FT-IR spectroscopy of CAI and chondrules in primitive chondrites: techniques and first results

From the Introduction: Here we present preliminary mid-infrared spectra of CAI, chondrules and matrix from the CV3.2 carbonaceous chondrite Allende. This is part of our ongoing project to compile a database of infrared and optical spectra of minerals and components of primitive meteorites. These spectra should allow a better comparison with spectra from astronomical sources e.g. from dust and molecular clouds or young solar systems

Laminitis in horses

Laminitis is a common and painful condition of adult equids that often results in permanent lameness or euthanasia. In recent years, our knowledge of the condition has developed and this article discusses the current understanding of laminitis and approaches to its treatment and prevention

Mid-infrared spectroscopy of CAI and AOA from the Allende CV3.2 chondrite

Mid-infrared spectra of bulk CAI from the CV3.2 chondrite Allende are presented and compared with astronomical spectra of cometary dust, zodiacal light,the circumstellar disk of beta Pictoris and dust around the red supergiant PR Per

Optimizing data collection for public health decisions: a data mining approach

Background: Collecting data can be cumbersome and expensive. Lack of relevant, accurate and timely data for research to inform policy may negatively impact public health. The aim of this study was to test if the careful removal of items from two community nutrition surveys guided by a data mining technique called feature selection, can (a) identify a reduced dataset, while (b) not damaging the signal inside that data. Methods: The Nutrition Environment Measures Surveys for stores (NEMS-S) and restaurants (NEMS-R) were completed on 885 retail food outlets in two counties in West Virginia between May and November of 2011. A reduced dataset was identified for each outlet type using feature selection. Coefficients from linear regression modeling were used to weight items in the reduced datasets. Weighted item values were summed with the error term to compute reduced item survey scores. Scores produced by the full survey were compared to the reduced item scores using a Wilcoxon rank-sum test. Results: Feature selection identified 9 store and 16 restaurant survey items as significant predictors of the score produced from the full survey. The linear regression models built from the reduced feature sets had R2 values of 92% and 94% for restaurant and grocery store data, respectively. Conclusions: While there are many potentially important variables in any domain, the most useful set may only be a small subset. The use of feature selection in the initial phase of data collection to identify the most influential variables may be a useful tool to greatly reduce the amount of data needed thereby reducing cost