Prediction modeling of isoscapes for stable isotopes in China’s rice based on environmental similarity

Rice quality is directly related to human health, so it is important to have traceability systems that can trace inferior or contaminated rice back to its geographical origin. This ensures farming practices in substandard regions become better regulated to improve rice quality, origin labelling and consumer trust. However, tracing the origin of rice on the marketplace requires an accurate database benchmarking the isotope distribution over areas of rice production. Large stable isotope data sets can be used to determine the geographical origin of rice through predictive isoscape models. This study presents the first rice isoscape based on environmental similarity to predict the geospatial distribution of δ13C, δ2H and δ18O values of Chinese rice and provides uncertainty at every location such prediction is made. For this study, 794 rice samples were collected in 2017 from primary rice production regions of China. An independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean squared error (RMSE) of 0.51‰, 7.09‰ and 2.06‰ for δ13C, δ2H and δ18O values respectively. In addition, uncertainty in the spatial distribution of isotopes can be used to indicate the prediction accuracy and to guide future sampling. Our results indicate that an isoscape prediction method based on environmental similarity is effective to predict the spatial distribution of stable isotope in rice, and is an effective tool for building isotope distribution in rice over large areas with complex environment. This method could also be used to predict potential isotopic variations in future years due to climate change.

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides

Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson’s χ² goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

Compositional and Isotopic Characteristics for The Longmaxi Shale Gas in The Northern Guizhou Area, South China

The organic-rich marine shale of the Lower Silurian Longmaxi formation in the northern Guizhou area (NGA), China, is characterized by its high thermal maturity (Ro values range in 2.18%~3.12%), high TOC values (0.92%~4.87%), high gas contents (0.47~2.69 m3/t) and type II1 organic matter, and has recently been a precursor for shale gas exploration and development. Compositional and isotopic parameters of 7 gas samples from Longmaxi shale from DY-1 well were analyzed in this study. Dry coefficient of the gases is up to 30~200 making the northern Guizhou Longmaxi shale gas among the driest gaseous hydrocarbons in the world. The δ13CCH4 values range from -38.6‰ to -18.6‰ and the δ13CC2H6 values vary in -36.2‰~-30.8‰. These results indicate that the Longmaxi shale gas is of thermogenic origin and oil derived. This Longmaxi shale gas has high proportion of non-hydrocarbon gases especially including nitrogen in response to complicate tectonic movements and strong hydrodynamic flushing. Tectonic movement and hydrodynamic flushing not only destroy hydrocarbon gases reservoirs but also change the isotope distribution of gaseous hydrocarbons. Isotopic reversal is frequent in closed system, and under relatively bad preserving condition, the isotope distribution will back to normal even at overmature evolution stage.