Reflectance spectroscopy analysis and lithium content estimation in lithium-rich rocks and stream sediments: Insights from Tuanjie Peak, Western Kunlun, China.

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.

Pathophysiological Relevance and Therapeutic Outlook of GPR43 in Atherosclerosis.

Atherosclerosis (AS) is an inflammatory arterial disorder that occurs due to the deposition of the excessive lipoprotein under the artery intima, mainly including low-density lipoprotein (LDL) and other apolipoprotein B-containing lipoproteins. G protein-coupled receptors (GPCRs) play a crucial role in transmitting signals in physiological and pathophysiological conditions. GPCRs recognize inflammatory mediators, thereby serving as important players during chronic inflammatory processes. It has been demonstrated that free fatty acids can function as ligands for various GPCRs, such as free fatty acid receptor (FFAR)1/GPR40, FFAR2/GPR43, FFAR3/GPR41, FFAR4/GPR120, and the lipid metabolite binding glucose-dependent insulinotropic receptor (GPR119). This review discusses GPR43 and its ligands in the pathogenesis of AS, especially focusing on its distinct role in regulating chronic vascular inflammation, inhibiting oxidative stress, ameliorating endothelial dysfunction and improving dyslipidemia. It is hoped that this review may provide guidance for further studies aimed at GPR43 as a promising target for drug development in the prevention and therapy of AS.

Compact tunable high power picosecond source based on Yb-doped fiber amplification of gain switch laser diode.

A compact tunable high power picosecond source based on Yb-doped fiber amplification of gain switch laser diode is demonstrated. A multi-stage single mode Yb-doped fiber preamplifier was combined with a single mode double-clad Yb-doped fiber main amplifier to construct the amplification system, which is seeded by a gain switch laser diode. By optimizing preamplifier???s parameters to compensate the seed spectrum gain, a "flat top" broadband spectrum is obtained to realize wavelength tunable output with a self-made tunable filter. The tunable pulses were further amplified to 3.5 W average power 90 ps pulses at 1 MHz repetition rate, and the center wavelength was tunable in the ranges from 1053 nm to 1073 nm with excellent beam quality.