RESUMO
The river valley forests of the Irtysh River Basin are a germplasm bank of Salicaceae species and rare plant resources in China, and the distribution varies with the river and is highly distinctive. However, there is a dearth of systematic research on the characteristics of plant resources. In this study, a comprehensive investigation was conducted in the trunk stream and six tributaries with valley forest distribution in the Irtysh River Basin, and 244 quadrats were set up. The analysis focused on the composition of the flora and resource characteristics. The results reveal the following: (1) The valley forests of the Irtysh River Basin contain 256 species of plants belonging to 57 families and 178 genera, among which 19 species of trees, 23 species of shrubs, and 214 species of herbs were investigated. (2) Among the identified species, 226 (88.67%) were recognized as resource plants, with medicinal plants being the most abundant (176 species, 68.75% of the total). (3) The distribution patterns of trees, shrubs, and herbs of each resource type vary across rivers. Elevation drop, river length, and river distance all significantly affect the number of specie. This study elucidated the current status and distributional characteristics of plant resources in the valley forests of the Irtysh River Basin, which is essential for both biodiversity conservation and sustainable resource utilization.
RESUMO
AIM: This study aims to evaluate the accuracy of scanned images of 4 clinically used intraoral scanners (CS3600, i500, Trios3, Omnicam) when scanning the surface of full arch models with various kinds of orthodontic brackets in the presence of artificial saliva. Materials and Methods. Four study models were prepared; bonded with ceramic, metal, and resin brackets, respectively, and without brackets. Reference images were taken by scanning the models with an industrial scanner. Study models were then applied with an artificial saliva and scanned 10 times, respectively, with the above 4 intraoral scanners. All images were converted to STL file format and analyzed with 3D analysis software. By superimposing with the reference images, mean maximum discrepancy values and mean discrepancy values were collected and compared. For statistical analysis, two-way ANOVA was used. RESULTS: Omnicam (1.247 ± 0.255) showed higher mean maximum discrepancy values. CS3600 (0.758 ± 0.170), Trios3 (0.854 ± 0.166), and i500 (0.975 ± 0.172) performed relatively favourably. Resin (1.119 ± 0.255) and metal (1.086 ± 0.132) brackets showed higher mean maximum discrepancy values. Nonbracket (0.776 ± 0.250) and ceramic bracket (0.853 ± 0.269) models generally showed lower mean maximum discrepancy values in studied scanners. In mean discrepancy values, the difference between scanners was not statistically significant whereas among brackets, resin bracketed models (0.093 ± 0.142) showed the highest value. CONCLUSION: Intraoral scanners and brackets had significant influences on the scanned images with application of artificial saliva on the study models. It may be expected to have similar outcomes in an intraoral environment. Some data showed the discrepancy values up to about 1.5 mm that would require more caution in using intraoral scanners for production of detailed appliances and records.