High visibility on-chip quantum interference of single surface plasmons

Quantum photonic integrated circuits (QPICs) based on dielectric waveguides have been widely used in linear optical quantum computation. Recently, surface plasmons have been introduced to this application because they can confine and manipulate light beyond the diffraction limit. In this study, the on-chip quantum interference of two single surface plasmons was achieved using dielectric-loaded surface-plasmon-polariton waveguides. The high visibility (greater than 90%) proves the bosonic nature of single plasmons and emphasizes the feasibility of achieving basic quantum logic gates for linear optical quantum computation. The effect of intrinsic losses in plasmonic waveguides with regard to quantum information processing is also discussed. Although the influence of this effect was negligible in the current experiment, our studies reveal that such losses can dramatically reduce quantum interference visibility in certain cases; thus, quantum coherence must be carefully considered when designing QPIC devices.Comment: 6 pages, 4 figure

Diaqua­bis­[5-(pyrazin-2-yl-κN 1)-3-(pyridin-4-yl)-1H-1,2,4-triazol-1-ido-κN 1]cobalt(II) methanol disolvate

The CoII ion in the title mononuclear compound, [Co(C11H7N6)2(H2O)2]·2CH3OH, is located on an inversion center and is six-coordinated in a distorted octa­hedral geometry defined by four N atoms from two deprotonated 5-(pyrazin-2-yl-κN)-3-(pyridin-4-yl)-1H-1,2,4-triazol-1-ide (ppt) ligands and two water mol­ecules. In the crystal, the complex mol­ecules and lattice methanol mol­ecules are linked via O—H⋯N and O—H⋯O hydrogen bonds, generating a two-dimensional supra­molecular network parallel to (001). π–π inter­actions between the triazole and pyrazine rings and between the pyridine rings are present [centroid–centroid distances = 3.686 (3) and 3.929 (4) Å, respectively]

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.Comment: Work in progres

ESX Secretion-Associated Protein C From Mycobacterium tuberculosis Induces Macrophage Activation Through the Toll-Like Receptor-4/Mitogen-Activated Protein Kinase Signaling Pathway

Mycobacterium tuberculosis, as a facultative intracellular pathogen, can interact with host macrophages and modulate macrophage function to influence innate and adaptive immunity. Proteins secreted by the ESX-1 secretion system are involved in this relationship. Although the importance of ESX-1 in host-pathogen interactions and virulence is well-known, the primary role is ascribed to EsxA (EAST-6) in mycobacterial pathogenesis and the functions of individual components in the interactions between pathogens and macrophages are still unclear. Here, we investigated the effects of EspC on macrophage activation. The EspC protein is encoded by an espA/C/D cluster, which is not linked to the esx-1 locus, but is essential for the secretion of the major virulence factors of ESX-1, EsxA and EsxB. Our results showed that both EspC protein and EspC overexpression in M. smegmatis induced pro-inflammatory cytokines and enhanced surface marker expression. This mechanism was dependent on Toll-like receptor 4 (TLR4), as demonstrated using EspC-treated macrophages from TLR4−/− mice, leading to decreased pro-inflammatory cytokine secretion and surface marker expression compared with those from wild-type mice. Immunoprecipitation and immunofluorescence assays showed that EspC interacted with TLR4 directly. Moreover, EspC could activate macrophages and promote antigen presentation by inducing mitogen-activated protein kinase (MAPK) phosphorylation and nuclear factor-κB activation. The EspC-induced cytokine expression, surface marker upregulation, and MAPK signaling activation were inhibited when macrophages were blocked with anti-TLR4 antibodies or pretreated with MAPK inhibitors. Furthermore, our results showed that EspC overexpression enhanced the survival of M. smegmatis within macrophages and under stress conditions. Taken together, our results indicated that EspC may be another ESX-1 virulence factor that not only modulates the host innate immune response by activating macrophages through TLR4-dependent MAPK signaling but also plays an important role in the survival of pathogenic mycobacteria in host cells

Relphormer: Relational Graph Transformer for Knowledge Graph Representations

Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transformer architectures struggle to capture the intrinsically heterogeneous structural and semantic information of knowledge graphs. To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the semantic information within entities and relations. Moreover, we utilize masked knowledge modeling for general knowledge graph representation learning, which can be applied to various KG-based tasks including knowledge graph completion, question answering, and recommendation. Experimental results on six datasets show that Relphormer can obtain better performance compared with baselines. Code is available in https://github.com/zjunlp/Relphormer.Comment: Work in progres

On the Detectability of Galactic Dark Matter Annihilation into Monochromatic Gamma-rays

Monochromatic gamma-rays are thought to be the smoking gun signal for identifying the dark matter annihilation. However, the flux of monochromatic gamma-rays is usually suppressed by the virtual quantum effects since dark matter should be neutral and does not couple with gamma-rays directly. In the work we study the detection strategy of the monochromatic gamma-rays in a future space-based detector. The monochromatic gamma-ray flux is calculated by assuming supersymmetric neutralino as a typical dark matter candidate. We discuss both the detection focusing on the Galactic center and in a scan mode which detects gamma-rays from the whole Galactic halo are compared. The detector performance for the purpose of monochromatic gamma-rays detection, with different energy and angular resolution, field of view, background rejection efficiencies, is carefully studied with both analytical and fast Monte-Carlo method

Neonatal outcome in 29 pregnant women with COVID-19 : A retrospective study in Wuhan, China

Funding: YTW: National Key Research and Development Program of China (2018YFC1002804), http://www.most.gov.cn; YTW: National Key Research and Development Program of China (2016YFC1000203), http://www.most.gov.cn. CL: COVID-19 Prevention and Control Program of International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University (2020-COVID-19-04), https://www.ipmch.com.cn. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability: All the research data are available at the ResMen Manager of Chinese Clinical Trial Registry (www.medresman.org), and the registration number is ChiCTR2000031954 (http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=1810).Peer reviewedPublisher PD

Detecting the dark matter annihilation at the ground EAS detectors

In this paper we study the possibility of detecting gamma rays from dark matter annihilation in the subhalos of the Milky Way by the ground based EAS detectors within the frame of the minimal supersymmetric standard model. Based on the Monte Carlo simulation we also study the properties of two specific EAS detectors, the ARGO and HAWC, and the sensitivities of these detectors on the detection of dark matter annihilation. We find the ground EAS detectors have the possibility to observe such signals. Conversely if no signal observed we give the constraints on the supersymmetric parameter space, which however depends on the subhalos properties.Comment: 23 pages, 9 figures, accepted by NP