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Abstract Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near–infrared reflectance (Vis–NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580–1850 nm based on Savitzky–Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg−1, MAE = 0.79 mg kg−1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis–NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

Familial idiopathic basal ganglia calcification (FIBGC) is a rare, autosomal dominant disorder involving bilateral calcification of the basal ganglia. To identify gene mutations related to a Chinese FIBGC lineage, we evaluated available individuals in the family using CT scans. DNA was extracted from the peripheral blood of available family members, and both exonic and flanking intronic sequences of the SLC20A2 gene were amplified by PCR and then sequenced. Non-denaturing polyacrylamide gel electrophoresis (PAGE) was used to confirm the presence of mutations. Allele imbalances of the SLC20A2 gene or relative quantity of SLC20A2 transcripts were evaluated using qRT-PCR. A novel heterozygous single base-pair deletion (c.510delA) within the SLC20A2 gene was identified. This deletion mutation was found to co-segregate with basal ganglia calcification in all of the affected family members but was not detected in unaffected individuals or in 167 unrelated Han Chinese controls. The mutation will cause a frameshift, producing a truncated SLC20A2 protein with a premature termination codon, most likely leading to the complete loss of function of the SLC20A2 protein. This mutation may also lead to a reduction in SLC20A2 mRNA expression by approximately 30% in cells from affected individuals. In conclusion, we identified a novel mutation in SLC20A2 that is linked to FIBGC. In addition to the loss of function at the protein level, decreasing the expression of SLC20A2 mRNA may be another mechanism that can regulate SLC20A2 function in IBGC individuals. We propose that the regional expression pattern of SLC20A1 and SLC20A2 might explain the unique calcification pattern observed in FIBGC patients.