Environmental vulnerability of the global ocean epipelagic plankton community interactome.

Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles and help regulate climate. Although global surveys are starting to reveal ecological drivers underlying planktonic community structure and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here, we leveraged Tara Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network-the community interactome-and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar) and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change and forecasted the most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios while identifying plausible plankton bioindicators for ocean monitoring of climate change.

Probabilistic Modeling of Microbial Metabolic Networks for Integrating Partial Quantitative Knowledge Within the Nitrogen Cycle.

Understanding the interactions between microbial communities and their environment sufficiently to predict diversity on the basis of physicochemical parameters is a fundamental pursuit of microbial ecology that still eludes us. However, modeling microbial communities is problematic, because (i) communities are complex, (ii) most descriptions are qualitative, and (iii) quantitative understanding of the way communities interact with their surroundings remains incomplete. One approach to overcoming such complications is the integration of partial qualitative and quantitative descriptions into more complex networks. Here we outline the development of a probabilistic framework, based on Event Transition Graph (ETG) theory, to predict microbial community structure across observed chemical data. Using reverse engineering, we derive probabilities from the ETG that accurately represent observations from experiments and predict putative constraints on communities within dynamic environments. These predictions can feedback into the future development of field experiments by emphasizing the most important functional reactions, and associated microbial strains, required to characterize microbial ecosystems.

Geant4-based Monte Carlo simulations on GPU for medical applications.

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography and radiotherapy. However MCS is also associated with long calculation times that prevent its use in routine clinical practice. Recently, graphics processing units (GPU) became in many domains a low cost alternative for the acquisition of high computational power. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was chosen as the MCS engine given the large variety of physics processes available for targeting different medical imaging and radiotherapy applications. In addition, Geant4 is the MCS engine behind GATE which is actually the most popular medical applications' simulation platform. We propose the definition of a global strategy and associated structures for such a GPU based simulation implementation. Different photon and electron physics effects are resolved on the fly directly on GPU without any approximations with respect to Geant4. Validations have shown equivalence in the underlying photon and electron physics processes between the Geant4 and the GPU codes with a speedup factor of 80-90. More clinically realistic simulations in emission and transmission imaging led to acceleration factors of 400-800 respectively compared to corresponding GATE simulations.