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[Purpose/significance] Under the background of novel coronavirus pneumonia, this paper proposes a method of identifying COVID-19 news elements in multi-task environment based on transfer learning to provide knowledge services of emergency for the public. [Method/process] Firstly, multiple tasks were used to identify news elements: Time elements were identified based on rules; besides, a cross domain element recognition model was constructed by integrating model transfer and deep learning methods. On this basis, the associated data of COVID-19 news elements was constructed, and the relationship between the elements was displayed by knowledge mapping. [Result/conclusion] The experimental results show that the F1 values of news elements except Drug are above 80%, which indicates that the transfer learning model can achieve fine recognition effect. Moreover, the knowledge map of associated data can intuitively display the key elements and main contents of news. In conclusion, the method proposed in this paper can effectively identify elements in COVID-19 news, thus it can help readers obtain important information from the news accurately and efficiently.

Changes in the demand for bulk cargo will have a significant impact on the industrial structure and transportation development planning, and trains and cargo ships are the main means of bulk cargo transportation. Accurately predicting the volume of bulk cargo transportation can be used to support transportation system management, such as operation planning and route selection design. This study uses the Elman model to conduct empirical analysis, predict the changing trend of bulk cargo transportation, and provide data reference for the formulation of transportation development plans.

Acute lung injury (ALI) is a common lung disease with increasing morbidity and mortality rates due to the lack of specific drugs. Impaired alveolar fluid clearance (AFC) is a primary pathological feature of ALI. Epithelial sodium channel (ENaC) is a primary determinant in regulating the transport of Na+ and the clearance of alveolar edema fluid. Therefore, ENaC is an important target for the development of drugs for ALI therapy. However, the role of ENaC in the progression of ALI remains unclear. Inhibition of early growth response factor (EGR-1) expression has been reported to induce a protective effect on ALI; therefore, we evaluated whether EGR-1 participates in the progression of ALI by regulating ENaC-α in alveolar epithelium. We investigated the potential mechanism of EGR-1-mediated regulation of ENaC in ALI. We investigated whether EGR-1 aggravates the pulmonary edema response in ALI by regulating ENaC. ALI mouse models were established by intrabronchial injection of lipopolysaccharides (LPS). Lentiviruses with EGR-1 knockdown were transfected into LPS-stimulated A549 cells. We found that EGR-1 expression was upregulated in the lung tissues of ALI mice and in LPS-induced A549 cells, and was negatively correlated with ENaC-α expression. Knockdown of EGR-1 increased ENaC-α expression and relieved cellular edema in ALI. Moreover, EGR-1 regulated ENaC-α expression at the transcriptional level, and correspondingly promoted pulmonary edema and aggravated ALI symptoms. In conclusion, our study demonstrated that EGR-1 could promote pulmonary edema by downregulating ENaC-α at the transcriptional level in ALI. Our study provides a new potential therapeutic strategy for treatment of ALI. EGR-1 expression was increased in LPS-induced ALI mice and associated with aggravated pulmonary edema EGR-1 induced pulmonary edema relying on regulating the expression of ENaC-α at the transcriptional level by manipulating the promoter [ABSTRACT FROM AUTHOR]

Drug–drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a H ierarchical T riple-view C ontrastive L earning framework for D rug– D rug I nteraction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view , respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations. [ABSTRACT FROM AUTHOR]

Background and aims: Knowledge of how intrinsic capacity (IC) shape functional ability (FA) trajectories in later life remains unclear. We investigated the changes in IC and their impact on 5-years FA trajectories in the Chinese older population. Methods: A total of 1640 older adults from the Rugao Longitudinal Ageing Study were included and analyzed. FA was assessed by The Lawton Instrumental Activities of Daily Living Scale (IADLs). We used cognition, psychology, locomotion, sensory capacity, and vitality to capture the multiple domains of IC according to the ICOPE method. The IC was derived retrospectively from variables collected before this was described by WHO. Results: At baseline, a higher IC was associated with higher IADLs (β = 0.98, 95% CI 0.90, 1.06, P < 0.001). Individuals with declines in IC between wave1 and wave2 experienced a faster decline in IADLs over time (β = − 0.28, 95% CI − 0.40, − 0.16, P < 0.001) after considering covariates. One or more impairment IC scores at baseline strongly predicted death (HR = 1.20, 95% CI 1.11, 1.30, P < 0.001). In addition, according to the IC scores at baseline, we stratify IC in low, middle, and high, compared with those in the high IC score, those in the low were associated with a 2.56-fold (95% CI 1.64, 4.01, P < 0.001) higher risk of mortality, after adjustment for variables. Conclusion: Changes in IC shape FA trajectories. IC impairment is associated with an increased risk of death. Assessing intrinsic capacity would facilitate early identification of older adults at high risk of adverse outcomes and prompt targeted interventions. [ABSTRACT FROM AUTHOR]

For the increasing demand for lipids and terpenoids in humans, biological fermentation has become an attractive choice due to the safety and sustainability. Thraustochytrids have been identified as promising producers of polyunsaturated fatty acids because of their high lipid content and simple fatty acid composition. In addition, thraustochytrids are also potential producers of terpenoids for their completed mevalonate pathway. Chemical regulators can be used to stimulate or inhibit metabolic pathways, which are equivalent to effects of overexpression and suppression approaches. The application of chemical regulators is potentially an easy and practical approach to improve the lipid and terpenoid yield in thraustochytrids, which has become a research focus. In this review, the mechanisms of chemical regulators promoting lipid and terpenoid biosynthesis in thraustochytrids are elucidated. Various chemical regulators which can directly enhance the biosynthesis of lipids and terpenoids are summarized depending on the type of chemicals. In addition, some chemical regulators are demonstrated to indirectly improve lipid and terpenoid yield by reducing the conversion of metabolites and reducing oxidative stress. Therefore, the addition of chemical regulators can be a useful alternative strategy for improving lipid and terpenoid accumulation in large-scale cultivation of thraustochytrids. [ABSTRACT FROM AUTHOR]

In the coherent signal environments caused by multipath propagation, the interference suppression performance of the global navigation satellite systems (GNSS) receivers decreases sharply. In this paper, a robust space-time adaptive processing (STAP) method for GNSS receivers is proposed to suppress interferences in coherent signal environments, by using the modified space-time two-dimensional iterative adaptive approach (ST2D-IAA) spectrum estimation. This method applies the IAA algorithm to the ST2D signal model of GNSS receivers, and further modifies the ST2D-IAA algorithm to accurately estimate the power spectrum and noise power simultaneously. The space-time interference-plus-noise covariance matrix (STINCM) is reconstructed by using the estimated power spectrum and noise power in the interference angle region. Based on the reconstructed STINCM, we construct the STAP beamforming optimization problem for the space-time steering vector (STSV) error vector, and further correct the STSV of GNSS signal. Finally, the weight vector of STAP beamforming is calculated by using the reconstructed STINCM and the corrected STSV of GNSS signal. Simulation results show that the proposed method can suppress interferences in coherent signal environments and outperforms the current methods. [ABSTRACT FROM AUTHOR]

This paper focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]