To compare current methods of pretreatment/determination for plant foliar pH,we proposed a method for longperiod sample preservation with little interference with the stability of foliar pH.Four hundred leaf samples f...To compare current methods of pretreatment/determination for plant foliar pH,we proposed a method for longperiod sample preservation with little interference with the stability of foliar pH.Four hundred leaf samples from 20 species were collected and four methods of pH determination were used:refrigerated(stored at 4°C for 4 days),frozen(stored at−16°C for 4 days),oven-dried and fresh green-leaf pH(control).To explore the effects of different leaf:water mixing ratio on the pH determination results,we measured oven-dried green-leaf pH by leaf:water volume ratio of 1:8 and mass ratio of 1:10,and measured frozen senesced-leaf pH by mass ratio of 1:10 and 1:15.The standard major axis regression was used to analyze the relationship and the conversion equation between the measured pH with different methods.Foliar pH of refrigerated and frozen green leaves did not signifcantly differ from that of fresh green-leaf,but drying always overrated fresh green-leaf pH.During the feld sampling,cryopreservation with a portable refrigerator was an advisable choice to get a precise pH.For long-duration feld sampling,freezing was the optimal choice,and refrigeration is the best choice for the shorttime preservation.The different leaf:water mixing ratio signifcantly infuenced the measured foliar pH.High dilution reduced the proton concentration and increased the measured pH.Our fndings provide the conversion relationships between the existing pretreatment and measurement methods,and establish a connection among pH determined by different methods.Our study can facilitate foliar pH measurement,thus contributing to understanding of this interesting plant functional trait.展开更多
This paper discusses the monitoring section layout, sampling point layout, sampling time and sampling frequency determination, preparation before water sample collection, collection method, collector, industrial sewag...This paper discusses the monitoring section layout, sampling point layout, sampling time and sampling frequency determination, preparation before water sample collection, collection method, collector, industrial sewage collection, flow determination, water sample transportation and the whole process of river water quality control.展开更多
Background Management of pediatric intestinal obstruction remains clinically challenging,particularly regarding the selection between surgical and conservative approaches.This study aimed to develop artificial intelli...Background Management of pediatric intestinal obstruction remains clinically challenging,particularly regarding the selection between surgical and conservative approaches.This study aimed to develop artificial intelligence(AI)models to support treatment decisionmaking.Methods A retrospective analysis was conducted on clinical data from pediatric intestinal obstruction patients.The dataset was split via stratified sampling(70%training/30%test),preserving outcome distribution.Predictive models incorporating clinical indicators were developed using machine learning,with evaluation metrics including accuracy,F1-score,Kappa value,positive predictive value(PPV),negative predictive value(NPV),precision-recall curves,calibration plots and decision curve analysis(DCA).Results Among 765 pediatric patients,425 responded to conservative treatment while 340 required surgery.The Random Forest model demonstrated optimal performance in the test cohort(area under the curve:0.953;sensitivity:0.879;specificity:0.901;accuracy:0.892;F1-score:0.878;Kappa value:0.780;PPV:0.878;NPV:0.905).Calibration,precision-recall,and DCAs indicated favorable clinical applicability.Conclusion Machine learning integration with clinical indicators shows potential as a decision-support tool for selecting surgical or conservative treatment in pediatric intestinal obstruction.展开更多
基金supported by the‘Strategic Priority Research Program’of the Chinese Academy of Sciences(XDA26040202)the National Natural Science Foundation of China(32001165)supported by Chinese Universities Scientifc Fund(2021TC117).
文摘To compare current methods of pretreatment/determination for plant foliar pH,we proposed a method for longperiod sample preservation with little interference with the stability of foliar pH.Four hundred leaf samples from 20 species were collected and four methods of pH determination were used:refrigerated(stored at 4°C for 4 days),frozen(stored at−16°C for 4 days),oven-dried and fresh green-leaf pH(control).To explore the effects of different leaf:water mixing ratio on the pH determination results,we measured oven-dried green-leaf pH by leaf:water volume ratio of 1:8 and mass ratio of 1:10,and measured frozen senesced-leaf pH by mass ratio of 1:10 and 1:15.The standard major axis regression was used to analyze the relationship and the conversion equation between the measured pH with different methods.Foliar pH of refrigerated and frozen green leaves did not signifcantly differ from that of fresh green-leaf,but drying always overrated fresh green-leaf pH.During the feld sampling,cryopreservation with a portable refrigerator was an advisable choice to get a precise pH.For long-duration feld sampling,freezing was the optimal choice,and refrigeration is the best choice for the shorttime preservation.The different leaf:water mixing ratio signifcantly infuenced the measured foliar pH.High dilution reduced the proton concentration and increased the measured pH.Our fndings provide the conversion relationships between the existing pretreatment and measurement methods,and establish a connection among pH determined by different methods.Our study can facilitate foliar pH measurement,thus contributing to understanding of this interesting plant functional trait.
文摘This paper discusses the monitoring section layout, sampling point layout, sampling time and sampling frequency determination, preparation before water sample collection, collection method, collector, industrial sewage collection, flow determination, water sample transportation and the whole process of river water quality control.
基金funded by the Fundamental Research Funds for the Central Universities(3012300076)the Postdoctoral Foundation of Jiangsu Province(1412300075)+2 种基金the Foundation of Jiangxi Provincial Health Department(No:2023A0321,202410462,202410443)the Project of Jiangxi Province Key Laboratory of Child Development(No:2024SSY06191)Jiangxi Provincial Natural Science Foundation(No:20242BAB25444)
文摘Background Management of pediatric intestinal obstruction remains clinically challenging,particularly regarding the selection between surgical and conservative approaches.This study aimed to develop artificial intelligence(AI)models to support treatment decisionmaking.Methods A retrospective analysis was conducted on clinical data from pediatric intestinal obstruction patients.The dataset was split via stratified sampling(70%training/30%test),preserving outcome distribution.Predictive models incorporating clinical indicators were developed using machine learning,with evaluation metrics including accuracy,F1-score,Kappa value,positive predictive value(PPV),negative predictive value(NPV),precision-recall curves,calibration plots and decision curve analysis(DCA).Results Among 765 pediatric patients,425 responded to conservative treatment while 340 required surgery.The Random Forest model demonstrated optimal performance in the test cohort(area under the curve:0.953;sensitivity:0.879;specificity:0.901;accuracy:0.892;F1-score:0.878;Kappa value:0.780;PPV:0.878;NPV:0.905).Calibration,precision-recall,and DCAs indicated favorable clinical applicability.Conclusion Machine learning integration with clinical indicators shows potential as a decision-support tool for selecting surgical or conservative treatment in pediatric intestinal obstruction.
