Flow injection analysis as a tool for enhancing oceanographic nutrient measurements—A review

Macronutrient elements (C, N and P) and micronutrient elements (Fe, Co, Cu, Zn and Mn) are widely measured in their various physico-chemical forms in open ocean, shelf sea, coastal and estuarine waters. These measurements help to elucidate the biogeochemical cycling of these elements in marine waters and highlight the ecological and socio-economic importance of the oceans. Due to the dynamic nature of marine waters in terms of chemical, biological and physical processes, it is advantageous to make these measurements in situ and in this regard flow injection analysis (FIA) provides a suitable shipboard platform. This review, therefore, discusses the role of FIA in the determination of macro- and micro-nutrient elements, with an emphasis on manifold design and detection strategies for the reliable shipboard determination of specific nutrient species. The application of various FIA manifolds to oceanographic nutrient determinations is discussed, with an emphasis on sensitivity, selectivity, high throughput analysis and suitability for underway analysis and depth profiles. Strategies for enhancing sensitivity and minimizing matrix effects, e.g. refractive index (schlieren) effects and the important role of uncertainty budgets in underpinning method validation and data quality are discussed in some detail.     

Improved Method for the Preparation of Oxadiazoles from Diacyl Hydrazines

N-methylimidazole (NMI) is used as the base in the triflic anhydride (Tf₂O)–mediated formation of oxadiazoles from diacyl hydrazines. This reagent is superior to pyridine-derived bases in terms of cost and reaction profile.     

Total syntheses of proposed (±)-trichodermatides B and C

Total syntheses of putative (±)-trichodermatides B and C are described. These efficient syntheses feature the oxa-[3+3] annulation strategy, leading to B and C along with their respective C2-epimers. However, these synthetic samples are spectroscopically very different from the natural products. DFT calculations of C13 chemical shifts are conducted and the predicted values are in good agreement with those of synthetic samples, thereby questioning the accuracy of structural assignments of trichodermatides B and C.     

A critical evaluation of the current state-of-the-art in quantitative imaging mass spectrometry

Mass spectrometry imaging (MSI) has evolved into a valuable tool across many fields of chemistry, biology, and medicine. However, arguably its greatest disadvantage is the difficulty in acquiring quantitative data regarding the surface concentration of the analyte(s) of interest. These difficulties largely arise from the high dependence of the ion signal on the localized chemical and morphological environment and the difficulties associated with calibrating such signals. The development of quantitative MSI approaches would correspond to a giant leap forward for the field, particularly for the biomedical and pharmaceutical fields, and is thus a highly active area of current research. In this review, we outline the current progress being made in the development and application of quantitative MSI workflows with a focus on biomedical applications. Particular emphasis is placed on the various strategies used for both signal calibration and correcting for various ion suppression effects that are invariably present in any MSI study. In addition, the difficulties in validating quantitative-MSI data on a pixel-by-pixel basis are highlighted.

Robust and Accurate Inference via a Mixture of Gaussian and Student’s t Errors

A Gaussian measurement error assumption, that is, an assumption that the data are observed up to Gaussian noise, can bias any parameter estimation in the presence of outliers. A heavy tailed error assumption based on Student’s t distribution helps reduce the bias. However, it may be less efficient in estimating parameters if the heavy tailed assumption is uniformly applied to all of the data when most of them are normally observed. We propose a mixture error assumption that selectively converts Gaussian errors into Student’s t errors according to latent outlier indicators, leveraging the best of the Gaussian and Student’s t errors; a parameter estimation can be not only robust but also accurate. Using simulated hospital profiling data and astronomical time series of brightness data, we demonstrate the potential for the proposed mixture error assumption to estimate parameters accurately in the presence of outliers. Supplemental materials for this article are available online.     

Jets in hadron–hadron collisions

In this article, we review some of the complexities of jet algorithms and of the resultant comparisons of data to theory. We review the extensive experience with jet measurements at the Tevatron, the extrapolation of this acquired wisdom to the LHC and the differences between the Tevatron and LHC environments. We also describe a framework (SpartyJet) for the convenient comparison of results using different jet algorithms.