Modeling physiological processes in plankton on enzyme kinetic principles

[EN] Many ecologically important chemical transformations in the ocean are controlled by biochemical enzyme reactions in plankton. Nitrogenase regulates the transformation of N2 to ammonium in some cyanobacteria and serves as the entryway for N2 into the ocean biosphere. Nitrate reductase controls the reduction of NO3 to NO2 and hence new production in phytoplankton. The respiratory electron transfer system in all organisms links the carbon oxidation reactions of intermediary metabolism with the reduction of oxygen in respiration. Rubisco controls the fixation of CO2 into organic matter in phytoplankton and thus is the major entry point of carbon into the oceanic biosphere. In addition to these, there are the enzymes that control CO2 production, NH4 excretion and the fluxes of phosphate. Some of these enzymes have been recognized and researched by marine scientists in the last thirty years. However, until recently the kinetic principles of enzyme control have not been exploited to formulate accurate mathematical equations of the controlling physiological expressions. Were such expressions available they would increase our power to predict the rates of chemical transformations in the extracellular environment of microbial populations whether this extracellular environment is culture media or the ocean. Here we formulate from the principles of bisubstrate enzyme kinetics, mathematical expressions for the processes of NO3 reduction, O2 consumption, N2 fixation, total nitrogen uptake.

On the use of simple geometric descriptors provided by RGB-D sensors for re-identification

[EN]The re-identification problem has been commonly accomplished using appearance features based on salient points and color information. In this paper, we focus on the possibilities that simple geometric features obtained from depth images captured with RGB-D cameras may offer for the task, particularly working under severe illumination conditions. The results achieved for different sets of simple geometric features extracted in a top-view setup seem to provide useful descriptors for the re-identification task, which can be integrated in an ambient intelligent environment as part of a sensor network.