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C20H26O5, orthorhombic, P212121 (no. 19), a = 5.2825(10) Å, b = 13.0652(2) Å, c = 26.4176(4) Å, V = 1823.26(5) Å3, Z = 4, R gt(F) = 0.0338, wR ref(F 2) = 0.0821, T = 150 K. CCDC No.: 2377464.

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.

This study investigates ionospheric anomalies associated with the Mw 7.1 Wushi earthquake that occurred in Xinjiang, China, on January 22, 2024. We analyze multi-GNSS data, including GPS and BeiDou (BDS), from the Crustal Movement Observation Network of China (CMONOC) to detect pre-seismic ionospheric anomalies and co-seismic ionospheric disturbances (CIDs). A 27-day sliding interquartile range method is applied to BDS geostationary satellite data to identify pre-seismic anomalies while minimizing the influence of solar and geomagnetic activity. The results show that BDS geostationary satellites significantly improve the spatiotemporal resolution of ionospheric monitoring, and M_GIM outperforms CODE-TEC in regions with sparse IGS stations. Pre-seismic anomalies are observed on January 14, 15, 16, and 18, with those southeast of the epicenter possibly caused by additional electric fields generated in the seismic preparation zone. CIDs are detected within 400 km of the epicenter using the Butterworth filtering technique, revealing a propagation speed of approximately 1 km/s, likely caused by acoustic-gravity waves generated by the earthquake. This study demonstrates the importance of multi-GNSS observations and high-resolution regional modeling for detecting and understanding seismo-ionospheric anomalies, providing insights into the earthquake preparation process and ionospheric disturbance propagation characteristics.

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.

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.

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.