Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

The proteolytic processing of bovine fibrinogen by MMP-2 (gelatinase A), which brings about the formation of a product unable to form fibrin clots, has been studied at 37 °C. Catalytic parameters, although showing a somewhat lower catalytic efficiency with respect to thrombin and plasmin, indeed display values indicating a pathophysiological significance of this process. A parallel molecular modelling study predicts preferential binding of MMP-2 to the β-chain of fibrinogen through its haemopexin-like domain, which has been directly demonstrated by the inhibitory effect in the presence of the exogenous haemopexin-like domain. However, the removal of this domain does not impair the interaction between MMP-2 and fibrinogen, but it dramatically alters the proteolytic mechanism, producing different fragmentation intermediates. The investigation at various pH values between 6.0 and 9.3 indicates a proton-linked behaviour, which is relevant for interpreting the influence on the process by environmental conditions occurring at the site of an injury. Furthermore, the action of MMP-2 on peroxynitrite-treated fibrinogen has been investigated, a situation possibly occurring under oxidative stress. The chemical alteration of fibrinogen, which has been shown to abolish its clotting activity, brings about only limited modifications of the catalytic parameters without altering the main enzymatic mechanism.

Efficient biological electron transfer may require a fluid association of redox partners. Two noncrystallographic methods (a new molecular docking program and 1H NMR spectroscopy) have been used to study the electron transfer complex formed between the cytochrome c peroxidase (CCP) of Paracoccus denitrificans and cytochromes c. For the natural redox partner, cytochrome c550, the results are consistent with a complex in which the heme of a single cytochrome lies above the exposed electron-transferring heme of the peroxidase. In contrast, two molecules of the nonphysiological but kinetically competent horse cytochrome bind between the two hemes of the peroxidase. These dramatically different patterns are consistent with a redox active surface on the peroxidase that may accommodate more than one cytochrome and allow lateral mobility.