V-I characteristics in the vicinity of order-disorder transition in vortex matter

The shape of the V-I characteristics leading to a peak in the differential resistance r_d=dV/dI in the vicinity of the order-disorder transition in NbSe2 is investigated. r_d is large when measured by dc current. However, for a small Iac on a dc bias r_d decreases rapidly with frequency, even at a few Hz, and displays a large out-of-phase signal. In contrast, the ac response increases with frequency in the absence of dc bias. These surprisingly opposite phenomena and the peak in r_d are shown to result from a dynamic coexistence of two vortex matter phases rather than from the commonly assumed plastic depinning.Comment: 12 pages 4 figures. Accepted for publication in PRB rapi

Asynchronous Training of Word Embeddings for Large Text Corpora

Word embeddings are a powerful approach for analyzing language and have been widely popular in numerous tasks in information retrieval and text mining. Training embeddings over huge corpora is computationally expensive because the input is typically sequentially processed and parameters are synchronously updated. Distributed architectures for asynchronous training that have been proposed either focus on scaling vocabulary sizes and dimensionality or suffer from expensive synchronization latencies. In this paper, we propose a scalable approach to train word embeddings by partitioning the input space instead in order to scale to massive text corpora while not sacrificing the performance of the embeddings. Our training procedure does not involve any parameter synchronization except a final sub-model merge phase that typically executes in a few minutes. Our distributed training scales seamlessly to large corpus sizes and we get comparable and sometimes even up to 45% performance improvement in a variety of NLP benchmarks using models trained by our distributed procedure which requires $1/10$ of the time taken by the baseline approach. Finally we also show that we are robust to missing words in sub-models and are able to effectively reconstruct word representations.Comment: This paper contains 9 pages and has been accepted in the WSDM201

Cross-protection against European swine influenza viruses in the context of infection immunity against the 2009 pandemic H1N1 virus : studies in the pig model of influenza

Pigs are natural hosts for the same influenza virus subtypes as humans and are a valuable model for cross-protection studies with influenza. In this study, we have used the pig model to examine the extent of virological protection between a) the 2009 pandemic H1N1 (pH1N1) virus and three different European H1 swine influenza virus (SIV) lineages, and b) these H1 viruses and a European H3N2 SIV. Pigs were inoculated intranasally with representative strains of each virus lineage with 6- and 17-week intervals between H1 inoculations and between H1 and H3 inoculations, respectively. Virus titers in nasal swabs and/or tissues of the respiratory tract were determined after each inoculation. There was substantial though differing cross-protection between pH1N1 and other H1 viruses, which was directly correlated with the relatedness in the viral hemagglutinin (HA) and neuraminidase (NA) proteins. Cross-protection against H3N2 was almost complete in pigs with immunity against H1N2, but was weak in H1N1/pH1N1-immune pigs. In conclusion, infection with a live, wild type influenza virus may offer substantial cross-lineage protection against viruses of the same HA and/or NA subtype. True heterosubtypic protection, in contrast, appears to be minimal in natural influenza virus hosts. We discuss our findings in the light of the zoonotic and pandemic risks of SIVs

Naturally occurring mutations in the PA gene are key contributors to increased virulence of pandemic H1N1/09 influenza virus in mice

We examined the molecular basis of virulence of pandemic H1N1/09 influenza viruses by reverse genetics based on two H1N1/09 virus isolates (A/California/04/2009 [CA04] and A/swine/Shandong/731/2009 [SD731]) with contrasting pathogenicities in mice. We found that four amino acid mutations (P224S in the PA protein [PA-P224S], PB2-T588I, NA-V106I, and NS1-I123V) contributed to the lethal phenotype of SD731. In particular, the PA-P224S mutation when combined with PA-A70V in CA04 drastically reduced the virus's 50% mouse lethal dose (LD50), by almost 1,000-fold

Electron correlation effects in electron-hole recombination in organic light-emitting diodes

We develop a general theory of electron--hole recombination in organic light emitting diodes that leads to formation of emissive singlet excitons and nonemissive triplet excitons. We briefly review other existing theories and show how our approach is substantively different from these theories. Using an exact time-dependent approach to the interchain/intermolecular charge-transfer within a long-range interacting model we find that, (i) the relative yield of the singlet exciton in polymers is considerably larger than the 25% predicted from statistical considerations, (ii) the singlet exciton yield increases with chain length in oligomers, and, (iii) in small molecules containing nitrogen heteroatoms, the relative yield of the singlet exciton is considerably smaller and may be even close to 25%. The above results are independent of whether or not the bond-charge repulsion, X_perp, is included in the interchain part of the Hamiltonian for the two-chain system. The larger (smaller) yield of the singlet (triplet) exciton in carbon-based long-chain polymers is a consequence of both its ionic (covalent) nature and smaller (larger) binding energy. In nitrogen containing monomers, wavefunctions are closer to the noninteracting limit, and this decreases (increases) the relative yield of the singlet (triplet) exciton. Our results are in qualitative agreement with electroluminescence experiments involving both molecular and polymeric light emitters. The time-dependent approach developed here for describing intermolecular charge-transfer processes is completely general and may be applied to many other such processes.Comment: 19 pages, 11 figure

Validating module network learning algorithms using simulated data

In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio

Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus

Identifying when past exposure to an infectious disease will protect against newly emerging strains is central to understanding the spread and the severity of epidemics, but the prediction of viral cross-protection remains an important unsolved problem. For foot-and-mouth disease virus (FMDV) research in particular, improved methods for predicting this cross-protection are critical for predicting the severity of outbreaks within endemic settings where multiple serotypes and subtypes commonly co-circulate, as well as for deciding whether appropriate vaccine(s) exist and how much they could mitigate the effects of any outbreak. To identify antigenic relationships and their predictors, we used linear mixed effects models to account for variation in pairwise cross-neutralization titres using only viral sequences and structural data. We identified those substitutions in surface-exposed structural proteins that are correlates of loss of cross-reactivity. These allowed prediction of both the best vaccine match for any single virus and the breadth of coverage of new vaccine candidates from their capsid sequences as effectively as or better than serology. Sub-sequences chosen by the model-building process all contained sites that are known epitopes on other serotypes. Furthermore, for the SAT1 serotype, for which epitopes have never previously been identified, we provide strong evidence - by controlling for phylogenetic structure - for the presence of three epitopes across a panel of viruses and quantify the relative significance of some individual residues in determining cross-neutralization. Identifying and quantifying the importance of sites that predict viral strain cross-reactivity not just for single viruses but across entire serotypes can help in the design of vaccines with better targeting and broader coverage. These techniques can be generalized to any infectious agents where cross-reactivity assays have been carried out. As the parameterization uses pre-existing datasets, this approach quickly and cheaply increases both our understanding of antigenic relationships and our power to control disease

Carbon dioxide fluxes across the Sierra de Guadarrama, Spain

Understanding the spatial and temporal variation in soil respiration within small geographic areas is essential to accurately assess the carbon budget on a global scale. In this study, we investigated the factors controlling soil respiration in an altitudinal gradient in a southern Mediterranean mixed pine–oak forest ecosystem in the north face of the Sierra de Guadarrama in Spain. Soil respiration was measured in five Pinus sylvestris L. plots over a period of 1 year by means of a closed dynamic system (LI-COR 6400). Soil temperature and water content were measured at the same time as soil respiration. Other soil physico-chemical and microbiological properties were measured during the study. Measured soil respiration ranged from 6.8 to 1.4 lmol m-2 s-1, showing the highest values at plots situated at higher elevation. Q10 values ranged between 1.30 and 2.04, while R10 values ranged between 2.0 and 3.6. The results indicate that the seasonal variation of soil respiration was mainly controlled by soil temperature and moisture. Among sites, soil carbon and nitrogen stocks regulate soil respiration in addition to soil temperature and moisture. Our results suggest that application of standard models to estimate soil respiration for small geographic areas may not be adequate unless other factors are considered in addition to soil temperature

Seroprevalence following the second wave of pandemic 2009 H1N1 influenza in Pittsburgh, PA, USA

Background: In April 2009, a new pandemic strain of influenza infected thousands of persons in Mexico and the United States and spread rapidly worldwide. During the ensuing summer months, cases ebbed in the Northern Hemisphere while the Southern Hemisphere experienced a typical influenza season dominated by the novel strain. In the fall, a second wave of pandemic H1N1 swept through the United States, peaking in most parts of the country by mid October and returning to baseline levels by early December. The objective was to determine the seroprevalence of antibodies against the pandemic 2009 H1N1 influenza strain by decade of birth among Pittsburgh-area residents. Methods and Findings: Anonymous blood samples were obtained from clinical laboratories and categorized by decade of birth from 1920-2009. Using hemagglutination-inhibition assays, approximately 100 samples per decade (n = 846) were tested from blood samples drawn on hospital and clinic patients in mid-November and early December 2009. Age specific seroprevalences against pandemic H1N1 (A/California/7/2009) were measured and compared to seroprevalences against H1N1 strains that had previously circulated in the population in 2007, 1957, and 1918. (A/Brisbane/59/2007, A/Denver/1/ 1957, and A/South Carolina/1/1918). Stored serum samples from healthy, young adults from 2008 were used as a control group (n = 100). Seroprevalences against pandemic 2009 H1N1 influenza varied by age group, with children age 10-19 years having the highest seroprevalence (45%), and persons age 70-79 years having the lowest (5%). The baseline seroprevalence among control samples from 18-24 year-olds was 6%. Overall seroprevalence against pandemic H1N1 across all age groups was approximately 21%. Conclusions: After the peak of the second wave of 2009 H1N1, HAI seroprevalence results suggest that 21% of persons in the Pittsburgh area had become infected and developed immunity. Extrapolating to the entire US population, we estimate that at least 63 million persons became infected in 2009. As was observed among clinical cases, this sero-epidemiological study revealed highest infection rates among school-age children. © 2010 Zimmer et al

Effect of D222G Mutation in the Hemagglutinin Protein on Receptor Binding, Pathogenesis and Transmissibility of the 2009 Pandemic H1N1 Influenza Virus

Influenza viruses isolated during the 2009 H1N1 pandemic generally lack known molecular determinants of virulence associated with previous pandemic and highly pathogenic avian influenza viruses. The frequency of the amino acid substitution D222G in the hemagglutinin (HA) of 2009 H1N1 viruses isolated from severe but not mild human cases represents the first molecular marker associated with enhanced disease. To assess the relative contribution of this substitution in virus pathogenesis, transmission, and tropism, we introduced D222G by reverse genetics in the wild-type HA of the 2009 H1N1 virus, A/California/04/09 (CA/04). A dose-dependent glycan array analysis with the D222G virus showed a modest reduction in the binding avidity to human-like (α2-6 sialylated glycan) receptors and an increase in the binding to avian-like (α2-3 sialylated glycan) receptors in comparison with wild-type virus. In the ferret pathogenesis model, the D222G mutant virus was found to be similar to wild-type CA/04 virus with respect to lethargy, weight loss and replication efficiency in the upper and lower respiratory tract. Moreover, based on viral detection, the respiratory droplet transmission properties of these two viruses were found to be similar. The D222G virus failed to productively infect mice inoculated by the ocular route, but exhibited greater viral replication and weight loss than wild-type CA/04 virus in mice inoculated by the intranasal route. In a more relevant human cell model, D222G virus replicated with delayed kinetics compared with wild-type virus but to higher titer in human bronchial epithelial cells. These findings suggest that although the D222G mutation does not influence virus transmission, it may be considered a molecular marker for enhanced replication in certain cell types.Centers for Disease Control and Prevention (U.S.)United States. National Institutes of Health (merit award R37 GM057073-13)Singapore-MIT Alliance for Research and Technolog