0. 5) in the patients with nephritis, uretericcalculi and other diseases. The renogram methodis simple, inexpensive, without blood samplingand is useful in clinical application.

Objective To explore the attention bias characteristics and impacts of attention training on negative attention bias of undergraduates with sub-clinical depression.Methods The undergraduates whose BDI scores being at the top of 5％ were recruited as participations and assigned to early attention training group and late attention training group using random number table.The dot probe paradigm was used to compare the difference of depressive symptoms and ingredients of attention bias made by different processing phases of attention training.Results (1)The BDI score after the training(87.91± 12.47) was significantly lower than that the former test (97.23±17.72) (F~,32)=4.78,P＜0.05),and the attention bias score in late attention training group (-5.97±2.92) was lower than that in the early attention training group(2.77±2.75) (P＜0.05).The interaction of the stimulus materials presenting time and the measuring time was significant(F(2,64) =4.76,P＜0.05).Simple effect analysis showed that when the time of stimulus material presenting was 1 000 ms,the score of negative attention bias after the test (-4.89 ± 23.66) was significantly lower than pre-test (7.73±26.14) (F(1,33) =5.11,P＜ 0.05).In the pre-test,the negative attention bias scores of the stimulus materials presenting time for 100 ms and 1 000 ms (8.62 ± 27.60,7.73 ± 26.14) were significantly higher than that for 500 ms (-12.80±29.09)(P＜0.05).(2)When the negative disengaged score as a dependent variable,the repetitive measure analysis of variance showed that the interaction effect of the stimulus materials' presenting time and training group type was significant (F(1,32) =4.41,P＜0.05).Simple effect analysis results indicated the negative disengaged score of the late attention training group at post-test (-5.84±7.79) was significantly lower than that at pre-test (24.16±7.35) (P＜0.05).Conclusion The attention training during the late stage of the attention process can efficiently intervene the negative attention bias of undergraduates with sub-clinical depression.

ObjectiveTo establish the identification method of Dalbergiae Odoriferae Lignum（DOL） and its counterfeits by nuclear magnetic resonance hydrogen spectrum（¹H-NMR） combined with multivariate statistical analysis. Method¹H-NMR spectra of DOL and its counterfeits were obtained by NMR， and the full composition information was established and transformed into a data matrix， and the detection conditions were as follows：taking dimethyl sulfoxide-d₆（DMSO-d₆） containing 0.03% tetramethylsilane（TMS） as the solvent， the constant temperature at 298 K（1 K=-272.15 ℃）， pulse interval of 1.00 s， spectrum width of 12 019.23 Hz， the scanning number of 16 times， and the sampling time of 1.08 s. Similarity examination and hierarchical cluster analysis（HCA） were performed on the data matrix of DOL and its counterfeits， and orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to analyze the data matrix and identify the differential components between them. In the established OPLS-DA category variable value model， the category variable value of DOL was set as 1， and the category variable value of the counterfeits was set as 0， and the threshold was set as ±0.3， in order to identify the commercially available DOL. The OPLS-DA score plot was used to determine the types of counterfeits in commercially available DOL， and it was verified by thin layer chromatography（TLC）. ResultThe results of similarity analysis and HCA showed that there was a significant difference between DOL and its counterfeits. OPLS-DA found that the differential component between DOL and its counterfeits was trans-nerolidol. The established category variable value model could successfully identify the authenticity of the commercially available DOL. The results of the OPLS-DA score plot showed that there were heartwood of Dalbergia pinnata and D. cochinchinensis in the commercially available DOL， and were consistent with the TLC verification results. ConclusionThere is a phenomenon that heartwood of D. pinnata and D. cochinchinensis are sold as DOL in the market. ¹H-NMR combined with multivariate statistical analysis can effectively distinguish DOL and its counterfeits， which can provide a reference for the identification of them.

ObjectiveTo screen the differential markers by analyzing volatile components in Dalbergia odorifera and its counterfeits， in order to provide reference for authentication of D. odorifera. MethodThe volatile components in D. odorifera and its counterfeits were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the GC conditions were heated by procedure（the initial temperature of the column was 50 ℃， the retention time was 1 min， and then the temperature was raised to 300 ℃ at 10 ℃ for 10 min）， the carrier gas was helium， and the flow rate was 1.0 mL·min^-1， the split ratio was 10∶1， and the injection volume was 1 mL. The MS conditions used electron bombardment ionization（EI） with the scanning range of m/z 35-550. The compound species were identified by database matching， the relative content of each component was calculated by the peak area normalization method， and principal component analysis（PCA）， orthogonal partial least squares-discrimination analysis（OPLS-DA） and cluster analysis were performed on the detection results by SIMCA 14.1 software， and the differential components of D. odorifera and its counterfeits were screened out according to the variable importance in the projection（VIP） value>2 and P<0.05. ResultA total of 26， 17， 8， 22， 24 and 7 volatile components were identified from D. odorifera， D. bariensis， D. latifolia， D. benthamii， D. pinnata and D. cochinchinensis， respectively. Among them， there were 11 unique volatile components of D. odorifera， 6 unique volatile components of D. bariensis， 3 unique volatile components of D. latifolia， 6 unique volatile components of D. benthamii， 8 unique volatile components of D. pinnata， 4 unique volatile components of D. cochinchinensis. The PCA results showed that， except for D. latifolia and D. cochinchinensis， which could not be clearly distinguished， D. odorifera and other counterfeits could be distributed in a certain area， respectively. The OPLS-DA results showed that D. odorifera and its five counterfeits were clustered into one group each， indicating significant differences in volatile components between D. odorifera and its counterfeits. Finally， a total of 31 differential markers of volatile components between D. odoriferae and its counterfeits were screened. ConclusionHS-GC-MS combined with SIMCA 14.1 software can systematically elucidate the volatile differential components between D. odorifera and its counterfeits， which is suitable for rapid identification of them.

The recent discovery of circular RNAs (circRNAs) and characterization of their functional roles have opened a new avenue for understanding the biology of genomes. circRNAs have been implicated to play important roles in a variety of biological processes, but their precise functions remain largely elusive. Currently, a few approaches are available for novel circRNA prediction, but almost all these methods are intended for animal genomes. Considering that the major differences between the organization of plant and mammal genomes cannot be neglected, a plant-specific method is needed to enhance the validity of plant circRNA identification. In this study, we present CircPlant, an integrated tool for the exploration of plant circRNAs, potentially acting as competing endogenous RNAs (ceRNAs), and their potential functions. With the incorporation of several unique plant-specific criteria, CircPlant can accurately detect plant circRNAs from high-throughput RNA-seq data. Based on comparison tests on simulated and real RNA-seq datasets from Arabidopsis thaliana and Oryza sativa, we show that CircPlant outperforms all evaluated competing tools in both accuracy and efficiency. CircPlant is freely available at http://bis.zju.edu.cn/circplant.
Arabidopsis/metabolism* ; Oryza/metabolism* ; RNA, Circular/metabolism* ; RNA, Plant/metabolism* ; Sequence Analysis, RNA/methods*