A simplified model of decontamination by BWR steam suppression pools

Phenomena that can decontaminate aerosol-laden gases sparging through steam suppression pools of boiling water reactors during reactor accidents are described. Uncertainties in aerosol properties, aerosol behavior within gas bubbles, and bubble behavior in plumes affect predictions of decontamination by steam suppression pools. Uncertainties in the boundary and initial conditions that are dictated by the progression of severe reactor accidents and that will affect predictions of decontamination by steam suppression pools are discussed. Ten parameters that characterize boundary and initial condition uncertainties, nine parameters that characterize aerosol property and behavior uncertainties, and eleven parameters that characterize uncertainties in the behavior of bubbles in steam suppression pools are identified. Ranges for the values of these parameters and subjective probability distributions for parametric values within the ranges are defined. These uncertain parameters are used in Monte Carlo uncertainty analyses to develop uncertainty distributions for the decontamination that can be achieved by steam suppression pools and the size distribution of aerosols that do emerge from such pools. A simplified model of decontamination by steam suppression pools is developed by correlating features of the uncertainty distributions for total decontamination factor, DF(total), mean size of emerging aerosol particles, d{sub p}, and the standard deviation of the emerging aerosol size distribution, {sigma}, with pool depth, H. Correlations of the median values of the uncertainty distributions are suggested as the best estimate of decontamination by suppression pools. Correlations of the 10 percentile and 90 percentile values of the uncertainty distributions characterize the uncertainty in the best estimates. 295 refs., 121 figs., 113 tabs

Erratum to: What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder

What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder

A Mathematical Model of Liver Cell Aggregation In Vitro

The behavior of mammalian cells within three-dimensional structures is an area of intense biological research and underpins the efforts of tissue engineers to regenerate human tissues for clinical applications. In the particular case of hepatocytes (liver cells), the formation of spheroidal multicellular aggregates has been shown to improve cell viability and functionality compared to traditional monolayer culture techniques. We propose a simple mathematical model for the early stages of this aggregation process, when cell clusters form on the surface of the extracellular matrix (ECM) layer on which they are seeded. We focus on interactions between the cells and the viscoelastic ECM substrate. Governing equations for the cells, culture medium, and ECM are derived using the principles of mass and momentum balance. The model is then reduced to a system of four partial differential equations, which are investigated analytically and numerically. The model predicts that provided cells are seeded at a suitable density, aggregates with clearly defined boundaries and a spatially uniform cell density on the interior will form. While the mechanical properties of the ECM do not appear to have a significant effect, strong cell-ECM interactions can inhibit, or possibly prevent, the formation of aggregates. The paper concludes with a discussion of our key findings and suggestions for future work

Fredholm determinants and the statistics of charge transport

Using operator algebraic methods we show that the moment generating function of charge transport in a system with infinitely many non-interacting Fermions is given by a determinant of a certain operator in the one-particle Hilbert space. The formula is equivalent to a formula of Levitov and Lesovik in the finite dimensional case and may be viewed as its regularized form in general. Our result embodies two tenets often realized in mesoscopic physics, namely, that the transport properties are essentially independent of the length of the leads and of the depth of the Fermi sea.Comment: 30 pages, 2 figures, reference added, credit amende

Movement ecology of a mobile predatory fish reveals limited habitat linkages within a temperate estuarine seascape

Large predatory fishes, capable of traveling great distances, can facilitate energy flow linkages among spatially separated habitat patches via extended foraging behaviors over expansive areas. Here, we tested this concept by tracking the movement of a large mobile estuarine fish, red drum (Sciaenops ocellatus). Specifically, we addressed the following two questions: (i) What are the spatial and temporal patterns of red drum movement (rates of dispersal) and activity space? (ii) Does red drum movement facilitate linkages among estuarine marsh complexes? Dispersal from the release location was greatest during the first 2 weeks at liberty before declining to less than 0.5 km·week–1 for the remainder of the study. Activity space initially increased rapidly before reaching an asymptote at 2.5 km2 2 weeks postrelease. Connectivity indices calculated among marsh complexes corroborated these observations, suggesting high residency and limited seascape-scale linkages via red drum movement behaviors. These data highlight potential within-estuary spatial structure for mobile fishes and could inform subsequent efforts to track energy flows in coastal food webs, predict the footprint of local habitat restoration benefits, and enhance the design of survey regimes to quantify overall population demography

Prospects for gulf of Mexico environmental recovery and restoration

Previous oil spills provide clear evidence that ecosystem restoration efforts are challenging, and recovery can take decades. Similar to the Ixtoc 1 well blowout in 1979, the Deepwater Horizon (DWH) oil spill was enormous both in volume of oil spilled and duration, resulting in environmental impacts from the deep ocean to the Gulf of Mexico coastline. Data collected during the National Resource Damage Assessment showed significant damage to coastal areas (especially marshes), marine organisms, and deep-sea habitat. Previous spills have shown that disparate regions recover at different rates, with especially long-term effects in salt marshes and deepsea habitat. Environmental recovery and restoration in the northern Gulf of Mexico are dependent upon fundamental knowledge of ecosystem processes in the region. PostDWH research data provide a starting point for better understanding baselines and ecosystem processes. It is imperative to use the best science available to fully understand DWH environmental impacts and determine the appropriate means to ameliorate those impacts through restoration. Filling data gaps will be necessary to make better restoration decisions, and establishing new baselines will require long-term studies. Future research, especially via NOAA’s RESTORE Science Program and the state-based Centers of Excellence, should provide a path to understanding the potential for restoration and recovery of this vital marine ecosystem

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types