Retrospective analysis of traumatic triradiate cartilage injury in children.

BACKGROUND: To summarize and analyze the epidemiological characteristics, treatment and corresponding curative effect of triradiate cartilage injury(TCI) in children after trauma, to provide a theoretical basis for early diagnosis and improvement of treatment. METHODS: The TCI was classified according to Bucholz classification, and the final curative effect was evaluated with Harris Hip Score and imaging examination during follow-up. Finally, a comprehensive analysis was made by reviewing the cases in the literature combined with the patients in our hospital. RESULTS: A total of 15 cases (18 hips) of triradiate cartilage injuries were collected in our hospital. There was 1 hip with type I injury, nine hips with type II injury, two hips with type IV injury, one hip with type V injury and five hips with type VI injury. Among the 12 cases with complete follow-up, the bone bridge was found in or around the triradiate cartilage in 8 cases, early fusion of triradiate cartilage occurred in 5 patients, 3 cases had hip dysplasia, 4 cases had a subluxation of the femoral head, and HHS was excellent in 8 cases and good in 4 cases. CONCLUSION: The early diagnosis of TCI is still a difficult problem. Conservative treatment is often the first choice. The overall prognosis of acetabular fractures involving triradiate cartilage is poor. The formation of the bone bridge in triradiate cartilage usually indicates the possibility of premature closure, which may lead to severe complications of post-traumatic acetabular dysplasia and subluxation of the femoral head.

Noniterative Sparse LS-SVM Based on Globally Representative Point Selection.

A least squares support vector machine (LS-SVM) offers performance comparable to that of SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity compared with SVMs, making LS-SVM unsuitable for handling large-scale data due to computation and memory costs. To obtain sparse LS-SVM, several pruning methods based on an iterative strategy were recently proposed but did not consider the quantity constraint on the number of reserved support vectors, as widely used in real-life applications. In this article, a noniterative algorithm is proposed based on the selection of globally representative points (global-representation-based sparse least squares support vector machine, GRS-LSSVM) to improve the performance of sparse LS-SVM. For the first time, we present a model of sparse LS-SVM with a quantity constraint. In solving the optimal solution of the model, we find that using globally representative points to construct the reserved support vector set produces a better solution than other methods. We design an indicator based on point density and point dispersion to evaluate the global representation of points in feature space. Using the indicator, the top globally representative points are selected in one step from all points to construct the reserved support vector set of sparse LS-SVM. After obtaining the set, the decision hyperplane of sparse LS-SVM is directly computed using an algebraic formula. This algorithm only consumes O(N2) in computational complexity and O(N) in memory cost which makes it suitable for large-scale data sets. The experimental results show that the proposed algorithm has higher sparsity, greater stability, and lower computational complexity than the traditional iterative algorithms.

Willingness to accept COVID-19 vaccine among the elderly and the chronic disease population in China.

A cross-sectional field survey was conducted from November 2020 to January 2021 among 7259 participants to investigate the public perception, willingness, and information sources for COVID-19 vaccination, with the focus on the elderly and non-communicable chronic disease (NCD) population. Multiple logistic regressions were performed to identify associated factors of the vaccination willingness. The willingness rate of the elderly to accept the future COVID-19 vaccine (79.08%) was lower than that of the adults aged 18-59 (84.75%). The multiple analysis didn't identify significant relationship between NCD status and the vaccination intention. The main reasons for vaccine hesitancy by the public were: concern for vaccine safety, low infection risk, waiting and seeing others getting vaccinated, concern of vaccine effectiveness and price. Their relative importance differed between adults aged 18-59 and the elderly, and between adults aged 18-59 with or without NCD. Perception for vaccination importance, vaccine confidence, and trust in health workers were significant predictors of the vaccination intention in both age groups. The elderly who perceived high infection risk or had trust in governments were more likely to accept the vaccine. Compared with the adults aged 18-59, the elderly used fewer sources for COVID-19 vaccination information and more trusted in traditional media and family, relatives, and friends for getting vaccination recommendations. To promote vaccine uptake, the vaccination campaigns require comprehensive interventions to improve vaccination attitude, vaccine accessibility and affordability, and tailor strategies to address specific concerns among different population groups and conducted via their trusted sources, especially for the elderly.

Relaxation oscillation in Er3+-doped and Yb3+/Er3+ co-doped fiber grating lasers.

Relaxation oscillations in Er(3+)-doped and Yb(3+)/Er(3+) co-doped fiber grating lasers were investigated. Intensity noise models were established for both cases in which the fluctuations of pump power and cavity loss were analyzed. Simulation indicates that the relaxation oscillation is induced by the pump power fluctuation, and fluctuation of the cavity loss can broaden the relaxation oscillation peak and enhance the amplitude. The relaxation oscillation of a laser with Er(3+)-doped fiber exhibits a lower frequency and larger amplitude than that of an Yb(3+)/Er(3+) co-doped fiber grating laser. Distributed Bragg reflector fiber grating lasers with Er(3+)-doped fiber and Yb(3+)/Er(3+) co-doped fiber were fabricated, and the relaxation oscillations were measured. For the former, the relaxation oscillation frequency is at the magnitude of several hundreds of kilohertz and linearly shifts toward the higher frequency with the pump power. For the latter, no obvious relaxation oscillation was measured.

Information Propagation Formalized Representation of Micro-blog Network Based on Petri Nets.

The description of user behavior in social networks is an important issue for studying social networks. Given that Petri nets can describe the resource flow problem, this study utilizes the features of Petri nets to portray the user behavior states during the message propagation of a micro-blog network and presents an information propagation formalized representation method of a micro-blog network. On this basis, this study analyzed the proposed formalized representation method in detail. We provide examples of applying formalized representation (e.g., micro-blog network addiction of users, user behavior influence, and public opinion analysis). In addition, we introduce the algorithms of formalized representation. We conduct experiments using Sina micro-blog data. Results show that the information propagation formalized representation method of micro-blog network based on Petri nets can depict user behaviors of micro-blog network intuitively and accurately. This study reveals a new perspective for information transmission of a micro-blog network and provides some tools to support public opinion monitoring and micro-blog marketing applications.

Cyberphysical Human Sexual Behavior Acquisition System (SeBA): Development and Implementation Study in China.

BACKGROUND: Sexual health is one of the principal components of human well-being. Traditional methods for observing human sexual behavior typically adopt manual intervention approaches (eg, interviews). However, the data obtained by such traditional approaches suffer from intrinsic bias and limited sample sizes. Sexual behavioral data that are more reflective of the actual situation can be collected by equipping sex toys with sensors. OBJECTIVE: To address the limitations of traditional human sexual behavior data observation methods, a novel cyberphysical system is proposed to capture natural human sexual behavior data in China at the nationwide level. METHODS: A cyberphysical human sexual behavior acquisition system (SeBA) was designed and implemented. SeBA jointly utilizes state of the art information and communication technologies such as smart sex toys, smartphones, and mobile social networks. Smart sex toys enable objective collection of data on human sexual behavior, while the mobile social network provides the possibility of partnered sex in a cyberphysical manner. The objectives and function settings are discussed, and the overall framework of the system architecture is presented. RESULTS: Operation and privacy policies are proposed and the technical solution of SeBA is described. The effectiveness of SeBA was verified based on analysis of users' human sexual behavior data collected from January 2016 to June 2017. A total of 103,424 solo sexual behaviors were recorded involving 13,047 users, and 61,007 partnered sexual behaviors from 7,140 users were observed. The proportions of males and females in the solo and partnered sex groups were fairly consistent with recent statistics on unmarried individuals in China. We also found that only a small portion of individuals provided information on at least one other attribute besides the required input of gender, such as age, height, location, job, sex preferences, purposes, and interests. CONCLUSIONS: To the best of our knowledge, this is the first study to analyze objective human sexual behavior data at the nationwide level. Although the data are restricted to China, this study can provide insight for further research on human sexual behavior based on the huge amount of data available from wireless smart sex toys worldwide. It is anticipated that findings from such objective big data analyses can help deepen our understanding of sexual behavior, as well as improve sexual health and sexual wellness.

A novel efficient broadband model to derive daily surface solar Ultraviolet radiation (0.280-0.400 µm).

The climatic characteristics of solar Ultraviolet radiation (UV) are of vital important for the climate change and photochemical reactions. High-quality records of solar UV radiation are the premise for solar UV researches and applications, but solar UV radiation observations are sparse around the world. Among all wavelength of UV radiations, only UVA (0.315-0.400 nm) and UVB (0.280-0.315 nm) could reach the earth surface. This study attempted to develop a novel efficient physically broadband parameterization (hereafter, FASTUV) for estimating surface solar UV radiation (0.280-0.400 µm) in all-sky conditions based on Leckner's spectral model for calculating shortwave solar radiation, using MERRA_2 reanalysis data. The Quadratic polynomial formula and artificial neural networks were used to calculate the cloud transmittance for UV, using sunshine durations measurements at 2474 CMA stations. The surface solar UV radiation measurements at 29 CERN (The Chinese Ecosystem Research Network) stations were used for validating the estimated UV values. The result showed the FASTUV model could be used for estimating UV values with high accuracy, strong robustness and fast speed. Then, the spatial and temporal variation of surface solar UV radiation in China were revealed. The result indicated that the Qinghai Tibetan Plateau and the Palmier Plateau has always been the areas with highest UV values, while the Northeastern China is the area with the lowest UV values. Meanwhile, the FASTUV model have been packaged into a software namely 'FASTUV_V1.0'. We provide the executable file of FASTUV model in publicly available repository: https://doi.org/10.6084/m9.figshare.11409666.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China.

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth's system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Identification of biomarkers associated with clinical severity of chronic obstructive pulmonary disease.

We sought to identify the biomarkers related to the clinical severity of stage I to stage IV chronic obstructive pulmonary disease (COPD). Gene expression profiles from the blood samples of COPD patients at each of the four stages were acquired from the Gene Expression Omnibus Database (GEO, accession number: GSE54837). Genes showing expression changes among the different stages were sorted by soft clustering. We performed functional enrichment, protein-protein interaction (PPI), and miRNA regulatory network analyses for the differentially expressed genes. The biomarkers associated with the clinical classification of COPD were selected from logistic regression models and the relationships between TLR2 and inflammatory factors were verified in clinical blood samples by qPCR and ELISA. Gene clusters demonstrating continuously rising or falling changes in expression (clusters 1, 2, and 7 and clusters 5, 6, and 8, respectively) from stage I to IV were defined as upregulated and downregulated genes, respectively, and further analyzed. The upregulated genes were enriched in functions associated with defense, inflammatory, or immune responses. The downregulated genes were associated with lymphocyte activation and cell activation. TLR2, HMOX1, and CD79A were hub proteins in the integrated network of PPI and miRNA regulatory networks. TLR2 and CD79A were significantly correlated with clinical classifications. TLR2 was closely associated with inflammatory responses during COPD progression. Functions associated with inflammatory and immune responses as well as lymphocyte activation may play important roles in the progression of COPD from stage I to IV. TLR2 and CD79A may serve as potential biomarkers for the clinical severity of COPD. TLR2 and CD79A may also serve as independent biomarkers in the clinical classification in COPD. TLR2 may play an important role in the inflammatory responses of COPD.

Global projections of future urban land expansion under shared socioeconomic pathways.

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50-63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1-4%, corresponding to the annual food needs for a certain crop of 122-1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production.

Preparation of polypyrrole nanoparticles in reverse micelle and its application to glucose biosensor.

Polypyrrole nanoparticles were successfully synthesized in cetyltrimethyl ammonium bromide (CTAB)/hexanol/water reverse micelle. The morphology and particle size of the obtained nanoparticles were characterized with transmission electron microscope (TEM) and scanning electron microscopy (SEM). Glucose biosensors were formed with glucose oxidase (GOx) immobilized in conducting composite material consisting of polypyrrole nanoparticles and ethyl cellulose. The effects of reaction conditions such as molar ratio of polypyrrole nanoparticles to ethyl cellulose, working voltage, glucose concentration, temperature and solution pH on the electrochemical response of the GOx electrode were studied. Experimental results showed that the linear range of GOx electrode was 1.0 x 10(-6)-6 x 10(-3) mol/L and the detection limit was 1.0 x 10(-7) mol/L. The electrode exhibited fine repeatability and selectability, and its lifetime was greater than one month. AFM showed that the surface of conducting composite material-glucose oxidase electrode's presents uniform granular after washing paraffin wax with cyclohexane, which was favorable for enzyme-catalyzed reaction.

A Multiple Kernel Learning Model Based on p-Norm.

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp -norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of Lp -norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (L1- and L2-norms based GMKL) method, in which L1 L2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p-norm, by joining L1- and Lp -norms. Consequently, the L1- and L2-norms based GMKL is a special case in our method when p = 2. Experiments demonstrated that our L1- and Lp -norms based MKL offers a higher accuracy than the L1- and L2-norms based GMKL in the classification, while keeping the properties of the L1- and L2-norms based on GMKL.

Cycle Based Network Centrality.

Paths and cycles are the two pivotal elements in a network. Here, we demonstrate that paths, particularly the shortest ones, are incomplete in information network. However, based on such paths, many network centrality measures are designed. While extensive explorations on paths have been made, modest studies focus on the cycles on measuring network centrality. We study the relationship between the shortest cycle and the shortest path from extensive real-world networks. The results illustrate the incompleteness of the shortest paths on measuring network centrality. Noticing that the shortest cycle is much more robust than the shortest path, we propose two novel cycle-based network centrality measures to address the incompleteness of paths: the shortest cycle closeness centrality (SCC) and the all cycle betweenness centrality (ACC). Notwithstanding we focus on the network centrality problem, our findings on cycles can be applied to explain the incompleteness of paths in applications and could improve the applicability into more scenarios where the paths are employed in network science.

Discrete time information diffusion in online social networks: micro and macro perspectives.

Opinions shared publicly in online social networks spread broadly and at an extremely high speed. However, modelling information diffusion in online social networks is still a challenge that is intriguing to many researchers. To monitor public opinions online, it is necessary to model the process of information dissemination. In this paper, we first study information diffusion based on the network structure and time occupation. By taking into consideration the availability of a user, e.g., his online or offline state, we present the discrete-time bi-probability independent cascade model. We next analyse the information diffusion from a macro perspective. A diffusion model is established by merging the interferences from other events and the cumulative effect that occurs over time. Finally, we observe the factors in online social networks that impact a message's diffusion from a micro perspective and discuss more complex user behaviour and various types of interferences with their effects from a macro perspective. Experiments are conducted with real world data, and the experimental results justify our models.

Micro-blog user community discovery using generalized SimRank edge weighting method.

Community discovery is one of the most popular issues in analyzing and understanding a network. Previous research suggests that the discovery can be enhanced by assigning weights to the edges of the network. This paper proposes a novel edge weighting method, which balances both local and global weighting based on the idea of shared neighbor ranging between users and the interpersonal significance of the social network community. We assume that users belonging to the same community have similar relationship network structures. By controlling the measure of "neighborhood", this method can adequately adapt to real-world networks. Therefore, the famous similarity calculation method-SimRank-can be regarded as a special case of our method. According to the practical significance of social networks, we propose a new evaluation method that uses the communication rate to measure its divided demerit to better express users' interaction relations than the ordinary modularity Q. Furthermore, the fast Newman algorithm is extended to weighted networks. In addition, we use four real networks in the largest Chinese micro-blog website Sina. The results of experiments demonstrate that the proposed method easily meets the balancing requirements and is more robust to different kinds of networks. The experimental results also indicate that the proposed algorithm outperforms several conventional weighting methods.

Simulating urban expansion and its impact on functional connectivity in the Three Gorges Reservoir Area.

Understanding the impact of urban expansion on functional connectivity is significant to biodiversity conservation. Particularly, in the Three Gorges Reservoir Area (TGRA, Southwest China), the urban land has rapidly expanded to provide settlements for an enormous population of TGRA migrants. However, the consequence of future land-use changes to the functional connectivity of the local habitat network has rarely been studied. To extend this knowledge, this paper proposes a framework that integrates a novel cellular automata (CA) simulation model and ecological network analysis, taking the TGRA as the study area, to predict how different urban expansion scenarios might affect functional connectivity for a nationally protected species, the leopard. The least-cost path modeling is used, and a set of connectivity indicators are adopted to evaluate functional connectivity. The results show that, the population-growth-based urban expansion maintains a higher connectivity than the business-as-usual and fast-urban-growth scenarios. In addition, the connectivity loss due to urban expansion can be offset by the reforestation efforts of the Green-for-Grain Project. Finally, we identify habitat patches that act as key connectivity providers, and suggest that those patches be prioritized for protection to avoid significant connectivity loss.

Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set.

The semisupervised least squares support vector machine (LS-S3VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S3VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S3VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model. Based on the distances between the unlabeled data and the decision hyperplane, a new indicator is designed to represent the possibility that the label of an unlabeled datum should be reversed in each iteration during training. Using the indicator, we construct an extended candidate set consisting of the indices of unlabeled data with high possibilities, which integrates more information from unlabeled data. Our algorithm is degenerated into a special scenario of the previous algorithm when the extended candidate set is reduced into a set with only one element. Two strategies are utilized to determine the descent directions based on the extended candidate set. Furthermore, we developed a novel method for locating a good starting point based on the properties of the equivalent IP model. Combined with the extended candidate set and the carefully computed starting point, a fast algorithm to solve LS-S3VM quasi-optimally is proposed. The choice of quasi-optimal solutions results in low computational cost and avoidance of overfitting. Experiments show that our algorithm equipped with the two designed strategies is more effective than other algorithms in at least one of the following three aspects: 1) computational complexity; 2) generalization ability; and 3) flexibility. However, our algorithm and other algorithms have similar levels of performance in the remaining aspects.

Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data.

Urbanization has profoundly altered the terrestrial ecosystem carbon cycle, especially the net primary productivity (NPP). Many attempts have been made to assess the influence of urbanization on NPP at coarse resolutions (e.g., 250m or larger), which may ignore many smaller and highly fragmented urban lands, and to a large extent, underestimate the NPP variations induced by urban sprawl. Hence, we attempted to analyze the NPP variations influenced by urban sprawl at a fine resolution (e.g., 30m), toward which the accuracy of NPP was improved using remotely sensed data fusion algorithm. In this paper, this assumption was tested in the Pearl River Delta of China. The land cover datasets from the Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) were acquired to quantify the urban sprawl. The synthetic Normal Differential Vegetation Index (NDVI) data was obtained by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI via spatiotemporal fusion algorithm. The Carnegie-Ames-Stanford Approach (CASA) model was driven by land cover map, synthetic NDVI and meteorological data to estimate the 30-m resolution NPP. Then, we analyzed the influence of urban sprawl on 30-m resolution NPP during the period of 2001-2009. Additionally, we also simulated the spatiotemporal change of future urban sprawl under different scenarios using the Future Land Use Simulation (FLUS) model, and further analyzed its influence on 30-m resolution NPP. Our results showed that the accuracy of 30-m resolution NPP from synthetic NDVI is better than 500-m resolution NPP from MODIS NDVI. The loss in 30-m resolution NPP due to urban sprawl was much higher than 500-m resolution NPP. Moreover, the harmonious development scenario, characterized by a reasonable size of urban sprawl and a corresponding lower NPP loss from 2009 to 2050, would be considered as a more human-oriented and sustainable development strategy.

Electroacupuncture modulates cortical activities evoked by noxious somatosensory stimulations in human.

A noninvasive high-resolution imaging technique of cerebral electric activities has been developed to directly link scalp potential measurement with the magnetic resonance images of the subjects, which is very helpful for the elucidation of the cortical processing following various stimulations. Here, we used a 64-channel Neuroscan ESI-128 system to explore the specific cortical activities elicited by electroacupuncture (EA) acupoint in normal volunteers and the modulatory effect of EA on cortical activities evoked by noxious somatosensory stimulation. A specific later-latency somatosensory-evoked potential (SEP, P150) located in bilateral anterior cingulated cortex was observed after EA acupoint but not non-acupoint. Two pain-specific SEP components (P170 and N280), located in bilateral suprasylvian operculum and anterior cingulated cortex respectively were observed following painful median nerve stimulation. Binding EA acupoint with painful median nerve stimulation, the amplitudes of P170 and N280 appeared to be attenuated significantly, 2D topography exhibited tremendous decrease of cortical activation between 120 ms and 296 ms in latency, and visual analogue scale (VAS) changes also showed a similar pattern to the change of amplitude. The bilateral anterior cingulated cortex recruited following acupoint stimuli might, to some extent, suggest that EA has the specific physiological effects. Decrease of pain-induced cortical activation by EA acupoint was considered to be mainly due to an interaction of the signals in anterior cingulated cortex ascending from the pain stimulation and EA.

A CRISPR/Cas9 and Cre/Lox system-based express vaccine development strategy against re-emerging Pseudorabies virus.

Virus evolves rapidly to escape vaccine-induced immunity, posing a desperate demand for efficient vaccine development biotechnologies. Here we present an express vaccine development strategy based on CRISPR/Cas9 and Cre/Lox system against re-emerging Pseudorabies virus, which caused the recent devastating swine pseudorabies outbreak in China. By CRISPR/Cas9 system, the virulent genes of the newly isolated strain were simultaneously substituted by marker genes, which were subsequently excised using Cre/Lox system for vaccine safety concern. Notably, single cell FACS technology was applied to further promote virus purification efficiency. The combination of these state-of-art technologies greatly accelerated vaccine development. Finally, vaccination and challenge experiments proved this vaccine candidate's protective efficacy in pigs and the promise to control current pseudorabies outbreak. This is, to our knowledge, the first successful vaccine development based on gene edit technologies, demonstrating these technologies leap from laboratory to industry. It may pave the way for future express antiviral vaccine development.