Single atom detection from low contrast-to-noise ratio electron microscopy images

Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.Team Raf Van de Pla

Guaranteed ellipse fitting with the Sampson distance

When faced with an ellipse fitting problem, practitioners frequently resort to algebraic ellipse fitting methods due to their simplicity and efficiency. Currently, practitioners must choose between algebraic methods that guarantee an ellipse fit but exhibit high bias, and geometric methods that are less biased but may no longer guarantee an ellipse solution. We address this limitation by proposing a method that strikes a balance between these two objectives. Specifically, we propose a fast stable algorithm for fitting a guaranteed ellipse to data using the Sampson distance as a data-parameter discrepancy measure. We validate the stability, accuracy, and efficiency of our method on both real and synthetic data. Experimental results show that our algorithm is a fast and accurate approximation of the computationally more expensive orthogonal-distance-based ellipse fitting method. In view of these qualities, our method may be of interest to practitioners who require accurate and guaranteed ellipse estimates.Zygmunt L. Szpak, Wojciech Chojnacki and Anton van den Henge

Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands: 2. Infestation Statistics on Economic Hosts

Detailed host records are listed for 39 species of Bactrocera and 2 species of Dacus fruit flies, infesting 98 species of commercial and edible fruits in the Pacific Island Countries and Territories, based on sampling and incubating in laboratory almost 13,000 field collected samples, or over 380,000 fruits. For each host-fly-country association, quantitative data are presented on the weight and number of fruits collected, the proportion of infested samples, the number of adult flies emerged per kg of fruits and, whenever available, the percentage of individual fruits infested. All the published records of each fly-host-country association are cited and erroneous or dubious published records are rectified or commented. Laboratory forced infestation data are also cited and reviewed

News place-making:applying ‘mental mapping’ to explore the journalistic interpretive community

Scholarship in visual communications, media, and geography explore how news media assign meanings to environment through narratives of and about place. In this study, however, the author aims to move scholarship from evaluating journalistic place representations to exploring the cultural and ideological processes of how these place representations come to be. Understanding how journalists construct place adds depth to knowledge about news as a social and cultural construction, and contributes to previous research on news place-characterizations. This study enacts a methodology called ‘mental mapping’ and serves as a call for communication scholars to consider such participatory methods. Data for this study come from interviews with 30 participants, including reporters from three newspapers, public officials, and residents of Iowa City, USA. In the end, this study identifies a visual methodology for exploring the role and influence of how journalists work and represent place in the news, a process the author calls news place-making

Reducing uncertainty in prediction of wheat performance under climate change

Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles