The effects of spacecraft environments on some hydrolytic enzyme patterns in bacteria

The effects of space flight on the production and characteristics of proteolytic enzymes are studied for a number of bacterial species isolated from crew members and spacecraft. Enzymatic make-up and cultural characteristics of bacteria isolated from spacecraft crew members are determined. The organism Aeromonas proteolytica and the proteolytic enzymes which it produces are used as models for future spacecraft experiments

Using the general equilibrium growth model to study great depressions: a reply to Temin

Three of the arguments made by Temin (2008) in his review of Great Depressions of the Twentieth Century are demonstrably wrong: that the treatment of the data in the volume is cursory; that the definition of great depressions is too general and, in particular, groups slow growth experiences in Latin America in the 1980s with far more severe great depressions in Europe in the 1930s; and that the book is an advertisement for the real business cycle methodology. Without these three arguments — which are the results of obvious conceptual and arithmetical errors, including copying the wrong column of data from a source — his review says little more than that he does not think it appropriate to apply our dynamic general equilibrium methodology to the study of great depressions, and he does not like the conclusion that we draw: that a successful model of a great depression needs to be able to account for the effects of government policy on productivity.

Blogging as a viable research methodology for young people with arthritis: a qualitative study.

The development of services that are responsive to the needs of users is a health policy priority. Finding ways of engaging young people in research to gain insights into their particular experiences, perspectives, and needs is vital but challenging. These data are critical to improving services in ways that meet the needs of young people

Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.Comment: 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6). Differences from first submission: some minor edits in response to feedback

XLVI. On the figure of the earth

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