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GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). GrassPlot collects plot records (releves) from grasslands and other open habitats of the Palaearctic biogeographic realm. It focuses on precisely delimited plots of eight standard grain sizes (0.0001; 0.001;... 1,000 m(2)) and on nested-plot series with at least four different grain sizes. The usage of GrassPlot is regulated through Bylaws that intend to balance the interests of data contributors and data users. The current version (v. 1.00) contains data for approximately 170,000 plots of different sizes and 2,800 nested-plot series. The key components are richness data and metadata. However, most included datasets also encompass compositional data. About 14,000 plots have near-complete records of terricolous bryophytes and lichens in addition to vascular plants. At present, GrassPlot contains data from 36 countries throughout the Palaearctic, spread across elevational gradients and major grassland types. GrassPlot with its multi-scale and multi-taxon focus complements the larger international vegetationplot databases, such as the European Vegetation Archive (EVA) and the global database " sPlot". Its main aim is to facilitate studies on the scale-and taxon-dependency of biodiversity patterns and drivers along macroecological gradients. GrassPlot is a dynamic database and will expand through new data collection coordinated by the elected Governing Board. We invite researchers with suitable data to join GrassPlot. Researchers with project ideas addressable with GrassPlot data are welcome to submit proposals to the Governing Board.
The Arabidopsis tandem-pore K+ (TPK) channels displaying four transmembrane domains and two pore regions share structural homologies with their animal counterparts of the KCNK family. In contrast to the Shaker-like Arabidopsis channels (six transmembrane domains/one pore region), the functional properties and the biological role of plant TPK channels have not been elucidated yet. Here, we show that AtTPK4 (KCO4) localizes to the plasma membrane and is predominantly expressed in pollen. AtTPK4 (KCO4) resembles the electrical properties of a voltage-independent K+ channel after expression in Xenopus oocytes and yeast. Hyperpolarizing as well as depolarizing membrane voltages elicited instantaneous K+ currents, which were blocked by extracellular calcium and cytoplasmic protons. Functional complementation assays using a K+ transport-deficient yeast confirmed the biophysical and pharmacological properties of the AtTPK4 channel. The features of AtTPK4 point toward a role in potassium homeostasis and membrane voltage control of the growing pollen tube. Thus, AtTPK4 represents a member of plant tandem-pore-K+ channels, resembling the characteristics of its animal counterparts as well as plant-specific features with respect to modulation of channel activity by acidosis and calcium
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.