Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies.

High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classification model, to automatically classify Google Earth images into pixel-based land cover maps. By combining domain adaptation (DA) and semi-supervised learning (SSL) techniques, our model demonstrates its effectiveness even when trained with a limited dataset derived from Gaofen2 (GF2) satellite images. The model's overall accuracy on the translated GF2 dataset improved significantly from 19.5% to 75.2%, and on the Google Earth image dataset from 23.1% to 61.5%. The overall accuracy is 2.9% and 3.4% higher than when using only DA. Furthermore, with this model, we derived land cover maps and investigated the impact of land surface composition on the local meteorological parameters and air pollutant concentrations in the three most developed urban agglomerations in China, i.e., Beijing, Shanghai and the Great Bay Area (GBA). Our correlation analysis reveals that air temperature exhibits a strong positive correlation with neighboring artificial impervious surfaces, with Pearson correlation coefficients higher than 0.6 in all areas except during the spring in the GBA. However, the correlation between air pollutants and land surface composition is notably weaker and more variable. The primary contribution of this paper is to provide an efficient method for urban land cover extraction which will be of great value for assessing the urban surface composition, quantifying the impact of land use/land cover, and facilitating the development of informed policies.

(±) Pestalactone D and pestapyrone F, two new isocoumarins from an endolichenic Pestalotiopsis rhododendri.

Three isocoumarins, including two new compounds, (±) pestalactone D (1) and pestapyrone F (2), as well as one known compound, pestapyrone D (3), were isolated from the culture of the endolichenic Pestalotiopsis rhododendri LF-19-12. The planar structures of all compounds were elucidated by NMR and MS spectra. And the absolute configurations of 1 were confirmed by single crystal X-ray diffraction analysis, indicative of it as a racemate of 4S/12S and 4R/12R enantiomers. Compound 1 exhibited weak anti-coronaviral activity against human coronavirus HCoV-229E with an EC50 of 77.61 µM. Based on the bioinformatics analysis, the biosynthetic pathway of 1 has been proposed.

Genome-Directed Discovery of Bicyclic Cinnamoyl-Containing Nonribosomal Peptides with Anticoronaviral Activity from Streptomyces griseus.

Two novel cinnamoyl-containing nonribosomal peptides (CCNPs) grisgenomycin A and B were identified in Streptomyces griseus NBRC 13350 (CGMCC 4.5718) and ATCC 12475, through genome mining using conserved adjacent LuxR family regulators as probes and activators. Notably, grisgenomycins represent a new group of bicyclic decapeptides featuring an unprecedented C-C bond between the tryptophan carbocycle and the cinnamoyl group. A plausible biosynthetic pathway for grisgenomycins was deduced by a bioinformatics analysis. Grisgenomycins exhibited activity against human coronaviruses at the micromolar level.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Omicsynin B4 potently blocks coronavirus infection by inhibiting host proteases cathepsin L and TMPRSS2.

The emergence of SARS-CoV-2 variants represents a major threat to public health and requires identification of novel therapeutic agents to address the unmet medical needs. Small molecules impeding viral entry through inhibition of spike protein priming proteases could have potent antiviral effects against SARS-CoV-2 infection. Omicsynin B4, a pseudo-tetrapeptides identified from Streptomyces sp. 1647, has potent antiviral activity against influenza A viruses in our previous study. Here, we found omicsynin B4 exhibited broad-spectrum anti-coronavirus activity against HCoV-229E, HCoV-OC43 and SARS-CoV-2 prototype and its variants in multiple cell lines. Further investigations revealed omicsynin B4 blocked the viral entry and might be related to the inhibition of host proteases. SARS-CoV-2 spike protein mediated pseudovirus assay supported the inhibitory activity on viral entry of omicsynin B4 with a more potent inhibition of Omicron variant, especially when overexpression of human TMPRSS2. Moreover, omicsynin B4 exhibited superior inhibitory activity in the sub-nanomolar range against CTSL, and a sub-micromolar inhibition against TMPRSS2 in biochemical assays. The molecular docking analysis confirmed that omicsynin B4 fits well in the substrate binding sites and forms a covalent bond to Cys25 and Ser441 in CTSL and TMPRSS2, respectively. In conclusion, we found that omicsynin B4 may serve as a natural protease inhibitor for CTSL and TMPRSS2, blocking various coronavirus S protein-driven entry into cells. These results further highlight the potential of omicsynin B4 as an attractive candidate for broad-spectrum antiviral therapy that could rapidly respond to emerging variants of SARS-CoV-2.

Tilianin improves lipid profile and alleviates atherosclerosis in ApoE-/- mice through up-regulation of SREBP2-mediated LDLR expression.

BACKGROUND: The huge global burden of atherosclerotic cardiovascular diseases (CVDs) represents an urgent unmet need for the development of novel therapeutics. Dracocephalum moldavica L. has been used as a traditional Uygur medicine to treat various CVDs for centuries. Tilianin is a major flavonoid component of D. moldavica L. and has potential for preventing atherosclerosis. However, the molecular mechanisms that tilianin attenuate atherosclerosis are far from fully understood. PURPOSES: The purpose of this study is to investigate the efficiency and underlying mechanisms of tilianin in controlling lipid profile and preventing atherogenesis. METHODS: The lipid-lowering effect of tilianin was evaluated in C57BL/6 and ApoE-/- mice by systematically determining serum biochemical parameters. The effects of tilianin on the atherosclerotic lesion were observed in aortic roots and whole aortas of ApoE-/- mice with oil red O staining. Caecal content from ApoE-/- mice were collected for 16S rRNA gene sequence analysis to assess the structure of the gut microbiota. The inhibition of hepatosteatosis was verified by histological examination, and a liver transcriptome analysis was performed to elucidate the tilianin-induced hepatic transcriptional alterations. Effects of tilianin on the expression and function of LDLR were examined in HepG2 cells and ApoE-/- mice. Further mechanisms underlying the efficacy of tilianin were investigated in HepG2 cells. RESULTS: Tilianin treatment improved lipid profiles in C57BL/6 and dyslipidemic ApoE-/- mice, especially reducing the serum LDL-cholesterol (LDL-C) level. Significant reductions of atherosclerotic lesion area and hepatosteatosis were observed in tilianin-treated ApoE-/- mice. The altered gut microbial composition in tilianin groups was associated with lipid metabolism and atherosclerosis. The liver transcriptome revealed that tilianin regulated the transcription of lipid metabolism-related genes. Then both in vitro and in vivo analyses revealed the potent effect of tilianin to enhance hepatic LDLR expression and its mediated LDL-C uptake. Further studies confirmed a critical role of SREBP2 in hepatic LDLR up-regulation by tilianin via increasing precursor and thus mature nuclear SREBP2 level. CONCLUSION: This study demonstrated the lipid-lowering effect of tilianin through SREBP2-mediated transcriptional activation of LDLR. Our findings reveal a novel anti-atherosclerotic mechanism of tilianin and underlie its potential clinical use in modulating CVDs with good availability and affordability.

Trans-scale relationship analysis between the pore structure and macro parameters of backfill and slurry.

The characteristics of the porous structure of backfill are directly related to the macro parameters of the flowability of the filling slurry and the mechanical features of the backfill, which are fundamental to the study of multiscale mechanics of backfill. Based on the geometry and fractal theory, scanning electron microscopy images of backfill were analysed by image analysis methods such as OTSU and box counting. The fractal dimension of the pore structure was calculated. By quantitatively characterizing the pore structure, the trans-scale relationships between the fractal dimension of the pore structure and the macro parameters of the filling slurry were established in terms of equilibrium shear stress (ESS) and equilibrium apparent viscosity (EAV). In addition, the correlations between the fractal dimension and macro parameters of backfill were obtained in terms of uniaxial compressive strength (UCS), water content (WC) and porosity. The influence of the microstructure on the macro parameters was discussed. The results show the following: (i) the fractal dimension of the backfill pore structure can characterize the complexity of the structure; (ii) the fractal dimension of the pore structure is negatively correlated with the ESS and EAV of the filling slurry. The UCS of the backfill is positively correlated with the flowability parameter; (iii) the fractal dimension of the pore structure has a certain correlation with some macro parameters of the backfill, i.e. the fractal dimension is negatively correlated with the UCS and positively correlated with the WC and (iv) the linear correlations between the pore fractal dimension and UCS and WC are established. The correlation coefficient between the fractal dimension and UCS has an R 2 value of -0.638, while the corresponding value of the fractal dimension and WC is 0.604. UCS and WC can be predicted by the fractal dimension of pores.