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This study investigates the application of hyperspectral image space-spectral fusion technology in lithologic classification, using data from China's GF-5 and Europe's Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space-spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The "GSA+RF" method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.
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Detritus geochemical information has been proven through research to be an effective prospecting method in mineral exploration. However, the traditional detritus metal content monitoring methods based on field sampling and laboratory chemical analysis are time-consuming and may not meet the requirements of large-scale metal content monitoring. In this study, we obtained 95 detritus samples and seven HySpex hyperspectral imagery scenes with a spatial resolution of 1 m from Karatag Gobi area, Xinjiang, China, and used partial least squares and wavebands selection methods to explore the usefulness of super-low-altitude HySpex hyperspectral images in estimating detritus feasibility and effectiveness of Cu element content. The results show that: (1) among all the inversion models of transformed spectra, power-logarithm transformation spectrum was the optimal prediction model (coefficient of determination(R2) = 0.586, mean absolute error(MAE) = 21.405); (2) compared to the genetic algorithm (GA) and continuous projection algorithm (SPA), the competitive weighted resampling algorithm (CARS) was the optimal feature band-screening method. The R2 of the inversion model was constructed based on the characteristic bands selected by CARS reaching 0.734, which was higher than that of GA (0.519) and SPA (0.691), and the MAE (19.926) was the lowest. Only 20 bands were used in the model construction, which is lower than that of GA (105) and SPA (42); (3) The power-logarithm transforms, and CARS combined with the model of HySpex hyperspectral images and the Cu content distribution in the study area were obtained, consistent with the actual survey results on the ground. Our results prove that the method incorporating the HySpex hyperspectral data to invert copper content in detritus is feasible and effective, and provides data and a reference method for obtaining geochemical element distribution in a large area and for reducing key areas of geological exploration in the future.
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Lithium, a rare metal of strategic importance, has garnered heightened global attention. This investigation delves into the laboratory visible-near infrared and short-wavelength infrared reflectance (VNIR-SWIR 350 nm-2500 nm) spectral properties of lithium-rich rocks and stream sediments, aiming to elucidate their quantitative relationship with lithium concentration. This research seeks to pave new avenues and furnish innovative technical solutions for probing sedimentary lithium reserves. Conducted in the Tuanjie Peak region of Western Kunlun, Xinjiang, China, this study analyzed 614 stream sediments and 222 rock specimens. Initial steps included laboratory VNIR-SWIR spectral reflectance measurements and lithium quantification. Following the preprocessing of spectral data via Savitzky-Golay (SG) smoothing and continuum removal (CR), the absorption positions (Pos2210nm, Pos1910nm) and depths (Depth2210, Depth1910) in the rock spectra, as well as the Illite Spectral Maturity (ISM) of the rock samples, were extracted. Employing both the Successive Projections Algorithm (SPA) and genetic algorithm (GA), wavelengths indicative of lithium content were identified. Integrating the lithium-sensitive wavelengths identified by these feature selection methods, A quantitative predictive regression model for lithium content in rock and stream sediments was developed using partial least squares regression (PLSR), support vector regression (SVR), and convolutional neural network (CNN). Spectral analysis indicated that lithium is predominantly found in montmorillonite and illite, with its content positively correlating with the spectral maturity of illite and closely related to Al-OH absorption depth (Depth2210) and clay content. The SPA algorithm was more effective than GA in extracting lithium-sensitive bands. The optimal regression model for quantitative prediction of lithium content in rock samples was SG-SPA-CNN, with a correlation coefficient prediction (Rp) of 0.924 and root-mean-square error prediction (RMSEP) of 0.112. The optimal model for the prediction of lithium content in stream sediment was SG-SPA-CNN, with an Rp and RMSEP of 0.881 and 0.296, respectively. The higher prediction accuracy for lithium content in rocks compared to sediments indicates that rocks are a more suitable medium for predicting lithium content. Compared to the PLSR and SVR models, the CNN model performs better in both sample types. Despite the limitations, this study highlights the effectiveness of hyperspectral technology in exploring the potential of clay-type lithium resources in the Tuanjie Peak area, offering new perspectives and approaches for further exploration.
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BACKGROUND: To explore a new method to reduce radiation-induced oral mucositis by scheduling radiotherapy for patients with nasopharyngeal carcinoma (NPC) in the corresponding time window of the cycle of oral mucosal cells. METHODS: Eighty-two NPC patients were randomly divided into a day group (n = 41) and a night group (n = 41). The radiotherapy was scheduled at noon (11:30-15:30) for the day group, while at night (19:00-23:00) for the night group. Oral mucositis and oral pain were recorded in both groups after each radiotherapy fraction. The short-term efficacy of primary tumor regression, weight loss, and bone marrow suppression was recorded. RESULTS: The incidence of Grade 2 oral mucositis was 87.8% (36/41) and 63.4% (26/41) in the night group and day group, respectively (p = 0.010). The incidence of Grade 3 oral mucositis was 65.9% (27/41) and 22.0% (9/41) in the night group and day group, respectively (p < 0.001). The mean number of radiotherapy for patients to develop Grade 2 oral mucositis was 15.67 ± 5.05 and 20.92 ± 6.21 in the night group and day group, respectively. The incidence of Grade 2 oral pain was 48.8% (20/41) and 22.0% (9/41) in the night group and day group, respectively (p = 0.011). There were no significant differences in tumor regression, weight loss, and bone marrow suppression between the two groups. CONCLUSION: By scheduling radiotherapy based on the corresponding time window of the cycle of oral mucosal cells, the severity of oral mucositis in NPC patients was reduced.
Assuntos
Neoplasias Nasofaríngeas , Estomatite , Humanos , Carcinoma Nasofaríngeo/radioterapia , Estudos Prospectivos , Neoplasias Nasofaríngeas/radioterapia , Neoplasias Nasofaríngeas/patologia , Estomatite/etiologia , Estomatite/prevenção & controle , Dor , Redução de PesoRESUMO
Barkol Lake, situated northeast of the Tianshan Mountains, Xinjiang, is a hypersaline lake with abundant sulfate and chloride minerals, which can be a potential analog for microbial saline paleolakes on Mars. The lake water, sediments, and surrounding soils of Barkol Lake were sampled for geochemical analysis and 16S rRNA gene sequencing to investigate the prokaryotic community structure, abundances, interactions, and ecological functions. Results show that (1) prokaryotic community structure differs significantly between biotopes (water, sediment, and soil), with the highest abundances of archaea occurring in water samples and highest prokaryotic diversities in soil samples; (2) archaeal communities are dominated by Halobacterota, Nanoarchaeota, Thermoplasmatota, and Crenarchaeota, while bacterial communities are mainly Proteobacteria, Bacteroidetes, Actinobacteria, Desulfobacterota, Chloroflexi, Gemmatimonadetes, Firmicutes, and Cyanobacteria; (3) the prokaryotic community network for soil is far more complicated and stable than those for water and sediment; (4) soil prokaryotic communities could be significantly affected by environmental factors such as salinity, pH, total sulfur, and Ca2+; (5) archaeal communities may play an important role in the nitrogen cycle, while bacterial communities may mainly participate in the sulfur cycle. This study extends the data set of prokaryotic communities for hypersaline environments, which will provide perspectives into identification of the counterparts and help to understand potential microbial interactions and biogeochemical cycles occurring on Mars.