Diffusion of small clusters on metal (100) surfaces: Exact master-equation analysis for lattice-gas models

Exact results are presented for the surface diffusion of small two-dimensional clusters, the constituent atoms of which are commensurate with a square lattice of adsorption sites. Cluster motion is due to the hopping of atoms along the cluster perimeter with various rates. We apply the formalism of Titulaer and Deutch [J. Chem. Phys. 77, 472 (1982)], which describes evolution in reciprocal space via a linear master equation with dimension equal to the number of cluster configurations. We focus on the regime of rapid hopping of atoms along straight close-packed edges, where certain subsets of configurations cycle rapidly between each other. Each such subset is treated as a single quasiconfiguration, thereby reducing the dimension of the evolution equation, simplifying the analysis, and elucidating limiting behavior. We also discuss the influence of concerted atom motions on the diffusion of tetramers and larger clusters

Comparison of ground based and TOMS measurements of SO2 from volcanic emissions

The Brewer Ozone Spectrometer is being used in the World Ozone Network to monitor ozone and SO sub 2. SO sub 2 from natural as well as anthropogenic sources are measured. It has been demonstrated that SO sub 2 interferes with total ozone values as measured by the Dobson Spectrophotometer and the Total Ozone Mapping Spectrometer (TOMS). A small amount of manmade SO sub 2 is difficult to detect and quantify by TOMS because it is located near the surface. However, larger amounts of SO sub 2 injected into the stratosphere from volcanic emissions are detected by TOMS

The use of a formal sensitivity analysis on epidemic models with immune protection from maternally acquired antibodies

This paper considers the outcome of a formal sensitivity analysis on a series of epidemic model structures developed to study the population level effects of maternal antibodies. The analysis is used to compare the potential influence of maternally acquired immunity on various age and time domain observations of infection and serology, with and without seasonality. The results of the analysis indicate that time series observations are largely insensitive to variations in the average duration of this protection, and that age related empirical data are likely to be most appropriate for estimating these characteristics

Multicluster growth via irreversible cooperative filling on lattices

Consider irreversible cooperative filling of sites on an infinite lattice where the filling rates ki depend on the number, i, of occupied sites adjacent to the site(s) being filled. If clustering is significantly enhanced relative to nucleation (k1/k0≡ρ≫1), then the process is thought of as a competition between nucleation, growth, and (possible) coalescence of clusters. These could be Eden clusters with or without permanent voids, Eden trees, or have modified but compact structure (depending on the ki, i≥1). Detailed analysis of the master equations in hierarchial form (exploiting an empty-site shielding property) produces results which are exact (approximate) in one (two or more) dimensions. For linear, square, and (hyper)cubic lattices, we consider the behavior of the average length of linear strings of filled sites, lav=J∞s=1 sls/J∞s=1 ls, where ls is the probability of a string of length s [lav=(1−CTHETA)−1 for random filling, at coverage CTHETA]. In one dimension, ls=ns gives the cluster size distribution, and we write lav=nav. We consider the scaling lav∼A(CTHETA)ρω as ρ→∞ (with CTHETA fixed), which is elucidated by the introduction of simpler models neglecting fluctuations in cluster growth or cluster interference. For an initially seeded lattice, there exists an upper bounding curve lav+ for lav (as a function of CTHETA), which is naturally obtained by switching off nucleation (setting k0=0). We consider scaling of lav+ as the initial seed coverage ε vanishes. The divergence, lav∼C(1−CTHETA)−1 as CTHETA→1, is also considered, focusing on the cooperativity dependence of C. Other results concerning single-cluster densities and ls behavior are discussed

Nested Markov Properties for Acyclic Directed Mixed Graphs

Directed acyclic graph (DAG) models may be characterized in at least four different ways: via a factorization, the d-separation criterion, the moralization criterion, and the local Markov property. As pointed out by Robins (1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of DAG models also imply equality constraints that are not conditional independences. The well-known `Verma constraint' is an example. Constraints of this type were used for testing edges (Shpitser et al., 2009), and an efficient marginalization scheme via variable elimination (Shpitser et al., 2011). We show that equality constraints like the `Verma constraint' can be viewed as conditional independences in kernel objects obtained from joint distributions via a fixing operation that generalizes conditioning and marginalization. We use these constraints to define, via Markov properties and a factorization, a graphical model associated with acyclic directed mixed graphs (ADMGs). We show that marginal distributions of DAG models lie in this model, prove that a characterization of these constraints given in (Tian and Pearl, 2002b) gives an alternative definition of the model, and finally show that the fixing operation we used to define the model can be used to give a particularly simple characterization of identifiable causal effects in hidden variable graphical causal models.Comment: 67 pages (not including appendix and references), 8 figure

Fluctuation-Induced Transitions in a Bistable Surface Reaction: Catalytic CO Oxidation on a Pt Field Emitter Tip

Fluctuations which arise in catalytic CO oxidation on a Pt field emitter tip have been studied with field electron microscopy as the imaging method. Fluctuation-driven transitions between the active and the inactive branch of the reaction are found to occur sufficiently close to the bifurcation point, terminating the bistable range. The experimental results are modeled with Monte Carlo simulations of a lattice-gas reaction model incorporating rapid CO diffusion

Sparse Nested Markov models with Log-linear Parameters

Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which captures equality constraints found in marginals of directed acyclic graph (DAG) models. Some of these constraints, such as the so called `Verma constraint', strictly generalize conditional independence. To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model. Placing such limits is similar in spirit to sparsity methods for undirected graphical models, and regression models. In this paper, we give a log-linear parameterization which allows sparse modeling with nested Markov models. We illustrate the advantages of this parameterization with a simulation study.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Application of Computational Fluid Dynamics (CFD) modelling to retail display and storage of food

This paper describes the work that has been conducted at the University of Bristol on the use of computational fluid dynamic (CFD) modelling to aid the design of retail display cabinets and storage rooms

Surfactant protein D contributes to ocular defense against Pseudomonas aeruginosa in a murine model of dry eye disease.

Dry eye disease can cause ocular surface inflammation that disrupts the corneal epithelial barrier. While dry eye patients are known to have an increased risk of corneal infection, it is not known whether there is a direct causal relationship between these two conditions. Here, we tested the hypothesis that experimentally-induced dry eye (EDE) increases susceptibility to corneal infection using a mouse model. In doing so, we also examined the role of surfactant protein D (SP-D), which we have previously shown is involved in corneal defense against infection. Scopolamine injections and fan-driven air were used to cause EDE in C57BL/6 or Black Swiss mice (wild-type and SP-D gene-knockout). Controls received PBS injections and were housed normally. After 5 or 10 days, otherwise uninjured corneas were inoculated with 10(9) cfu of Pseudomonas aeruginosa strain PAO1. Anesthesia was maintained for 3 h post-inoculation. Viable bacteria were quantified in ocular surface washes and corneal homogenates 6 h post-inoculation. SP-D was measured by Western immunoblot, and corneal pathology assessed from 6 h to 4 days. EDE mice showed reduced tear volumes after 5 and 10 days (each by ∼75%, p<0.001) and showed fluorescein staining (i.e. epithelial disruption). Surprisingly, there was no significant difference in corneal pathology between EDE mice and controls (∼10-14% incidence). Before bacterial inoculation, EDE mice showed elevated SP-D in ocular washes. After inoculation, fewer bacteria were recovered from ocular washes of EDE mice (<2% of controls, p = 0.0004). Furthermore, SP-D knockout mice showed a significant increase in P. aeruginosa corneal colonization under EDE conditions. Taken together, these data suggest that SP-D contributes to corneal defense against P. aeruginosa colonization and infection in EDE despite the loss of barrier function to fluorescein

Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems

Summary 1. Significant advances in both mathematical and molecular approaches in ecology offer unprecedented opportunities to describe and understand ecosystem functioning. Ecological networks describe interactions between species, the underlying structure of communities and the function and stability of ecosystems. They provide the ability to assess the robustness of complex ecological communities to species loss, as well as a novel way of guiding restoration. However, empirically quantifying the interactions between entire communities remains a significant challenge. 2. Concomitantly, advances in DNA sequencing technologies are resolving previously intractable questions in functional and taxonomic biodiversity and provide enormous potential to determine hitherto difficult to observe species interactions. Combining DNA metabarcoding approaches with ecological network analysis presents important new opportunities for understanding large-scale ecological and evolutionary processes, as well as providing powerful tools for building ecosystems that are resilient to environmental change. 3. We propose a novel ‘nested tagging’ metabarcoding approach for the rapid construction of large, phylogenetically structured species-interaction networks. Taking tree–insect–parasitoid ecological networks as an illustration, we show how measures of network robustness, constructed using DNA metabarcoding, can be used to determine the consequences of tree species loss within forests, and forest habitat loss within wider landscapes. By determining which species and habitats are important to network integrity, we propose new directions for forest management. 4. Merging metabarcoding with ecological network analysis provides a revolutionary opportunity to construct some of the largest, phylogenetically structured species-interaction networks to date, providing new ways to: (i) monitor biodiversity and ecosystem functioning; (ii) assess the robustness of interacting communities to species loss; and (iii) build ecosystems that are more resilient to environmental change