The frequency of epstein-barr virus infection and associated lymphoproliferative syndrome after transplantation and its manifestations in children

Twenty cases of Epstein-Barr virus (EBV)-associated lymphoproliferative syndrome (LPS), defined by the presence of EBV nuclear antigen and/or EBV DNA in tissues, were diagnosed in 1467 transplant recipients in Pittsburgh from 1981—1985. The frequency of occurrence in pediatric transplant recipients was 4% (10/ 253), while in adults it was 0.8% (10/1214) (P < .0005). The frequency of LPS in adults declined after 1983 coincidental with the introduction of cyclosporine monitoring. However there was no apparent decline of LPS in children. We describe these ten pediatric cases and one additional case of LPS in a child who received her transplant before 1981. The frequency of EBV infection in 92 pediatric liver recipients was 63%. Of these subjects, 49% were sero-negative and 77% of those acquired primary infection. Of 11 cases of pediatric EBV-associated LPS, 10 were in children who had primary infection shortly before or after transplantation. These results reinforce the impor-tance of primary EBV infection in producing LPS, which was previously shown in adults. Children are at greater risk because they are more likely to be seronegative for EBV and to acquire primary infection. Three clinical types of LPS were recognized in children. The first (5 cases) was a self-limited mononucleo-sislike syndrome. The second syndrome (4 cases) began similarly, but then progressed over the next two months to widespread lymphoproliferation in internal organs and death. The third type (2 cases) was an extranodal intestinal monoclonal B cell lymphoma, occurring late after primary infection. © 1988 by The Williams and Wilkins Co

Nickel Isotopic Composition and Nickel/Iron Ratio in the Solar Wind: Results from SOHO/CELIAS/MTOF

Using the Mass Time-of-Flight Spectrometer (MTOF)—part of the Charge, Elements, Isotope Analysis System (CELIAS)—onboard the Solar Heliospheric Observatory (SOHO) spacecraft, we derive the nickel isotopic composition for the isotopes with mass 58, 60 and 62 in the solar wind. In addition we measure the elemental abundance ratio of nickel to iron. We use data accumulated during ten years of SOHO operation to get sufficiently high counting statistics and compare periods of different solar wind velocities. We compare our values with the meteoritic ratios, which are believed to be a reliable reference for the solar system and also for the solar outer convective zone, since neither element is volatile and no isotopic fractionation is expected in meteorites. Meteoritic isotopic abundances agree with the terrestrial values and can thus be considered to be a reliable reference for the solar isotopic composition. The measurements show that the solar wind elemental Ni/Fe-ratio and the isotopic composition of solar wind nickel are consistent with the meteoritic values. This supports the concept that low-FIP elements are fed without relative fractionation into the solar wind. Our result also confirms the absence of substantial isotopic fractionation processes for medium and heavy ions acting in the solar win

Determination of Sulfur Abundance in the Solar Wind

Solar chemical abundances are determined by comparing solar photospheric spectra with synthetic ones obtained for different sets of abundances and physical conditions. Although such inferred results are reliable, they are model dependent. Therefore, one compares them with the values for the local interstellar medium (LISM). The argument is that they must be similar, but even for LISM abundance determinations models play a fundamental role (i.e., temperature fluctuations, clumpiness, photon leaks). There are still two possible comparisons—one with the meteoritic values and the second with solar wind abundances. In this work we derive a first estimation of the solar wind element ratios of sulfur relative to calcium and magnesium, two neighboring low-FIP elements, using 10 years of CELIAS/MTOF data. We compare the sulfur abundance with the abundance determined from spectroscopic observations and from solar energetic particles. Sulfur is a moderately volatile element, hence, meteoritic sulfur may be depleted relative to non-volatile elements, if compared to its original solar system valu

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

EuroSL – a European taxonomic backbone for vegetation databases and other taxon- related databases: version 1.0

Background: A taxonomic reference list is an indispensable tool to sample, manage and match biodiversity data from different sources. Merging vegetation databases or combining them with taxon-related attributes needs reliable and consistent information about the taxon concepts used and an appropriate naming. Aim: Creating a “taxonomic backbone” of European vascular plants and bryophytes with links to widespread taxonomic references. Methods: We used the Euro+Med plant list (Euro+Med 2006ff), version 2015/04. For all families not yet covered there we used taxa from Flora Europaea (Tutin et al. 1980ff). Additionally we included the aggregates from the Ehrendorfer (1973) list. For bryophytes we rely on Grolle & Long (2000) and Hill et al. (2006). Results: EuroSL 1.0 covers > 45T accepted taxa and >77T synonyms from approx. 370 families. At the species level this means approx. 32T accepted names and >44T synonyms. EuroSL list will be published open access to allow referencing and connecting taxon-related databases beyond country borders. Future releases of EuroSL might contain additional taxonomic groups (algae and lichens), aggregates or new names as needed. However, a thorough documentation and transparency regarding taxon concepts, i.e. name usage = taxon circumscription, given by citing the source lists, will remain the highest priority. The first application of EuroSL will be the compilation of Ecological Indicator Values for Europe (EIVE version 1.0)

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

Solar-Wind Bulk Velocity Throughout the Inner Heliosphere from Multi-Spacecraft Measurements

We extrapolate solar-wind bulk velocity measurements for different in-ecliptic heliospheric positions by calculating the theoretical time lag between the locations. The solar-wind bulk velocity dataset is obtained from in-situ plasma measurements by STEREO A and B, SOHO, Venus Express, and Mars Express. During their simultaneous measurements between 2007 and 2009 we find typical solar activity minimum conditions. In order to validate our extrapolations of the STEREO A and B data, we compare them with simultaneous in-situ observations from the other spacecraft. This way of cross-calibration we obtain a measure for the goodness of our extrapolations over different heliospheric distances. We find that a reliable solar-wind dataset can be provided in case of a longitudinal separation less than 65 degrees. Moreover, we find that the time lag method assuming constant velocity is a good basis to extrapolate from measurements in Earth orbit to Venus or to Mars. These extrapolations might serve as a good solar-wind input information for planetary studies of magnetospheric and ionospheric processes. We additionally show how the stream-stream interactions in the ecliptic alter the bulk velocity during radial propagation

BDNF is a mediator of glycolytic fiber-type specification in mouse skeletal muscle

Brain-derived neurotrophic factor (BDNF) influences the differentiation, plasticity, and survival of central neurons and likewise, affects the development of the neuromuscular system. Besides its neuronal origin, BDNF is also a member of the myokine family. However, the role of skeletal muscle-derived BDNF in regulating neuromuscular physiology in vivo remains unclear. Using gain- and loss-of-function animal models, we show that muscle-specific ablation of BDNF shifts the proportion of muscle fibers from type IIB to IIX, concomitant with elevated slow muscle-type gene expression. Furthermore, BDNF deletion reduces motor end plate volume without affecting neuromuscular junction (NMJ) integrity. These morphological changes are associated with slow muscle function and a greater resistance to contraction-induced fatigue. Conversely, BDNF overexpression promotes a fast muscle-type gene program and elevates glycolytic fiber number. These findings indicate that BDNF is required for fiber-type specification and provide insights into its potential modulation as a therapeutic target in muscle diseases

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.Comment: 10 pages and 7 figure

Epidemics on contact networks: a general stochastic approach

Dynamics on networks is considered from the perspective of Markov stochastic processes. We partially describe the state of the system through network motifs and infer any missing data using the available information. This versatile approach is especially well adapted for modelling spreading processes and/or population dynamics. In particular, the generality of our systematic framework and the fact that its assumptions are explicitly stated suggests that it could be used as a common ground for comparing existing epidemics models too complex for direct comparison, such as agent-based computer simulations. We provide many examples for the special cases of susceptible-infectious-susceptible (SIS) and susceptible-infectious-removed (SIR) dynamics (e.g., epidemics propagation) and we observe multiple situations where accurate results may be obtained at low computational cost. Our perspective reveals a subtle balance between the complex requirements of a realistic model and its basic assumptions.Comment: Main document: 16 pages, 7 figures. Electronic Supplementary Material (included): 6 pages, 1 tabl