Photodissociation of OCS: deviations between theory and experiment, and the importance of higher order correlation effects.

The photodissociation of carbonyl sulfide (OCS) was investigated theoretically in a series of studies by Schmidt and co-workers. Initial studies [J. A. Schmidt, M. S. Johnson, G. C. McBane, and R. Schinke, J. Chem. Phys. 136, 131101 (2012); J. A. Schmidt, M. S. Johnson, G. C. McBane, and R. Schinke, J. Chem. Phys. 137, 054313 (2012)] found photodissociation in the first UV-band to occur mainly by excitation of the 2(1)A' (A) excited state. However, in a later study [G. C. McBane, J. A. Schmidt, M. S. Johnson, and R. Schinke, J. Chem. Phys. 138, 094314 (2013)] it was found that a significant fraction of photodissociation must occur by excitation of 1(1)A″ (B) excited state to explain the product angular distribution. The branching between excitation of the A and B excited states is determined by the magnitude of the transition dipole moment vectors in the Franck-Condon region. This study examines the sensitivity of these quantities to changes in the employed electronic structure methodology. This study benchmarks the methodology employed in previous studies against highly correlated electronic structure methods (CC3 and MRAQCC) and provide evidence in support of the picture of the OCS photodissociation process presented in [G. C. McBane, J. A. Schmidt, M. S. Johnson, and R. Schinke, J. Chem. Phys. 138, 094314 (2013)] showing that excitation of A and B electronic states both contribute significantly to the first UV absorption band of OCS. In addition, this study presents evidence in support of the assertion that the A state potential energy surface employed in previous studies underestimates the energy at highly bent geometries (γ ∼ 70°) leading to overestimated rotational energy in the product CO.

Meta-analysis and other approaches for synthesizing structured and unstructured data in plant pathology.

The term data deluge is used widely to describe the rapidly accelerating growth of information in the technical literature, in scientific databases, and in informal sources such as the Internet and social media. The massive volume and increased complexity of information challenge traditional methods of data analysis but at the same time provide unprecedented opportunities to test hypotheses or uncover new relationships via mining of existing databases and literature. In this review, we discuss analytical approaches that are beginning to be applied to help synthesize the vast amount of information generated by the data deluge and thus accelerate the pace of discovery in plant pathology. We begin with a review of meta-analysis as an established approach for summarizing standardized (structured) data across the literature. We then turn to examples of synthesizing more complex, unstructured data sets through a range of data-mining approaches, including the incorporation of 'omics data in epidemiological analyses. We conclude with a discussion of methodologies for leveraging information contained in novel, open-source data sets through web crawling, text mining, and social media analytics, primarily in the context of digital disease surveillance. Rapidly evolving computational resources provide platforms for integrating large and complex data sets, motivating research that will draw on new types and scales of information to address big questions.