Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion.

This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 µm × 710 µm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.

Estimation of Soil Arsenic Content with Hyperspectral Remote Sensing.

With the continuous application of arsenic-containing chemicals, arsenic pollution in soil has become a serious problem worldwide. The detection of arsenic pollution in soil is of great significance to the protection and restoration of soil. Hyperspectral remote sensing is able to effectively monitor heavy metal pollution in soil. However, due to the possible complex nonlinear relationship between soil arsenic (As) content and the spectrum and data redundancy, an estimation model with high efficiency and accuracy is urgently needed. In response to this situation, 62 samples and 27 samples were collected in Daye and Honghu, Hubei Province, respectively. Spectral measurement and physical and chemical analysis were performed in the laboratory to obtain the As content and spectral reflectance. After the continuum removal (CR) was performed, the stable competitive adaptive reweighting sampling algorithm coupled the successive projections algorithm (sCARS-SPA) was used for characteristic band selection, which effectively solves the problem of data redundancy and collinearity. Partial least squares regression (PLSR), radial basis function neural network (RBFNN), and shuffled frog leaping algorithm optimization of the RBFNN (SFLA-RBFNN) were established in the characteristic wavelengths to predict soil As content. These results show that the sCARS-SPA-SFLA-RBFNN model has the best universality and high prediction accuracy in different land-use types, which is a scientific and effective method for estimating the soil As content.

Comparative Metabolome and Transcriptome Analyses Reveal Differential Enrichment of Metabolites with Age in Panax notoginseng Roots.

Panax notoginseng is a perennial plant well known for its versatile medicinal properties, including hepatoprotective, antioxidant, anti-inflammatory, anti-tumor, estrogen-like, and antidepressant characteristics. It has been reported that plant age affects the quality of P. notoginseng. This study aimed to explore the differential metabolome and transcriptome of 2-year (PN2) and 3-year-old (PN3) P. notoginseng plant root samples. Principal component analysis of metabolome and transcriptome data revealed major differences between the two groups (PN2 vs. PN3). A total of 1813 metabolites and 28,587 genes were detected in this study, of which 255 metabolites and 3141 genes were found to be differential (p < 0.05) between PN2 vs. PN3, respectively. Among differential metabolites and genes, 155 metabolites and 1217 genes were up-regulated, while 100 metabolites and 1924 genes were down-regulated. The KEGG pathway analysis revealed differentially enriched metabolites belonging to class lipids ("13S-hydroperoxy-9Z, 11E-octadecadionic acid", "9S-hydroxy-10E, 12Z-octadecadionic acid", "9S-oxo-10E, 12Z-octadecadionic acid", and "9,10,13-trihydroxy-11-octadecadionic acid"), nucleotides and derivatives (guanine and cytidine), and phenolic acids (chlorogenic acid) were found to be enriched (p < 0.05) in PN3 compared to PN2. Further, these differentially enriched metabolites were found to be significantly (p < 0.05) regulated via linoleic acid metabolism, nucleotide metabolism, plant hormone signal transduction, and arachidonic acid metabolism pathways. Furthermore, the transcriptome analysis showed the up-regulation of key genes MAT, DMAS, SDH, gallate 1-beta-glucosyltransferase, and beta-D-glucosidase in various plants' secondary metabolic pathways and SAUR, GID1, PP2C, ETR, CTR1, EBF1/2, and ERF1/2 genes observed in phytohormone signal transduction pathway that is involved in plant growth and development, and protection against the various stressors. This study concluded that the roots of a 3-year-old P. notoginseng plant have better metabolome and transcriptome profiles compared to a 2-year-old plant with importantly enriched metabolites and genes in pathways related to metabolism, plant hormone signal transduction, and various biological processes. These findings provide insights into the plant's dynamic biochemical and molecular changes during its growth that have several implications regarding its therapeutic use.

Study on the Raman spectral characteristics of dynamic and static blood and its application in species identification.

This paper proposes a method to identify the blood of 4 poultry species (chicken, duck, goose and pigeon) based on Raman spectroscopy and its baseline. Samples were prepared by pretreatment methods of freezing, thawing, and dilution. The Raman spectra of dynamic blood and static blood were measured, respectively, and the spectral differences between the two research schemes were analyzed. The four species of poultry blood were identified based on the Raman spectroscopy and its baseline. The results show that the method can realize the identification of four species of poultry blood. In addition, the potential of Raman spectroscopy as a technique for determining carotenoids in blood has been clearly confirmed, which opens up the possibility to quickly determine whether poultry eats feed containing carotenoids without sample preparation.