> JLI{ ,.bjbjzz4@&+8.4bhvpaaa$\CaaaaaCXaazsq+R n0}X4aaaaaaaCCaaaaaaaaaaaaaaaa:Supplements
Timeofflight secondary ion mass spectrometry (TOFSIMS)
Timeofflight secondary ion mass spectrometry (TOFSIMS) is a sensitive, semiquantitative mass spectroscopic technique for imaging of material properties ADDIN EN.CITE Belu200354[29]545417Belu, Anna M.Graham, Daniel J.Castner, David G.Timeofflight secondary ion mass spectrometry: techniques and applications for the characterization of biomaterial surfacesBiomaterials363536532421TOFSIMSBiomaterialsSurface characterizationImagingMultivariate analysis200301429612http://www.sciencedirect.com/science/article/pii/S014296120300159510.1016/s01429612(03)001595[ HYPERLINK \l "_ENREF_29" \o "Belu, 2003 #54" 29]. With careful handling of samples and standards it is possible to obtain approximately quantitative data but several factors require control. The technique penetrates only the first 13 molecular layers, so that contamination control of the sample surface is important. The secondary analytical ions are extracted by collision with the sample target. The combined probabilities of collision and ionization range over several orders of magnitude. There are also matrix and surface effects, i.e. the chemical composition of the region of the analysed spot influences the ionization yield and the yield will be greater from a tilted surface than from a flat surface, so that rough surfaces give higher responses. The high variability associated with these factors requires normalized measurements and standardized conditions during analysis. When univariate mass spectral data are to be used, it may be advantageous to normalize the response against a common component. A further alternative is to use normalized multivariate full mass spectrum data, in which relationships between m/z responses are included in the calibration model. Despite the many sources of possible variability, multivariate TOFSIMS calibration for protein analysis has been shown to provide accuracy of 525 % when compared to iodine radiolabeled protein analysis ADDIN EN.CITE ADDIN EN.CITE.DATA [ HYPERLINK \l "_ENREF_19" \o "Wagner, 2006 #47" 19, HYPERLINK \l "_ENREF_30" \o "Hu, 2005 #93" 30], which indicates that it is reasonably reliable as a quantitative method.
Orthogonal Partial Least Squares
Orthogonal partial least squares (OPLS) analysis is an extension of partial least squares (PLS) and has the same predictive ability as PLS and increased functionality for the interpretation of data. PLS is a multidimensional linear regression method in which it is possible to incorporate many correlated or noncorrelated x and y variables in the regression equation. PLS can deliver regression coefficients for the direct calculation of new y values from xvariables but the regression coefficients are not useful for the interpretation of data ADDIN EN.CITE Seasholtz199055[31]555517Seasholtz, Mary BethKowalski, Bruce R.Qualitative information from multivariate calibration modelsAppl. Spectrosc.133713484481990[ HYPERLINK \l "_ENREF_31" \o "Seasholtz, 1990 #55" 31]. Therefore, PLS divides the regression model into PLScomponents, for which there is one set of loadingcoefficients (or one loadingvector) for each component. The loadingvectors are more relevant for interpretation than the PLS regression coefficients ADDIN EN.CITE Seasholtz199055[31]555517Seasholtz, Mary BethKowalski, Bruce R.Qualitative information from multivariate calibration modelsAppl. Spectrosc.133713484481990[ HYPERLINK \l "_ENREF_31" \o "Seasholtz, 1990 #55" 31]. However, the variation in loadingvectors will contain both the patterns in xvariables that support the variation in y and patterns of interfering variations not related to y. The advantage of OPLS is that the variation in x is split into two parts, the predictive part and the orthogonal part ADDIN EN.CITE Trygg200240[20]404017Trygg, JohanWold, SvanteOrthogonal projections to latent structures (OPLS)J. Chemom119128162002[ HYPERLINK \l "_ENREF_20" \o "Trygg, 2002 #40" 20]. The interfering variation in xvariables that is not related to y is placed in the orthogonal loadings and the variation that is directly related to y is placed in the predictive loadings. When the variation in y is dichotomous, this is usually presented in the OPLS regression as ones or zeroes in the y variable. This form of regression is called OPLSDiscriminant Analysis (OPLSDA) ADDIN EN.CITE Bylesj200645[26]454517Bylesj, MaxRantalainen, MattiasCloarec, OlivierNicholson, Jeremy K.Holmes, ElaineTrygg, JohanOPLS discriminant analysis: combining the strengths of PLSDA and SIMCA classificationJ Chemom34135120810OPLSDAorthogonalmultivariateclassificationPLSDASIMCA2006John Wiley & Sons, Ltd.1099128Xhttp://dx.doi.org/10.1002/cem.100610.1002/cem.1006[ HYPERLINK \l "_ENREF_26" \o "Bylesj, 2006 #45" 26].
Both PLS and OPLS generate sets of scores for each PLScomponent. These scores represent the observations and can be scatterplotted to show the relations between observations in a given PLS or OPLS model. Scores in OPLS are rotated when compared to PLS scores. The OPLS scores will always be aligned with the predictive component and an OPLSDA model will try to divide the scores in two groups along the predictive dimension in the score plot, commonly plotted horizontally.
Any meaningful data set will contain variation in some dimensions (degrees of freedom). The similarity between multiple linear regression (MLR) and PLS is that both methods cannot work beyond these degrees of freedom. In MLR it is necessary to identify a set of relatively independent variables to use in the regression whereas in PLS it is necessary to determine the number of PLScomponents that are needed for a robust model using diagnostic tools. The number of MLR variables and the number of PLScomponents or latent variables should be about the same. In both cases mathematical orthogonality is created, in MLR by selecting variables and in PLS by arranging orthogonal loading vectors.
Skin prick test
The Phadiatop test determines the presence of raised levels of specific serum IgE antibodies to common inhaled allergens (dog, cat, horse, timothy, birch, mugwort, house dust mite, and Cladosporium). The test result is expressed as positive or negative with a cutoff of 0.35 kU/l of the specific IgE.
Lung function
FEV1(pred%) FEV1% predicted is defined as FEV1% of the patient divided by the average FEV1% in the population for any person of similar age, sex and body composition.
PAGE \* MERGEFORMAT 1
FGy
z
{

ƷxaxxQQh!h*6OJQJmH sH ,jh*OJQJUmHnHsH u&jh*OJQJUmHnHsH uh*OJQJmHnHsH uh*OJQJmH sH jh*OJQJUmH sH h!h*OJQJmH sH hUh*6mH sH hUh*6OJQJmH sH hUh*mH sH h*mH sH h{rh*mH sH
Gw'x'U)V),
,,KY...... ...(.).$a$ddgd*gd*gd*EFGIJLMyz{}~͋͢l]NBh*OJQJmH sH hAdh*OJQJmH sH hah*OJQJmH sH hUh*6OJQJmH sH h!h*OJQJmH sH ,jZh*OJQJUmHnHsH u,jh*OJQJUmHnHsH u&jh*OJQJUmHnHsH uh*OJQJmHnHsH ujh*OJQJUmH sH %juh*OJQJUmH sH $% !!!l,j
h*OJQJUmHnHsH u,jD
h*OJQJUmHnHsH uh*OJQJmH sH hAdh*OJQJmH sH jh*OJQJUmH sH ,jh*OJQJUmHnHsH u&jh*OJQJUmHnHsH uh*OJQJmHnHsH u&!"6"b"c"d":';'<'='n'o'p'r's't'u'v'x''''''',,!,Ʒ}}}}mZ%h9oh*B*OJQJmH phsH huPh*6OJQJmH sH hAdh*OJQJmH sH ,j.h*OJQJUmHnHsH u&jh*OJQJUmHnHsH uh*OJQJmHnHsH ujh*OJQJUmH sH h*OJQJmH sH hdRKh*OJQJmH sH hah*OJQJmH sH !,,,,JKOPY\]dt...... .
..
.$.쵡}i}U}MIMIMIMIA=hTqjhTqUhVFjhVFU'h5h*CJOJQJ\aJmH sH 'h5h*CJH*OJQJaJmH sH $h5h*CJOJQJaJmH sH !h*6CJOJQJaJmH sH 'h5h*6CJOJQJaJmH sH hAdh*OJQJmH sH (hAdh*6B*OJQJmH phsH %hAdh*B*OJQJmH phsH %hAdh*B*OJQJmH phsH $.%.&.'.*.+.,.$h5h*CJOJQJaJmH sH hVFhTqjhTqUmHnHuhCOmHnHujhTqU).*.+.,.dgd*21h:pTq. A!"#$%uDyK
_ENREF_29pDHu200593[19, 30]939317Hu, S.Xie, Y.Ramachandran, P.Ogorzalek Loo, R. R.Li, Y.Loo, J. A.Wong, D. T.School of Dentistry & Dental Research Institute, University of California, Los Angeles, CA 90095, USA.Largescale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and twodimensional gel electrophoresismass spectrometryProteomics171428562005/04/01Chromatography, LiquidElectrophoresis, Gel, TwoDimensionalHumansProteome/*metabolismSaliva/*metabolismSpectrometry, Mass, Electrospray IonizationSpectrometry, Mass, MatrixAssisted Laser DesorptionIonization2005Apr16159853 (Print)
16159853 (Linking)15800970http://www.ncbi.nlm.nih.gov/pubmed/1580097010.1002/pmic.200401037engWagner200647474717Wagner, M. S.Graham, D. J.Castner, D. G.Simplifying the interpretation of ToFSIMS spectra and images using careful application of multivariate analysisAppl. Surf. Sci.6575658125219ToFSIMSMultivariate analysisImage analysisPrincipal component analysisMultivariate curve resolution200601694332http://www.sciencedirect.com/science/article/pii/S016943320600332110.1016/j.apsusc.2006.02.073uDyK
_ENREF_19uDyK
_ENREF_30uDyK
_ENREF_31uDyK
_ENREF_31uDyK
_ENREF_20uDyK
_ENREF_26j666666666vvvvvvvvv666666>6666666666666666666666666666666666666666666666666hH6666666666666666666666666666666666666666666666666666666666666666662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~ OJPJQJ_HmHnHsHtHN`NStandarddCJ_HaJmHsHtH x@x*
berschrift 2$$@&/5B*CJOJPJQJ\aJmHphOsHtHJA J
AbsatzStandardschriftartXiX
0Normale Tabelle4
l4a0k 0
0Keine Listelol*berschrift 2 Zchn/5B*CJOJPJQJ\aJmHphOsHtHL @L*0Fuzeile
p#CJaJmHsHtH>o>*0
Fuzeile ZchnmHsHtHl^@"l*0Standard (Web)ddd[$\$ CJOJPJQJaJmHsHtHPK![Content_Types].xmlN0EHJ@%ǎǢș$زULTB l,3;rØJB+$G]7O٭VL1Lg?zd9qw}틇_Veۏ@蟙9ϿzϿV7Cn#cpDŵv#L;6Pk{*r7eq7Gc E+4߈b9pQZW)IV+2n*]8MR`μ,ǻqc8Q8$ QH?TxwR'\Bw(`Z!94۴Mc˴gȷۨY[EBW`Va0'DJǬ*#Sq= !aϴH@YieE
Ed`~ЍpVa4(F{\Uw!;'}'ݧ:&
D?(^'ܩ11TpuL"nF#n)d]A^5Jز?Mϲs/?lvx Rma0`d_nBX/ʁ
6:+kPo
Z2_]Zѱ_3*E
ЈM>R*}ahG.9gMW~'fpv4;
!6o%JۍswŴR.+<+/FS*BiDpTPTz/ȡanjHP8T$Z2wzvvY,d*dL#rHPs>=A6hN֟=Qr9LV638h&T;ƬFtEyZƚxY(HH>#:~ l/i#&ʆ6t'B`kΒs\uN/^d;kC
=٢4o2&1GYt?L*a>֍PK!
ѐ'theme/theme/_rels/themeManager.xml.relsM
0wooӺ&݈Э5
6?$Q
,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}$b{P8g/]QAsم(#L[PK![Content_Types].xmlPK!֧60_rels/.relsPK!kytheme/theme/themeManager.xmlPK!`theme/theme/theme1.xmlPK!
ѐ' theme/theme/_rels/themeManager.xml.relsPK]
,&i@ ***!!,$.,.).,.y{
FILz}$c:<ort,&QX̕QQDXX̕QX̕QX̕QX̕QX̕$&!8@0(
B
S ? ##!$*$k$n$$$$$1%3%F%I%^%b%&&&&&& &&&'&*&&Y%^%&&&&&& &&&*&&3
K%Y%&&&&&& &&&*&&* =S>VFCO_fTq&&@,&H@UnknownG*Ax Times New Roman5Symbol3.*Cx Arial7@Cambria7.@ CalibriA$BCambria Math"1gMG4 E4 E%20%%KQHX $P*2!xxsesslersDietwiler, EstherOh+'0T
(4<DLsesslersNormal.dotmDietwiler, Esther3Microsoft Office Word@.N*@lsq4 ՜.+,D՜.+,4hp
S.Karger AGE%TitelD 8@_PID_HLINKSA*C%
_ENREF_26C
_ENREF_20B
_ENREF_31B
_ENREF_31B
_ENREF_30@
_ENREF_19C
_ENREF_29
"#$%&'(*+,./012345678:;<=>?@BCDEFGHKRoot Entry FPWzsqMData
!1Table)WordDocument4@SummaryInformation(9DocumentSummaryInformation8ACompObjr
F Microsoft Word 972003Dokument
MSWordDocWord.Document.89q