EmoCycling – Analysen von Radwegen mittels Humansensorik und Wearable Computing

Radfahren erfreut sich einer zunehmenden Wertschätzung. Einerseits als neuer Lifestyle, andererseits als wichtiges Thema der städtischen Mobilitätsplanung: Bike-Sharing-Angebote, Radwegekonzepte und Förderung eines umweltfreundlichen Mobilitätsmix sind hierbei wichtige Stichworte. Daher fördern zunehmend mehr Städte den Ausbau der Radwege-Infrastruktur, um das Radfahren attraktiver zu gestalten. Wie stark Radfahren aber tatsächlich angenommen und praktiziert wird, hängt von ganz verschiedenen Faktoren ab: Verkehrslage, Quantität und Qualität der Infrastruktur, Topografie sowie das subjektive Sicherheitsempfinden z.B. an unübersichtlichen Kreuzungen beeinflussen die Verkehrsmittelwahl. Insbesondere die Erfassung und Analyse des subjektiven Sicherheitsempfindens stellt hierbei eine große Herausforderung dar – wird aber durch neue Methoden der Humansensorik (Exner et al. 2012) möglich. Entwicklungen in den Bereichen des Wearable Computing sowie der Geoinformatik ermöglichen es, das subjektive Sicherheitsempfinden während der Fahrt genauer zu analysieren. Anknüpfend an Projekte zur emotionalen Stadtkartierung (Höffken et al. 2008, Zeile et al. 2010) erfolgt ein Live-Monitoring der Probanden während der Fahrt. Mittels eines Sensorarmbands (Smartband) zur Erfassung psychophysiologischer Reaktionen des Körpers in Kombination mit Video-Kamera-Daten und GPS-Koordinaten wird der emotionale Zustand der Probanden sekundengenau gemessen. Dadurch lassen sich Emotionen, insbesondere Stress, interpretieren und auf einer Karte verorten sowie die Auslöser (Trigger) identifizieren. Zudem kann auf diese Weise der Verkehr kontinuierlich erfasst und in die Analyse mit aufgenommen werden, um Gefahrenstellen zu lokalisieren. Nach einer Einführung in das Thema Radfahren in der Untersuchungsgemeinde Kaiserslautern, gibt das Paper einen Überblick über den aktuellen Stand der Methodik, die Konzeptionierung der Teststrecken sowie die Methodik im konkreten Projekt EmoCycling. Darauf basierend werden die Ergebnisse des Projektes vorgestellt und daraus resultierende weiterführende Fragenstellungen aufgezeigt

Self-supervised learning methods for label-efficient dental caries classification

High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three selfsupervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ě45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive

A LAK of Direction Misalignment Between the Goals of Learning Analytics and its Research Scholarship

Learning analytics defines itself with a focus on data from learners and learning environments, with corresponding goals of understanding and optimizing student learning. In this regard, learning analytics research, ideally, should be characterized by studies that make use of data from learners engaged in education systems, should measure student learning, and should make efforts to intervene and improve these learning environments

Characteristics and sources of fluorescent aerosols in the central Arctic Ocean

The Arctic is sensitive to cloud radiative forcing. Due to the limited number of aerosols present throughout much of the year, cloud formation is susceptible to the presence of cloud condensation nuclei and ice nucleating particles (INPs). Primary biological aerosol particles (PBAP) contribute to INPs and can impact cloud phase, lifetime, and radiative properties. We present yearlong observations of hyperfluorescent aerosols (HFA), tracers for PBAP, conducted with a Wideband Integrated Bioaerosol Sensor, New Electronics Option during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (October 2019–September 2020) in the central Arctic. We investigate the influence of potential anthropogenic and natural sources on the characteristics of the HFA and relate our measurements to INP observations during MOSAiC. Anthropogenic sources influenced HFA during the Arctic haze period. But surprisingly, we also found sporadic “bursts” of HFA with the characteristics of PBAP during this time, albeit with unclear origin. The characteristics of HFA between May and August 2020 and in October 2019 indicate a strong contribution of PBAP to HFA. Notably from May to August, PBAP coincided with the presence of INPs nucleating at elevated temperatures, that is, &amp;gt;−9°C, suggesting that HFA contributed to the “warm INP” concentration. The air mass residence time and area between May and August and in October were dominated by the open ocean and sea ice, pointing toward PBAP sources from within the Arctic Ocean. As the central Arctic changes drastically due to climate warming with expected implications on aerosol–cloud interactions, we recommend targeted observations of PBAP that reveal their nature (e.g., bacteria, diatoms, fungal spores) in the atmosphere and in relevant surface sources, such as the sea ice, snow on sea ice, melt ponds, leads, and open water, to gain further insights into the relevant source processes and how they might change in the future.</jats:p

Stakeholder views on secondary findings in whole-genome and whole-exome sequencing:a systematic review of quantitative and qualitative studies

Purpose: As whole-exome and whole-genome sequencing (WES/WGS) move into routine clinical practice, it is timely to review data that might inform the debate around secondary findings (SF) and the development of policies that maximize participant benefit. Methods: We systematically searched for qualitative and quantitative studies that explored stakeholder views on SF in WES/WGS. Framework analysis was undertaken to identify major themes. Results: 44 articles reporting the views of 11,566 stakeholders were included. Stakeholders were broadly supportive of returning ‘actionable’ findings, but definitions of actionability varied. Stakeholder views on SF disclosure exist along a spectrum: potential WES/WGS recipients’ views were largely influenced by a sense of rights, while views of genomics professionals were informed by a sense of professional responsibility. Experience of genetic illness and testing resulted in greater caution about SF, suggesting that truly informed decisions require an understanding of the implications and limitations of WES/WGS and possible findings. Conclusion: This review suggests that bidirectional interaction during consent might best facilitate informed decision-making about SF, and that dynamic forms of consent, allowing for changing preferences, should be considered. Research exploring views from wider perspectives and from recipients who have received SF is critical if evidence-based policies are to be achieved.</p

Characteristics and sources of fluorescent aerosols in the central Arctic Ocean

The Arctic is sensitive to cloud radiative forcing. Due to the limited number of aerosols present throughout much of the year, cloud formation is susceptible to the presence of cloud condensation nuclei and ice nucleating particles (INPs). Primary biological aerosol particles (PBAP) contribute to INPs and can impact cloud phase, lifetime, and radiative properties. We present yearlong observations of hyperfluorescent aerosols (HFA), tracers for PBAP, conducted with a Wideband Integrated Bioaerosol Sensor, New Electronics Option during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (October 2019–September 2020) in the central Arctic. We investigate the influence of potential anthropogenic and natural sources on the characteristics of the HFA and relate our measurements to INP observations during MOSAiC. Anthropogenic sources influenced HFA during the Arctic haze period. But surprisingly, we also found sporadic “bursts” of HFA with the characteristics of PBAP during this time, albeit with unclear origin. The characteristics of HFA between May and August 2020 and in October 2019 indicate a strong contribution of PBAP to HFA. Notably from May to August, PBAP coincided with the presence of INPs nucleating at elevated temperatures, that is, >−9°C, suggesting that HFA contributed to the “warm INP” concentration. The air mass residence time and area between May and August and in October were dominated by the open ocean and sea ice, pointing toward PBAP sources from within the Arctic Ocean. As the central Arctic changes drastically due to climate warming with expected implications on aerosol–cloud interactions, we recommend targeted observations of PBAP that reveal their nature (e.g., bacteria, diatoms, fungal spores) in the atmosphere and in relevant surface sources, such as the sea ice, snow on sea ice, melt ponds, leads, and open water, to gain further insights into the relevant source processes and how they might change in the future