Some like it organic, some like it purple and some like it ancient:consumer preferences and WTP for value-added attributes in whole grain bread

The German bread market is considered one of the most developed and diverse bread markets worldwide. However, until so far no empirical evidence exists with respect to consumers liking and willingness to pay for different value-added attributes in whole grain bread. Thus, our study is the first one providing empirical evidence on how German consumers perceive different value-added attributes in whole grain bread, how much they are willing to pay for these attributes and to which extent liking scores and willingness to pay (WTP) estimates are influenced by extrinsic information. Our analysis is based on a combination of hedonic evaluations with experimental auctions under three different information scenarios. The attributes considered in our study are: (i) organic, (ii) functional, and (iii) ancient grain variety. The collected data are analyzed via cluster analysis and random effects Tobit models. The results indicate the following. First, significant heterogeneity exists across consumer clusters with respect to the responsiveness to extrinsic information. Second, results differ depending on the hedonic measure chosen, i.e. whether a taste or an overall liking score is employed. Third, the chosen functional bread, purple wheat bread would be accepted as long as taste expectations are met. Put differently, consumers would not comprise on taste for health. However, at the same time our results highlight that expecting that a bread has “natural” health-enhancing properties significantly increases the sensory evaluation and thus product liking. Consequently, bread with functional properties will be only successful on the market if consumers are satisfied with the taste experience, whereas extrinsic information might increase the perceived taste experience

Combining Simulation and Augmented Reality Methods for Enhanced Worker Assistance in Manual Assembly

Lampen E, Teuber J, Gaisbauer F, Bär T, Pfeiffer T, Wachsmuth S. Combining Simulation and Augmented Reality Methods for Enhanced Worker Assistance in Manual Assembly. Procedia CIRP. 2019;81:588-593.Due to mass customization product variety increased steeply in the automotive industry, entailing the increment of worker’s cognitive load during manual assembly tasks. Although worker assistance methods for cognitive automation already exist, they proof insufficient in terms of usability and achieved time saving. Given the rising importance of simulation towards autonomous production planning, a novel approach is proposed using human simulation data in context of worker assistance methods to alleviate cognitive load during manual assembly tasks. Within this paper, a new concept for augmented reality-based worker assistance is presented. Additionally, a comparative user study (N=24) was conducted with conventional worker assistance methods to evaluate a prototypical implementation of the concept. The results illustrate the enhancing opportunity of the novel approach to save cognitive abilities and to induce performance improvements. The implementation provided stable information presentation during the entire experiment. However, with regard to the recentness, there has to be carried out further developments and research, concerning performance adaptions and investigations of the effectiveness

The Ras Dimer Structure

Oncogenic mutated Ras is a key player in cancer, but despite intense and expensive approaches its catalytic center seems undruggable. The Ras dimer interface is a possible alternative drug target. Dimerization at the membrane affects cell growth signal transduction. In vivo studies indicate that preventing dimerization of oncogenic mutated Ras inhibits uncontrolled cell growth. Conventional computational drug-screening approaches require a precise atomic dimer model as input to successfully access drug candidates. However, the proposed dimer structural models are controversial. Here, we provide a clear-cut experimentally validated N-Ras dimer structural model. We incorporated unnatural amino acids into Ras to enable the binding of labels at multiple positions via click chemistry. This labeling allowed for the determination of multiple distances of the membrane-bound Ras-dimer measured by fluorescence and electron paramagnetic resonance spectroscopy. In combination with protein-protein docking and biomolecular simulations we identified key residues for dimerization. Site‑directed mutations of these residues prevent dimer formation in our experiments, proving our dimer model to be correct. The presented dimer structure enables now computational drug-screening studies exploiting the Ras dimer interface as alternative drug target

A Projection Method on Measures Sets

International audienceWe consider the problem of projecting a probability measure π on a set MN of Radon measures. The projection is defined as a solution of the following variational problem: inf µ∈M N h (µ − π) 2 2 , where h ∈ L 2 (Ω) is a kernel, Ω ⊂ R d and denotes the convolution operator. To motivate and illustrate our study, we show that this problem arises naturally in various practical image rendering problems such as stippling (representing an image with N dots) or continuous line drawing (representing an image with a continuous line). We provide a necessary and sufficient condition on the sequence (MN) N ∈N that ensures weak convergence of the projections (µ * N) N ∈N to π. We then provide a numerical algorithm to solve a discretized version of the problem and show several illustrations related to computer-assisted synthesis of artistic paintings/drawings

SoilTemp: A global database of near-surface temperature.

Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes

SoilTemp: A global database of near-surface temperature

© 2020 John Wiley & Sons Ltd Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes