A synthetic electric force acting on neutral atoms

Electromagnetism is a simple example of a gauge theory where the underlying potentials -- the vector and scalar potentials -- are defined only up to a gauge choice. The vector potential generates magnetic fields through its spatial variation and electric fields through its time-dependence. We experimentally produce a synthetic gauge field that emerges only at low energy in a rubidium Bose-Einstein condensate: the neutral atoms behave as charged particles do in the presence of a homogeneous effective vector potential. We have generated a synthetic electric field through the time dependence of an effective vector potential, a physical consequence even though the vector potential is spatially uniform

A Case Study of Crowdsourcing Imagery Coding in Natural Disasters

Crowdsourcing and open licensing allow more people to participate in research and humanitarian activities. Open data, such as geographic information shared through OpenStreetMap and image datasets from disasters, can be useful for disaster response and recovery work. This chapter shares a real-world case study of humanitarian-driven imagery analysis, using open-source crowdsourcing technology. Shared philosophies in open technologies and digital humanities, including remixing and the wisdom of the crowd, are reflected in this case study.This research was funded through the European Commission FP7-ICT project: Citizen Cyberlab: Technology Enhanced Creative Learning in the field of Citizen Cyberscience

Intestinal lesions in dogs with acute hemorrhagic diarrhea syndrome associated with netF-positive Clostridium perfringens type A

Acute hemorrhagic diarrhea syndrome (AHDS), formerly named canine hemorrhagic gastroenteritis, is one of the most common causes of acute hemorrhagic diarrhea in dogs, and is characterized by acute onset of diarrhea, vomiting, and hemoconcentration. To date, histologic examinations have been limited to postmortem specimens of only a few dogs with AHDS. Thus, the aim of our study was to describe in detail the distribution, character, and grade of microscopic lesions, and to investigate the etiology of AHDS. Our study comprised 10 dogs with AHDS and 9 control dogs of various breeds, age, and sex. Endoscopic biopsies of the gastrointestinal tract were taken and examined histologically (H&E, Giemsa), immunohistochemically (Clostridium spp., parvovirus), and bacteriologically. The main findings were acute necrotizing and neutrophilic enterocolitis (9 of 10) with histologic detection of clostridia-like, gram-positive bacteria on the necrotic mucosal surface (9 of 10). Clostridium perfringens isolated from the duodenum was identified as type A (5 of 5) by multiplex PCR (5 of 5). In addition, each of the 5 genotyped isolates encoded the pore-forming toxin netF. Clostridium spp. (not C. perfringens) were cultured from duodenal biopsies in 2 of 9 control dogs. These findings suggest that the pore-forming netF toxin is responsible for the necrotizing lesions in the intestines of a significant proportion of dogs with AHDS. Given that the stomach was not involved in the process, the term acute hemorrhagic diarrhea syndrome seems more appropriate than the frequently used term hemorrhagic gastroenteritis

Low genotypic diversity and long-term ecological decline in a spatially structured seagrass population

In isolated or declining populations, viability may be compromised further by loss of genetic diversity. Therefore, it is important to understand the relationship between long-term ecological trajectories and population genetic structure. However, opportunities to combine these types of data are rare, especially in natural systems. Using an existing panel of 15 microsatellites, we estimated allelic diversity in seagrass, Zostera marina, at five sites around the Isles of Scilly Special Area of Conservation, UK, in 2010 and compared this to 23 years of annual ecological monitoring (1996–2018). We found low diversity and long-term declines in abundance in this relatively pristine but isolated location. Inclusion of the snapshot of genotypic, but less-so genetic, diversity improved prediction of abundance trajectories; however, this was spatial scale-dependent. Selection of the appropriate level of genetic organization and spatial scale for monitoring is, therefore, important to identify drivers of eco-evolutionary dynamics. This has implications for the use of population genetic information in conservation, management, and spatial planning

Haplotype Analysis Improved Evidence for Candidate Genes for Intramuscular Fat Percentage from a Genome Wide Association Study of Cattle

In genome wide association studies (GWAS), haplotype analyses of SNP data are neglected in favour of single point analysis of associations. In a recent GWAS, we found that none of the known candidate genes for intramuscular fat (IMF) had been identified. In this study, data from the GWAS for these candidate genes were re-analysed as haplotypes. First, we confirmed that the methodology would find evidence for association between haplotypes in candidate genes of the calpain-calpastatin complex and musculus longissimus lumborum peak force (LLPF), because these genes had been confirmed through single point analysis in the GWAS. Then, for intramuscular fat percent (IMF), we found significant partial haplotype substitution effects for the genes ADIPOQ and CXCR4, as well as suggestive associations to the genes CEBPA, FASN, and CAPN1. Haplotypes for these genes explained 80% more of the phenotypic variance compared to the best single SNP. For some genes the analyses suggested that there was more than one causative mutation in some genes, or confirmed that some causative mutations are limited to particular subgroups of a species. Fitting the SNPs and their interactions simultaneously explained a similar amount of the phenotypic variance compared to haplotype analyses. Haplotype analysis is a neglected part of the suite of tools used to analyse GWAS data, would be a useful method to extract more information from these data sets, and may contribute to reducing the missing heritability problem

Citizen science breathes new life into participatory agricultural research : A review

Participatory research can improve the efficiency, effectiveness, and scope of research processes, and foster social inclusion, empowerment and sustainability. Yet despite four decades of agricultural research institutions exploring and developing methods for participatory research, it has never become mainstream in the agricultural technology development cycle. Citizen science promises an innovative approach to participation in research, using the unique facilities of new digital technologies, but its potential in agricultural research participation has not been systematically probed. To this end, we conducted a critical literature review. We found that citizen science opens up four opportunities for creatively reshaping research: i) new possibilities for interdisciplinary collaboration, ii) rethinking configurations of socio-computational systems, iii) research on democratization of science more broadly, and iv) new accountabilities. Citizen science also brings a fresh perspective on the barriers to institutionalizing participation in the agricultural sciences. Specifically, we show how citizen science can reconfigure cost-motivation-accountability combinations using digital tools, open up a larger conceptual space of experimentation, and stimulate new collaborations. With appropriate and persistent institutional support and investment, citizen science can therefore have a lasting impact on how agricultural science engages with farming communities and wider society, and more fully realize the promises of participation

Diet-related chronic disease in the northeastern United States: a model-based clustering approach

Background: Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. Methods: We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. Results: We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Conclusions: Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease