Multiplexed in situ protein imaging using DNA-barcoded antibodies with extended hybridization chain reactions.

Antibodies have long served as vital tools in biological and clinical laboratories for the specific detection of proteins. Conventional methods employ fluorophore or horseradish peroxidase-conjugated antibodies to detect signals. More recently, DNA-conjugated antibodies have emerged as a promising technology, capitalizing on the programmability and amplification capabilities of DNA to enable highly multiplexed and ultrasensitive protein detection. However, the nonspecific binding of DNA-conjugated antibodies has impeded the widespread adoption of this approach. Here, we present a novel DNA-conjugated antibody staining protocol that addresses these challenges and demonstrates superior performance in suppressing nonspecific signals compared to previously published protocols. We further extend the utility of DNA-conjugated antibodies for signal-amplified in situ protein imaging through the hybridization chain reaction (HCR) and design a novel HCR DNA pair to expand the HCR hairpin pool from the previously published 5 pairs to 13, allowing for flexible hairpin selection and higher multiplexing. Finally, we demonstrate highly multiplexed in situ protein imaging using these techniques in both cultured cells and tissue sections.

Big data-driven carbon emission traceability list and characteristics of ships in maritime transportation-a case study of Tianjin Port.

As Chi na's shipping industry continues to develop, ship emissions have become a significant source of pollutants. Consequently, it has become imperative to comprehend accurately the nature and attributes of ship pollutant emissions and understand their causation and effect as a crucial aspect of pollution control and legislation. This paper employs high-precision automatic identification system (AIS) dynamic and static data, along with pollutant emission parameters, to estimate the pollutant emissions from a ship's main engine, auxiliary engine, and boiler using a dynamic approach. Additionally, the study considers the sailing state and trajectory of the vessel and analyzes the characteristics of ship carbon emissions. Taking Tianjin Port as an example, this study conducts a multi-dimensional analysis of pollutant emissions to gain insight into the causation and effect of pollutants based on the collected big AIS data. The results show that the pollutant emissions in this region are mainly concentrated in the vicinity of Tianjin Port land port area, Dagusha Channel, and the Main Shipping Channel of Tianjin Xingang Fairway. Carbon emissions peak in September and are lower in June and December. Through accurate analysis of pollutant emission sources and emission characteristics in the region, this paper establishes the regular relationship between pollutant emissions and possible influencing factors and provides data support for China to formulate accurate pollutant emission reduction policies and regulate ship construction technology and carbon trading.

Optimizing Graph Neural Network With Multiaspect Hilbert-Schmidt Independence Criterion.

The graph neural network (GNN) has demonstrated its superior power in various data mining tasks and has been widely applied in diversified fields. The core of GNN is the aggregation and combination functions, and mainstream GNN studies focus on the enhancement of these functions. However, GNNs face a common challenge, i.e., useless features contained in neighbor nodes may be integrated into the target node during the aggregation process. This leads to poor node embedding and undermines downstream tasks. To tackle this problem, this article proposes a novel GNN optimization framework GNN-MHSIC by introducing the nonparametric dependence method Hilbert-Schmidt independence criterion (HSIC) under the guidance of information bottleneck. HSIC is utilized to guide the information propagation among layers of a GNN from multiaspect views. GNN-MHSIC aims to achieve three main objectives: 1) minimizing the HSIC between the input features and the propagation layers; 2) maximizing the HSIC between the propagation layers and the ground truth; and 3) minimizing the HSIC between the propagation layers. With a multiaspect design, GNN-MHSIC can minimize the propagation of redundant information while preserving relevant information about the target node. We prove GNN-MHSIC's finite upper and lower bounds theoretically and evaluate it experimentally with four classic GNN models, including the graph convolutional network, the graph attention network (GAT), the heterogeneous GAT, and the heterogeneous graph (HG) propagation network on three widely used HGs. The results illustrate the usefulness and performance of GNN-MHSIC.

Impact of innovative education on the professionalism of undergraduate nursing students in China.

AIM: To explore the relationships among innovative atmosphere, innovative behavior, professional self-efficacy, professional identity, and professionalism of undergraduate nursing students in China. BACKGROUND: In lieu of the global shortage of nurses and low professional willingness of nursing students, innovative qualities are closely related to the professionalism of nurses. METHODS: The participants of this cross-sectional study consisted of 320 nursing students recruited from the Nursing College of a comprehensive university in Jiangsu Province, China who voluntarily completed an anonymous questionnaire from May to October 2019. Structural equation modeling analyses were performed. RESULTS: There was a positive correlation between all hypothetical pairwise variables (r = 0.496-0.795, p < 0.01). The final research model fits well. The results revealed that innovation atmosphere had a positive effect on innovative behavior and innovative behavior could affect nursing professionalism through self-efficacy and identity. CONCLUSION: Innovative education plays a very important role in the professionalism of undergraduate nursing students. Nursing educators can promote the development of professionalism in future nurses by fostering innovative behaviors.

Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues.

Spatial mapping of proteins in tissues is hindered by limitations in multiplexing, sensitivity and throughput. Here we report immunostaining with signal amplification by exchange reaction (Immuno-SABER), which achieves highly multiplexed signal amplification via DNA-barcoded antibodies and orthogonal DNA concatemers generated by primer exchange reaction (PER). SABER offers independently programmable signal amplification without in situ enzymatic reactions, and intrinsic scalability to rapidly amplify and visualize a large number of targets when combined with fast exchange cycles of fluorescent imager strands. We demonstrate 5- to 180-fold signal amplification in diverse samples (cultured cells, cryosections, formalin-fixed paraffin-embedded sections and whole-mount tissues), as well as simultaneous signal amplification for ten different proteins using standard equipment and workflows. We also combined SABER with expansion microscopy to enable rapid, multiplexed super-resolution tissue imaging. Immuno-SABER presents an effective and accessible platform for multiplexed and amplified imaging of proteins with high sensitivity and throughput.