A small molecule compound targeting hemagglutinin inhibits influenza A virus and exhibits broad-spectrum antiviral activity.

Influenza A virus (IAV) is a widespread pathogen that poses a significant threat to human health, causing pandemics with high mortality and pathogenicity. Given the emergence of increasingly drug-resistant strains of IAV, currently available antiviral drugs have been reported to be inadequate to meet clinical demands. Therefore, continuous exploration of safe, effective and broad-spectrum antiviral medications is urgently required. Here, we found that the small molecule compound J1 exhibited low toxicity both in vitro and in vivo. Moreover, J1 exhibits broad-spectrum antiviral activity against enveloped viruses, including IAV, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human coronavirus OC43 (HCoV-OC43), herpes simplex virus type 1 (HSV-1) and HSV-2. In this study, we explored the inhibitory effects and mechanism of action of J1 on IAV in vivo and in vitro. The results showed that J1 inhibited infection by IAV strains, including H1N1, H7N9, H5N1 and H3N2, as well as by oseltamivir-resistant strains. Mechanistic studies have shown that J1 blocks IAV infection mainly through specific interactions with the influenza virus hemagglutinin HA2 subunit, thereby blocking membrane fusion. BALB/c mice were used to establish a model of acute lung injury (ALI) induced by IAV. Treatment with J1 increased survival rates and reduced viral titers, lung index and lung inflammatory damage in virus-infected mice. In conclusion, J1 possesses significant anti-IAV effects in vitro and in vivo, providing insights into the development of broad-spectrum antivirals against future pandemics.

Investigation on application of filling mining technology pass through abandoned roadway of reclaimed working face.

This paper aims to analyze the roof stability when the reclaimed working face passes through abandoned roadway. The mechanical model of main roof in abandoned roadway was established for the purpose of theoretical analyses. To ensure the stability of the abandoned roadway, the strength formula of backfill body was deduced. The optimum ratios among different compositions of the filling material were determined by experiment, while the viscosity, bleeding, hydration temperature and compressive strength of filling material were also studied. Test results indicated that the optimum ratio among coal ash, lime and compound activator is 80:15:5, and the ideal water cement ratio is 0.7:1. It was also found that no bleeding occurred, the rheological behavior of slurry presented shear thinning fluid and the hydration temperature of filling body was relatively stable which is mainly maintained at 40°C. The uniaxial compressive strength of filling material with 28 and 90 days curing were 3.35 and 6.62 MPa respectively. Under a confining pressure environment, the filling material presented an obviously plastic deformation. Field test showed that the filling rate was almost 100%, when working face passed through abandoned roadway, the surface of filled body was complete and no roof collapse was triggered. Therefore, a better bonding effect was proved for the filling body.

Preparation of High-Performance CdS@C Catalyst Using Cd-Enriched Biochar Recycled From Plating Wastewater.

Biochar is widely used for the adsorptive removal of Cd from water and soil, but the Cd-enriched biochar produced carries a risk of secondary pollution. In this work, biochar derived from rice straw was used to adsorb Cd from plating wastewater. The Cd-enriched biochar showed a saturated adsorption capacity of about 63.5 mg/g and could be recycled and used in a mesoporous carbon-supported CdS (CdS@C) photocatalyst after pyrolysis carbonization and a hydrothermal reaction. The results demonstrated that the as-prepared CdS@C photocatalyst contained mixed cubic and hexagonal CdS phases, with a considerably lower band gap (2.1 eV) than pure CdS (2.6 eV). CdS@C exhibited an enhanced photocatalytic performance for the degradation of organic dyes under visible light irradiation compared with pure CdS due to its excellent light-harvesting capacity and efficient electron-hole separation. Moreover, the continuous formation of active species (h+, •OH, and O2•-) was responsible for the photodegradation of organic dyes using CdS@C. This work provides new insights for the safe disposal of Cd-enriched wastewater and for improving the economic viability of Cd-contaminated resources by recovering a value-added photocatalyst.

Sensor Classification Using Convolutional Neural Network by Encoding Multivariate Time Series as Two-Dimensional Colored Images.

This paper proposes a framework to perform the sensor classification by using multivariate time series sensors data as inputs. The framework encodes multivariate time series data into two-dimensional colored images, and concatenate the images into one bigger image for classification through a Convolutional Neural Network (ConvNet). This study applied three transformation methods to encode time series into images: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF). Two open multivariate datasets were used to evaluate the impact of using different transformation methods, the sequences of concatenating images, and the complexity of ConvNet architectures on classification accuracy. The results show that the selection of transformation methods and the sequence of concatenation do not affect the prediction outcome significantly. Surprisingly, the simple structure of ConvNet is sufficient enough for classification as it performed equally well with the complex structure of VGGNet. The results were also compared with other classification methods and found that the proposed framework outperformed other methods in terms of classification accuracy.