The irruption of gas and oil into the Gulf of Mexico during the Deepwater Horizon event fed a deep sea bacterial bloom that consumed hydrocarbons in the affected waters, formed a regional oxygen anomaly, and altered the microbiology of the region. In this work, we develop a coupled physical-metabolic model to assess the impact of mixing processes on these deep ocean bacterial communities and their capacity for hydrocarbon and oxygen use. We find that observed biodegradation patterns are well-described by exponential growth of bacteria from seed populations present at low abundance and that current oscillation and mixing processes played a critical role in distributing hydrocarbons and associated bacterial blooms within the northeast Gulf of Mexico. Mixing processes also accelerated hydrocarbon degradation through an autoinoculation effect, where water masses, in which the hydrocarbon irruption had caused blooms, later returned to the spill site with hydrocarbon-degrading bacteria persisting at elevated abundance. Interestingly, although the initial irruption of hydrocarbons fed successive blooms of different bacterial types, subsequent irruptions promoted consistency in the structure of the bacterial community. These results highlight an impact of mixing and circulation processes on biodegradation activity of bacteria during the Deepwater Horizon event and suggest an important role for mixing processes in the microbial ecology of deep ocean environments.oil spill | well blowout | intrusion layers O il and gas from the Macondo well flowed freely into the deep Gulf of Mexico for a period of 83 d after the explosion and sinking of the Deepwater Horizon (DWH) mobile offshore drilling unit. The environmental release of crude oil occurred at ∼1.5 km water depth with an estimated magnitude of 4.1 × 10 6 barrels (1). Large volumes of gas also emanated from the ruptured well, with reported ratios of gas to oil ranging from 1,600 to 2,800 standard cubic feet of gas (15.6°C, 1 bar) per barrel of oil (2-4). Mass fluxes estimated from these values are 5.4 × 10 11 g for liquid oil and 1.8-3.1 × 10 11 g for natural gases, defined here as alkanes with one to five carbons.Oil and gas entered the ocean initially through multiple openings in the ruptured riser pipe and later, from the top of the blowout preventer after the riser pipe was cut away on June 1, 2010 (1). The hydrocarbon droplets ejected ranged in size from several millimeters down to small droplets with slow ascent rates; 771,000 gal dispersant were applied at depth to promote formation of such small, slow-rising droplets. On release, the bulk of oil and gas began a rapid ascent from the sea floor, entraining sea water as it rose. The entrainment of sea water cooled the oil and gas rapidly (3) and initiated both dissolution of the soluble components and formation of gas hydrate. Kinetically controlled chemical fractionation seems to have persisted for several hundred meters of ascent until the entrained waters separated from the ascending oil (3, 5, 6). These waters f...

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With an increasing number of endosomal cargo molecules studied, it is becoming clear that endocytic routes are diverse, and the cell uses more pathways to adjust expression of cell surface proteins. Intracellular itinerary of integral membrane proteins that avoid the early endosomal recycling route is not enough studied. Therefore, we studied endocytic trafficking of empty L (eL ) molecules, an open form of murine MHC-I allele, in fibroblast-like cells. Pulse labeling of cell surface eL with mAbs and internalization kinetics suggest two steps of endosomal recycling: rapid and late. The same kinetics was also observed for human open MHC-I conformers. Kinetic modeling, using in-house developed software for multicompartment analysis, colocalization studies and established protocols for enriched labeling of the late endosomal (LE) pool of eL demonstrated that the late step of recycling occurs from an LE compartment. Although the majority of eL distributed into pre-degradative multivesicular bodies (MVBs), these LE subsets were not a source for eL recycling. The LE recycling of eL did not require Rab7 membrane domains, as demonstrated by Rab7-silencing, but required vectorial LE motility, suggesting that LE recycling occurs from dynamic tubulovesicular LE domains prior segregation of eL in MVBs. Thus, our study indicates that LE system should not be simply considered as a feeder for loading of the degradative tract of the cell but also as a feeder for loading of the plasma membrane and thereby contribute to the maintenance of homeostasis of plasma membrane proteins. J. Cell. Physiol. 232: 872-887, 2017. © 2016 Wiley Periodicals, Inc.

For every non-autonomous system, there is the related family of Koopman operators K (t,t 0 ) , parameterized by the time pair (t, t 0 ). In this paper we are investigating the time dependency of the spectral properties of the Koopman operator family in the linear non-autonomous case and we propose an algorithm for computation of its spectrum from observed data only. To build this algorithm we use the concept of the fundamental matrix of linear non-autonomous systems and some specific aspects of Arnoldi-like methods. In particular, we use Arnoldi-like methods on local data stencils, we exploit the information contained in the Krylov subspace projection error, and discover limitations in the application of Arnoldi-like methods to cases with continous time dependency. We present results of this data-driven algorithm on various linear non-autonomous systems, hybrid as well as continuous in time. In all the examples comparison with exact eigenvalues and eigenfunctions shows excellent performance of the proposed algorithm. on projection to the eigenfunctions of Koopman operator associated with the dynamical evolution of the observables. Due to the linearity of the Koopman operator, this approach is applicable even if the dynamics is nonlinear. Thus, the dynamical evolution of the system can be described by using Koopman mode analysis, which consists of determining the eigenvalues, eigenfunctions and eigenmodes of the Koopman operator, so that the considered dynamical system can be represented by the corresponding Koopman Mode Decomposition (KMD). An overview of the spectral properties of Koopman operator and its application to the analysis of the fluid flow is given in [13,10,2].A variety of methods for determining the numerical approximation of the KMD has been developed. A general method for computing the Koopman modes, based on the rigorous theoretical results for the generalized Laplace transform, is known under the name Generalized Laplace Analysis (GLA) [10,2]. It reduces to the Wiener's generalized harmonic analysis in the case when all the eigenvalues are on the unit circle [8]. Another method that is closely related to the Koopman operator and is based on its spectral properties is the Dynamic Mode Decomposition (DMD) method [13]. Like GLA, DMD method belongs to the class of data driven algorithms, so that it can be applied to the time series of data, even if the underlying mathematical model is not known. The first DMD method for evaluating the Koopman mode and Koopman eigenvalues was the Arnoldi-like method based on the companion matrix [13]. The more stable algorithm using the similar approach was based on the DMD decomposition and was proposed independently and with no relation to Koopman mode analysis by Schmid in [14,15]. Tu et.al. provide in their paper [19] several alternative algorithms for evaluating DMD modes and eigenvalues and give comparison between them. They introduced the algorithm known under the name exact DMD. Williams et.al. introduced the extension of the DMD algorithm in [21], which they...