Diel rhythmicity in amino acid uptake by Prochlorococcus

The marine cyanobacterium Prochlorococcus, the most abundant phototrophic organism on Earth, numerically dominates the phytoplankton in nitrogen (N)-depleted oceanic gyres. Alongside inorganic N sources such as nitrite and ammonium, natural populations of this genus also acquire organic N, specifically amino acids. Here, we investigated using isotopic tracer and flow cytometric cell sorting techniques whether amino acid uptake by Prochlorococcus is subject to a diel rhythmicity, and if so, whether this was linked to a specific cell cycle stage. We observed, in contrast to diurnally similar methionine uptake rates by Synechococcus cells, obvious diurnal rhythms in methionine uptake by Prochlorococcus cells in the tropical Atlantic. These rhythms were confirmed using reproducible cyclostat experiments with a light-synchronized axenic Prochlorococcus (PCC9511 strain) culture and S-35-methionine and H-3-leucine tracers. Cells acquired the tracers at lower rates around dawn and higher rates around dusk despite > 10(4) times higher concentration of ammonium in the medium, presumably because amino acids can be directly incorporated into protein. Leucine uptake rates by cells in the S+G(2) cell cycle stage were consistently 2.2 times higher than those of cells at the G(1) stage. Furthermore, S+G(2) cells upregulated amino acid uptake 3.5 times from dawn to dusk to boost protein synthesis prior to cell division. Because Prochlorococcus populations can account from 13% at midday to 42% at dusk of total microbial uptake of methionine and probably of other amino acids in N-depleted oceanic waters, this genus exerts diurnally variable, strong competitive pressure on other bacterioplankton populations.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.