Soil Organic Carbon Density in Hebei Province, China: Estimates and Uncertainty

In order to improve the precision of soil organic carbon (SOC) estimates, the sources of uncertainty in soil organic carbon density (SOCD) estimates and SOC stocks were examined using 363 soil profiles in Hebei Province, China, with three methods: the soil profile statistics (SPS), GIS-based soil type (GST), and kriging interpolation (KI). The GST method, utilizing both pedological professional knowledge and GIS technology, was considered the most accurate method of the three estimations, with SOCD estimates for SPS 10% lower and KI 10% higher. The SOCD range for GST was 84% wider than KI as KI smoothing effect narrowed the SOCD range. Nevertheless, the coefficient of variation for SOCD with KI (41.7%) was less than GST and SPS. Comparing SOCD’s lower estimates for SPS versus GST, the major sources of uncertainty were the conflicting area of proportional relations. Meanwhile, the fewer number of soil profiles and the necessity of using the smoothing effect with KI were its sources of uncertainty. Moreover, for local detailed variations of SOCD, GST was more advantageous in reflecting the distribution pattern than KI.

Visualization of Multivariate Metabolomic Data

Objective Although principal components analysis profiles greatly facilitate the visualization and interpretation of the multivariate data,the quantitative concepts in both scores plot and loading plot are rather obscure.This article introduced three profiles that assisted the better understanding of metabolomic data.Methods The discriminatory profile,heat map, and statistic profile were developed to visualize the multivariate data obtained from high-throughput GC-TOF-MS analysis. Results The discriminatory profile and heat map obviously showed the discriminatory metabolites between the two groups,while the statistic profile showed the potential markers of statistic significance.Conclusion The three types of profiles greatly facilitate our understanding of the metabolomic data and the identification of the potential markers.

METABOLIC AND TRANSCRIPTOME ANALYSIS REVEALS METABOLITE VARIATION AND FLAVONOID REGULATORY NETWORKS IN FRESH SHOOTS OF TEA(CAMELLIA SINENSIS)OVER THREE SEASONS

Metabolites,especially secondary metabolites,are very important in the adaption of tea plants and the quality of tea products.Here,we focus on the seasonal variation in metabolites of fresh tea shoots and their regulatory mechanism at the transcriptional level.The metabolic profiles of fresh tea shoots of 10 tea accessions collected in spring,summer,and autumn were analyzed using ultra-performance liquid chromatography coupled with quadrupole-obitrap mass spectrometry.We focused on the metabolites and key genes in the phenylpropanoid/flavonoid pathway integrated with transcriptome analysis.Multivariate statistical analysis indicates that metabolites were distinctly different with seasonal alternation.Flavonoids,amino acids,organic acids and alkaloids were the predominant metabolites.Levels of most key genes and downstream compounds in the flavonoid pathway were lowest in spring but the catechin quality index was highest in spring.The regulatory pathway was explored by constructing a metabolite correlation network and a weighted gene co-expression network.