THE STEAM-JET AEROSOL COLLECTOR

A new principle of sampling aerosol particles by means of steam injection with the consequent collection of grown droplets has been established. An air stream free of water-soluble gases is rapidly mixed with steam. The resulting supersaturation causes aerosol particles to grow into droplets. The droplets containing dissolved aerosol species are then collected by two cyclones in series. The solution collected in the cyclones is constantly pumped out and can be on- or off-line analysed by means of ion chromatography or flow injection analysis. On the basis of the new sampling principle a prototype of an aerosol sampler was designed which is capable of sampling particles quantitatively down to several nanometres in diameter. The mass sampling efficiency of the instrument was found to be 99\%. The detection limit of the sampler for ammonium, sulphate, nitrate and chloride ions is below 0.7 mu g m(-3). By reduction of an already identified source of contamination, much lower detection limits can be achieved. During measurements the sampler proved to be stable, working without any assistance for extended periods of time. Comparison of the sampler with filter packs during measurements of ambient air aerosols showed that the sampler gives good results.

Selecting renal replacement therapies: what do African American and non-African American patients and their families think others should know? A mixed methods study.

BACKGROUND: Little is known regarding the types of information African American and non-African American patients with chronic kidney disease (CKD) and their families need to inform renal replacement therapy (RRT) decisions. METHODS: In 20 structured group interviews, we elicited views of African American and non-African American patients with CKD and their families about factors that should be addressed in educational materials informing patients' RRT selection decisions. We asked participants to select factors from a list and obtained their open-ended feedback. RESULTS: Ten groups of patients (5 African American, 5 non-African American; total 68 individuals) and ten groups of family members (5 African American, 5 non-African American; total 62 individuals) participated. Patients and families had a range (none to extensive) of experiences with various RRTs. Patients identified morbidity or mortality, autonomy, treatment delivery, and symptoms as important factors to address. Family members identified similar factors but also cited the effects of RRT decisions on patients' psychological well-being and finances. Views of African American and non-African American participants were largely similar. CONCLUSIONS: Educational resources addressing the influence of RRT selection on patients' morbidity and mortality, autonomy, treatment delivery, and symptoms could help patients and their families select RRT options closely aligned with their values. Including information about the influence of RRT selection on patients' personal relationships and finances could enhance resources' cultural relevance for African Americans.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Health coaching for glaucoma care: a pilot study using mixed methods.

INTRODUCTION: Adherence to glaucoma medications is essential for successful treatment of the disease but is complex and difficult for many of our patients. Health coaching has been used successfully in the treatment of other chronic diseases. This pilot study explores the use of health coaching for glaucoma care. METHODS: A mixed methods study design was used to assess the health coaching intervention for glaucoma patients. The health coaching intervention consisted of four to six health coaching sessions with a certified health coach via telephone. Quantitative measures included demographic and health information, adherence to glaucoma medications (using the visual analog adherence scale and medication event monitoring system), and an exit survey rating the experience. Qualitative measures included a precoaching health questionnaire, notes made by the coach during the intervention, and an exit interview with the subjects at the end of the study. RESULTS: Four glaucoma patients participated in the study; all derived benefits from the health coaching. Study subjects demonstrated increased glaucoma drop adherence in response to the coaching intervention, in both visual analog scale and medication event monitoring system. Study subjects' qualitative feedback reflected a perceived improvement in both eye and general health self-care. The subjects stated that they would recommend health coaching to friends or family members. CONCLUSION: Health coaching was helpful to the glaucoma patients in this study; it has the potential to improve glaucoma care and overall health.

Percutaneous Revision of a Testicular Prosthesis is Safe, Cost-effective, and Provides Good Patient Satisfaction.

Office-based percutaneous revision of a testicular prosthesis has never been reported. A patient received a testicular prosthesis but was dissatisfied with the firmness of the implant. In an office setting, the prosthesis was inflated with additional fluid via a percutaneous approach. Evaluated outcomes included patient satisfaction, prosthesis size, recovery time, and cost savings. The patient was satisfied, with no infection, leak, or complication after more than 1 year of follow-up, at significantly less cost than revision surgery. Percutaneous adjustment of testicular prosthesis fill-volume can be safe, inexpensive, and result in good patient satisfaction.