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Abstract Ligand-receptor interaction (LRI) prediction has great significance in biological and medical research and facilitates to infer and analyze cell-to-cell communication. However, wet experiments for new LRI discovery are costly and time-consuming. Here, we propose a computational model called THGB to uncover new LRIs. THGB first extracts feature information of Ligand-Receptor (LR) pairs using iFeature. Next, it adopts a tree boosting model to obtain representative LR features. Finally, it devises the histogram-based gradient boosting model to capture high-quality LRIs. To assess the THGB performance, we compared it with three new LRI prediction models (i.e., CellEnBoost, CellGiQ, and CellComNet) and one classical protein-protein interaction inference model PIPR. The results demonstrated that THGB achieved the best overall predictions in terms of six evaluation indictors (i.e., precision, recall, accuracy, F1-score, AUC, and AUPR). To measure the effect of LR feature selection on the prediction, THGB was compared with four feature selection methods (i.e., PCA, NMF, LLE, and TSVD). The results showed that the tree boosting model was more appropriate to select representative LR features and improve LRI prediction. We also conducted ablation study and found that THGB with feature selection outperformed THGB without feature selection. We hope that THGB is a useful tool to find new LRIs and further infer cell-to-cell communication.

Abstract In this paper, we study the discovery potential of a Vector-Like B quark (VLB) via the process $$pp \rightarrow B(\rightarrow bZ)j\rightarrow b(Z \rightarrow \nu _l\bar{\nu _l})j$$ p p → B ( → b Z ) j → b ( Z → ν l ν l ¯ ) j at the Large Hadron Collider (LHC) with $$\sqrt{s}=14$$ s = 14 TeV. In the framework of a simplified model, we perform a scan over its parameter space and test its viability following a Monte Carlo analysis developed to include all production and decay dynamics. We use cut-and-count combined with Extreme Gradient Boosting (XGBoost) methods to classify the signal and background events in order to improve the efficiency of signal identification and background rejection. We find that this approach can reduce background events significantly while the signal retention rate is much higher than that of traditional methods, thereby improving the VLB discovery potential. We then calculate the exclusion and discovery capabilities for VLBs and find that the advantages of the cut-and-count plus XGBoost method especially lie in the high-mass region, i.e., $$m_B > 1500 \text { GeV}$$ m B > 1500 GeV . We finally obtain the following LHC results in terms of the coupling and chiral structure of a singlet heavy VLB interactions: (i) for $$g^{*}=0.2$$ g ∗ = 0.2 and $$R_L=0$$ R L = 0 with 3000 fb $$^{-1}$$ - 1 , the B quark mass can be be excluded (discovered) up to 3000 GeV (2500 GeV); (ii) for $$g^{*}=0.2$$ g ∗ = 0.2 and $$R_L=0.5$$ R L = 0.5 with 3000 fb $$^{-1}$$ - 1 , the exclusion (discovery) region can reach up to 4750 GeV (4250 GeV).

Background Chinese resident physicians confront challenges such as staff shortages and heavy workloads, leading to a heightened prevalence of mental distress. This study aims to investigate the symptom network of stress, burnout, anxiety, depression (SBAD) and adverse personal outcomes in this cohort.Methods From October 2020 to April 2022, 994 physicians were recruited across China through snowball sampling. Stress, burnout, anxiety and depression were assessed using the 10-item Perceived Stress Scale, a two-item burnout questionnaire, Generalized Anxiety Disorder Scale-7 and the Patient Health Questionnaire-9, respectively. Data on adverse personal outcomes (low quality of life [QOL] and suicidal ideation) were collected. We constructed and visualized two networks, calculating expected influence (EI) and bridge EI indices to identify central and bridge symptoms.Results In the SBAD network, perceived helplessness was the most central and critical bridge symptom connecting stress and mental distress, with emotional exhaustion identified as the secondary bridge symptom. Perceived helplessness, perceived self-efficacy and emotional exhaustion exhibited the highest negative correlations with QOL. Worthless (PHQ6) and motor (PHQ8) symptoms were strongly correlated with suicidal ideation. The estimated SBAD network showed excellent stability and accuracy.Conclusions Our study emphasizes that perceived helplessness may be a high-priority target for preventing and intervening in mental distress and improving QOL among residents. Burnout transcends workplace problems and is widely connected to depression, anxiety and QOL. Implementing early detection and intervention measures at three levels—the individual physician, health system and professional colleges, and external regulators—is crucial for preventing and alleviating stress and mental distress among residents.

Abstract To evaluate the influence distance and evolution law of the jet blast under the full thrust state of the aircraft engine, we constructed a refined single-engine nozzle model in this study. Using a specific B737-800 engine as an illustrative example, this model takes into account both the internal combustion of the engine and the shearing effect of the tail cone on the airflow; based on a structured grid, the grid and far-field independence of the numerical solution were verified. The numerical simulation results obtained via the Fluent software were compared with the data outlined in the Boeing aircraft characteristics manual for a thorough analysis. Our results indicated that the calculated jet influence distance from the refined nozzle model aligns more closely with the data in the aircraft characteristics manual, albeit with superior accuracy compared to the simplified nozzle model employed in previous studies. The findings of this study can serve as a valuable reference for computing jet influence distances across various aircraft types and engine models, providing data support for the study of safety intervals for aircraft crossing the runway behind the takeoff point.

Among the largest transcription factor families in plants, bZIPs are crucial for various developmental and physiological processes, particularly abiotic stress resistance. Setaria italica has become a model for understanding stress resistance mechanisms. In this study, we identified 90 bZIP transcription factors in the Setaria italica genome. SibZIPs were classified into 13 groups based on references to Arabidopsis bZIPs. Members in the same group shared similar motifs and gene structure pattern. In addition, gene duplication analysis indenfied 37 pairs of segmental duplicated genes and none tandem duplicated genes in S. italica suggesting segmental duplication contributed to the expansion of the S. italica bZIP gene family. Moreover, the number of SibZIPs genes (39) exhibiting higher expression in roots was significantly more than that in other organs. Twelve SibZIP genes were upregulated in response to dehydration stress. In conclusion, our study advances the current understanding of SibZIP genes and provide a number of candidates for functional analysis of drought tolerance in S. italica.

Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.

With an increasing emphasis on energy conservation, emission reduction, and power consumption management, port enterprises are focusing on enhancing their electricity load forecasting capabilities. Accurate electricity load forecasting is crucial for understanding power usage and optimizing energy allocation. This study introduces a novel approach that transcends the limitations of single prediction models by employing a Binary Fusion Weight Determination Method (BFWDM) to optimize and integrate three distinct prediction models: Temporal Pattern Attention Long Short-Term Memory (TPA-LSTM), Multi-Quantile Recurrent Neural Network (MQ-RNN), and Deep Factors. We propose a two-phase process for constructing an optimal combined forecasting model for port power load prediction. In the initial phase, individual prediction models generate preliminary outcomes. In the subsequent phase, these preliminary predictions are used to construct a combination forecasting model based on the BFWDM. The efficacy of the proposed model is validated using two actual port data, demonstrating high prediction accuracy with a Mean Absolute Percentage Error (MAPE) of only 6.23% and 7.94%. This approach not only enhances the prediction accuracy but also improves the adaptability and stability of the model compared to other existing models.

Carbon trading prices are crucial for carbon emissions and transparent carbon market pricing. Previous studies mainly focused on data mining in the prediction direction to quantify carbon trading prices. Although the prospect of high-frequency data forecasting mechanisms is considerable, more mixed-frequency ensemble forecasting is needed for carbon trading prices. Therefore, this article designs a new type of ensemble prediction model to increase the scope of model research. The module is divided into three parts: data denoising, mixed frequency and machine learning, multi-objective optimization, and ensemble forecasting. Precisely, the data preprocessing technology enhanced by adopting a self-attention mechanism can better remove noise and extract effective features. Furthermore, mixed frequency technology is introduced into the machine learning model to achieve more comprehensive and efficient prediction, and a new evaluation criterion is proposed to measure the optimal submodel. Finally, the ensemble model based on deep learning strategy can effectively integrate the advantages of high-frequency and low-frequency data in complex datasets. At the same time, a new multi-objective optimization algorithm is proposed to optimize the parameters of the ensemble model, significantly improving the predictive ability of the integrated module. The results of four experiments and the Mean Absolute Percent Error index of the proposed model improved by 28.3526% compared to machine learning models, indicating that the ensemble model established can effectively address the time distribution characteristics and uncertainty issues predicted by carbon trading price models, which helps to mitigate climate change and develop a low-carbon economy.

Abstract Background Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA–disease associations predicted by computational methods are the best complement to biological experiments. Results In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA–disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA–disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA–disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP. Conclusion A new computational model KATZNCP was proposed for predicting potential miRNA–drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA–disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

To comprehend the etiology and pathogenesis of many illnesses, it is essential to identify disease-associated microRNAs (miRNAs). However, there are a number of challenges with current computational approaches, such as the lack of "negative samples", that is, confirmed irrelevant miRNA-disease pairs, and the poor performance in terms of predicting miRNAs related with "isolated diseases", i.e. illnesses with no known associated miRNAs, which presents the need for novel computational methods. In this study, for the purpose of predicting the connection between disease and miRNA, an inductive matrix completion model was designed, referred to as IMC-MDA. In the model of IMC-MDA, for each miRNA-disease pair, the predicted marks are calculated by combining the known miRNA-disease connection with the integrated disease similarities and miRNA similarities. Based on LOOCV, IMC-MDA had an AUC of 0.8034, which shows better performance than previous methods. Furthermore, experiments have validated the prediction of disease-related miRNAs for three major human diseases: colon cancer, kidney cancer, and lung cancer.

ABSTRACT Replication of influenza A virus (IAV) is highly reliant on host cell function, and to identify the key host factor required in the influenza A virus life cycle, a genome-wide CRISPR/Cas9 knockout (KO) screen was conducted in A549 cells infected by H1N1 influenza virus. The results showed that glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (GNE) plays a crucial role in the replication of influenza A virus, and knockout of the GNE significantly reduced the replication of multiple subtype influenza A viruses in A549 cells, and restoration of the GNE expression in the GNE-knockout cells partially recovered IAV infection. Additionally, overexpression of the GNE in wild-type (WT) cells promoted IAV infection to a certain extent. Further study showed that the GNE is involved in α-2,3- and α-2,6-linked sialic acid (Sia) synthesis, which is the major receptor of the influenza A virus, and the GNE knockout downregulated the α-2,3- and α-2,6-linked sialic acid expression. In summary, the knockout of the GNE inhibits adsorption and endocytosis of IAV. This study provides a new approach to elucidate the replication mechanism of IAV and identifies GNE as a potential target for anti-influenza drug development. IMPORTANCE Influenza A virus infection requires the assistance of the host proteins. Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase (GNE) was identified as an important host factor in influenza A virus infection by genome-wide CRISPR screening. GNE knockout (KO) extensively inhibited the replication of multiple subtype influenza A viruses. It indirectly participated in IAV adsorption and endocytosis by regulating the expression of sialic acid (Sia), the main receptor of IAV. This finding provides novel insights into the replication mechanism of IAV.

ABSTRACT: DNA vaccination has great potential to treat or prevent avian influenza pandemics, but the technique may be limited by low immunogenicity and gene delivery in clinical testing. Here, to improve the immune efficacy of DNA vaccines against avian influenza, we prepared and tested the immunogenicity of 4 recombinant DNA vaccines containing 2 or 3 AIV antigens. The results revealed that chickens and mice immunized with plasmid DNA containing 3 antigens (HA gene from H9N2, and NA and HA genes from H5N1) exhibited a robust immune response than chickens and mice immunized with plasmid DNAs containing 2 antigenic genes. Subsequently, this study used pβH9N1SH5 as a model antigen to study the effect of dendritic polylysine (DGL) nanoparticles as a gene delivery system and adjuvant on antigen-specific immunity in mice models. At a ratio of 1:3 DGL/pβH9N1SH5 (w/w), the pβH9N1SH5/DGL NPs showed excellent physical and chemical properties, induced higher levels of HI antibodies, and larger CD3+/CD4+ T lymphocyte and CD3+/CD8+ T lymphocyte population, as well as the production of cytokines, namely, interferon (IFN)-γ, interleukin (IL)-2 compared with the naked pβH9N1SH5. Therefore, multiantigen gene expression methods using DGL as a delivery system may have broad application prospects in gene therapy.

Effective crude oil price forecasting is essential for energy supply stabilization, investment decisions, policy formulation, and economic impact assessment. However, previous studies of crude oil futures price forecasting either consider only a single variable without adequately accounting for the influence of various factors on crude oil prices, or they consider only the same frequency of influences and ignore the importance of mixed frequencies, resulting in forecasts that do not achieve the desired results. To overcome these problems, this paper proposes a new multivariate combinatorial mixed frequency forecasting system to predict the weekly closing price of crude oil futures. The system is divided into four modules: Data denoising, Feature selection, Combined forecasting, and Performance evaluation module. To obtain smooth data, ICEEMDAN is used to denoise the original data. Furthermore, to select appropriate variables and reduce model redundancy, recursive feature elimination is used to select appropriate variables with low frequency. Considering the importance of mixed-frequency data, the mutual information method was used to select appropriate high-frequency variables for modeling the crude oil price forecast model. To overcome the shortcomings of Back Propagation Neural Network, Gate Recurrent Unit, and Radial Basis Function Neural Network models, integrate their advantages, and obtain accurate and stable prediction results, a combined forecasting mechanism based on a Multi-Objective Sparrow Search algorithm was developed to obtain both point and interval forecasting results, and finally, two data sets were selected for empirical analysis. The results show that the mean absolute percentage errors of the point forecast of this model are 1.96% and 1.84%, respectively, about 31% and 15% higher than those of the competing models (mean absolute percentage errors 2.57% and 2.13%, respectively). For interval forecasting, the accumulated width deviation is 0.0037 and 0.002, respectively, about 35% and 25% higher than those of the competing models (accumulated width deviation 0.005 and 0.0025, respectively). Thus, the proposed forecasting framework outperforms all comparative models and can be used effectively for forecasting crude oil prices.

BackgroundMicrobes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe–disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.MethodsWe developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe–disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.ResultsGPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.ConclusionThe proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases.Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA–disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma.Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA‐disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross‐validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma.Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.

Since its outbreak in 2010, Tembusu virus (TMUV) has spread widely throughout China and Southeast Asia, causing significant economic losses to the poultry industry. In 2018, an attenuated vaccine called FX2010-180P (180P) was licensed for use in China. The 180P vaccine has demonstrated its immunogenicity and safety in mice and ducks. The potential use of 180P as a backbone for flavivirus vaccine development was explored by replacing the pre-membrane (prM) and envelope (E) genes of the 180P vaccine strain with those of Japanese encephalitis virus (JEV). Two chimeric viruses, 180P/JEV-prM-E and 180P/JEV-prM-ES156P with an additional E protein S156P mutation were successfully rescued and characterized. Growth kinetics studies showed that the two chimeric viruses replicated to similar titers as the parental 180P virus in cells. Animal studies also revealed that the virulence and neuroinvasiveness of the 180P/JEV-prM-E chimeric virus was decreased in mice inoculated intracerebrally (i.c.) and intranasally (i.n.), respectively, compared to the wild-type JEV strain. However, the chimeric 180P/JEV-prM-E virus was still more virulent than the parent 180P vaccine in mice. Additionally, the introduction of a single ES156P mutation in the chimeric virus 180P/JEV-prM-ES156P further attenuated the virus, which provided complete protection against challenge with a virulent JEV strain in the mouse model. These results indicated that the FX2010-180P could be used as a promising backbone for flavivirus vaccine development.

IntroductionDrug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.MethodsIn this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest.ResultsEnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation.DiscussionWe anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

Abstract The H9N2 subtype avian influenza virus (AIV) inactivated vaccine has been used extensively in poultry farms, but it often fails to stimulate a sufficiently high immune response in poultry in the field, although it works well in laboratory experiments; hence, the virus still causes economic damage every year and poses a potential threat to public health. Based on surveillance data collected in the field, we found that broilers with high levels of maternal-derived antibodies (MDAs) against H9N2 virus did not produce high levels of antibodies after vaccination with a commercial H9N2 inactivated vaccine. In contrast, specific pathogen-free (SPF) chickens without MDAs responded efficiently to that vaccination. When MDAs were mimicked by administering passively transferred antibodies (PTAs) into SPF chickens in the laboratory, similar results were observed: H9N2-specific PTAs inhibited humoral immunity against the H9N2 inactivated vaccine, suggesting that H9N2-specific MDAs might hinder the generation of antibodies when H9N2 inactivated vaccine was used. After challenge with homologous H9N2 virus, the virus was detected in oropharyngeal swabs of the vaccinated and unvaccinated chickens with PTAs but not in the vaccinated chickens without PTAs, indicating that H9N2-specific MDAs were indeed one of the reasons for H9N2 inactivated vaccine failure in the field. When different titers of PTAs were used to mimic MDAs in SPF chickens, high (HI = 12 log2) and medium (HI = log 9 log2) titers of PTAs reduced the generation of H9N2-specific antibodies after the first vaccination, but a booster dose would induce a high and faster humoral immune response even of PTA interference. This study strongly suggested that high or medium titers of MDAs might explain H9N2 inactivated vaccine failure in the field.

Outbreaks of Tembusu virus (TMUV) infection have caused huge economic losses to the poultry industry in China since 2010. However, the potential threat of TMUV to mammals has not been well studied. In this study, a TMUV HB strain isolated from diseased ducks showed high virulence in BALB/c mice inoculated intranasally compared with the reference duck TMUV strain. Further studies revealed that the olfactory epithelium is one pathway for the TMUV HB strain to invade the central nervous system of mice. Genetic analysis revealed that the TMUV HB virus contains two unique residues in E and NS3 proteins (326K and 519T) compared with duck TMUV reference strains. K326E substitution weakens the neuroinvasiveness and neurovirulence of TMUV HB in mice. Remarkably, the TMUV HB strain induced significantly higher levels of IL-1β, IL-6, IL-8, and interferon (IFN)-α/β than mutant virus with K326E substitution in the brain tissue of the infected mice, which suggested that TMUV HB caused more severe inflammation in the mouse brains. Moreover, application of IFN-β to infected mouse brain exacerbated the disease, indicating that overstimulated IFN response in the brain is harmful to mice upon TMUV infection. Further studies showed that TMUV HB upregulated RIG-I and IRF7 more significantly than mutant virus containing the K326E mutation in mouse brain, which suggested that HB stimulated the IFN response through the RIG-I-IRF7 pathway. Our findings provide insights into the pathogenesis and potential risk of TMUV to mammals.

Abstract Background Reassortment between human and avian influenza viruses (AIV) may result in novel viruses with new characteristics that may threaten human health when causing the next flu pandemic. A particular risk may be posed by avian influenza viruses of subtype H9N2 that are currently massively circulating in domestic poultry in Asia and have been shown to infect humans. In this study, we investigate the characteristics and compatibility of a human H1N1 virus with avian H9N2 derived genes. Methods The polymerase activity of the viral ribonucleoprotein (RNP) complex as combinations of polymerase-related gene segments derived from different reassortment events was tested in luciferase reporter assays. Reassortant viruses were generated by reverse genetics. Gene segments of the human WSN-H1N1 virus (A/WSN/1933) were replaced by gene segments of the avian A2093-H9N2 virus (A/chicken/Jiangsu/A2093/2011), which were both the Hemagglutinin (HA) and Neuraminidase (NA) gene segments in combination with one of the genes involved in the RNP complex (either PB2, PB1, PA or NP). The growth kinetics and virulence of reassortant viruses were tested on cell lines and mice. The reassortant viruses were then passaged for five generations in MDCK cells and mice lungs. The HA gene of progeny viruses from different passaging paths was analyzed using Next-Generation Sequencing (NGS). Results We discovered that the avian PB1 gene of H9N2 increased the polymerase activity of the RNP complex in backbone of H1N1. Reassortant viruses were able to replicate in MDCK and DF1 cells and mice. Analysis of the NGS data showed a higher substitution rate for the PB1-reassortant virus. In particular, for the PB1-reassortant virus, increased virulence for mice was measured by increased body weight loss after infection in mice. Conclusions The higher polymerase activity and increased mutation frequency measured for the PB1-reassortant virus suggests that the avian PB1 gene of H9N2 may drive the evolution and adaptation of reassortant viruses to the human host. This study provides novel insights in the characteristics of viruses that may arise by reassortment of human and avian influenza viruses. Surveillance for infections with H9N2 viruses and the emergence of the reassortant viruses in humans is important for pandemic preparedness.

Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status. The conventional destructive methods, although reliable, demand extensive laboratory work for measuring various traits. On the other hand, nondestructive techniques, while efficient and adaptable, often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure. Striking a delicate balance between efficiency and accuracy, we have developed the Bio-Master phenotyping system. This system is capable of simultaneously measuring four vital biochemical components of the canopy profile: dry matter, water, chlorophyll, and nitrogen content. Bio-Master initiates the process by addressing structural influences, through segmenting the fresh plant and then further chopping the segment into uniform small pieces. Subsequently, the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber, utilizing an independent light source. The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample. In this study, we established a comprehensive training dataset encompassing a wide range of rice varieties, nitrogen levels, and growth stages. Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master. Leave-one-out validation revealed the model’s capacity to accurately estimate these contents at both leaf and plant scales. With Bio-Master, measuring a single rice plant takes approximately only 5 min, yielding around 10 values for each of the four biochemical components across the vertical profile. Furthermore, the Bio-Master system allows for immediate measurements near the field, mitigating potential alterations in plant status during transportation and processing. As a result, our measurements are more likely to faithfully represent in situ values. To summarize, the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling. It harnesses the benefits of remote sensing techniques, providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.

Although vaccines have been widely used for many years, they have failed to control H9N2 avian influenza virus (AIV) in the field in China. The high level of maternal-derived antibodies (MDAs) against H9N2 virus contributes to the H9N2 influenza vaccine failure in poultry. The study aimed to generate a new vaccine to overcome MDAs interference in H9N2 vaccination in chickens. We used turkey herpesvirus (HVT) as a vaccine vector to express H9 hemagglutinin (HA) proteins. The recombinant HVT expressing H9 HA proteins (rHVT-H9) was successfully generated and characterized in primary chicken embryonic fibroblasts (CEFs). Western blot and indirect immunofluorescence assay (IFA) showed that the rHVT-H9 consistently expressed HA proteins. In addition, the rHVT-H9 had similar growth kinetics to the parent HVT. Preliminary animal experiments showed that compared to the conventional inactivated whole virus (IWV) vaccine, the rHVT-H9 stimulated robust humoral immunity in chickens with passively transferred antibodies (PTAs) that were used to mimic MDAs. Transmission experiments showed that the rHVT-H9 induced both humoral and cellular immunity in chickens with PTAs. Furthermore, we used mathematical models to quantify the vaccine’s efficacy in preventing the transmission of H9N2 AIV. The results showed that the rHVT-H9 reduced the virus shedding period and decreased the reproduction ratio (R) value in chickens with PTAs after homologous challenge. However, the vaccination in this trial did not yet bring R

Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.

BackgroundNurses are at high risk for depression and anxiety symptoms after the outbreak of the COVID-19 pandemic. We aimed to assess the network structure of anxiety and depression symptoms among Chinese nurses in the late stage of this pandemic.MethodA total of 6,183 nurses were recruited across China from Oct 2020 to Apr 2021 through snowball sampling. We used Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder scale-7 (GAD-7) to assess depression and anxiety, respectively. We used the Ising model to estimate the network. The index “expected influence” and “bridge expected influence” were applied to determine the central symptoms and bridge symptoms of the anxiety-depression network. We tested the stability and accuracy of the network via the case-dropping procedure and non-parametric bootstrapping procedure.ResultThe network had excellent stability and accuracy. Central symptoms included “restlessness”, “trouble relaxing”, “sad mood”, and “uncontrollable worry”. “Restlessness”, “nervous”, and “suicidal thoughts” served as bridge symptoms.ConclusionRestlessness emerged as the strongest central and bridge symptom in the anxiety-depression network of nurses. Intervention on depression and anxiety symptoms in nurses should prioritize this symptom.

Since the outbreak of COVID-19, hundreds of millions of people have been infected, causing millions of deaths, and resulting in a heavy impact on the daily life of countless people. Accurately identifying patients and taking timely isolation measures are necessary ways to stop the spread of COVID-19. Besides the nucleic acid test, lung CT image detection is also a path to quickly identify COVID-19 patients. In this context, deep learning technology can help radiologists identify COVID-19 patients from CT images rapidly. In this paper, we propose a deep learning ensemble framework called VitCNX which combines Vision Transformer and ConvNeXt for COVID-19 CT image identification. We compared our proposed model VitCNX with EfficientNetV2, DenseNet, ResNet-50, and Swin-Transformer which are state-of-the-art deep learning models in the field of image classification, and two individual models which we used for the ensemble (Vision Transformer and ConvNeXt) in binary and three-classification experiments. In the binary classification experiment, VitCNX achieves the best recall of 0.9907, accuracy of 0.9821, F1-score of 0.9855, AUC of 0.9985, and AUPR of 0.9991, which outperforms the other six models. Equally, in the three-classification experiment, VitCNX computes the best precision of 0.9668, an accuracy of 0.9696, and an F1-score of 0.9631, further demonstrating its excellent image classification capability. We hope our proposed VitCNX model could contribute to the recognition of COVID-19 patients.

To efficiently address the challenge of thermoelastic coupling in functionally graded materials, we propose an approach that combines the radial integral boundary element method (RIBEM) with proper orthogonal decomposition (POD). This integration establishes a swift reduced-order model to transform the high-dimensional system of equations into a more manageable, low-dimensional counterpart. The implementation of this reduced-order model offers the potential for rapid numerical simulations of functionally graded materials (FGMs) under thermal shock loading. Initially, the RIBEM is utilized to resolve the thermal coupling issue within the FGMs. From these solutions, a snapshot matrix is constructed, capturing the solved temperature and displacement fields. Subsequently, the POD modes are established and a POD reduced-order model is constructed for the boundary element format of the thermally coupled problem. Finally, a system of low-order discrete differential equations is solved. Numerical experiments demonstrate that the results obtained from the reduced-order model closely align with those of the full-order model, even when considering variations in structural parameters or impact loads. Thus, the introduction of the reduced-order model not only guarantees solution accuracy but also significantly enhances computational efficiency.

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

BackgroundOlder adults are more susceptible to severe health outcomes for coronavirus disease 2019 (COVID-19). Universal vaccination has become a trend, but there are still doubts and research gaps regarding the COVID-19 vaccination in the elderly. This study aimed to investigate the efficacy, immunogenicity, and safety of COVID-19 vaccines in older people aged ≥ 55 years and their influencing factors.MethodsRandomized controlled trials from inception to April 9, 2022, were systematically searched in PubMed, EMBASE, the Cochrane Library, and Web of Science. We estimated summary relative risk (RR), rates, or standardized mean difference (SMD) with 95% confidence interval (CI) using random-effects meta-analysis. This study was registered with PROSPERO (CRD42022314456).ResultsOf the 32 eligible studies, 9, 21, and 25 were analyzed for efficacy, immunogenicity, and safety, respectively. In older adults, vaccination was efficacious against COVID-19 (79.49%, 95% CI: 60.55−89.34), with excellent seroconversion rate (92.64%, 95% CI: 86.77−96.91) and geometric mean titer (GMT) (SMD 3.56, 95% CI: 2.80−4.31) of neutralizing antibodies, and provided a significant protection rate against severe disease (87.01%, 50.80−96.57). Subgroup and meta-regression analyses consistently found vaccine types and the number of doses to be primary influencing factors for efficacy and immunogenicity. Specifically, mRNA vaccines showed the best efficacy (90.72%, 95% CI: 86.82−93.46), consistent with its highest seroconversion rate (98.52%, 95% CI: 93.45−99.98) and GMT (SMD 6.20, 95% CI: 2.02−10.39). Compared to the control groups, vaccination significantly increased the incidence of total adverse events (AEs) (RR 1.59, 95% CI: 1.38−1.83), including most local and systemic AEs, such as pain, fever, chill, etc. For inactivated and DNA vaccines, the incidence of any AEs was similar between vaccination and control groups (p > 0.1), while mRNA vaccines had the highest risk of most AEs (RR range from 1.74 to 7.22).ConclusionCOVID-19 vaccines showed acceptable efficacy, immunogenicity and safety in older people, especially providing a high protection rate against severe disease. The mRNA vaccine was the most efficacious, but it is worth surveillance for some AEs it caused. Increased booster coverage in older adults is warranted, and additional studies are urgently required for longer follow-up periods and variant strains.

Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins.

Abstract H6 subtype avian influenza viruses spread widely in birds and pose potential threats to poultry and mammals, even to human beings. In this study, the evolution and pathogenicity of H6 AIVs isolated in live poultry markets from 2011 to 2017 were investigated. These H6 isolates were reassortant with other subtypes of influenza virus with increasing genomic diversity. However, no predominant genotype was found during this period. All of the H6N2 and most of the H6N6 isolates replicated efficiently in lungs of inoculated mice without prior adaptation. All of the H6N2 and two H6N6 isolates replicated efficiently in nasal turbinates of inoculated mice, which suggested the H6N2 viruses were more adaptive to the upper respiratory tract of mice than the H6N6 viruses. One of H6N2 virus caused systemic infection in one out of three inoculated mice, which indicated that H6 avian influenza virus, especially the H6N2 viruses posed a potential threat to mammals. Five H6 strains selected from different genotypes caused no clinical signs to inoculated chickens, and their replication were limited in chickens since the viruses have been detected only from a few tissues or swabs at low titers. Our study strongly suggests that the H6 avian influenza virus isolated from live poultry markets pose potential threat to mammals.

Recent studies indicated that numerous long noncoding RNAs (lncRNAs) are closely related to human diseases and can serve as potential biomarkers and drug targets for complex diseases. Therefore, identifying lncRNAs associated with diseases through computational methods is conducive to the exploration of disease pathogenesis. Most previous studies had shortcomings, such as low prediction accuracy, the need for negative samples, and weak generalization. Such studies established shallow prediction models and failed to fully capture the complex relationships among lncRNA-disease associations, lncRNA similarity, and disease similarity. LRLSSP, a new computational method based on Laplacian regularized least squares (LRLS) and space projection was used to predict candidate disease lncRNAs in this study. LRLSSP deeply integrates information on lncRNA similarity, disease similarity, and known lncRNA-disease associations. The estimated score of lncRNA-disease association was obtained through LRLS, and network projection was utilized to reliably predict disease-related lncRNAs. Leave-one-out cross validation(LOOCV) was implemented to evaluate the prediction performance of LRLSSP. Results showed that LRLSSP performed was better than other state-of-the-art methods in predicting lncRNA-disease associations. In addition, case studies conducted on melanoma,cervical cancer, ovarian cancer and breast cancer indicated that LRLSSP can discover potential and novel lncRNA-disease associations. Overall, the results demonstrated that LRLSSP may serve as a reliable and effective computational tool for disease-related lncRNAs prediction.

Growing lines of evidence supported the importance of CD8+ lung tissue resident memory T (TRM) cells in protection against respiratory viruses, exemplified by influenza A virus. However, the underlying in vivo mechanism remains largely undetermined. Here, we used mouse infection models to dissect in vivo cross-protective activity of lung CD8+ TRM cells. By simultaneously interrogating transcriptional dynamics in lung CD8+ TRM cells and surrounding tissues during the early course of infection, we demonstrated that lung CD8+ TRM cells react to antigen re-exposure within hours, manifested by IFN-γ upregulation, and a tissue-wide interferon-stimulated gene (ISG) program is subsequently elicited. Using antibody-mediated IFN-γ neutralization and IFN-γ receptor knockout mice, we could show that the induction of several important antiviral ISGs required IFN-γ signaling, so did the suppression of key inflammatory cytokines. Interestingly, there were also examples of ISGs unaffected in the absence of IFN-γ activity. Collectively, focusing on in situ characterization of lung CD8+ TRM cells during very early stage of infection, a critical period of host antiviral defense that has been poorly investigated, our studies highlight that these cells, once triggered by antigen re-exposure, are programmed to produce IFN-γ expeditiously to promote a lung-wide antiviral response for effective virus control.

ABSTRACT The H9N2 subtype avian influenza virus (AIV) has become endemic in poultry globally; however due to its low pathogenicity, it is not under primary surveillance and control in many countries. Recent reports of human infection caused by H9N2 AIV has increased public concern. This study investigated the genetic and antigenic characteristics of H9N2 AIV isolated from local markets in nine provinces in Southern China from 2013 to 2018. We detected an increasing annual isolation rate of H9N2 AIV. Phylogenetic analyses of hemagglutinin (HA) genes suggests that isolated strains were rooted in BJ94 lineage but have evolved into new subgroups (II and III), which derived from subgroup I. The estimated substitution rate of the subgroup III strains was 6.23 × 10−3 substitutions/site/year, which was 1.5-fold faster than that of the average H9N2 HA rate (3.95 × 10−3 substitutions/site/year). Based on the antigenic distances, subgroup II and III strains resulted in two clear antigenic clusters 2 and 3, separated from the vaccine strain F98, cluster 1. New antigenic properties of subgroup III viruses were associated with 11 amino acid changes in the HA protein, suggesting antigenic drift in H9N2 viruses. Our phylogenetic and antigenic analyses of the H9N2 strains circulating in local markets in Southern China provide new insights on the antigenic diversification of H9N2 viruses. IMPORTANCE The H9N2 low pathogenicity avian influenza (LPAI) virus has become endemic in poultry globally. In several Asian countries, vaccination against H9N2 avian influenza virus (AIV) was approved to reduce economic losses in the poultry industry. However, surveillance programs initiated after the introduction of vaccination identified the persistence of H9N2 AIV in poultry (especially in chicken in South Korea and China). Recent reports of human infection caused by H9N2 AIV has increased public concern. Surveillance of H9N2 circulating in poultry in the fields or markets was essential to update the vaccination strategies. This study investigated the genetic and antigenic characteristics of H9N2 AIVs isolated from local markets in nine provinces in Southern China from 2013 to 2018. The discovery of mutations in the hemagglutinin (HA) gene that result in antigenic changes provides a baseline reference for evolutionary studies of H9N2 viruses and vaccination strategies in poultry.

Two-dimensional metal chalcogenides (2D-MCs) with complex interactions are usually rich in phase transition behavior, such as superconductivity, charge density wave (CDW), and magnetic transitions, which hold great promise for the exploration of exciting physical properties and functional applications. Interlayer chemical modulation, as a renewed surface modification method, presents congenital advantages to regulate the phase transitions of 2D-MCs due to its confined space, strong guest–host interactions, and local and reversible modulation without destructing the host lattice, whereby new phenomena and functionalities can be produced. Herein, recent achievements in the interlayer chemical modulation of 2D-MCs are reviewed from the aspects of superconducting transition, CDW transition, semiconductor-to-metal transition, magnetic phase transition, and lattice transition. We systematically discuss the roles of charge transfer, spin coupling, and lattice strain on the modulation of phase transitions in the guest–host architectures of 2D-MCs established by electrochemical intercalation, solution-processed intercalation, and solid-state intercalation. New physical phenomena, new insight into the mechanism of phase transitions, and derived functional applications are presented. Finally, a prospectus of the challenges and opportunities of interlayer chemical modulation for future research is pointed out.

One big threat from influenza A viruses (IAVs) is that novel viruses emerge from mutation alongside reassortment. Some of them have gained the capability to transmit into human from the avian reservoir. Understanding the molecular events and the involved factors in breaking the cross-species barrier holds important implication for the surveillance and prevention of potential influenza outbreaks. In this review, we summarize recent progresses, including several ground-breaking findings, in how the interaction between host and viral factors, exemplified by the PB2 subunit of the influenza virus RNA polymerase co-opting host ANP32 protein to facilitate transcription and replication of the viral genome, shapes the evolution of IAVs from host specificity to cross-species infection.

Formaldehyde (FA) is involved in multiple physiological regulatory processes and plays a crucial role in memory storage. Meanwhile, FA has a notorious reputation as a toxic compound, and it will cause a variety of diseases if its level is unbalanced in the human body. To date, there have been numerous fluorescent probes for FA imaging reported. Among them, the probes based on the 2−aza−Cope rearrangement have attracted the most attention, and their applications in cell imaging have been greatly expanded. Herein, we screened the various trigger moieties of FA fluorescent probes based on the mechanism of 2−aza−Cope rearrangement. FA−2, in which a fluorophore is connected to a 4−nitrobenzylamine group and an allyl group, demonstrated the highest sensitivity, selectivity, and reaction kinetics. Furthermore, FA−Lyso, derived from FA−2, has been successfully designed and applied to monitor exogenous and endogenous FA fluctuations in lysosomes of living cells.

H9N2 subtype avian influenza has spread dramatically in China ever since first reported in the 1990s. A national vaccination program for poultry was initiated in 1998. Field isolation data show that the widely used inactivated H9N2 vaccine does not provide effective control of the transmission of this low pathogenic avian influenza (LPAI) virus in poultry. Current research has focused on two reasons: (i) insufficient immune response triggered by the vaccination with the inactivated virus, (ii) the occurrence of escape mutants selected by vaccine-induced immune pressure. However, the lack of effectivity of the inactivated virus vaccine to sufficiently reduce transmission has been noticed. We mimicked the natural infection and transmission process of the H9N2 virus in vaccinated and non-vaccinated chickens. A statistical model was used to estimate the transmission rate parameters among vaccinated chickens, varying in serum hemagglutinin inhibition titers (HIT) and non-vaccinated chickens. We demonstrate, for the first time, that the transmission is not sufficiently reduced by the H9N2 vaccine, even when vaccinated chickens have an IgG serum titer (HIT>23), which is considered protective for vaccination against homologous highly pathogenic avian influenza (HPAI) virus. Our study does, on the other hand, cast new light on virus transmission and immune escape of LPAI H9N2 virus in vaccinated chickens populations, and shows that new mitigation strategies against LPAI viruses in poultry are needed.

Abstract Background Compared with chickens, ducks are normally resistant to avian influenza virus without clinical signs while they habor almost all subtypes of influenza A viruses. To date, however the mechanism for duck anti-influenza has not been completely understood. The H9N2 avian influenza virus (AIV) is the most prevalent subtype of influenza A virus that infects chickens and ducks in China. However, H9N2 AIV replication and the host immune response in these domestic birds has not been systematically investigated. Methods In the present study, we compared the kinetics and magnitudes of antibody responses in chickens and ducks after infection with H9N2 AIV by the intranasal route or intravenous route. Furthermore, we determined the viral replication and distribution in chickens and ducks after infection with H9N2 AIV by the intravenous route. Results Our results revealed that the antibody response was rapid and robust in ducks than in chickens at early time (2-3dpi) after intravenous infection with H9N2 AIVs, while delayed and lower antibody detected in ducks than in chickens after intranasal infection with H9N2 AIVs. The virus was detected in multiple organs tissues in chickens but not in ducks infected by the intravenous route. Conclusions Our results provide the evidence that humoral immune response could play a critical role in duck resistance for influenza, which expands our knowledge on duck anti-influenza characteristics.

By taking 22 OECD countries from 2010 to 2017 as sample, we study the effect of pharmaceutical manufacturing innovation on perceived health by using the panel Tobit model from the entire sample and sub-samples, respectively, as well as analyze their transmission channels by adding moderating effect. Based on the above, we get the following results: first, the pharmaceutical manufacturing innovation 4 years ago has a positive influence on perceived health, which means the improvement of perceived health is closely related to pharmaceutical manufacturing innovation 4 years ago. Second, pharmaceutical manufacturing innovation has a heterogeneous impact on perceived health, which, including the size and direction of the impact effect, is mainly reflected in different pharmaceutical manufacturing innovation levels, population aging degrees, and education levels. Third, income level can positively regulate the impact of pharmaceutical manufacturing innovation on perceived health.

This paper from the perspective of productivity changes examines the impact of innovation activities and foreign direct investment (FDI) on improved green productivity (IGP) in developing countries. We divide the sample into two sub-groups; the BRICS and the other developing countries so as to account for underlying country heterogeneity. The analysis follows a panel data approach over the period 1991 to 2014, and used the global Malmquist-Luenberger productivity index to measure IGP. The results indicate that IGP in developing countries has declined. Innovation activities have a positive impact on IGP. FDI has a significant negative impact on IGP. Further study finds that there are threshold effects between FDI and IGP based on innovation activities, when the developing countries with a low-level of innovation, FDI has a negative impact on IGP; when the developing countries innovation activities above the threshold, innovation activities and FDI both can promote IGP.

Abstract Correlated electronic materials (CEMs) with strong electron−electron interactions are often associated with exotic properties, such as metal‐insulator transition (MIT), charge density wave (CDW), superconductivity, and magnetoresistance (MR), which are fundamental to next generation condensed matter research and electronic devices. When the dimension of CEMs decreases, exposing extremely high specific surface area and enhancing electronic correlation, the surface states are equally important to the bulk phase. Therefore, surface/interface chemical interactions provide an alternative route to regulate the intrinsic properties of low‐dimensional CEMs. Here, recent achievements in surface/interface chemistry engineering of low‐dimensional CEMs are reviewed, using surface modification, molecule−solid interaction, and interface electronic coupling, toward modulation of conducting solids, phase transitions including MIT, CDW, superconductivity, and magnetism transition, as well as external‐field response. Surface/interface chemistry engineering provides a promising strategy for exploring novel properties and functional applications in low‐dimensional CEMs. Finally, the current challenge and outlook of the surface/interface engineering are also pointed out for future research development.

The long non-coding RNA (lncRNA)–protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA–protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA–protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA–lncRNA or the protein–protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA–protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA–protein interaction prediction.

Infectious laryngotracheitis virus (ILTV) causes severe respiratory disease in chickens and results in huge economic losses in the poultry industry worldwide. To correlate the genomic difference with the replication and pathogenicity, phenotypes of three ILTVs isolated from chickens in China from 2016 to 2018 were sequenced by high-throughput sequencing. Based on the entire genome, the isolates GD2018 and SH2017 shared 99.9% nucleotide homology, while the isolate SH2016 shared 99.7% nucleotide homology with GD2018 and SH2017, respectively. Each virus genome contained 82 ORFs encoding 77 kinds of protein, 31 of which share the same amino acid sequence in the three viruses. GD2018 and SH2017 shared 57 proteins with the same amino acid sequence, while SH2016 shared 42 and 41 proteins with the amino acid sequences of GD2018 and SH2017, respectively. SH2016 propagated efficiently in allantoic fluid and on chorioallantoic membranes (CAMs) of SPF chicken embryo eggs, while GD2018 and SH2017 proliferated well only on CAMs. GD2018 propagated most efficiently on CAMs and LMH cells among three isolates. SH2016 caused serious clinical symptoms, while GD2018 and SH2017 caused mild and moderate clinical symptoms in chickens, although the sero of the chickens infected with those three isolates were all positive for anti-ILTV antibody at 14 and 21 days after challenge. Three ILTVs with high genetic homology showed significant differences in the replication in different culture systems and the pathogenicity of chickens, providing basic materials for studying the key determinants of pathogenicity of ILTV.

Since 2016, frequent outbreaks of egg-reducing syndromes caused by an unknown virus in duck farms have resulted in huge economic losses in China. The causative virus was isolated and identified as a novel species in Avihepatovirus of the picornavirus family according to the current guidelines of the International Committee on Taxonomy of Viruses (ICVT), and was named the duck egg-reducing syndrome virus (DERSV). The DERSV was most closely related to wild duck avihepatovirus-like virus (WDALV) with 64.0%, 76.8%, 77.5%, and 70.7% of amino acid identities of P1, 2C, 3C, and 3D proteins, respectively. The DERSV had a typical picornavirus-like genomic structure, but with the longest 2A region in the reported picornaviruses so far. Importantly, the clinical symptoms were successfully observed by artificially infecting ducks with DERSV, even in the contact exposed ducks, which suggested that DERSV transmitted among ducks by direct contact. The antibody levels of DERSV were correlated with the emergence of the egg-reducing syndromes in ducks in field. These results indicate that DERSV is a novel emerging picornavirus causing egg-reducing syndrome in ducks.

H9N2 avian influenza virus is one of the most widely circulating viruses in poultry and poses a huge potential threat to human health due to its frequent gene reassortment with other influenza viruses. In this study, we generated a series of H9N2-H7N9 reassortant viruses and examined their pathogenicity in a mouse model. We found that HA or combined HA and NA replacement on the H9N2 background led to no substantial change in the virus-induced pathogenicity, whereas H9N2 virus containing H7N9 internal genes had significantly higher virulence in comparison to the parental H9N2 virus. This increased pathogenicity is associated with enhanced viral replication both in mice and in MDCK cells. We further demonstrated that the viral ribonucleoprotein complex from H7N9 virus possessed higher activity than that from its H9N2 counterpart. Collectively, our data demonstrated that genetic compatibility between H9N2 and H7N9 viruses facilitated the reassortment between H7N9 and H9N2 viruses co-circulated in poultry and that internal gene replacement would convert H9N2 virus into a novel threat to human health.

A live attenuated duck Tembusu virus (TMUV) vaccine FX2010-180P (180P) was successfully utilized to prevent TMUV infections in ducks in China. Compared with wild-type TMUV, 180P was highly attenuated and lost transmissibility in ducks. However, the mechanism of the attenuation of 180P remains poorly understood. To explore the key molecular basis of attenuation, chimeric and site mutant viruses in the background of the wild-type TMUV-FX2010 (FX) strain were rescued, and the replication, tissue tropism, and transmissibility were characterized in ducks. The results show that the envelope (E) protein was responsible for attenuation and loss of transmission in ducks. Further studies showed that a D120N amino acid mutation located in domain II of the E protein was responsible for the attenuation and transmissibility loss of 180P in ducks. The D120N substitution resulted in an extra high-mannose type N-linked glycosylation (NLG) in the E protein of 180P compared with the wild-type TMUV, which might restrict the tissue tropism and transmissibility of TMUV in ducks. Our findings elucidate that N120 in the E protein is a key molecular basis of TMUV attenuation in ducks and provide new insight into the role of NLG in TMUV tissue tropism and transmissibility.

Abstract microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA–disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA–disease association can be verified by our experiment. This study indicates that this method is feasible and effective.

The influenza A virus (IAV) is an important cause of respiratory disease worldwide. It is well known that alveolar epithelial cells are the target cells for the IAV, but there is relatively limited knowledge regarding the role of macrophages during IAV infection. Here, we aimed to analyze transcriptome differences in mouse lungs and macrophage (RAW264.7) cell lines infected with either A/California/04/2009 H1N1 (CA09) or A/chicken/SD/56/2015 H9N2 (SD56) using deep sequencing. The uniquely differentially expressed genes (UDEGs) were analyzed with the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases; the results showed that the lungs infected with the two different viruses had different enrichments of pathways and terms. Interestingly, CA09 virus infection in mice was mostly involved with genes related to the extracellular matrix (ECM), while the most significant differences after SD56 infection in mice were in immune-related genes. Gene set enrichment analysis (GSEA) of RAW264.7 cells revealed that regulation of the cell cycle was of great significance after CA09 infection, whereas the regulation of the immune response was most enriched after SD56 infection, which was consistent with analysis results in the lung. Similar results were obtained from weighted gene co-expression network analysis (WGCNA), where cell cycle regulation was extensively activated in RAW264.7 macrophages infected with the CA09 virus. Disorder of the cell cycle is likely to affect their normal immune regulation, which may be an important factor leading to their different prognoses. These results provide insight into the mechanism of the CA09 virus that caused a pandemic and explain the different reactivities of monocytes/macrophages infected by H9N2 and H1N1 IAV subtypes.

Identifying intermediates during phase transitions is critical for our understanding of correlated materials, but difficult to achieve experimentally. Here, the authors report a surface coordination route to stabilize and directly image a phase-transition intermediate during the metal-insulator transition in vanadium dioxide.