Seed dispersal distances: a typology based on dispersal modes and plant traits

Vittoz P. and Engler R. 2007. Seed dispersal distances: a typology based on dispersal modes and plant traits. Bot. Helv. 117: 109-124. The ability of plants to disperse seeds may be critical for their survival under the current constraints of landscape fragmentation and climate change. Seed dispersal distance would therefore be an important variable to include in species distribution models. Unfortunately, data on dispersal distances are scarce, and seed dispersal models only exist for some species with particular dispersal modes. To overcome this lack of knowledge, we propose a simple approach to estimate seed dispersal distances for a whole regional flora. We reviewed literature about seed dispersal in temperate regions and compiled data for dispersal distances together with information about the dispersal mode and plant traits. Based on this information, we identified seven "dispersal types” with similar dispersal distances. For each type, upper limits for the distance within which 50% and 99% of a species' seeds will disperse were estimated with the 80th percentile of the available values. These distances varied 5000-fold among the seven dispersal types, but generally less than 50-fold within the types. Thus, our dispersal types represented a large part of the variation in observed dispersal distances. The attribution of a dispersal type to a particular species only requires information that is already available in databases for most Central European species, i.e. dispersal vector (e.g. wind, animals), the precise mode of dispersal (e.g. dyszoochory, epizoochory), and species traits influencing the efficiency of dispersal (e.g. plant height, typical habitats). This typology could be extended to other regions and will make it possible to include seed dispersal in species distribution model

How to Describe and Measure Obstacles of Narrative Immersion in a Film? The Wheel of Immersion as a Framework

Abstract The aim of this study is to describe and measure obstacles of narrative immersion in a film. Inspired by a literature review within both game research and film studies, we propose a circular model to describe the dynamic process of different levels of involvement viewers can be in while watching a film. The evaluation is based on a 3D animation short film we have developed to achieve total immersion among viewers. The methodological design involved an attempt to decrease viewers’ involvement in the animation film by using distractions during the viewing. The study follows a mixed method strategy combining observation, a questionnaire and a structured interview. The results revealed that viewers react very differently to the distractions. For some viewers, the animation film was not the perceptual focus, where others were totally immersed. The number of distractions was not dependent on whether the film was watched individually or in groups, and for all participants, the distractions occurred in certain rhythms.</jats:p

A novel approach for electric load curve holistic modelling and simulation

International audienceThis paper presents a novel approach of an electric load curve simulator using a set of grey box models that results to an efficient trade-off between complete and complex physical models and fast simplified statistical models. The input parameters are macroscopic data coming from large databases such as national census, DSO’s client information and meteorological data such as temperature or irradiation data. The problem of matching between the different databases is investigated to assess comparable load curves. Validation is performed using load measurements at the medium voltage level. Once the model is calibrated it can be turned into a good prediction tool useful for planning studies since it permits easily to incorporate the evolution of usages, the characteristics of consumption devices, as well as the evolution of the building’s characteristics

A Novel Protocol Using Small-Scale Spray-Drying for the Efficient Screening of Solid Dispersions in Early Drug Development and Formulation, as a Straight Pathway from Screening to Manufacturing Stages

This work describes a novel screening strategy that implements small-scale spray-drying in early development of binary amorphous solid dispersions (ASDs). The proposed methodology consists of a three-stage decision protocol in which small batches (20&ndash;100 mg) of spray-dried solid dispersions (SDSDs) are evaluated in terms of drug&ndash;polymer miscibility, physical stability and dissolution performance in bio-predictive conditions. The objectives are to select the adequate carrier and drug-loading (DL) for the manufacturing of robust SDSD; and the appropriate stabilizer dissolved in the liquid vehicle of SDSD suspensions, which constitutes the common dosage form used during non-clinical studies. This methodology was verified with CDP146, a poorly water soluble (&lt;2 &micro;g/mL) API combined with four enteric polymers and four stabilizers. CDP146/HPMCAS-LF 40:60 (w/w) and 10% (w/v) PVPVA were identified as the lead SDSD and the best performing stabilizer, respectively. Lead SDSD suspensions (1&ndash;50 mg/mL) were found to preserve complete amorphous state during 8 h and maintain supersaturation in simulated rat intestinal fluids during the absorption window. Therefore, the implementation of spray-drying as a small-scale screening approach allowed maximizing screening effectiveness with respect to very limited API amounts (735 mg) and time resources (9 days), while removing transfer steps between screening and manufacturing phases

Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry

The presented work addresses the problem of particle detection with neural networks (NNs) in defocusing particle tracking velocimetry. A novel approach based on synthetic training data refinement is introduced, with the scope of revising the well documented performance gap of synthetically trained NNs, applied to experimental recordings. In particular, synthetic particle image (PI) data is enriched with image features from the experimental recordings by means of deep learning through an unsupervised image-to-image translation. It is demonstrated that this refined synthetic training data enables the neural-network-based particle detection for a simultaneous increase in detection rate and reduction in the rate of false positives, beyond the capability of conventional detection algorithms. The potential for an increased accuracy in particle detection is revealed with NNs that utilise small scale image features, which further underlines the importance of representative training data. In addition, it is demonstrated that NNs are able to resolve overlapping PIs with a higher reliability and accuracy in comparison to conventional algorithms, suggesting the possibility of an increased seeding density in real experiments. A further finding is the robustness of NNs to inhomogeneous background illumination and aberration of the images, which opens up defocusing PTV for a wider range of possible applications. The successful application of synthetic training-data refinement advances the neural-network-based particle detection towards real world applicability and suggests the potential of a further performance gain from more suitable training data

Aviator: a web service for monitoring the availability of web services

With Aviator, we present a web service and repository that facilitates surveillance of online tools. Aviator consists of a user-friendly website and two modules, a literature-mining based general and a manually curated module. The general module currently checks 9417 websites twice a day with respect to their availability and stores many features (frontend and backend response time, required RAM and size of the web page, security certificates, analytic tools and trackers embedded in the webpage and others) in a data warehouse. Aviator is also equipped with an analysis functionality, for example authors can check and evaluate the availability of their own tools or those of their peers. Likewise, users can check the availability of a certain tool they intend to use in research or teaching to avoid including unstable tools. The curated section of Aviator offers additional services. We provide API snippets for common programming languages (Perl, PHP, Python, JavaScript) as well as an OpenAPI documentation for embedding in the backend of own web services for an automatic test of their function. We query the respective APIs twice a day and send automated notifications in case of an unexpected result. Naturally, the same analysis functionality as for the literature-based module is available for the curated section. Aviator can freely be used at https://www.ccb.uni-saarland.de/aviator

Wirewall a Follow Up: Labratory and Field Measurements of Wave Overtopping

At the Protections 2018 conference, the WireWall wave overtopping research project was introduced. WireWall uses recent advances in high frequency capacitance wire technology that can measure overtopping data. Wave overtopping has now been measured in the laboratory and in the field using the WireWall system. Here we provide an update on the validation of the system in flume tests and results from the first field measurement campaign. Before deployment in the field, an extensive set of tests were carried out in one of the 2D wave flumes at HR Wallingford. These tests simulated known wave conditions from a buoy near the field measurement site, and a representation of the sea wall at the site. The structure (shown in Figure 1) underwent extensive testing and was used to validate the WireWall rig. Using traditional methods of assessing wave overtopping in the flume, the WireWall measurements were directly validated against the known volumes collected in the overtopping tanks. The WireWall field system was deployed at Crosby, Liverpool during the winter of 2018/19, where waves regularly overtop the sea wall. Comparison between the WireWall measurements and the BayonetGPE predictions for one of the Crosby deployments shows good agreement, with the predictions and the WireWall measurements being within the uncertainties estimated for the BayonetGPE predictions

Prediction of Phase Behavior of Spray-Dried Amorphous Solid Dispersions: Assessment of Thermodynamic Models, Standard Screening Methods and a Novel Atomization Screening Device with Regard to Prediction Accuracy

The evaluation of drug–polymer miscibility in the early phase of drug development is essential to ensure successful amorphous solid dispersion (ASD) manufacturing. This work investigates the comparison of thermodynamic models, conventional experimental screening methods (solvent casting, quench cooling), and a novel atomization screening device based on their ability to predict drug–polymer miscibility, solid state properties (Tg value and width), and adequate polymer selection during the development of spray-dried amorphous solid dispersions (SDASDs). Binary ASDs of four drugs and seven polymers were produced at 20:80, 40:60, 60:40, and 80:20 (w/w). Samples were systematically analyzed using modulated differential scanning calorimetry (mDSC) and X-ray powder diffraction (XRPD). Principal component analysis (PCA) was used to qualitatively assess the predictability of screening methods with regards to SDASD development. Poor correlation was found between theoretical models and experimentally-obtained results. Additionally, the limited ability of usual screening methods to predict the miscibility of SDASDs did not guarantee the appropriate selection of lead excipient for the manufacturing of robust SDASDs. Contrary to standard approaches, our novel screening device allowed the selection of optimal polymer and drug loading and established insight into the final properties and performance of SDASDs at an early stage, therefore enabling the optimization of the scaled-up late-stage development