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Abstract Pasteurella multocida type A (PmA) mainly causes respiratory diseases such as pneumonia in bovines, leading to great economic losses to the breeding industry. At present, there is still no effective commercial vaccine against PmA infection. In this study, a mutant strain (PmCQ2Δ4555–4580) with brand-new phenotypes was obtained after serially passaging at 42 °C. Whole genome resequencing and PCR analysis showed that PmCQ2Δ4555–4580 missed six genes, including PmCQ2_004555, PmCQ2_004560, PmCQ2_004565, PmCQ2_004570, PmCQ2_004575, and PmCQ2_004580. Importantly, the virulence of PmCQ2Δ4555–4580 was reduced by approximately 2.8 × 109 times in mice. Notably, live PmCQ2Δ4555–4580 could provide 100%, 100% and 40% protection against PmA, PmB and PmF, respectively; and inactivated PmCQ2Δ4555–4580 could provide 100% and 87.5% protection against PmA and PmB. Interestingly, immune protection-related proteins were significantly upregulated in PmCQ2Δ4555–4580 based on RNA-seq and bioinformatics analysis. Meaningfully, by in vitro expression, purification and in vivo immunization, 12 proteins had different degrees of immune protective effects. Among them, PmCQ2_008205, PmCQ2_010435, PmCQ2_008190, and PmCQ2_004170 had the best protective effect, the protection rates against PmA were 50%, 40%, 30%, and 30%, respectively, and the protective rates against PmB were 62.5%, 42.9%, 37.5%, and 28.6%, respectively. Collectively, PmCQ2Δ4555–4580 is a potential vaccine candidate for the prevention of Pasteurellosis involving in high expression of immune protective related proteins.

Flexible lithium metal batteries with high capacity and power density have been regarded as the core power resources of wearable electronics. However, the main challenge lies in the limited electrochemical performance of solid-state polymer electrolytes, which hinders further practical applications. Incorporating functional inorganic additives is an effective approach to improve the performance, including increasing ionic conductivity, achieving dendrite inhibiting capability, and improving safety and stability. Herein, this review summarizes the latest developments of functional inorganic additives in composite solid-state electrolytes for flexible metal batteries with special emphasis on their mechanisms, strategies, and cutting-edge applications, in particular, the relationship between them is discussed in detail. Finally, the perspective on future research directions and the key challenges on this topic are outlooked.

The coupling processes among the lithosphere, atmosphere, and ionosphere (LAI) during the earthquake preparation phase are still an open scientific debate. Comprehensive LAI coupling effects around the 2022 Ms6.8 Luding earthquake in China are investigated with a multi-parameter and multi-layer approach, including the b-value, revised accelerated moment release, Earth resistivity, ELF magnetic field emissions, atmospheric electric field, surface temperature, foF2 from ionosonde, GNSS TEC, Ne and magnetic field from CSES and Swarm satellites, and energetic electrons from CSES and NOAA satellites. It is found that the anomalies start from the lithospheric parameters as Earth resistivity and b-values 1–2 years before to reflect the local stress loading in the seismic region, then the ionospheric and atmospheric disturbances occur and accelerate −50 days before and −15 days before, and finally the electrons precipitate a few days before. The simultaneous perturbations in LAI illustrate the thermodynamic coupling channel, such as on 24 August, −12 days before. Meanwhile, the abundant developed ionospheric anomalies without atmospheric disturbances demonstrate the electromagnetic coupling way from the lithosphere to the ionosphere directly. Finally, the results demonstrate a two-way model of LAIC: one way is characterized by a slow chain of processes, of thermodynamic nature, starting from the ground and proceeding to the above atmosphere and ionosphere, showing an exponential trend in the cumulative number of anomalies; the second way is characterized by oscillating electromagnetic coupling between the lithosphere and ionosphere, showing intermittent fluctuations in the corresponding cumulative number of anomalies.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes—the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

Geomagnetic diurnal variation significantly influences the precision of marine magnetic measurements. Precise estimation of this variation is crucial for enhancing the accuracy of offshore magnetic surveys. To address the challenges in achieving the desired accuracy with current estimation methods for geomagnetic diurnal variation, this study introduces a high-precision estimation model that integrates support vector machine (SVM) and random forest (RF) techniques. Initially, the data preprocessing phase includes an innovative extreme value adjustment method to rectify the temporal discrepancies across different stations, alongside employing the base period technique for daily baseline correction. Subsequently, we construct models to capture the daily variation trends at various times, facilitating an in-depth analysis of the diurnal variation patterns. The culmination of this process involves employing a fusion model algorithm to compute the diurnal variations across all stations comprehensively. Comparative analyses with conventional methods, such as distance weighting, bifactor weighting, and latitude weighting, reveal that our proposed model achieves a significant reduction in the root mean square error (RMSE) by an average of 31%, decreases the mean absolute error (MAE) by 35%, and enhances the Pearson correlation coefficient by 20% on average. These improvements underscore the superior accuracy of our geomagnetic diurnal variation estimation model.

Bovine parainfluenza virus type 3 (BPIV-3), also known as bovine respirovirus 3, is a common respiratory pathogen associated with bovine respiratory disease (BRD). BPIV-3 has currently circulated worldwide; however, data on the prevalence and genetic characteristics of BPIV-3 are still scarce and limited. In this study, the BPIV-3 strain SC was identified and isolated from cattle presenting with clinical signs of BRD in China. Animal experiments indicated that BPIV-3 SC can successfully infect C57BL/6 mice and induce weight loss, lung inflammatory cell infiltration, and inflammatory cytokine expression in mice. In addition, the complete genome of BPIV-3 SC was obtained using next-generation sequencing and was 15,473 bp in length. Phylogenetic analysis indicated that BPIV-3 SC belonged to genotype C, which clustered in the same large clade consisting of a population of Chinese genotype C strains but was found to be different from the other strains upon further differentiation. Compared to other Chinese genotype C strains, the BPIV-3 SC showed 70 unique nucleotide mutations and 13 unique amino acid mutations in the HN, P, and L proteins, suggesting a unique genetic evolution of BPIV-3 SC. In conclusion, we isolated and characterized a differential Chinese genotype C BPIV-3, which contributed to an understanding of the prevalence and evolution of BPIV-3 in China.

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismogenic activity. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five surface categories of the epicenter area were defined contingent on position (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated the identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postseismic remission signatures. The casework found 10 GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36 GHz horizontal-polarized performed best for arid, vegetated landmasses. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.

Abstract Hydrovoltaic energy technology that generates electricity directly from the interaction of materials with water has been regarded as a promising renewable energy harvesting method. With the advantages of high specific surface area, good conductivity, and easily tunable porous nanochannels, two‐dimensional (2D) nanomaterials have promising potential in high‐performance hydrovoltaic electricity generation applications. Herein, this review summarizes the most recent advances of 2D materials for hydrovoltaic electricity generation, including carbon nanosheets, layered double hydroxide (LDH), and layered transition metal oxides and sulfides. Some strategies were introduced to improve the energy conversion efficiency and the output power of hydrovoltaic electricity generation devices based on 2D materials. The applications of these devices in self‐powered electronics, sensors, and low‐consumption devices are also discussed. Finally, the challenges and perspectives on this emerging technology are outlined.

This article takes the measured Total Electron Content (TEC) from the GPS points of the China Regional Crust Observation Network as the starting point to establish a regional ionospheric empirical model. The model’s performance is enhanced by considering solar flux and geomagnetic activity data. The refinement function model of the ionospheric TEC diurnal variation component, seasonal variation component, and geomagnetic component is studied. Using the nonlinear least squares method to fit undetermined coefficients, MEFM-ITCR (Multi-parameter Empirical Fusion Model–Ionospheric TEC China Regional Model) is proposed to forecast the regional ionosphere TEC in China. The results show that the standard deviation of MEFM-ITCR residuals is 3.74TECU, and MEFM-ITCR fits the modeling dataset well. Analyses of geographic location variation, seasonal variation, and geomagnetic disturbance were carried out for MEFM-ITCR performance. The results indicate that in the Chinese region, MEFM-ITCR outperforms IRI2020 and NeQuick2 models in terms of forecast accuracy, linear correlation, and model precision for TEC measured using GPS points under different latitudes and longitudes, different seasons, and different geomagnetic disturbances. The empirical TEC model built for the Chinese region in this paper provides a new ionospheric delay correction method for GNSS single frequency users and is of great significance for establishing other new and improving existing ionospheric empirical models.

In this article, a new method for seismic ionospheric Global Navigation Satellite System (GNSS) total electron content (TEC) based anomaly detection using a deep learning framework is proposed. The performance of the proposed encoder–decoder long short-term memory extended model is compared with those of five other benchmarking predictors. The proposed model achieves the best performance (R2 = 0.9105 and root-mean-square error (RMSE) = 2.6759) in predicting TEC time series data, demonstrating a 20% improvement in R2 and 39.1% improvement in the RMSE over the autoregressive integrated moving average model. To detect the pre-earthquake ionospheric disturbances more accurately, a reasonable strategy for determining anomaly limits is also proposed. Finally, the method is applied to analyze the pre-earthquake ionospheric TEC disturbance of the 2016 Xinjiang Ms 6.2 earthquake. By excluding the effects of solar activity and geomagnetic activity, obvious ionospheric anomalies could be observed, occurring during 4–8 days prior to, and on 1 day before, the earthquake. Negative anomalies tended to occur in the earlier period, whereas positive anomalies occurred closer to the earthquake time, with increasing anomaly intensity with temporal proximity. Furthermore, confusion analysis is used in this article to verify the reliability of the proposed model. The proposed model achieves significant improvements in predicting GNSS TEC time series and is shown to advance the performance of earthquake anomaly detection technology.

An optimized transport of anions and cations is essential for the development of practical alkali metal batteries. Here, the authors report the use of Ti0.87O2 nanosheets as coating material of polypropylene separators for Li-S, Li-Se and Na-Se cells.

Abstract Graphitic carbon nitride (g-C3N4)-based photocatalysts have shown great potential in the splitting of water. However, the intrinsic drawbacks of g-C3N4, such as low surface area, poor diffusion, and charge separation efficiency, remain as the bottleneck to achieve highly efficient hydrogen evolution. Here, a hollow oxygen-incorporated g-C3N4 nanosheet (OCN) with an improved surface area of 148.5 m2 g−1 is fabricated by the multiple thermal treatments under the N2/O2 atmosphere, wherein the C–O bonds are formed through two ways of physical adsorption and doping. The physical characterization and theoretical calculation indicate that the O-adsorption can promote the generation of defects, leading to the formation of hollow morphology, while the O-doping results in reduced band gap of g-C3N4. The optimized OCN shows an excellent photocatalytic hydrogen evolution activity of 3519.6 μmol g−1 h−1 for ~ 20 h, which is over four times higher than that of g-C3N4 (850.1 μmol g−1 h−1) and outperforms most of the reported g-C3N4 catalysts.

Beyond-Li+-ion batteries are promising energy storage systems but suffer from lack of suitable electrode materials. Here the authors report a new type of zero-strain cathodes for Na+, K+, Zn2+, and Al3+ ion batteries through strain engineering of a 2D multilayered VOPO4-graphene heterostructure.

Tracheobronchial obstruction in children due to benign stenosis or tracheobronchomalacia still remains a challenging matter of concern. Currently, there is 10%–20% complication rate in clinical treatment. The non-biodegradable property of silicone stents and nickel-titanium memory alloy stents take the primary responsibility for drawbacks including stimulating local granulation tissue proliferation, displacement, and stent-related infections. Permanent tracheobronchial stent will be a persistent foreign object for a long time, causing excessive secretion of tracheal mucosa, ulceration and even perforation, which is particularly unsuitable for young children with persistent tracheal growth. In this study, the degradation and biocompatibility performance of three typical biodegradable metals were investigated as potential tracheobronchial stent materials. The results exhibited that these materials showed different degradation behaviors in the simulating respiratory fluid environment compared with SBF. Except for pure iron group, high purity magnesium and zinc showed favorable cell adhesion and proliferation in three culture methodologies (direct culture, indirect culture and extraction culture). The proper corrosion rate and good biocompatibility indicated that high purity magnesium and zinc may be good candidates as tracheobronchial stent materials. Keywords: Biomaterials, Biodegradable metals, Corrosion, Tracheobronchial stents, Cytocompatibility

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky radius and an input sequence length of 20 consecutive observations during night time. We further explore a transferring learning approach, which initially trains the model with the larger Electro-Magnetic Emissions Transmitted from the Earthquake Regions (DEMETER) dataset, and then tunes the model with the CSES dataset. The transfer-learning performance is substantially higher than that of direct learning, yielding a 12% improvement in the F1 score and a 29% improvement in the MCC value. Moreover, we compare the proposed model SeqNetQuake with other five benchmarking classifiers on an independent test set, which shows that SeqNetQuake demonstrates a 64.2% improvement in MCC and approximately a 24.5% improvement in the F1 score over the second-best convolutional neural network model. SeqNetSquake achieves significant improvement in identifying pre-earthquake ionospheric perturbation and improves the performance of earthquake prediction using the CSES data.

Hydroxyapatite (HA) is an attractive bioceramic for hard tissue repair and regeneration due to its physicochemical similarities to natural apatite. However, its low fracture toughness, poor tensile strength and weak wear resistance become major obstacles for potential clinical applications. One promising method to tackle with these problems is exploiting graphene and its derivatives (graphene oxide and reduced graphene oxide) as nanoscale reinforcement fillers to fabricate graphene-based hydroxyapatite composites in the form of powders, coatings and scaffolds. The last few years witnessed increasing numbers of studies on the preparation, mechanical and biological evaluations of these novel materials. Herein, various preparation techniques, mechanical behaviors and toughen mechanism, the in vitro/in vivo biocompatible analysis, antibacterial properties of the graphene-based HA composites are presented in this review.

Low Earth orbit satellites collect and study information on changes in the ionosphere, which contributes to the identification of earthquake precursors. Swarm, the European Space Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has successfully shown that in some cases it is able to observe many several ionospheric perturbations that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model using 9017 recent (2014–2020) independent earthquakes of magnitude 4.8 or greater, as well as the corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent performance has been achieved. In addition, the influence of different model inputs and spatial window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime data provided the highest performance in discriminating pre-earthquake perturbations, yielding an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation identification can be significantly improved by utilizing SafeNet, which is capable of detecting precursor effects within electromagnetic satellite data.

In order to ensure the blade strength of large-scale wind turbine, the blunt trailing edge airfoil structure is proposed, aiming at assessing the impact of the trailing edge shape on the flow characteristics and airfoil performance. In this paper, a Joukowsky airfoil is modified by adding the tail thickness parameter K to achieve the purpose of accurately modifying the thickness of the blunt tail edge of the airfoil. Using Ansys Fluent as a tool, a large eddy simulation (LES) model was used to analyze the vortex structure of the airfoil trailing edge. The attack angles were used as variables to analyze the aerodynamic performance of airfoils with different K-values. It was found that when α = 0°, α = 4°, and α = 8°, the lift coefficient and lift–drag ratio increased with increasing K-value. With the increase in the angle of attack from 8° to 12°, the lift–drag ratio of the airfoil with the blunt tail increased from +70% to −7.3% compared with the original airfoil, which shows that the airfoil with the blunt trailing edge has a better aerodynamic performance at a small angle of attack. The aerodynamic characteristics of the airfoil are affected by the periodic shedding of the wake vortex and also have periodic characteristics. By analyzing the vortex structure at the trailing edge, it was found that the value of K can affect the size of the vortex and the position of vortex generation/shedding. When α = 0°, α = 4°, and α = 8°, the blunt trailing edge could improve the aerodynamic performance of the airfoil; when α = 12°, the position of vortex generation changed, which reduced the aerodynamic performance of the airfoil. Therefore, when designing the trailing edge of an airfoil, the thickness of the trailing edge can be designed according to the specific working conditions. It can provide valuable information for the design and optimization of blunt trailing edge airfoil.

As alkali-ion battery anodes, metal oxide nanomaterials suffer from severe structural degradation after charging/discharging cycling. Here the authors develop two-dimensional holey nanosheet anodes which display minimal structural changes during electrochemical operation.

The low-altitude satellite DEMETER recorded many cases of ionospheric perturbations observed on occasion of large seismic events. In this paper, we explore 16 spot-checking classification algorithms, among which, the top classifier with low-frequency power spectra of electric and magnetic fields was used for ionospheric perturbation analysis. This study included the analysis of satellite data spanning over six years, during which about 8760 earthquakes with magnitude greater than or equal to 5.0 occurred in the world. We discover that among these methods, a gradient boosting-based method called LightGBM outperforms others and achieves superior performance in a five-fold cross-validation test on the benchmarking datasets, which shows a strong capability in discriminating electromagnetic pre-earthquake perturbations. The results show that the electromagnetic pre-earthquake data within a circular region with its center at the epicenter and its radius given by the Dobrovolsky’s formula and the time window of about a few hours before shocks are much better at discriminating electromagnetic pre-earthquake perturbations. Moreover, by investigating different earthquake databases, we confirm that some low-frequency electric and magnetic fields’ frequency bands are the dominant features for electromagnetic pre-earthquake perturbations identification. We have also found that the choice of the geographical region used to simulate the training set of non-seismic data influences, to a certain extent, the performance of the LightGBM model, by reducing its capability in discriminating electromagnetic pre-earthquake perturbations.

The paper has developed Robust Satellite data analysis Technique (RST) to detect seismic anomalies in the case of the Wenchuan earthquake occurred on May 12, 2008, using the bi-angular Advanced Along-Track Scanning Radiometer (AATSR) gridded brightness temperature (BT) data based on spatial/temporal continuity and confutation analysis. The proposed method has been applied to analyze the Wenchuan earthquake with longitude from 95°E to 111°E and latitude from 23°N to 39°N, and a full data-set of 7 years data from 2003 to 2009 during the months of April and May has been analyzed. Combining with the rupture structure data of the Wenchuan earthquake, the analyzed results indicate that: the main structure activity (the Yingxiu-Beichuan fault) characteristics of the Wenchuan earthquake can be identified and extracted using the bi-angular AATSR nadir and forward brightness temperature data, and the seismic infrared radiation anomalies detected by the AATSR nadir brightness temperature is more close to the Yingxiu-Beichuan fault.