Epithiospecifier protein activity in broccoli: The link between terminal alkenyl glucosinolates and sulphoraphane nitrile

The chemical nature of the hydrolysis products from the glucosinolate-myrosinase system depends on the presence or absence of supplementary proteins, such as epithiospecifier proteins (ESPs). ESPs (non-catalytic cofactors of myrosinase) promote the formation of epithionitriles from terminal alkenyl glucosinolates and as recent evidence suggests, simple nitriles at the expense of isothiocyanates. The ratio of ESP activity to myrosinase activity is crucial in determining the proportion of these nitriles produced on hydrolysis. Sulphoraphane, a major isothiocyanate produced in broccoli seedlings, has been found to be a potent inducer of phase 2 detoxification enzymes. However, ESP may also support the formation of the non-inductive sulphoraphane nitrile. Our objective was to monitor changes in ESP activity during the development of broccoli seedlings and link these activity changes with myrosinase activity, the level of terminal alkenyl glucosinolates and sulphoraphane nitrile formed. Here, for the first time, we show ESP activity increases up to day 2 after germination before decreasing again to seed activity levels at day 5. These activity changes paralleled changes in myrosinase activity and terminal alkenyl glucosinolate content. There is a significant relationship between ESP activity and the formation of sulforaphane nitrile in broccoli seedlings. The significance of these findings for the health benefits conferred by eating broccoli seedlings is briefly discussed.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.