3,643 research outputs found

    IFN-gamma is associated with risk of Schistosoma japonicum infection in China.

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    Before the start of the schistosomiasis transmission season, 129 villagers resident on a Schistosoma japonicum-endemic island in Poyang Lake, Jiangxi Province, 64 of whom were stool-positive for S. japonicum eggs by the Kato method and 65 negative, were treated with praziquantel. Forty-five days later the 93 subjects who presented for follow-up were all stool-negative. Blood samples were collected from all 93 individuals. S. japonicum soluble worm antigen (SWAP) and soluble egg antigen (SEA) stimulated IL-4, IL-5 and IFN-gamma production in whole-blood cultures were measured by ELISA. All the subjects were interviewed nine times during the subsequent transmission season to estimate the intensity of their contact with potentially infective snail habitats, and the subjects were all re-screened for S. japonicum by the Kato method at the end of the transmission season. Fourteen subjects were found to be infected at that time. There was some indication that the risk of infection might be associated with gender (with females being at higher risk) and with the intensity of water contact, and there was evidence that levels of SEA-induced IFN-gamma production were associated with reduced risk of infection

    Graphene nanoribbons with zigzag and armchair edges prepared by scanning tunneling microscope lithography on gold substrates

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    The properties of graphene nanoribbons are dependent on both the nanoribbon width and the crystallographic orientation of the edges. Scanning tunneling microscope lithography is a method which is able to create graphene nanoribbons with well defined edge orientation, having a width of a few nanometers. However, it has only been demonstrated on the top layer of graphite. In order to allow practical applications of this powerful lithography technique, it needs to be implemented on single layer graphene. We demonstrate the preparation of graphene nanoribbons with well defined crystallographic orientation on top of gold substrates. Our transfer and lithography approach brings one step closer the preparation of well defined graphene nanoribbons on arbitrary substrates for nanoelectronic applications

    Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words For Predicting Medical Codes

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    Word embeddings are a useful tool for extracting knowledge from the free-form text contained in electronic health records, but it has become commonplace to train such word embeddings on data that do not accurately reflect how language is used in a healthcare context. We use prediction of medical codes as an example application to compare the accuracy of word embeddings trained on health corpora to those trained on more general collections of text. It is shown that both an increase in embedding dimensionality and an increase in the volume of health-related training data improves prediction accuracy. We also present a comparison to the traditional bag-of-words feature representation, demonstrating that in many cases, this conceptually simple method for representing text results in superior accuracy to that of word embeddings

    Multilayer metamaterial absorbers inspired by perfectly matched layers

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    We derive periodic multilayer absorbers with effective uniaxial properties similar to perfectly matched layers (PML). This approximate representation of PML is based on the effective medium theory and we call it an effective medium PML (EM-PML). We compare the spatial reflection spectrum of the layered absorbers to that of a PML material and demonstrate that after neglecting gain and magnetic properties, the absorber remains functional. This opens a route to create electromagnetic absorbers for real and not only numerical applications and as an example we introduce a layered absorber for the wavelength of 88~\mum made of SiO2_2 and NaCl. We also show that similar cylindrical core-shell nanostructures derived from flat multilayers also exhibit very good absorptive and reflective properties despite the different geometry

    Radio-loud Quasars above Redshift 4: VLBI Imaging of an Extended Sample

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    High-redshift radio sources provide plentiful opportunities for studying the formation and evolution of early galaxies and supermassive black holes. However, the number of known radio-loud active galactic nuclei (AGN) above redshift 4 is rather limited. At high redshifts, it appears that blazars, with relativistically beamed jets pointing towards the observer, are in majority compared to radio-loud sources with jets misaligned with respect to the line of sight. To find more of these misaligned AGN, milliarcsec-scale imaging studies carried out with very long baseline interferometry (VLBI) are needed, as they allow us to distinguish between compact core--jet radio sources and those with more extended emission. Previous high-resolution VLBI studies revealed that some of the radio sources among blazar candidates in fact show unbeamed radio emission on milliarcsecond scales. The most accurate optical coordinates determined with the Gaia astrometric space mission are also useful in the classification process. Here, we report on dual-frequency imaging observations of 13 high-redshift (4 < z < 4.5) quasars at 1.7 and 5 GHz with the European VLBI Network. This sample increases the number of z>4 radio sources for which VLBI observations are available by about a quarter. Using structural and physical properties, such as radio morphology, spectral index, variability, brightness temperature, as well as optical coordinates, we identified six blazars and six misaligned radio AGNs, with the remaining one tentatively identified as blazar

    SG-VAE: Scene Grammar Variational Autoencoder to generate new indoor scenes

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    Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high-quality images. In this work, we propose a neural network to learn a generative model for sampling consistent indoor scene layouts. Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts. In contrast to existing grammar-based methods with a user-specified grammar, we construct the grammar automatically by extracting a set of production rules on reasoning about object co-occurrences in training data. The extracted grammar is able to represent a scene by an augmented parse tree. The proposed auto-encoder encodes these parse trees to a latent code, and decodes the latent code to a parse tree, thereby ensuring the generated scene is always valid. We experimentally demonstrate that the proposed auto-encoder learns not only to generate valid scenes (i.e. the arrangements and appearances of objects), but it also learns coherent latent representations where nearby latent samples decode to similar scene outputs. The obtained generative model is applicable to several computer vision tasks such as 3D pose and layout estimation from RGB-D data

    SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes

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    Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high-quality images. In this work, we propose a neural network to learn a generative model for sampling consistent indoor scene layouts. Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts. In contrast to existing grammar-based methods with a user-specified grammar, we construct the grammar automatically by extracting a set of production rules on reasoning about object co-occurrences in training data. The extracted grammar is able to represent a scene by an augmented parse tree. The proposed auto-encoder encodes these parse trees to a latent code, and decodes the latent code to a parse tree, thereby ensuring the generated scene is always valid. We experimentally demonstrate that the proposed auto-encoder learns not only to generate valid scenes (i.e. the arrangements and appearances of objects), but it also learns coherent latent representations where nearby latent samples decode to similar scene outputs. The obtained generative model is applicable to several computer vision tasks such as 3D pose and layout estimation from RGB-D data

    The effect of ultrasound pretreatment on some selected physicochemical properties of black cumin (Nigella Sativa)

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    Background In the present study, the effects of ultrasound pretreatment parameters including irradiation time and power on the quantity of the extracted phenolic compounds quantity as well as on some selected physicochemical properties of the extracted oils including oil extraction efficiency, acidity and peroxide values, color, and refractive index of the extracted oil of black cumin seeds with the use of cold press have been studied. Methods For each parameter, three different levels (30, 60, and 90 W) for the ultrasound power and (30, 45, and 60 min) and for the ultrasound irradiation time were studied. Each experiment was performed in three replications. Results The achieved results revealed that, with enhancements in the applied ultrasound power, the oil extraction efficiency, acidity value, total phenolic content, peroxide value, and color parameters increased significantly (P 0.05). Conclusions In summary, it could be mentioned that the application of ultrasound pretreatment in the oil extraction might improve the oil extraction efficiency, the extracted oil鈥檚 quality, and the extracted phenolic compounds content.info:eu-repo/semantics/publishedVersio

    A comparative study on different parallel solvers for nonlinear analysis of complex structures

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    The parallelization of 2D/3D software SAPTIS is discussed for nonlinear analysis of complex structures. A comparative study is made on different parallel solvers. The numerical models are presented, including hydration models, water cooling models, modulus models, creep model, and autogenous deformation models. A finite element simulation is made for the whole process of excavation and pouring of dams using these models. The numerical results show a good agreement with the measured ones. To achieve a better computing efficiency, four parallel solvers utilizing parallelization techniques are employed: (1) a parallel preconditioned conjugate gradient (PCG) solver based on OpenMP, (2) a parallel preconditioned Krylov subspace solver based on MPI, (3) a parallel sparse equation solver based on OpenMP, and (4) a parallel GPU equation solver. The parallel solvers run either in a shared memory environment OpenMP or in a distributed memory environment MPI. A comparative study on these parallel solvers is made, and the results show that the parallelization makes SAPTIS more efficient, powerful, and adaptable
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