Impact of ocean acidification on phytoplankton growth and aggregation

Since the industrial revolution, the ocean has absorbed around one third of the anthropogenic CO2, which induced a profound alteration of the carbonate system commonly known as ocean acidification. Since the preindustrial times, the average ocean surface water pH has fallen by 0.1 units, from approximately 8.2 to 8.1 and a further decrease of 0.4 pH units is expected for the end of the century. Despite their microscopic size, marine diatoms are bio-geo-chemically a very important group, responsible for the export of massive amount of carbon to deep waters and sediments. The knowledge of the potential effects of ocean acidification on the phytoplankton growth and on biological pump is still at its infancy. This study wants to investigate the effect of ocean acidification on the growth of the diatom Skeletonema marinoi and on its aggregation, using a mechanistic approach. The experiment consisted of two treatments (Present and Future) representing different pCO2 conditions and two sequential experimental phases. During the cell growth phase a culture of S. marinoi was inoculated into transparent bags and the effect of ocean acidification was studied on various growth parameters, including DOC and TEP production. The aggregation phase consisted in the incubation of the cultures into rolling tanks where the sinking of particles through the water column was simulated and aggregation promoted. Since few studies investigated the effect of pH on the growth of S. marinoi and none used pH ranges that are compatible with the OA scenarios, there were no baselines. I have shown here, that OA does not affect the cell growth of S. marinoi, suggesting that the physiology of this species is robust in respect to the changes in the carbonate chemistry expected for the end of the century. Furthermore, according to my results, OA does not affect the aggregation of S. marinoi in a consistent manner, suggesting that this process has a high natural variability but is not influenced by OA in a predictable way. The effect of OA was tested over a variety of factors including the number of aggregates produced, their size and sinking velocity, the algal, bacterial and TEP content. Many of these variables showed significant treatment effects but none of these were consistent between the two experiments.

Evaluation of confidence-driven cost aggregation strategies

In this thesis I describe eight new stereo matching algorithms that perform the cost-aggregation step using a guided filter with a confidence map as guidance image, and share the structure of a linear stereo matching algorithm. The results of the execution of the proposed algorithms on four pictures from the Middlebury dataset are shown as well. Finally, based on these results, a ranking of the proposed algorithms is presented.

Mathematical models for cellular aggregation: the chemotactic instability and clustering formation

In this thesis we present a mathematical formulation of the interaction between microorganisms such as bacteria or amoebae and chemicals, often produced by the organisms themselves. This interaction is called chemotaxis and leads to cellular aggregation. We derive some models to describe chemotaxis. The first is the pioneristic Keller-Segel parabolic-parabolic model and it is derived by two different frameworks: a macroscopic perspective and a microscopic perspective, in which we start with a stochastic differential equation and we perform a mean-field approximation. This parabolic model may be generalized by the introduction of a degenerate diffusion parameter, which depends on the density itself via a power law. Then we derive a model for chemotaxis based on Cattaneo's law of heat propagation with finite speed, which is a hyperbolic model. The last model proposed here is a hydrodynamic model, which takes into account the inertia of the system by a friction force. In the limit of strong friction, the model reduces to the parabolic model, whereas in the limit of weak friction, we recover a hyperbolic model. Finally, we analyze the instability condition, which is the condition that leads to aggregation, and we describe the different kinds of aggregates we may obtain: the parabolic models lead to clusters or peaks whereas the hyperbolic models lead to the formation of network patterns or filaments. Moreover, we discuss the analogy between bacterial colonies and self gravitating systems by comparing the chemotactic collapse and the gravitational collapse (Jeans instability).