Biosensors based on peptide exposure show single molecule conformations in live cells

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An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Semiparametric Regression in Size-Biased Sampling

Summary .  Size-biased sampling arises when a positive-valued outcome variable is sampled with selection probability proportional to its size. In this article, we propose a semiparametric linear regression model to analyze size-biased outcomes. In our proposed model, the regression parameters of covariates are of major interest, while the distribution of random errors is unspecified. Under the proposed model, we discover that regression parameters are invariant regardless of size-biased sampling. Following this invariance property, we develop a simple estimation procedure for inferences. Our proposed methods are evaluated in simulation studies and applied to two real data analyses.     

Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

Does Mother Nature really prefer rare species or are log‐left‐skewed SADs a sampling artefact?

Intensively sampled species abundance distributions (SADs) show left‐skew on a log scale. That is, there are too many rare species to fit a lognormal distribution. I propose that this log‐left‐skew might be a sampling artefact. Monte Carlo simulations show that taking progressively larger samples from a log‐unskewed distribution (such as the lognormal) causes log‐skew to decrease asymptotically (move towards ?∞) until it reaches the level of the underlying distribution (zero in this case). In contrast, accumulating certain types of repeated small samples results in a log‐skew that becomes progressively more log‐left‐skewed to a level well beyond the underlying distribution. These repeated samples correspond to samples from the same site over many years or from many sites in 1 year. Data from empirical datasets show that log‐skew generally goes from positive (right‐skewed) to negative (left‐skewed) as the number of temporally or spatially replicated samples increases. This suggests caution when interpreting log‐left‐skew as a pattern that needs biological interpretation.     

Identification of a four copper folding intermediate in mammalian copper metallothionein by electrospray ionization mass spectrometry

 Mammalian metallothioneins (MT) are known to maximally bind 12 copper ions in two six-Cu(I) ion clusters. Using electrospray ionization mass spectrometry of MT at pH 4.5, a four-Cu(I) ion cluster was observed intermediate to a fully formed six Cu(I) in a single domain or a fully formed Cu₁₂MT species. The four-Cu(I) cluster was observed in both MT1 and MT3 isoforms. Addition of increasing amounts of Cu(I) to MT at pH 4.5 resulted in prominent ions whoses masses were consistent with apo-MT, Cu₄MT, Cu₆MT, and Cu₁₂MT. The cooperativity of cluster formation was reduced at pH 2.5. Addition of Cu(I) to apo-MT at a reduced pH resulted in a series of ions consistent with Cu₄ to Cu₁₂MT species. However, formation of the tetracopper MT species remained cooperative at low pH, suggesting that this species is very stable. To determine whether the tetracopper cluster was formed in either the α or β domain, domain peptides of MT3 were used. Addition of Cu(I) to the apo β domain resulted in a peak consistent with the formation of a four-Cu(I) cluster. This is consistent with reports that Cu(I) ions bind preferentially to the β domain of MTs. Received: 2 June 1998 / Accepted: 21 August 1998     

EPR Monitored Redox Titration of the Cofactors of Saccharomyces cerevisiae Nar1

Electron Paramagnetic Resonance (EPR) monitored redox titrations are a powerful method to determine the midpoint potential of cofactors in proteins and to identify and quantify the cofactors in their detectable redox state.The technique is complementary to direct electrochemistry (voltammetry) approaches, as it does not offer information on electron transfer rates, but does establish the identity and redox state of the cofactors in the protein under study. The technique is widely applicable to any protein containing an electron paramagnetic resonance (EPR) detectable cofactor.A typical titration requires 2 ml protein with a cofactor concentration in the range of 1-100 µM. The protein is titrated with a chemical reductant (sodium dithionite) or oxidant (potassium ferricyanide) in order to poise the sample at a certain potential. A platinum wire and a Ag/AgCl reference electrode are connected to a voltmeter to measure the potential of the protein solution. A set of 13 different redox mediators is used to equilibrate between the redox cofactors of the protein and the electrodes. Samples are drawn at different potentials and the Electron Paramagnetic Resonance spectra, characteristic for the different redox cofactors in the protein, are measured. The plot of the signal intensity versus the sample potential is analyzed using the Nernst equation in order to determine the midpoint potential of the cofactor.