Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Laser & Catalysis. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Stomatal conductance of forest species after long‐term exposure to elevated CO2 concentration: a synthesis

Abstract: • Data from 13 long-term (> 1 yr), field-based studies of the effects of elevated CO2 concentration ([CO2]) on European forest tree species were analysed using meta-analysis and modelling. Meta-analysis was used to determine mean responses across the data sets, and data were fitted to two commonly used models of stomatal conductance in order to explore response to environmental conditions and the relationship with assimilation. • Meta-analysis indicated a significant decrease (21%) in stomatal conductance in response to growth in elevated [CO2] across all studies. The response to [CO2] was significantly stronger in young trees than old trees, in deciduous compared to coniferous trees, and in water stressed compared to nutrient stressed trees. No evidence of acclimation of stomatal conductance to elevated [CO2] was found. • Fits of data to the first model showed that growth in elevated [CO2] did not alter the response of stomatal conductance to vapour pressure deficit, soil water content or atmospheric [CO2]. Fits of data to the second model indicated that conductance and assimilation responded in parallel to elevated [CO2] except when water was limiting. • Data were compared to a previous meta-analysis and it was found that the response of gs to elevated [CO2] was much more consistent in long-term (> 1 yr) studies, emphasising the need for long-term elevated [CO2] studies. By interpreting data in terms of models, the synthesis will aid future modelling studies of responses of forest trees to elevated [CO2].

Nanostructured electrocatalysts with tunable activity and selectivity

Abstract: The field of electrocatalysis has undergone tremendous advancement in the past few decades, in part owing to improvements in catalyst design at the nanoscale. These developments have been crucial for the realization of and improvement in alternative energy technologies based on electrochemical reactions such as fuel cells. Through the development of novel synthesis methods, characterization techniques and theoretical methods, rationally designed nanoscale electrocatalysts with tunable activity and selectivity have been achieved. This Review explores how nanostructures can be used to control electrochemical reactivity, focusing on three model reactions: O2 electroreduction, CO2 electroreduction and ethanol electrooxidation. The mechanisms behind nanoscale control of reactivity are discussed, such as the presence of low-coordinated sites or facets, strain, ligand effects and bifunctional effects in multimetallic materials. In particular, studies of how particle size, shape and composition in nanostructures can be used to tune reactivity are highlighted. New catalysis materials are required for electrochemical reactions that are vital for clean energy production and environmental remediation. The use of nanostructured materials for improving catalytic reactivity is analysed in this Review in the context of model reactions of O2 reduction, CO2 electroreduction and ethanol oxidation.

Physically Based Deformable Models in Computer Graphics

Abstract: Physically based deformable models have been widely embraced by the Computer Graphics community. Many problems outlined in a previous survey by Gibson and Mirtich [ GM97] have been addressed, thereby making these models interesting and useful for both offline and real-time applications, such as motion pictures and video games. In this paper, we present the most significant contributions of the past decade, which produce such impressive and perceivably realistic animations and simulations: finite element/difference/volume methods, mass-spring systems, meshfree methods, coupled particle systems and reduced deformable models based on modal analysis. For completeness, we also make a connection to the simulation of other continua, such as fluids, gases and melting objects. Since time integration is inherent to all simulated phenomena, the general notion of time discretization is treated separately, while specifics are left to the respective models. Finally, we discuss areas of application, such as elastoplastic deformation and fracture, cloth and hair animation, virtual surgery simulation, interactive entertainment and fluid/smoke animation, and also suggest areas for future research.

BoxPlotR: a web tool for generation of box plots

Abstract: To the Editor In biomedical research, it is often necessary to compare multiple data sets with different distributions. The bar plot, or histogram, is typically used to compare data sets on the basis of simple statistical measures, usually the mean with s.d. or s.e.m. However, summary statistics alone may fail to convey underlying differences in the structure of the primary data (Fig. 1a), which may in turn lead to erroneous conclusions. The box plot, also known as the box-and-whisker plot, represents both the summary statistics and the distribution of the primary data. The box plot thus enables visualization of the minimum, lower quartile, median, upper quartile and maximum of any data set (Fig. 1b). The first documented description of a box plot–like graph by Spear1 defined a range bar to show the median and interquartile range (IQR, or middle 50%) of a data set, with whiskers extended to minimum and maximum values. The most common implementation of the box plot, as defined by Tukey2, has a box that represents the IQR, with whiskers that extend 1.5 times the IQR from the box edges; it also allows for identification of outliers in the data set. Whiskers can also be defined to span the 95% central range of the data3. Other variations, including bean plots4 and violin plots, reveal additional details of the data distribution. These latter variants are less statistically informative but allow better visualization of the data distribution, such as bimodality (Fig. 1b), that may be hidden in a standard box plot. Figure 1 Data visualization with box plots Despite the obvious advantages of the box plot for simultaneous representation of data set and statistical parameters, this method is not in common use, in part because few available software tools allow the facile generation of box plots. For example, the standard spreadsheet tool Excel is unable to generate box plots. Here we describe an open-source application, called BoxPlotR, and an associated web portal that allow rapid generation of customized box plots. A user-defined data matrix is uploaded as a file or pasted directly into the application to generate a basic box plot with options for additional features. Sample size may be represented by the width of each box in proportion to the square root of the number of observations5. Whiskers may be defined according to the criteria of Spear1, Tukey2 or Altman3. The underlying data distribution may be visualized as a violin or bean plot or, alternatively, the actual data may be displayed as overlapping or nonoverlapping points. The 95% confidence interval that two medians are different may be illustrated as notches defined as ±(1.58 × IQR/√n) (ref. 5). There is also an op on to plot the sample means and their confidence intervals. More complex statistical comparisons may be required to ascertain significance according to the specific experimental design6. The output plots may be labeled; customized by color, dimensions and orientation; and exported as publication-quality .eps, .pdf or .svg files. To help ensure that generated plots are accurately described in publications, the application generates a description of the plot for incorporation into a figure legend. The interactive web application is written in R (ref. 7) with the R packages shiny, beanplot4, vioplot, beeswarm and RColorBrewer, and it is hosted on a shiny server to allow for interactive data analysis. User data are held only temporarily and discarded as soon as the session terminates. BoxPlotR is available at http://boxplot.tyerslab.com/ and may be downloaded to run locally or as a virtual machine for VMware and VirtualBox.

Minimum Information about a Biosynthetic Gene cluster.

Abstract: A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.