Spatial Distribution of Glomalin-related Soil Proteins in Coniferous and Broadleaf mixed Temperate Forest

Glomalin-related soil protein (GRSP), as an important component of soil organic carbon (SOC) pool, is a glycoprotein produced by the hyphae of arbuscular mycorrhizal fungi (AMF), which play a vital role in carbon and nutrient cycling in forest ecosystem. Here we investigated the spatial distribution of GRSP in plant community of the dominated species not associated with AMF based on a typical coniferous and broad-leaved temperate forest in Mt. Changbai, Northeastern China. Spatial distribution of GRSP including easily extractable GRSP (EEG) and total GRSP (TG) is represented by Moran’s I on different soil depth among seven soil layers of 0-5 cm, 5-10 cm, 10-20 cm, 20-30 cm, 30-50 cm, 50-70 cm and 70-100 cm. The concentrations of EEG and TG decreased with the increase of soil depth according to a logarithmic function. The Moran’s I coefficient of GRSP was negative in all soil layers except TG in 20-30 cm and 50-70 cm soil layers. When EEG and TG were considered, the Moran’s I coefficient was positive in majority of soil layers within the separation distance of less than 4 m but in soil layers of 10-20 cm and 20-30 cm for EEG and in 30-50 cm for TG. The largest Moran’s I coefficient including EEG and TG was observed in the soil layer of 5-10 cm. The spatial distribution of GRSP was discrete in typical coniferous and broad-leaved temperate forest, and was affected by mycorrhizal colonization rate, soil organic carbon and total nitrogen

Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera

The anthocyanin content in leaves can reveal valuable information about a plant’s physiological status and its responses to stress. Therefore, it is of great value to accurately and efficiently determine anthocyanin content in leaves. The selection of calibration method is a major factor which can influence the accuracy of measurement with visible and near infrared (NIR) spectroscopy. Three multivariate calibrations including principal component regression (PCR), partial least squares regression (PLSR), and back-propagation neural network (BPNN) were adopted for the development of determination models of leaf anthocyanin content using reflectance spectra data (450–600 nm) in Prunus cerasifera and then the performance of these models was compared for three multivariate calibrations. Certain principal components (PCs) and latent variables (LVs) were used as input for the back-propagation neural network (BPNN) model. The results showed that the best PCR and PLSR models were obtained by standard normal variate (SNV), and BPNN models outperformed both the PCR and PLSR models. The coefficient of determination (R2), the root mean square error of prediction (RMSEp), and the residual prediction deviation (RPD) values for the validation set were 0.920, 0.274, and 3.439, respectively, for the BPNN-PCs model, and 0.922, 0.270, and 3.489, respectively, for the BPNN-LVs model. Visible spectroscopy combined with BPNN was successfully applied to determine leaf anthocyanin content in P. cerasifera and the performance of the BPNN-LVs model was the best. The use of the BPNN-LVs model and visible spectroscopy showed significant potential for the nondestructive determination of leaf anthocyanin content in plants

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients’ quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan–Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients

Molecular mechanisms and topological consequences of drastic chromosomal rearrangements of muntjac deer

Muntjac deer underwent rapid species radiation and dramatic chromosome fusions within a short period of time. Here the authors reveal that repeat sequences likely mediated illegitimate recombination to result in chromosome fusions and that 3D chromatin architecture around fusion sites have no significant change, while significant interactions across fusion sites were gradually established after speciation