Spin transport and spin dephasing in zinc oxide

The wide bandgap semiconductor ZnO is interesting for spintronic applications because of its small spin-orbit coupling implying a large spin coherence length. Utilizing vertical spin valve devices with ferromagnetic electrodes (TiN/Co/ZnO/Ni/Au), we study the spin-polarized transport across ZnO in all-electrical experiments. The measured magnetoresistance agrees well with the prediction of a two spin channel model with spin-dependent interface resistance. Fitting the data yields spin diffusion lengths of 10.8nm (2K), 10.7nm (10K), and 6.2nm (200K) in ZnO, corresponding to spin lifetimes of 2.6ns (2K), 2.0ns (10K), and 31ps (200K).Comment: 7 pages, 5 figures; supplemental material adde

The Characteristic Structural Features of the Blood Vessels of the Lewis Lung Carcinoma (A Light Microscopic and Scanning Electron Microscopic Study)

Vascular corrosion casts of Lewis lung carcinomas (LLC) grown subcutaneously in C57BL/6-mice are correlated with histological sections and with tumor tissue prepared for scanning electron microscopy (SEM). By making low, medium and high pressure cast preparations we studied the influence of perfusion and injection pressure on the resulting cast sample. Three types of vascular proliferations are distinguishable in LLC: 1) Small globular outgrowths on sinusoidal dilated tumor capillaries, caused by proliferation of their endothelial cells. 2) New sprouts on surrounding host vessels, invading the small, still avascular implant. 3) Superficially located, centrifugally running sprouts in peripheral regions of large tumors. They invade the surrounding host tissue. Vascular sprouts are of venous origin, have a fragmentary endothelium and are rather leaky if casted. High pressure preparations of large tumors reveal central avascular cavities surrounded by centripetally running, compressed and blind ending tumor vessels. Irrespective of the applied injection pressure, the casts always exhibit extravasal channels caused by degeneration of the endothelium of central tumor vessels. We show that SEM of vascular corrosion casts combined with histology not only demonstrates such contrary processes as the development of tumor blood vessels and the simultaneously occurring vascular degeneration, but also elucidates all other morphological characteristics of the tumor vascular system

The Angioarchitecture of the Lewis Lung Carcinoma in Laboratory Mice (A Light Microscopic and Scanning Electron Microscopic Study)

53 Lewis lung carcinomas implanted subcutaneously into C57BL/6-mice were examined. The animals were killed at various stages of tumor growth (TG) and prepared for histology and for scanning electron microscopy (critical-point-dried tissue; vascular corrosion casts). Prior to casting animals were rinsed using different perfusion pressures. Casting was done by manual injection of the resin, whereby different influx-rates were applied resulting in low, medium and high pressure preparations. We discern 3 phases of tumor angiogenesis (TA) occurring during 4 stages of TG among which vasodilation establishes the first reaction of the host vascular system to a growing tumor implant. During this stage 1 of TG, tumor nidation, nearby sinusoidal dilated host capillaries form globular outgrowings (phase 1 of TA) Subsequently radially arranged sprouts, which preferentially arise from venous host vessels, grow into the centre of the implant (phase 2 of TA). Stage 2 of TG, early tumor growth, is characterized by necrosis of the central tumor tissue and the development of a central avascular cavity. Thus the tumor vascular system is organized like a hollow sphere with a central cavity and a peripheral vascular envelope with large vessels embracing the tumor and centrifugally growing vascular sprouts, which arise from the venous part of the vascular envelope and invade the surrounding host tissue (phase 3 of TA). During stage 3 of TG, late tumor growth, many vessels of the basket-like vascular envelope obliterate. In stage 4 of TG, prefinal phase, the peripheral vascular density decreases continuously. Thus vascular sprouting and proliferation of viable tumor cells is confined to basal regions of the tumor

Quality of life of survivors of paediatric intensive care

Objective: The mortality rate in paediatric intensive care units (PICU) has fallen over the last two decades. More advanced treatment is offered to children with life-threatening disease and there is substantial interest in knowing whether long term outcome and quality of life after intensive care are acceptable

Stochastic blockmodels and community structure in networks

Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure

Effects of Contact Network Models on Stochastic Epidemic Simulations

The importance of modeling the spread of epidemics through a population has led to the development of mathematical models for infectious disease propagation. A number of empirical studies have collected and analyzed data on contacts between individuals using a variety of sensors. Typically one uses such data to fit a probabilistic model of network contacts over which a disease may propagate. In this paper, we investigate the effects of different contact network models with varying levels of complexity on the outcomes of simulated epidemics using a stochastic Susceptible-Infectious-Recovered (SIR) model. We evaluate these network models on six datasets of contacts between people in a variety of settings. Our results demonstrate that the choice of network model can have a significant effect on how closely the outcomes of an epidemic simulation on a simulated network match the outcomes on the actual network constructed from the sensor data. In particular, preserving degrees of nodes appears to be much more important than preserving cluster structure for accurate epidemic simulations.Comment: To appear at International Conference on Social Informatics (SocInfo) 201

Beyond clustering: mean-field dynamics on networks with arbitrary subgraph composition

Clustering is the propensity of nodes that share a common neighbour to be connected. It is ubiquitous in many networks but poses many modelling challenges. Clustering typically manifests itself by a higher than expected frequency of triangles, and this has led to the principle of constructing networks from such building blocks. This approach has been generalised to networks being constructed from a set of more exotic subgraphs. As long as these are fully connected, it is then possible to derive mean-field models that approximate epidemic dynamics well. However, there are virtually no results for non-fully connected subgraphs. In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes, to a high degree of accuracy, the expected values of system-level quantities, such as the prevalence of infection. Our approach offers a previously unattainable degree of control over the arrangement of subgraphs and network characteristics such as classical node degree, variance and clustering. The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network metrics constant. Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating networks based on different subgraphs but with identical degree distribution and clustering, leads to non-negligible differences in epidemic dynamics

Time to cut: Population models reveal how to mow invasive common ragweed cost-effectively

Roadsides are an important habitat for invasive common ragweed, Ambrosia artemisiifolia L., by facilitating seed dispersal. Reducing the size of roadside populations is therefore essential for confining this highly allergenic species. Here, we aim to determine the cost-effectiveness of mowing regimes varying in frequency and timing, by analysing population-level effects and underlying demographic processes. We constructed population models of A. artemisiifolia parameterised by demographic data for four unmanaged reference populations across Europe in two years. We integrated the effects of four experimental mowing regimes along Austrian road sides on plant performance traits of five years and experimental data on seed viability after cutting. All four experimental regimes reduced the projected intrinsic population growth rates (r) compared to the unmanaged controls by reducing plant height and seed viability, thereby counteracting increased size-dependent fecundity. The prevailing 2-cut regime in Austria (cutting during vegetative growth, here in June and just before seed ripening, here in September) performed least well and the reduction in r was mainly due to reduced seed viability after the second cut. The efficacy of the two best experimental regimes (alternative schemes for 2 or 3 cuts) was mainly due to cutting just before female flowering (here in August) by decreasing final adult plant height dramatically and thereby reducing seed numbers. Patterns were consistent across reference populations and years. Whether regimes reduced r below replacement level, however, varied per population, year and the survival rate of the seeds in the soil bank. Our model allowed projecting effects of five theoretical mowing regimes with untested combinations of cuts on r. By plotting r-cost relationships for all regimes, we identified the most cost-effective schemes for each cutting frequency (1-3 cuts). They all included the cut just before female flowering, highlighting the importance of cutting at this moment (here in August). Our work features i) the suitability of a modelling approach for the demography of an annual species with a seed bank, ii) the importance of seed viability in assessing mowing effects, iii) the use of population models in designing cost-effective mowing regimes

An efficient and principled method for detecting communities in networks

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint. Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models. We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times. We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods. We also show that the same approach can be used to extract nonoverlapping community divisions via a relaxation method, and demonstrate that the algorithm is competitively fast and accurate for the nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl

Fast variables determine the epidemic threshold in the pairwise model with an improved closure

Pairwise models are used widely to model epidemic spread on networks. These include the modelling of susceptible-infected-removed (SIR) epidemics on regular networks and extensions to SIS dynamics and contact tracing on more exotic networks exhibiting degree heterogeneity, directed and/or weighted links and clustering. However, extra features of the disease dynamics or of the network lead to an increase in system size and analytical tractability becomes problematic. Various `closures' can be used to keep the system tractable. Focusing on SIR epidemics on regular but clustered networks, we show that even for the most complex closure we can determine the epidemic threshold as an asymptotic expansion in terms of the clustering coefficient.We do this by exploiting the presence of a system of fast variables, specified by the correlation structure of the epidemic, whose steady state determines the epidemic threshold. While we do not find the steady state analytically, we create an elegant asymptotic expansion of it. We validate this new threshold by comparing it to the numerical solution of the full system and find excellent agreement over a wide range of values of the clustering coefficient, transmission rate and average degree of the network. The technique carries over to pairwise models with other closures [1] and we note that the epidemic threshold will be model dependent. This emphasises the importance of model choice when dealing with realistic outbreaks