Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses

Citation: Boysen, C., Davis, E. G., Beard, L. A., Lubbers, B. V., & Raghavan, R. K. (2015). Bayesian Geostatistical Analysis and Ecoclimatic Determinants of Corynebacterium pseudotuberculosis Infection among Horses. Plos One, 10(10), 15. doi:10.1371/journal.pone.0140666Kansas witnessed an unprecedented outbreak in Corynebacterium pseudotuberculosis infection among horses, a disease commonly referred to as pigeon fever during fall 2012. Bayesian geostatistical models were developed to identify key environmental and climatic risk factors associated with C. pseudotuberculosis infection in horses. Positive infection status among horses (cases) was determined by positive test results for characteristic abscess formation, positive bacterial culture on purulent material obtained from a lanced abscess (n = 82), or positive serologic evidence of exposure to organism (>= 1:512)(n = 11). Horses negative for these tests (n = 172)(controls) were considered free of infection. Information pertaining to horse demographics and stabled location were obtained through review of medical records and/or contact with horse owners via telephone. Covariate information for environmental and climatic determinants were obtained from USDA (soil attributes), USGS (land use/land cover), and NASA MODIS and NASA Prediction of Worldwide Renewable Resources (climate). Candidate covariates were screened using univariate regression models followed by Bayesian geostatistical models with and without covariates. The best performing model indicated a protective effect for higher soil moisture content (OR = 0.53, 95% CrI = 0.25, 0.71), and detrimental effects for higher land surface temperature (>= 35 degrees C) (OR = 2.81, 95% CrI = 2.21, 3.85) and habitat fragmentation (OR = 1.31, 95% CrI = 1.27, 2.22) for C. pseudotuberculosis infection status in horses, while age, gender and breed had no effect. Preventative and ecoclimatic significance of these findings are discussed

Letter from the Consistory of Rijssen to Rev. A. C. Van Raalte

A letter from the consistory in Rijsen to Rev. Albertus C. Van Raalte releasing Van Raalte from his service to the Rijssen congregation. Apparently V.R. assisted this congregation in becoming established while he was serving the Ommen congregation.https://digitalcommons.hope.edu/vrp_1840s/1001/thumbnail.jp

El rol de les xarxes socials en el sentit de pertinença dels espanyols a Islàndia

A causa de la crisi econòmica del 2008-2014, molts espanyols van emigrar per buscar feina a slàndia. Allà, el nombre d'espanyols s'ha quintuplicat entre el 2008 i el 2021, i molts dels nous migrants són joves altament qualificats. Un nou estudi explora com les xarxes socials els van ajudar a integrar-se al país nòrdic, que continua sent poc multicultural, i alhora mantenir les seves relacions socials a Espanya.A causa de la crisis económica de 2008-2014, muchos españoles emigraron para buscar empleo a Islandia. Allí, el número de españoles se ha quintuplicado entre el 2008 y el 2021, y muchos de estos nuevos migrantes son jóvenes altamente cualificados. Un nuevo estudio explora cómo las redes sociales les ayudaron a integrarse en el país nórdico, que sigue siendo poco multicultural, y a la vez a mantener sus relaciones sociales en España.Due to the economic crisis of 2008-2014, many Spaniards emigrated to search for work in Iceland. There, the number of Spaniards has increased fivefold between 2008 and 2021, and many of these new migrants are highly qualified young people. A new study explores how social media helped them integrate into the Nordic country, which is still not very multicultural, and at the same time helped them maintain their social relationships in Spain

Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness

Machine learning regression can predict macroscopic fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. Here we show that a similar approach is successful using event catalogs derived from the continuous data. Our methods are applicable to catalogs of arbitrary scale and magnitude of completeness. We investigate how machine learning regression from an event catalog of laboratory earthquakes performs as a function of the catalog magnitude of completeness. We find that strong model performance requires a sufficiently low magnitude of completeness, and below this magnitude of completeness, model performance saturates