Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Stereocomplexes Formed From Select Oligomers of Polymer d-lactic Acid (PDLA) and l-lactate May Inhibit Growth of Cancer Cells and Help Diagnose Aggressive Cancers-Applications of the Warburg Effect.

It is proposed that select oligomers of polymer d-lactic acid (PDLA) will form a stereocomplex with l-lactate in vivo, producing lactate deficiency in tumor cells. Those cancer cells that utilize transport of lactate to maintain electrical neutrality may cease to multiply or die because of lactate trapping, and those cancer cells that benefit from utilization of extracellular lactate may be impaired. Intracellular trapping of lactate produces a different physiology than inhibition of LDH because the cell loses the option of shuttling pyruvate to an alternative pathway to produce an anion. Conjugated with stains or fluorescent probes, PDLA oligomers may be an agent for the diagnosis of tissue lactate and possibly cell differentiation in biopsy specimens. Preliminary experimental evidence is presented confirming that PDLA in high concentrations is cytotoxic and that l-lactate forms a presumed stereocomplex with PDLA. Future work should be directed at isolation of biologically active oligomers of PDLA.