A novel role for the TIR domain in association with pathogen-derived elicitors

Plant innate immunity is mediated by Resistance (R) proteins, which bear a striking resemblance to animal molecules of similar function. Tobacco N is a TIR-NB-LRR R gene that confers resistance to Tobacco mosaic virus, specifically the p50 helicase domain. An intriguing question is how plant R proteins recognize the presence of pathogen-derived Avirulence (Avr) elicitor proteins. We have used biochemical cell fraction and immunoprecipitation in addition to confocal fluorescence microscopy of living tissue to examine the association between N and p50. Surprisingly, both N and p50 are cytoplasmic and nuclear proteins, and N's nuclear localization is required for its function. We also demonstrate an in planta association between N and p50. Further, we show that N's TIR domain is critical for this association, and indeed, it alone can associate with p50. Our results differ from current models for plant innate immunity that propose detection is mediated solely through the LRR domains of these molecules. The data we present support an intricate process of pathogen elicitor recognition by R proteins involving multiple subcellular compartments and the formation of multiple protein complexes. © 2007 Burch-Smith et al

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Supercontinuum generation in hydrogenated amorphous silicon waveguides at telecommunication wavelengths

We report supercontinuum (SC) generation centered on the telecommunication C-band (1550 nm) in CMOS compatible hydrogenated amorphous silicon waveguides. A broadening of more than 550 nm is obtained in 1cm long waveguides of different widths using as pump picosecond pulses with on chip peak power as low as 4 W. © 2014 Optical Society of America.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

The effect of surveillance and appreciative inquiry on puerperal infections : a longitudinal cohort study in India

Peer reviewedPublisher PD

Bio-nanotechnology application in wastewater treatment

The nanoparticles have received high interest in the field of medicine and water purification, however, the nanomaterials produced by chemical and physical methods are considered hazardous, expensive, and leave behind harmful substances to the environment. This chapter aimed to focus on green-synthesized nanoparticles and their medical applications. Moreover, the chapter highlighted the applicability of the metallic nanoparticles (MNPs) in the inactivation of microbial cells due to their high surface and small particle size. Modifying nanomaterials produced by green-methods is safe, inexpensive, and easy. Therefore, the control and modification of nanoparticles and their properties were also discussed

Probing Colored Particles with Photons, Leptons, and Jets

If pairs of new colored particles are produced at the Large Hadron Collider, determining their quantum numbers, and even discovering them, can be non-trivial. We suggest that valuable information can be obtained by measuring the resonant signals of their near-threshold QCD bound states. If the particles are charged, the resulting signatures include photons and leptons and are sufficiently rich for unambiguously determining their various quantum numbers, including the charge, color representation and spin, and obtaining a precise mass measurement. These signals provide well-motivated benchmark models for resonance searches in the dijet, photon+jet, diphoton and dilepton channels. While these measurements require that the lifetime of the new particles be not too short, the resulting limits, unlike those from direct searches for pair production above threshold, do not depend on the particles' decay modes. These limits may be competitive with more direct searches if the particles decay in an obscure way.Comment: 39 pages, 9 figures; v2: more recent searches include

Interleukin-1 beta-converting enzyme-like protease cleaves DNA-dependent protein kinase in cytotoxic T cell killing.

Cytotoxic T cells (CTL) represent the major defense mechanism against the spread of virus infection. It is believed that the pore-forming protein, perforin, facilitates the entry of a series of serine proteases (particularly granzyme B) into the target cell which ultimately leads to DNA fragmentation and apoptosis. We demonstrate here that during CTL-mediated cytolysis the catalytic subunit of DNA-dependent protein kinase (DNA-PKcs), an enzyme implicated in the repair of double strand breaks in DNA, is specifically cleaved by an interleukin (IL)-1 beta-converting enzyme (ICE)-like protease. A serine protease inhibitor, 3,4-dichloroisocoumarin (DCl), which is known to block granzyme B activity, inhibited CTL-induced apoptosis and prevented the degradation of DNA-PKcs in cells but failed to prevent the degradation of purified DNA-PKcs by CTL extracts. However, Tyr-Val-Ala-Asp-CH2Cl (YVAD-CMK) and other cysteine protease inhibitors prevented the degradation of purified DNA-PKcs by CTL extracts. Furthermore, incubation of DNA-PKcs with granzyme B did not produce the same cleavage pattern observed in cells undergoing apoptosis and when this substrate was incubated with either CTL extracts or the ICE-like protease, CPP32. Sequence analysis revealed that the cleavage site in DNA-PKcs during CTL killing was the same as that when this substrate was exposed to CPP32. This study demonstrates for the first time that the cleavage of DNA-PKcs in this intact cell system is exclusively due to an ICE-like protease

Evolution of Th2 responses : Characterization of IL-4/13 in sea bass (Dicentrarchus labrax L.) and studies of expression and biological activity

Acknowledgements This research was funded by the European Commission under the 7th Framework Programme for Research and Technological Development (FP7) of the European Union (Grant Agreement 311993 TARGETFISH). T.W. received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). MASTS is funded by the Scottish Funding Council (grant reference number HR09011) and contributing institutions.Peer reviewedPublisher PD

Naturalness bounds in extensions of the MSSM without a light Higgs boson

Adopting a bottom-up point of view, we make a comparative study of the simplest extensions of the MSSM with extra tree level contributions to the lightest Higgs boson mass. We show to what extent a relatively heavy Higgs boson, up to 200-350 GeV, can be compatible with data and naturalness. The price to pay is that the theory undergoes some change of regime at a relatively low scale. Bounds on these models come from electroweak precision tests and naturalness, which often requires the scale at which the soft terms are generated to be relatively low.Comment: 18 pages, 5 figures. v2: minor revision, added references. v3,v4: some numerical correction

Atomic-scale combination of germanium-zinc nanofibers for structural and electrochemical evolution

Alloys are recently receiving considerable attention in the community of rechargeable batteries as possible alternatives to carbonaceous negative electrodes; however, challenges remain for the practical utilization of these materials. Herein, we report the synthesis of germanium-zinc alloy nanofibers through electrospinning and a subsequent calcination step. Evidenced by in situ transmission electron microscopy and electrochemical impedance spectroscopy characterizations, this one-dimensional design possesses unique structures. Both germanium and zinc atoms are homogenously distributed allowing for outstanding electronic conductivity and high available capacity for lithium storage. The as-prepared materials present high rate capability (capacity of similar to 50% at 20 C compared to that at 0.2 C-rate) and cycle retention (73% at 3.0 C-rate) with a retaining capacity of 546 mAh g(-1) even after 1000 cycles. When assembled in a full cell, high energy density can be maintained during 400 cycles, which indicates that the current material has the potential to be used in a large-scale energy storage system