CN107024558A - A kind of organic compound molecule structure parameterization characterizing method - Google Patents
A kind of organic compound molecule structure parameterization characterizing method Download PDFInfo
- Publication number
- CN107024558A CN107024558A CN201710014952.2A CN201710014952A CN107024558A CN 107024558 A CN107024558 A CN 107024558A CN 201710014952 A CN201710014952 A CN 201710014952A CN 107024558 A CN107024558 A CN 107024558A
- Authority
- CN
- China
- Prior art keywords
- hydrogen atom
- compound
- variable
- formula
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8693—Models, e.g. prediction of retention times, method development and validation
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a kind of organic compound molecule structure parameterization characterizing method, belong to volatile organic contaminant QSRR (quantitative structure retention relationship) research method technical field.Purpose is to select VOC in the drinking water of part to be research sample, new structured descriptor is built with the relation between compound non-hydrogen atom and non-hydrogen atom, Parameter Expression is carried out to compound structure, compound structure and gas chromatography retention time (t are built with the method that PLS (PLS) is combined using successive Regression (SMR)R) between relational model, the chromatographic retention (t for predicting volatile organic contaminantR), auxiliary identification volatile organic contaminant.Method comprises the following steps：Step one chooses sample；Step 2 determines variable；Step 3 Variable Selection and modeling.Model can accurately predict the gas chromatography retention time (t of VOC in drinking waterR), there is very high reference value for the QSPR/QSAR researchs of organic pollution.
Description
Technical field
Present invention relates particularly to a kind of organic compound molecule structure parameterization characterizing method, belong to volatility organic contamination
Thing QSRR (D-M (Determiner-Measure) construction-Retention Relationship) research method technical field.
Background technology
Volatile organic contaminant low boiling point, species are various, are common and important pollutants in water body.Most of volatilizations
Property organic matter in an atmosphere have reactivity, be the reactant of photochemical fog, see paper：The day of atmospheric volatile organic compounds
Variation characteristic and effect-by taking Guangzhou during Summer as an example [J] ACTA Scientiae Circumstantiaes, 2009,29 (1) in ozone generation：54-62.
In air volatile organic contaminant harm animal and plant growth, threaten human health, into water body after cause drinking water pollution.It is right
It is significant that the species and property of Organic Pollutants of Drinking Water carry out research.See paper：VOCs's is quick in 57 in water
GCMS analyzes [J] water purification technologies, 2016,35 (s1)：83-88,112, drink is analyzed using Gas chromatographyMass spectrometry
With the volatile organic contaminant in water, more than 50 kinds of organic compound is finally identified in drinking water.Organic compound QSRR
Research is significant for predictive compound chromatographic retention, interpretative reservation mechanism, auxiliary authenticating compound etc., is having
In machine compound Q SRR/QSAR (Quantitative Structure-Property Relationship/activity relationship) researchs, it is necessary first to parameterized molecular structure
Expression, then builds the relational model between structure and properties from suitable method, and researchers did many in this respect
Work, but there is presently no highly effective easy method appearance.
The content of the invention
Therefore, for the above-mentioned deficiency of prior art, the present invention seeks to from volatile organic in the drinking water of part
Compound is research sample, new structured descriptor is built with the relation between compound non-hydrogen atom and non-hydrogen atom, to chemical combination
Thing structure carries out Parameter Expression, the method structureization being combined using successive Regression (SMR) with PLS (PLS)
Compound structure and gas chromatography retention time (tR) between relational model, for predict volatile organic contaminant chromatogram protect
Stay time (tR), auxiliary identification volatile organic contaminant.
The method of the present invention comprises the following steps：
Step one chooses sample
It is research sample to choose volatile compound in 56 drinking water, and compound gas phase chromatographic retention is with tRTable
Show；
Step 2 molecular structure of compounds is characterized
By in sample with 1, the non-hydrogen atom that 2,3,4 other non-hydrogen atoms are joined directly together respectively provide as first and second,
3rd, four class non-hydrogen atom, then carries out parametrization dyeing by formula one by non-hydrogen atom,
In formula, i is the coding of non-hydrogen atom in the molecule, niFor non-hydrogen atom i principal quantum number, xiFor electronegativity, xCFor
The electronegativity of carbon atom, miFor outermost electron number, hiFor the number of hydrogen atoms directly connected,
Influence for different type non-hydrogen atom itself to compound property, carries out classification by formula two and adds up,
In formula, k represents non-hydrogen atom i atomic type, ZiCalculated by formula one, according to the classification of non-hydrogen atom, for
4 class non-hydrogen atoms are contained up in one organic compound molecule, therefore finally can obtain 4 non-hydrogen atoms itself to compound
Property contributes item, uses x1、x2、x3And x4Represent,
It is public affairs using Gaussian shape distance relation functions for the influence of the relation pair molecular property between non-hydrogen atom
Formula three is calculated,
Zi、ZjCalculated by formula one；dijThe relative distance between non-hydrogen atom i, j, i.e. bond distance's sum and carbon-carbon single bond
The ratio of bond distance, if there is mulitpath between i, j, is defined by most short, and n and l are the affiliated type of atom, and α takes 0.5, change
4 class non-hydrogen atoms can be combined into following 10 kinds of relational terms in adduct molecule：m11、m12、…、m44, it is abbreviated as x5、x6、…、x14,
So will at most there are 14 variables.
Step 3 Variable Selection and modeling
The variable that step 2 is obtained, is screened, so using successive Regression according to variable conspicuousness to variable first
Dependent variable X is combined as with the variable filtered out afterwards, with compound gas phase chromatographic retention (tR) it is dependent variable Y, with partially most
A young waiter in a wineshop or an inn multiplies recurrence (PLS) and sets up model.
The beneficial effects of the present invention are：The present invention provides a kind of organic compound molecule structure parameterization characterizing method,
Non-hydrogen atom in molecule is classified and dyeing is parameterized, by the relation between non-hydrogen atom itself and different non-hydrogen atoms
As Molecular structure descriptor, parametrization is carried out to VOC structure in the drinking water of part and has been characterized.Using by
Step returns and constructs compound structure and gas chromatography retention time (t with the method (SMR-PLS) that offset minimum binary is combinedR)
Relational model, model coefficient correlation (R2) and cross-verification coefficient correlation (Q2) ideal, disclose to a certain extent
Influence compound gas phase chromatographic retention (tR) structural factor.Model can accurately predict there there is volatility in drinking water
Gas chromatography retention time (the t of machine compoundR), there is very high reference price for the QSPR/QSAR researchs of organic pollution
Value.
Brief description of the drawings
Fig. 1 is 56 samples in embodiment in PLS the first two principal component scores space scatter diagram；
Fig. 2 is offset minimum binary X spatial normalization modal distance schematic diagrames in embodiment；
Fig. 3 is variable importance (VIP) perspective view in PLS in embodiment；
Fig. 4 be embodiment in sample the first two principal component load diagram；
Fig. 5 is embodiment predicted value figure related to experiment value；
Fig. 6 is embodiment predicated error distribution map.
Embodiment
The embodiment to the present invention is illustrated below in conjunction with the accompanying drawings：
Experiment material
It is research sample to choose volatile compound in 56 drinking water, and compound gas phase chromatographic retention is with tRTable
Show, experiment value is derived from paper：VOCs fast GC MS analysis [J] water purification technologies, 2016,35 (s1) in 57 in water：83-88,
112.Compound and its gas chromatography retention time (tR) it is listed in table 1.
Table 1
Molecular structure of compounds is characterized
Chromatographic retention (the t of organic compoundR) except having outside the Pass with measurement factor, it is related also to the structure of molecule.
Connected mode between composition compound atomic species, number, atom etc. all can be to tRProduce influence.In molecular structure hydrogen suppressed graph
In, the relation pair compound property between different non-hydrogen atoms and non-hydrogen atom has material impact, and the influence of hydrogen atom can
To ignore.Non-hydrogen atom is divided into 4 classes first, with 1, the non-hydrogen atom that 2,3,4 other non-hydrogen atoms are joined directly together advises respectively
It is set to first, second, third and fourth class non-hydrogen atom, the secondary carbon being such as connected with 2 non-hydrogen atoms belongs to Equations of The Second Kind non-hydrogen atom.
Then non-hydrogen atom is subjected to parametrization dyeing.
In formula, i is the coding of non-hydrogen atom in the molecule, niFor non-hydrogen atom i principal quantum number, xiFor electronegativity, xCFor
The electronegativity (Pauling scales) of carbon atom, miFor outermost electron number, hiFor the number of hydrogen atoms directly connected.
Influence for different type non-hydrogen atom itself to compound property, carries out classification cumulative by formula (2).
In formula, k represents non-hydrogen atom i atomic type, ZiCalculated by formula (1).According to the classification of non-hydrogen atom, for one
4 class non-hydrogen atoms are contained up in individual organic compound molecule, therefore finally can obtain 4 non-hydrogen atoms itself to chemical combination physical property
Matter contributes item, uses x1、x2、x3And x4Represent.
For the influence of the relation pair molecular property between non-hydrogen atom, using Gaussian shape distance relation functional expressions
(3) calculate, this relation is not the specific mode of action between atom, but to reflect the degree of correlation between non-hydrogen atom
Change with distance increase and decrease in inverse change and with atomic property in positive change.
Zi、ZjCalculated by formula (1)；dijIt is relative distance (i.e. the bond distance's sum and carbon-carbon single bond key between non-hydrogen atom i, j
Long ratio, if there is mulitpath between i, j, is defined by most short)；N and l is the affiliated type of atom；α takes 0.5.Chemical combination
4 class non-hydrogen atoms can be combined into following 10 kinds of relational terms in thing molecule：m11、m12、…、m44, it is abbreviated as x5、x6、…、x14.This
Sample will at most have 14 variables (structured descriptor) to describe compound structure information.
QSRR is modeled with examining
For a sample set, not each Structural descriptors is right containing the information related to Compound Retention time
In the little descriptor of those correlations be noise, if they are stayed can not only influence regression effect in a model, but also meeting
Substantially reduce predictive ability.Therefore, variable is screened before modeling and just seems particularly necessary, and successive Regression (Stepwise
Regression, SMR) be selection variables common method.PLS (Partial least squares, PLS)
It is a kind of modeling method more popular at present, this method can effectively solve the Problems of Multiple Synteny between variable.Using progressively returning
(SMR) is returned to be modeled analysis with the method that PLS (PLS) is combined, with " leaving-one method " to model prediction energy
Power is tested.It is generally believed that modeling coefficient correlation (R2) between 0.64-1.0, show model height correlation；Standard deviation
(SD) when being less than 10% with the ratio of number range, show that model prediction accuracy is good；Cross-verification coefficient correlation Q2≥
0.5, show that model has good robustness and predictive ability.
Compound structure is subjected to parametrization sign and obtains structured descriptor, first using successive Regression (SMR) according to change
Amount conspicuousness size extracts variable successively.Then dependent variable X is combined as with the variable picked out, retained with compound gas phase chromatogram
Time (tR) it is dependent variable Y, then set up model with PLS (PLS).Final SMR picks out 12 variables altogether
Combine for modeling, Variable Selection and corresponding PLS the results are shown in Table 2.
Table 2
One good forecast model not only has preferable capability of fitting to internal specimen, but also should be to external samples
With stronger predictive ability.Therefore in preference pattern, in the case where ensureing to there is internal specimen good fit effect,
Cross-verification coefficient correlation (Q is selected as far as possible2) larger model, to ensure that model has stronger predictive ability.Can be with table 2
Find out, it should which the variable that selection is selected by the step of successive Regression (SMR) the 8th combines (selected structured descriptor is listed in table 1) modeling institute
Model is obtained, now compound gas phase chromatographic retention (tR) be with original argument's regression equation：tR=-4.602+
1.735·x1+0.995·x2+1.573·x3+1.906·x4-1.659·x5-0.820·x6-0.818·x7-0.734·x8。
Model coefficient correlation (R2) reach 0.955 (close to maximum 0.959), and cross-verification coefficient correlation (Q2) reach maximum
0.894。R2Between 0.64-1.0, Q2>=0.5, illustrate that this model height correlation, robustness be good, predictive ability is strong.Modeling mark
Quasi- deviation (SD) is 0.803, and the number range (maximum 15.236- minimum values 1.300) of sample is 13.936, and standard deviation
(0.803) ratio with number range (13.936) is 5.76%, and the standard much smaller than 10% illustrates that forecasting accuracy is good.
Fig. 1 be 56 samples in PLS the first two principal component scores space scatter diagram, more than 90% sample point all falls
In 95% confidence level Hotelling T2In oval confidence circle, illustrate that structured descriptor can appropriately show volatile organic compound
Thing molecular characterization, and correctly reflected in statistical model.In order to investigate fit solution of the sample in X spaces, to sample
Originally the Standardized Model distance in X spaces has been made to analyze (Fig. 2), it can be seen that more than 95% sample Standardized Model distance
All in 95% confidential interval (only 1 sample exceeds this scope), less than critical value 2.414, equally illustrate model quality
Well.What it is beyond this scope is that, containing a tert-butyl group in No. 42 samples, No. 42 samples, and other compounds are free of this group,
With certain particularity.
Fig. 3 is variable importance (VIP) perspective view in PLS, it is generally recognized that VIP is more than between 1 variable and Y with larger
Correlation, notable is influenceed on Y.It can be found that x in Fig. 33、x2VIP values be more than 1, illustrate gas of the two variables to compound
Phase chromatographic retention (tR) influence is notable, and variable x3Corresponding to the own contribution of the 3rd class non-hydrogen atom, variable x2Correspondence
In the own contribution of Equations of The Second Kind non-hydrogen atom,
Thus the 3rd class atom, Equations of The Second Kind atom are more, and compound may have larger retention time (tR) value；Secondly
It is x7To Compound Retention time (tR) also certain influence, x7Corresponding to the relation of first kind atom and the 3rd class atom, the above
All illustrate the chromatographic retention (t of substituent species, quantity and distribution situation to compoundR) there is important influence.
Fig. 4 is load diagram of the sample in the first two principal component.Fig. 4 can be found that x3、x2、x7The upper right side region of figure is in,
There is larger value in two principal components, and be proportionate with Y, equally illustrate x3、x2、x7It is notable on Y influences, it is and upper
State analysis result consistent.x1、x4、x5、x8It is negatively correlated with Y in first principal component, it is proportionate in Second principal component, with Y；
x6Be in the bottom-left quadrant of figure, i.e., it is negatively correlated with Y in two principal components.x1、x2、x3、x4、x5、x6、x7、x8In figure
There is a certain distance between present position, illustrate that selected variable does not have synteny between great similitude, variable small.
Gas chromatography retention time (t of the model to sampleR) predicted, predicted value is listed in the Cal. row of table 1, error
It is listed in Err. row.Fig. 5 is the related figure between model predication value and experiment value, and Fig. 6 is predicated error distribution map.Can be with from Fig. 5
Find out, all sample points are all distributed on 45 ° of diagonal or against diagonal, illustrate predicted value and experiment value closely,
Macro-forecast effect is good.Equally, the forecasting accuracy height of model, error are small as can be seen from Figure 6, the prediction of most samples
Error is in the range of ± 2SD, and the error of only 4 samples slightly exceeds this scope, and same explanation global error can receive.Its
In No. 52 sample error maximums, it is relevant to contain most chlorine atoms (6) with it, and experimental data is also possible to certainly in itself
There is larger error to influence result of calculation.
Compared with prior art, the structured descriptor of structure need not carry out molecular structure optimization, be easily understood, amount of calculation
It is small, and non-hydrogen atom dye number consider principal quantum number, electronegativity, outermost electron number, connection number of hydrogen atoms etc. it is abundant
Information.
Non-hydrogen atom in molecule is classified and dyeing is parameterized, by non-hydrogen atom itself and different non-hydrogen atoms it
Between relation as Molecular structure descriptor, parametrization table has been carried out to VOC structure in the drinking water of part
Levy.Compound structure is constructed using successive Regression with the method (SMR-PLS) that offset minimum binary is combined to protect with gas-chromatography
Stay time (tR) relational model, model coefficient correlation (R2) and cross-verification coefficient correlation (Q2) ideal, certain journey
Influence compound gas phase chromatographic retention is disclosed on degree
(tR) structural factor.Model can accurately predict the gas-chromatography of VOC in drinking water
Retention time (tR), there is higher reference value for the QSPR/QSAR researchs of organic pollution.
Described above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, on the premise of principle of the present invention is not departed from, some improvements and modifications can also be made, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (1)
1. a kind of organic compound molecule structure parameterization characterizing method, it is characterised in that the described method comprises the following steps：
Step one chooses sample
It is research sample to choose volatile compound in 56 drinking water, and compound gas phase chromatographic retention is with tRRepresent；
Step 2 molecular structure of compounds is characterized
By in sample with 1, the non-hydrogen atom that 2,3,4 other non-hydrogen atoms are joined directly together respectively provided as first, second, third and fourth
Class non-hydrogen atom, then carries out parametrization dyeing by formula one by non-hydrogen atom,
In formula, i is the coding of non-hydrogen atom in the molecule, niFor non-hydrogen atom i principal quantum number, x1For electronegativity, xCIt is former for carbon
The electronegativity of son, miFor outermost electron number, hiFor the number of hydrogen atoms directly connected,
Influence for different type non-hydrogen atom itself to compound property, carries out classification by formula two and adds up,
In formula, k represents non-hydrogen atom i atomic type, ZiCalculate, according to the classification of non-hydrogen atom, have for one by formula one
4 class non-hydrogen atoms are contained up in machine compound molecule, therefore finally can obtain 4 non-hydrogen atoms itself to compound property tribute
Item is offered, x is used1、x2、x3And x4Represent,
It is formula three using Gaussian shape distance relation functions for the influence of the relation pair molecular property between non-hydrogen atom
Calculate,
Zi、ZjCalculated by formula one；dijIt is the relative distance between non-hydrogen atom i, j, i.e., bond distance's sum is with carbon-carbon single bond bond distance's
Ratio, if there is mulitpath between i, j, is defined by most short, and n and l are the affiliated type of atom, and a takes 0.5, compound point
4 class non-hydrogen atoms can be combined into following 10 kinds in son
Relational term：m11、m12、…、m44, it is abbreviated as x5、x6、…、x14, so at most by the structure for thering are 14 variables to characterize compound
Information.
Step 3 Variable Selection and modeling
The variable that step 2 is obtained, is screened according to variable conspicuousness using successive Regression to variable first, then with
The variable filtered out is combined as dependent variable X, with compound gas phase chromatographic retention tRFor dependent variable Y, with offset minimum binary
Model is set up in recurrence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710014952.2A CN107024558A (en) | 2017-01-10 | 2017-01-10 | A kind of organic compound molecule structure parameterization characterizing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710014952.2A CN107024558A (en) | 2017-01-10 | 2017-01-10 | A kind of organic compound molecule structure parameterization characterizing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107024558A true CN107024558A (en) | 2017-08-08 |
Family
ID=59526088
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710014952.2A Pending CN107024558A (en) | 2017-01-10 | 2017-01-10 | A kind of organic compound molecule structure parameterization characterizing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107024558A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107516012A (en) * | 2017-08-21 | 2017-12-26 | 内江师范学院 | A kind of structured descriptor calculated based on organic compound molecule three-dimensional structure |
| CN113358808A (en) * | 2021-07-01 | 2021-09-07 | 佳木斯大学 | Method for qualitatively identifying polar compounds by using reversed-phase chromatographic retention index |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000079263A2 (en) * | 1999-06-18 | 2000-12-28 | Synt:Em S.A. | Identifying active molecules using physico-chemical parameters |
| CN102507630A (en) * | 2011-11-30 | 2012-06-20 | 大连理工大学 | Method for forecasting oxidation reaction rate constant of chemical substance and ozone based on molecular structure and environmental temperature |
| CN104834831A (en) * | 2015-04-08 | 2015-08-12 | 北京工业大学 | Consistency model building method based on 3-dimensional quantitative structure-activity relationship model |
| CN104915563A (en) * | 2015-06-16 | 2015-09-16 | 中国环境科学研究院 | Fresh water chronic standard prediction method based on metal quantitative structure-activity relation |
-
2017
- 2017-01-10 CN CN201710014952.2A patent/CN107024558A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2000079263A2 (en) * | 1999-06-18 | 2000-12-28 | Synt:Em S.A. | Identifying active molecules using physico-chemical parameters |
| CN102507630A (en) * | 2011-11-30 | 2012-06-20 | 大连理工大学 | Method for forecasting oxidation reaction rate constant of chemical substance and ozone based on molecular structure and environmental temperature |
| CN104834831A (en) * | 2015-04-08 | 2015-08-12 | 北京工业大学 | Consistency model building method based on 3-dimensional quantitative structure-activity relationship model |
| CN104915563A (en) * | 2015-06-16 | 2015-09-16 | 中国环境科学研究院 | Fresh water chronic standard prediction method based on metal quantitative structure-activity relation |
Non-Patent Citations (4)
| Title |
|---|
| QIN SONG 等: "Estimation and Prediction of Retention Time for a Variety of Volatile Organic Compounds", 《结构化学》 * |
| 廖立敏 等: "分子顶点相关指数用于卤代苯IgKow的预估", 《计算机与应用化学》 * |
| 李建凤 等: "地下水中挥发性有机物结构与色谱保留关系研究", 《分析科学学报》 * |
| 田菲菲: "定量谱效关系的研究与应用", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑 》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107516012A (en) * | 2017-08-21 | 2017-12-26 | 内江师范学院 | A kind of structured descriptor calculated based on organic compound molecule three-dimensional structure |
| CN113358808A (en) * | 2021-07-01 | 2021-09-07 | 佳木斯大学 | Method for qualitatively identifying polar compounds by using reversed-phase chromatographic retention index |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hille et al. | The impact of FDI on regional air pollution in the Republic of Korea: a way ahead to achieve the green growth strategy? | |
| Gidden et al. | Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century | |
| Albert et al. | A multi‐trait approach reveals the structure and the relative importance of intra‐vs. interspecific variability in plant traits | |
| Luecken et al. | Sensitivity of ambient atmospheric formaldehyde and ozone to precursor species and source types across the United States | |
| Swenson | Phylogenetic imputation of plant functional trait databases | |
| Walker et al. | The relationship of leaf photosynthetic traits–Vcmax and Jmax–to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta‐analysis and modeling study | |
| Karl et al. | Urban flux measurements reveal a large pool of oxygenated volatile organic compound emissions | |
| Bergmann et al. | Root traits are more than analogues of leaf traits: the case for diaspore mass | |
| Cottenie | Integrating environmental and spatial processes in ecological community dynamics | |
| Šímová et al. | The enigma of terrestrial primary productivity: measurements, models, scales and the diversity–productivity relationship | |
| Van Bodegom et al. | Going beyond limitations of plant functional types when predicting global ecosystem–atmosphere fluxes: exploring the merits of traits‐based approaches | |
| Kreft et al. | Contrasting environmental and regional effects on global pteridophyte and seed plant diversity | |
| Serra‐Varela et al. | Does phylogeographical structure relate to climatic niche divergence? A test using maritime pine (P inus pinaster A it.) | |
| Sguera et al. | Functional outlier detection by a local depth with application to no x levels | |
| Archibald et al. | On the changes in surface ozone over the twenty-first century: sensitivity to changes in surface temperature and chemical mechanisms | |
| Peppler‐Lisbach et al. | Long‐term vegetation changes in Nardus grasslands indicate eutrophication, recovery from acidification, and management change as the main drivers | |
| CN107024558A (en) | A kind of organic compound molecule structure parameterization characterizing method | |
| Haq et al. | Human-driven disturbances change the vegetation characteristics of temperate forest stands: A case study from Pir Panchal mountain range in Kashmir Himalaya | |
| Poyatos et al. | Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information | |
| Zhang et al. | A phylogenetic and trait‐based analysis of community assembly in a subtropical forest in central China | |
| Matsumoto et al. | CGE models in energy economics | |
| Martinelli et al. | Partitioning of environmental and taxonomic controls on Brazilian foliar content of carbon and nitrogen and stable isotopes | |
| Menefee et al. | Cropping management in a livestock–pasture–crop integration modifies microbial communities, activity, and soil health score | |
| Erdélyi et al. | Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: A comparative assessment of random forest variants | |
| Serna‐Chavez et al. | Spatial scale dependence of factors driving climate regulation services in the Americas |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170808 |
|
| RJ01 | Rejection of invention patent application after publication |