Expression profiling of Chrysanthemum crassum under salinity stress and the initiation of morphological changes.

Chrysanthemum crassum is a decaploid species of Chrysanthemum with high stress tolerance that allows survival under salinity stress while maintaining a relatively ideal growth rate. We previously recorded morphological changes after salt treatment, such as the expansion of leaf cells. To explore the underlying salinity tolerance mechanisms, we used an Illumina platform and obtained three sequencing libraries from samples collected after 0 h, 12 h and 24 h of salt treatment. Following de novo assembly, 154,944 transcripts were generated, and 97,833 (63.14%) transcripts were annotated, including 55 Gene Ontology (GO) terms and 128 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The expression profile of C. crassum was globally altered after salt treatment. We selected functional genes and pathways that may contribute to salinity tolerance and identified some factors involved in the salinity tolerance strategies of C. crassum, such as signal transduction, transcription factors and plant hormone regulation, enhancement of energy metabolism, functional proteins and osmolyte synthesis, reactive oxygen species (ROS) scavenging, photosystem protection and recovery, and cell wall protein modifications. Forty-six genes were selected for quantitative real-time polymerase chain reaction detection, and their expression patterns were shown to be consistent with the changes in their transcript abundance determined by RNA sequencing

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p

Chemical Reaction Spectrum

We proposed a new method of chemical reaction spectrum (CRS) in terms of chemical characterization, and established a method to fulfill it by combining with 3D chemical printing technology and 2D sampling. The CRS can provide a graphical data set for pure or mixed substances, which can comprehensively describe the reaction characteristics of the research object. Compared with common characterization methods (NMR, UV/vis, IR, Raman, GC or LC), it is more capable of revealing chemical behaviors enough, and is much lower in cost. It is expected to be an important data acquisition approach for the application of artificial intelligence in the field of chemistry in the future