gas emissions and lead adaptation efforts. These compound challenges and opportunities require a comprehensive perspective on the public policy of human settlements. Drawing on core literature that has driven debate around cities and climate over recent decades, we put forward a set of boundary objects that can be applied to connect the knowledge of epistemic communities and support an integrated urbanism. We then use these boundary objects to develop the Goals-Intervention-Stakeholder-Enablers (GISE) framework for a public policy of human settlements that is both place-specific and provides insights and tools useful for climate action in cities and other human settlements worldwide. Using examples from Berlin, we apply this framework to show that climate mitigation and adaptation, public health, and well-being goals are closely linked and mutually supportive when a comprehensive approach to urban public policy is applied.
Tuning systems of traditional Georgian singing determined from a new corpus of field recordings
(2022)
In this study we examine the tonal organization of the 2016 GVM dataset, a newly-created corpus of high-quality multimedia field recordings of traditional Georgian singing with a focus on Svaneti. For this purpose, we developed a new processing pipeline for the computational analysis of non-western polyphonic music which was subsequently applied to the complete 2016 GVM dataset.
To evaluate under what conditions a single tuning system is representative of current Svan performance practice, we examined the stability of the obtained tuning systems from an ensemble-, a song-, and a corpus-related perspective.
Furthermore, we compared the resulting Svan tuning systems with the tuning systems obtained for the Erkomaishvili dataset (Rosenzweig et al., 2020) in the study by Scherbaum et al. (2020). In comparison to a 12-TET (12-tone-equal-temperament) system, the Erkomaishvili and the Svan tuning systems are surprisingly similar.
Both systems show a strong presence of pure fourths (500 cents) and fifths (700 cents), and 'neutral' thirds (peaking around 350 cents) as well as 'neutral' sixths.
In addition, the sizes of the melodic and the harmonic seconds in both tuning systems differ systematically from each other, with the size of the harmonic second being systematically larger than the melodic one.
"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it.
Duplicates - multiple but different representations of the same real-world entities are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential.
Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results.
To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform.
It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results.
Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.
How much influence did the former communist state security service Darzhavna sigurnost (DS) have during the transformation period in Bulgaria?
For the first time in history, there is empirical data available that allow for an analysis of the role of the Bulgarian secret police and its "afterlife" after 1990.
Bulgarian intelligence archives, which were made partly accessible following the country's admission to the European Union in 2007, provide an excellent basis for an analysis of the relationship between the DS and the Soviet State Committee for Security, the transformation of the Bulgarian security apparatus in 1990, attempts to disclose the state security archives, and continuous infiltration of Bulgarian politics, institutions, and security apparatus by former agents of the communist intelligence and security apparatus.
The empirical data suggest that personal connections, dependencies, and informal networks of former DS agents and officials played an important role during the transformation period in Bulgaria and are at least partly responsible for political corruption, continuous Russian influence, a high degree of politicization of the Bulgarian security apparatus, misuse of intelligence and illegal surveillance practices, and a high degree of domestic and international mistrust and (dis-)loyalty.
Uncovering the digitalization impact on consumer decision-making for checking accounts in banking
(2022)
Checking account providers must understand the importance of digital and non-digital service attributes across different customer segments to achieve a product-market fit in digitalization. In particular, various latent personal characteristics influence customer choices in digital banking. However, there is only limited research on banking customer behavior beyond the technology acceptance model, and none that explores customer preferences for checking accounts experimentally. Against this background, we present the results of a discrete choice experiment on customer preferences towards checking accounts in Germany. The outcome of the paper is a detailed quantitative assessment of the relationships between checking account service attributes and a set of latent influencing factors on choice. While customer service experience, the scope of services, and professional expertise are identified as re-occurring critical aspects for customers when choosing their banking service provider, the type of provider and digital product innovation showed little impact on customer choice overall. In multigroup analyses, we reveal the moderating impact of influencing factors on the preference of checking account service attributes. Additional segmentation analyses point to six customer segments from which four still prefer a traditional operating model. The largest segment of traditional product-innovative customers prefers digitalized, i.e., data-driven checking accounts in a mixed-mode with human customer advisory and on-site branch services from a traditional bank. At the other end of the spectrum, a small innovative Fintech customer segment, influenced by non-pragmatism and social norms, prefers a purely digital operating model with data-driven applications in banking.
Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection
(2022)
Background:
Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied.
Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens.
No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.
Results:
We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes.
We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning.
We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity.
Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.
Conclusions:
The neural networks trained using our data collection enable accurate detection of novel fungal pathogens.
A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.
After a long history of floodplain degradation and substantial losses of inundation areas over the last decades, a rethinking of floodplain management has taken place in Germany.
Floodplains are now acknowledged as important areas for both biodiversity and society. This transformation has been significantly supported by nationwide research activities. A systematic assessment of the current floodplain management is still lacking.
We therefore developed a scheme to assess floodplain management through the steps of identification, analysis, implementation, and evaluation.
Reviewing the data and literature on nationwide floodplain-related research and activities, we defined key elements of floodplain management for Germany.
We concluded that research activities already follow a strategic nationwide approach of identifying and analyzing floodplains.
Progress in implementation is slow, however, and potentials are far from being reached.
Nevertheless, new and unique initiatives enable Germany to stay on the long-term path of giving rivers more space and improving floodplain conditions.
Pichia pastoris (syn. Komagataella phaffii) represents a commonly used expression system in the biotech industry. High clonal variation of transformants, however, typically results in a broad range of specific productivities for secreted proteins. To isolate rare clones with exceedingly high product titers, an extensive number of clones need to be screened.
In contrast to high-throughput screenings of P. pastoris clones in microtiter plates, secrete-and -capture methodologies have the potential to efficiently isolate high-producer clones among millions of cells through fluorescence-activated cell sorting (FACS).Here, we describe a novel approach for the non-covalent binding of fragment antigen-binding (Fab) proteins to the cell surface for the isolation of high-producing clones.
Eight different single-chain variable fragment (scFv)-based capture matrices specific for the constant part of the Fabs were fused to the Saccharomyces cerevisiae alpha -agglutinin (SAG1) anchor protein for surface display in P. pastoris. By encoding the capture matrix on an episomal plasmid harboring inherently unstable autonomously replicating sequences (ARS), this secrete-and -capture system offers a switchable scFv display.
Efficient plasmid clearance upon removal of selective pres-sure enabled the direct use of isolated clones for subsequent Fab production. Flow-sorted clones (n = 276) displaying high amounts of Fabs showed a significant increase in median Fab titers detected in the cell-free supernatant (CFS) compared to unsorted clones (n = 276) when cells were cultivated in microtiter plates (fac-tor in the range of-21-49). Fab titers of clones exhibiting the highest product titer observed for each of the two approaches were increased by up to 8-fold for the sorted clone.
Improved Fab yields of sorted cells vs. unsorted cells were confirmed in an upscaled shake flask cultivation of selected candidates (factor in the range of-2-3). Hence, the developed display-based selection method proved to be a valuable tool for efficient clone screening in the early stages of our bioprocess development.
In this paper, we define a variant of Roe algebras for spaces with cylindrical ends and use this to study questions regarding existence and classification of metrics of positive scalar curvature on such manifolds which are collared on the cylindrical end.
We discuss how our constructions are related to relative higher index theory as developed by Chang, Weinberger, and Yu and use this relationship to define higher rho-invariants for positive scalar curvature metrics on manifolds with boundary.
This paves the way for the classification of these metrics.
Finally, we use the machinery developed here to give a concise proof of a result of Schick and the author, which relates the relative higher index with indices defined in the presence of positive scalar curvature on the boundary.