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"Cao, Jie"
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Modern emergency management
This book provides essential information on emergency management. It is composed of two parts, addressing the basic theory and related methods of emergency management, including risk management, coordination management, crisis management and disaster management. By putting the emphasis on interdisciplinary, systematic perspectives and building a bridge between basic knowledge and further research, it is well suited as an emergency management textbook and offers a valuable guide to prepare readers for their future emergency management careers.
Book
Peer Effects of Corporate Social Responsibility
We investigate how firms react to their product-market peers’ commitment to and adoption of corporate social responsibility (CSR) using a regression discontinuity design approach. Relying on the passage or failure of CSR proposals by a narrow margin of votes during shareholder meetings, we find the passage of a close-call CSR proposal and its implementation are followed by the adoption of similar CSR practices by peer firms. In addition, peers that have greater difficulty in catching up with the voting firm in CSR experience significantly lower stock returns around the passage, consistent with the notion that the spillover effect of the adoption of CSR is a strategic response to competitive threat. Using alternative definitions of peers and examining underlying mechanisms, we further rule out alternative explanations, such as that based on propagation by financial intermediaries.
This paper was accepted by Gustavo Manso, finance department.
Journal Article
DeepGhost: real-time computational ghost imaging via deep learning
The potential of random pattern based computational ghost imaging (CGI) for real-time applications has been offset by its long image reconstruction time and inefficient reconstruction of complex diverse scenes. To overcome these problems, we propose a fast image reconstruction framework for CGI, called “DeepGhost”, using deep convolutional autoencoder network to achieve real-time imaging at very low sampling rates (10–20%). By transferring prior-knowledge from STL-10 dataset to physical-data driven network, the proposed framework can reconstruct complex unseen targets with high accuracy. The experimental results show that the proposed method outperforms existing deep learning and state-of-the-art compressed sensing methods used for ghost imaging under similar conditions. The proposed method employs deep architecture with fast computation, and tackles the shortcomings of existing schemes i.e., inappropriate architecture, training on limited data under controlled settings, and employing shallow network for fast computation.
Journal Article
Strategies to improve photodynamic therapy efficacy by relieving the tumor hypoxia environment
Photodynamic therapy (PDT) is an emerging technology for tumor treatment in which photosensitizer (PS)-mediated light irradiation reduces oxygen, producing high levels of reactive oxygen species (ROS) that can cause vascular injury and effectively kill tumor cells. However, the naturally hypoxic tumor microenvironment is the main obstacle that hinders the photodynamic response in vivo and prevents its extensive application to tumor treatment. Moreover, PDT-mediated oxygen consumption further increases tumor hypoxia, potentially causing a variety of adverse consequences, such as angiogenesis, tumor invasion, and metastasis. To overcome these limitations caused by hypoxia, multiple strategies have been investigated, including the use of oxygen carriers and reactive oxygen supply materials, the regulation of tumor microenvironments, and multimodal therapy including PDT. In this review, we summarize the latest progress in the development of strategies to relieve tumor hypoxia for improved PDT efficacy and better therapeutic effects.In this review, we summarize the latest progress in the development of strategies to relieve tumor hypoxia for improved PDT efficacy, from the design of novel nonreactive oxygen carriers to reactive materials and other strategies, including the regulation of tumor microenvironments and PDT-involved multimodal therapy.
Journal Article
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Single-cell RNA-seq (scRNA-seq) is being used widely to resolve cellular heterogeneity. With the rapid accumulation of public scRNA-seq data, an effective and efficient cell-querying method is critical for the utilization of the existing annotations to curate newly sequenced cells. Such a querying method should be based on an accurate cell-to-cell similarity measure, and capable of handling batch effects properly. Herein, we present Cell BLAST, an accurate and robust cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric. Through extensive benchmarks and case studies, we demonstrate the effectiveness of Cell BLAST in annotating discrete cell types and continuous cell differentiation potential, as well as identifying novel cell types. Powered by a well-curated reference database and a user-friendly Web server, Cell BLAST provides the one-stop solution for real-world scRNA-seq cell querying and annotation.
Single-cell RNA-seq (scRNA-seq) is being widely used to resolve cellular heterogeneity. Here, the authors present a cell-querying method built on a neural network-based generative model and a customized cell-to-cell similarity metric.
Journal Article
Spatial-linked alignment tool (SLAT) for aligning heterogenous slices
Spatially resolved omics technologies reveal the spatial organization of cells in various biological systems. Here we propose SLAT (Spatially-Linked Alignment Tool), a graph-based algorithm for efficient and effective alignment of spatial slices. Adopting a graph adversarial matching strategy, SLAT is the first algorithm capable of aligning heterogenous spatial data across distinct technologies and modalities. Systematic benchmarks demonstrate SLAT’s superior precision, robustness, and speed over existing state-of-the-arts. Applications to multiple real-world datasets further show SLAT’s utility in enhancing cell-typing resolution, integrating multiple modalities for regulatory inference, and mapping fine-scale spatial-temporal changes during development. The full SLAT package is available at
https://github.com/gao-lab/SLAT
.
Spatial omics technologies reveal the organisation of cells in various biological systems. Here, authors propose SLAT, a graph-based algorithm for aligning heterogenous data across technologies, modalities and timepoints, enabling spatiotemporal reconstruction of complex developmental processes.
Journal Article
Dual-YOLO Architecture from Infrared and Visible Images for Object Detection
With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.
Journal Article
Advances in 5-Aminolevulinic Acid Priming to Enhance Plant Tolerance to Abiotic Stress
Priming is an adaptive strategy that improves plant defenses against biotic and abiotic stresses. Stimuli from chemicals, abiotic cues, and pathogens can trigger the establishment of priming state. Priming with 5-aminolevulinic acid (ALA), a potential plant growth regulator, can enhance plant tolerance to the subsequent abiotic stresses, including salinity, drought, heat, cold, and UV-B. However, the molecular mechanisms underlying the remarkable effects of ALA priming on plant physiology remain to be elucidated. Here, we summarize recent progress made in the stress tolerance conferred by ALA priming in plants and provide the underlying molecular and physiology mechanisms of this phenomenon. Priming with ALA results in changes at the physiological, transcriptional, metabolic, and epigenetic levels, and enhances photosynthesis and antioxidant capacity, as well as nitrogen assimilation, which in turn increases the resistance of abiotic stresses. However, the signaling pathway of ALA, including receptors as well as key components, is currently unknown, which hinders the deeper understanding of the defense priming caused by ALA. In the future, there is an urgent need to reveal the molecular mechanisms by which ALA regulates plant development and enhances plant defense with the help of forward genetics, multi-omics technologies, as well as genome editing technology.
Journal Article
Metagenomic Next-Generation Sequencing versus Traditional Pathogen Detection in the Diagnosis of Peripheral Pulmonary Infectious Lesions
The aim of this study was to evaluate the value of metagenomic next-generation sequencing (mNGS) in peripheral pulmonary infection management by comparing the diagnostic yield of mNGS and traditional pathogen detection methods on interventional specimens obtained by bronchoscopy.
This study enrolled patients suspected with pulmonary infection who were admitted to Tianjin Medical University General Hospital from June 2018 to August 2019. Specimens were obtained from bronchoscopy for mNGS analysis and traditional pathogen detection (including bronchoalveolar lavage fluid microbial culture, smear microscopy, and lung biopsy histopathology), and the diagnostic yields were compared between mNGS and traditional methods to evaluate the diagnostic value of mNGS in peripheral pulmonary infection diagnosis.
In this study, by comparing mNGS with traditional pathogen detection, the results indicated that, first, mNGS identified at least one microbial species in almost 89% of the patients with pulmonary infection; second, mNGS detected microbes related to human diseases in 94.49% of samples from pulmonary infection patients who had received negative results from traditional pathogen detection; third, the accuracy and sensitivity of mNGS are higher than those of traditional pathogen detection; and, finally, mNGS could simultaneously detect and identify a large variety of pathogens.
Metagenomic NGS analysis provided fast and precise pathogen detection and identification, contributing to prompt and accurate treatment of peripheral pulmonary infection.
Journal Article
Multimaterial cryogenic printing of three-dimensional soft hydrogel machines
Hydrogel-based soft machines are promising in diverse applications, such as biomedical electronics and soft robotics. However, current fabrication techniques generally struggle to construct multimaterial three-dimensional hydrogel architectures for soft machines and robots, owing to the inherent hydrogel softness from the low-density polymer network nature. Herein, we present a multimaterial cryogenic printing (MCP) technique that can fabricate sophisticated soft hydrogel machines with accurate yet complex architectures and robust multimaterial interfaces. Our MCP technique harnesses a universal all-in-cryogenic solvent phase transition strategy, involving instant ink solidification followed by in-situ synchronous solvent melting and cross-linking. We, therefore, can facilely fabricate various multimaterial 3D hydrogel structures with high aspect ratio complex geometries (overhanging, thin-walled, and hollow) in high fidelity. Using this approach, we design and manufacture all-printed all-hydrogel soft machines with versatile functions, such as self-sensing biomimetic heart valves with leaflet-status perception and untethered multimode turbine robots capable of in-tube blockage removal and transportation.
Hydrogel-based machines have potential in a range of applications, but it can be challenging to prepare multimaterial devices with complex structures. Here, the authors report the development of a multimaterial cryogenic printing technique that can be used to prepare three-dimensional structures with geometries such as overhanging, thin-walled and hollow.
Journal Article