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Objective: To investigate the effects of different thermal reaction conditions (temperature, pH, reaction time and substrate molar ratio) on the simultaneous generation pattern of 3 important dicarbonyl compounds (DCs) and the corresponding flavor substances in the glucose-alanine model system. Methods: The contents of glyoxal (GO), methylglyoxal (MGO) and 3-deoxyglucuronide (3-DG) in the glucose-alanine system were determined synchronously using o-phenylenediamine derivatization coupled with HPLC, and 5 types of important flavor compounds in the system were determined using SPME-GC-MS. Results: Increasing temperature promoted the production of DCs. Alkaline conditions were unfavorable to 3-DG but favorable to GO and MGO. Increasing the proportion of reducing sugars favored the production of 3-DG, while increasing the proportion of amino acids favored the production of GO and MGO. Conclusion: During the thermal processing of sweet and aromatic foods, the MGO in the system should be focused on. When adjusting the processing technology to control the generation of DCs or the conversion to hazardous substances, it was necessary to focus on the impact on ketone and pyrazine flavor substances.

Abstract In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

The Ehlers-Danlos syndromes (EDS) are a group of rare hereditary connective tissue disorders characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. The clinical and genetic hetero- geneity of EDS frequently leads to underdiagnosis and misdiagnosis. Genetic testing is an essential approach to clarify the underlying diagnosis. Recent research has preliminarily established genotype-phenotype correlations and introduced the novel concept of " disease spectrum" in some subtypes. These studies deepen our understanding of EDS etiology and provide important insights into clinical management. Published in 2023, the Chinese Guidelines for Diagnosis and Treatment of the Ehlers-Danlos Syndromes(the Guidelines) recommend performing genetic testing with deep phenotyping for patients who meet the clinical diagnostic criteria or are suspected of having EDS. However, it should be noted that the clinical diagnosis might differ from the molecular diagnosis. Furthermore, cutting-edge approaches such as periodic data reanalysis, integration of RNA sequencing into family-based whole-genome sequencing, and third-generation sequencing may facilitate the reclassification of variants of uncertain significance or resolve undiagnosed cases. This article summarizes recent progress in the genetics research of EDS, with the hope of offering a valuable resource for clinical diagnosis, treatment and scientific research to optimize the quality of life of patients with EDS.

Abstract Proprotein convertase subtilisin/kexin type 9 (PCSK9) has emerged as a promising therapeutic target to reduce lipids. In 2020, we reported a chimeric camelid-human heavy chain antibody VHH-B11-Fc targeting PCSK9. Recently, it was verified that VHH-B11 binds one linear epitope in the PCSK9 hinge region. To enhance its druggability, we have developed a novel biparatopic B11-H2-Fc Ab herein. Thereinto, surface plasmon resonance (SPR) confirmed the epitope differences in binding-PCSK9 among VHH-B11, VHH-H2 and the approved Repatha. Additionally, SPR revealed the B11-H2-Fc exhibits an avidity of approximately 0.036 nM for PCSK9, representing a considerable increase compared to VHH-B11-Fc (~ 0.69 nM). Moreover, we found the Repatha and B11-H2-Fc exhibited > 95% PCSK9 inhibition efficiency compared to approximately 48% for the VHH-Fc at 7.4 nM (P 0.05). These findings provide the first evidence of the efficacy of a novel Ab targeting PCSK9 in the field of lipid-lowering drugs.

Ocular inoculation of a clade 2.3.4.4b highly pathogenic avian influenza A(H5N1) virus caused severe and fatal infection in ferrets. Virus was transmitted to ferrets in direct contact. The results highlight the potential capacity of these viruses to cause human disease after either respiratory or ocular exposure.

【Objective】As one of the four major nutrients required by plants, sulfur is a crucial for plant growth and development, playing a pivotal role in enhancing plant resistance against both biotic and abiotic stresses. Sulfate transporters (SULTR) serve as primary regulators in the absorption and transport of sulfate in plants, playing an irreplaceable role in this process. The understanding of the fundamental characteristics and expression patterns of CaSULTR genes could help to provide theoretical support for advancing researches on SULTR in peppers.【Method】Based on the pepper genome, members of the epper CaSULTR gene family were identified at the whole-genome level. A comprehensive analysis of their physicochemical properties, conserved Motifs and gene structures was conducted. By utilizing publicly available transcriptome data and qPCR technology, the expression patterns of CaSULTR genes under various hormone stress conditions were analyzed. Additionally, differentially expressed genes were cloned for further investigation.【Result】A total of 13 CaSULTR genes were identified and designated as CaSULTR1;1~1;3, CaSULTR2;1~2;2, CaSULTR3;1~3;7 and CaSULTR4;1. These genes were distributed on seven chromosomes in pepper, with amino acid lengths ranging from 641 to 947 aa, molecular weights from 69.65 to 104.36 kD, and isoelectric points between 8.42 to 9.62. Characterized by the hydrophobicity, CaSULTR proteins were classified into four subgroups (Ⅰ-Ⅳ), with subgroup Ⅲ containing the highest number of members. Within the same subgroup, members exhibited relatively small differences in physicochemical properties, and Motifs and gene structures were relatively conserved. With the exception of CaSULTR2;1 and CaSULTR2;2, all members possessed Motif 5. Tissue expression analysis indicated that CaSULTR genes may be involved in the development of organs such as leaves, fruits and flowers, with CaSULTR4;1 exhibiting expression in leaves, roots and stems. Expression profile analysis under hormone stress, coupled with qPCR results, revealed significant up-regulation of CaSULTR1;1 and CaSULTR3;4 under hormone induction.【Conclusion】The expression patterns of CaSULTR genes under hormone stress are identified, suggesting that of CaSULTR1;1 and CaSULTR3;4 may be involved in the regulation of sulfur metabolism by exogenous hormones.

Abstract The distribution of current/voltage can be further regulated by optimising the electrical connection topology, considering a particular battery thermal management systems. This study numerically investigates a 4P6S battery module with two connection topologies: 1) a straight connection topology, where the sub-modules consist of parallel-connected cells that are serial connected in a linear configuration, and 2) a parallelogram connection topology, where the sub-modules are serial connected in a parallelogram configuration. We find that the straight topology is more advantageous, as it allows the temperature gradient to be distributed among the parallel-connected cells in the sub-modules, mitigating over(dis)charging. Consequently, it achieves a 0.8% higher effective capacity than the parallelogram topology at 1C discharge, along with a higher state of health at 80.15% compared to 80% for the parallelogram topology. Notably, the straight topology results in a maximum current maldistribution of 0.24C at 1C discharge, which is considered an acceptable trade-off.

Owing to the integration of energy digitization and artificial intelligence technology, smart energy grids can realize the stable, efficient and clean operation of power systems. However, the emergence of cyber-physical attacks, such as dynamic load-altering attacks (DLAAs) has introduced great challenges to the security of smart energy grids. Thus, this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids. The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer. First, a data-driven method was proposed to predict the DLAA sequence in the cyber layer. By designing a double radial basis function network, the influence of disturbances on attack prediction can be eliminated. Based on the prediction results, an unknown input observer-based detection and localization method was further developed for the physical layer. In addition, an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs. Consequently, through the collaborative work of the cyber-physics layer, injected DLAAs were effectively detected and located. Compared with existing methodologies, the simulation results on IEEE 14-bus and 118- bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

Traffic data imputation is crucial for the reliability and efficiency of intelligent transportation systems (ITSs), forming the foundation for downstream tasks like traffic prediction and management. However, existing deep learning-based imputation methods struggle with two significant challenges: poor performance under high missing data rates and the limited incorporation of external traffic-related factors. To address these challenges, we propose a novel knowledge graph-enhanced generative adversarial network (KG-GAN) for traffic data imputation. Our approach uniquely integrates external knowledge with traffic spatiotemporal dependencies to improve data imputation quality. Specifically, we construct a fine-grained knowledge graph (KG) that differentiates attributes and relationships of external factors such as points of interest (POI) and weather conditions, facilitating more robust knowledge representation learning. We then introduce a knowledge-aware embedding cell (EM-cell) that merges traffic data with these learned external representations, providing richer inputs for the spatiotemporal GAN. Extensive experiments on a large-scale real-world traffic dataset demonstrate that KG-GAN significantly outperforms state-of-the-art methods under various missing data scenarios. Additionally, ablation studies confirm the superior performance gained from incorporating external knowledge, underscoring the importance of this approach in addressing complex missing data patterns.

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

PurposeTo develop a predictive model for orthokeratology (Ortho-K) lens decentration 1 month after wear.MethodsThis study included myopic children who were fitted with Ortho-K lenses at Fujian Provincial Hospital between December 2022 and May 2024. Corneal topography parameters and other relevant metrics were collected pre- and post-treatment. Feature selection was conducted using univariate logistic regression and Lasso regression analysis. A machine learning approach was used to develop multiple predictive models, including Decision Tree, Logistic Regression, Multilayer Perceptron, Random Forest, and Support Vector Machine. Model performance was evaluated using accuracy, sensitivity, specificity, ROC curves, DCA curves, and calibration curves. SHAP values were employed to interpret the models.ResultsThe Logistic Regression model demonstrated the best predictive performance, with an AUC of 0.82 (95% CI: 0.69–0.95), accuracy of 77.59%, sensitivity of 85%, and specificity of 61.11%. The most significant predictors identified were age, 8 mm sag height difference, 5 mm Kx1, and 7 mm Kx2. SHAP analysis confirmed the importance of these features, particularly the 8 mm sag height difference.ConclusionsThe Logistic Regression model successfully predicted the risk of Ortho-K lens decentration using key corneal morphological metrics and age. This model provides valuable support for clinicians in optimizing Ortho-K lens fitting strategies, potentially reducing the risk of adverse outcomes and improving the quality of vision for patients. Further validation in clinical settings is recommended.

BackgroundPostoperative fever frequently indicates surgical complications and is commonly used to evaluate the efficacy of interventions against surgical stress. However, the presence of circadian rhythms in body temperature may compromise the accurate detection of fever. ObjectiveThis study aimed to investigate the detection rate of fever under intermittent measurement. MethodsWe retrospectively reviewed the clinical records of patients who underwent nonemergency gastrointestinal surgery between November 2020 and April 2021. Patients’ temperature data were continuously collected every 4 seconds using a wireless axillary thermometer, and fever was defined as a temperature exceeding 38 °C within a day. To simulate intermittent measurement in clinical practice, the body temperature at each hour was selected from the continuously collected temperature dataset. Considering that temperatures are measured multiple times per day, all possible measurement plans using intermittent measurement were composed by combining 1-24 time points from the 24-hour daily cycle. Fever was clinically diagnosed based on the temperature readings at the selected time points per day. The fever detection rates for each plan, with varying measurement times, were listed and ranked. ResultsBased on the temperature data continuously collected by the thermometer, fever occurred in 60 (40.8%) of the 147 included patients within 3 days after surgery. Of the measurement plans that included 1-24 measurements daily, the fever detection rates ranged from 3.3% (2/60) to 85% (51/60). The highest detection rates and corresponding timings for measurement plans with 1, 2, 3, and 4 measurements daily were 38.3% (23/60; at 8 PM), 56.7% (34/60; at 3 AM and 7 or 8 PM), 65% (39/60; at 3 AM, 8 PM, and 10 or 11 PM), and 70% (42/60; at 12 AM, 3 AM, 8 PM, and 11 PM), respectively; and the lowest detection rates were 3.3% (2/60), 6.7% (4/60), 6.7% (4/60), and 8.3% (5/60), respectively. Although fever within 3 days after surgery was not correlated with an increased incidence of postoperative complications (5/60, 8.3% vs 6/87, 6.9%; P=.76), it was correlated with a longer hospital stay (median 7, IQR 6-9 days vs median 6, IQR 5-7 days; P

ABSTRACT Porcine reproductive and respiratory syndrome viruses (PRRSVs) exhibit high mutability and recombination, posing challenges to their immunization and control. This study isolated two new PRRSV strains, GD-7 and GX-3, from samples collected in Guangdong and Guangxi in 2023. Whole-genome sequencing, along with phylogenetic and recombination analyses, confirmed that GD-7 and GX-3 are natural novel recombinant strains of NADC30 PRRSV. Moreover, we established a pathogenicity model for piglets and sows based on the two isolates. The results of piglet pathogenicity revealed that both GD-7 and GX-3 caused clinical symptoms such as fever, loss of appetite, depression, and slow weight gain. Moreover, we observed that the mortality rate of GD-7-inoculated group piglets was 33.3%, which was similar to that of piglets infected with other highly pathogenic PRRSV strains and exceeded the mortality rate of most NADC30-like PRRSV. In pregnant sow models, the survival rate of sows in the GD-7 group was 75%, in contrast to the GX-3 group, where no sow mortality was observed, and both strains resulted in abortion, mummified fetuses, and stillbirths. These results highlight the elevated pathogenicity of these recombinant strains in sows, with GD-7 mainly causing sows to abort, and GX-3 mainly causing sows to give birth to mummified fetuses. This study introduces two distinct clinical recombinant PRRSV strains that differ from the prevalent strains in China. This research furthers our understanding of the epidemiology of PRRSV and underscores the significance of ongoing monitoring and research in the face of evolving virus strains. Moreover, these discoveries act as early warnings, underscoring the necessity for active control and immunization against PRRSV.IMPORTANCESince the discovery of NADC30-like PRRSV in China in 2013, it has gradually become the dominant strain of PRRSV in China. NADC30-like PRRSV exhibits high recombination characteristics, constantly recombining with different strains, leading to the emergence of numerous novel strains. Of particular importance is the observation that NADC30-like PRRSV with different recombination patterns exhibits varying pathogenicity, which has a significant impact on the pig farming industry. This emphasizes the necessity of monitoring and responding to evolving PRRSV strains to develop effective immunization and control strategies. In this paper, we conducted pathogenicity studies on the isolated NADC30-like PRRSV and analyzed the differences in the genomes and pathogenicity of the different strains by recording clinical symptoms, temperature changes, detoxification tests, and changes in viremia and histopathology in infected pigs. This was done to provide a theoretical basis for the epidemiological situation and epidemic prevention and control of PRRSV.

Hepatic diseases pose a significant threat to community health, impacting the quality of life and longevity of millions worldwide. Despite revolutionary advancements in treatment, liver diseases remain a pressing issue, necessitating the development of more effective therapeutic approaches. Here, we conducted a comprehensive multi-omics analysis to investigate the underlying mechanism of Swertiamarin in alleviating hepatic injuries induced by CCl4 in mice. We divided 100 Kunming mice into five groups: RC (control), RM (CCl4), RD (15 mg/Kg Swertiamarin), RZ (30 mg/Kg Swertiamarin), and RG (60 mg/Kg Swertiamarin). Animals in groups RD, RZ, and RG received daily Swertiamarin via gavage, while those in groups RM, RD, RZ, and RG were treated with CCl4 solution intraperitoneally every four days, nine times in total. Our findings revealed that mice in the RM group exhibited slightly lower average weights compared to other groups, along with significantly higher liver weight (p