This study photolytically generates, from 2-bromoethanol photodissociation, the 2-hydroxyethyl radical intermediate of the OH + ethene reaction and measures the velocity distribution of the stable radicals. We introduce an impulsive model to characterize the partitioning of internal energy in the C(2)H(4)OH fragment. It accounts for zero-point and thermal vibrational motion to determine the vibrational energy distribution of the nascent C(2)H(4)OH radicals and the distribution of total angular momentum, J, as a function of the total recoil kinetic energy imparted in the photodissociation. We render this system useful for the study of the subsequent dissociation of the 2-hydroxyethyl radical to the possible asymptotic channels of the OH + ethene reaction. The competition between these channels depends on the internal energy and the J distribution of the radicals. First, we use velocity map imaging to separately resolve the C(2)H(4)OH + Br((2)P(3/2)) and C(2)H(4)OH + Br((2)P(1/2)) photodissociation channels, allowing us to account for the 10.54 kcal/mol partitioned to the Br((2)P(1/2)) cofragment. We determine an improved resonance enhanced multiphoton ionization (REMPI) line strength for the Br transitions at 233.681 nm (5p (4)P(1/2) <-- 4p (2)P(3/2)) and 234.021 nm (5p (2)S(1/2) <-- 4p (2)P(1/2)) and obtain a spin-orbit branching ratio for Br((2)P(1/2)):Br((2)P(3/2)) of 0.26 +/- 0.03:1. Energy and momentum conservation give the distribution of total internal energy, rotational and vibrational, in the C(2)H(4)OH radicals. Then, using 10.5 eV photoionization, we measure the velocity distribution of the radicals that are stable to subsequent dissociation. The onset of dissociation occurs at internal energies much higher than those predicted by theoretical methods and reflects the significant amount of rotational energy imparted to the C(2)H(4)OH photofragment. Instead of estimating the mean rotational energy with an impulsive model from the equilibrium geometry of 2-bromoethanol, our model explicitly includes weighting over geometries across the quantum wave function with zero, one, and two quanta in the harmonic mode that most strongly alters the exit impact parameter. The model gives a nearly perfect prediction of the measured velocity distribution of stable radicals near the dissociation onset using a G4 prediction of the C-Br bond energy and the dissociation barrier for the OH + ethene channel calculated by Senosiain et al. (J. Phys. Chem. A 2006, 110, 6960). The model also indicates that the excited state dissociation proceeds primarily from a conformer of 2-bromoethanol that is trans across the C-C bond. We discuss the possible extensions of our model and the effect of the radical intermediate's J-distribution on the branching between the OH + ethene product channels.

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LicenceAuthors of JOSS papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY). SummaryThe drake R package (Landau 2018) is a workflow manager and computational engine for data science projects. Its primary objective is to keep results up to date with the underlying code and data. When it runs a project, drake detects any pre-existing output and refreshes the pieces that are outdated or missing. Not every runthrough starts from scratch, and the final answers are reproducible. With a user-friendly R-focused interface, comprehensive documentation, and extensive implicit parallel computing support, drake surpasses the analogous functionality in similar tools such as Make (Stallman 1998), remake (FitzJohn 2017), memoise (Wickham et al. 2017), and knitr (Xie 2017).In reproducible research, drake's role is to provide tangible evidence that a project's results are re-creatable. Drake quickly detects when the code, data, and output are synchronized. In other words, drake helps determine if the starting materials would produce the expected output if the project were to start over and run from scratch. This approach decreases the time and effort it takes to evaluate research projects for reproducibility.Regarding high-performance computing, drake interfaces with a wide variety of technologies to deploy the steps of a data analysis project. Options range from local multicore computing to serious distributed computing on a cluster. In addition, the parallel computing is implicit. In other words, drake constructs the directed acyclic network of the workflow and determines which steps can run simultaneously and which need to wait for dependencies. This automation eases the cognitive and computational burdens on the user, enhancing the readability of code and thus reproducibility.

A central goal of RNA sequencing (RNA-seq) experiments is to detect differentially expressed genes. In the ubiquitous negative binomial model for RNA-seq data, each gene is given a dispersion parameter, and correctly estimating these dispersion parameters is vital to detecting differential expression. Since the dispersions control the variances of the gene counts, underestimation may lead to false discovery, while overestimation may lower the rate of true detection. After briefly reviewing several popular dispersion estimation methods, this article describes a simulation study that compares them in terms of point estimation and the effect on the performance of tests for differential expression. The methods that maximize the test performance are the ones that use a moderate degree of dispersion shrinkage: the DSS, Tagwise wqCML, and Tagwise APL. In practical RNA-seq data analysis, we recommend using one of these moderate-shrinkage methods with the QLShrink test in QuasiSeq R package.