Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective To explore the biosynthesis of Dipsacus asper Wall.ex Henry triterpenoid saponin,and the UGT gene in Dipsacus asper Wall.ex Henry has been analyzed by the identification of whole genome,genome and prokaryotic expression.Methods The laboratory self-tested sequenced protein sequence files of the Dipsacus asper Wall.ex Henry genome were used.To validate the conserved domains of the sequence of the Dipsacus asper Wall.ex Henry UGT gene,BLASTP and hmmsearch were utilized.Prot-Param,SOMPA,MAGA7.0,Tbtools and other tools were used to investigate the protein physicochemical properties,protein structure,and covariance analysis of the Dipsacus asper Wall.ex Henry UGT gene family,and using the joint analysis of transcriptomic data and metabolomics data,two glycosyltransferases that might be related to triterpene saponin biosynthesis were screened,and expression vectors were constructed for prokaryotic expression.Results 44 Dipsacus asper Wall.ex Henry UGT genes were identified from the Dipsacus asper Wall.ex Henry genome.The length of Dipsacus asper Wall.ex Henry UGT proteins ranged from 49 to 1083 amino acids,with an average molecular weight of 24.86 kDa and an isoelectric point of 4.31-8.59.Dipsacus asper Wall.ex Henry UGT gene family was distributed on eight chromosomes.The phylogenetic tree constructed from the sequences of Dipsacus asper Wall.ex Henry,Arabidopsis thaliana and identified UGTs showed that glycosyltransferase gene families in Dipsacus asper Wall.ex Henry were mainly in the UGT73,UGT81,UGT85,and UGT80 families.Cis-acting element analysis showed that light-responsive elements were the most prevalent elements in the promoter regions of UGT gene family members.Two glycosyltransferases potentially related to triterpene saponin biosynthesis were screened using combined transcriptomics and metabolomics analysis,and were successfully expressed in prokaryotic form.Conclusion In this study,two candidate genes related to the biosynthesis of Dipsacus asper Wall.ex Henry triterpenoid saponins were jointly screened for prokaryotic expression using multi-omics,and were subjected to prokaryotic expression for further validation of the function of the genes.

Objective:To analyze the spatiotemporal correlation between the surveillance data of influenza in students reported by medical institutions and school absenteeism due to illness, and evaluate the application of Bayesian Structural Time Series model (BSTS) in the prediction of school influenza epidemic.Methods:A total of 13 schools in Dapeng new district of Shenzhen were selected. The incidence data of influenza in schools in Shenzhen from January 1, 2015 to December 31, 2019 were collected from China Disease Control and Prevention Information System and the illness related school absentence data during this period were collected from Shenzhen Student Health Surveillance System, and the spatiotemporal correlation between the data from two systems was analyzed and compared. BSTS was used to make long-term predictions of the monthly incidence of influenza in students in 2019 and short-term predictions of the weekly incidence of influenza in week 1-8 and week 45-52 of 2019 by using the data from two systems.Results:There was a temporal correlation between the data from China Disease Control and Prevention Information System and the data from Shenzhen Student Health Surveillance System ( r=0.93, P<0.001), and the lag of the former one was 1 day ( r=0.73, P<0.001). Influenza outbreaks were randomly distributed in different schools in Shenzhen, and there was no spatial correlation. The root mean square error ( RMSE) and mean absolute error ( MAE) were 0.35 and 0.28, respectively, in the long-term prediction, and the RMSE was 0.33 and 0.34, and the MAE was 0.26 and 0.28, respectively, in the short-term predictions of week 1-8 and week 45-52 of 2019, respectively, showing good prediction accuracy and fitting effect. Conclusion:By analyzing the data from China Disease Control and Prevention Information System and Shenzhen Student Health Surveillance System with BSTS, the dynamics of the school influenza epidemic can be accurately predicted, and effective technical support can be provided for the early warning and prevention and control of influenza epidemic.

Objective To explore the biosynthesis of Dipsacus asper Wall.ex Henry triterpenoid saponin,and the UGT gene in Dipsacus asper Wall.ex Henry has been analyzed by the identification of whole genome,genome and prokaryotic expression.Methods The laboratory self-tested sequenced protein sequence files of the Dipsacus asper Wall.ex Henry genome were used.To validate the conserved domains of the sequence of the Dipsacus asper Wall.ex Henry UGT gene,BLASTP and hmmsearch were utilized.Prot-Param,SOMPA,MAGA7.0,Tbtools and other tools were used to investigate the protein physicochemical properties,protein structure,and covariance analysis of the Dipsacus asper Wall.ex Henry UGT gene family,and using the joint analysis of transcriptomic data and metabolomics data,two glycosyltransferases that might be related to triterpene saponin biosynthesis were screened,and expression vectors were constructed for prokaryotic expression.Results 44 Dipsacus asper Wall.ex Henry UGT genes were identified from the Dipsacus asper Wall.ex Henry genome.The length of Dipsacus asper Wall.ex Henry UGT proteins ranged from 49 to 1083 amino acids,with an average molecular weight of 24.86 kDa and an isoelectric point of 4.31-8.59.Dipsacus asper Wall.ex Henry UGT gene family was distributed on eight chromosomes.The phylogenetic tree constructed from the sequences of Dipsacus asper Wall.ex Henry,Arabidopsis thaliana and identified UGTs showed that glycosyltransferase gene families in Dipsacus asper Wall.ex Henry were mainly in the UGT73,UGT81,UGT85,and UGT80 families.Cis-acting element analysis showed that light-responsive elements were the most prevalent elements in the promoter regions of UGT gene family members.Two glycosyltransferases potentially related to triterpene saponin biosynthesis were screened using combined transcriptomics and metabolomics analysis,and were successfully expressed in prokaryotic form.Conclusion In this study,two candidate genes related to the biosynthesis of Dipsacus asper Wall.ex Henry triterpenoid saponins were jointly screened for prokaryotic expression using multi-omics,and were subjected to prokaryotic expression for further validation of the function of the genes.

ObjectiveTo screen and validate key enzyme genes affecting the polysaccharide content in different Polygonatum species and perform in-depth amino acid sequence analysis by transcriptomic analysis of P. zanlanscianense， P. kingianum， and P. cyrtonema rhizomes to enrich the transcriptome data of Polygonatum plants and provide references for polysaccharide biosynthesis mechanism and genetic improvement. MethodThe Polygonatum transcriptome was sequenced and analyzed using the Illumina NovaSeq high-throughput sequencing platform， and the differences in the transcriptomes of the three Polygonatum species were compared and according to the annotations of Nr， Gene Ontology （GO）， and Kyoto Encyclopedia of Genes and Genomes （KEGG） databases. The key enzymes in the polysaccharide metabolism pathway were screened， and the expression of key enzyme genes was clustered and correlated with the polysaccharide content. Finally， Real-time polymerase chain reaction （Real-time PCR） was performed to validate the eight key enzyme genes， and the key genes of polysaccharide biosynthesis were further screened for homologous gene sequence analysis in combination with sequencing results， followed by constructing phylogenetic trees， predicting motifs， conserved structural domains， protein sequence isoelectric points， and molecular weights， and constructing 3D protein structures by using homology modeling method. ResultThe annotation of the Nr database revealed that three Polygonatum species had the highest gene homology with Asparagus officinalis. GO database annotation results showed that three Polygonatum species differed significantly in binding， catalytic activity， metabolic processes， and cellular components， while the KEGG pathway annotation results indicated that three Polygonatum species differed significantly in the starch and sucrose metabolic pathway and galactose metabolic pathway. According to clustering analysis， correlation analysis， Real-time PCR， expression profiles， and structural and functional predictions of amino acid sequences， the key enzyme significantly affecting the polysaccharide content in different Polygonatum species was inferred to be β-fructofuranosidase （sacA）. ConclusionSacA may be the main influencing factor for the difference in polysaccharide content of Polygonatum， and is also an important reason why Polygonatum polysaccharides are mainly fructans.