Estimating the mean and standard deviation of concentration distributions using censored samples

Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

The dynamics of calmodulin signaling revealed using optical methods

The Ca2+-binding protein calmodulin (CaM) is a key transducer of Ca2+ oscillations by virtue of its ability to bind Ca 2+ selectively and then interact specifically with a large number of downstream enzymes and proteins. It remains unclear whether Ca2+ -dependent signaling alone can activate the full range of Ca 2+/CaM regulated processes or whether other regulatory schemes in the cell exist that allow specific targeting of CaM to subsets of Ca 2+/CaM binding sites or regions of the cell. Here we investigate the possibility that alterations of the availability of CaM may serve as a potential cellular mechanism for regulating the activation of CaM-dependent targets. By utilizing sensitive optical techniques with high spatial and temporal resolution, we examine the intracellular dynamics of CaM signaling at a resolution previously unattainable. After optimizing and characterizing both the optical methods and fluorescently labeled probes for intracellular measurements, the diffusion of CaM in the cytoplasm of HEK293 cells was analyzed. It was discovered that the diffusion characteristics of CaM are similar to that of a comparably sized inert molecule. Independent manipulation of experimental parameters, including increases in total concentrations of CaM and intracellular Ca2+ levels, did not change the diffusion of CaM in the cytoplasm. However, changes in diffusion were seen when the concentration of Ca2+/CaM-binding targets was increased in conjunction with elevated Ca2+. This indicates that CaM is not normally limiting for the activation of Ca 2+/CaM-dependent enzymes in HEK293 cells but reveals that the ratio of CaM to CaM-dependent targets is a potential mechanism for changing CaM availability. Next we considered whether cellular compartmentalization may act to regulate concentrations of available Ca2+/CaM in hippocampal neurons. We discovered changes in diffusion parameters of CaM under elevated Ca2+ conditions in the soma, neurite and nucleus which suggest that either the composition of cytoplasm is different in these compartments and/or they are composed of unique families of CaM-binding proteins. Finally, we return to the HEK293 cell and for the first time directly show the intracellular binding of CaM and CaMKII, an important target for CaM critical for neuronal function and plasticity. Furthermore, we analyzed the complex binding stoichiometry of this molecular interaction in the basal, activated and autophosphorylated states of CaMKII and determined the impact of this binding on CaM availability in the cell. Overall these results demonstrate that regulation of CaM availability is a viable cellular mechanism for regulating the output of CaM-dependent processes and that this process is tuned to the specific functional needs of a particular cell type and subcellular compartment. ^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Development of combined micro -preparation, separation, and mass spectrometry methods and application in the microdialysis study of neuropeptide metabolism

Two sets of mass spectrometry-based methods were developed specifically for the in vivo study of extracellular neuropeptide biochemistry. First, an integrated micro-concentration/desalting/matrix-addition device was constructed for matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) to achieve attomole sensitivity for microdialysis samples. Second, capillary electrophoresis (CE) was incorporated into the above micro-liquid chromatography (LC) and MALDI MS system to provide two-dimensional separation and identification (i.e. electrophoretic mobility and molecular mass) for the analysis of complex mixtures. The latter technique includes two parts of instrumentation: (1) the coupling of a preconcentration LC column to the inlet of a CE capillary, and (2) the utilization of a matrix-precoated membrane target for continuous CE effluent deposition and for automatic MALDI MS analysis (imaging) of the CE track.^ Initial in vivo data reveals a carboxypeptidase A (CPA) activity in rat brain involved in extracellular neurotensin metabolism. Benzylsuccinic acid, a CPA inhibitor, inhibited neurotensin metabolite NT1-12 formation by 70%, while inhibitors of other major extracellular peptide metabolizing enzymes increased NT1-12 formation. CPA activity has not been observed in previous in vitro experiments. Next, the validity of the methodology was demonstrated in the detection and structural elucidation of an endogenous neuropeptide, (L)VV-hemorphin-7, in rat brain upon ATP stimulation. Finally, the combined micro-LC/CE/MALDI MS was used in the in vivo metabolic study of peptide E, a mu-selective opioid peptide with 25 amino acid residues. Profiles of 88 metabolites were obtained, their identity being determined by their mass-to-charge ratio and electrophoretic mobility. The results indicate that there are several primary cleavage sites in vivo for peptide E in the release of its enkephalin-containing fragments. ^