Search Results (468)

Background: Children are at high risk of influenza infections and may spread the disease to vulnerable family members. Quadrivalent influenza vaccines (QIV) provide protection against four strains of influenza recommended annually by the World Health Organization (WHO) and have the potential to provide improved protection during seasons with B-strain mismatch between vaccine and circulating virus strains. Methods: We evaluated the reactogenicity and safety of a QIV (Afluria Quad and Afluria Quad Junior, Seqirus, Parkville, Australia) in children aged 6 months to <3 years and 3 to <9 years over two Southern Hemisphere influenza seasons (2019 and 2020). The rates of solicited local and systemic adverse events (AEs) occurring on Days 1–7 after each vaccine dose were compared between three vaccine batches during each of the two seasons. Results: Overall, 73.7% of participants aged 6 months to <3 years and 77.5% of those aged 3 to <9 years reported any solicited AE between Day 1 and 7 of SH2019, and 66.7% and 69.2%, respectively, reported any solicited AE in SH2020, consistent with results from prior paediatric studies of QIV. The majority of solicited AEs were mild to moderate in severity. No consistent patterns of batch variation in solicited local or systemic reactogenicity were observed, suggesting no clinically significant differences between vaccine batches. No serious AEs or AEs of special interest (i.e., anaphylactic reaction or febrile convulsion) were reported during Days 1–7 after each vaccination, and no new safety concerns were identified. Conclusions: Together, these results support a clinically acceptable safety and tolerability profile of QIV in children aged 6 months to <9 years. Full article

With the advancement of UAV trajectory planning and sensing technologies, unmanned aerial vehicles (UAVs) are now capable of performing high-performance ground detection and search tasks. Mountainous regions, due to their complex terrain, have long been a focal point in the field of remote sensing. Effective UAV search tasks in such areas must consider not only horizontal coverage but also variations in detection range and angle caused by changes in elevation. Conventional algorithms typically require complete prior knowledge of the environment for trajectory optimization and often depend on scenario-specific policy models, limiting their generalizability. To address these challenges, this paper proposes a Feature-Aware Elite Imitation Multi-Agent Reinforcement Learning (FA-EIMARL) algorithm that leverages partial terrain information to construct a feature extraction network. This approach enables batch training across diverse terrains without the need for full environmental maps. In addition, an elite imitation mechanism has been proposed for convergence acceleration and task performance enhancement. Simulation results demonstrate that the proposed method achieves superior reward performance, convergence rate, and computational efficiency while maintaining strong adaptability to varying terrains. Full article

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete Kalman filter for latent state estimation, and Hotelling’s T² statistical criterion for deviation detection. This paper implements an online learning mechanism (“on the fly”) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model’s stable convergence and anomalies’ precise detection with the metrics precision is ≈0.83, recall is ≈0.83, the F1-score is ≈0.83, and the end-to-end delay of 1.5–6.5 ms under 100–1000 sessions/s load was demonstrated experimentally. The computational estimate for typical model parameters is ≈5152 operations for a forward pass and ≈38,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m³) term in the Kalman step, was identified. The obtained results’ practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems. Full article

Porcine enteric coronaviruses (CoVs), including swine acute diarrhea syndrome coronavirus (SADS-CoV), porcine epidemic diarrhea virus (PEDV), porcine deltacoronavirus (PDCoV), and porcine transmissible gastroenteritis virus (TGEV), are major pathogens causing porcine viral diarrhea syndrome (VDS), which brings significant economic losses to the swine industry; distinguishing between these clinically similar viruses has become a serious challenge. We developed a highly specific and interference-resistant porcine CoV multiplex digital PCR (dPCR) assay. The assay exhibited robust anti-interference capabilities, as the concentrations of the four viruses did not affect their accurate quantification. The coefficients of variation (CV%) of intra-batch and inter-batch repeatability for all target viruses were less than 11%. The limit of quantification (LoQ) of this dPCR assay reached 7.5 copies/reaction for each target, and it was one order of magnitude more sensitive than qPCR. The limits of detection (LoD) for SADS-CoV, PEDV, PDCoV, and TGEV were 2.72, 3.00, 3.56, and 3.19 copies/reaction, respectively. A total of 408 known samples were used for validation tests, and the results were highly consistent with the known conditions, showing a compliance rate of 97–100%. The diagnostic specificity (Dsp) of the method was 99–100%. In conclusion, the developed multiplex dPCR assay is highly suitable for early detection and quarantine in four porcine CoVs. The results indicate that this dPCR method is characterized by high specificity, anti-interference capabilities, repeatability, and high sensitivity. It also demonstrates a high compliance rate and diagnostic specificity in sample detection. This multiplex dPCR will contribute to the control of porcine enteric CoV-caused VDS and provide clues for subsequent research. Full article

The red-claw crayfish Cherax quadricarinatus has been widely introduced and cultured in China and has become a crucial economic freshwater species. However, individuals reared from the same batch of seedlings in uniform aquaculture systems exhibit significant size variation within and between genders, which notably impedes the optimization of both their quality and yield. Gut microbiota plays an important role in the metabolism, development, and immunity of aquatic animals. However, the knowledge on the intestinal microbiota of red-claw crayfish with various sizes and genders is poor. In this study, the intestinal microbiota of red-claw crayfish cultured in consistent farming conditions were separated to larger-sized female (GUBF), larger-sized male (GUBM), smaller-sized female (GUSF), and smaller-sized male (GUSM) groups based on their body size (weight) and gender, before being analyzed via high-throughput 16S rRNA gene sequencing. The intestinal microbiota results showed that alpha diversity tended to generally decrease in the order of GUBF, GUBM, GUSF, and GUSM, indicating that the richness and evenness of the gut flora were gradually improved with the increase in body weight or from male to female. Community richness and diversity were highest in the GUBF group, followed by the GUBM, GUSF, and GUSM groups, respectively. Beta diversity indicated significant differences in gut microbiota between the GUBF and GUSF, GUBM and GUSM, GUBF, and GUBM groups. Further analysis showed that the dominant phyla in the intestine of the red-claw crayfish were Firmicutes, Proteobacteria, Fusobacteriota, Bacteroidota, and Deinococcota, and the dominant genera were Vibrio, Tyzzerella, Candidatus Bacilloplasma, Citrobacter, and Candidatus Hepatoplasma. Moreover, nine phyla and 106 genera were identified to be significantly different in abundance among all four groups. Pairwise comparisons revealed that the phylum Dependentiae and Planctomycetota and genus Babeliaceae_unclassified were significantly abundant in the gut of female crayfishes, regardless of body size. On the other hand, irrespective of genders, the abundance of Novosphingobium, Piscinibacter, and Citrobacter was significantly increased or declined in the larger or smaller crayfishes, respectively. PICRUSt2 analysis based on the KEGG database suggested that the pathway bacterial secretion system, isoflavonoid biosynthesis, and pathway glycerolipid metabolism were significantly up- and down-regulated in female individuals, respectively, regardless of body sizes. Meanwhile, the adipocytokine signaling pathway, pyruvate metabolism, and pathway electron transfer carriers were significantly up- and down-regulated in larger individuals, respectively, regardless of gender. Gender differences may induce gut microbiota to exert a greater impact on hormonal regulation, whereas differences in individual size seem to lead gut microbiota to develop a preference for food intake and energy sources. In summary, this study revealed key differences in the intestinal microbiota of the crayfish with different sizes and genders, even in those which were cultured in the same environment and period, which potentially suggest that the intestinal microbiota may be influenced by some other factors in the culture system, such as hormone secretion, metabolism, and immunity. This study will contribute to improving growth performance and animal quality in the aquaculture of C. quadricarinatus. Full article

Under the severe situation of increasing global climate change, it is urgent to improve the ability of cities to cope with climate change and achieve sustainable development. As a key institutional arrangement for China’s climate adaptation, the climate-resilient city initiative has been piloted in 67 cities across two batches since 2017, aiming to foster urban resilience through systematic governance. Based on complex adaptive system theory, this study constructs an urban ecological resilience evaluation framework under the “Pressure–State–Response” (PSR) model. Using panel data from 243 prefecture-level cities from 2010 to 2022 and a difference-in-differences model, it empirically examines the impact of climate-resilient city construction on ecological resilience, further exploring the moderating mechanism of government attention to environmental protection and spatial heterogeneity effects. Key findings include the following: (1) climate-resilient city construction significantly enhances urban ecological resilience, with pilot cities experiencing an average increase of approximately 0.74%; (2) government attention to environmental protection strengthens policy effectiveness, demonstrating a significant positive moderating effect; and (3) policy effects show notable regional variations, with more pronounced improvements in resource-based cities, western regions, and ecologically vulnerable areas. Full article

In this study, the combined effects of influent carbon-to-nitrogen ratio (C/N = 0.8, 1.5, 2.5, 3.5, 4.5) and nitrate (NO₃⁻-N) concentration (40 and 80 mg/L, labeled as R₄₀ and R₈₀) on the partial denitrification (PD) performance were investigated using an intermittent sequencing batch reactor (SBR) process. With sodium acetate as an additional carbon source, the substrate variation, microbial diversity, and functional bacteria evolution were also explored to reveal the nitrite (NO₂⁻-N) accumulation mechanism at low temperatures (3–12 °C). The results showed that the 3.5-R₄₀ and 2.5-R₈₀ systems both presented the optimal NO₂⁻-N accumulation at a temperature of 10 °C, with the NO₂⁻-N transformation rate (NTR) of 66.89% and 76.79%, respectively. In addition, as the temperature reduced from 10 °C to 5 °C, the NO₂⁻-N accumulation performance was significantly suppressed, where the average effluent NO₂⁻-N of 3.5-R₄₀ (20.00 → 11.00 mg/L) and 2.5-R₈₀ (43.00 → 18.90 mg/L) systems reduced by nearly half. It is worth noting that there was almost no NO₂⁻-N accumulation at a C/N ratio of 0.8, although higher NO₃⁻-N concentration promoted NTR under the same C/N ratio. The high-throughput sequencing showed that the minimum Shannon value of 3.81 and the maximum Simpson value of 0.095 both occurred at a C/N ratio of 2.5, suggesting the downshifted microbial richness. Proteobacteria and Bacteroides increased significantly from 35.31% and 18.34% to 51.69–60.35% and 18.08–35.21%, as compared with the seeding sludge. Thauera and Flavobacterium as the main contributors to NO₂⁻-N accumulation accounted for 31.83% and 20.30% at the C/N ratio of 2.5 under a low temperature of 5 °C. The above discussion suggested that higher temperature (10 °C), lower C/N ratio (2.5–3.5), and higher NO₃⁻-N concentration (80 mg/L) were more favorable for the stable PD formation. Full article

Shell color is a commercially valuable trait in eggs, and blue-green eggshells typically exhibit multiple color subtypes. To explore the relationship between the CIELab system and visual color classification and develop simplified discrimination indices, 2274 blue-green eggs across seven batches were selected. The L*, a*, and b* values of each egg were measured, and average visual classification (AveObs) was calculated from four numeric categories (Light = 1, Blue = 2, Green = 3, Olive = 4) separately assigned by four observers. After batch correction using ComBat, four algorithms—linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and neural network (NNET)—were compared. Correction substantially reduced the coefficients of variation of the L*, a*, and b* values. Correlations emerged: L* and b* (−0.722), a* and b* (0.451), and L* and a* (−0.088), while correlations of the L*, a*, and b* values with AveObs were −0.713, 0.218, and 0.771, respectively. The LDA model achieved superior comprehensive performance across all data scenarios, with the highest accuracy and efficiency as compared to the SVM, NNET, and RF models. Among the LDA functions, LD1 explained 78.53% of the variance, with L*, a*, and b* coefficients of −0.134, 0.063, and 0.349, respectively (ratio ≈ 1:0.47:2.60). Simplified formulas based on the L*, a*, and b* values were constructed and compared to the existing indices C* (=$\sqrt{{a*}^2+{b*}^2}$) and SCI (=L* − a* − b*). The correlation between L* − 2b* and AveObs was −0.803, similar to those for C* (0.797) and SCI (−0.782), while the correlation between L* − 4C* and AveObs was −0.810, significantly higher than that for SCI (p < 0.05). In conclusion, the LDA model demonstrated optimal performance in predicting color classification, and L* − 4C* is an ideal index for grading of blue-green eggs. Full article

This study systematically investigated the decolorization efficacy and detoxification effect of crude laccase derived from Pleurotus ostreatus yang1 on azo and triphenylmethane dyes. This research encompassed decolorization efficiencies for 15 dyes (7 azo dyes and 8 triphenylmethane dyes), time course decolorization kinetics, and detoxification assessment using rice (Oryza sativa) and wheat (Triticum aestivum) seed germination as phytotoxicity indicators for both single-dye and mixed-dye systems. Molecular docking was employed to elucidate the laccase–dye interaction mechanisms. The results demonstrated that crude laccase from Pleurotus ostreatus yang1 exhibited significant decolorization efficiency and effective detoxification capacity toward both azo dyes and triphenylmethane dyes. It also displayed considerable decolorization efficiency for mixtures of azo and triphenylmethane dyes (mixture of two types of dyes), along with strong detoxification capability against the phytotoxicity of mixed dyes. Crude laccase showed robust continuous batch decolorization capability for azo dyes Alpha-naphthol Orange (α-NO) and Mordant Blue 13 (MB13). Similarly, it achieved high continuous batch decolorization efficiency for triphenylmethane dyes (e.g., Cresol Red, Acid Green 50) while maintaining stable laccase activity throughout the decolorization process. Crude laccase demonstrated excellent reusability and sustainable degradation performance during the continuous batch decolorization. The decolorization of crude laccase could significantly reduce or completely eliminate the phytotoxicity of both single dyes and mixtures of two dyes (pairwise mixtures of different types of dyes, totaling 18 different combinations). The results of molecular docking between the laccase protein and structurally diverse dyes further elucidated the underlying causes and potential mechanisms for variations in the catalytic ability of laccase toward different structural dyes. In summary, crude laccase from Pleurotus ostreatus yang1 possessed great application value and potential for efficiently degrading and detoxifying dye pollutants of different structural types. Full article

Background: Bradykinin (BK) is an inflammatory mediator. The degradation of labeled synthetic BK in biofluids can be used to report on the activity of angiotensin-converting enzyme (ACE) and basic carboxypeptidases N and CBP2, for which the neuropeptide is a substrate. Clinical studies have shown significant changes in the serum activity of these enzymes in patients with inflammatory diseases. Methods: Here, we investigated variation in the cleavage of dabsylated synthetic BK (DBK) in serum and the formation of the major enzymatic fragments using a thin-layer chromatography-based neuropeptide reporter assay (NRA) in a large cohort of healthy volunteers from the international human Personal Omics Profiling consortium based at Stanford University. Results: Four major outcomes were reported. First, a set of NRA reference data for the healthy population was delivered, which is important for future investigations of patient sera. Second, it was shown that the measured serum degradation capacity for DBK was significantly higher in males than in females. There was no significant correlation of the NRA results with ethnicity, body mass index or overnight fasting. Third, a batch effect was noted among sampling sites (HUPO conferences). Thus, we used subcohorts rather than the entire collection for data mining. Fourth, as the low-cost and robust NRA is sensitive to enzyme activity, it provides such a necessary quick test to eliminate degraded and/or otherwise questionable samples. Conclusions: The results reiterate the critical importance of a high level of standardization in pre-analytical sample collection and processing—most notably, sample quality should be evaluated before conducting any large and expensive omics analyses. Full article

Accurate real-time detection of hawthorn by vision systems is a fundamental prerequisite for automated harvesting. This study addresses the challenges in hawthorn orchards—including target overlap, leaf occlusion, and environmental variations—which lead to compromised detection accuracy, high computational resource demands, and poor real-time performance in existing methods. To overcome these limitations, we propose YOLO-DCL (group shuffling convolution and coordinate attention integrated with a lightweight head based on YOLOv8n), a novel lightweight hawthorn detection model. The backbone network employs dynamic group shuffling convolution (DGCST) for efficient and effective feature extraction. Within the neck network, coordinate attention (CA) is integrated into the feature pyramid network (FPN), forming an enhanced multi-scale feature pyramid network (HSPFN); this integration further optimizes the C2f structure. The detection head is designed utilizing shared convolution and batch normalization to streamline computation. Additionally, the PIoUv2 (powerful intersection over union version 2) loss function is introduced to significantly reduce model complexity. Experimental validation demonstrates that YOLO-DCL achieves a precision of 91.6%, recall of 90.1%, and mean average precision (mAP) of 95.6%, while simultaneously reducing the model size to 2.46 MB with only 1.2 million parameters and 4.8 GFLOPs computational cost. To rigorously assess real-world applicability, we developed and deployed a detection system based on the PySide6 framework on an NVIDIA Jetson Xavier NX edge device. Field testing validated the model’s robustness, high accuracy, and real-time performance, confirming its suitability for integration into harvesting robots operating in practical orchard environments. Full article

Quality control of fresh Lycium barbarum during storage presents significant challenges, particularly regarding the unclear relationship between quality characteristics and storage conditions. This study analyzes the changes in qualitative and structural characteristics, including fruit hardness, soluble solid content (SSC), titratable acidity (TA), and vitamin C (Vc), under various storage conditions (temperature, duration, and initial maturity). We employed optimized Latin hypercubic sampling to develop radial basis function neural networks (RBFNNs) and Elman neural networks to establish predictive models for the quality characteristics of fresh wolfberry. Additionally, we applied the Particle Swarm Optimization (PSO) algorithm to determine the optimal solution for the constructed models. The results indicate a significant variation in how different storage conditions affect the quality characteristics. The established RBFNN predictive model exhibited the highest accuracy for TA and Vc during the storage of fresh wolfberry (R² = 0.99, RMSE = 0.21 for TA; R² = 0.99, RMSE = 0.19 for Vc), while the predictive performance for hardness and SSC was slightly lower (R² = 0.98, RMSE = 385.78 for hardness; R² = 0.94, RMSE = 2.611 for SSC). Multi-objective optimization led to the conclusion that the optimal storage conditions involve harvesting Lycium barbarum fruits at an initial maturity of 60% or greater and storing them for approximately 10 days at a temperature of 10 °C. Under these conditions, the fruit hardness was observed to be 15 N, with SSC at 17.5%, TA at 1.22%, and Vc at 18.5 mg/100 g. The validity of the prediction model was confirmed through multi-batch experimental verification. This study provides theoretical insights for predicting nutritional quality and informing storage condition decisions for other fresh fruits, including wolfberries. Full article

Precise recognition of individual ovine specimens plays a pivotal role in implementing smart agricultural platforms and optimizing herd management systems. With the development of deep learning technology, sheep face recognition provides an efficient and contactless solution for individual sheep identification. However, with the growth of sheep, their facial features keep changing, which poses challenges for existing sheep face recognition models to maintain accuracy across the dynamic changes in facial features over time, making it difficult to meet practical needs. To address this limitation, we propose the lifelong biometric learning of the sheep face network (LBL-SheepNet), a feature decoupling network designed for continuous adaptation to ovine facial changes, and constructed a dataset of 31,200 images from 55 sheep tracked monthly from 1 to 12 months of age. The LBL-SheepNet model addresses dynamic variations in facial features during sheep growth through a multi-module architectural framework. Firstly, a Squeeze-and-Excitation (SE) module enhances discriminative feature representation through adaptive channel-wise recalibration. Then, a nonlinear feature decoupling module employs a hybrid channel-batch attention mechanism to separate age-related features from identity-specific characteristics. Finally, a correlation analysis module utilizes adversarial learning to suppress age-biased feature interference, ensuring focus on age-invariant identifiers. Experimental results demonstrate that LBL-SheepNet achieves 95.5% identification accuracy and 95.3% average precision on the sheep face dataset. This study introduces a lifelong biometric learning (LBL) mechanism to mitigate recognition accuracy degradation caused by dynamic facial feature variations in growing sheep. By designing a feature decoupling network integrated with adversarial age-invariant learning, the proposed method addresses the performance limitations of existing models in long-term individual identification. Full article

This work aims to solve the problem of product quality fluctuations caused by batch-to-batch variations in the adsorption capacity of activated carbon during the production of traditional Chinese medicine (TCM) injections. In this work, Shenqi Fuzheng injection was selected as an example. Diluted Shenqi Extract (DSE), an intermediate in the production process of Shenqi Fuzheng injection, was adsorbed with different batches of activated carbon. The adsorption capacities of adenine, adenosine, calycosin-7-glucoside, and astragaloside IV in DSE were selected as evaluation indices for activated carbon absorption. Characterization methods such as nitrogen adsorption, X-ray photoelectron spectrum (XPS), and Fourier transform infrared (FTIR) were chosen to explore the quantitative relationships between the properties of activated carbon (i.e., specific surface area, pore volume, surface elements, and spectrum) and the adsorption capacities of these four components. It was found that the characteristic wavelengths from FTIR characterization, i.e., 1560 cm⁻¹, 2325 cm⁻¹, 3050 cm⁻¹, and 3442 cm⁻¹, etc., showed the strongest correlation with the adsorption capacities of these four components. Prediction models based on the transmittance at characteristic wavelengths were successfully established via multiple linear regression. In validation experiments of models, the relative errors of predicted adsorption capacities of activated carbon were mostly within 5%, indicating good predictive ability of the models. The results of this work suggest that the prediction method of adsorption capacity based on the mid-infrared spectrum can provide a new way for the quality control of activated carbon. Full article

Background/Objectives: Chemically or genetically detoxified pertussis toxin (PTx) is a crucial antigen component of the acellular pertussis vaccine. Chemical detoxification using glutaraldehyde generally causes significant structural changes to the toxin. However, how these structural changes in PTx affect its antigenic properties remains unclear. Additionally, there is limited knowledge regarding how many alterations in antigenic properties impact immunogenicity. Methods: To investigate the impact of structural changes on antigenic properties, we developed a sandwich ELISA to quantify the neutralizing epitopes on PTx. Subsequently, we analyzed different PTx toxoid (PTd) preparations with the assay. Additionally, we assessed the immunogenicity of various acellular pertussis vaccine candidates containing these PTd preparations. Finally, the assay was applied to evaluate the consistency of commercial batches of PTx and PTd intermediates. Results: The assay demonstrated reasonable specificity, accuracy, and precision, and it was sensitive enough to quantify variations in neutralizing epitopes among different PTd samples that shared the same protein concentration. Importantly, we found a positive correlation between the number of neutralizing epitopes in detoxified PTx and its immunogenicity, indicating that the amount of neutralizing epitopes present determines the immunogenicity of glutaraldehyde-inactivated PTx. Moreover, commercial batches of PTx and PTd intermediates exhibited minor variations in neutralizing epitopes. Conclusions: These findings have significant implications for developing acellular pertussis vaccines as they highlight the importance of preserving the neutralizing epitopes of PTx during detoxification to ensure the vaccine’s effectiveness. This assay is also valuable for the quality control of PTd as it more accurately represents the actual antigenic changes of PTx. Full article