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In 2020, the Chinese government set the objective of achieving carbon neutrality, making it essential to understand the urbanization process in order to effectively implement future sustainable development strategies. Here, we demonstrate the urbanization development of 31 provincial capitals in mainland China from 1985 to 2020, including changes in impervious surfaces, socio-economic development, carbon emissions, and population. Using elasticity coefficients and the carbon emissions causal equation for quantitative analysis, we explore the relationships among these factors. We found that the national urban expansion rate experienced rapid growth, followed by a slowdown, with the peak occurring between 2000 and 2005. In just 40 years, total urban area expanded from 11,280 km2 to 40,429 km2, with eastern cities growing faster than those in the west. Despite similar trends, different cities displayed four distinct spatial characteristics, shaped by factors such as topography, transportation, and policies. Moreover, urban economic development is gradually decoupling from carbon emissions, accompanied by a declining reliance on urban area expansion and a reduced growth rate of carbon emissions. A strong decoupling trend between economic growth and carbon emissions has already emerged in the South & Central and Northwest regions, offering a preliminary explanation for the observed slowdown in carbon emissions. The study provides a comprehensive overview of the urbanization process in mainland China, highlighting low-carbon urban transformations and revealing how government policies have significantly shaped the development patterns of Chinese cities. These findings offer valuable insights for policymakers to further promote sustainable development goals.

Infectious bovine rhinotracheitis (IBR), characterized by acute respiratory lesions in cattle, is a major infectious disease caused by bovine alphaherpesvirus-1 (BoAHV-1). Control of this disease is primarily depending on vaccination. Madin-Darby bovine kidney cells (MDBK) being the main host cells and the important production platform for IBR vaccines. However, innate immune genes inhibit viral replication. Accordingly, the aim of this study was developing of IRF7 gene deleted MDBK cells to facilitate the production of high-titer vaccines. The CRISPR/Cas9 technology was used to knock out the IRF7 gene in MDBK cells and the impact on virus replication was examined using virus growth curves, CCK-8 assays, cell scratch assays, and qPCR. The knockout of the IRF7 gene in MDBK cells led to an increased replication capacity of IBRV and a significant reduction in type I interferons expression, specifically IFN-α and IFN-β. This indicates that IRF7−/−MDBK cell lines can effectively result in production of IBRV with high-titer, which will enhance the development of inactivated or attenuated vaccines.

This paper reports a case of closed reduction internal fixation of pelvic fracture and minimally invasive hemostasis procedure and hematoma catheterization and drainage for the active pelvic bleeding site and giant hematoma, guided by a robot-assisted teleultrasound. In this case, the robot-assisted teleultrasound-guided minimally invasive interventional hemostasis and catheterization utilized a partial master-slave approach. It was preliminarily confirmed that robot-assisted teleultrasound-guided minimally invasive hemostasis and puncture catheterization for hematoma were accurate and effective. The robot-assisted teleultrasound overcomes the reliance on physician experience in ultrasound-guided interventional diagnostics and treatment, and promoting the use of minimally invasive "visualized" technology across any distance.

With the development of network technology and intelligent robot technology, Robot-assisted teleultrasound has played an important role in clinical fields. The application of real-time remote ultrasound technology has made the ultrasonic diagnosis break through the limitation of time and space distance, and solved the problem of shortage of medical resources to a certain extent. This article introduces the development and application basis of robot-assisted teleultrasound, summarizes the clinical application status, and discusses the advantages and limitations of its current application. In addition, we discuss the value in application scenario, interventional therapy and intracavitary ultrasound in the future.

Background: Meckel Syndrome (MKS, OMIM #249000) is a rare and fatal autosomal recessive ciliopathy with high clinical and genetic heterogeneity. MKS shows complex allelism with other related ciliopathies such as Joubert Syndrome (JBTS, OMIM #213300). In MKS, the formation and function of the primary cilium is defective, resulting in a multisystem disorder including occipital encephalocele, polycystic kidneys, postaxial polydactyly, liver fibrosis, central nervous system malformations and genital anomalies. This study aimed to analyze the genotype of MKS patients and investigate the correlation between genotype and phenotype.Methods: A nonconsanguineous couple who conceived four times with a fetus affected by multiorgan dysfunction and intrauterine fetal death was studied. Whole exome sequencing (WES) was performed in the proband to identify the potentially pathogenic variant. Sanger sequencing was performed in family members. In silico tools were used to analyse the pathogenicity of the identified variants. cDNA TA-cloning sequencing was performed to validate the effects of intronic variants on mRNA splicing. Quantitative real-time PCR was performed to investigate the effect of the variants on gene expression. Immunofluorescence was performed to observe pathological changes of the primary cilium in kidney tissue from the proband.Results: Two splice site variants of TMEM231 (NM_001077418.2, c.583-1G>C and c.583-2_588delinsTCCTCCC) were identified in the proband, and the two variants have not been previously reported. The parents were confirmed as carriers. The two variants were predicted to be pathogenic by in silico tools and were classified as pathogenic/likely pathogenic variants according to the American College of Medical Genetics and Genomics guideline. cDNA TA cloning analysis showed that both splice site variants caused a deletion of exon 5. RT-PCR revealed that the expression of TMEM231 was significantly decreased and immunofluorescence showed that the primary cilium was almost absent in the proband’s kidney tissue.Conclusion: We reported the clinical, genetic, molecular and histochemical characterisation of a family affected by MKS. Our findings not only extended the mutation spectrum of the TMEM231 gene, but also revealed for the first time the pathological aetiology of primary cilia in humans and provide a basis for genetic counselling of the parents to their offspring.

Abstract Objective Resveratrol (RSV) is a polyphenol compound found in grapes, veratrum and other plants. It has been reported that RSV has anti-inflammatory, anti-oxidant, anti-cancer and other pharmacological effects. However, the impacts of RSV on development of nervous system are not understood well. The study aims to investigate RSV’s neuroprotective effect during development and to provide a health care for pregnant women and their fetuses with RSV supplementation. Methods In this study, we induced human induced pluripotent stem cells (hiPSCs) to form the embryoid bodies (EBs) and cerebral organoids (COs) with 3 dimensional (3D) culture. In the meantime, D-galactose (D-gal, 5 mg/ml) was used to make nervous injury model, and on the other hand, RSV with various doses, such as 2 μm/L, 10 μm/L, 50 μm/L, were applied to understand its neuroprotection. Therefore, the cultures were divided into control group, D-gal nervous injury group and RSV intervention groups. After that, the diameters of EBs and COs were measured regularly under a reverted microscope. In the meantime, the neural proliferation, cell apoptosis and the differentiation of germ layers were detected via immunofluorescence. Results (1) D-gal could delay the development of EBs and COs; (2) RSV could rescue the atrophy of EBs and COs caused by D-gal; (3) RSV showed its neuroprotection, through promoting the neural cell proliferation, inhibiting apoptosis and accelerating the differentiation of germ layers. Conclusion RSV has a neuroprotective effect on the development of the nervous system, suggesting RSV supplementation may be necessary during the health care of pregnancy and childhood.

Pancreatic cancer (PC) is a fatal malignancy, threatening human health in worldwide. Long non-coding RNAs (lncRNAs) have been acknowledged to be essential regulators in various biological processes of human cancers. However, the role of some novel lncRNAs in PC remain to be explored. In this study, we focused on the function and molecular mechanism of a novel lncRNA linc-UROD (also named TCONS_00002016 or XLOC_000166) in PC. The expression of linc-UROD was found to be upregulated in PC cells. The results of loss-of-function assays demonstrated that linc-UROD knockdown suppressed cell proliferation and migration, induced cell cycle G0/G1 arrest, and accelerated apoptosis of PC cells. Through mechanistic experiments, we found that IGF2BP3 stabilized linc-UROD through METTL3-mediated m6A modification. In addition, linc-UROD enhances the stability of ENO1 and PKM through interacting with them to inhibit ubiquitination. Detection on glucose consumption, pyruvate kinase activity and lactate production indicated that linc-UROD accelerated glycolysis of PC cells through PKM/ENO1-mediated pathway. To summarize, linc-UROD stabilized by IGF2BP3/METTL3 contributes to glycolysis and malignant phenotype of PC cells by stabilizing ENO1 and PKM. The findings suggest that linc-UROD may be a novel therapeutic target for PC patients.

Compared with the urban aging population, the rural aging population in China is larger, and is subject to a lower per capita income, lower social security coverage, and insufficient security capacity. Therefore, ensuring the health of middle-aged and older farmers is an inevitable requirement for maintaining the stability of rural areas and society. This study uses data from the China Health and Retirement Longitudinal Survey (CHARLS) 2018, an ordered probit model and instrumental variable approach to empirically analyze the effect of land circulation out on the physical health of middle-aged and older farmers over 45. The results indicate that land circulation out positively and significantly affects farmers’ health. Again, farmer’s non-agricultural work and household income play a significant role in this positive relationship between land circulation out and farmers’ health. Thus, the promotion of non-agricultural work for farmers and household income enhancement could alleviate household budget constraints while increasing health investment. The findings provide policies advocating for rural health development from land circulation perspectives.

Magnetic anomaly detection (MAD) is an effective method to detect the existence and localization of magnetic targets. Magnetic signal processing technology can extract target signals from complex background noise. However, traditional magnetic signal processing methods cannot greatly improve the signal-to-noise ratio (SNR) while also restoring information concerning the target. This is because the main existing method to calculate a target’s magnetic moment requires a pure target signal. Our research regarding the full magnetic gradient orthonormal basis function (FMG-OBF) addresses the problem of low SNRs for the target magnetic anomaly (TMA) signal. However, this method can only detect the presence of the target and cannot obtain the magnetic moment characteristics of the target. Benefiting from the FMG tensor, which contains large amounts of spatial magnetic field information, this paper is devoted to characterizing TMA in different orientations from the signal energy point of view. We analyze the influences of the target magnetic moment’s variation on the energy components of the TMA signal in various orientations, and further propose a target magnetic moment orientation estimation method. Compared with the traditional signal processing method, the proposed method can estimate the magnetic moment orientation of the target while greatly improving the SNR. Therefore, this method has significant application potential for the classification and identification of weak TMA signals in MAD.

Allobetulin is structurally similar tobetulinic acid, inducing the apoptosis of cancer cells with low toxicity. However, both of them exhibited weak antiproliferation against several tumor cell lines. Therefore, the new series of allobetulon/allobetulin–nucleoside conjugates 9a–10i were designed and synthesized for potency improvement. Compounds 9b, 9e, 10a, and 10d showed promising antiproliferative activity toward six tested cell lines, compared to zidovudine, cisplatin, and oxaliplatin based on their antitumor activity results. Among them, compound 10d exhibited much more potent antiproliferative activity against SMMC-7721, HepG2, MNK-45, SW620, and A549 human cancer cell lines than cisplatin and oxaliplatin. In the preliminary study for the mechanism of action, compound 10d induced cell apoptosis and autophagy in SMMC cells, resulting in antiproliferation and G0/G1 cell cycle arrest by regulating protein expression levels of Bax, Bcl-2, and LC3. Consequently, the nucleoside-conjugated allobetulin (10d) evidenced that nucleoside substitution was a viable strategy to improve allobetulin/allobetulon’s antitumor activity based on our present study.

To achieve high-precision vector measurement values in a geomagnetic field, it is necessary to develop methods for overcoming alternating magnetic interference (AMI), which is generated by electrical equipment. This paper proposes the adaptive alternating magnetic interference suppression (AAIS) algorithm. In this algorithm, first, only a triaxial fluxgate sensor measures the magnetic field data. The time–frequency diagram of the total magnetic field is obtained quickly through short-time Fourier transform and wavelet transform. Additionally, the time and frequency of AMI appearance are analyzed. Then, the triaxial adaptive notch filter suppresses the three-component related magnetic interference. Herein, simulation and actual experiments are performed to verify the effectiveness of AAIS. The results indicate that the algorithm can quickly detect the frequencies of AMI from the total magnetic field and adaptively fit their amplitude and phase on the vector magnetic field. Finally, AAIS can suppress the interference effectively. The AAIS algorithm realizes error compensation for the vector measurement values by the total magnetic field, which effectively improves the vector measurement accuracy of the geomagnetic field. We highlight that the AAIS algorithm is effective for AMIs of different frequencies, numbers, and intensities without reference sensors. Our work has practical implications in airborne, vehicle-mounted, and shipborne geomagnetic vector detection.

Abstract Background CRC is one of the most common malignancies worldwide, and its molecular mechanisms remain unclear. Elevated levels of BAG3 have been reported in various tumors. The present study aimed to explore the expression and function of BAG3 in CRC. Methods BAG3 protein expression was evaluated in 90 CRC specimens using immunohistochemistry in tissue microarrays, and the correlation between BAG3 expression and the clinicopathological features were assessed. In HCT116 cells BAG3 overexpression cell models were constructed, and CRISPR/Cas9 was used for BAG3 knockout. Western blotting and quantitative real-time PCR were used to determine BAG3 expression in HCT-116 Cells. Cell proliferation, migration and invasion were analyzed by cell counting, colony formation assay, EdU cell proliferation assay, RTCA growth curve assays, wound-healing migration assay and transwell invasion assay. The influence of BAG3 expression level on chemoresistance in HCT-116 cells was examined. Gene expression microarray and IPA analyses were employed to explore signaling pathways associated with the control of BAG3. Results Using immunohistochemistry, this study found that BAG3 was markedly upregulated in colorectal cancer tissues and that BAG3 levels were significantly associated with tumor size and gender. BAG3 overexpression promoted HCT-116 cell growth, migration and invasion in vitro. In contrast, BAG3 knockout inhibited HCT-116 cell growth, migration and invasion. HCT-116 cells with high expression of BAG3 had higher cell viability and lower apoptosis rate than control cells after treatment with 5-FU, while the BAG3 knockout group demonstrated the opposite effects. So BAG3 expression level was associated with chemoresistance to 5-FU in HCT-116 cells. Gene expression microarrays and bioinformatics analyses of HCT-116 cells with BAG3 knockout demonstrated the involvement of BAG3 in signaling pathways associated with the control of cell proliferation, migration, invasion and chemoresistance in CRC. Conclusions In conclusion, this study provided evidence that BAG3 has a relevant role in CRC biology, and defined potential molecular pathways and networks. So BAG3 may be considered as a potential therapeutic target for anti-tumor therapy in colorectal cancer.

Abstract This study introduces a multifunctional device based on Cu2O/g-C3N4 monitoring and purification p–n heterojunctions (MPHs), seamlessly integrating surface-enhanced Raman scattering (SERS) detection with photocatalytic degradation capabilities. The SERS and photocatalytic performances of the Cu2O in various morphologies, g-C3N4 nanosheets (NSs) and Cu2O/g-C3N4 MPHs with different g-C3N4 mass ratios were systematically evaluated, with a particular emphasis on the Cu2O/g-C3N4-0.2 MPH, where g-C3N4 constituted 20% of the total mass. Multiple optical and electrochemical tests revealed that the Cu2O/g-C3N4-0.2 MPH effectively enhances charge separation and reduces charge transfer resistance. The Cu2O/g-C3N4-0.2 SERS sensor exhibited a relative standard deviation (RSD) below 15% and achieved an enhancement factor (EF) of 2.43 × 106 for 4-ATP detection, demonstrating its high sensitivity and consistency. Additionally, it demonstrated a 98.3% degradation efficiency for methyl orange (MO) under visible light within 90 min. Remarkably, even after 216 days, its photocatalytic efficiency remained at 93.7%, and it retained an 84.0% efficiency after four cycles. XRD and SEM analyses before and after cycling, as well as after 216 days, confirmed the structural and morphological stability of the composite, demonstrating its cyclic and long-term stability. The excellent performance of the Cu2O/g-C3N4 MPH is attributed to its Z-type mechanism, as verified by radical trapping experiments. The evaluation of the self-cleaning performance of the Cu2O/g-C3N4-0.2 SERS sensor demonstrated that its Z-scheme structure not only provides excellent self-cleaning capability but also enables the detection of both individual and mixed pollutants, while significantly enhancing the SERS signal response through an effective charge transfer enhancement mechanism.

The dynamics of the Rosen–Zener transition (RZT) in interacting two-level systems within nonreciprocal systems is examined, where nonreciprocity stems from asymmetric couplings between energy levels. For the linear case, an analytical expression for the RZT probability is derived. In the nonlinear scenario, numerical experiments are utilized to investigate the impact of various initial states and scanning periods on transition probabilities under adiabatic conditions. Additionally, an equivalent classical Hamiltonian is formulated to elucidate the origin of transition asymmetry under different initial state conditions. The influence of energy level detuning on the RZT is explored, leading to the suggestion of new applications for studying parameters of two-level systems related to nonlinearity and nonreciprocity. This opens avenues for potential Rosen–Zener–Stückelberg interferometry implementations.

Abstract Facile and efficient photocatalysts using sunlight, as well as fast and sensitive surface-enhanced Raman spectroscopy (SERS) substrates, are urgently needed for practical degradation of tetracycline (TC). To meet these requirements, a new paradigm for PI/TiO2/Ag organic‒inorganic ternary flexible microfibers based on semiconducting titanium dioxide (TiO2), the noble metal silver (Ag) and the conjugated polymer polyimide (PI) was developed by engineering a simple method. Under sunlight, the photocatalytic characteristics of the PI/TiO2/Ag flexible microfibers containing varying amounts of Ag quantum dots (QDs) were evaluated with photocatalytic degradation of TC in aqueous solution. The results demonstrated that the amount of Ag affected the photocatalytic activity. Among the tested samples, PI/TiO2/Ag-0.07 (93.1%) exhibited a higher photocatalytic degradation rate than PI/TiO2 (25.7%), PI/TiO2/Ag-0.05 (77.7%), and PI/TiO2/Ag-0.09 (63.3%). This observation and evaluation conducted in the present work strongly indicated a charge transfer mechanism. Moreover, the PI/TiO2/Ag-0.07 flexible microfibers exhibited highly sensitive SERS detection, as demonstrated by the observation of the Raman peaks for TC even at an extremely low concentration of 10–10 moles per liter. The excellent photocatalytic performance and SERS detection capability of the PI/TiO2/Ag flexible microfibers arose from the Schottky barrier formed between Ag and TiO2 and also from the outstanding plasmonic resonance and visible light absorptivity of Ag, along with immobilization by the PI. The successful synthesis of PI/TiO2/Ag flexible microfibers holds significant promise for sensitive detection and efficient photocatalytic degradation of antibiotics.

Sustainable development of power and energy systems (PES) can effectively handle challenges of fuel shortage, environmental pollution, climate change, energy security, etc. Data of PES presents distinctive characteristics including large collection, wide coverage, diverse temporal and spatial scales, inconsistent sparsity, multiple structures and low value density, putting forward higher requirements for real-time and accuracy of data analysis, and bringing great challenges to operation analysis and coordinated control of PES. In order to realize data quality improvement and further support flexible choice of operating mode, safe and efficient coordinated control, dynamic and orderly fault recovery of sustainable PES, this paper proposes an unscented particle filter algorithm, adopting unscented Kalman filter to construct importance density functions and KLD resampling to dynamically adjust the particle number. Simulation results obtained by taking an 85-node system as a benchmark for simulation verification show that compared with traditional PF algorithm and UKF algorithm, UPF algorithm has higher estimation accuracy.

Virtual synchronous control has been widely studied for the advantages of emulating inertia for voltage source converters (VSCs). A constant dc-link voltage is usually assumed in existing literature to estimate transient stability of virtual synchronous generators (VSGs). However, actual power supply in the dc-side of VSGs is limited and different dc-link voltage controllers are needed to achieve power balance between DC side and AC side. Addition of dc-link voltage controller has great influence on transient behavior of VSGs, which has not been investigated by previous research. To fill this gap, this paper gives insights into the effect of dc-link voltage dynamics on transient stability of VSGs. First, two typical kinds of VSGs with dc-link voltage controllers are introduced. Then, mathematical models considering dc-link dynamics are established and the effect of dc-link voltage controllers on transient synchronization stability of VSGs is revealed through equal area criterion (EAC). It is found that dc-link voltage controller would reduce stability margin of VSGs and design-oriented transient stability analysis is carried out quantitively using critical clearing time (CCT). Finally, simulation results are given to validate correctness of theoretical analysis.

Photocatalytic technology holds significant promise for sustainable development and environmental protection due to its ability to utilize renewable energy sources and degrade pollutants efficiently. In this study, BiOI nanosheets (NSs) were synthesized using a simple water bath method with varying amounts of mannitol and reaction temperatures to investigate their structural, morphological, photoelectronic, and photocatalytic properties. Notably, the introduction of mannitol played a critical role in inducing a transition in BiOI from an n-type to a p-type semiconductor, as evidenced by Mott–Schottky (M-S) and band structure analyses. This transformation enhanced the density of holes (h+) as primary charge carriers and resulted in the most negative conduction band (CB) position (−0.822 V vs. NHE), which facilitated the generation of superoxide radicals (·O2−) and enhanced photocatalytic activity. Among the samples, the BiOI-0.25-60 NSs (synthesized with 0.25 g of mannitol at 60 °C) exhibited the highest performance, characterized by the largest specific surface area (24.46 m2/g), optimal band gap energy (2.28 eV), and efficient photogenerated charge separation. Photocatalytic experiments demonstrated that BiOI-0.25-60 NSs achieved superior methylene blue (MB) degradation efficiency of 96.5% under simulated sunlight, 1.14 times higher than BiOI-0-70 NSs. Additionally, BiOI-0.25-60 NSs effectively degraded tetracycline (TC), 2,4-dichlorophenol (2,4-D), and rhodamine B (Rh B). Key factors such as photocatalyst concentration, MB concentration, and solution pH were analyzed, and the BiOI-0.25-60 NSs demonstrated excellent recyclability, retaining over 94.3% of their activity after three cycles. Scavenger tests further identified ·O2− and h+ as the dominant active species driving the photocatalytic process. In this study, the pivotal role of mannitol in modulating the semiconductor characteristics of BiOI nanomaterials is underscored, particularly in promoting the n-type to p-type transition and enhancing photocatalytic efficiency. These findings provide a valuable strategy for designing high-performance p-type photocatalysts for environmental remediation applications.

Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles.

With the intensification of the energy crisis and the growing concern over environmental pollution, particularly the discharge of organic dye pollutants in industrial wastewater, photocatalytic degradation of these contaminants using solar energy has emerged as an effective, eco-friendly solution. In this study, we successfully synthesized 2D/2D g-C3N4/BiOI p-n heterojunctions via a simple precipitation method and a high-temperature calcination method. The unique 2D structures of g-C3N4 nanosheets (NSs) and BiOI NSs, coupled with the synergistic effect between the two materials, significantly enhanced the photocatalytic degradation performance of the heterojunctions under simulated sunlight. The band structures, as determined by Tauc curves, Mott–Schottky curves and XPS-VB analysis, revealed a Z-scheme charge transfer mechanism that efficiently reduced charge carrier recombination and improved electron–hole separation. The photocatalytic activity of 2D/2D g-C3N4/BiOI p-n heterojunctions for rhodamine B (Rh B) degradation reached 99.7% efficiency within 60 min, a 2.37-fold and 1.27-fold improvement over pristine BiOI NSs and g-C3N4 NSs, respectively. Furthermore, the heterojunction exhibited excellent recyclability stability, with the degradation efficiency decreasing by only 1.2% after five cycles. Radical scavenging experiments confirmed the involvement of superoxide radicals (∙O2−) and hydroxyl radicals (∙OH) as the primary reactive species in the degradation process. This work highlights the potential of 2D/2D g-C3N4/BiOI p-n heterojunctions for efficient photocatalytic applications in environmental remediation.

Abstract Quantum vortices in Bose–Einstein condensates (BECs) are essential phenomena in condensed matter physics, and precisely locating their positions, especially the vortex core, is a precondition for studying their properties. With the rise of machine learning, there is a possibility to expedite the localization process and provide accurate predictions. However, traditional machine learning requires particular considerable amount of manual data annotation, leading to uncontrollable accuracy. In this paper, we utilize the U-Net method to detect vortex positions accurately at the pixel level and propose an Automatic Correction Labeling (ACL) approach to optimize the acquisition of data sets for vortex localization in BECs. This approach addresses inaccuracies in the labeled vortex positions and improves the accuracy of vortex localization, especially the vortex core positions, while enhancing the tolerance for human mislabeling. The main process involves Rough Labeling $$\rightarrow $$ → Machine Learning $$\rightarrow $$ → Probability Region Search $$\rightarrow $$ → Data Relabeling $$\rightarrow $$ → Machine Learning again. The objective of ACL is to secure more accurate labeled data for model retraining. Through vortex localization experiments conducted in a two-dimensional Bose-Einstein condensate, our results establish the following: 1. Even under conditions of biased and missing manual annotations, U-Net can still accurately locate vortex positions; 2. Vortices exhibit certain regularities, and training U-Net with a small number of samples yields excellent predictive consequences; 3. The machine learning vortex locator based on the ACL method effectively corrects errors in manually annotated data, significantly improving the model’s performance metrics, thus enhancing the precision and metrics of vortex localization. This substantial advancement in the application of machine learning in vortex localization provides an effective way for vortex dynamics localization. Furthermore, this method of obtaining more accurate positions of approximate human labels through machine learning offers new insights for machine learning in other types of image recognition problems.

Abstract Breast cancer is the most common malignant tumour in women. The early silk-splitting inhibitor protein 1 Emi1 is responsible for mediating ubiquitin protein degradation. The present study investigated the effects of the decreased expression of the Emil gene on the proliferation and invasion of breast cancer cells. The interference efficiency of small interfering ribonucleic acid (siRNA) was quantitatively verified using fluorescence real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting, and the effect of Emi1 gene silencing on cell vitality and invasion was determined using MTT and Transwell assays, respectively. The expression of the proliferation genes programmed cell death receptor 4 (PDCD-4), fatty acid synthase ligand (FasL), PTEN and RhoB, along with the invasive genes Maspin, TIMP3 and RECK, was measured using fluorescence RT-qPCR. In breast cancer cells, siRNA successfully reduced the expression of the Emi1 gene, and the expression level of the cell proliferation genes PDCD-4, FasL, PTEN and RhoB, along with invasive genes Maspin, TIMP3 and RECK, decreased significantly (P

Abstract Panicle architecture is a key determinant of rice grain yield and is mainly determined at the 1-2 mm young panicle stage. Here, we investigated the transcriptome of the 1-2 mm young panicles from 275 rice varieties and identified thousands of genes whose expression levels were associated with panicle traits. Multimodel association studies suggested that many small-effect genetic loci determine spikelet per panicle (SPP) by regulating the expression of genes associated with panicle traits. We found that alleles at cis-expression quantitative trait loci of SPP-associated genes underwent positive selection, with a strong preference for alleles increasing SPP. We further developed a method that integrates the associations of cis- and trans-expression components of genes with traits to identify causal genes at even small-effect loci and construct regulatory networks. We identified 36 putative causal genes of SPP, including SDT (MIR156j) and OsMADS17, and inferred that OsMADS17 regulates SDT expression, which was experimentally validated. Our study reveals the impact of regulatory variants on rice panicle architecture and provides new insights into the gene regulatory networks of panicle traits.

Large-scale photovoltaic (PV) penetration reduces system damping and causes stability problems on off-grid distribution systems. The single-machine equivalent method is typically used to simplify the full-order model by ignoring the differences in PVs. However, this results in substantial errors. When the differences between the control models of PVs are greater, the calculation results lead to greater error. Then the stability of the PV system cannot be judged. This study validated that the oscillation mode of the PV in the subsynchronous frequency band has a linear correlation with the operating conditions and control parameters. Accordingly, the authors proposed to divide the oscillation mode interval by taking the maximum and minimum PV operating conditions as the boundary to assess the stability of the system. Considering the difference in the operating conditions of the PVs, the average power was used to dynamically aggregate the PV electric farm to determine whether the system was at risk of instability. Finally, the effectiveness of the proposed method was verified through case studies on a 12-PV off-grid distribution system and a large-scale PV farm system. The verification results show that the proposed method can determine the stability of the distribution system accurately. The proposed method is significant for developing strategies to enhance the stability of off-grid distribution systems.

Abstract The access of large‐scale distributed generation (DG) easily leads to energy imbalance in distribution network. To deal with this issue, this paper proposes an energy optimal schedule method for distribution network considering the participation of source‐load‐storage aggregation groups (SAGs). Firstly, the system model consisting of distribution network layer and SAGs layer is established, and the schedule objectives and constraints of each layer are also given. Secondly, considering the fluctuation on the load side, a forecasting method based on Adaboost integrated convolutional neural networks and bidirectional long‐short term memory is proposed. Then, the improved sparrow search algorithm (ISSA) is proposed by using the tent map and Levy flight on the original sparrow search algorithm. At the same time, by introducing Pareto dominance relation and adaptive grid algorithm, the multi‐objective sparrow search algorithm (MOSSA) is derived. After that, a two‐layer optimization framework (ISSA–MOSSA) is proposed to solve the studied system. The simulation results verify the accuracy of the proposed load forecasting model, the superiority of ISSA as well as MOSSA, and the effectiveness of ISSA–MOSSA in solving the energy optimal schedule problem of the distribution system with the access of DG.

The particle filter (PF) algorithm is a powerful method for tackling non-Gaussian noise interference in distribution network state measurement. However, this algorithm suffers from slow solving speed and lengthy calculation time. To overcome this, a state estimation method based on parallel particle filter (PPF) is proposed, which leverages the independent computation features of each particle in the PF model to improve computational efficiency. This study utilizes the parallel architecture of Compute Unified Device Architecture (CUDA) and General Purpose Graphics Processing Units (GPGPU) to establish a one-to-one correspondence between particles and computing threads. An improved rejecting-resampling method is introduced to solve the problem of low execution efficiency caused by unmerged access to GPGPU memory. In addition, according to the relationship between the particle number and estimation accuracy of state variable of the PPF, the optimal particle number suitable for parallel computation is solved. Ultimately, the simulation results indicate that the proposed method can be used to effectively filter the non-Gaussian-colored noises from the collected data, which meets the requirements of the distribution network state estimation for the accuracy and real-time performance.

Abstract With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi‐equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi‐objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi‐objective optimization problem (MOP) is transformed into a single‐objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust‐region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33‐bus, PG&E 69‐bus, 292‐bus and 1180‐bus test cases. The experimental results show the rapidity and robustness of the proposed method.

A bi-level optimization configuration model of user-side photovoltaic energy storage (PVES) is proposed considering of distributed photovoltaic power generation and service life of energy storage. The upper layer takes the user’s lowest annual comprehensive cost as the objective function to optimize the capacity of photovoltaic & energy storage and power of energy storage considering the user’s annual electricity bill, basic electricity bill, and investment, operation and maintenance cost of PVES. The lower level takes the minimum daily operation scheduling cost as the objective function to optimize the charging and discharging control strategy of energy storage. The YALMIP solver is used to solve the optimization model, and the effectiveness of the model is verified through an example, which shows that reasonable configuration of photovoltaic & energy storage can reduce the user’s overall annual cost. In the context of the current national electricity price adjustment policy, by analyzing the impact of different time-of-use (TOU) electricity prices and basic electricity charges on the configuration capacity of user-side PVES to find the break-even point of the user’s PVES investment return rate in order to provide guidance to users for installing PVES systems. By analyzing the impact of different TOU electricity prices and basic electricity rates on the benefits of multiple entities, and taking into account the benefits of users, power grids and society at the same time to provide a theoretical basis for formulating a reasonable electricity price system.

With the penetration of distributed resources into power distribution networks, power distribution networks are transforming into active distribution networks with a high proportion of distributed generations and power electronic equipment. Efficient modeling and simulation methods are essential to perform dynamic response analysis. In order to satisfy the fast/steady/slow multiple time-scale simulation requirements of active distribution networks, a fast/medium/slow time partition model and a network decoupling method for short line characteristic lines is proposed in this paper. Through the decomposition coordination simulation method, the network is decomposed into multiple regions that can be simulated in parallel. Based on the interconnection of fiber optic network cards, a multi-rate parallel simulation and synchronization strategy is proposed, which significantly improves the simulation speed of active distribution networks while ensuring simulation accuracy. The numerical experiments have been conducted based on a modified IEEE 33-bus and a PG&E 69-bus, and simulation results show the feasibility of the proposed method. The verification results of the example show that using adaptive variable-step-size multi-rate parallel simulation technology can increase the subnet computation-time balance rate and simulation acceleration ratio to 119.90% and 121.31% in the same rate-parallel mode.

The necessity to understand plant adaptations to environmental stressors is underscored by the role of polyploidy in species evolution. This study focuses on the superior stress resistance exhibited by autotetraploid rice, which arises from chromosome doubling, in comparison to its diploid donor. We provide a quantitative analysis that highlights the differing susceptibilities of diploid (GFD-2X) and autotetraploid (GFD-4X) rice to rice blast disease, with GFD-2X being significantly more susceptible. Our investigation centers on transcription factors (TFs), which are crucial in regulating biological stress responses, by analyzing their expression in the face of a pathogen attack. This study uncovers variations in the number and expression timing of differentially expressed TF genes, providing a quantitative view of GFD-4X’s resistance. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses confirm the role of specific pathways, including “response to stimulus” and the “MAPK signaling pathway,” in resistance mechanisms. An extensive analysis of protein–protein interaction networks further clarifies the complex role of TFs during stress responses. The rationale for our experimental approach is rooted in the imperative to decipher the molecular basis of disease resistance across different ploidies, which has implications for crop enhancement. The conclusion from our research is that autotetraploid rice has a unique and more effective defense response regulation system, facilitated by transcription factors, when faced with rice blast disease. This finding provides a foundation for future genetic strategies aimed at improving crop resistance.

Whole-genome duplication is a significant evolutionary mechanism in plants, with polyploid plants often displaying larger organs and enhanced adaptability to unfavorable conditions compared to their diploid counterparts. The cell wall acts as a primary defense for plant cells against external stresses, playing an essential role in the plant’s resistance to various stressors. In this study, we utilized both autotetraploid and its donor diploid rice (Oryza sativa L.) to analyze their phenotypic differences comparatively, the composition of key cell wall components, and the expression of related genes under normal conditions, as well as under stress from Magnaporthe oryzae (M. oryzae) and salt. Our findings indicated that autotetraploid rice exhibits significantly larger phenotypic characteristics under normal conditions than diploid rice. At the seedling stage, the lignin, cellulose, hemicellulose, and pectin levels in autotetraploid rice were markedly lower than in diploid rice. Additionally, 24 genes associated with major cell wall components showed differential expression between diploid and tetraploid rice. At the filling stage, the lignin and pectin content in autotetraploid rice were significantly higher than in diploid rice, while the levels of cellulose and hemicellulose were notably lower. Under M. oryzae stress or salt stress, autotetraploid rice showed smaller lesion areas and less wilting than diploid rice. The increased lignin content in autotetraploid rice under M. oryzae stress suggested a stronger adaptive capacity to adverse conditions. Compared to salt stress, M. oryzae stress induced more differential expression of genes related to major cell wall components. In this study, we explored the differences in the major cell wall components of diploid and homologous tetraploid rice under various treatment conditions. This study provides valuable insights into understanding the cell wall’s adaptive mechanisms in autotetraploid rice when facing blast disease and salt stress, and it reveals the differential gene expression linked to these adaptive capabilities.

Abstract Nowadays, the reliable supply of electric power is vital in all aspects of social life. With the development and participation of distributed generations, not only does an accurate fault location lets repair of a fault line as quickly as possible, but also it is of great significance to ensure the safe and stable economic operation of the power system. This study proposes a method to determine the fault location in distribution networks, which is a combination of Extreme Gradient Boosting and Support Vector Machine. The effectiveness of the proposed method is validated on an IEEE34‐bus distribution network under single‐phase‐to‐ground faults, using voltage measurements available at each node in the distribution network. The comparison in accuracy, precision, recall, F1‐score and time‐cost of the method in this study with K‐Nearest Neighbour and Multi‐Layer Perceptron demonstrates the feasibility of applying the proposed method in distribution system fault diagnosis.

Abstract The simulation of complex power grid adopts the method of model segmentation. The system is divided into two or more subsystems. Each subsystem is calculated and solved in different computing units. Because the simulation model runs on different hardware, the time synchronization between subsystems may bring errors and stability problems. In this paper, a time synchronization method based on virtual power is proposed. It is assumed that there is a virtual load composed of resistance and inductance between different simulation subsystems, and two identical voltage sources are set at both terminals of the virtual load. When there is no time error between the subsystems, the power consumed by the virtual load should be zero, otherwise virtual power will be generated. The virtual power is sent to the PI regulator as the error signal, and the output of the regulator is used as the time error correction signal of the subsystem until the virtual power is zero, so as to realize the clock synchronization between multiple computers. In this paper, the theoretical analysis and implementation method of this time synchronization scheme are given in detail, and the simulation results show that the method is correct.

Abstract With the many characteristics of cyber–physical systems (CPS), including the complex evolution of various dynamic processes in distribution networks (DNs), communication modes and serious network security events have become important issues. From the perspective of DN analysis, traditional electrical digital simulations cannot access current models of communication systems and cyber network security events. The interactions between cyber and physical networks cannot be simulated and analysed simultaneously, which is challenging to the development of CPS simulations of DNs. To evaluate the impact of cyber networks on physical networks, a CPS simulation platform that contains physical, communication and control layers is discussed. Three‐phase short‐circuit fault processing and islanding operation control are simulated by data interactions among three‐layer models that are used to analyse the impact of communication data tampering on fault handling processes. The influence of data tampering on fault processing and the influence of false data injection and data interception on islanding operation control processes are analysed. The simulation results show that the network information security has a large impact on fault protection and islanding operation control, which leads to the expansion of the outage scope and the failure of islanding operation frequency adjustment. This article also provides a simulation basis for the analysis of CPS operation characteristics and the identification and prevention of cyber network security events.

Soil salinity is a severe constraint on crop productivity that triggers plant salt stress response pathways. The salt stress responses are controlled by transcriptional regulatory networks that enhance plant stress tolerance by manipulating the expression of transcription factor (TFs)-encoding genes. Identifying TFs associated with salt tolerance contributes to salt-tolerant crop breeding. In this study, a comparative transcriptome analysis was performed to investigate the global gene expression of entire TFs in diploid and autotetraploid rice with different salt tolerance levels, considering NaCl stress, NaOH stress, and Na2CO3 stress. A total of 54, 54 and 55 TF families were co-expressed in diploid and tetraploid roots under three stresses, respectively. Furthermore, we investigated differentially expressed TFs (DE-TFs) based on different comparisons, and the statistical analysis indicated that the DE-TFs derived from the three types of stress were as follows: Na2CO3 (53 TF families, 1356 DE-TFs) > NaCl (19 TF families, 214 DE-TFs) > NaOH (18 TF families, 152 DE-TFs). These results imply that Na2CO3 stress induced a more obvious biological responses in the roots than the other two stresses. GO and KEGG pathway enrichment analysis of DE-TFs revealed the importance of plant hormone signal transduction and MAPK signaling pathways that may contribute to the saline–alkaline tolerance of tetraploid rice. This study provides gene resources that are valuable for exploring the regulatory mechanism of TFs involved in the saline–alkaline tolerance of polyploid rice.

The use of frequency-changers is widespread in daily electric power generation. If a frequency-changer could automatically adjust its power output according to the load status and power network operation, it would have a positive impact on power system stability. To achieve this a novel control strategy based on virtual synchronous motors (VSM) is proposed in this paper. The frequency-changer consists of two main parts: the rectifier and the inverter. In the rectifier part, a UDC-Q control strategy is adopted to control the DC-link voltage and the reactive power. In the inverter part, a VSM control strategy is used; it includes a power-frequency controller and an excitation controller, and can change the inverter's output frequency and voltage with the variation of load, can adjust the active power with change of the power system frequency. The proposed control strategy is simulated using Simulink/Matlab. The simulation results show that the proposed control strategy can follow the mechanical inertia and electrical characteristics of the synchronous moto and can effectively improve the system power quality and system stability.

In this paper, a neural network model called Long Short-Term Temporal Convolutional Network (LST-TCN) model is proposed for short-term load forecasting. This model refers to the 1-D fully convolution network, causal convolution, and void convolution structure. In the convolution layer, a residual connection layer is added. Additionally, the model makes use of two networks to extract features from long-term data and periodic short-term data, respectively, and fuses the two features to calculate the final predicted value. Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) are used as comparison algorithms to train and forecast 3 h, 6 h, 12 h, 24 h, and 48 h ahead of daily electricity load together with LST-TCN. Three different performance metrics, including pinball loss, root mean squared error (RMSE), and mean absolute error (RASE), were used to evaluate the performance of the proposed algorithms. The results of the test set proved that LST-TCN has better generalization effects and smaller prediction errors. The algorithm has a pinball loss of 1.2453 for 3 h ahead forecast and a pinball loss of 1.4885 for 48 h ahead forecast. Generally speaking, LST-TCN has better performance than LSTM, TCN, and other algorithms.

B and T lymphocyte attenuator (BTLA) is one of the most important cosignaling molecules. It belongs to the CD28 superfamily and is similar to programmed cell death-1 (PD-1) and cytotoxic T lymphocyte associated antigen-4 (CTLA-4) in terms of its structure and function. BTLA can be detected in most lymphocytes and induces immunosuppression by inhibiting B and T cell activation and proliferation. The BTLA ligand, herpesvirus entry mediator (HVEM), does not belong to the classic B7 family. Instead, it is a member of the tumor necrosis factor receptor (TNFR) superfamily. The association of BTLA with HVEM directly bridges the CD28 and TNFR families and mediates broad and powerful immune effects. Recently, a large number of studies have found that BTLA participates in numerous physiopathological processes, such as tumor, inflammatory diseases, autoimmune diseases, infectious diseases, and transplantation rejection. Therefore, the present work aimed to review the existing knowledge about BTLA in immunity and summarize the diverse functions of BTLA in various immune disorders.

Abstract The engineering of metal–organic frameworks (MOFs) into membranes and films is being investigated, to transform laboratory‐synthesized MOFs into industrially viable products for a range of attractive applications. However, rational design and construction of highly permeable MOF thin films, without trade‐offs in terms of structural mechanical stability, remains a significant challenge. Herein, a simple, general strategy is reported to prepare thin MOF nanosheet (NS)‐assembled frame film via heteroepitaxial growth from metal hydroxide film. As the thin MOF NS‐assembled film significantly enhances the permeability of mass though the film, the resultant gold nanoparticle (Au NP)@MOF film exhibits much higher catalytic efficiency than the Au NP@MOF bulk film. Meanwhile, the unique framework of the MOF NS‐assembled film resists torsion and collapse, so the composite catalyst exhibits long‐term stability.

In order to apply the cyber-physical system (CPS) co-simulation method based on state cache to a complex distribution network (CDN), a physical model corresponding to multiple operating states should be proposed. As the different spatial resolution of the physical model leads to different performances of the cyber-physical models, a multi-resolution model based on time-state-machine is presented for CDN here. First, the operation process of CDN is divided into five operating states by using time-state-machine method, and the boundary conditions between states are qualitatively discussed. Second, three different spatial resolution physical models of CDN are built, and their simulation performances under different operating states are quantitatively analysed to form a multi-resolution physical model of CDN. Finally, the multi-resolution model of CDN is validated by use of a cyber-physical co-simulation example. Simulation results show that the multi-resolution model is capable to automatically transmission from anyone operating state to another.

This paper proposed an entropy weight optimum seeking method (EWOSM) based on the typical scenarios partitioning and load distribution matching, to solve the reactive power optimization problem in distribution network under the background of big data. Firstly, the mathematic model of reactive power optimization is provided to analyze the relationship between the data source and the optimization schemes in distribution network, which illustrate the feasibility of using large amount of historical data to solve reactive power optimization. Then, the typical scenarios partitioning method and load distribution matching method are presented, which can select out some loads that have the same or similar distributions with the load to be optimized from historical database rapidly, and the corresponding historical optimization schemes are used as the alternatives. As the reactive power optimization is a multi-objective problem, the multi-attribute decision making method based on entropy weight method is used to select out the optimal scheme from the alternatives. The objective weights of evaluation indexes are determined by entropy weight method, and then the multi-attribute decision making problem is transformed to a single attribute decision making problem. Finally, the proposed method is tested on several systems with different scales and compared with existing methods to prove the validity and superiority.

In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS) is defined to measure the degree of similarity between the loads in different days and the calculation method of the load similarity of load random matrix (LRM) is presented. By calculating the load similarity between the forecasting random matrix and the random matrix of historical load, the historical reactive power optimization dispatching scheme that most matches the forecasting load can be found for reactive power control usage. The differences of daily load curves between working days and weekends in different seasons are considered in the proposed method. The proposed method is tested on a standard 14 nodes distribution network with three different types of load. The computational result demonstrates that the proposed method for reactive power optimization is fast, feasible and effective in distribution network.

Although the outbreak in China has been effectively controlled, the international situation of prevention and control of coronavirus disease 2019 (COVID-19) still poses a serious public health risk. COVID-19 has been declared a pandemic by World Health Organization (WHO) due to its highly contagious nature, rapid transmission, swift clinical course, profound worldwide impact, and high mortality among critically ill patients. COVID-19 is mainly transmitted by respiratory droplets and contact routes, which could be effectively prevented by proper medical equipment and hand hygiene. However, due to the lack of effective protective measures and personal protective equipment of healthcare personnel in some area, the infectious rate of COVID-19 remains high, especially in clinical settings. At present, due to its portability and accuracy, ultrasound has been playing an indispensable role in the diagnosis, evaluation, and follow-up of COVID-19 patients, especially those in severe conditions. In the course of diagnosis and treatment, because sonographers often work closely with patients, coupled with the increasing number of patients and relatively poor ventilation in the working area, the risk of occupational exposure is undoubtedly high. This article combined the standard precaution methods with current Chinese government's COVID-19 strategy and polices, aiming to provide suggestions on the prevention and control of COVID-19 pandemic for ultrasound professionals.

Objective: To investigate the value of lung ultrasound (LUS) in asymptomatic patients with confirmed COVID-19.Methods: A retrospective analysis was performed on nine patients in a designated isolation hospital in Sanya from February 22nd, 2020 to February 23rd, 2020. All patients were confirmed with COVID-19 pneumonia by PCR test, but none had the typical symptoms of COVID-19. All patients first underwent LUS examination and then chest computed tomography (CT) scanning. The application value of LUS in asymptomatic confirmed patients with COVID-19 was evaluated, compared with chest CT which was regarded as the golden standard.Results: Among nine asymptomatic patients with COVID-19, there were two cases (22.22%) with abnormal ultrasonic manifestations, of which one (11.11%) showed a fusion B3-line in zone 5 of the right lung, and the other showed localized pulmonary consolidation in zone 6 of the left lung. The remaining seven cases (77.78%) showed no abnormal changes in LUS, but only clear pleura sliding sign and A-line. Chest CT showed abnormal changes in three cases (33.33%). Two of them (22.22%) showed flocculent high-density shadow at the base of both lungs (especially in the right lung), while the other case showed ground-glass opacity with thickened interlobular septal in the left lower lobe, involving the pleura. There were no abnormalities on chest CT of the remaining six cases (66.67%). The two (22.22%) LUS-positive patients were in complete coincidence with CT-positive patients. This study showed that the coincidence rate of the two examination methods was 88.89%. Taking chest CT as golden standard, the sensitivity, specificity, positive predictive value, negative predictive value, and Kappa value of lung ultrasound in the diagnosis of COVID-19 were 66.67%, 100%, 100%, 85.71%, and 0.727, respectively.Conclusion: LUS can evaluate lung lesions in asymptomatic patients with COVID-19. Compared with chest CT, the diagnostic coincidence rate and diagnostic consistency of LUS are relatively higher. For this asymptomatic type of patient, ultrasound can be used as a diagnosis method, which can avoid the risk of radiation exposure in a short period of time. As a rapid and dynamic assessment method, LUS can cooperate with remote consultation to provide timely and accurate guidance for clinical diagnosis and treatment when necessary.

Neuromyelitis optica spectrum disorder (NMOSD) is an idiopathic autoimmune inflammatory disorder which is characterized by central nervous system (CNS) demyelinating. Common symptoms of area postrema clinical syndrome (APS) include intractable hiccup, nausea and vomit which may lead to aspiration pneumonia. In this article, we report a patient with suspicious COVID-19 infection and complicated with NMOSD and aspiration pneumonia. The question still remains that whether 2019-nCoV could infect CNS and cause NMO.

Real-time ultrasound imaging has been used for the evaluation of pulmonary abnormalities and other complications during the outbreak of COVID-19. However, conventional ultrasound examination requires operators to conduct scanning either in the examination room or patient ward, which may increase the potential infective risk. Tele-operated ultrasound robotics can provide a unique technique for doctors to remotely scan patients. During the COVID-19 pandemic, we attempted to apply a 5G network-based scanning robot to conduct remote ultrasound examination on COVID-19 patients and to explore the feasibility of this technique for teleultrasound diagnosis and consultation during critical infectious situations.