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In this report, we present a compilation of reported cetane numbers for pure chemical compounds. The compiled database contains cetane values for 299 pure compounds, including 156 hydrocarbons and 143 oxygenates. Cetane number is a relative ranking of fuels based on the amount of time between fuel injection and ignition. The cetane number is typically measured either in a combustion bomb or in a single-cylinder research engine. This report includes cetane values from several different measurement techniques -each of which has associated uncertainties. Additionally, many of the reported values are determined by measuring blending cetane numbers, which introduces significant error. In many cases, the measurement technique is not reported nor is there any discussion about the purity of the compounds. Nonetheless, the data in this report represent the best pure compound cetane number values available from the literature as of August 2004.

α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors mediate fast excitatory neurotransmission by converting chemical signals into electrical signals. Thus, it is important to understand the relationship between their chemical biology and their function. Single molecule fluorescence resonance energy transfer (smFRET) was used to examine the conformations explored by the agonist binding domain of the AMPA receptor for wild type and T686 mutant proteins. Each form of the agonist binding domain exhibited a dynamic, multi-state sequential equilibrium, which could only be identified using wavelet shrinkage, a signal processing technique that removes experimental shot-noise. These results illustrate that the extent of activation is dependent not on a rigid closed cleft, but instead on the probability that a given subunit will occupy a closed cleft conformation, which in turn is not only determined by the lowest energy state but by the range of states that the protein explores.

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We introduce a step transition and state identification (STaSI) method for piecewise constant single-molecule data with a newly derived minimum description length equation as the objective function. We detect the step transitions using the Student’s t test and group the segments into states by hierarchical clustering. The optimum number of states is determined based on the minimum description length equation. This method provides comprehensive, objective analysis of multiple traces requiring few user inputs about the underlying physical models and is faster and more precise in determining the number of states than established and cutting-edge methods for single-molecule data analysis. Perhaps most importantly, the method does not require either time-tagged photon counting or photon counting in general and thus can be applied to a broad range of experimental setups and analytes.

This paper presents a new approach to estimating the conditional probability distribution of multiperiod _nancial returns[ Estimation of the tails of the distribution is particularly important for risk management tools\ such as Value!at!Risk models[ A popular approach is to assume a Gaussian dis! tribution\ and to use a theoretically derived variance expression which is a non!linear function of the holding period\ k\ and the one!step!ahead vola! tility forecast\ s ¼ t ¦ 0 [ The new method avoids the need for a distributional assumption by applying quantile regression to the historical returns from a range of di}erent holding periods to produce quantile models which are functions of k and s ¼ t ¦ 0 [ A neural network is used to estimate the potentially non!linear quantile models[ Using daily exchange rates\ the approach is compared to GARCH!based quantile estimates [ The results suggest that the new method o}ers a useful alternative for estimating the conditional density[ Copyright Þ 1999 John Wiley + Sons\ Ltd[ KEY WORDS quantile regression^neural networks^multiperiod returnsĉ onditional density Correspondence to] James W[ Taylor\ Sa( Ãd Business School\ University of Oxford\ 48 George Street\ Oxford\ OX0 1BE\ UK[ E!mail] james[taylorÝsbs[ox[ac[uk 299 J[ W[ Taylor

A method to denoise single-molecule fluorescence resonance energy (smFRET) trajectories using wavelet detail thresholding and Bayesian inference is presented. Bayesian methods are developed to identify fluorophore photoblinks in the time trajectories. Simulated data are used to quantify the improvement in static and dynamic data analysis. Application of the method to experimental smFRET data shows that it distinguishes photoblinks from large shifts in smFRET efficiency while maintaining the important advantage of an unbiased approach. Known sources of experimental noise are examined and quantified as a means to remove their contributions via soft thresholding of wavelet coefficients. A wavelet decomposition algorithm is described, and thresholds are produced through the knowledge of noise parameters in the discrete-time photon signals. Reconstruction of the signals from thresholded coefficients produces signals that contain noise arising only from unquantifiable parameters. The method is applied to simulated and observed smFRET data, and it is found that the denoised data retain their underlying dynamic properties, but with increased resolution.

Progress toward competitive and integrated employment (CIE) for people with intellectual and developmental disabilities (I/DD) over the last 40 years has been mixed. Despite evidence showing that supported employment interventions can enable adults with I/DD to effectively get and keep jobs, national rates of integrated employment remain below a third of the working age population (Hiersteiner et al., 2016). Progress is being made to improve these outcomes. Pathways have been identified that lead to CIE through supported employment, customized employment, internship experiences, and postsecondary education (Siperstein, Heyman, & Stokes, 2014; Wehman, 2011). The recent passage of the Workforce Innovation and Opportunity Act (WIOA) has created fresh momentum and increased the onus on interagency collaboration. This article examines what is known about promoting CIE through these pathways and highlights recommendations for future research and policy change. Recommendations for the future are provide direction toward positive change in CIE into the 21 st century.