Concreteness, context-availability, and image-ability ratings and word associations for abstract, concrete, and emotion words.

Normative values on various word characteristics were obtained for abstract, concrete, and emotion words in order to facilitate research on concreteness effects and on the similarities and differences among the three word types. A sample of 78 participants rated abstract, concrete, and emotion words on concreteness, context availability,and imagery scales, Word associations were also gathered for abstract, concrete, and emotion words. The data were used to investigate similarities and differences among these three word types on word attributes, association strengths, and number of associations. These normative data can be used to further research on concreteness effects, word type effects, and word recognition for abstract, concrete, and emotion words

Challenges in the Automatic Analysis of Students' Diagnostic Reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

Extension of the Dip-test Repertoire -- Efficient and Differentiable p-value Calculation for Clustering

Over the last decade, the Dip-test of unimodality has gained increasing interest in the data mining community as it is a parameter-free statistical test that reliably rates the modality in one-dimensional samples. It returns a so called Dip-value and a corresponding probability for the sample's unimodality (Dip-p-value). These two values share a sigmoidal relationship. However, the specific transformation is dependent on the sample size. Many Dip-based clustering algorithms use bootstrapped look-up tables translating Dip- to Dip-p-values for a certain limited amount of sample sizes. We propose a specifically designed sigmoid function as a substitute for these state-of-the-art look-up tables. This accelerates computation and provides an approximation of the Dip- to Dip-p-value transformation for every single sample size. Further, it is differentiable and can therefore easily be integrated in learning schemes using gradient descent. We showcase this by exploiting our function in a novel subspace clustering algorithm called Dip'n'Sub. We highlight in extensive experiments the various benefits of our proposal

Nationwide Survey Reveals High Prevalence of Non-Swimmers among Children with Congenital Heart Defects

Background: Physical activity is important for children with congenital heart defects (CHD), not only for somatic health, but also for neurologic, emotional, and psychosocial development. Swimming is a popular endurance sport which is in general suitable for most children with CHD. Since we have previously shown that children with CHD are less frequently physically active than their healthy peers, we hypothesized that the prevalence of non-swimmers is higher in CHD patients than in healthy children. Methods: To obtain representative data, we performed a nationwide survey in collaboration with the German National Register of Congenital Heart Defects (NRCHD) and the Institute for Sport Sciences of the Karlsruhe Institute for Technology (KIT). The questionnaire included questions capturing the prevalence of swimming skills and the timing of swim learning and was part of the “Motorik-Modul” (MoMo) from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS). A representative age-matched subset of 4569 participants of the MoMo wave two study served as a healthy control group. Results: From 894 CHD-patients (mean age of 12.5 ± 3.1 years), the proportion of non-swimmers in children with CHD was significantly higher (16% versus 4.3%; p < 0.001) compared to healthy children and was dependent on CHD severity: Children with complex CHD had an almost five-fold increased risk (20.4%) of being unable to swim, whereas in children with simple CHD, the ability to swim did not differ significantly from their healthy reference group (5.6% vs. 4.3% non-swimmers (p = not significant). Conclusions: According to our results, one in five patients with complex CHD are non-swimmers, a situation that is concerning in regard of motoric development, inclusion and integration, as well as prevention of drowning accidents. Implementation of swim learning interventions for children with CHD would be a reasonable approach

Challenges in the automatic analysis of students' diagnostic reasoning

Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges

Challenging the Postwar Narrative: The Art and Agenda of Boris Lurie

Art history is shaped, studied, and taught based on narratives, artistic movements, and the biographies of celebrated artists. While contributing to an understanding of prevalent traditions and artists working in those traditions, these narratives are also constructions of inclusion and exclusion that establish art historical placement for certain artists while relegating others to historical obscurity. It is clear what happens to the critical fortunes of artists who are placed within these narratives. Yet what happens to the artists who do not fit within any of the categories established by these constructions? Are they then to be understood as simply minor artists or perhaps even “outsider artists?” Using the example of Boris Lurie and his critical fortune within the context of the standard art historical narrative of American art of the post World War Two period, this thesis argues for an expanded vision of modern and contemporary art that would accommodate lesser-known artists and offer a nuanced understanding of what American art has been after 1945

Analysis of automatic annotation suggestions for hard discourse-level tasks in expert domains

Many complex discourse-level tasks can aid domain experts in their work but require costly expert annotations for data creation. To speed up and ease annotations, we investigate the viability of automatically generated annotation suggestions for such tasks. As an example, we choose a task that is particularly hard for both humans and machines: the segmentation and classification of epistemic activities in diagnostic reasoning texts. We create and publish a new dataset covering two domains and carefully analyse the suggested annotations. We find that suggestions have positive effects on annotation speed and performance, while not introducing noteworthy biases. Envisioning suggestion models that improve with newly annotated texts, we contrast methods for continuous model adjustment and suggest the most effective setup for suggestions in future expert tasks

Salutogenesis for thriving societies

Settings are defined by the World Health Organization (1998) as “the place or social context in which people engage in daily activities in which environmental, organizational, and personal factors interact to affect health and well-being.” Such settings range from small-scale home/family to (international) organizations and large cities and thus differ in size, in their degree of formalized organization and their relationships to society. The chapters in Part V review how salutogenesis has been applied to health promotion research and practice in a broad range of settings: organizations in general, schools, higher education, workplace, military settings, neighborhood/communities, cities, and restorative environments. The following synthesis demonstrates that applying salutogenesis to various settings and linking salutogenesis with other models established in these settings has the great potential to generate ideas on how to advance the general salutogenic model

Parathyroid hormone stimulates bone regeneration in an atrophic non-union model in aged mice

Background Non-union formation still represents a major burden in trauma and orthopedic surgery. Moreover, aged patients are at an increased risk for bone healing failure. Parathyroid hormone (PTH) has been shown to accelerate fracture healing in young adult animals. However, there is no information whether PTH also stimulates bone regeneration in atrophic non-unions in the aged. Therefore, the aim of the present study was to analyze the efect of PTH on bone regeneration in an atrophic non-union model in aged CD-1 mice. Methods After creation of a 1.8 mm segmental defect, mice femora were stabilized by pin-clip fxation. The animals were treated daily with either 200 mg/kg body weight PTH 1–34 (n=17) or saline (control; n=17) subcutaneously. Bone regeneration was analyzed by means of X-ray, biomechanics, micro-computed tomography (µCT) imaging as well as histological, immunohistochemical and Western blot analyses. Results In PTH-treated animals bone formation was markedly improved when compared to controls. This was associated with an increased bending stifness as well as a higher number of tartrate-resistant acid phosphatase (TRAP)- positive osteoclasts and CD31-positive microvessels within the callus tissue. Furthermore, PTH-treated aged animals showed a decreased infammatory response, characterized by a lower number of MPO-positive granulocytes and CD68-positive macrophages within the bone defects when compared to controls. Additional Western blot analyses demonstrated a signifcantly higher expression of cyclooxygenase (COX)-2 and phosphoinositide 3-kinase (PI3K) in PTH-treated mice. Conclusion Taken together, these fndings indicate that PTH is an efective pharmacological compound for the treatment of non-union formation in aged animals

FAMULUS: interactive annotation and feedback generation for teaching diagnostic reasoning

Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student’s diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases