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Due to the special environment of disruption of offline business form at the beginning of the epidemic, more and more merchants have started live-streaming with goods in order to seek development, which has become the main force of sales. As Generation Z enters the workplace and their income gradually rises, they will become a powerful growth point for the future consumer market. In this context, on basis of the theory of planned behavior and cognitive mediation model, this paper constructed a research model on the factors influencing the consumption behavior of Generation Z's e-commerce live broadcast, and analyzed the data through questionnaires and using SPSS 26.0. The study showed that online attention has no significant positive influence on consumption behavior intention, and behavioral attitude, subjective norm and perceived behavioral control (PBC) have significant positive influence on consumption behavior intention. Accordingly, this paper presented a study on the consumption behavior intention of Generation Z as well as suggestions for consumers and businesses.

The eigenvalue bounds of interval matrices are often required in some mechanical and engineering fields. In this paper, we improve the theoretical results presented in a previous paper “A property of eigenvalue bounds for a class of symmetric tridiagonal interval matrices” and provide a fast algorithm to find the upper and lower bounds of the interval eigenvalues of a class of symmetric tridiagonal interval matrices.

Existing deep learning algorithms for point cloud analysis mainly concern discovering semantic patterns from global configuration of local geometries in a supervised learning manner. However, very few explore geometric properties revealing local surface manifolds embedded in 3D Euclidean space to discriminate semantic classes or object parts as additional supervision signals. This paper is the first attempt to propose a unique multi-task geometric learning network to improve semantic analysis by auxiliary geometric learning with local shape properties, which can be either generated via physical computation from point clouds themselves as self-supervision signals or provided as privileged information. Owing to explicitly encoding local shape manifolds in favor of semantic analysis, the proposed geometric self-supervised and privileged learning algorithms can achieve superior performance to their backbone baselines and other state-of-the-art methods, which are verified in the experiments on the popular benchmarks., Comment: 11 pages, 8 figures, 9 tables; accepted by the IEEE Transactions on Cybernetics

Background The contrast-enhanced ultrasound (CEUS) has been recently used for the assessment of cervical lymph node metastasis to guide the surgical operation in the patient with papillary thyroid carcinoma (PTC). However, the specificity and sensitivity of CEUS reported from previous studies is not consistent. The objective of this study was to evaluate the diagnostic value of CEUS to the metastasis of cervical lymph node in PTC patients based on the data from one regional central hospital. Methods The diagnostic value of CEUS in preoperative LNM of PTC patients was concluded by comparing the results of CEUS on lymph node status with postoperative pathology examination. In addition, this study conducted hierarchical analysis of PTC patients to explore whether tumor size, different lymph node regions, and hashimoto's thyroiditis have an influence on the assessment of CEUS. Results This research enrolled 965 PTC patients finally, including 266 male and 699 female patients with a mean age of 42.27±11.34 years. 527 patients were supposed to be clinical-node negative and 438 were clinical-node positive before surgery. The specificity, sensitivity, PPV, NPV and accuracy of CEUS in the assessment of LNM in PTC patients were 56.00%, 71.00%, 57.06%, 69.76% and 62.59% respectively. For central and lateral lymph node, the accuracy of CEUS in PTC patients were 49.43% and 54.30%, respectively. Besides, it was showed that the accuracy of CEUS in PTC patients with HT slightly dropped to 58.44%, and the accuracy of CEUS in PTC patients with non-HT in turns increased to 64.17%. The accuracy of CEUS in non-PTMC and PTMC patients were 65.68% and 61.24%, respectively. The accuracy of CEUS in predicting central lymph node metastases was statistically different in PTC patients with or without Hashimoto's thyroiditis(P＜0.001) in this study, but not in lateral lymph nodes (P=0.114). Conclusion The accuracy of CEUS on the assessment of LNM in PTC is not consistently satisfactory, especially for central lymph nodes, small tumor diameter, or the patient with HT. More diagnostic technologies for abnormal lymph node should be considered in PTC patients.

BackgroundHuman adenovirus type 55 (HAdV55) has a re-emerged as pathogen causing an acute respiratory disease presenting as a severe lower respiratory illness that can cause death. To date, there is no HAdV55 vaccine or treatment available for general use.MethodsHerein, a monoclonal antibody specific for HAdV55, mAb 9-8, was isolated from an scFv-phage display library derived from mice immunized with the purified inactived-HAdV55 virions. By using ELISA and a virus micro-neutralization assay, we evaluated the binding and neutralizing activity of mAb 9-8 following humanization. Western blotting analysis and antigen-antibody molecular docking analysis were used to identify the antigenic epitopes that the humanized monoclonal antibody 9-8-h2 recognized. After that, their thermal stability was determined.ResultsMAb 9-8 showed potent neutralization activity against HAdV55. After humanization, the humanized neutralizing monoclonal antibody (9-8-h2) was identified to neutralize HAdV55 infection with an IC50 of 0.6050 nM. The mAb 9-8-h2 recognized HAdV55 and HAdV7 virus particles, but not HAdV4 particles. Although mAb 9-8-h2 could recognize HAdV7, it could not neutralize HAdV7. Furthermore, mAb 9-8-h2 recognized a conformational neutralization epitope of the fiber protein and the crucial amino acid residues (Arg 288, Asp 157, and Asn 200) were identified. MAb 9-8-h2 also showed favorable general physicochemical properties, including good thermostability and pH stability.ConclusionsOverall, mAb 9-8-h2 might be a promising molecule for the prevention and treatment of HAdV55.

The emerging coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the causative agent of coronavirus disease 2019 (COVID-19), which has become a severe threat to global public health and local economies. In this study, several single-chain antibody fragments that bind to the receptor-binding domain (RBD) of the SARS-CoV-2 spike (S) protein were identified and used to construct human-mouse chimeric antibodies and humanized antibodies. These antibodies exhibited strong binding to RBD and neutralization activity towards a SARS-CoV-2 pseudovirus. Moreover, these antibodies recognize different RBD epitopes and exhibit synergistic neutralizing activity. These provide candidate to combination use or bispecific antibody to potential clinical therapy for COVID-19.

Antibiotic contamination is becoming a prominent global issue. Therefore, sensitive, specific and simple technology is desirable the demand for antibiotics detection. Biosensors based on split aptamer has gradually attracted extensive attention for antibiotic detection due to its higher sensitivity, lower cost, false positive/negative avoidance and flexibility in sensor design. Although many of the reported split aptamers are antibiotics aptamers, the acquisition and mechanism of splitting is still unknow. In this review, six reported split aptamers in antibiotics are outlined, including Enrofloxacin, Kanamycin, Tetracycline, Tobramycin, Neomycin, Streptomycin, which have contributed to promote interest, awareness and thoughts into this emerging research field. The study introduced the pros and cons of split aptamers, summarized the assembly principle of split aptamer and discussed the intermolecular binding of antibiotic-aptamer complexes. In addition, the recent application of split aptamers in antibiotic detection are introduced. Split aptamers have a promising future in the design and development of biosensors for antibiotic detection in food and other field. The development of the antibiotic split aptamer meets many challenges including mechanism discovery, stability improvement and new biosensor development. It is believed that split aptamer could be a powerful molecular probe and plays an important role in aptamer biosensor.

The intron-based stabilization approach is a very useful strategy for construction of stable flavivirus infectious clones. SA14-14-2 is a highly attenuated Japanese encephalitis (JE) live vaccine strain that has been widely used in China since 1989. To develop safe and effective recombinant vaccines with SA14-14-2 as a backbone vector, we constructed the DNA-based infectious clone pCMW-JEV of SA14-14-2 using the intron-based stabilization approach and acquired the rescued virus rDJEV, which retained the biological properties of the parental virus. Unexpectedly, a rescued virus strain with altered virulence, designated rHV-DJEV, was accidentally acquired in one of the transfection experiments. rHV-DJEV showed up to 105-fold increased neurovirulence compared with the SA14-14-2 parental strain. Genome sequencing showed that the inserted introns were still present in the genome of rHV-DJEV. Therefore, we think that the intron-based stabilization approach should be used with caution in vaccine development and direct iDNA immunization.

As a key environment perception technology of autonomous driving or driver assistance systems, lane detection is to ensure vehicles to drive safely in corresponding lane. However, existing lane detection algorithms for two-lane detection focus on using various filtering methods to reduce the impact of useless information, resulting in low accuracy and low efficiency. In this paper, a novel Adaptive Region of Interest (A-ROI) extraction method is proposed to improve the accuracy and real-time performance of the two-lane detection algorithm. Three key technologies are introduced to solve the problems. First, A-ROI, which only focuses on the lane where the vehicle is located, is applied to the Bird’s-Eye-View image obtained by using Inverse Perspective Mapping (IPM). Next, based on Bayesian framework and Likelihood models, a lane feature extraction method with a lane-like feature filter is used for edge detection. Finally, an improved Random Sample Consensus (RANSAC) algorithm is introduced by using a filter that can remove noisy lane data. The performance of the proposed A-ROI method together with the improved lane detection method is evaluated via simulation of various scenarios. Experimental results show the proposed method has better accuracy and real-time performance than the traditional lane detection methods.

Big challenges are usually occurring in non-rigid 3D shape retrieval, for the shapes undergoing arbitrarily non-affine transformations. In this work, a novel design of feature learning approach is proposed for non-rigid 3D shape retrieval, dubbed Structured Sparsity Regularized Multi-Modality Method (SSR-MM). The shape signatures which capture the deformation-invariant characteristics are averaged and stacked for a multi-modality machine learning approach, and a transform matrix based on the structure sparsity regularization is utilized to map those signatures obtaining the discriminative features for retrieval. The proposed framework is evaluated on the publicly available non-rigid 3D human benchmarks, and the experimental results show the efficacy of our contributions and the advantages of our method over existing ones.

The intron-based stabilization approach is a very useful strategy for construction of stable flavivirus infectious clones. SA14-14-2 is a highly attenuated Japanese encephalitis (JE) live vaccine strain and widely used since 1989 in China. To develop safe and effective recombinant vaccines with SA14-14-2 as a backbone vector, we constructed the DNA-based infectious clone pCMW-JEV of SA14-14-2 using the intron-based stabilization approach, and acquired the rescued virus rDJEV which retained the identical biological properties of the parental virus. Unexpectedly, a rescued virus strain, designated rHV-DJEV, with altered virulence was accidentally acquired in one of the transfection experiments. rHV-DJEV showed up to 105-fold increased neurovirulence compared with SA14-14-2 parental strain. Genome sequencing found that the inserted introns still existed in the genome of rHV-DJEV. Therefore, we think that the intron-based stabilization approach should be used prudently in vaccine development and direct iDNA immunization.

Enhancement of alternative activation (M2) in microglia is a promising therapeutic target for microglia-mediated neuroinflammation. Shenqi Fuzheng Injection (SFI) is a clinical adjuvant treatment for cancer to reduce the side effects during cancer treatment, including boosting mood and improving appetite. However, the mechanism of SFI's effects on central symptoms is not clear. Therefore, using arginase 1 (Arg1) and transforming growth beta-1 (Tgfb1) as markers for M2 microglia activation, we found that compounds 1, 5, 12, 14, and 15 are the major M2-promoting constituents in SFI, which significantly upregulated Arg1 or Tgfb1 gene expression. Our results suggested that these compounds in SFI may promote M2 microglial activation and have potential uses in modulating microglial activation and alleviating neuroinflammation.

BACKGROUND Norcantharidin (NCTD) is suitable for the treatment of primary liver cancer, especially early and middle primary liver cancer. This compound can reduce tumors and improve immune function. However, the side effects of NCTD have limited its application. There is a marked need to reduce the side effects and increase the efficacy of NCTD. AIM To develop a nanomaterial carrier, NCTD-loaded metal-organic framework IRMOF-3 coated with a temperature-sensitive gel (NCTD-IRMOF-3-Gel), aiming to improve the anticancer activity of NCTD and reduce the drug dose. METHODS NCTD-IRMOF-3-Gel was obtained by a coordination reaction. The apparent characteristics and in vitro release of NCTD-IRMOF-3-Gel were investigated. Cell cytotoxicity assays, flow cytometry, and apoptosis experiments in mouse hepatoma (Hepa1-6) cells were used to determine the anti-liver cancer activity of NCTD-IRMOF-3-Gel in in vitro models. RESULTS The particle size of NCTD-IRMOF-3-Gel was 50-100 nm, and the particle size distribution was uniform. The release curve showed that NCTD-IRMOF-3-Gel had an obvious sustained-release effect. The cytotoxicity assays showed that the free drug NCTD and NCTD-IRMOF-3-Gel treatments markedly inhibited Hepa1-6 cell proliferation, and the inhibition rate increased with increasing drug concentration. By flow cytometry, NCTD-IRMOF-3-Gel was observed to block the Hepa1-6 cell cycle in the S and G2/M phases, and the thermosensitive gel nanoparticles may inhibit cell proliferation by inducing cell cycle arrest. Apoptosis experiments showed that NCTD-IRMOF-3-Gel induced the apoptosis of Hepa1-6 cells. CONCLUSION Our results indicated that the NCTD-IRMOF-3-Gel may be beneficial for liver cancer disease treatment.

Online remaining charging-discharging cycle (RCDC) prognosis is of great significance for lithium-ion batteries. The conventional method is usually based on whether the state-of-health (SOH) of capacity reaches the end-of-life (EoL) threshold. However, the most available prediction methods have two problems that need to be solved. First, the SOH degradation curve of the lithium-ion battery is nonlinear and non-Gaussian, and the battery capacity regeneration phenomena (CRP) has a direct impact on RCDC estimation efficiency. These factors challenge the precise forecast of RCDC and increase the risk of prediction failure. Second, existing methods have insufficient early-stage prediction ability for capacity degradation because too little data are available to facilitate establishing and optimizing the prediction models. To overcome the above-mentioned drawbacks, this study introduces the Mann-Kendall trend analysis to generate an equivalent degradation indicator (EDI), and to replace the capacity-based SOH. The proposed EDI has good linearity and monotonicity, and is conducive to adopt a simple structured prediction model to determine the RCDC. Besides, this study is based on the “SOH-EDI” synchronization mapping relationship and applies an one-degree polynomial regression model to estimate the EoL threshold on the EDI curve. From the perspective of computational complexity, the proposed framework uses two polynomial prediction models with simple structures, which realizes a low computational burden and online RCDC prediction. To verify the efficiency of the proposed method, this paper introduces three methods for comparison. Experimental results show that the proposed framework has satisfied early-stage prediction ability of RCDC and has a superior prognosis efficiency.

In this paper, we mainly focus on the valuation for the risk-based premium of private pension plan with termination provided by the Pension Benefit Guaranty Corporation (PBGC). The dynamics for ass...

In tropical and subtropical countries, dengue virus (DENV) infections have been increasing; however, we still lack effective therapy. In the present study, we aimed to engineer a bispecific antibody (subsequently named LUZ-8F2-6B1), based on monoclonal antibody 6B1, which has anti DENV-1, 2, and 3 activity, and 8F2, which has anti DENV-4 activity. LUZ-8F2-6B1 displayed potent neutralization activity against four serotypes of DENV by binding to the envelop protein. In vivo, we demonstrated that LUZ-8F2-6B1 could provide protection against infection by four serotypes of DENV in a mouse model. In addition, the deletion of nine amino acids in the Fc region (LUZ-8F2-6B1-9del) completely abolished the antibody-dependent enhancement observed at lower doses of the antibody. Thus, LUZ-8F2-6B1 is a promising, safe, and effective agent for the prophylaxis and treatment of DENV infection.

The intron-based stabilization approach is a very useful strategy for construction of stable flavivirus infectious clones. SA14-14-2 is a highly attenuated Japanese encephalitis live vaccine and widely used in China. To develop safe and effective recombinant vaccines with SA14-14-2 as a backbone vector, we constructed the DNA-based infectious clone pCMW-JEV of the vaccine strain using the intron-based stabilization approach, and acquired the rescued virus rDJEV which retained the identical biological properties of the parental virus. Unexpectedly, a rescued virus strain, designated rHV-DJEV, with altered virulence was acquired in one of the transfection experiments. rHV-DJEV showed up to 105-fold increased neurovirulence compared with SA14-14-2 parental strain. Genome sequencing found that the inserted introns still existed in the genome of rHV-DJEV. Therefore, we think that the intron-based stabilization approach should be used carefully in vaccine development and direct iDNA immunization.

Soil erosion of sloping farmland in the Miyun reservoir area in Beijing has become a serious issue and has threatened the ecological environment and safety of the reservoir area. We used the Taishizhuang Village Non-point Source Pollution Prevention & Control Base in the Miyun reservoir as a study area and performed a comparative analysis of the physicochemical properties of soil of the upper, middle, and lower slopes of the Scutellaria baicalensis + Buchloe dactyloides plot (Treatment 1, T1), Morus alba + Buchloe dactyloides plot (Treatment 2, T2), Salvia miltiorrhiza + Cynodon dactylon plot (Treatment 3, T3), Platycodon grandiflorus + Cynodon dactylon plot (T4), and a barren land control plot (Control check, CK), to explore how different hedgerow patterns affect the soil’s physicochemical properties, anti-erodibility, and fractal characteristics. We found the following: (1) The primary soil mechanical composition included sand particles in the upper slopes, whereas it was soil fine particles in the middle and lower slopes. (2) The fractal dimension of the slope soil showed a significant negative correlation with sand particles (R2 = 0.9791) while being positively correlated with silt particles (R2 = 0.9635) and clay particles (R2 = 0.9408). (3) All hedgerow patterns increased soil nutrients, with the Morus alba + Buchloe dactyloides hedgerow plot increasing the soil total nitrogen (STN), soil total phosphorus (STP), and soil organic matter (SOM) content by 213.89–282.69%, 55.56–58.15%, and 29.77–56.04%, respectively. (4) The Morus alba + Buchloe dactyloides hedgerow plot significantly decreased the soil erodibility factor K value, improved soil anti-erodibility, and reduced soil erosion. (5) The K value of the soil erodibility was significantly negatively correlated with clay particles, soil fractal dimension, and STP (p < 0.01); positively correlated with sand particles; and negatively correlated with silt particles, STN, and SOM. Therefore, the Morus alba + Buchloe dactyloides hedgerow planting contributes to clay particle conservation, soil nutrient content improvement, soil structure optimization, and soil anti-erodibility enhancement.

High-molecular-weight PAHs (HMW-PAHs) in soil cannot be easily degraded. However, nutrient supplementation could stimulate the growth of exogenously added strains to enhance the degradation of HMW-PAHs in polluted soil. This study evaluated the applicability of Fusarium sp. ZH-H2, a polycyclic aromatic hydrocarbon (PAH)-degrading strain isolated by our research group, for the bioremediation of contaminated soil from the Hebei coal mining area in China. A soil incubation experiment was conducted to investigate the effect of two carbon sources and different carbon, nitrogen, and phosphorus (C:N:P) ratios on the remediation of high-molecular-weight PAHs (HMW-PAHs) in soil by Fusarium sp. ZH-H2, as well as the induction of lignin peroxidase activity. Our findings indicated that the HDF2 treatment (equal parts of humic acid and starch as carbon sources at a 50:1:0.5 C:N:P ratio) enhanced the removal rate of total HMW-PAHs from soil, reaching a maximum removal rate of 37.15 %. The removal rates of Pyr (a 4-ring PAH), BaP (a 5-ring PAH), and BghiP (a 6-ring PAH) were the highest in HDF2 treatment, and the removal rates were 39.51 %, 54.63 %, and 38.60 %, respectively. Compared with the ZH-H2 treatment, different carbon sources and C:N:P ratios significantly induced soil lignin peroxidase activity and the HDF2 treatment also resulted in the highest enzyme activity (up to 34.68 U/L). Furthermore, there was a significant or highly significant linear positive correlation between the removal rate of HMW-PAHs and enzyme activity in all cases. Our findings suggest that the optimal HMW-PAH degradation performance and enhancement of lignin peroxidase activity by ZH-H2 were achieved when both starch and humic acid were used as carbon sources at a C:N:P ratio of 50:1:0.5.

In this article, a novel self-evolving data cloud-based proportional integral derivative (PID) (SEDCPID) like controller is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using fuzzy rules with nonparametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameter concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like subcontrollers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems.

Background The interaction of gas and liquid can produce many interesting phenomena, such as bubbles rising from the bottom of the liquid. The simulation of two-phase fluids is a challenging topic in computer graphics. To animate the interaction of a gas and liquid, MultiFLIP samples the two types of particles, and a Euler grid is used to track the interface of the liquid and gas. However, MultiFLIP uses the fluid implicit particle (FLIP) method to interpolate the velocities of particles into the Euler grid, which suffer from additional noise and instability. Methods To solve the problem caused by fluid implicit particles (FLIP), we present a novel velocity transport technique for two individual particles based on the affine particle-in-cell (APIC) method. First, we design a weighed coupling method for interpolating the velocities of liquid and gas particles to the Euler grid such that we can apply the APIC method to the simulation of a two-phase fluid. Second, we introduce a narrowband method to our system because MultiFLIP is a time-consuming approach owing to the large number of particles. Results Experiments show that our method is well integrated with the APIC method and provides a visually credible two-phase fluid animation. Conclusions The proposed method can successfully handle the simulation of a twophase fluid.

This paper investigates the problems of the observer-based fault estimation (FE) and fault-tolerant control (FTC) for nonlinear systems subjected to external disturbances by the T–S fuzzy model method with local nonlinear models. Firstly, an FE strategy is proposed based on the unknown input observer technology, where the local nonlinear terms can be decoupled from the FE error system for relaxing the design of observer. Then, by using the FE information, a fuzzy fault-tolerant controller is designed to guarantee the stability of the system. Additionally, in the design schemes of the FE observer and the fault-tolerant controller, the $${L_\infty }$$ method is used to deal with the problems of FE and FTC for the system with persistent disturbance such that the robustness of the system against the persistent external disturbance can be increased. Compared with the traditional $${H_\infty }$$ method which is only applicable for dealing with the energy bounded signals, the $${L_\infty }$$ FTC strategy proposed in this paper can deal with the persistent disturbance such that the shortcomings of the traditional $${H_\infty }$$ approach are overcome. Finally, the effectiveness of the proposed method is verified by simulation results.

Many image-denoising approaches seek to remove either additive white Gaussian noise (AWGN) or impulse noise (IN), because both types are easier to process when considered separately. However, images can be corrupted by a mixture of AWGN and IN during image acquisition and transmission. The major difficulty of mixed noise removal arises through the complex distribution of noise, which cannot be fitted by a simple parametric model. In this paper, a new nonlocal means based framework (NMF) is proposed. A median-type filter is used to detect the locations of outlier pixels; these pixels are then replaced by their nonlocal means, which makes the mixed noise distribution approximately Gaussian. To prove the effectiveness of our NMF, a low rank approximation combined with NMF (LRNM) model is presented for mixed noise removal. In the LRNM, we group similar nonlocal patches in a matrix and apply a low rank approximation to reconstruct the clean image. Gradient regularization is added to better preserve the image texture details. A convolutional neural network (CNN) combined with the NMF (NMF-CNN) is also presented, to prove the generality of the NMF. Experimental results show that LRNM and NMF-CNN achieve a strong mixed noise removal performance and also produce visually pleasing denoising results.

Obtaining reliable GNSS uncalibrated phase delay (UPD) or integer clock products is the key to achieving ambiguity-fixed solutions for real-time (RT) precise point positioning (PPP) users. However, due to the influence of RT orbit errors, the quality of UPD/integer clock products estimated with a globally distributed GNSS network is difficult to ensure, thereby affecting the ambiguity resolution (AR) performance of RT-PPP. In this contribution, by fully utilising the consistency of orbital errors in regional GNSS network coverage areas, a method is proposed for estimating regional integer clock products to compensate for RT orbit errors. Based on Centre National d’Etudes Spatiales (CNES) RT precise products, the regional GPS/BDS integer clock was estimated with a CORS network in the west of China. Results showed that the difference between the estimated regional and CNES global integer clock/bias products was generally less than 5 cm for GPS, whereas clock differences of greater than 10 cm were observed for BDS due to the large RT orbit error. Compared with PPP using global integer clock/bias products, the AR performance of PPP using the regional integer clock was obviously improved for four rover stations. For single GPS, the horizontal and vertical accuracies of ambiguity-fixed PPP solutions were improved by 56.2% and 45.3% on average, respectively, whereas improvements of 67.5% and 50.5% in the horizontal and vertical directions, respectively, were observed for the combined GPS/BDS situation. Based on a regional integer clock, the RMS error of a kinematic ambiguity-fixed PPP solution in the horizontal direction could reach 0.5 cm. In terms of initialisation time, the average time to first fix (TTFF) in combined GPS/BDS PPP was shortened from 18.2 min to 12.7 min. In view of the high AR performance realised with the use of regional integer clocks, this method can be applied to scenarios that require high positioning accuracy, such as deformation monitoring.

The fan is one of the key components of the power transformer cooling system. The operating condition of fans determines transformers’ internal temperature rise and long-term reliability. However, at present, the fans’ condition monitoring only includes switch status (online) and regular maintenance (offline), online direct monitoring of the fans’ operating condition is lacking due to economic costs. In view of the above-mentioned problem, this paper proposes a transformer fan early fault detection method based on the oil exponent, which is monitored by the existing transformer top-oil temperature data, thereby detecting the abnormality of the fans. In this method, the oil exponent was chosen as the characteristic criterion. First, to obtain the range of oil exponent in different cooling modes, a set of physical models describing global oil flow and its interaction with air was established based on fluid dynamics and heat transfer principle. Then, regarding the constantly changing top-oil temperature, ambient temperature and load current, an oil exponent tracking algorithm using particle swarm optimization (PSO) was proposed within an improved IEC dynamic thermal model. The operation data from an oil-immersed transformer with a rated capacity of 120-MVA and rated voltage of 220-kV was selected to verify the above methods under two different scenarios.

TIGIT is an emerging novel checkpoint target that is expressed on both tumor-infiltrating T cells and NK cells. Some current investigational antibodies targeting TIGIT have also achieved dramatic antitumor efficacy in late clinical research. Most recently, the relevance of NK cell–associated TIGIT signaling pathway to tumors’ evasion of the immune system has been clearly revealed, which endows NK cells with a pivotal role in the therapeutic effects of TIGIT blockade. In this article, we describe a novel anti-TIGIT monoclonal antibody, AET2010, which was acquired from a phage-displayed human single-chain antibody library through a cell panning strategy. With emphasis on its regulation of NK cells, we confirmed the excellent ex vivo and in vivo antitumor immunity of AET2010 mediated by the NK-92MI cells. Intriguingly, our work also revealed that AET2010 displays a lower affinity but parallel avidity and activity relative to MK7684, an investigational monoclonal antibody from MSD, implying a reasonable balance of potency and potential side effects for AET2010. Together, these results are promising and warrant further development of AET2010.

Oil-immersed transformer is one of the most important electrical equipment in power distribution and transmission systems. Dielectric response method is a well-recognized method to diagnose the insulation defect of oil-immersed transformers. However, the applicability of this method is restricted due to the long testing time. Under some special field conditions, the method is not even applicable. In this article, a novel testing method is proposed based on the following ideas: first, the low-frequency dielectric parameters are extracted by using mixing frequency excitation; then, parameters of the extended Debye equivalent circuit are determined based on cuckoo search optimization algorithm; finally, the specific parameters are used to the established simulation model and obtain the recovery voltage curve. Compared with the traditional method, the testing time of the proposed method has been greatly reduced. Besides, dielectric parameters in both frequency domain and time domain can be obtained simultaneously. The applicability of the proposed method is verified by the dielectric response measurements on a laboratory transformer and a real power transformer in a substation.

Dengue virus (DENV) is a prevalent mosquito-transmitted human pathogen, causing about 100 million cases of acute dengue fever and 21,000 deaths annually worldwide. Therapeutic neutralizing antibodies against dengue virus might be effective to treat severe dengue fever. Here, we showed that human monoclonal antibody (HMAb) 9C7 bound to all four intact serotypes of DENV but not to the recombinant envelope protein, suggesting HMAb 9C7 recognized a conformational epitope of the envelope protein. Taken together our results suggested that HMAb 9C7 neutralized all four serotypes of DENV in vitro and, for DENV-1, indicated activity at the pre- and post-attachment steps in the viral life cycle. HMAb 9C7 potently protected suckling mice from lethal challenge with all four serotypes of DENV. FcγRII-mediated uptake of immune complexes and antibody-dependent enhancement at low doses of the antibody were abolished by two Leu-to-Ala (9C7-LALA) mutations or deletion of nine amino acids (9C7-9del) in HMAb 9C7 Fc. Therefore, HMAb 9C7 represented a promising prophylactic and therapeutic agent against all four serotypes of DENV.

Investigating how aquatic animals respond to hypoxia brought about by changes in environmental temperature may be of great significance to avoid oxidative injury and maintain the quality of farmed fish in the background of global warming. Here, we investigated the effects of hypoxia on oxidative injury and environment-sensing pathway in blood cells of Micropterus salmoides. The total blood cell count (TBCC) and Giemsa staining showed that hypoxia could lead to damage of blood cells. Flow cytometry analysis confirmed that the apoptosis rate, Ca2+ level, NO production and ROS of blood cells were significantly increased under hypoxia stress. Environment-sensing pathways, such as Nrf2 pathway showed that hypoxia resulted in significant up-regulation of hiF-1 alpha subunit (Hif-1α), nuclear factor erythroid 2-related factor 2 (Nrf2) and kelch-1ike ECH- associated protein l (Keap1) expression. Meanwhile, the expression of Hippo pathway-related genes such as MOB kinase activator 1 (MOB1), large tumor suppressor homolog 1/2 (Lats1/2), yes-associated protein/transcriptional co-activator with PDZ-binding motif (YAP/TAZ), protein phosphatase 2A (PP2A) were significantly increased in blood cells after hypoxia exposure. In addition, hypoxia stress also increased the expression of catalase (CAT) and glutathione peroxidase (GPx), but decreased the expression of superoxide dismutase (SOD). Consequently, our results suggested that hypoxia could induce oxidative injury and apoptosis via mediating environment-sensing pathway such as Nrf2/Hippo pathway in blood cells of M. salmoides.

In this article, a novel recursive maximum correntropy-based evolving fuzzy system (RMCEFS) is proposed. The proposed system has the capability of reorganizing the structure and adapting itself in a dynamically changing environment with non-Gaussian noises. The system generates a new rule based on the correntropy criterion which represents a robust nonlinear similarity measure between two random variables and avoids recruiting the noises as the rules. Maximizing the cross-correntropy between the system output and the desired response leads to the maximum correntropy criterion for system self-adaptation. In our article, a recursive solution of the maximum correntropy criterion is derived to update the parameters of the evolving rules. This avoids the convergence problem produced by the learning size in the gradient-based learning. Also, the steady-state convergence performance of the proposed RMCEFS is studied, where the analytical solutions of the steady-state excess mean square error for the Gaussian noise and non-Gaussian noises are derived. The simulation studies show that the proposed RMCEFS using the recursive maximum correntropy converges much faster and is more accurate than the existing evolving fuzzy systems in the case of noise-free and noisy conditions.

We propose an effective approach to make segmentation for objects in videos with an initial input of the object masks in a few frames of the source video. In this method, we cast the segmentation task as a Markov Random Field (MRF) labeling problem. Different from the conventional MRF models, our model uses an additional term of higher-order potential to better propagate the global consistency among frames. The higher-order potential presented in this paper is significant for the proposed method because of its capability to keep the long-range consistency during segmentation. In order to make the MRF energy minimized, we also introduce a smart skill that makes the intractable higher-order potential “invisible” during the optimization so that the problem can be solved simply by applying a standard graph cut algorithm. Besides, the entire process is operated in a bilateral space, where the labeling can be inferred efficiently on the vertices that are sampled regularly from the bilateral grid. The results of a comparison of our method with a number of recently developed methods show that it performs favorably against state-of-the-art algorithms on multiple benchmark data sets in view of accuracy and achieves a much faster runtime performance.

Macrophages (Mø) are immune cells with natural phagocytic ability and play an important role in tumorigenesis, development and metastasis. Mø play a dual role of tumour inhibition and tumour promotion in tumour development due to their two different phenotypes. Mø in the tumour microenvironment have long been referred to as tumour-associated Mø (TAMs). Mø are mainly involved in tumour resistance, cancer metastasis and mediating immunosuppression. Nowadays, Mø and Mø membranes have been widely used in drug delivery systems (DDSs) because of their good biocompatibility, natural phagocytosis and their important role in tumour development. In this review, from the perspective of Mø's role in tumour development, we present strategies and drugs of Mø targeting and focusing on the several types of biomimetic nanoparticles constructed by Mø and Mø membranes in tumour therapy, and discuss the problem of this delivery system in present research and future directions.

Effective fault diagnosis of rotating machinery plays a pretty important role in the enhanced reliability and improved safety of industrial informatics applications. Although traditional intelligent fault diagnosis techniques, such as support vector machine, extreme learning machine, and convolutional neural network, might achieve satisfactory accuracy, a very high price is caused by marking all samples manually. In this article, a novel fault diagnosis method of the rotating machinery is proposed by integrating semisupervised generative adversarial nets with wavelet transform (WT-SSGANs). The proposed WT-SSGANs’ method involves two parts. In the first part, WT is adopted to transform 1-D raw vibration signals into 2-D time–frequency images. In the second part, the 2-D time–frequency images are inputted into the built SSGANs’ model to realize fault diagnosis with little labeled samples. The advantage of the built model is that the unlabeled samples might be made full use of through an adversarial learning mechanism. Finally, two case studies are implemented to verify the proposed method. The results indicate that it can achieve higher accuracy and use less labeled samples than the other existing methods in the literature. In addition, its performance in stability is pretty good as well. Competitive and promising results are still achieved when working conditions are changed.

Piezo-driven precision positioning platforms with large stroke are greatly demanded in both scientific research and industrial applications. Although various principles have been proposed to design piezo-driven positioning platforms, the problem of backward motion commonly exists, greatly hindering their applications. To suppress the backward motion, in this paper, an idea by generating a forward frictional force to balance the reversed frictional force was proposed. Correspondingly, a specific arc-shape flexure hinge was designed to generate the forward frictional force via its elastic recovery. To verify the feasibility of the proposed idea, a positioning platform with alterable initial gap between contact surfaces is designed by the parasitic motion principle (PMP). By measuring the output displacement under various initial gaps, a critical initial gap was identified, at which the positioning platform could output stepping displacement without backward motion. Under this critical initial gap, effects of driving voltage and frequency on the backward motion are further investigated. In addition, the natural frequency and loading capacity of the positioning platform are tested. This paper confirms that using PMP, it is feasible to design piezo-driven positioning platforms with completely suppressed backward motion, which would enhance the applications of the PMP and PMP positioning platforms.

Urban acoustic classification (UAC) plays a vital role in smart city engineering, urban security, noise pollution analysis, etc. Convolutional neural networks (CNNs) based feature transfer learning have been shown competitive performance in many applications but little attention has been paid to UAC. In this study, a novel UAC algorithm exploiting the deep CNNs based feature transfer learning and the deep belief net (DBN) based classification is developed. The spectrogram is first adopted for the urban acoustic stream representation. Then, three deep CNNs pre-trained on ImageNet database are applied as feature extractors. The extracted features are concatenated and fed to a DBN for classifier learning. To achieve a good generalization performance, three restricted boltzmann machines (RBM) trained by the contrastive divergence algorithm (CD) followed by a back-propagation (BP) based fine parameter tuning is adopted in DBN. The proposed UAC is evaluated on a real acoustic database, including 11 categories of acoustic signals recorded from the urban environment. Performance comparisons to many state-of-the-art algorithms are presented to demonstrate the superiority of the proposed method.

In modern manufacturing industry featured with automation and flexibility, the intelligent tool management for Computer Numeric Control (CNC) machine plays an essential role in manufacturing automation. The automatic tool recognition in terms of geometric shapes, materials and usage functions could facilitate the seamless integration with downstream process planning and scheduling processes. In this paper, a intelligent tool recognition system is proposed with a novel hybrid framework of multi-channel deep learning network with non-iterative and fast feedforward neural network to meet high efficiency and accuracy requirement in intelligent manufacturing. The combination of the fine-tuning Convolutional Neural Networks (CNNs) with the random parameter assignment mechanism of Extreme Learning Machines (ELMs) reach a balance in accurate feature extraction and fast recognition. In the proposed hybrid framework, features extracted from efficient CNNs are aggregated into robust ELM auto-encoders (ELM-AEs) to generate the compact but rich feature information, which are then feed to the subsequent single layer ELM network for tool recognition. The performance of proposed framework is verified on several standardized 3D shape retrieval and classification dataset, as well as on a self-constructed multi-view 3D data represented tool library database. Numerical experiments reveal a promising application perspective of proposed intelligent recognition system on manufacturing automation.