Different dark conformations function in color-sensitive photosignaling by the sensory rhodopsin I-HtrI complex.

The haloarchaeal phototaxis receptor sensory rhodopsin I (SRI) in complex with its transducer HtrI delivers an attractant signal from excitation with an orange photon and a repellent signal from a second near-UV photon excitation. Using a proteoliposome system with purified SRI in complex with its transducer HtrI, we identified by site-directed fluorescence labeling a site (Ser(155)) on SRI that is conformationally active in signal relay to HtrI. Using site-directed spin labeling of Ser(155)Cys with a nitroxide side chain, we detected a change in conformation following one-photon excitation such that the spin probe exhibits a splitting of the outer hyperfine extrema (2A'(zz)) significantly smaller than that of the electron paramagnetic resonance spectrum in the dark state. The dark conformations of five mutant complexes that do not discriminate between orange and near-UV excitation show shifts to lower or higher 2A'(zz) values correlated with the alterations in their motility behavior to one- and two-photon stimuli. These data are interpreted in terms of a model in which the dark complex is populated by two conformers in the wild type, one that inhibits the CheA kinase (A) and the other that activates it (R), shifted in the dark by mutations and shifted in the wild-type SRI-HtrI complex in opposite directions by one-photon and two-photon reactions.

High-sensitivity rod photoreceptor input to the blue-yellow color opponent pathway in macaque retina.

Small bistratified cells (SBCs) in the primate retina carry a major blue-yellow opponent signal to the brain. We found that SBCs also carry signals from rod photoreceptors, with the same sign as S cone input. SBCs exhibited robust responses under low scotopic conditions. Physiological and anatomical experiments indicated that this rod input arose from the AII amacrine cell-mediated rod pathway. Rod and cone signals were both present in SBCs at mesopic light levels. These findings have three implications. First, more retinal circuits may multiplex rod and cone signals than were previously thought to, efficiently exploiting the limited number of optic nerve fibers. Second, signals from AII amacrine cells may diverge to most or all of the approximately 20 retinal ganglion cell types in the peripheral primate retina. Third, rod input to SBCs may be the substrate for behavioral biases toward perception of blue at mesopic light levels.

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. ^

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^