Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relati...Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this field.For a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or modules.However,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification problem.At the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs.Finally,we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction.Our model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches.Finally,we evaluated our model on two publicly accessible datasets.The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal model.On the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal model.Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.展开更多
Ozonation of oxalate in aqueous phase was performed with a commercial activated carbon（AC）in this work. The effect of AC dosage and solution pH on the contribution of hydroxyl radicals（HOU） in bulk solution and ox...Ozonation of oxalate in aqueous phase was performed with a commercial activated carbon（AC）in this work. The effect of AC dosage and solution pH on the contribution of hydroxyl radicals（HOU） in bulk solution and oxidation on the AC surface to the removal of oxalate was studied. We found that the removal of oxalate was reduced by tert-butyl alcohol（tBA） with low dosages of AC,while it was hardly affected by tBA when the AC dosage was greater than 0.3 g/L. tBA also inhibited ozone decomposition when the AC dosage was no more than 0.05 g/L, but it did not work when the AC dosage was no less than 0.1 g/L. These observations indicate that HOUin bulk solution and oxidation on the AC surface both contribute to the removal of oxalate. HOU oxidation in bulk solution is significant when the dosage of AC is low, whereas surface oxidation is dominant when the dosage of AC is high. The oxalate removal decreased with increasing pH of the solution with an AC dosage of 0.5 g/L. The degradation of oxalate occurs mainly through surface oxidation in acid and neutral solution, but through HOUoxidation in basic bulk solution. A mechanism involving both HOUoxidation in bulk solution and surface oxidation was proposed for AC enhanced ozonation of oxalate.展开更多
The degradation of phenol using ozone with activated carbon(O_(3)/AC system)was investigated in this study.The O_(3)/AC system was also compared with the single O_(3) and AC systems.The total organic carbon(TOC)remova...The degradation of phenol using ozone with activated carbon(O_(3)/AC system)was investigated in this study.The O_(3)/AC system was also compared with the single O_(3) and AC systems.The total organic carbon(TOC)removal efficiency in the O_(3)/AC system was roughly 26%and 30%higher than the single AC and O_(3) systems,respectively.It was demonstrated that the phenol degradation rate and TOC removal efficiency were significantly affected by the ozone concentration,AC dosage,and solution pH.The pseudo-first-order and pseudo-second-order kinetic models were fitted to identify the mechanisms of the phenol removal process.The results of Scanning Electron Microscopy,Brunauer-Emmett-Teller,and Fourier-transform infrared spectroscopy of raw and used AC indicated that the surface morphology,microstructure,and functional group properties had been changed during the reaction process.The possible O_(3)/AC system mineralization mechanism for phenol removal was tentatively proposed using scavenging active species such as·OH,O_(2)^(·-),and H_(2)O_(2).The transformation byproducts generated during the application of the O_(3)/AC system were identified by High Performance Liquid Chromatography and Gas Chromatography-Mass Spectrometry analyses.Therefore,the mineralization pathway of phenol in detail was proposed in acidic(pH 3.0)and alkaline(pH 11.0)conditions.This study provided a more systematic explanation of the mineralization mechanism for phenol in the O_(3)/AC system.展开更多
Identification of differentially expressed genes （DEGs） in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few exis...Identification of differentially expressed genes （DEGs） in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a fiat gene filter based on the Ljung-Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization ap- proach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62002206 and 62202373)the open topic of the Green Development Big Data Decision-Making Key Laboratory(DM202003).
文摘Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of scholars.The biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this field.For a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or modules.However,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification problem.At the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs.Finally,we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction.Our model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches.Finally,we evaluated our model on two publicly accessible datasets.The experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal model.On the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal model.Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.
基金supported by the National Natural Science Foundation of China (No. 21177130)the National Key Technology R&D Program (No. 2011BAC06B09)the Chinese Academy of Sciences Visiting Professorships for Senior International Scientists (No. 2009G2-28)
文摘Ozonation of oxalate in aqueous phase was performed with a commercial activated carbon（AC）in this work. The effect of AC dosage and solution pH on the contribution of hydroxyl radicals（HOU） in bulk solution and oxidation on the AC surface to the removal of oxalate was studied. We found that the removal of oxalate was reduced by tert-butyl alcohol（tBA） with low dosages of AC,while it was hardly affected by tBA when the AC dosage was greater than 0.3 g/L. tBA also inhibited ozone decomposition when the AC dosage was no more than 0.05 g/L, but it did not work when the AC dosage was no less than 0.1 g/L. These observations indicate that HOUin bulk solution and oxidation on the AC surface both contribute to the removal of oxalate. HOU oxidation in bulk solution is significant when the dosage of AC is low, whereas surface oxidation is dominant when the dosage of AC is high. The oxalate removal decreased with increasing pH of the solution with an AC dosage of 0.5 g/L. The degradation of oxalate occurs mainly through surface oxidation in acid and neutral solution, but through HOUoxidation in basic bulk solution. A mechanism involving both HOUoxidation in bulk solution and surface oxidation was proposed for AC enhanced ozonation of oxalate.
基金financial support from the National Natural Science Foundation of China(NSFC)(No.21876159,No.51578519)the Major Science and Technology Program for Water Pollution Control and Treatment(No.2017ZX07202002).
文摘The degradation of phenol using ozone with activated carbon(O_(3)/AC system)was investigated in this study.The O_(3)/AC system was also compared with the single O_(3) and AC systems.The total organic carbon(TOC)removal efficiency in the O_(3)/AC system was roughly 26%and 30%higher than the single AC and O_(3) systems,respectively.It was demonstrated that the phenol degradation rate and TOC removal efficiency were significantly affected by the ozone concentration,AC dosage,and solution pH.The pseudo-first-order and pseudo-second-order kinetic models were fitted to identify the mechanisms of the phenol removal process.The results of Scanning Electron Microscopy,Brunauer-Emmett-Teller,and Fourier-transform infrared spectroscopy of raw and used AC indicated that the surface morphology,microstructure,and functional group properties had been changed during the reaction process.The possible O_(3)/AC system mineralization mechanism for phenol removal was tentatively proposed using scavenging active species such as·OH,O_(2)^(·-),and H_(2)O_(2).The transformation byproducts generated during the application of the O_(3)/AC system were identified by High Performance Liquid Chromatography and Gas Chromatography-Mass Spectrometry analyses.Therefore,the mineralization pathway of phenol in detail was proposed in acidic(pH 3.0)and alkaline(pH 11.0)conditions.This study provided a more systematic explanation of the mineralization mechanism for phenol in the O_(3)/AC system.
文摘Identification of differentially expressed genes （DEGs） in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a fiat gene filter based on the Ljung-Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization ap- proach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.
