A valve-free continuous flow method and instrument were established,with only a multi-channel pump for delivering the sample and reagent,and without any injection or solenoid valves and sample loop for selecting and adding the sample or reagent.Nitrate was reduced to nitrite with Cu-Cd reductant column,and then detected with spectrophotometric detector.The proposed method was suitable for determination of nitrate at normal level in most of estuary and coastal seawaters.With the optimum parameters,the linear range and detection limit were 5-180 μmol/L and 0.27 μmol/L,respectively.The samples of 10 and 80 μmol/L nitrate were continually measured for 11 times,and the relative standard deviations were 1.4％ and 1.3％,respectively.The recovery of real samples at different salinity ranged between 99.4％ and 106.1％.There was no significant difference in the analytical results between the proposed method and the flow injection analysis (FIA).In comparison with FIA,the method and instrument were less cost and easy to operate,and was suitable to be applied in general laboratories and field for continuous monitoring.The method was successfully used to measure the nitrate in seawater samples in Xiamen's Western Harbor and monitor nitrate in Jiulongjiang estuary.

Objective:To study a classifier used to classify arrhythmia electrocardiogram (ECG) signals under the inter-patient paradigm to improve the accuracy of automatic classification and solve the limitations of manual diagnosis of arrhythmia.Methods:A SVM+XGBoost ensemble classifier with four modules including preprocessing, feature extraction, support vector machine (SVM) training and ensemble classification was constructed. ECG signal was preprocessed, and R-R interval, high-order statistics, local binary patterns and wavelet components were used as features to train independent SVM classifiers. Then, XGboost algorithm was used to integrate independent SVM classifiers and output arrhythmia classification results. The integrated classifiers were trained and tested on MIT-BIH database.Results:The overall classification accuracy of the ensemble classifier for arrhythmia was 0.867 and the average sensitivity was 0.782.Conclusions:The proposed ensemble classifier can realize automatic and accurate classification of arrhythmia ECG signals under the inter-patient paradigm, and can be used for clinical auxiliary diagnosis.

Objective To characterize the distribution and assess the exposure to phthalic acid esters （PAEs） in the indoor dust of Shanghai City. Methods Samples were collected from 33 sampling sites， including homes， hotels， offices and public places， in Shanghai in 2018， 2019， and 2020. The samples were pretreated by 100 sieves， extracted and concentrated， and then analyzed by gas chromatography-mass spectrometry in selected ion mode （SIM）. Results Results on the characteristics of PAEs in indoor dust in different places showed that concentrations of PAEs were in a range of <0.01-2 464 mg·kg^-1.The average concentration of 16 PAEs was 613 mg·kg^-1. Bis（2-ethylhexyl） phthalate （DEHP）， di-iso-butyl phthalate （DiBP）， di-n-butyl phthalate （DBP） and di-n-octyl phthalate （DnOP） were the main components of PAEs in indoor dust， accounting for approximately 99.5% of 16 PAEs. The intake of DEHP， DBP， DEP and BBP was lower than the tolerable daily intake （TDI） and reference doses （RfD） set by EU CSTEE and U.S. EPA. Conclusion Average daily dose （ADD） via indoor dust is estimated， and the order of intake through different pathways is hand-oral intake>skin contact>respiratory inhalation. Exposure risk of PAEs in children is greater than that in adults.