Generating a multi-mode vortex beam based on spoof surface plasmon polaritons.

The vortex beam provides a promising alternative for next-generation wireless communication, but it is a long-standing challenge to generate a multi-mode and robust vortex beam. In this Letter, a multi-mode vortex beam emitter is introduced and experimentally verified based on spoof surface plasmon polaritons (SSPP). The SSPP on a helical grating carries multi-mode orbital angular momentum and can be converted into a high-purity vortex beam via the diffraction of a ring array. The operation frequency and topological charge are determined by that of the SSPP. This emitter can achieve the function of beam-scanning in each radiation band. The beam-scanning and vortex characteristics are experimentally verified. The designed emitter is compact and robust, and we are confident that this work will have great application prospects in communication systems.

Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA.

Human Ensemble Cell Atlas (hECA) provides a unified informatics framework and the cell-centric-assembled single-cell transcriptome data of 1,093,299 labeled human cells from 116 published datasets. In this protocol, we provide three applications of hECA: "quantitative portraiture" exploration with websites, customizable reference creation for automatic cell type annotation, and "in data" cell sorting with logical conditions. We provide detail steps of connecting to the database, searching cell with conditions, downloading data, and annotating new datasets with customized reference. For complete details on the use and execution of this protocol, please refer to Chen et al. (2022).

Risk Factor Analysis of Hepatic Encephalopathy and the Establishment of Diagnostic Model.

To identify laboratory diagnostic indicators of hepatic encephalopathy (HE), the present study established a HE diagnostic model to explore the diagnostic value of serum homocysteine, lactic acid, procalcitonin, and bile acid levels in HE identification. 371 patients with liver cirrhosis were selected as research objects, who were admitted to the Department of Hepatic Diseases, Affiliated Hospital of Northwest Minzu University from August 2019 to August 2020. The Spearman correlation results indicated that between lactic acid, procalcitonin, bile acid, serum homocysteine, and HE, the coefficients were -0.15, 0.41, 0.29, and -0.19, respectively. Univariate and multivariate analysis methods were adopted for inpatient analysis to identify the influencing factors of HE occurrence, and the diagnosis of the HE identification model was subsequently constructed. The univariate logistic regression showed that risk of developing HE increased as bile acid level (P = 0.00434) and serum homocysteine (P = 0.058) increased. Multivariate logistic regression diagnostic model of bile acid level and serum homocysteine revealed that the AUC value of the area under the ROC curve was 0.7201, indicating that the diagnostic model produced a satisfactory evaluation effect. The model formula referred logistic (P) = -2.4544 + 0.0117 bile acid levels + 0.0198 serum homocysteine. In this study, the HE diagnostic model was established using logistic regression analysis, which could benefit patients in early HE differential diagnosis. Particularly, combined detection of serum homocysteine and bile acid levels was considered to be more significant.

hECA: The cell-centric assembly of a cell atlas.

The accumulation of massive single-cell omics data provides growing resources for building biomolecular atlases of all cells of human organs or the whole body. The true assembly of a cell atlas should be cell-centric rather than file-centric. We developed a unified informatics framework for seamless cell-centric data assembly and built the human Ensemble Cell Atlas (hECA) from scattered data. hECA v1.0 assembled 1,093,299 labeled human cells from 116 published datasets, covering 38 organs and 11 systems. We invented three new methods of atlas applications based on the cell-centric assembly: "in data" cell sorting for targeted data retrieval with customizable logic expressions, "quantitative portraiture" for multi-view representations of biological entities, and customizable reference creation for generating references for automatic annotations. Case studies on agile construction of user-defined sub-atlases and "in data" investigation of CAR-T off-targets in multiple organs showed the great potential enabled by the cell-centric ensemble atlas.

Toward a unified information framework for cell atlas assembly.

This perspective discusses the need and directions for the development of a unified information framework to enable the assembly of cell atlases and a revolution in medical research on the virtual body of assembled cell systems.

Efficient magnetic-coupling excitation of LSSPs on high-Q multilayer planar-circular-grating resonators.

Recently, ultrathin localized spoof surface plasmon (LSSP) resonators are found to have intrinsic defects of relatively low quality factors (Q-factors) because of unavoidable material and radiation losses. In this paper, multilayer structures of planar-circular-grating resonators and their magnetic-coupling schemes are proposed to achieve effective excitation of high-Q LSSPs modes. By adopting the multilayer structures with air between the layers, the power dissipation effected by both material and radiation losses is significantly suppressed. Experimental results show that the Q-factors could reach more than 200 and the excitation efficiencies could reach more than 90%. Numerical simulations show the distribution of the electromagnetic field and illustrate the principle of magnetic coupling. Besides, the Q-factors of resonators with different structural parameters were measured and analyzed. This study aims to provide some inspirations on planar gyro-devices and to improve the performance of existing applications, such as sensors and filters.

An Experiment on Ab Initio Discovery of Biological Knowledge from scRNA-Seq Data Using Machine Learning.

Expectations of machine learning (ML) are high for discovering new patterns in high-throughput biological data, but most such practices are accustomed to relying on existing knowledge conditions to design experiments. Investigations of the power and limitation of ML in revealing complex patterns from data without the guide of existing knowledge have been lacking. In this study, we conducted systematic experiments on such ab initio knowledge discovery with ML methods on single-cell RNA-sequencing data of early embryonic development. Results showed that a strategy combining unsupervised and supervised ML can reveal major cell lineages with minimum involvement of prior knowledge or manual intervention, and the ab initio mining enabled a new discovery of human early embryonic cell differentiation. The study illustrated the feasibility, significance, and limitation of ab initio ML knowledge discovery on complex biological problems.