Description: With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model their most common type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they were familiar with to model synthetic titration data that are typical of those obtained by applying state-of-the-art electrochemical methods - anodic stripping voltammetry (ASV) and competitive ligand equilibration-adsorptive cathodic stripping voltammetry (CLE-ACSV) - to the analysis of natural waters. Herein, we compare their estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations ([Mf]), and evaluate the influence of the various methods and assumptions used on these results.The ASV-type titrations were designed to test each participant's ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameters. For the four samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate 'measurements' of ambient [Mf]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one's parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean-squared relative error between the fitted observations and the simulated curves should be comparable to the expected precision of the analytical method and upon visual inspection the distribution of residuals should not be skewed.Modeling the synthetic, CLE-ACSV-type titration dataset, which comprises 5 titration curves generated at different analytical windows or levels of competing ligand added to the virtual sample, proved to be more challenging due to the random measurement error that was incorporated. Comparison of the submitted results was complicated by the participants' differing interpretations of their task. Most adopted the provided 'true' instrumental sensitivity in modeling the CLE-ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since most fitted sensitivities were biased low, systematic error in inferred ambient [Mf] and in estimated weak ligand (L2) concentrations resulted.The main distinction between the mathematical approaches taken by participants lies in the functional form of the speciation model equations, with their implicit definition of independent and dependent or manipulated variables. In 'direct modeling', the dependent variable is the measured [Mf] (or I p) and the total metal concentration ([M]T) is considered independent. In other, much more widely used methods of analyzing titration data - classical linearization, best known as van den Berg/Ružić, and isotherm fitting by nonlinear regression, best known as the Langmuir or Gerringa methods - [Mf] is defined as independent and the dependent variable calculated from both [M]T and [Mf]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [Mf] revealed that the best results were obtained by the direct approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability.Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using direct modeling of the unified multiwindow dataset, while the single most accurate set of results also included simultaneous calibration. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that more practical experimental designs for multi-window titrations be developed.Finally, while all mathematical approaches proved to be adequate for some datasets, matrix-based equilibrium models proved to be most naturally suited for the most challenging cases encountered in this work, i.e., experiments where the added ligand in ACSV became titrated. The ProMCC program (Omanović et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercomparison exercise.

Description: In 2005, the complex permittivity of the surface of Saturn's moon Titan was measured by the PWA-MIP/HASI (Permittivity Wave Altimetry-Mutual Impedance Probe/Huygens Atmospheric Structure Instrument) experiment on board the Huygens probe. The analysis of these measurements was recently refined but could not be interpreted in terms of composition due to the lack of knowledge on the low-frequency/low-temperature electrical properties of Titan's organic material, a likely key ingredient of the surface composition. In order to fill that gap, we developed a dedicated measurement bench and investigated the complex permittivity of analogs of Titan's organic aerosols called "tholins". These laboratory measurements, together with those performed in the microwave domain, are then used to derive constraints on the composition of Titan's first meter below the surface based on both the PWA-MIP/HASI and the Cassini Radar observations. Assuming a ternary mixture of water-ice, tholin-like dust and pores (filled or not with liquid methane), we find that at least 10% of water ice and 15% of porosity are required to explain observations. On the other hand, there should be at most 50-60% of organic dust. PWA-MIP/HASI measurements also suggest the presence of a thin conductive superficial layer at the Huygens landing site. Using accurate numerical simulations, we put constraints on the electrical conductivity of this layer as a function of its thickness (e.g., in the range 7-40 nS/m for a 7-mm thick layer). Potential candidates for the composition of this layer are discussed.

Description: We have recently reported that cannabinoid agonists can up-regulate and enhance the activity of serotonin 2A (5-HT2A) receptors in the prefrontal cortex (PFCx). Increased expression and activity of cortical 5-HT2A receptors has been associated with neuropsychiatric disorders, such as anxiety and schizophrenia. Here we report that repeated CP55940 exposure selectively up-regulates GRK5 proteins in rat PFCx and in a neuronal cell culture model. We sought to examine the mechanism underlying the regulation of GRK5 and to identify the role of GRK5 in the cannabinoid agonist-induced up-regulation and enhanced activity of 5-HT2A receptors. Interestingly, we found that cannabinoid agonist-induced up-regulation of GRK5 involves CB2 receptors, β-arrestin 2, and ERK1/2 signaling because treatment with CB2 shRNA lentiviral particles, β-arrestin 2 shRNA lentiviral particles, or ERK1/2 inhibitor prevented the cannabinoid agonist-induced up-regulation of GRK5. Most importantly, we found that GRK5 shRNA lentiviral particle treatment prevented the cannabinoid agonist-induced up-regulation and enhanced 5-HT2A receptor-mediated calcium release. Repeated cannabinoid exposure was also associated with enhanced phosphorylation of CB2 receptors and increased interaction between β-arrestin 2 and ERK1/2. These latter phenomena were also significantly inhibited by GRK5 shRNA lentiviral treatment. Our results suggest that sustained activation of CB2 receptors, which up-regulates 5-HT2A receptor signaling, enhances GRK5 expression; the phosphorylation of CB2 receptors; and the β-arrestin 2/ERK interactions. These data could provide a rationale for some of the adverse effects associated with repeated cannabinoid agonist exposure.