Virtual Field Studies: Conducting Studies on Public Displays in Virtual Reality

Field studies on public displays can be difficult, expensive, and time-consuming. We investigate the feasibility of using virtual reality (VR) as a test-bed to evaluate deployments of public displays. Specifically, we investigate whether results from virtual field studies, conducted in a virtual public space, would match the results from a corresponding real-world setting. We report on two empirical user studies where we compared audience behavior around a virtual public display in the virtual world to audience behavior around a real public display. We found that virtual field studies can be a powerful research tool, as in both studies we observed largely similar behavior between the settings. We discuss the opportunities, challenges, and limitations of using virtual reality to conduct field studies, and provide lessons learned from our work that can help researchers decide whether to employ VR in their research and what factors to account for if doing so

Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region.Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia

Resistance to mesenchymal reprogramming sustains clonal propagation in metastatic breast cancer

The acquisition of mesenchymal traits is considered a hallmark of breast cancer progression. However, the functional relevance of epithelial-to-mesenchymal transition (EMT) remains controversial and context dependent. Here, we isolate epithelial and mesenchymal populations from human breast cancer metastatic biopsies and assess their functional potential in vivo. Strikingly, progressively decreasing epithelial cell adhesion molecule (EPCAM) levels correlate with declining disease propagation. Mechanistically, we find that persistent EPCAM expression marks epithelial clones that resist EMT induction and propagate competitively. In contrast, loss of EPCAM defines clones arrested in a mesenchymal state, with concomitant suppression of tumorigenicity and metastatic potential. This dichotomy results from distinct clonal trajectories impacting global epigenetic programs that are determined by the interplay between human ZEB1 and its target GRHL2. Collectively, our results indicate that susceptibility to irreversible EMT restrains clonal propagation, whereas resistance to mesenchymal reprogramming sustains disease spread in multiple models of human metastatic breast cancer, including patient-derived cells in vivo

Innovation und Trends für Mobiles Lernen

Der Beitrag zeigt aktuelle Trends im Bereich der mobilen und ubiquitären Lerntechnologien auf, welche die klassischen Konzepte von Mobilem Lernen erweitern: a) Mobiler und allgegenwärtiger Zugang zu Lerninhalten b) unterbrechungsfreie Lernunterstützung oder "Seamless Learning Support", die nahtlose Integration von Lernunterstützung in gemischten Lernszenarien, c) Smartphones und Sensoren im Mobilen Lernen, d) Mobile Gaming und mobile Augmented Reality und e) situierte eingebettete Displays. Anhand dieser Trends werden die Konsequenzen für das didaktische Design und darunter liegende Lernkonzepte diskutiert

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS