Combining metagenomics with metaproteomics and stable isotope probing reveals metabolic pathways used by a naturally occurring marine methylotroph

A variety of culture-independent techniques have been developed that can be used in conjunction with culture-dependent physiological and metabolic studies of key microbial organisms, in order to better understand how the activity of natural populations influences and regulates all major biogeochemical cycles. In this study, we combined DNA-stable isotope probing with metagenomics and metaproteomics to characterize an as yet uncultivated marine methylotroph that actively incorporated carbon from 13C-labeled methanol into biomass. By metagenomic sequencing of the heavy DNA, we retrieved virtually the whole genome of this bacterium and determined its metabolic potential. Through protein-stable isotope probing, the RuMP cycle was established as the main carbon assimilation pathway, and the classical methanol dehydrogenase-encoding gene mxaF, as well as three out of four identified xoxF homologues were found to be expressed. This proof-of-concept study is the first in which theculture-independent techniques of DNA- and protein-stable isotope probing have been used to characterize the metabolism of a naturally-ocurring Methylophaga-like bacterium in the marine environment (i.e. M. thiooxydans L4) and thus provides a powerful approach to access the genome and proteome of uncultivated microbes involved in key processes in the environment

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

Geometry- and diffraction-independent ionization probabilities in intense laser fields: Probing atomic ionization mechanisms with effective intensity matching

We report an experimental technique for the comparison of ionization processes in ultrafast laser pulses irrespective of pulse ellipticity. Multiple ionization of xenon by 50 fs 790 nm, linearly and circularly polarized laser pulses is observed over the intensity range 10 TW/cm(2) to 10 PW/cm(2) using effective intensity matching (EIM), which is coupled with intensity selective scanning (ISS) to recover the geometry-independent probability of ionization. Such measurements, made possible by quantifying diffraction effects in the laser focus, are compared directly to theoretical predictions of multiphoton, tunnel and field ionization, and a remarkable agreement demonstrated. EIM-ISS allows the straightforward quantification of the probability of recollision ionization in a linearly polarized laser pulse. Furthermore, the probability of ionization is discussed in terms of the Keldysh adiabaticity parameter gamma, and the influence of the precursor ionic states present in recollision ionization is observed.

Probing quantum communication rules by addition and subtraction of single photons to/from a light field

The possibility of arbitrarily "adding" and "subtracting" single photons to and from a light field may give access to a complete engineering of quantum states and to fundamental quantum phenomena. We experimentally implemented simple alternated sequences of photon creation and annihilation on a thermal field and used quantum tomography to verify the peculiar character of the resulting light states. In particular, as the final states depend on the order in which the two actions are performed, we directly observed the noncommutativity of the creation and annihilation operators, one of the cardinal concepts of quantum mechanics, at the basis of the quantum behavior of light. These results represent a step toward the full quantum control of a field and may provide new resources for quantum information protocols

Proton Probing Measurement of Electric and Magnetic Fields Generated by ns and ps Laser-Matter Interactions

The use of laser-accelerated protons as a particle probe for the detection of electric fields in plasmas has led in recent years to a wealth of novel information regarding the ultrafast plasma dynamics following high intensity laser-matter interactions. The high spatial quality and short duration of these beams have been essential to this purpose. We will discuss some of the most recent results obtained with this diagnostic at the Rutherford Appleton Laboratory (UK) and at LULI - Ecole Polytechnique (France), also applied to conditions of interest to conventional Inertial Confinement Fusion. In particular, the technique has been used to measure electric fields responsible for proton acceleration from solid targets irradiated with ps pulses, magnetic fields formed by ns pulse irradiation of solid targets, and electric fields associated with the ponderomotive channelling of ps laser pulses in under-dense plasmas.