Ottochoi1354

An acoustic plasmon mode in a graphene-dielectric-metal structure has recently been spotlighted as a superior platform for strong light-matter interaction. It originates from the coupling of graphene plasmon with its mirror image and exhibits the largest field confinement in the limit of a sub-nm-thick dielectric. Although recently detected in the far-field regime, optical near-fields of this mode are yet to be observed and characterized. Linrodostat research buy Here, we demonstrate a direct optical probing of the plasmonic fields reflected by the edges of graphene via near-field scattering microscope, revealing a relatively small propagation loss of the mid-infrared acoustic plasmons in our devices that allows for their real-space mapping at ambient conditions even with unprotected, large-area graphene grown by chemical vapor deposition. We show an acoustic plasmon mode that is twice as confined and has 1.4 times higher figure of merit in terms of the normalized propagation length compared to the graphene surface plasmon under similar conditions. We also investigate the behavior of the acoustic graphene plasmons in a periodic array of gold nanoribbons. Our results highlight the promise of acoustic plasmons for graphene-based optoelectronics and sensing applications.Atomic metal wires have great promise for practical applications in devices due to their unique electronic properties. Unfortunately, such atomic wires are extremely unstable. Here we fabricate stable atomic silver wires (ASWs) with appreciably unoccupied states inside the parallel tunnels of α-MnO2 nanorods. These unoccupied Ag 4d orbitals strengthen the Ag-Ag bonds, greatly enhancing the stability of ASWs while the presence of delocalized 5s electrons makes the ASWs conducting. These stable ASWs form a coherently oriented three-dimensional wire array of over 10 nm in width and up to 1 μm in length allowing us to connect it to nano-electrodes. Current-voltage characteristics of ASWs show a temperature-dependent insulator-to-metal transition, suggesting that the atomic wires could be used as thermal electrical devices.The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV  0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.The humoral immune response to SARS-CoV-2 is a benchmark for immunity and detailed analysis is required to understand the manifestation and progression of COVID-19, monitor seroconversion within the general population, and support vaccine development. The majority of currently available commercial serological assays only quantify the SARS-CoV-2 antibody response against individual antigens, limiting our understanding of the immune response. To overcome this, we have developed a multiplex immunoassay (MultiCoV-Ab) including spike and nucleocapsid proteins of SARS-CoV-2 and the endemic human coronaviruses. Compared to three broadly used commercial in vitro diagnostic tests, our MultiCoV-Ab achieves a higher sensitivity and specificity when analyzing a well-characterized sample set of SARS-CoV-2 infected and uninfected individuals. We find a high response against endemic coronaviruses in our sample set, but no consistent cross-reactive IgG response patterns against SARS-CoV-2. Here we show a robust, high-content-enabled, antigen-saving multiplex assay suited to both monitoring vaccination studies and facilitating epidemiologic screenings for humoral immunity towards pandemic and endemic coronaviruses.Zaire ebolavirus (EBOV) is a highly pathogenic filovirus which can result in Ebola virus disease (EVD); a serious medical condition that presents as flu like symptoms but then often leads to more serious or fatal outcomes. The 2013-16 West Africa epidemic saw an unparalleled number of cases. Here we show characterisation and identification of T cell epitopes in surviving patients from Guinea to the EBOV glycoprotein. We perform interferon gamma (IFNγ) ELISpot using a glycoprotein peptide library to identify T cell epitopes and determine the CD4+ or CD8+ T cell component response. Additionally, we generate data on the T cell phenotype and measure polyfunctional cytokine secretion by these antigen specific cells. We show candidate peptides able to elicit a T cell response in EBOV survivors and provide inferred human leukocyte antigen (HLA) allele restriction. This data informs on the long-term T cell response to Ebola virus disease and highlights potentially important immunodominant peptides.Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization.