ABSTRACT
Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.
ABSTRACT
The recent emergence of highly pathogenic avian influenza A virus strains with subtype H5N1 pose a global threat to human health. Elucidation of the underlying mechanisms of viral replication is critical for development of anti-influenza virus drugs. The influenza RNA-dependent RNA polymerase (RdRp) heterotrimer has crucial roles in viral RNA replication and transcription. It contains three proteins: PA, PB1 and PB2. PB1 harbours polymerase and endonuclease activities and PB2 is responsible for cap binding; PA is implicated in RNA replication and proteolytic activity, although its function is less clearly defined. Here we report the 2.9 ångström structure of avian H5N1 influenza A virus PA (PA(C), residues 257-716) in complex with the PA-binding region of PB1 (PB1(N), residues 1-25). PA(C) has a fold resembling a dragon's head with PB1(N) clamped into its open 'jaws'. PB1(N) is a known inhibitor that blocks assembly of the polymerase heterotrimer and abolishes viral replication. Our structure provides details for the binding of PB1(N) to PA(C) at the atomic level, demonstrating a potential target for novel anti-influenza therapeutics. We also discuss a potential nucleotide binding site and the roles of some known residues involved in polymerase activity. Furthermore, to explore the role of PA in viral replication and transcription, we propose a model for the influenza RdRp heterotrimer by comparing PA(C) with the lambda3 reovirus polymerase structure, and docking the PA(C) structure into an available low resolution electron microscopy map.
