Systematic calibration of epitranscriptomic maps using a synthetic modification-free RNA library.

Recent years have witnessed rapid progress in the field of epitranscriptomics. Functional interpretation of the epitranscriptome relies on sequencing technologies that determine the location and stoichiometry of various RNA modifications. However, contradictory results have been reported among studies, bringing the biological impacts of certain RNA modifications into doubt. Here, we develop a synthetic RNA library resembling the endogenous transcriptome but without any RNA modification. By incorporating this modification-free RNA library into established mapping techniques as a negative control, we reveal abundant false positives resulting from sequence bias or RNA structure. After calibration, precise and quantitative mapping expands the understanding of two representative modification types, N6-methyladenosine (m6A) and 5-methylcytosine (m5C). We propose that this approach provides a systematic solution for the calibration of various RNA-modification mappings and holds great promise in epitranscriptomic studies.

A Multi-Clustering Algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study.

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.

RNA demethylation increases the yield and biomass of rice and potato plants in field trials.

RNA N6-methyladenosine (m6A) modifications are essential in plants. Here, we show that transgenic expression of the human RNA demethylase FTO in rice caused a more than threefold increase in grain yield under greenhouse conditions. In field trials, transgenic expression of FTO in rice and potato caused ~50% increases in yield and biomass. We demonstrate that the presence of FTO stimulates root meristem cell proliferation and tiller bud formation and promotes photosynthetic efficiency and drought tolerance but has no effect on mature cell size, shoot meristem cell proliferation, root diameter, plant height or ploidy. FTO mediates substantial m6A demethylation (around 7% of demethylation in poly(A) RNA and around 35% decrease of m6A in non-ribosomal nuclear RNA) in plant RNA, inducing chromatin openness and transcriptional activation. Therefore, modulation of plant RNA m6A methylation is a promising strategy to dramatically improve plant growth and crop yield.

A new characterization methodology for starch gelatinization.

A gelatinization degree control system, with a combination of Artificial Neural Networks (ANNs) and computer vision, was successfully developed. An intelligent measurement framework was purposely designed to achieve a precise investigation on phase transition and morphology change of starch in real time, as well as a process control during gelatinization. Base on a variation of birefringence number, the degree of gelatinization (DG) control system provided a direct and fast methodology without subjective uncertainty in studying starch gelatinization. In the course, the whole system was a cascade structure with the hot-stage temperature chosen as the inner-loop parameter, thus the granule morphology and birefringence at different DG could be easily observed and compared in real time, and the relative transition temperature was simultaneously calculated.