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SPAS 2023.3.31版 Strati

Package‘SPAS’April21,2023Type PackageTitle Stratiﬁed-Petersen Analysis SystemVersion2023.3.31Date2023-03-31Author Carl James SchwarzMaintainer Carl James Schwarz<******************************>LinkingTo TMB,RcppEigenImports MASS,Matrix,msm,numDeriv,plyr,reshape2,TMB(>=1.7.15)Description The Stratiﬁed-Petersen Analysis System(SPAS)is designedto estimate abundance in two-sample capture-recapture experimentswhere the capture and recaptures are stratiﬁed.This is a generalizationof the simple Lincoln-Petersen estimator.Strata may be deﬁned in time or in space or both,and the s strata in which marking takes placemay differ from the t strata in which recoveries take place.When s=t,SPAS reduces to the method described byDarroch(1961)<https:///stable/2332748>.When s<t,SPAS implements the methods described inPlante,Rivest,and Tremblay(1988)<https:///stable/2533994>.Schwarz and Taylor(1998)<https:///doi/10.1139/f97-238>describe the use of SPAS in estimating return of salmon stratiﬁed bytime and geography.A related package,BTSPAS,deals with temporal stratiﬁcation wherea spline is used to model the distribution of the populationover time as it passes the second capture location.This is the R-version of the(now obsolete)standalone Windowsprogram available at<https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>. License GPL(>=2)RoxygenNote7.2.3Suggests knitr,rmarkdownVignetteBuilder knitrEncoding UTF-81NeedsCompilation yesRepository CRANDate/Publication2023-04-2022:12:40UTCR topics documented:SPAS.autopool (2)SPAS.ﬁt.model (4)SPAS.print.model (6)Index7 SPAS.autopool Autopooling a Stratiﬁed-Petersen(SP)data set.This function appliespooling rules to pool a SPAS dataset to meeting minimum sparsityrequirements.DescriptionAutopooling a Stratiﬁed-Petersen(SP)data set.This function applies pooling rules to pool a SPAS dataset to meeting minimum sparsity requirements.UsageSPAS.autopool(rawdata,min.released=100,min.inspected=50,min.recaps=50,min.rows=1,min.cols=1)Argumentsrawdata An(s+1)x(t+1)of the raw data BEFORE pooling.The s x t upper left matrix is the number of animals released in row stratum i and recovered in column stratumj.Row s+1contains the total number of UNMARKED animals recovered incolumn stratum j.Column t+1contains the number of animals marked in eachrow stratum but not recovered in any column stratum.The rawdata[s+1,t+1]isnot used and can be set to0or NA.The sum of the entries in each of theﬁrsts rows is then the number of animals marked in each row stratum.The sum ofthe entries in each of theﬁrst t columns is then the number of animals captured(marked and unmarked)in each column stratum.The row/column names of thematrix may be set to identify the entries in the output.min.released Minimum number of releases in a pooled rowmin.inspected Minimum number of inspections in a pooled columnmin.recaps Minimum number of recaptures before any rows can be pooledmin.rows,min.colsMinimum number or rows and columns after poolingDetailsIn many cases,the stratiﬁed set of releases and recapture is too sparse(many zeroes)or count are very small.Pooling rows and columns may be needed.Data needs to be pooled both row wise and column wise if the data are sparse to avoid singularities in theﬁt.This function automates pooling rows or columns following Schwarz and Taylor(1998).•All rows that have0releases are discarded•All columns that have0recaptures of taggedﬁsh and0ﬁsh inspected are discarded•Starting at theﬁrst row and working forwards in time,and then working from theﬁnal rowand working backwards in time,.rows are pooled until a minimum of min.released arereleased.An alternating pooling(from the top,from the bottom,from the top,etc)is used•Starting at theﬁrst column and working forwards in time,.and then working from theﬁnalcolumn and working backwards in time,columns are pooled until a minimum of min.inspected are inspected.An alternating pooling(from the left,from the right,from the left,etc)is used.•If the sum of the total recaptures from releasedﬁsh is<=min.recaps,then all rows are pooled(which reduces to a Chapman estimator)ValueA list with a suggest pooling.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4340,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)SPAS.autopool(conne.data)SPAS.fit.model Fit a Stratiﬁed-Petersen(SP)model using TMB.DescriptionThis functionﬁts a Stratiﬁed-Petersen(Plante,1996)to data and specify which rows/columns of the data should be pooled.The number of rows after pooling should be<=number of columns after pooling.UsageSPAS.fit.model(model.id="Stratified Petersen Estimator",rawdata,autopool=FALSE,row.pool.in=NULL,col.pool.in=NULL,row.physical.pool=TRUE,theta.pool=FALSE,CJSpool=FALSE,optMethod=c("nlminb"),optMethod.control=list(maxit=50000),svd.cutoff=1e-04,chisq.cutoff=0.1,min.released=100,min.inspected=50,min.recaps=50,min.rows=1,min.cols=1)Argumentsmodel.id Character string identifying the name of the model including any pooling..rawdata An(s+1)x(t+1)of the raw data BEFORE pooling.The s x t upper left matrix is the number of animals released in row stratum i and recovered in column stratumj.Row s+1contains the total number of UNMARKED animals recovered incolumn stratum j.Column t+1contains the number of animals marked in eachrow stratum but not recovered in any column stratum.The rawdata[s+1,t+1]isnot used and can be set to0or NA.The sum of the entries in each of theﬁrsts rows is then the number of animals marked in each row stratum.The sum ofthe entries in each of theﬁrst t columns is then the number of animals captured(marked and unmarked)in each column stratum.The row/column names of thematrix may be set to identify the entries in the output.autopool Should the automatic pooling algorithms be used.Give more details here on these rule work.row.pool.in,col.pool.inVectors(character/numeric)of length s and t respectively.These identify therows/columns to be pooled before the analysis is done.The vectors consists ofentries where pooling takes place if the entries are the same.For example,if s=4,then row.pool.in=c(1,2,3,4)implies no pooling because all entries are distinct;row.pool.in=c("a","a","b","b")implies that theﬁrst two rows will be pooled andthe last two rows will be pooled.It is not necessary that row/columns be contin-uous to be pooled,but this is seldom sensible.A careful choice of pooling labelshelps to remember what as done,e.g.row.pool.in=c("123","123","123","4")in-dicates that theﬁrst3rows are pooled and the4th row is not pooled.Characterentries ensure that the resulting matrix is sorted properly(e.g.if row.pool.in=c(123,123,123,4),then the same pooling is done,but the matrix rows are sorted rather strangely.row.physical.poolShould physical pooling be done(default)or should logical pooling be done.Forexample,if there are3rows in the data matrix and row.pool.in=c(1,1,3),then inphysical pooling,the entries in rows1and2are physically added together tocreate2rows in the data matrix beforeﬁtting.Because the data has changed,you cannot compare physical pooling using AIC.In logical pooling,the datamatrix is unchanged,but now parameters p1=p2but the movement parametersfor the rest of the matrix are not forced equal.theta.pool,CJSpoolNOT YET IMPLEMENTED.DO NOT CHANGE.optMethod What optimization method is used.Defaults is the nlminb()function..optMethod.controlControl parameters for optimization method.See the documentation on the dif-ferent optimization methods for details.svd.cutoff Whenﬁnding the variance-covariance matrix,a singular value decomposition isused.This identiﬁes the smallest singular value to retain.chisq.cutoff Whenﬁnding a goodness ofﬁt statistic using(obs-exp)^2/exp,all cell whoseExp<gof.cutoff are ignored to try and remove structural zero cells.min.released Minimum number of releases in a pooled rowmin.inspected Minimum number of inspections in a pooled columnmin.recaps Minimum number of recaptures before any rows can be pooledmin.rows,min.colsMinimum number or rows and columns after poolingValueA list with many entries.Refer to the vignettes for more details.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4346SPAS.print.model 0,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)mod1<-SPAS.fit.model(conne.data,model.id="Pooling rows1/2,5/6;pooling columns5/6",row.pool.in=c("12","12","3","4","56","56"),col.pool.in=c(1,2,3,4,56,56))mod2<-SPAS.fit.model(conne.data,model.id="Auto pool",autopool=TRUE)SPAS.print.model Print the results from aﬁt of a Stratiﬁed-Petersen(SP)model whenusing the TMB optimizerDescriptionThis function makes a report of the results of the modelﬁtting.UsageSPAS.print.model(x)Argumentsx A result from the modelﬁtting.See SPAS.fit.model.ValueA report to the console.Refer to the vignettes.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4340,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)mod1<-SPAS.fit.model(conne.data,model.id="Pooling rows1/2,5/6;pooling columns5/6",row.pool.in=c("12","12","3","4","56","56"),col.pool.in=c(1,2,3,4,56,56))SPAS.print.model(mod1)IndexSPAS.autopool,2SPAS.fit.model,4SPAS.print.model,67。

Onpattro (patisiran lipid complex) 泛型说明文档说明书

Onpattro (patisiran lipid complex)(Intravenous)Document Number: IC-0379 Last Review Date: 10/01/2019Date of Origin: 09/05/2018Dates Reviewed: 09/2018, 10/2019I.Length of AuthorizationCoverage will be provided for six months and may be renewed.II.Dosing LimitsA.Quantity Limit (max daily dose) [Pharmacy Benefit]:∙Onpattro 10 mg injection: 3 vials every 3 weeksB.Max Units (per dose and over time) [Medical Benefit]:∙300 billable units every 3 weeksIII.Initial Approval CriteriaCoverage is provided in the following conditions:∙Must not be used in combination with other transthyretin (TTR) reducing agents (e.g., inotersen, tafamidis, etc.); ANDPolyneuropathy due to Hereditary Transthyretin-Mediated (hATTR) Amyloidosis /FamilialAmyloidotic Polyneuropathy (FAP) †∙Patient must be at least 18 years old; AND∙Patient has a definitive diagnosis of hATTR amyloidosis/FAP as documented by amyloid deposition on tissue biopsy and identification of a pathogenic TTR variant using moleculargenetic testing; AND∙Used for the treatment of polyneuropathy as demonstrated by at least TWO of the following criteria:o Subjective patient symptoms are suggestive of neuropathyo Abnormal nerve conduction studies are consistent with polyneuropathyo Abnormal neurological examination is suggestive of neuropathy; AND∙Patient’s peripheral neuropathy is attributed to hATTR/FAP and other causes of neuropathy have been excluded; AND∙Baseline in strength/weakness has been documented using an objective clinical measuring tool (e.g., Medical Research Council (MRC) muscle strength, etc.); AND∙Patient has not been the recipient of an orthotopic liver transplant (OLT); AND∙Patient is receiving supplementation with vitamin A at the recommended daily allowance †FDA Approved Indication(s); ‡ Compendium Recommended Indication(s)IV.Renewal CriteriaAuthorizations can be renewed based on the following criteria:∙Patient continues to meet the criteria identified in section III; AND∙Absence of unacceptable toxicity from the drug. Examples of unacceptable toxicity include the following: severe infusion-related reactions, ocular symptoms related tohypovitaminosis A, etc.; AND∙Disease response compared to pre-treatment baseline as evidenced by stabilization or improvement in one or more of the following:o Signs and symptoms of neuropathyo MRC muscle strengthV.Dosage/AdministrationVI.Billing Code/Availability InformationHCPCS code:∙J0222 - Injection, patisiran, 0.1 mg; 1 billable unit = 0.1 mgNDC:Onpattro 10 mg/5 mL single-dose vial: 71336-1000-xxVII.References1.Onpattro [package insert]. Cambridge, MA; Alnylam Pharmaceuticals, Inc., August 2018.Accessed August 2019.2.Adams D, Gonzalez-Duarte A, O’Riordan WD, et al. Patisiran, an RNAi Therapeutic, forHereditary Transthyretin Amyloidosis. N Engl J Med. 2018 Jul 5;379(1):11-21. doi:10.1056/NEJMoa17161533.Adams D, Suhr OB, Dyck PJ, et al. Trial design and rationale for APOLLO, a Phase 3,placebo-controlled study of patisiran in patients with hereditary ATTR amyloidosis withpolyneuropathy. BMC Neurol. 2017;17(1):1814.Sekijima Y, Yoshida K, Tokuda T, et al. Familial Transthyretin Amyloidosis. Gene Reviews.Adam MP, Ardinger HH, Pagon RA, et al., editors. Seattle (WA): University of Washington,Seattle; 1993-2018.5.Ando Y, Coelho T, Berk JL, et al. Guideline of transthyretin-related hereditary amyloidosisfor clinicians. Orphanet J Rare Dis. 2013;8:31.Appendix 1 – Covered Diagnosis CodesAppendix 2 – Centers for Medicare and Medicaid Services (CMS)Medicare coverage for outpatient (Part B) drugs is outlined in the Medicare Benefit Policy Manual (Pub. 100-2), Chapter 15, §50 Drugs and Biologicals. In addition, National CoverageDetermination (NCD) and Local Coverage Determinations (LCDs) may exist and compliance with these policies is required where applicable. They can be found at: /medicare-coverage-database/search/advanced-search.aspx. Additional indications may be covered at the discretion of the health plan.Medicare Part B Covered Diagnosis Codes (applicable to existing NCD/LCD): N/A。

kisssoft-anl-003-E-din743-intro

KISSsoft, shaft analysis:Introduction to DIN 743, October 2000Shafts and axles, calculation of load capacityIntroductionPurposeThe DIN 743 for strength analysis of shafts and axles is a most helpful analysis method available in the KISSsoft software, [1], for analysis of machine elements. The standard however is available in German only and the theory behind the software KISSsoft is not readily available for non german speaking customers. Therefore, a short introduction to the said standard is given herewith. The DIN 743 The German standard DIN 743 [2] has been prepared by the German institute for standardisation and the Institut für Maschinenelemente und Maschinenkonstruktion of the technical university of Dresden, Germany. The objective was to make available for the engineering community a standard focusing on strength analysis of shafts and axles. The standard is based on the standard TGL 19340 of the former German Democratic Republic, the VDI 2226 of the Federal Republic of Germany and the FKMguideline compiled by the IMA Dresden, Germany, see [3], [4], [13]. The proof of strength is based on the calculation of a safety factor against fatigue and against static failure. The safety factors have to be higher than a required minimal safety factor. If this condition is fulfilled, proof is delivered. The standard consists of four parts:Part 1: Introduction, analysis methodPart 2: Stress concentration factors and fatigue notch factorsPart 3: Materials dataPart 4: ExamplesLimitationsThe analytical proof considers bending, tensile/compressive and shear stresses due to torsion.However, shear stresses due to shear forces are not considered, hence use of this standard for short shafts requires caution.Only the fatigue limit is used in the proof, no proof for finite life strength is delivered. However, an extension is planned, see section 0.Materials data are based on 107 stress cycles with a probability of survival of 97.5%.The safety factor required in the standard covers only the uncertainty in the analysis procedure.Additional safety factors or an increased safety factor due to uncertainties in the load assumptions and due to the effects of a failure are not defined. They have to be defined by the engineer.I n t r o d u c t i o n t o D I N 743The notch factors for feather keys are questionable since no difference is made for the different key forms.All loads (bending, tensile/compression, shear) are in phase.The standard does not cover the calculation of the load acting.The standard is limited to non-welded steels in the range of –40C ° to 150C °. The environment has to be non-corrosive for application of this standard.LoadsThe loads acting on the part are defined by describing the effective load amplitudes and the mean loads (for the fatigue proof) and the maximum acting load (for static proof). These loads are to be calculated according to the nominal stress concept, using standard engineering formulas.Proof against fatigue failure SafetyIn order to deliver the proof against fatigue failure, the safety S calculated has to be higher than the minimal required safety S min . According to the standard, S min has to be at least 1.2. Uncertainties in the load assumptions and severe effects in case of failure require higher safety factors, to be defined by the engineer. The safety S is calculated from partial safeties according to the following formula: 221⎟⎟⎠⎞⎜⎜⎝⎛+⎟⎟⎠⎞⎜⎜⎝⎛+=tADK ta bADK ba zdADK zda S ττσσσσwhereta ba zda τσσ,, Stress amplitudes due to tension/compression, bending, torsiontADK bADK zdADK τσσ,, Permissible stress amplitudes, strengthsThe form of the above formula is based on the idea of partial safeties for the specific load types (normal stresses / shear stresses) combined in elliptic form.The stress amplitudes are calculated based on the nominal stress concept, considering the cross section of the shaft (A, I, W b , W p ) and external loads (moments, forces).For calculation of the permissible stress amplitudes, see section 0.Condition for delivery of proof is that20.1min ≥≥S STwo different cases are distinguished:Case 1: The safety factor is based only on the comparison between actual and permissible stress amplitude, leaving the mean stress on a constant levelCase 2: The safety is based on the assumption that the stress ratio used for calculation of permissible stress amplitude is equal to the stress ratio as it is for the actual stress amplitudeThe latter is the more conservative approach and recommended.Strength of the partThe strength of the part (index ADK) is being calculated from the strength of the un-notched material specimen. It is a nominal stress considering- Technological size factor K 1(d eff ): effect of heat treatment (size of shaft), depending on diameter at time of treatment), see section 0- Geometrical size factor K 2(d): effect of stress gradient on bending strength compared to tensilestrength of specimen, see section 0- Notches, notch factor βσ(d), βτ(d): effect of local stress increasers/notches- Surface roughness factor K F σ, K F τ, see section 0- Surface hardening factor K V : effect of compressive residual stresses, see section 0Mean stress sensitivity ψσK and ψτK : Effect of mean stress level on permissible stress amplitude.Basic assumption is that the ultimate strength R m or σB is based on a reference diameter d B for which the ultimate strength R m is tabulated (see part 3 of the standard).R m / σB may also be estimated from measured Brinell hardness values according to:HB B H *3.0≈σFor a part with diameter d>d B lower strength applies, the difference being considered using the technological size factor K 1(d). This factor depends on the type of material used and its hardenability / heat treatability:)(*)()(1B B B d d K d σσ=WhereσzB (d)Strength of the part with diameter dσzB (d B ) Strength of the specimen with diameter d BK 1(d) Technological size coefficientBased on this ultimate strength of the part, the fatigue strengths are estimated as follows (for bending, tension/compression and shear):)(*3.0)()(*4.0)()(*5.0)(d d d d d d B tw B zdw B bw στσσσσ===The fatigue strength of the notched part then is (influence of mean stress not yet considered, see section 0), for tension/compression (index zd), bending (index b) and torsion (index t):τσσττσσσσK d K d K d K d K d K d eff B tW tWK eff B bW bWK eff B zdW zdWK )(*)()(*)()(*)(111===WhereK 1(d eff )Technological size factortW bW zdW τσσ,, Strength of the un-notched specimen with diameter d B These values are listed in Part 3 of the standardV F V F K K d K K K K d K K 1*11)(1*11)(22⎟⎟⎠⎞⎜⎜⎝⎛−+=⎟⎟⎠⎞⎜⎜⎝⎛−+=τττσσσββWhereK 2(d) Geometrical size coefficient, see section 0βσ,τNotch factor for compression/tension, bending and torsion Listed in Part 2 of the standardHence, the effect of the notch is considered in the permissible stress and not in the actual stress (calculated as nominal stress). Influence of mean stressTwo different cases are to be distinguished, see case 1 and case 2 in section 0. In both cases, thestrength of the notched part considering the mean stress is a function of the strength of the notched part not considering the mean stress, the mean stress and the mean stress sensitivity:),,,,,,,(,,K K b K zd mv mv tWK bWK zdWK tADK bADK zdADK f τσσψψψτστσστσσ=For case 1, we find:mvK tWK zdADK mv K b bWK bADK mvK zd zdWK zdADK τψτσσψσσσψσστσσ***−=−=−=And for case 2 (limiting conditions not shown, see standard for more details)ta mvK tWK tADK bamvK b bWKbADK zdamvK zd zdWKzdADK a ττψττσσψσσσσψσστσσ**1*1+=+=+=Where()3*322mvmv tmbm zdm mv σττσσσ=++=The mean stress sensitivity factors ψ themselves depend on the ultimate strength of the material and the alternating strength. According to the formulas used in the standard, the mean stress sensitivity factor is independent of the level of the mean stress, although new findings show that it is higher for low mean stresses and lower for higher mean stresses, see [11].Static proofSafetyFor this proof, the maximum stress occurring during the lifetime of the part is to be compared to it’s strength. The resulting safety is calculated as follows:2max 2max max 1⎟⎟⎠⎞⎜⎜⎝⎛+⎟⎟⎠⎞⎜⎜⎝⎛+=tFK t bFK b zdFK zd S ττσσσσNote that for the static proof, the effect of notches is not considered.Strength of the partIn the above formula, strengths of the part (in the denominators) are compared to the acting stresses (in the numerators). The comparisons are than added up in the sense of a elliptic safety criterion.Generally, the yield strength of the part of diameter d is not known (e.g. from measurement) and is to be estimated from the values of the specimen with diameter d B . The yield strengths of the part, generally written as σs (d) (yield strength for a part of diameter d) are calculated as follws:3/)(***)()(***)()(***)(212121B s F F eff tFK B s F F eff bFK B s F F eff zdFK d K d K d K d K d K d K σγτσγσσγσ===Where- K 1(d eff ) is the technological size coefficient- K 2F is the static support coefficient depending on the presence of a hardened outer layer of the material- γF is a coefficient for increasing the yield strength due to a multi-axial stress state in a notch - σS (d B ) is the yield strength of the specimen with diameter d BRemarks Notch effectsThe notch factor β is defined through the permissible stress amplitude against fatigue failure of the un-notched specimen of diameter d compared to the one of the notched part:tWK tW bWKzd bW zd d d ττβσσβτσ)()(,,==Whereas the form factor α is the coefficient of the stress value in the notch compared to the stress value in the un-disturbed cross section (nominal stress). Hence, whereas the form factor is a function only of the geometry of the part, the notch factor β is also a function of the material and the stress state. The notch factor β can be calculated from the form factor α as follows:n τστσαβ,,=Where n is the support coefficient to be calculated from the materials strength and the stress gradient in the notch. The stress gradient can either be calculated by means of FEM or estimated through formulas given in the standard.Different notch factors are given for all three load types considered (bending, tension/compression and torsion), Influence of sizeThe influence of the size of a part on it’s strength is to be considered in three factors:1) Technological size coefficient K 1(d eff ): With this factor the fact that the effect of hardening / heat treatment and hence the strength is reduced with increasing part diameter. This coefficient is independent of the type of load (tension/compression, bending, shear). The coefficient is to be estimated using the effective diameter during heat treatment. The coefficient is to beconsidered if the strength of the part is not measured but calculated from the strength of the specimen as tabulated in the standard.2) Geometrical size coefficient K 2(d): With this factor, the effect that the strength against bending converges towards the strength against tension/compression with increasing part diameter andthat the strength against shear due to torsion is also being reduced. This is due to the decreasing stress gradient with increasing part diameter. With the decreasing stress gradient, the support coefficient n also decreases.3) Geometrical size coefficient K 3(d): Same effect as in 2) but here for the notched part.Influence of surface hardeningWith the coefficient K V effects due to surface hardening depending on the procedure applied areconsidered. The underlying effects covered are the surface hardness and induced compressive stresses. It is recommended to use the lower range of the tabulated values or to conduct experiments if the higher values are to be used, see for example [6].Finite life calculationThe standard DIN 743 covers only the proof against fatigue failure for infinite life. However, anextension to finite life calculation is planned, see [12], [8]. With this extension, load spectrums can be considered. Based on the load spectrum, a damage equivalent load is calculated. Based on this damage equivalent load, using a S-N curve, the lifetime is estimated or the proof for infinite life is delivered.The damage equivalent load amplitude is calculated as follows:koll a a f /1σσ=Whereσa is the damage equivalent load amplitudeσa1 is the highest load amplitude of the collectivef koll describes the shape of the load sepctrum, calculated according to different variations of Miners rule, see [12]The permissible stress amplitude for a required life time is calculated as follows:ADK q LD ANK N N σσ*=WhereσANK is the permissible stress amplitude for finite lifeσADK is the permissible stress amplitude for infinite lifeN D = 1*106 cyclesN L is the required life (cycles), 1*103<N L <N Dq=5 for bending and tension/compression, q=8 for torsion Comparison to other analysis methodsIn [7], [9] and [5], comparisons of the analysis method according to DIN 743 with other methods, FKM-Richtlinie [13], Nieman [14] and Hänchen + Decker [15], are described.In comparison with the FKM-guideline, the following differences are found:- In the FKM guideline, shear stress due to shear forces are considered- The minimal safety factors required also depend on the material, load assumptions and effect of failure-High temperature is considered in static proof-Differences in notch factorsA comparison with the other two methods cited shows that the complete analysis procedure is quite different.All authors agree that the deviations in the results strongly depend on the notch considered. No clear tendencies were found when comparing the results, but the usefulness of the standard and the soundness of the design resulting from its use were confirmed in all cases.References[1]KISSsoft Software, Calculation software for machine design, www.KISSsoft.ch[2]DIN 743, Teile 1-3, Tragfähigkeitsberechnung von Wellen und Achsen, 2000[3]TGL 19340, Dauerfestigkeit der Maschinenbauteile, März 1983[4]VDI Richtlinie 2226, Empfehlung für die Festigkeitsberechnung metallischer Bauteile, VDI-Verlag, 1965[5] B. Schlecht, Vergleichende Betrachtungen zum Tragfähigkeitsnachweis der Wellen in Sondergetriebenvon Tagebaugrossgeräten, VDI Berichte Nr. 1442, 1998[6]T. Körner, H. Depping, J. Häckh, G. Willmerding, W. Klos, Rechnerische Lebensdauerabschätzung unterBerücksichtigung realer Belastungskollektive für die Hauptwelle eines Nutzfahrzeuggetriebes, VDIBerichte Nr. 1689, 2002[7]M. Bachmann, Anwendung der DIN 743 in der Antriebstechnik, VDI Berichte Nr. 1442, 1998[8]H. Linke, Praxisorientierte Berechnung von Wellen und Achsen nach DIN 743, VDI Berichte 1442, 1998[9]U. Kissling, Festigkeitsberechnung von Wellen – Einführung von neuen Normen, internal report,KISSsoft AG[10]U. Kissling, Festigkeitsberechnung von Wellen und Achsen nach neuen Normen, Antriebstechnik 36 Nr.10, 1997[11]H. Linke, I. Römhild, Belastbarkeit von Wellen und Achsen nach DIN 743, VDI/EKV Tagung Welle-Nabe-Verbindungen, April 1998, Fulda[12]Entwurf Berechnungsvorschlag FVA – BV 743, Tragfähigkeitsberechnung von Achsen und Wellen,Ergänzung zu DIN 743, Erfassung von Lastkollektiven und Berechnungen der Sicherheit im Dauer- und Zeitfestigkeitsbereich[13]FKM-Richtlinie 154, Rechnerischer Festigkeitsnachweis für Maschinenbauteile[14]G. Niemann, Maschinenelemente, Band 1, Springer 1981[15]R. Hänchen, K.-H. Decker, Neue Festigkeitslehre für den Maschinenbau, 2.te Auflage, Carl HanserVerlag, 1967.。

用php写whatsapp发送模板的范文

用php写whatsapp发送模板的范文下载提示：该文档是本店铺精心编制而成的，希望大家下载后，能够帮助大家解决实际问题。

文档下载后可定制修改，请根据实际需要进行调整和使用，谢谢!本店铺为大家提供各种类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by this editor. I hope that after you download it, it can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you! In addition, this shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts, other materials and so on, want to know different data formats and writing methods, please pay attention!在当今社会，随着智能手机的普及和即时通讯工具的飞速发展，WhatsApp已经成为了人们日常生活中不可或缺的一部分。

CMMI ML3 最新访谈问题集 for EPG_QA

文档审计工作是如何开展的？
PPQA SP1.2
How do you handle findings of the audit?
审计的结果你是如何处理的？
PPQA SP 2.1 AND SP 2.2
Would you please describe how senior management is involved in QA activities
and all PAs GP 2.8/3.2
Would you please describe how do you communicate and use measurement results?
你怎样交流和利用度量结果的？
MA SP 2.4
Can you give examples of process improvements carried out based on data analysis
Scripted questions for EPG/QA FAR
Session:Date and Time:Duration:
Questions
PA Ref
Would you please describe how do you plan for QA activities?
你是如何编制质量保证计划的？
过程是怎样被评价的？
OPF SP 1.2
Would you please describe how do you establish and implement process action items?
怎样建立和实施过程改进项的？
OPF SP 2.1 and 2.2
Would you please describe how process training are carried out in your organization

磷脂酰肌醇3激酶β(PI3Kβ)和PI3Kδ在KIT突变介导的细胞转化中起不同作用

细胞与分子免疫学杂志（Chin J Cell Mollmmunol)2021，37( 1)39•论著•文章编号：1007-8738(2021 )01>0039~08磷脂酰肌醇3激酶p(PI3Kp)和PI3K8在K IT突变介导的细胞转化中起不同作用张少婷，朱光荣，石君，杨继辉，蒋宗英，张良颖，窦凯凯，孙建民*(宁夏医科大学基础医学院病原生物学与医学免疫学系，宁夏银川750004)[摘要]目的探究磷脂酰肌醇3激酶(P I3K)的不同亚型在三型酪氨酸激酶受体KIT突变介导的信号传递及细胞增殖中的作用。

方法在B aF3细胞中稳定表达野生型KIT及胃肠间质瘤中常见的KIT突变V560D、W557K558del,分别用PI3K a、P I3KP、P I3K5亚型特异性抑制剂或者广谱P I3K抑制剂处理细胞，免疫沉淀法和W estern blot法检测KIT及其下游信号活化情况。

胃肠间质瘤G IST-T1细胞采用相同药物及浓度处理，免疫共沉淀和W estern blot法检测KIT及其下游信号的活化情况，噻唑蓝 (M T T)法检测细胞增殖，流式细胞术检测细胞凋亡。

结果与对照组相比，在表达野生型KIT及其突变体的B aF3细胞中，P I3K8亚型特异性抑制剂对KIT及其下游信号分子蛋白激酶B(AKT)和胞外信号调节激酶（ERK)活化的抑制作用最强，其次为 P I3K a和P I3KP亚型特异性抑制剂。

在G IST-T1细胞中，P I3KP亚型特异性抑制剂对KJT及其下游信号活化的抑制作用最强,其次为P I3K&和P I3K a亚型特异性抑制剂。

结论在B aF3细胞中，P I3KS亚型在KIT活化及其下游信号传递中起主要作用，而在G IST-T1细胞中，P I3Kp亚型在KIT活化及其下游信号传递中起主要作用，这些结果表明不同P I3K亚型在KIT突变介导的细胞转化中起不同作用，且在不同的细胞中其作用也有不同。

一篇影响因子超40的蛋白组学Protein Analysis by Shotgun Bottom-up Proteomics

† ‡
Department of Chemical Physiology, The Scripps Research Institute, La Jolla, California 92037, United States Department of Molecular Medicine, Cell and Matrix Biology Research Institute, School of Medicine, Kyungpook National University, Daegu 700-422, Republic of Korea
1. INTRODUCTION According to Genome Sequencing Project statistics (http:// /genomes/static/gpstat.html), as of February 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1,2 The availability of full genome sequences has greatly facilitated biological research in many ﬁelds, including the growth of mass spectrometry-based proteomics. Proteins are important because they are the direct biofunctional molecules in living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3,4 As a postgenomic discipline, proteomics encompasses eﬀorts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, posttranslational modiﬁcations (PTMs), and turnover as a function of time, space, and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome.

spatstat.Knet 3.0-2 软件包说明说明书

Package‘spatstat.Knet’November13,2022Type PackageTitle Extension to'spatstat'for Large Datasets on a Linear NetworkVersion3.0-2Date2022-11-12Depends R(>=3.5.0),spatstat.data(>=3.0),spatstat.sparse(>=3.0),spatstat.geom(>=3.0),spatstat.random(>=3.0),spatstat.explore,spatstat.model,spatstat.linnet(>=3.0),spatstat(>=3.0)Imports spatstat.utils(>=3.0),MatrixMaintainer Adrian Baddeley<**************************.au>Description Extension to the'spatstat'family of packages,for analysinglarge datasets of spatial points on a network.The geometrically-corrected K function is computed using a memory-efﬁcienttree-based algorithm described by Rakshit,Baddeley and Nair(2019).License GPL(>=2)NeedsCompilation yesByteCompile trueAuthor Suman Rakshit[aut,cph](<https:///0000-0003-0052-128X>), Adrian Baddeley[cre,cph](<https:///0000-0001-9499-8382>)Repository CRANDate/Publication2022-11-1305:40:08UTCR topics documented:spatstat.Knet-package (2)Knet (3)Knetinhom (4)wacrashes (5)Index712spatstat.Knet-packagespatstat.Knet-package Extension to’spatstat’for Large Datasets on a Linear NetworkDescriptionExtension to the’spatstat’family of packages,for analysing large datasets of spatial points on anetwork.The geometrically-corrected K function is computed using a memory-efﬁcient tree-basedalgorithm described by Rakshit,Baddeley and Nair(2019).DetailsThis is an extension to the spatstat package for the analysis of large data sets on linear networks.Its main functionality is a memory-efﬁcient algorithm for computing the estimate of the K functionon a linear network,described in Rakshit et al(2019).The main functions are Knet and Knetinhom.These are counterparts of the functions linearK andlinearKinhom in the spatstat.linnet package.The spatstat.linnet functions linearK and linearKinhom are usable(and slightly faster)for smalldatasets,but require substantial amounts of memory.For larger datasets,the functions Knet andKnetinhom are much more efﬁcient.The DESCRIPTIONﬁle:Package:spatstat.KnetType:PackageTitle:Extension to’spatstat’for Large Datasets on a Linear NetworkVersion: 3.0-2Date:2022-11-12Depends:R(>=3.5.0),spatstat.data(>=3.0),spatstat.sparse(>=3.0),spatstat.geom(>=3.0),spatstat.random(>= Imports:spatstat.utils(>=3.0),MatrixAuthors@R:c(person(given="Suman",family="Rakshit",role=c("aut","cph"),email="************************. Maintainer:Adrian Baddeley<**************************.au>Description:Extension to the’spatstat’family of packages,for analysing large datasets of spatial points on a network License:GPL(>=2)NeedsCompilation:yesByteCompile:trueAuthor:Suman Rakshit[aut,cph](<https:///0000-0003-0052-128X>),Adrian Baddeley[cre,cph](<httIndex of help topics:Knet Geometrically-Corrected K Function on NetworkKnetinhom Geometrically-Corrected Inhomogeneous KFunction on Networkspatstat.Knet-package Extension to spatstat for Large Datasets on aLinear Networkwacrashes Road Accidents in Western AustraliaKnet3Author(s)NAMaintainer:Adrian Baddeley<**************************.au>ReferencesRakshit,S.,Baddeley,A.and Nair,G.(2019)Efﬁcient code for second order analysis of events ona linear network.Journal of Statistical Software90(1)1–37.DOI:10.18637/jss.v090.i01 Knet Geometrically-Corrected K Function on NetworkDescriptionCompute the geometrically-corrected K function for a point pattern on a linear network.UsageKnet(X,r=NULL,freq,...,verbose=FALSE)ArgumentsX Point pattern on a linear network(object of class"lpp").r Optional.Numeric vector of values of the function argument r.There is a sensible default.freq Vector of frequencies corresponding to the point events on the network.The length of this vector should be equal to the number of points on the network.The default frequency is one for every point on the network....Ignored.verbose A logical for printing iteration number corresponding to each point event on the network.DetailsThis command computes the geometrically-corrected K function,proposed by Ang et al(2012), from point pattern data on a linear network.The algorithm used in this computation is discussed in Rakshit et al(2019).The spatstat function linearK is usable(and slightly faster)for the same purpose for small datasets, but requires substantial amounts of memory.For larger datasets,the function Knet is much more efﬁcient.ValueFunction value table(object of class"fv").4KnetinhomAuthor(s)Suman Rakshit(modiﬁed by Adrian Baddeley)ReferencesAng,Q.W.,Baddeley,A.and Nair,G.(2012)Geometrically corrected second-order analysis of events on a linear network,with applications to ecology and criminology.Scandinavian Journal of Statistics39,591–617.Rakshit,S.,Baddeley,A.and Nair,G.(2019)Efﬁcient code for second order analysis of events ona linear network.Journal of Statistical Software90(1)1–37.DOI:10.18637/jss.v090.i01ExamplesUC<-unmark(chicago)r<-seq(0,1000,length=41)K<-Knet(UC,r=r)Knetinhom Geometrically-Corrected Inhomogeneous K Function on NetworkDescriptionCompute the geometrically-corrected inhomogeneous K function for a point pattern on a linear network.UsageKnetinhom(X,lambda,r=NULL,freq,...,verbose=FALSE)ArgumentsX Point pattern on a linear network(object of class"lpp").lambda Fitted intensity of the point process.Either a numeric vector giving values of the ﬁtted intensity at each data point of X,or an object of class"linim","linfun"or"lppm"from which theﬁtted intensity at each data point can be extracted.r Optional.Numeric vector of values of the function argument r.There is a sensible default.freq Vector of frequencies corresponding to the point events on the network.The length of this vector should be equal to the number of points on the network.The default frequency is one for every point on the network....Ignored.verbose Logical value indicating whether to print progress reports during the computa-tion.DetailsThis command computes the inhomogeneous version of the geometrically-corrected K function, proposed by Ang et al(2012),from point pattern data on a linear network.The algorithm used in this computation is described in Rakshit et al(2019).The spatstat function linearKinhom is usable(and slightly faster)for this purpose for small datasets,but requires substantial amounts of memory.For larger datasets,the function Knetinhom is much more efﬁcient.ValueFunction value table(object of class"fv").Author(s)Suman Rakshit(modiﬁed by Adrian Baddeley)ReferencesAng,Q.W.,Baddeley,A.and Nair,G.(2012)Geometrically corrected second-order analysis of events on a linear network,with applications to ecology and criminology.Scandinavian Journal of Statistics39,591–617.Rakshit,S.,Baddeley,A.and Nair,G.(2019)Efﬁcient code for second order analysis of events ona linear network.Journal of Statistical Software90(1)1–37.DOI:10.18637/jss.v090.i01ExamplesUC<-unmark(chicago)fit<-lppm(UC~x+y)r<-seq(0,1000,length=41)K<-Knetinhom(UC,lambda=fit,r=r)wacrashes Road Accidents in Western AustraliaDescriptionThis dataset gives the spatial locations of all road accidents recorded in the state of Western Aus-tralia for the year2011,on the state road network.These data were published and analysed in Rakshit et al(2019).Usagedata(wacrashes)FormatA object of class"lpp"representing the spatial point pattern of accident locations on the networkof roads in Western Australia.DetailsThe road network has88,512intersections and115,169road segments.The spatial coordinates are expressed in metres,and the total network length is97,165,540metres(97,165km).The number of accident locations on the network is14,562.SourceMain Roads,Western Australia.Made available as part of the Western Australian Whole of Gov-ernment Open Data Policy.ReferencesRakshit,S.,Baddeley,A.and Nair,G.(2019)Efﬁcient code for second order analysis of events ona linear network.Journal of Statistical Software90(1)1–37.DOI:10.18637/jss.v090.i01Examplesdata(wacrashes)wacrashessummary(wacrashes)plot(wacrashes,cols="red",cex=0.5)Index∗datasetswacrashes,5∗nonparametricKnet,3Knetinhom,4∗packagespatstat.Knet-package,2∗spatialKnet,3Knetinhom,4wacrashes,5Knet,2,3Knetinhom,2,4linearK,2,3linearKinhom,2,5spatstat.Knet-package,2wacrashes,57。

非诺贝特单独或联合他汀类药物治疗血脂异常

© 2010 Moutzouri et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.Vascular Health and Risk Management 2010:6 525–539Vascular Health and Risk ManagementDove presssubmit your manuscript | Dove press525R e V i e wopen access to scientific and medical researchOpen Access Full Text Article5593Management of dyslipidemias with fibrates, alone and in combination with statins: role of delayed-release fenofibric acidelisavet Moutzouri Anastazia Kei Moses S elisafHaralampos J MilionisDepartment of internal Medicine, School of Medicine, University of ioannina, ioannina, GreeceCorrespondence: Haralampos J Milionis Department of internal Medicine, School of Medicine, University of ioannina, 451 10 ioannina, Greece T el +30 265 100 7516 Fax +30 265 100 7016 email hmilioni@uoi.grAbstract: Cardiovascular disease (CVD) represents the leading cause of mortality worldwide. Lifestyle modifications, along with low-density lipoprotein cholesterol (LDL-C) reduction, remain the highest priorities in CVD risk management. Among lipid-lowering agents, statins are most effective in LDL-C reduction and have demonstrated incremental benefits in CVD risk reduction. However, in light of the residual CVD risk, even after LDL-C targets are achieved, there is an unmet clinical need for additional measures. Fibrates are well known for theirb eneficial effects in triglycerides, high-density lipoprotein cholesterol (HDL-C), and LDL-C subspecies modulation. Fenofibrate is the most commonly used fibric acid derivative, exertsb eneficial effects in several lipid and nonlipid parameters, and is considered the most suitable fibrate to combine with a statin. However, in clinical practice this combination raises concerns about safety. ABT -335 (fenofibric acid, Trilipix ®) is the newest formulation designed to overcome the drawbacks of older fibrates, particularly in terms of pharmacokinetic properties. It has been extensively evaluated both as monotherapy and in combination with atorvastatin, rosuvastatin, and simvastatin in a large number of patients with mixed dyslipidemia for up to 2 years and appears to be a safe and effective option in the management of dyslipidemia.Keywords: atherogenic dyslipidemia, cardiovascular disease prevention, lipid-loweringt reatment, fenofibric acid, statins IntroductionCardiovascular disease (CVD) constitutes the leading cause of death in developed coun-tries. Current treatment guidelines focus on lowering low-density lipoprotein cholesterol (LDL-C) as the primary strategy for reducing CVD risk (Table 1).1–5 Hydroxymethyl-glutamyl-coenzyme A reductase inhibitors (HMG-CoA) or statins have demonstrated a significant CVD risk reduction in a large number of landmark trials.6 There is growing evidence that both diabetic and nondiabetic patients are still at risk for CVD events even if they are receiving optimal statin treatment (termed as “residual CVD risk”). In the Pravastatin or Atorvastatin Evaluation and I nfection Therapy – Thrombolysis in Myocardial Infarction (PROVE IT -TIMI) 22 study, 4162 patients with acute coronary syndromes were treated with either pravastatin 40 mg or a torvastatin 80 mg. A substan-tial number of patients (10%) died from CVD events within 30 months, despite having LDL-C levels below 70 mg/dL (1.8 mmol/L).7 Similar were the fi ndings in the Treat-ing to New Targets (TNT) study, where the level of residual CVD risk remained high in patients treated with atorvastatin 10 mg/dL or 80 mg/dL.8,9 This residual CVD risk seems to depend at least partly on increased levels of triglycerides (TG) and decreased levels of high-density lipoprotein cholesterol (HDL-C). Recent data indicate that upNumber of times this article has been viewedThis article was published in the following Dove Press journal: Vascular Health and Risk Management 29 June 2010Vascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press526Moutzouri et al to 50% of patients treated with a statin who have achieved LDL-C target levels have low HDL-C levels.10,11 In addition, based on current data, increased TG is nowadays considered to be a significant CVD risk factor.12The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) recognized both low HDL-C (,40 mg/dL [1.03 mmol/L] for men, ,50 mg/dL [1.29 mmol/L] for women) and elevated TG levels ($150 mg/dL [1.69 mmol/L]) as markers of increased CVD risk, independently of LDL-C levels.1 Mixed dyslipidemia, which is characterized by elevated TG ($50 mg/dL [1.69 mmol/L]) and low HDL-C l evels (,40 mg/dL [1.03 mmol/L] for men, ,50 mg/dL [1.29 mmol/L] for women) with or without increased LDL-C, apolipoprotein (apo) B or non-HDL-C levels is typical in patients with type 2 diabetes and/or the metabolic syn-drome.1,2,13 Mixed dyslipidemia is also characterized by an altered LDL subfraction profile with a preponderance of small dense LDL-C particles.14 Small dense LDL-C particles are considered to be highly atherogenic.2,15 Statins reduce LDL-C levels effectively, but they manifest limited effects on TG and HDL-C levels, as well as on LDL-C particle size modification, especially in patients with mixed dyslipidemia. There is accumulating evidence suggesting that treatment of patients with increased CVD risk, the metabolic syndrome, or diabetes should be oriented not only against decreas-ing LDL-C, but also raising HDL-C levels.16,17 It becomes apparent that agents effective against these components of atherogenic dyslipidemia have an intriguing role to play in CVD risk reduction.18,19 However, there is no overwhelmingevidence that treating these targets will alter major CVD outcomes. Furthermore, specific treatment goals for non-LDL parameters are not currently defined.Forty years since the introduction of the first fibrate in clinical practice, the exact role of these pharmacologic com-pounds remains ill-defined.20 Fenofibrate is one of the most commonly prescribed lipid-lowering agents in the world. Trilipix ® (fenofibric acid, ABT -335), is the newest formula-tion of a fibric acid derivative approved by the Food and Drug Administration (FDA).21Both statins and fibrates have favorable effects on several lipid and nonlipid parameters.22,23 Combining a statin with a fibrate may have a global beneficial effect because these two groups of pharmacologic agents differ in a substantial number of lipid and nonlipid parameters, and may in fact act in a complementary fashion.23,24 Updated guidelines from the NCEP ATP III recognize the potential beneficial effects of fibrates used in combination with a statin in patients with mixed dyslipidemia and coronary heart disease (CHD) or CHD risk equivalents.25Aspects of pharmacologyFenofibrate chemically is a 2-[4[(4-chlorobenzoyl)phenoxy]-2-methyl-propanoic acid, 1-methylethyl ester. Hydrolysis of the ester bond converts fenofibrate to its active form, namely fenofibric acid.26 Fenofibrate is a lipophilic compound, and its absolute bioavailability is hard to estimate because it is highly insoluble in water. It is highly protein bound (99%),p rimarily to albumin.26,27 Under normal conditions no unmodified fenofibrate is found in plasma.28 Plasma levelsTable 1 Treatment targets for total cholesterol, LDL-C, and non-HDL-C, and cutoff values for triglycerides and HDL-C in the NCeP and eSC guidelinesTotal cholesterol LDL-C non-HDL-C a Triglycerides b HDL-C b mg/dLmmol/Lmg/dLmmol/Lmg/dLmmol/Lmg/dL mmol/L mg/dL mmol/L NCEP guidelines 1,4 General population 1501.7401.00 or 1 RF160 4.1190 4.9More than 2 RF or 130 3.4160 4.1CAD event risk ,20%CAD or risk equivalent 1100 2.6130 3.4Optional in very high risk 701.81002.6ESC guidelines 5General population 190 4.9115 3.01501.740 (M) 45 (w)1.0 (M) 1.2 (w)CAD, CVD or DM 175 4.5100 2.6Optional1554.0802.1Notes: a in the fasting state, non-HDL-C is calculated by subtracting HDL-C from total cholesterol and serves as a secondary target of therapy in patients with elevated triglycerides (.200 mg/dL, 2.26 mmol/L); b No specific treatment goals are defined for HDL-C and fasting TG, but these concentrations serve as markers of increased CVD risk; c Defined as other clinical forms of atherosclerotic disease, diabetes mellitus, or a 10-year risk for CAD greater than 20%.Abbreviations: CAD, coronary artery disease; CVD, cardiovascular disease; DM, diabetes mellitus; eSC, european Society of Cardiology; M, men; NCeP, National Cholesterol education Program; RF, risk factor; w, women.Vascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press 527Fenofibric acid in the treatment of dyslipidemiaspeak in six to eight hours, while steady-state plasma levels are reached within 5 days. Its absorption is increased with meals, and the half-life is 16 hours.26,27,29Fenofibric acid is inactivated by UDP-glucuronyltranferase into fenofibric acid glucuronide,30 and is mainly excreted in urine (60%) as fenofibric acid and fenofibric acid glucuronide (ester glucuronidation takes place in hepatic and renal tissues).26 As a result, fenofibric acid may accumulate in severe kidney disease (creatinine clearance, [CrCl] , 30 mL/min),28,31 and is not eliminated by hemodialysis.31Since the initial introduction of a fenofibrate in clinical practice, several other formulations have been developed in order to optimize its pharmacologic properties. The major drawbacks of the original fenofibrate formulation were its low availability and the necessitation of taking it with meals, especially fat meals. The new formulation is Trilipix (also known as fenofibric acid delayed-release or cholinef enofibrate) which is the choline salt of fenofibrate. Trilipix does not require enzymatic cleavage to become active. It rapidly dissociates to the active form of free fenofibric acid within the gastrointestinal tract and does not undergo first-pass hepatic metabolism.21Trilipix is manufactured as delayed-release 45 mg and 135 mg capsules. The chemical name for cholinef enofibrate is ethanaminium, 2-hydroxy-N,N,N-trimethyl, 2-{4-(4- c hlorobenzoyl)phenoxy] -2-methylpropanoate (1:1)32 (see Figure 1). It is freely soluble in water. Trilipix delayed-release capsules can be taken without regard to meals. Of great importance, fenofibric acid is well absorbed throughout the gastrointestinal tract, and has statistically greater b ioavailability than prior fenofibrate formulations, as has been demonstrated in healthy human volunteers.33PharmacokineticsFenofibric acid is the circulating pharmacologically active moiety in plasma after oral administration of Trilipix. Feno-fibric acid is also the circulating pharmacologically active moiety in plasma after oral administration of fenofibrate, the ester of fenofibric acid. Plasma concentrations of fenofibricacid after one 135 mg delayed-release capsule are equivalent to those after one 200 mg capsule of micronized fenofibrate administered under fed conditions.AbsorptionFenofibric acid is well absorbed throughout the g astrointestinal tract. The absolute bioavailability of fenofibric acid is approximately 81%. The absolute bioavailability in the stomach, proximal small bowel, distal small bowel, and colon has been shown to be approximately 81%, 88%, 84%, and 78%, respectively, for fenofibric acid and 69%, 73%, 66%, and 22%, respectively, for fenofibrate (P , 0.0001 and P = 0.033 for fenofibric acid versus fenofibrate in the colon and distal small bowel, respectively).33 Fenofibric acid e xposure in plasma, as measured by time to peakc oncentration in plasma and area under the concentration curve (AUC), is not significantly different when a single 135 mg dose of Trilipix is administered under fasting or nonfasting conditions.34DistributionUpon multiple dosing of Trilipix, fenofibric acid levels reach steady state within 8 days.34 Plasma concentrations of fenofi-bric acid at steady state are approximately slightly more than double those following a single dose.MetabolismFenofibric acid is primarily conjugated with glucuronic acid and then excreted in urine. A small amount of fenofibric acid is reduced at the carbonyl moiety to a benzhydrol metabo-lite which is, in turn, conjugated with glucuronic acid and excreted in urine.34 In vivo metabolism data after fenofibrate administration indicate that fenofibric acid does not undergo oxidative metabolism, eg, by cytochrome (CYP) P450, to a significant extent.eliminationAfter absorption, Trilipix is primarily excreted in the urine in the form of fenofibric acid and fenofibric acid glucuronide.HOCH 3CH 3H 3CCO 2−N +ClOO CH 3H 3CFigure 1 Chemical structure of ABT -335. The chemical name for choline fenofibrate is ethanaminium, 2-hydroxy-N,N,Ntrimethyl, 2-{4-(4-chlorobenzoyl)phenoxy]-2- methylpropanoate. The empirical formula is C 22H 28ClNO 5 and the molecular weight is 421.91.Vascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press528Moutzouri et al Fenofibric acid is eliminated with a half-life of approximately 20 hours,34 allowing once-daily administration of Trilipix.Use in specific populationsIn five elderly volunteers aged 77–87 years, the oral clearance of fenofibric acid following a single oral dose of fenofibrate was 1.2 L/hour, which compares with 1.1 L/hour in young adults. This indicates that an equivalent dose of fenofibric acid tablets can be used in elderly subjects with normal renal function, without increasing accumulation of the drug or metabolites. Trilipix has not been investigated in well-controlled trials in pediatric patients. No pharmacokinetic difference between males and females has been observed for Trilipix. The influence of race on the pharmacokinetics of Trilipix has not been studied.The pharmacokinetics of fenofibric acid were examined in patients with mild, moderate, and severe renal impairment. Patients with severe renal impairment (CrCl , 30 mL/min showed a 2.7-fold increase in exposure to fenofibric acid and increased accumulation of fenofibric acid during chronic dosing compared with healthy subjects.30 Patients with mild-to-moderate renal impairment (CrCl 30–80 mL/min) had similar exposure but an increase in the half-life for fenofibric acid compared with that in healthy subjects. Based on these findings, the use of Trilipix should be avoided in patients who have severe renal impairment, and dose reduction is required in patients having mild to moderate renal impairment. No pharmacokinetic studies have been conducted in patients with hepatic impairment.Drug–drug interactionsIn vitro studies using human liver microsomes i ndicate that fenofibric acid is not an inhibitor of CYP P450 i soforms CYP3A4, CYP2D6, CYP2E1, or CYP1A2. It is a weaki nhibitor of CYP2C8, CYP2C19, and CYP2A6, and a mild-to-moderate inhibitor of CYP2C9 at therapeutic concentrations.34,35 Accordingly, fenofibric acid may have the potential to cause various pharmacokinetic drug interactions.Since they are highly protein-bound, all fibric acid derivatives may increase the anticoagulant effect ofc oumarin derivatives. Serial monitoring of the InternationalN ormalized Ratio should be performed. Caution should be exercised when drugs that are highly protein-bound are given concomitantly with fenofibrate.36Interaction with cyclosporine has been reported to increase the risk of nephrotoxicity, myositis, and rhabdomyolysis, partly due to the fact that both are metabolized through CYP 3A4.37 Careful consideration should be given when fenofibric acidis administered with other potential nephrotoxic drugs and, if necessary, lower doses of fenofibric acid may be used.21Bile acid sequestrants may decrease the absorption of fenofibrate and therefore the bioavailability of fenofibric acid. It is recommended that fenofibrate be taken at least one hour before or 4–6 hours after bile acid resins.21Concerning the pharmacokinetic interactions betweenf enofibric acid and statins, no clinically significantp harmacokinetic drug interaction between fenofibrate simvastatin, pravastatin, atorvastatin, and rosuvastatin has been observed in humans.38–41 Not all fibrates share the same p harmacokinetic properties. In vitro studies have d emonstrated that g emfibrozil interacts with the same family of glucuronidation enzymes involved in statin metabolism.35 As a result of inhibiting statin glucuronidation, gemfibrozil coadministration with statins generally produces increases in the statin AUC. Gemfibrozil is also an inducer of CYP3A4, but acts as both an inducer and an inhibitor of CYP2C8.35 In con-trast, fenofibrate is metabolized by different g lucuronidation enzymes and as a result, does not lead to pharmacokinetic interactions with statins in a clinically relevant way.35Mode of action effects on lipidsFenofibric acid derivatives exert their primary effects on lipid metabolism via the activation of peroxisomep roliferator-activated receptor-alpha (PPAR-α) by the active fenofibric acid. Several target genes modulating lipid metabolism are encoded through the activation of these receptors.42,43 Fenofibrate affects the metabolism of TG and HDL-C through several pathways.Fenofibrate is able to reduce plasma TG levels by inhibit-ing their synthesis and stimulating their clearance. Primarily, fenofibrate induces fatty acid β-oxidation and, in this way, the availability of fatty acids for very LDL-C (VLDL-C) synthesis and secretion is reduced.44,45 Furthermore, it aug-ments the activity of lipoprotein lipase (LPL) activity, which hydrolyzes TG on several lipoproteins.46Apo C proteins are crucial for TG metabolism. Apo C III delays catabolism of TG-rich lipoproteins by inhibiting their binding to the endothelial surface and subsequent lipolysis by LPL. Fenofibrate decreases both apo C II and apo C III expression in the liver via PPAR-α activation.47–50 Apo C III reductions have also been shown to be the only significant and independent predictor of fenofibrate-induced TG alterations in obese patients with the metabolic syndrome.51Apart from TG reduction, fenofibrate is well known for its favorable actions on HDL-C levels. The fundamentalVascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press 529Fenofibric acid in the treatment of dyslipidemiasaction of fenofibrate is the promotion of apo A I and II synthesis in the liver, which represent the main HDL-C apoproteins.52 Fenofibrate modifies HDL and the reverse cholesterol transport pathway through several mechanisms. Specifically, fenofibrate is able to increase pre-β1-HDL-C levels in patients with the metabolic syndrome,53 reduce total plasma cholesteryl ester transfer protein activity,54,55 induce the activity of adenosine triphosphate-binding cassette transporter (ABCA1,56,57 member 1 of the human transporter subfamily ABCA), also known as the cholesterol efflux regulatory protein (CERP), and induce hepatic lipase activity.46Some recent clinical reports have suggested that HDL-C levels may be paradoxically decreased after fenofibrate treatment.58,59 This appears to occur mainly in patients with combined fibrate plus statin therapy and possibly in those with low baseline HDL-C. A survey of 581 patients treated with the combination for 1 year or longer indicated that paradoxical HDL-C reductions are a relatively uncommon phenomenon.59 Approximately 15% of patients showed mod-est reduction in HDL-C levels. These reductions in HDL-C occurred mainly in individuals with significant HDL-C elevations (ie, .50 mg/dL, 1.3 mmol/L) and almost never in patients with low HDL-C. There was no impact of a pre-vious diagnosis of diabetes or hypertension on the HDL-C changes.59In addition, fibrates have been shown to decrease cho-lesterol synthesis by inhibiting hydroxymethylglutamyl-c oenzyme A reductase and to increase cholesterol excretion in the bile pool.55,60,61 Fenofibrate is able to reduce apo B levels, primarily as a result of reduced synthesis and secretion of TG, and not by directly influencing apo B production.45effects on nonlipid parametersFenofibrate has beneficial effects on several nonlipidp arameters which are independent of its action on lipopro-teins.62 As widely known, fibrates reduce fi brinogen levels. F enofibric acid has been shown to inhibit p lasminogen acti-vator inhibitor-1 and tissue factor expression on e ndothelial cells and macrophages.63 Fenofibrate also m odulates platelet aggregation and endothelial dysfunction, via an incompletely elucidated molecular mechanism.64,65Significant reductions in serum high sensitivity C- r eactive protein levels have been observed with f enofibrate treat-ment.63,66 Fenofibrate effectively decreases serum interleu-kin-6 levels, as well as plasma platelet-activating factor acetylohydrolase, which represents a novel inflammatory marker.66,67Of importance, fenofibrate can significantly decrease serum uric acid levels by increasing renal urate expression, and considerable reductions in serum alkaline phosphatase and gamma glutamyltransferase activity are commonly observed during therapy with fenofibrate.63,68 The latter effects may have an application in patients with liver diseases, including nonalcoholic fatty liver disease.Recent reports have stressed the role of fenofibrate in glucose and carbohydrate metabolism. However, this issue remains controversial, with some studies demonstrating beneficial effects on insulin secretion 51,66 and others s howing no effect.69Studies evaluating the efficacy of fenofibrateFenofibrate as monotherapyFenofibrate is indicated for the treatment of h ypercholesterolemia, combined dyslipidemia, remnant hyperlipidemia, hypertrig-lyceridemia, and mixed hyperlipidemia ( F rederickson types IIa, IIb, III, IV , and V , respectively).Fenofibrate as monotherapy decreases serum TG levels by 20%–50% and increases HDL-C levels by 10%–50%.48,70,71 The rise in HDL-C levels depends on baseline HDL-Cc oncentrations, with the greatest elevations observed when baseline HDL-C is ,39 mg/dL (1.03 mmol/L).72 It also decreases LDL-C levels by 5%–20%.73 LDL-C response is directly related to baseline LDL-C levels and inversely related to baseline TG levels.73 In clinical practice, TG r eduction is greater in hypertriglyceridemia phenotypes (up to 50%) and lower in T ype IIa hypercholesterolemia (,30%). Fenofibrate also exerts beneficial effects on several apolipoprotein levels. Apo A I and apo A II levels are significantly increased, while apo C III and apo B levels are reduced.48Fenofibrate has been shown to modify favorably both LDL and HDL subclass distributions. Treatment with fenofibrate shifts the ratio of LDL-C particle subspecies from small, dense, atherogenic LDL particles (LDL 4 and LDL 5) to large, buoyant ones (LDL 3).54 These larger, less dense LDL particles show higher affinity for the LDL receptor, while an association between small dense LDL and increased CVD risk has long been established. In addition, fenofibrate is able to alter HDL particle size.54 The HDL-C rise is accompanied with a shift of HDL from large to small particles.48,53,54,74–76 The antiatherogenic, antioxidative, and antiapoptotic properties of HDL have been attributed mainly to its small subfractions.77,78 Furthermore, plasma levels of small HDL subclasses has been shown to be a strong p redictor of protection against atherosclerosis.79,80Vascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press530Moutzouri et al Several large clinical and angiographic trials have e valuated the efficacy of fibrates as monotherapy in halting the p rogression of atherosclerotic disease (Table 2).81–85 TheF enofibrate Intervention and Event Lowering in Diabetes (FIELD) study was a 5-year, randomized, p lacebo-controlled trial testing the safety and efficacy of fenofibrate 200 mg in 9795 type 2 diabetic patients.86 The primary e ndpoint was CHD death or nonfatal myocardial infarction (MI).F enofibrate failed to alter the primary endpoint s ignificantly. However, f enofibrate reduced the composite of CVD death, MI, stroke, and coronary or carotid revascularization by 11% (P = 0.035). I nterestingly, in this study, fenofibrate significantly reduced the need for retinal laser therapy (by 30%, P , 0.001), the rate of nontraumatic amputation (by 38%, P = 0.011), and the p rogression of albuminuria (P , 0.002). Of note, only 21% of the patients enrolled hadTable 2 Major clinical and angiographic trials with fibratesStudyPatientsDuration Fibrate Comparator ResultsHelsinki heart study 814081 male patients (40–55 years) with primary dyslipidemia and non-HDL-C levels . 200 mg/dL (5.17 mmol/L) 5 yearsGemfibrozil 1200 mg dailyPlacebo34% decrease in fatal and nonfatal Mi (95% Ci 8.2–52.6, P , 0.02)Bezafibrate coronary atherosclerosis intervention study 8392 post-Mipatients , 45 years5 yearsBezafibrate 200 mg (3 times daily)PlaceboLess disease progression in focal lesions as assessed coronaryangiograms in segments with ,50% diameter stenosis at baseline LOPiD coronary angiography trial 82395 post-coronary bypass men # 70 years with HDL-C , 42.46 mg/dL (1.1 mmol/L) and LDL-C # 170 mg/dL (4.5 mmol/L)32 monthsGemfibrozil 1200 mg dailyPlaceboDecrease in rate of change in native coronary segments and minimum luminal diameter, and new lesions (2% forgemfibrozil vs 14% for placebo, P , 0.001)Bezafibrate infarction prevention study 853090 patients (45–74 years) with CHD6.2 yearsBezafibrate 400 mg dailyPlaceboDecrease by 9% in fatal and nonfatal Mi and sudden death (nonsignificant vs placebo)Veterans affairs high-density lipoprotein cholesterolintervention study 842531 patients (,74 years) with CHD andHDL-C , 39 mg/dL (1.03 mmol/L)5.1 yearsGemfibrozil 1200 mg dailyPlaceboDecrease by 24% in composite of CHD death, nonfatal Mi, stroke; by 24% in CVD events; by 25% in stroke, and by 22% in CHD death Diabetes atherosclerosis intervention study 88418 patients aged 40–65 years withDM and TC/HDL-C , 4 or LDL-C , 170 mg/dL (4.5 mmol/L) or LDL-C 135–170 mg/dL(3.5–4.5 mmol/L) and TG # 495 mg/dL (5.2 mmol/L)3 yearsFenofibrate 200 mg dailyPlacebo40% decrease in minimum lumen diameter (P = 0.029 vs placebo); 42%decrease in progression in percentage diameter stenosis (P = 0.02 vs placebo)Fenofibrateintervention and event lowering in diabetes (FieLD) study 869795 patients with type 2 diabetes, 50–75 years,(2131 patients with documented CVD)5 yearsFenofibrate 200 mg dailyPlacebo24% decrease innonfatal Mi (P = 0.01 vs placebo); 11%decrease in total CVD (P = 0.04 vs placebo)Abbreviations: CHD, coronary heart disease; CVD, cardiovascular disease; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides.Vascular Health and Risk Management 2010:6submit your manuscript | Dove pressDove press 531Fenofibric acid in the treatment of dyslipidemiasmixed dyslipidemia (TG $ 200 mg/dL [2.25 mmol/L] and HDL-C , 40 mg/dL [1.03 mmol/L] for men and ,50 mg/dL [1.29 mmol/L] for women). F enofibrate decreased TG and LDL-C moderately (by 29% and 12%, respectively) and increased HDL-C by 5% at 4 months.86 Using the NCEP ATP III definition of metabolic syndrome, more than 80% of FIELD participants qualified as having the condition.87 Each feature of metabolic syndrome, e xcluding increased waist circumference, was associated with an increase in the absolute 5-year risk for CVD events by at least 3%. Marked dyslipidemia (defined as elevated t riglycerides $2.3 mmol/L and low HDL-C) was associated with the highest risk of CVD events (17.8%). The largest effect of fenofibrate on CVD risk reduction was observed in subjects with marked dyslipidemia, in whom a 27% relative risk reduction (95% confidence interval [CI] 9%–42%, P = 0.005; number needed to treat = 23) was observed.87In the earlier Diabetes Atherosclerosis Interven-tion Study (DAIS) study, 418 type 2 diabetic patients were enrolled (baseline lipid profile: LDL-C 132 mg/dL [3.4 mmol/L]; TG 221 mg/dL [2.49 mmol/L]; and HDL-C 40 mg/dL [1.03 mmol/L]). DAIS was a 3-year, randomized, p lacebo-controlled a ngiographic trial.88 F enofibrate slowed the a ngiographic p rogression of coronary a therosclerosis, along with considerable improvement in the lipid profile (LDL-C reduction by 6%, TG reduction by 28%, and HDL-C increase by 7%). Fenofibrate decreased the p rogression of focal c oronary atheroma by 40% versus placebo. A dditionally, fenofibrate decreased the incidence of microalbuminuria by 54%.Fibrate and statin combination therapyIt is well accepted that statins are the primary and more effi-cient method of reducing LDL-C levels even at low doses.89 However, statins manifest minimal effects in raising HDL-C levels (5%–15%) and in decreasing TG levels (7%–30%).19 Fenofibrate has small or minimal effects on LDL-C levels, which depends on baseline TG levels.89 These data imply that a combination of a statin and a fibrate may have additional benefits, especially in patients with mixed bining fenofibrate with a statin appeared to be safe and effective in several short-term studies (Table 3).76,90–94 The combination of fenofibrate with a statin, along with better improvements in lipid profile, has been shown to induce a marked increase in the ratio of large to small LDL subspecies compared with statin monotherapy.75,95Long-term, placebo-controlled trials with the combina-tion of a fibrate and a statin with hard CVD outcomes arelacking. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Lipid study, researchers evaluated whether adding fenofibrate to statin therapy prevents adversec ardiovascular events in patients with type 2 diabetes.96 A total of 5518 diabetic patients (mean age, 62 years; 31% women; glycosylated hemoglobin $ 7.5%; LDL-C 60–180 mg/dL [1.55–4.65 mmol/L]; HDL-C , 55 mg/dL [1.42 mmol/L] for women and blacks and ,50 mg/dL [1.29 mmol/L] for all other groups) were enrolled. All p articipants received simvastatin 20–40 mg/day and also were assigned to daily fenofibrate 160 mg or p lacebo. Mean follow-up was 4.7 years. Participants were also randomized to either intensive or standard glycemic control and to either intensive or standard blood pressure control. Glycemic control in the ACCORD study was stopped early in February 2008 because of higher mortality in the intensive glycemic control group. All patients were then transferred to a standard glycemic control regimen.In both groups, mean LDL-C levels dropped from 100.0 mg/dL (2.59 mmol/L) to about 80.0 mg/dL (2.07 mmol/L). Mean HDL-C levels increased from 38.0 mg/dL (0.98 mmol/L) to 41.2 mg/dL (1.07 mmol/L) in the fenofibrate group and to 40.5 mg/dL (1.05 mmol/L) in the placebo group. Median TG levels decreased from about 189 mg/dL (2.13 mmol/L) to 147 mg/dL (1.66 mmol/L) in the fenofibrate group and to 170 mg/dL (1.92 mmol/L) in the placebo group.96 The primary endpoint, adverse (major fatal or nonfatal) cardiovascular events, occurred with similar frequency in the two groups (2.2% versus 2.4% per year; hazard ratio 0.92; P = 0.32).96 Among the secondary endpoints, there was also no statistically significant differ-ence between the two treatments. No subgroup analysis was strongly positive. Only gender showed evidence of an interaction according to study group. The primary outcome for men was 11.2% in the fenofibrate group versus 13.3% in the placebo group, whereas the rate for women was 9.1% in the fenofibrate group versus 6.6% in the placebo group (P = 0.01 for interaction). A p ossible benefit was also sug-gested for patients who had a TG level in the highest third ($204 mg/dL [$2.30 mmol/L]) and an HDL-C in the lowest third (#34 mg/dL [#0.88 mmol/L]). The primary out-come rate was 12.4% in the fenofibrate group versus 17.3% in the placebo group, whereas such rates were 10.1% in both study groups for all other patients (P = 0.057 for interaction). Although patients receiving fenofibrate had higher rates of treatment discontinuation due to an increase in glomerular filtration rate (GFR), a lower incidence of both microalbu-minuria (38.2% versus 41.6, P = 0.01) and macroalbuminuria。

MKP383S资料

MKP Metallized Polypropylene Film CapacitorsMKP 380S -386SConstruction of capacitors:Reference standards:Climatic category:Metallized polypropylen film capacitors noninductive construction,cylindric shape,self-healing ability,Leads:tinned cooper wire Surface coating by yellow polyester film tape wraped,epoxy resin sealed Flame retardand construction available upon request also UL 94V-0General specifications:IEC 60384-1,EN 130000Sectional specifications:IEC 60386-16,CECC 3120055/100/56(IEC 60068-1)Nominal capacitanceC :Tolerance of capacitance:Insulation resistance Ris:Time constant tis:Test voltage between terminations:R see tableOther values on request.Nominal capacitance values are based on the E6serie in accordance to IEC 63publ.or arbitrary values in capacitance range on request.±20%(M),±10%(K),±5%(J),arbitrary tolerances on requestC Î0,33µF Ris min.100000MÂC >0,33µF tis min.30000sec.tis=Ris .C [sec;MÂ;µF]U =1,6×U for 2sec.at anbient temperature+25°C±5°CRated voltage U Working Temperature range:50-60Hz]-see table.55°C ÷+100°CT R R [DC/AC -L max (mm)d (mm)L (mm)1110,616190,625310,837,5140,620260,832,5360,8411kHz10kHz 100kHzC Î0,1µF 0,00060,00100,0030R 0,1µFÎC Î1µF0,00060,0020R C >1µF 0,0006R Dissipation factor tgçat +25°C max.Maximum pulse rise time dU/dt [V/µsec]Lmax (mm)U 160250400630100016002000R Î14100120200250300500750î313045601001401752501975100150200250300650264560100120160200450dU/dt [V/µs]max.10080604020U /U [%]o p R -60-40-2020406080100[°C]Operating voltage dependence on ambient temperatureType Nominal voltage U DC/AC Nom.capac.C R R1000pF 150022003300470068000,010µF 0,0150,0220,0330,0470,0680,10µF 0,150,220,330,470,681,0µF 1,52,23,34,76,810µF5x115x115x115x115,5x115,5x145,5x146x145,5x196,5x197x198x197,5x268x268,5x269x3110x3112x3115x3117,5x3620,5x365x115x145x145x145,5x195,5x195,5x195,5x197x268x269x268x319,5x3111x3113x3114,5x3117x3119x3621x365x115x146x148,5x145x196x197x197,5x268x269x269,5x3111x3113,5x3116x3117,5x365x115x145,5x146,5x145,5x196x196,5x197x197x268x269,5x2610x3111,5x3114x3116x3118x3621,5x367x117,5x148,5x1410x148x199x1910,5x1910x2612,5x2614,5x2616x3119x3121x36Maximal dimensionsD x L (mm)MKP 380S 160100MKP 382S 400220*MKP 383S 630250*MKP 384S 1000400*MKP 385S 1600630MKP 386S20007005x195x195x196x195x26,55,5x26,56,5x26,57x26,58x26,59,5x26,510,5x26,513,5x26,512,5x31,515,5x31,516x31,522x31,55x26,55x26,55x26,57x26,55x31,56,5x31,57x31,57,5x31,58x31,59x31,510,5x31,512,5x31,515x31,518x31,522x31,5*this capacitors are not suitable for across the line applicationsThe manufacturer is not responsible for any damages,caused by the improper instalation and application.K150302-3Electroniccomponents CZSyllabova 380/41,70300OSTRAVA -Vítkovice Phone:+420/595781623,5966233856623386Fax:+420/595781612,59E -mail:eso es-ostrava.czWeb Site:http://www.es-ostrava.cz@MKP 381S 250160MKP Metallized PolypropyleneFilm Capacitors Syllabova380/41,70300OSTRAVA-VítkovicePhone:+420/595781623,5966233856623386Fax:+420/595781612,59E-mail:eso es-ostrava.cz@Web Site:http://www.es-ostrava.cz。

JAMP-AIS003 MSD Splus Format Image

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3GPP 5G基站(BS)R16版本一致性测试英文原版(3GPP TS 38.141-1)

4.2.2
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All rights reserved. UMTS™ is a Trade Mark of ETSI registered for the benefit of its members 3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners LTE™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM Association

PI3KAKT pathway plays a pivotal role in breast cancer development and maintenance

Gene Expression Profiling Reveals New Aspects ofPIK3CA Mutation in ERalpha-Positive Breast Cancer: Major Implication of the Wnt Signaling PathwayMagdalena Cizkova1,2.,Ge´raldine Cizeron-Clairac1.,Sophie Vacher1,Aure´lie Susini1,Catherine Andrieu1,Rosette Lidereau1,Ivan Bie`che1*1Laboratoire d’Oncoge´ne´tique,Institut National de la Sante´et de la Recherche,U745,Institut Curie/Hoˆpital Rene´Huguenin,St-Cloud,France,2Laboratory of Experimental Medicine,Department of Paediatrics,Faculty of Medicine and Dentistry,Palacky University and University Hospital Olomouc,Olomouc,Czech RepublicAbstractBackground:The PI3K/AKT pathway plays a pivotal role in breast cancer development and maintenance.PIK3CA,encoding the PI3K catalytic subunit,is the oncogene exhibiting a high frequency of gain-of-function mutations leading to PI3K/AKT pathway activation in breast cancer.PIK3CA mutations have been observed in30%to40%of ER a-positive breast tumors.However the physiopathological role of PIK3CA mutations in breast tumorigenesis remains largely unclear.Methodology/Principal Findings:To identify relevant downstream target genes and signaling activated by aberrant PI3K/ AKT pathway in breast tumors,we first analyzed gene expression with a pangenomic oligonucleotide microarray in a series of43ER a-positive tumors with and without PIK3CA mutations.Genes of interest were then investigated in249ER a-positive breast tumors by real-time quantitative RT-PCR.A robust collection of19genes was found to be differently expressed in PIK3CA-mutated tumors.PIK3CA mutations were associated with over-expression of several genes involved in the Wnt signaling pathway(WNT5A,TCF7L2,MSX2,TNFRSF11B),regulation of gene transcription(SEC14L2,MSX2,TFAP2B,NRIP3)and metal ion binding(CYP4Z1,CYP4Z2P,SLC40A1,LTF,LIMCH1).Conclusion/Significance:This new gene set should help to understand the behavior of PIK3CA-mutated cancers and detailed knowledge of Wnt signaling activation could lead to novel therapeutic strategies.Citation:Cizkova M,Cizeron-Clairac G,Vacher S,Susini A,Andrieu C,et al.(2010)Gene Expression Profiling Reveals New Aspects of PIK3CA Mutation in ERalpha-Positive Breast Cancer:Major Implication of the Wnt Signaling Pathway.PLoS ONE5(12):e15647.doi:10.1371/journal.pone.0015647Editor:James McCubrey,East Carolina University,United States of AmericaReceived August2,2010;Accepted November19,2010;Published December30,2010Copyright:ß2010Cizkova et al.This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use,distribution,and reproduction in any medium,provided the original author and source are credited.Funding:This work was supported by INCA(Institut National du CAncer),Programme Cance´ropoˆles2003-2007Ile de France,and Comite´De´partemental des Hauts-de-Seine of the Ligue Nationale Contre le Cancer.This work is part of the Cartes d’Identite´des Tumeurs(CIT)program(/index.php/en)launched and directed by the Ligue Nationale Contre le Cancer.The funders had no role in study design,data collection and analysis,decision to publish, or preparation of the manuscript.Competing Interests:The authors have declared that no competing interests exist.*E-mail:i.bieche@.These authors contributed equally to this work.IntroductionDeregulation of the phosphatidylinositol3-kinase(PI3K) signaling pathway is frequent in human cancers.Activation of PI3K,which catalyzes inositol lipid phosphorylation to produce phosphatidylinositol-3,4,5-trisphosphate,is one of the most important downstream molecular events following tyrosine kinase receptor activation.Phosphatidylinositol-3,4,5-trisphosphate acti-vates the serine/threonine kinase AKT,which in turn regulates several signaling pathways controlling cell survival,apoptosis, proliferation,motility,and adhesion[1].PI3K is a heterodimeric enzyme composed of a p110a catalytic subunit encoded by the PIK3CA gene,and a p85regulatory subunit encoded by the PIK3R1gene[2].Gain-of-function mutations in PIK3CA have recently been found in several malignancies,including breast cancer[1,3,4].PIK3CA is frequently mutated at‘‘hotspots’’in exons9and20,corresponding to the helical(E542K and E545K)and kinase(H1047R)domains,activity,transforming primary fibroblasts in culture,inducing anchorage-independent cell growth,and causing tumors in animals[5,6].After the TP53suppressor gene,the PIK3CA oncogene is the most frequently mutated gene in human breast cancers(up to40% of breast tumors)[7,8].Activating somatic mutations of other oncogenes(EGFR,KRAS,HRAS,NRAF,BRAF and AKT1)involved in downstream molecular events following tyrosine kinase receptor activation are frequent in several malignancies but rare in breast cancer.Several studies suggest that PIK3CA mutations are more frequent in estrogen receptor alpha(ER a)-positive breast tumors (30–40%)than in ER a-negative breast tumors(10–20%)[7]. The pathological role of these gain-of-function PIK3CA mutations in breast tumors,and particularly in ER a-positive breast tumors,is largely unknown.Better knowledge of PIK3CA mutation impact requires the identification of downstream target genes and signaling pathways activated by aberrant PI3K/AKT signaling.Here,we compared gene expression in PIK3CA-mutatedgenome-wide microarray and subsequently real-time quantitative reverse transcriptase-polymerase chain reaction(RT-PCR). Materials and MethodsPatients and SamplesWe analyzed samples of292primary unilateral non metastatic ER a-positive postmenopausal breast tumors excised from women at Rene´Huguenin Hospital(Saint-Cloud,France)from1978to 2008.Other characteristics of the patients are listed in Table S1. Each patient gave written informed consent and this study was approved by the Local Ethical Committee(Breast Group of Rene´Huguenin Hospital).Immediately after surgery the tumor samples were stored in liquid nitrogen until RNA extraction.The samples analyzed contained more than70%of tumor cells.ER a status was determined at the protein level by using biochemical methods (Dextran-coated charcoal method until1988and enzyme immunoassay thereafter)and was confirmed at mRNA level by real-time RT-PCR.Forty-three samples were used as a microarray and RT-PCR screening set to identify differentially expressed genes.These genes were then validated in the remaining249ER a-positive tumors by means of RT-PCR.Control samples consisted of eight specimens of normal breast tissue collected from women undergoing cosmetic breast surgery or adjacent normal breast tissue from breast cancer patients.RNA extractionTotal RNA was extracted from breast tissue by using the acid-phenol guanidium method,and its quality was determined by agarose gel electrophoresis and ethidium bromide staining.The 18S and28S RNA bands were visualized under ultraviolet light. PIK3CA mutation screeningPIK3CA mutation screening was performed on cDNA fragments obtained by RT-PCR amplification of exons9and20and their flanking exons.Details of the primers and PCR conditions are available on request.The amplified products were sequenced with the BigDye Terminator kit on an ABI Prism3130automatic DNA sequencer(Applied Biosystems,Courtabœuf,France).Sequences thus obtained were compared with the corresponding cDNA reference sequence(NM_006218).Microarray analysisMicroarray experiments used Human Genome U133Plus2.0 arrays from Affymetrix,containing54675probe sets.Gene chips were hybridized and scanned using standard Affymetrix protocols. Expression data were obtained as CEL files.BRB ArrayTools (version 3.6.0available on /BRB-Array Tools.html)were used to import CEL files with Robust Method Average(RMA)normalization,and to analyze gene expression.A class comparison based on a univariate t test applied to log-normalized data was used to identify genes differentially expressed in breast tumors with and without PIK3CA mutations.Supervised class prediction analysis was implemented with the Prediction Analysis for Microarrays(PAM)algorithm to identify genes required for optimal prediction[9].The Database for Annotation,Visualization and Integrated Discovery(DAVID,available on /) was used to interpret the lists of differentially expressed probes and to identify statistically overrepresented biological function catego-ries of Gene Ontology(GO)and biological pathways,as defined in the Kyoto Encyclopedia of Genes and Genomes(KEGG).In compliance with the Minimun Information About a were deposited in the Gene Expression Omnibus(GEO)database (/geo/)under series accession num-ber GSE22035.Real-time quantitative RT-PCRRT-PCR was applied to the selected genes,as well as ER a (NM_000125),MKI67(NM_002417),and TBP(NM_003194; endogenous mRNA control).Primers and PCR conditions are available on request,and the RT-PCR protocol using the SYBR Green Master Mix kit on the ABI Prism7900Sequence Detection System(Perkin-Elmer Applied Biosystems,Foster City,CA,USA) is described in detail elsewhere[10].The relative mRNA expression level of each gene,expressed as the N-fold difference in target gene expression relative to the TBP gene,and termed ‘‘N target’’,was calculated as N target=2D Ct sample.The value of the cycle threshold(D Ct)of a given sample was determined by subtracting the average Ct value of the target gene from the average Ct value of the TBP gene.The N target values of the samples were subsequently normalized such that the median N target value of the normal breast samples was1.The relative expression of each gene was characterized by the median and range,and the differences in gene expression between tumors with and without PIK3CA mutations were analysed for significance with the non parametric Mann-Whitney U test.Clustering analysisHierarchical clustering analyses of gene expression and samples were performed using BRB ArrayTools.Classification perfor-mance was calculated as overall accuracy,defined as the proportion of correctly classified tumors in each cluster,using Matthews’correlation coefficient(MCC)[11].This parameter was used to discriminate identical accuracies.The chi-square test was used to determine the statistical significance of the clustering. ResultsAnalysis of differentially expressed genes in43ER a-positive tumorsOverview of transcriptome changes in PIK3CA-mutated tumors.To identify PIK3CA mutation-related genes, microarray analysis(Affymetrix U133Plus2.0arrays)was first applied to43ER a-positive breast tumors,of which14were PIK3CA-mutated and29were wild-type(Table S1).We found that 6124probes were differentially expressed between breast tumors with and without PIK3CA mutations,with P values,0.05.Of these,2538probes(1630unique genes)were up-regulated(Table S2)and3586(2672unique genes)were down-regulated(Table S3).Only216up-regulated probes(153unique genes)and28 down-regulated probes(18unique genes)showed at least a2-fold change(FC).Gene ontology analysis of differentially expressed genes.To identify families of genes that might have significant roles related to specific biological or molecular processes,we used the DAVID database to annotate the6124probes and categorize them by function.As shown in Table1,these genes were mainly involved in the regulation of transcription,cell cycling, proliferation,death,adhesion and cytoskeleton organization,and also ion binding and transport,and ATP and RNA binding activity.The2672down-regulated genes were mainly associated with ATP binding,acetylation and ion transport(Table1).Among the down-regulated genes with FC$2,no significantly overrepresent-Most of the1630up-regulated genes were involved in transcriptional regulation(17.3%)(biological process)and ion binding(25.6%)(molecular function)(Table1).The latter included the metal ion-binding and zinc ion-binding categories(Table1). As shown in Figure1A,the216probes most strongly up-regulated in PIK3CA-mutated tumors(153unique genes)belonged mainly to the ion-binding category(35.5%)but also to categories of structural molecule activity(including structural cytoskeleton constituents)(9.3%),transcription regulatory activity(9.3%)and nucleotide binding(including ATP and GTP binding)(7.5%).In the ion-binding category,the genes corresponded to genes encoding metal ion-binding proteins in95%of cases:28% encoding iron ion-binding and23%with zinc ion-binding proteins (Figure1B),pointing to a role of ion-binding proteins,and especially iron ion-binding proteins,in breast cancer with PIK3CA mutations.Interestingly,the genes belonging to the metal ion-binding category(Table2)included two families of genes that were among the most strongly up-regulated in PIK3CA-mutated tumors. They comprised four genes of cytochrome P450family4(CYP4Z1, CYP4X1,CYP4B1and the pseudogene(CYP4Z2P)and two solute carrier genes(SLC4A4and SLC40A1).All these genes,with exception of SLC4A4,are associated with iron ion binding.In addition to these genes,we found on the top of the list lactoferrin genes encoding zinc ion-binding proteins,three(ANPEP,LIMCH1 and NR2F2)are known to be cancer-related.Besides NR2F2,five other transcription factors,all known to be involved in tumorigenesis,were identified(Table2):(a)TFAP2B,a tumor suppressor gene in breast cancer[12],(b)SEC14L2,a gene possibly involved in the antiproliferative effect of vitamin E in cancer[13],(c)ID4,which has been proposed to be involved in breast cancer,inhibiting mammary epithelial cell differentiation and stimulating mammary epithelial cell growth[14],(d)TCF7L2, also named TCF4,a cancer-promoting gene involved in the Wnt signaling pathway[15],and(e)MSX2,a gene implicated in mammary gland and breast cancer development[16],and which is also activated by Wnt signaling[17].These five transcriptional factors(TFAP2B,SEC14L2,ID4, TCF7L2and MSX2),as well as ten genes involved in metal ion binding(CYP4Z1,CYP4X1,CYP4B1,CYP4Z2P,SLC4A4, SLC40A1,LTF,ANPEP,LIMCH1and NR2F2),were selected for validation by RT-PCR.Pathway analysis of differentially expressed genes.By applying KEGG pathway analysis to the6124probes differentially expressed in PIK3CA-mutated tumors,we identified physiological pathways directly or indirectly associated with PIK3CA mutations. The most significantly overrepresented pathways are shown inTable1.Selected categories significantly over-represented in PIK3CA-mutated breast tumors.Up-and down-regulated genes Up-regulated genes Down-regulated genesGene Category Number of genes P value Number of genes P value Number of genes P value GENE ONTOLOGYN Biological ProcessRegulation of transcription581(14%)0.0100282(17%),0.000122 Regulation of cell cycle and proliferation203(4.8%)0.000294(5.8%)0.0004115(4.3%)ns Regulation of cell death198(4.7%)0.005284(5.2%)0.0360120(4.5%)0.0430 Cell adhesion171(4.1%)0.007381(5.0%)0.002794(3.5%)nsIon transport169(4.0%)ns22130(4.9%)0.0003 Cytoskeleton organization116(2.8%)0.001458(3.6%)0.000463(2.4%)nsN Molecular FunctionIon binding936(22%)0.0040417(26%)0.0007543(20%)nsMetal ion binding920(15%)0.0019411(25%)0.0003533(20%)nsZinc ion binding518(22%)0.0140268(16%),0.0001269(10%)nsATP binding339(8.1%)0.0130130(8.0%)ns218(8.2%)0.0048 RNA binding182(4.4%)0.000987(5.3%)0.0008108(4.0%)0.0260 Acetylation2222378(14%)0.0004 KEGG PATHWAYPathways in cancer100(2.4%),0.000155(3.4%),0.000147(1.1%)nsMAPK signaling pathway76(1.8%)0.001132(2.0%)0.020047(1.1%)0.0190 Calcium signaling pathway50(1.2%)0.009310(0.6%)ns44(1.0%),0.0001 Jak-STAT signaling pathway43(1.0%)0.021017(1.0%)ns28(0.7%)0.0470 Wnt signaling pathway41(1.0%)0.037024(1.5%)0.001517(0.4%)ns Apoptosis27(0.6%)0.013012(0.7%)ns15(0.4%)nsns:not significant.The biological processes,molecular functions and physiological pathways of genes were obtained from the DAVID database using GOTERM_BP_FAT,GOTERM_MF_FAT and KEGG PATHWAY,respectively.The two first tools(Gene Ontology)annotated4202genes(1630up-and2672down-regulated genes)while KEGG annotated960 genes(385up-and601down-regulated genes).The gene enrichment of a given class was measured by determining the number of genes belonging to the class in the list of significantly altered genes,weighed against the total human genome,and was tested using Fisher exact probability test.Not all significant categories are included here in order to reduce redundancy.A given gene can belong to several processes.doi:10.1371/journal.pone.0015647.t001following five signaling networks were thus identified:MAPK, Calcium,Jak-STAT,Wnt and apoptosis.The Calcium signaling pathway was specifically altered by the down-regulated genes, whereas the Wnt signaling pathway was specifically altered by the up-regulated genes.The same method applied to the216probes (153unique genes)that were up-regulated with FC$2also revealed the Wnt signaling pathway(P=0.015)(data not shown), highlighting the importance of this pathway in PIK3CA-mutated tumors.Five major genes of the Wnt signaling were thus recognized among the216probes(Table S2):MSX2and TCF7L2(already cited),and WNT5A,VANGL2and TNFRSF11B/osteoprotegerin.These genes were also selected for RT-PCR validation.Finally,among the genes up-regulated with FC$2(Table S2), we identified PIK3R1,the gene encoding the PI3K regulatory subunit,and two other genes of interest:HMGCS2,a nuclear gene encoding a mitochondrial matrix enzyme involved in ketogenesis and cholesterol synthesis,processes possibly implicated in the etiology or progression of breast cancer[18]and MAPT,a protein involved in taxane resistance[19].These three genes were added to the RT-PCR validation set.Two-class prediction analysis of differentially expressed genes.Two-class prediction analysis with the PAM algorithm was used to identify the group of genes that best characterized PIK3CA-mutated and wild-type tumors and that classified the tumors with the smallest number of predictive features.A threshold of 2.81,that minimized the error,identified56 differentially expressed probes corresponding to39unique genes (Table S4).Thirty-eight of these39unique genes were over-expressed in ER a-positive breast tumors with PIK3CA mutations, 16being up-regulated at least3-fold,while only one gene (NKAIN1,encoding Na+/K+ATPase interacting protein)was down-regulated,with a FC of3.52.Interestingly,two major genes involved in the Wnt signaling pathway were also identified by PAM,namely WNT5A(the most predictive gene)and TCF7L2, further confirming the importance of this pathway in PIK3CA-mutated tumors.The previously selected up-regulated genes were almost all included in the list of the most predictive genes.Figure1.Molecular function classifications of genes up-regulated with a FC$2in the PIK3CA-mutated tumors.Molecular functions were attributed to107of the153genes using GOTERM_MF_FAT from the DAVID database.Categories with at least three genes are represented in A. Subclassification of the36genes belonging to the metal ion-binding category is shown in B.All categories were represented and several genes were common to more than one category.Genes belonging to the metal ion-binding and transcription activity categories are listed in Table2.doi:10.1371/journal.pone.0015647.g001Table2.List of genes belonging to the metal ion-binding and transcription regulation categories.Probe set FC P value Gene symbol Probe set FC P value Gene symbol METAL ION BINDING221584_s_at 2.110.0023KCNMA1Iron ion binding1564241_at 2.070.0257ATP1A4 202018_s_at*10.520.0005LTF230364_at 2.000.0217CHPT1237395_at*7.760.0035CYP4Z1Sodium ion binding227702_at* 5.570.0032CYP4X1203908_at* 4.810.0005SLC4A4 239723_at* 4.420.0005SLC40A1201242_s_at 2.760.0001ATP1B1 210096_at* 4.120.0011CYP4B1201243_s_at 2.710.0002ATP1B1 1553434_at* 3.800.0009CYP4Z2P210738_s_at* 2.130.0023SLC4A4 225871_at 2.340.0188STEAP2211494_s_at* 2.130.0025SLC4A4 1555497_a_at* 2.340.0061CYP4B1Potassium ion binding233123_at* 2.290.0139SLC40A1244623_at 2.300.0152KCNQ5223044_at* 2.260.0066SLC40A1221584_s_at 2.110.0023KCNMA1 205542_at 2.170.0266STEAP11564241_at 2.070.0257ATP1A4 219232_s_at 2.150.0006EGLN3Cobalt ion binding222453_at 2.140.0119CYBRD1205513_at 2.870.0009TCN1204446_s_at 2.190.0003ALOX5Manganese ion binding224996_at 2.100.0135ASPH230364_at 2.000.0217CHPT1Zinc ion binding202888_s_at* 3.520.0008ANPEP TRANSCRIPTION REGULATION212774_at 2.970.0320ZNF238214451_at* 6.680.0020TFAP2B 212325_at* 2.960.0002LIMCH11553394_a_at* 4.340.0035TFAP2B 225728-at 2.720.0141SORBS2223864_at 4.250.0399ANKRD30A 207981_s_at 2.690.0213ESRRG230316_at* 3.050.0006SEC14L2 212328_at* 2.690.0001LIMCH1204541_at* 3.030.0004SEC14L2 204288_s_at 2.690.0073SORBS2209292_at* 3.030.0002ID4212327_at* 2.490.0008LIMCH1212774_at 2.970.0320ZNF238 241459_at* 2.350.0003LIMCH1209291_at* 2.960.0001ID4227811_at 2.200.0051FGD3207981_s_at 2.690.0213ESRRG211965_at 2.180.0002ZFP36L1226847_at 2.610.0020FST215073_s_at* 2.080.0063NR2F2243030_at 2.490.0006MAP3K1 231929_at 2.070.0039IKZF2226992_at 2.230.0064NOSTRIN 214761_at 2.050.0016ZNF423212762_s_at* 2.180.0000TCF7L2 Calcium ion binding210319_x_at* 2.170.0011MSX2219197_s_at 3.080.0173SCUBE2216511_s_at* 2.160.0000TCF7L2204455_at 2.700.0065DST224975_at 2.130.0003NFIA229030_at 2.420.0370CAPN8240024_at* 2.120.0016SEC14L2 209369_at 2.420.0174ANXA3209706_at 2.120.0292NKX361 203887_s_at 2.200.0006THBD221666_s_at 2.090.0050PYCARD 204446_s_at 2.190.0003ALOX5215073_s_at* 2.080.0063NR2F2224996_at 2.100.0135ASPH216035_x_at* 2.080.0000TCF7L2221584_s_at 2.110.0023KCNMA1231929_at 2.070.0039IKZF21564241_at 2.070.0257ATP1A4214761_at 2.050.0016ZNF423 Magnesium ion binding220625_s_at 2.020.0286ELF5227556_at 2.990.0007NME7226806_s_at 2.020.0006NFIA243030_at 2.490.0006MAP3K1These genes are ranked according to the fold change(FC)in tumors with PIK3CA mutations relative to non mutated tumors.Several genes were common to more than one category.The genes marked with an asterisk were selected for RT-PCR validation.doi:10.1371/journal.pone.0015647.t002up-regulated with FC$3,namely VTCN1,TMC5,NTN4,REEP1 and NRIP3,which were added to the RT-PCR validation set. Among the down-regulated genes,NKAIN1was selected for RT-PCR validation,along with two other genes known to be involved in cancer biology:TUSC3and TPD52,that were among the28 most strongly down-regulated probes(FC$2)(Table S3)and that were also among the most predictive genes in PAM analysis with a lower FC threshold of2.5(data not shown).Combined analysis of the GO,KEGG and PAM approaches identified29most promising genes(26up-regulated and3down-regulated)for RT-PCR validation.The expression status of these genes was first confirmed in the same series of43breast tumors (Table3).Strong positive correlations were observed between the microarray and RT-PCR expression levels of each gene(Spear-man’s correlation coefficients ranged from0.69to0.97and were all significant,at P,0.0001;data not shown).mRNA expression of the29genes of interest in249ER a-positive breast tumorsOverall expression of the29differentially expressed genes.The expression levels of the29genes selected by microarray analysis were then verified by RT-PCR in a large independent cohort of249ER a-positive breast tumors,of which 157were PIK3CA wild-type and92were PIK3CA-mutated(Table S1).This PIK3CA mutation frequency of37%was in keeping with the results of previous studies showing a mutation rate of up to 40%in ER a-positive breast tumors[7,8].Almost all the tumors had a single mutation,44(47.8%)in exon9(helical domain)and 46(50%)in exon20(kinase domain)[7].Two tumors(2.2%) carried two mutations,located in exons9and20in one case,and in exon20in the second case.Nineteen(66%)of the29selected genes showed significantly different expression between mutated and wild-type tumors in theTable3.Microarray and RT-PCR analyses of the29genes in43ER a-positive breast tumors.Microarray analysis RT-PCR analysisSymbol Gene GenBank FC P value PIK3CA non mutated(n=29)PIK3CA mutated(n=14)FC P valueUP-REGULATED GENESANPEP*NM_001150 3.520.00080.17(0.03–1.52)0.37(0.10–23.7) 2.160.0033CYP4B1*NM_000779 4.120.0011 3.13(0.11–71.5)10.6(2.14–431) 3.400.0033CYP4X1NM_178033 5.570.0032 1.04(0.05–73.3) 5.85(0.63–97.7) 5.620.0124CYP4Z1NM_1718347.760.00350.36(0.01–220)9.17(0.10–311)25.150.0085CYP4Z2P*NR_002788 3.800.000934.8(0.12–1457)160(22.9–2103) 4.590.0007 HMGCS2*NM_005518 5.310.00030.10(0.00–11.1) 3.40(0.07–16.3)32.560.0011ID4*NM_001546 3.030.00020.07(0.02–0.61)0.16(0.05–1.03) 2.130.0133 LIMCH1*NM_014988 2.960.00020.54(0.10–3.83) 1.66(0.48–2.87) 3.060.0014LTF*NM_00234310.520.00050.03(0.00–11.3)0.86(0.00–37.4)31.540.0012MAPT*NM_016835 2.820.0004 1.09(0.02–12.1) 4.40(0.04–10.2) 4.020.0010MSX2NM_002449 2.170.0011 1.74(0.09–4.56) 3.32(1.56–8.57) 1.910.0025NR2F2NM_021005 2.080.00630.51(0.14–2.02) 1.06(0.58–2.20) 2.090.0009NRIP3*NM_020645 3.280.00020.94(0.05–18.9) 3.64(0.64–33.9) 3.870.0025NTN4*NM_021229 4.210.00080.48(0.05–3.07) 1.87(0.68–3.19) 3.910.0004PIK3R1*NM_181523 2.45,0.00010.28(0.07–0.89)0.49(0.18–1.61) 1.740.0053REEP1*NM_022912 3.300.0005 1.15(0.16–14.4) 3.49(1.36–8.99) 3.040.0446SEC14L2*NM_012429 3.030.00060.98(0.13–16.1) 5.54(0.37–24.4) 5.680.0049SLC4A4*NM_003759 4.810.00050.28(0.10–8.45) 3.45(0.00–116)12.150.0190SLC40A1*NM_014585 4.420.00050.37(0.11–7.79) 1.14(0.26–6.62) 3.120.0068TCF7L2NM_030756 2.08,0.00010.24(0.00–0.64)0.35(0.23–0.91) 1.450.0010TFAP2B*NM_003221 6.680.00200.09(0.00–26.0) 1.32(0.00–34.7)15.230.0164TMC5*NM_024780 4.270.0022 2.53(0.05–36.4)9.45(1.26–37.8) 3.740.0177 TNFRSF11B NM_002546 2.120.00230.67(0.13–10.6) 2.64(0.44–31.3) 3.910.0004 VANGL2NM_020335 2.490.00090.64(0.03–3.44) 1.90(0.13–5.37) 2.990.0018VTCN1*NM_024626 5.470.00070.19(0.00–4.89) 1.12(0.22–23.3) 5.970.0025WNT5A*NM_003392 3.43,0.00010.56(0.05–6.03) 2.10(0.37–6.17) 3.750.0013 DOWN-REGULATED GENESNKAIN1*NM_02452223.520.0006137.8(0.94–560)12.13(1.39–389)211.360.0124TPD52NM_00507922.170.0014 6.29(3.13–81.8) 3.88(1.20–11.68)21.620.0020TUSC3NM_00676522.480.00260.58(0.09–9.35)0.32(0.12–0.63)21.830.0092For each gene,we report the fold change(FC)between tumors with and without PIK3CA mutations.RT-PCR results are expressed as the median(range)mRNA level for each gene relative to normal breast tissues.Genes identified by PAM analysis are marked with an asterisk.validation cohort,with a distribution similar to that observed in the screening cohort(Table4).Among the three down-regulated genes of interest in the screening set,only NKAIN1was significantly down-regulated in the validation set.Among the26up-regulated genes,18were also up-regulated in the validation set.With exception of VANGL2,up-regulation of the genes involved in Wnt signaling pathway,namely WNT5A,MSX2,TCF7L2and TNFRSF11B,was confirmed in the validation set,further emphasizing the important role of the Wnt signaling pathway in PIK3CA-mutated breast cancer.Up-regulation was also confirmed for genes related to breast cancer(MAPT,HMGCS2,NR2F2and TFAP2B),genes involved in metal ion binding(CYP4Z1,CYP4Z2P, SLC40A1,LTF and LIMCH1)and also NRIP3,NTN4,REEP1,SEC14L2and TMC5.Deregulation of these genes was not related to ER a status or proliferation since similar expression levels of ER a and MKI67were observed in PIK3CA-mutated and-non mutated tumors(Table4).Only2of the29selected genes showed significantly different expression between PIK3CA exon9-and exon20-mutated tumors,namely TFAP2B and NRIP3(Table S5). Interestingly,TFAP2B was over-expressed in exon20-mutated tumors and NRIP3in exon9-mutated tumors.Identification of the most discriminatory genes.PAM prediction analysis was then used to test the ability of each gene to classify the249ER a-positive breast tumors according to PIK3CA mutation status.NKAIN1was the most predictive gene(PAM rank) (Table4).NKAIN1was also an essential classifier in supervisedTable4.Relative mRNA expression levels of the29genes in249ER a-positive breast tumors.Symbol Gene GenBank PIK3CA non mutated(n=157)PIK3CA mutated(n=92)FC P value Rank in PAMUP-REGULATED GENESANPEP NM_0011500.46(0.00–154)0.39(0.06–18.3)20.84ns15CYP4B1NM_000779 6.59(0.00–222) 5.72(0.00–178)21.12ns20CYP4X1NM_178033 2.34(0.02–59) 3.78(0.05–101) 1.62ns11CYP4Z1NM_171834 1.15(0.01–140) 2.97(0.01–254) 2.580.01344CYP4Z2P NR_00278838.3(0.00–1815)66.4(0.00–1069) 1.740.00608HMGCS2NM_0055180.29(0.00–24.8)0.60(0.00–25.7) 2.090.048710ID4NM_0015460.13(0.00–9.10)0.17(0.02–9.57) 1.30ns28LIMCH1NM_0149880.73(0.05–6.59) 1.09(0.08–8.58) 1.490.000719LTF NM_0023430.08(0.00–14.7)0.14(0.00–41.8) 1.740.00367MAPT NM_016835 3.03(0.07–71.1) 4.52(0.15–26.2) 1.490.003914MSX2NM_002449 2.26(0.00–13.9) 3.69(0.11–39.3) 1.630.00035NR2F2NM_0210050.79(0.06–10.8) 1.00(0.11–7.27) 1.250.041524NRIP3NM_020645 1.55(0.00–168) 2.69(0.10–105) 1.730.025016NTN4NM_0212290.75(0.03–5.47) 1.17(0.04–10.2) 1.570.000212PIK3R1NM_1815230.32(0.06–1.38)0.37(0.08–1.30) 1.16ns27REEP1NM_022912 1.85(0.00–12.1) 2.59(0.19–21.8) 1.400.00536SEC14L2NM_012429 2.49(0.00–24.0) 4.51(0.16–39.1) 1.81,0.00012SLC4A4NM_0037590.29(0.00–178)0.42(0.00–128) 1.43ns17SLC40A1NM_0145850.88(0.03–7.81) 1.22(0.00–17.9) 1.380.031113TCF7L2NM_0307560.26(0.03–1.05)0.32(0.06–1.26) 1.210.037326TFAP2B NM_003221 1.28(0.00–35.7) 5.53(0.00–179) 4.310.00553TMC5NM_024780 4.79(0.01–69.0) 5.77(0.11–46.2) 1.200.03319TNFRSF11B NM_002546 1.25(0.00–50.3) 1.90(0.15–21.8) 1.520.006821VANGL2NM_0203350.73(0.03–4.64)0.82(0.07–9.09) 1.12ns23VTCN1NM_0246260.61(0.00–10.3)0.64(0.01–15.4) 1.05ns22WNT5A NM_0033920.74(0.03–12.4) 1.17(0.18–7.27) 1.59,0.000118DOWN-REGULATED GENESNKAIN1NM_02452281.1(0.54–1648)57.7(0.71–560)21.410.04711TPD52NM_005079 6.01(1.30–115) 5.34(1.75–80.9)21.12ns25TUSC3NM_0067650.68(0.08–3.72)0.63(0.08–6.31)21.09ns29 CONTROL GENESER a NM_0001258.77(1.27–68.9)8.86(1.59–39.8) 1.01ns2MKI67NM_00241712.1(0.86–57.2)11.0(1.79–313)0.91ns2ns:not significant.Results are expressed as the median(range)mRNA level for each gene relative to normal breast tissues.For each gene,we report the fold change(FC)between tumors with and without PIK3CA mutations and the PAM rank.。

F8总结材料之SP和TOC

TOCTests of control /test /effectiveness / controls (in preventing, detecting or correcting material misstatements)Substantive procedure s /detecting material misstatements/ at the assertion level. They include tests of detail of transactions, balances, disclosures and substantive analytical procedures.案例题尽量结合实际经验Management letter format:地址The Directors(公司名称)Blake Co（121 happy） Street（Happy） Town5 December 2013起头Management letterDear SirsWe write to bring to your attention （题目requirement容）正文结尾If you require any further information on the above, please do not hesitate to contact us. Yours faithfully（审计公司名称）Sales system TOC存在问题：Authorization人不对：not senior enough/not sufficiently senior (without enough expertise and experience),not sufficiently independentCredit limit的问题：too high ,irrecoverable debt/too low, loss of salesControl 的方法：为防止customer发错单：an order acceptance is automatically sent to the customer by mail/email confirming the goods ordered and a likely despatch date.为防止遗漏处理：Flag标记（sales order）as (fulfilled)为防止拖延outstanding receivable ,sales order; 为了避免拖延所设期限predetermined period+为保证所设期限有效执行Credit limit, outstanding sales order, discount level等等很多东西都需要review,且on a regular basis ，且by a responsible official(financial director..)TOC方法：找人：找一手证据：Cash systemPayroll system有的在requiement中虽然写了identify and explain，但是后面有一个小问给你继续explain这时前面就直接identify就好通用Approval/number consecutively/segregation of duties/sign to take responsibility/review by senior regularlySP万能procedure：➢Presentation1)在报表上的分类 2）披露对不对➢Compare&InquireRario: 1)与同行比 2）与往年比➢Cast and agree➢Subsequent events➢Supporting documents➢Confirmation letter/enquiry letterSend a(上面两者)to(可以提供信息的人)to obtain (想获得的信息)➢Representation letter➢Discuss with (responsible people)about(讨论的项目) to (讨论的目的如：assess the reasonableness of …)and obtain a written management representation letter confirming()➢SAP and compare(在流量中比较常用)：大致估算，如receivable days, estimated revenueAccounts Receivable倾向于多记Describe substantive procedures the auditor should perform to confirm XX’s year end receivables.给一个balance，要判断它是否真实公平地反映了事实找可能导致没有真实反映的原因，及相应的procedure可以与provision结合起来Provision& current liability 作为一种会计估计要recognition:评估可能性+可以计量AP倾向于少记Revenue:倾向于数据的分析Payroll expenseSalary，wage和pay这三个词都可以表示“工资”，但其含义不同。

Pharmacokinetic–Pharmacodynamic Modelling

Journal of Pharmacokinetics and Pharmacodynamics,Vol.33,No.3,June2006(©2006) DOI:10.1007/s10928-005-9002-0Pharmacokinetic–Pharmacodynamic Modelling: History and PerspectivesChantal Csajka1and Davide Verotta1,2,∗Received April13,2005—Final October11,2005—Published Online January11,2006A major goal in clinical pharmacology is the quantitative prediction of drug effects.Theﬁeld of pharmacokinetic–pharmacodynamic(PK/PD)modelling has made many advances from the basic concept of the dose–response relationship to extended mechanism-based models.The purpose of this article is to review,from a historical perspective,the progression of the mod-elling of the concentration–response relationship from theﬁrst classic models developed in the mid-1960s to some of the more sophisticated current approaches.The emphasis is on general models describing key PD relationships,such as:simple models relating drug dose or con-centration in plasma to effect,biophase distribution models and in particular effect compart-ment models,models for indirect mechanism of action that involve primarily the modulation of endogenous factors,models for cell trafﬁcking and transduction systems.We show the evo-lution of tolerance and time-variant models,non-and semi-parametric models,and brieﬂy dis-cuss population PK/PD modelling,together with some example of more recent and complex pharmacodynamic models for control system and nonlinear HIV-1dynamics.We also discuss some future possible directions for PK/PD modelling,report equations for general classes of novel semi-parametric models,as well as describing two new classes,additive or set-point, of regulatory,additive feedback models in their direct and indirect action variants.KEY WORDS:pharmacokinetics;pharmacodynamics;modelling;review;history.INTRODUCTIONOver the last40years,pharmacokinetics–pharmacodynamics(PK/PD) has evolved from the basic concept of the dose–response relationship to sophisticated models enabling the understanding of the underlying mech-anism of drug action.This shift has primarily resulted from improved1Department of Biopharmaceutical Sciences,University of California,San Francisco,CA, USA.2Department of Biostatistics,University of California,San Francisco,CA,USA.∗To whom correspondence should be addressed.Telephone:+1-415-476-1556;e-mail: davide.verotta@2271567-567X/06/0600-0227/0©2006Springer Science+Business Media,Inc.228Csajka and Verotta analytical methodologies,advances in computer hardware and software, increased regulatory and academic interest and the continuous reﬁnement of pharmacodynamic models based on physiological mechanisms.In this article,we review,from a historical perspective,the progression of the understanding of the concentration–response relationship from the pio-neering works in the1960s to PK/PD modelling,established nowadays as a well-known scientiﬁc discipline.This review is not exhaustive,but rather traces models that symbolize key pharmacodynamic relationships or illus-trate important features in use today.We only consider non-steady-state PK/PD experiments,thus models devised to describe the dynamics of the effects in response to changing drug concentrations(see also the recent minireview by(1)).Weﬁrst discuss a general modelling framework describing PK/PD models,and in particular so-called direct and indirect response.We will then chronologically examine the succession of important pharmacody-namic models from the1960s until now(year2004).In particular,we will focus on direct concentration–response rela-tionships describing:linear models and nonlinear models relating drug dose or concentration in plasma to effect,biophase distribution models and in particular effect compartment models,and early models describ-ing irreversible interaction of chemotherapeutic agents with target cells. In the next section,we review models for indirect mechanism of action that involve primarily the modulation of endogenous factors and in a more general context and models for cell trafﬁcking.Transduction sys-tems will be then described.In the next section we will navigate through the1980s and early1990s,developmental years,which saw the emergence of tolerance,time-variant,non-and semi-parametric models and popula-tion PK/PD modelling.we will then give some examples of more recent and complex pharmacodynamic models,in particular control system and nonlinear HIV-1dynamics and set-point models for oscillatory behaviour. The last part of this article will brieﬂy discuss some future directions of pharmacodynamic modelling,such as computing,semi-parametric model-ling and regulatory feedback modelling.To limit the size of the review,we will only brieﬂy mention models for multiple drugs administration.In gen-eral all the models described below extends naturally to the case of mul-tiple drugs once their respective pharmacokinetics are taken into account, and models for additivity,synergism or antagonism(2)are introduced to describe their interactions.For purposes of clarity,efforts were made to keep equations as gen-eral as possible and the number of symbols to a minimum,for this reason the equations reported in the original references were changed to make notation uniform.Pharmacokinetic–Pharmacodynamic Modelling229 GENERAL FRAMEWORKIn the following we will take the point of view that the input to a PK/PD model is drug concentration in an observed compartment.We will assume that either parametric(e.g.multi-exponentials)or non-parametric functions(e.g.interpolants or regression splines3)adequately represent the time course of drug concentration in an observed site.This separation of pharmacokinetics from pharmacodynamics is of course appropriate for the case that the drug’s dynamics do not inﬂuence its kinetics.Any PK/PD model so deﬁned can be viewed as a model for the response(effect)of a system to an input(drug concentration).A classiﬁcation of different kinds of PK/PD models has been proposed and discussed in Refs.(3–5).We now give somewhat formal description of PK/PD systems.The response(effect,E)to an input in an observed site is functionally related to time(t)relative to the time of input of drug(t=0),and observed drug concentration C,by means of a function(G):E(t)=G[t,C(−∞,t)](1)where C(−∞,t)represents drug concentration in the observed site from time−∞to t.A system so deﬁned depends on the input applied before and at time t:such as system is called causal or non-anticipatory.We will not consider anticipatory systems here,which depend on future inputs or expectations(as in mind/body interaction in,e.g.,an addictive drug).First,a system can be static or dynamic.For a static system:E(t)=G[t,C(t)](2)i.e.the effect depends only on the current time and concentrations of drug,but not on past concentrations.Such a system is also called mem-ory-less.Equation(1)represents the more general dynamic or memory sys-tem,where the response depends on present and past inputs.In the case thatG[t,0]=0,−∞<t<∞(3)the system is said to be relaxed or at rest,i.e.,its response is excited by drug concentration,but zero otherwise.Many physiological responses3Splines areﬂexible functions,which are basically piecewise polynomials that join at cer-tain locations called breakpoints and match their derivatives up to the degree minus one of the polynomials.For example a linear spline matches only the values of the polyno-mials at the breakpoints to give the somewhat familiar“broken line”.230Csajka and Verotta can be considered relaxed once a constant baseline value is subtracted out.More complicated unrelaxed physiological responses are ones exhib-iting circadian variation and/or non-constant production of endogenous substances.Second,the response of a relaxed system to an input can be linear or nonlinear with respect to the input.For a linear system,the following holds:G[t,αC1(−∞,t]+βC2(−∞,t]]=αG[t,C1(−∞,t]]+βG[t,C2(−∞,t]](4)i.e.,the response to the sum of two inputs equals the sum of the responses to each one,whereas in a nonlinear system,this is not true.Finally,the system can be time-invariant or time-variant.For a time-invariant system,the response does not change irrespective of the time that an input is applied,while this does not hold for a time-variant one.The convolution between two functions,h and I,represents a linear, time-invariant dynamic system at rest,i.e.th(τ)I(t−τ)d t def=h∗I(5)can represent a PK system when,e.g.,h(t)is a disposition function,unit–impulse response,for a site of interest,and I(t)an input function(6).Most PK/PD models can be built by composing cascades of dynamic linear and static nonlinear sub-systems(7).Nonlinear dynamic systems (i.e.,most interesting PK/PD systems)can be built up by combining dynamic and static functions,obtaining so-called cascade structured mod-els.In such models,the output of one function is the input to another.The so-called“effect compartment”model(8)is an example of what we call a direct-response model to contrast it with indirect action mod-els(4)(see below).Both direct-response models and indirect-response models are composed of two sub-system cascades,one nonlinear static and one linear dynamic sub-model,but the order of the sub-models differs as described above.DIRECT ACTION MODELSIn a direct-response model,a linear-dynamic model is followed by a static non-linear model.From a physiological point of view,such a model has the interpretation of a kinetic(often called the link)PK system fol-lowed by a memory-less interaction of drug with the body that describes the effect.Pharmacokinetic–Pharmacodynamic Modelling231 Linear E vs.Dose/C RelationshipsIn the early days of pharmacodynamics,it was recognized that the intensity of many pharmacological effects is linearly related to the loga-rithm of the amount of drug(A)(9,10):E=s log A+e(6) where s and e are the slope and the intercept terms.Levy(9,10)and Levy and Nelson(11)published a number of mathematical expressions,which related the time course of pharmacological activity of a drug to itsﬁrst-order elimination:log A=log A0−k2.3t(7)where A0is an extrapolated intercept at time0and k is theﬁrst-order elimination rate constant.Substituting log A and log A0in Eq.(6)from Eq.(5)and rearranging yieldsE−e s =E0−es−k2.3t(8)which can be simpliﬁed and rearranged toE=E0−ks2.3t(9)where E0is a theoretical intercept.This relationship(Eq.(9))was supported by studies of drugs that showed plasma concentrations decreasing expo-nentially and the effect decreasing linearly with time(9,10).Although lim-ited by the assumptions of monoexponential drug elimination and linear dose–response relationship,these simple models provided pharmacody-namic parameters(slope values)that could be easily calculated graphically by simple linear regression.They provided quantitative measurements of the intensity of the pharmacological effect and were used to compare different drugs or combination of drugs.Concepts of duration of the response and effect after repeated drug administration(9,12)also evolved based on these simple relationships.232Csajka and Verotta Nonlinear E vs.Dose/C RelationshipsThe apparent linear relationship between concentration and pharma-cological response reﬂected the fact that effects were analysed over only a very limited concentration range.Moreover,linear models were recognized to be only valid when the effect was either less than20%(linear)or within 20–80%(log-linear).Owing to these limitations,Wagner(13)proposed the use of the Hill equation to describe the concentration–response relation-ship.The rationale for this approach was based on the law of mass action and classical receptor occupancy theory(14).The rate of change of the drug–receptor complex(RC)is given by the following equation:d RCdt=k on(R M−RC)C−k off RC(10)where R M is the maximum receptor density(therefore,R M−RC=R,the concentration of free receptor),C is the drug concentration at the site of action,k on is the association rate constant and k off is a dissociation rate constant.At equilibrium,Eq.(10)can be rearranged to yieldRC=R M CK d+C(11)where K d is the equilibrium dissociation constant k off/k on.What is now needed is a“transduction”function,z(RC),which relates occupancy at the receptor level with pharmacological effect.E=z(RC)=zR M CK d+C(12)Assuming that the response produced by the concentration of drug C is simply proportional to the fraction of occupied receptorsσ,that is E∝σRC,the effect is as follow(13):E=E max CEC50+C(13)where E max is the maximal effect(proportional to R M)and EC50is theconcentration of drug producing50%of E max.We note that an alterna-tive approach is proposed by(15),in which the simple model z(RC)=σ(RC)is criticized because,among other reasons,it would predict EC50+ C=K d.The transduction function proposed by(15)is itself a hyperbolic(Michaelis–Menten)relationship,under whichE=z(RC)=E max RCK E+RC(14)Pharmacokinetic–Pharmacodynamic Modelling233 where K E is the concentration of RC producing50%of E bining Eq.(11)with the previous equation yieldsE=E max R M CK d K E M E(15)This approach allows differentiating between occupancy at the receptor level and K E,which might prove important in in-vitro/in-vivo PK/PD cor-relation studies(16,17).Because Eqs.(13)and(15)collapse to the same model when no information about K d is available,in the following we will only refer to model(13)and derive models according to the correspond-ing representation,it is understood that model(15)could be substituted in any place where model(13)will be encountered in the following.Borrowing again from receptor theory,one can change Eq.(13)(as well as Eq.(15),see(15)to take into account for multiple receptor,and obtain the familiar Hill equation:E=E0+E max CγECγ50+Cγ(16)where we add E0to indicate baseline effect and when integer,γrepre-sents the number of molecules bound to the receptor.These relationships are curvilinear and allow a more general description of the entire dose–response relationship.A feature common to all models discussed above is that maximum effects are predicted to occur simultaneously with peak drug concentra-tions,since obviously if C∗is the maximum observed drug concentration, the maximum observed effect,E∗,isE∗=E0+E∗C∗γECγ50(17)However,it was soon observed that for many responses E∗lags behind or ahead of C∗,because of temporal displacements caused by kinetics,phys-iological or pharmacological mechanisms.In these situations a plot of E vs.C shows an hysteresis loop(C∗is before E∗,see e.g.bottom panel of Figs.1and11)or proteresis(C∗is after E∗,see Fig.13).A hysteresis or proteresis loop can in general be justiﬁed assuming a time-variant model (e.g.tolerance,see below),but hysteresis or proteresis cannot be justiﬁed by any model in which E is related to C by a time-invariant function, since if C(t1)=C(t2)than G[C(t1)]=G[C(t2)]for any t1,t2.234Csajka and Verotta Biophase Distribution ModelsIn1968,Segre(8)ﬁrst introduced the concept of a biophase compart-ment(the term can be found back in Furchgott(18)in1955,and(19), where“it indicates a space containing the receptors,or to be interposed between the receptors and...extracellularﬂuid”.This is theﬁrst model that explicitly introduces the concept of an hypothetical compartment which is driven by plasma concentrations and that is directly related to the effect,and it was used to explain the fact that the pressor effects of nor-epinephrine lagged appreciably behind its concentration proﬁle in blood. Taking advantage of the emergence of sophisticated computing programs (in particular early versions of the SAAM program(20)currently distrib-uted,in evolved form derived from CONSAAM(21)by the University of Washington(22)),he derived a model capable of describing the time delay between plasma concentrations and effect time curves.This model was further reﬁned later in(23)and it will be described in detail below.Around the same time,the awareness of delayed pharmacological effect relative to drug concentrations in plasma raised the alternative,and ulti-mately incorrect,hypothesis that the site of drug action might be associated with one of the compartments used to characterize the kinetics of drug dis-position(for example the peripheral compartment in a two-compartment model).Wagner in1968(13)suggested that either plasma concentrations or another compartment could be associated to the effect.Shortly afterwards, Levy et al.(24)reported that the pharmacological effect of LSD was corre-lated to the drug level in the slowly equilibrating compartment.The same idea was still used years later,with the study of Sheiner and co-workers.(25)who reported that the prolongation of the QT interval more closely paralleled the saliva concentration curve of procainamide than the plasma concentration curve.Kramer et al.(26)also reported a closer relationship between the time course of inotropic effects and estimated digoxin concen-trations in the slowly equilibrating peripheral compartment.Although,in some cases,it was thought plausible to associate a physiological compart-ment to the site of drug action,most authors emphasized correctly that a pharmacological response associated with a peripheral compartment of a disposition function model was probably a result of coincidence without any obvious physiologic interpretation,and that often observed compartments which were not of relevance for drug disposition appeared to be more,but imperfectly so,associated to drug effect.In1978,Dahlstrom et al.’s study(23),characterizing the relationship between morphine concentrations in rat plasma and whole brain and drug effects(vocalisation,and vocalisation after discharge)introduce a model that could solve the problem.In their study the time delay between thePharmacokinetic–Pharmacodynamic Modelling235dt =k b C b−k12C e1(18)dC e2 dt =k12C e1−k eC e2236Csajka and VerottaFig.2.Schematic representation of the model proposed by Segre (8,23),linking pharmaco-kinetics and pharmacodynamics.The brain concentrations C b are linked to a ﬁrst and then to a second biophase compartment (with concentrationsand C e 1and C e 2)by ﬁrst-order rate constants (k b and k 12),k eo characterizes the rate of loss from C e 2.Effect is a weighed sum of C e 1and C e 2:E =αC e 1+βC e 2(see text).where C b is concentration in brain,C e 1and C e 2are the concentrations in hypothetical effect compartments 1and 2,respectively,k b is the transfer rate constant from the brain compartment to the ﬁrst effect compartment,k 12is the transfer rate constant from the ﬁrst to the second effect com-partment and k eo (k 21in the original manuscript)is the elimination rate constant from the second effect compartment.The responses,vocalisation and vocalisation after discharge,were represented using a linear combina-tion of the concentrations in the effect compartments (using summer func-tions in SAAM),that is,E =αC e 1+βC e 2(19)where α,βwere coefﬁcients to be estimated.Their model was used to simultaneously ﬁt the pharmacokinetic data from plasma and (whole)brain,and the kinetics of the analgesic response.Since C e 1and C e 2are unobserved,the rate constant from the brain compartment to the ﬁrst effect compartment was ﬁxed,with no loss of generality,to an arbitrary unit value,and the kinetic of loss to the effect compartment was set up so that it did not inﬂuence the pharmacokinetics of the drug in brain.4The model recognized not only that an hypothetical effect compartment must be decoupled from PK disposition using a kinetic “link”model,but that often a complex link is necessary to represent effect data (a feature that was to be rediscovered almost 20years later by,e.g.(27)).4Using SAAM,this was simply achieved by adding a positive ﬂux (k b C b )in the equation for the brain compartment.The same approach was used by Sheiner et al.(28)by incorporating an effect compartment as a kinetic link between the plasma concentrations of a neuromuscular blocking drug and the skeletal muscle paralysing effect. As proposed by Segre,the amount in the effect compartment was linked to the plasma compartment by aﬁrst-order process:dC edt=k1e C−k e0C e(20)where C e is the drug concentration in the effect compartment,C is the plasma drug concentration,k1e isﬁrst-order rate constant from central to effect compartments and k e0theﬁrst-order rate constant of drug elimi-nation from the effect compartment,characterizing the temporal aspects of drug equilibration with the site of action.The rate constant k1e was set to a negligible value to avoid inﬂuencing the pharmacokinetics of the drug.The effect was then modeled by directly incorporating C e into the Hill equation:E=CγeCγe+ECγ50(21)This,as it become to be known,effect compartment model,or link model became very popular and was used to explain the hysteresis in the C−E plot of many drugs.In applications following(28),k1e has been arbitrarily set equal to k e0,as opposed to,e.g.the unit value of Segre’s model.This choice adds the convenience that at steady state the concentration in the hypothetical compartment,C ss e,directly equals the steady-state concentra-tion in plasma C ss,instead of C ss e=C ss k1e/k e0as for model(20).In gen-eral the value of k1e is irrelevant for the purposes of the PK/PD analysis.Irreversible EffectsThe use of log-linear or Michaelis–Menten models was justiﬁed by the assumption of reversible and fast,relative to PK,interaction between drug and receptor.However,the reversibility aspect of this mechanism precluded application to therapy with certain antibiotics,antimetabolites and alkylating agents,which usually depend on the irreversible or covalent incorporation of drug into cell metabolic sites or pathways.Jusko(29)described a pharmacodynamic modelling approach for cell-cycle non-speciﬁc chemotherapeutic drugs(Fig.3a).It introduced a com-partment for chemotherapeutic effect,deﬁned as separate from the central compartment and linked to it byﬁrst-order rate constants.Considering theFig.3.(a)The site of chemotherapeutic effect C p is a homogenous compartment in equilib-rium with the central compartment C withﬁrst-order links(k cp and k pc).A small portion of the dose is involved in an irreversible reaction with the receptor on the target cells(TC= TCs for the cell-cycle speciﬁc inhibition model see below),which ultimately produces mitotic arrest of these cells(TC x).It is assumed that viable cells increase in number(k s)and are subject to physiologic degradation(k d)(29).(b)Extension of the model to cell-cycle speciﬁc inactivation.The pool of targeted cells was divided into the sensitive cells,T s and the insen-sitive cells T r to the drug and is inter-convertible.Cells in each compartment are intercon-versible with rate constants k rs and k sr(30).molecular interaction of drug and cell receptor and the natural cell turnover,the rate of change in quantity of target cells(TC)with time was written asd TCdt=(k s−k d)TC−k i T C A p(22)where k s and k d are rate constants for increase in cells number at their natural mitotic rate and physiologic degradation,respectively,k i the rate constant for the irreversible reaction leading to the mitotic arrest of target cells,and A p is the amount in the peripheral chemotherapeutic compart-ment(receptor site).If F is the fraction of surviving cells(F=TC/TC0where(TC0= T C(0)),after all potentially effective drug was eliminated at time t,it can be derived by integration of Eq.(22)to yieldlog(F)=(k s−k d)t/2.3−k i tA p(t)dt(23)Under the assumption that only a negligible fraction of A p is involved in an irreversible reaction with the receptors(and therefore the distributionof the drug to the tissue site could be approximated by a standard two-compartment model),it can be shown that after all the drug is eliminated, the relationship(23)becomeslog(F)≈(k s−k d)/2.3t−k12/k10k i dose=α−K dose(24) which predicts a log-linear relationship between the fraction of surviving cells and the dose of the drug.The parameter K was used to quantify the chemotherapeutic efﬁcacy of different drugs.Under the main assumption of the model,negligible fraction of drug lost to interaction,the right side of Eq.(24)is basically independent on the distribution pharmacokinetics of the drug.As pointed out in the orig-inal reference,the model could be used to follow the dynamics of cell sur-vival and the kinetics of drug,but in this case the distribution part of the model(which“disappears”in the asymptotic relationship with dose was derived)would need elaboration,while still retaining the assumption of receptors contained in a homogeneous compartment which does not alter drug kinetics.Partial incorporation of drug kinetics together with a fur-ther reﬁnement to cycle-speciﬁc irreversible inactivation was described a couple of years later(30).This model was based on a two-compartment cell system where the pool of targeted cells was divided into the sensitive cells,TC s and the insensitive cells TC r to the drug(Fig.3a,b).Cells in each compartment are inter-convertible,with transformation rate constants k rs and k sr.Equation(22)was rewritten asd TC s dt =k rs TC r+(k s−k sr)TC s−k i TC s A p(25)d TC rdt=k sr TC s−k rs)TC r−k d TC rwhere drug only interacts with the sensitive cells.The dynamics of cell sur-vival were analysed by simplifying the model using the negligible fraction of drug lost to interaction assumption.Thus,model(25)becomesd TC s dt =k rs TC r+(k s−k sr)TC s−TC s K dose N(26)d TC rdt=k sr TC s−k rs TC r−k d TC rwhere N is the total number of given doses.Equation(26)can be seen immediately as describing a bi-exponential(decay)function,and the solu-tion to(26)wasﬁt to the cell-survival data(Fig.4corresponding to Fig.4in the original manuscript).The incorporation of multiple dosesFig.6.Basic concept of indirect pharmacodynamic response models(4).Effects are mediated by an endogenous substance(mediator).Indirect acting drugs modulate these effects(E)by either stimulating or inhibiting the production rate(k in)or the elimination rate(k out)of the mediator.INDIRECT ACTION MODELSTwo basic conceptual approaches have been developed for analysing hysteresis between drug plasma levels and pharmacodynamic response:in the previous sections,we discussed direct models,in which the pharma-cological effect was considered a direct consequence of drug action and the delay in response was thought to reﬂect the time required for the drug to reach its site of pharmacological action.Alternatively,the drug recep-tor interaction initiates a series of downstream biochemical events that account for the observed time lag.Indirect mechanisms of action involve primarily the modulation of endogenous factors that mediate the drug effect.In a more general context,we present in this section other types of indirect models,namely models for cell trafﬁcking.In indirect dynamic models,a dynamic linear sub-system follows a static nonlinear one.The dynamic model describes the formation and loss of an endogenous variable.From a physiological point of view,such a model has the interpretation of a memory-less interaction of drug with body structure,followed by an endogenous(linear)kinetic system directly related to the effect.Modulation of Endogenous FactorsThe earliest description of drugs acting through indirect mechanisms came from Ariens(14).He explained how drugs induced their effects not because of their interactions directly linked to an observable effect with receptors,but because the interaction affected the fate of endogenous。

母体与样本.

3
Exp3.1 Find the mean of 18, 15, 12, 20, 19, 11, 14, 38, 18, and 17.
總和是：平均數是
Exp 3.2 十天中，貨運包裏的數量為： 110， 112，120，128，115， 150， 151，91，88， 162。求此十天的日平均包裹量。
從最小值開始前五個組別的累積次數為51均分佈的所以我們要從這組的下組界上再加上組距10當中的439亦即stst0c328stst0c329stst0c33038stst0c331
STST0_C3
1
3.1 母體與樣本

母體(population) 樣本(sample) 例：The trial of flipping a coin The weights of a baby 由 400 ceramic tiles 抽出 20 片測量強度
數偏右。

一般資料，常用平均數來代表中心；至於偏態資料，以中位數代表中心較適合。
STST0_C3
20
3.5 其它的分位數

Quartiles, deciles, percentiles Q1，Q3: 第一、三個四分位數
計算方式： Q1是小於中位數的資料的中位數

Q3是大於中位數的資料的中位數
STST0_C3
2
3.2 Mean 平均數

定義
n 個數的平均數 = 總和除以 n
Sample mean: X
i
Xi
i
n
Population mean:
Xi
N
(N 表示母體個數)
Sample ←→ Population Statistics ←→ parameters

Jackson Tool 636 Jumbo Jack Pipe Stand Assembly, O

1. Inspect the Pipe Stand before each use. Never use if any part of a Pipe Stand is bent,deformed or broken. Never modify or use a Pipe Stand that has been modified.2. Place the Pipe Stand on a smooth, level, firm surface that will support the weight ofthe load. Be sure all casters of the Pipe Stand are making contact with the floor.3. Always support the pipe with a minimum of two (2) Pipe Stands. Pipe size, weight andlength are some of the factors to consider when supporting pipe with the Pipe Stands.4. Using the adjusting handle, adjust the V-heads of the Pipe Stands so they are thesame height. ALWAYS adjust the V-head height prior to loading the pipe onto the Pipe Stands. Be sure all the casters are unlocked and pointing in the same direction before loading the pipe onto the Pipe Stands.5. Place the pipe onto the Pipe Stands. Never drop the pipe onto the Pipe Stands. Neverexceed the maximum rated capacity of the Pipe Stands. Never allow anyone under the pipe while it is supported by the pipe stands.6. Each caster on the Pipe Stand has a locking brake. To engage the brake, push downon the brake lever. To unlock the brake, lift up on the brake lever.NOTE: The V-head rollers have an oilite bearing and do not need regular lubrication.OPERATING INSTRUCTIONSSPECIFICATIONSModel No.636636NC (No Casters)Pipe Size Capacity 4" to 36"4" to 36"Max. Weight Capacity 2500 lbs. per Pipe Stand 2500 lbs. per Pipe Stand Overall DimensionsW 35" H 361⁄4"W 35" H 28"Weight130 lbs.90 lbs.TO UNLOCK TO LOCKSET SCREWASSEMBL Y INSTRUCTIONS — 636 JUMBO JACK1. Fully insert five casters into base as shown and secure each with set screw provided.2. Insert V-head into center tube of base.3. Using adjusting handle, raise or lower V-head.STEEL ROLLERADJUSTING HANDLEBASEV-HEADSET SCREWS (5)ONE PER CASTERCASTERS (5)EXPLODED VIEW — 636 JUMBO JACKPARTS LIST — 636ITEM#PART#QTYDESCRIPTION1 .........636-861 ..........1 .............Base2 .........636-855 ..........1 .............V-Head3 ...........636-2 ............5 .............Caster4 .........636-846 ..........8 .............Spacer5 ...........636-3 ............5 .............Set Screw – 5⁄16"-18 X 1⁄4" Long6 ...........302-2 ............4 .............Nut – Hex, 3⁄8-16 (Nylon Insert – Thin)7 ...........636-4 ............4 .............Screw – Shoulder 1⁄2" X 2" Long8 .........636-895 ..........4 .............Roller84276153ASSEMBL Y INSTRUCTIONS — 636NC1. Fully insert five foot assemblies into the baseas shown and secure each with set screw provided.2. Insert V-head into center tube of base.3. Using adjusting handle, raise or lower V-head.V-HEADSTEEL ROLLERADJUSTING HANDLEBASESET SCREWS (5)ONE PER FOOT ASSEMBLY FOOT ASSEMBLYALWAYS fully insert the foot assemblies into the base and secure with the set screws. The foot assemblies are NOT designed to level theJumbo Jack.CAUTIONEXPLODED VIEW – 636NCPARTS LIST — 636NCITEM#PART#QTYDESCRIPTION1 ....................302-2 ..................4 .........Nut – Hex, 3⁄8-16 (Nylon Insert – Thin) 2 ....................636-3 ...................5 .........Screw – Socket Set (5⁄16"-18 X 1⁄4") Cup Pt 3 ....................636-4 ...................4 .........Screw – Shoulder, (1⁄12" X 2") 4 ..................636-846 .................8 .........Spacer5 ..................636-855 .................1 .........V-Head6 ..................636-861 .................1 .........Base7 ..................636-895 .................4 .........Roller8 ..................636-902 .................5 .........Foot Assembly26874135Photo 1Photo 2In order that you have themaximum amount of adjustmenttravel, adjust the handle/nut sothat the nut is flush with the endof the acme screw. (See photo 3.)Photo 3Photo 4ALWAYS use a hold down device with each pipe stand when transporting pipe.Before transporting, align the casters as follows:A. Unlock ALL caster brakes (See page 4).B. Rotate the pipe stand base in a circular motion to align casters.NEVER use a hammer to reposition the casters. Damage Right WrongPhoto 5。

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Examples for Using Speech Signal Processing ToolkitVer.3.3SPTK working groupDecember25,2009Contents1Basics31.1Help message (3)1.2Data type conversion between“little endian”and“big endian.” (3)1.3Dump a binary dataﬁle (3)1.4Data type conversion from“short int”to“ﬂoat” (3)1.5Plotting speech waveform on X-window (3)1.6Save theﬁgure in an Encapsulated PostScriptﬁle (4)1.7Play a soundﬁle (4)1.8Cut a portion out of aﬁle (4)2Pitch Extraction from Speech Waveform52.1A(very simple)pitch extractor (5)2.2Plotting the extracted pitch contour (6)3Speech Analysis/Synthesis Based on Mel-Cepstral Representation63.1Mel-cepstral analysis of speech (6)3.2Plotting spectral estimates from mel-cepstrum (6)3.3Plotting the spectral estimate with the FFT spectrum (7)3.4Speech synthesis from mel-cepstrum (8)4Speech Analysis/Synthesis based on LPC94.1LPC analysis of speech (9)4.2Plotting spectral estimates from LPC coeﬃcients (10)4.3Plotting the spectral estimate with the FFT spectrum (10)4.4Speech synthesis from LPC coeﬃcients (11)4.5Obtain PARCOR coeﬃcients from LPC coeﬃcients (12)4.6Speech synthesis from PARCOR coeﬃcients (12)4.7Obtain LSP coeﬃcients from LPC coeﬃcients (13)4.8Speech synthesis from LSP coeﬃcients (13)5Speech Analysis/Synthesis Based on Mel-Generalized Cepstral Representation145.1Mel-generalized cepstral analysis of speech (14)5.2Plotting spectral estimates from mel-generalized cepstrum (15)5.3Plotting the spectral estimate with the FFT spectrum (16)5.4Speech synthesis from mel-generalized cepstrum (17)16Vector Quantization of Mel-Cepstrum186.1Train a(very small)Codebook (18)6.2Encode(training vectors) (18)6.3Decode(training vectors) (19)6.4Plotting original and quantized spectra (19)6.5Performance evaluation on the training data (20)6.6Speech synthesis from quantized mel-cepstrum (21)7Preparation of Speech Parameter for Speech Recognition227.1Cepstrum derived from LPC analysis(LPC cepstrum) (22)7.2Mel-cepstrum derived from LPC analysis(LPC mel-cepstrum) (22)7.3Mel-cepstrum obtained by mel-cepstral analysis (22)7.4Mel-cepstrum derived from mel-generalized cepstral analysis (23)7.5Plotting spectra for each speech recognition parameter (23)8Playing with the Vocoder Based on Mel-Cepstrum248.1High-or low-pitched voice (24)8.2Fast-or slow-speaking voice (25)8.3Hoarse voice (25)8.4Robotic voice (25)8.5Child-like or deep voice (25)8.6Various voices (25)9Speech Synthesis Based on HMM269.1Speech parameter generation from a sequence of HMMs (26)9.2Plotting spectra calculated from generated mel-cepstrum (26)9.3Speech synthesis from the generated mel-cepstrum (26)9.4Check the given mean and variance vectors (27)9.4.1Dump static feature vectors (27)9.4.2Dump variance vectors of static feature vectors (28)9.4.3Dump dynamic feature vectors(delta) (28)9.4.4Dump variance vectors of dynamic feature vectors(delta) (28)9.5Speech synthesis without dynamic feature (28)21Basics1.1Help messageimpulse-h1.2Data type conversion between“little endian”and“big endian.”Files:data.short:speech data included in this example(short integer,16kHz sampling,little endian) data.short-b:speech data(short integer,16kHz sampling,big endian)swab+s<data.short>data.short-b1.3Dump a binary dataﬁleFiles:data.short:speech data included in this example(short integer,16kHz sampling)dmp+s data.short|less1.4Data type conversion from“short int”to“ﬂoat”Files:data.short:speech data included in this example(short integer,16kHz sampling)data.ﬂoat:speech data(ﬂoat,16kHz sampling)12x2x+sf<data.short>data.float1.5Plotting speech waveform on X-windowFiles:data.short:speech data included in this example(short integer,16kHz sampling)gwave+s data.short|xgr1By clicking links in this PDFﬁle,your PC may play some speechﬁles,which were converted from“ﬂoat”format into“wav”format(16 kHz sampling,16-bit integer).2If you compiled SPTK with"--enable-double"option,please use"+sd"option instead of"+sf"and"+d"option instead of"+f".303847681152153619202304268830723456-14507145073841422546094993537757616145652969137297-14507145077682806684508834921896029986103701075411138-145071450711523119071229112675130591344313827142111459514979-145071450715364157481613216516169001728417668180521843618820-14507145071.6Save the ﬁgure in an Encapsulated PostScript ﬁleFiles:data.short:speech data included in this example (short integer,16kHz sampling)ﬁgure.eps:Encapsulated PostScript ﬁle gwave +s data.short |psgr >figure.eps1.7Play a sound ﬁleFiles:data.short:speech data included in this example (short integer,16kHz sampling)Note:This works only on Linux,Solaris,and FreeBSD.da +s -s 16-a 100data.short1.8Cut a portion out of a ﬁleFiles:data.short:speech data included in this example (short integer,16kHz sampling)4bcut +s -s 1000-e 11000<data.short |\gwave +s |xgr020040060080010001200140016001800-14507145072001220124012601280130013201340136013801-14507145074002420244024602480250025202540256025802-14507145076003620364036603680370037203740376037803-14507145078004820484048604880490049204940496049804-14507145072Pitch Extraction from Speech Waveform2.1A (very simple)pitch extractorFiles:data.short:speech data included in this example (short integer,16kHz sampling)Conditions:frame length:640points (40ms)frame period:80points (5ms)window:Blackman window Note:Options should be adjusted for each speech data.x2x +sf data.short |frame +f -l 640-p 80|\window -l 640|pitch -s 16-l 640-t 6.0-L 80-H 165>data.pitch52.2Plotting the extracted pitch contourFiles:data.pitch:pitch data extracted from speech data”data.short”Conditions:Minimum value of vertical axis:0.0Maximum value of vertical axis:250.0Width:15cmHeight:4cmfdrw-y0250-W 1.5-H0.4<data.pitch|xgr0 -x- 236 0 -y- 2503Speech Analysis/Synthesis Based on Mel-Cepstral Representation 3.1Mel-cepstral analysis of speechFiles:data.short:speech data included in this example(short integer,16kHz sampling) data.mcep:mel-cepstrum(ﬂoat)Conditions:frame length:400points(25ms)frame period:80points(5ms)window:Blackman windowanalysis order:20frequency warping parameter:α=0.42FFT size:512pointsx2x+sf<data.short|frame+f-l400-p80|window-l400-L512|\mcep-l512-m20-a0.42>data.mcep3.2Plotting spectral estimates from mel-cepstrumFiles:data.mcep:mel-cepstrum(ﬂoat)Conditions:analysis order:20frequency warping parameter:α=0.42FFT size:512pointsplotted frames:from10-th to135-thsampling frequency:16kHzbcut+f-n20-s10-e135<data.mcep|\mgc2sp-m20-a0.42-g0-l512|grlogsp-l512-x8|xgr602468Frequency (kHz)bcut +f -n 20-s 10-e 135<data.mcep |\mgc2sp -m 20-a 0.42-g 0-l 512|grlogsp -l 512-x 8-t |xgr2468F r e q u e n c y (k H z )3.3Plotting the spectral estimate with the FFT spectrumFiles:data.mcep:mel-cepstrum (ﬂoat)Conditions:analysis order:20frequency warping parameter:α=0.42FFT size:512points7plotted frame:65-thsampling frequency:16kHz(x2x +sf <data.short |frame +f -l 400-p 80|\bcut +f -l 400-s 65-e 65|\window -l 400-L 512|spec -l 512|\glogsp -l 512-x 8-p 2;\\bcut +f -n 20-s 65-e 65<data.mcep |\mgc2sp -m 20-a 0.42-g 0-l 512|glogsp -l 512-x 8)|xgr20468Frequency (kHz)20406080100L o g m a g n i t u d e (d B )3.4Speech synthesis from mel-cepstrumFiles:data.pitch:pitch data extracted from speech data ”data.short”data.mcep:mel-cepstrum (ﬂoat)data.mcep.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:20frequency warping parameter:α=0.42excite -p 80data.pitch |\mlsadf -m 20-a 0.42-p 80data.mcep >data.mcep.syn gwave data.mcep.syn |xgr803777541131150818852262263930163393-16408164083776415345304907528456616038641567927169-16408164087552792983068683906094379814101911056810945-164081640811328117051208212459128361321313590139671434414721-164081640815104154811585816235166121698917366177431812018497-1640816408da +f -s 16data.mcep.syn4Speech Analysis /Synthesis based on LPC4.1LPC analysis of speechFiles:data.short:speech data included in this example (short integer,16kHz sampling)data.lpc:LPC coe ﬃcients (ﬂoat)Conditions:frame length:400points (25ms)frame period:80points (5ms)window:Blackman window analysis order:20x2x +sf <data.short |frame +f -l 400-p 80|window -l 400|\lpc -l 400-m 20>data.lpc94.2Plotting spectral estimates from LPC coeﬃcients Files:data.lpc:LPC coeﬃcients(ﬂoat)Conditions:analysis order:20bcut+f-n20-s10-e135<data.lpc|\spec-l512-n20|grlogsp-l512-x8|xgrorbcut+f-n20-s10-e135<data.lpc|\mgc2sp-m20-a0-g-1-n-u-l512|\grlogsp-l512-x8|xgr02468Frequency (kHz)4.3Plotting the spectral estimate with the FFT spectrum Files:data.lpc:LPC coeﬃcients(ﬂoat)10Conditions:analysis order:20plotted frame:65-thsampling frequency:16kHz(x2x +sf <data.short |frame +f -l 400-p 80|\bcut +f -l 400-s 65-e 65|\window -l 400-L 512|spec -l 512|\glogsp -l 512-x 8-p 2;\\bcut +f -n 20-s 65-e 65<data.lpc >data.tmp ;\spec -l 512-n 20-p data.tmp |glogsp -l 512-x 8;\\rm data.tmp )|xgr 20468Frequency (kHz)020406080100L o g m a g n i t u d e (d B )4.4Speech synthesis from LPC coe ﬃcientsFiles:data.pitch:pitch data extracted from speech data ”data”data.lpc:LPC coe ﬃcients (ﬂoat)data.lpc.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:20excite -p 80data.pitch |poledf -m 20-p 80data.lpc >data.lpc.syngwave +f data.lpc.syn |xgr1103777541131150818852262263930163393-16795167953776415345304907528456616038641567927169-16795167957552792983068683906094379814101911056810945-167951679511328117051208212459128361321313590139671434414721-167951679515104154811585816235166121698917366177431812018497-1679516795da +f -s 16data.lpc.syn4.5Obtain PARCOR coe ﬃcients from LPC coe ﬃcientsFiles:data.lpc:LPC coe ﬃcients (ﬂoat)data.par:PARCOR coe ﬃcients (ﬂoat)Conditions:analysis order:20lpc2par -m 20<data.lpc >data.par4.6Speech synthesis from PARCOR coe ﬃcientsFiles:data.pitch:pitch data extracted from speech data ”data”(ﬂoat)data.par:PARCOR coe ﬃcients (ﬂoat)data.par.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:2012excite -p 80data.pitch |ltcdf -m 20-p 80data.par >data.par.syngwave +f data.par.syn |xgr 03777541131150818852262263930163393-16456164563776415345304907528456616038641567927169-16456164567552792983068683906094379814101911056810945-164561645611328117051208212459128361321313590139671434414721-164561645615104154811585816235166121698917366177431812018497-16456164564.7Obtain LSP coe ﬃcients from LPC coe ﬃcientsFiles:data.lpc:LPC coe ﬃcients (ﬂoat)data.lsp:LSP coe ﬃcients (ﬂoat)Conditions:analysis order:20split number of unit circle:256lpc2lsp -m 20-n 256<data.lpc >data.lsp4.8Speech synthesis from LSP coe ﬃcientsFiles:data.pitch:pitch data extracted from speech data ”data”data.lsp:LSP coe ﬃcients (ﬂoat)data.lsp.syn :synthesize speech (ﬂoat)13Conditions:frame period:80points (5ms)analysis order:20excite -p 80data.pitch |lspdf -m 20-p 80data.lsp >data.lsp.syngwave +f data.lsp.syn |xgr 03777541131150818852262263930163393-16952169523776415345304907528456616038641567927169-16952169527552792983068683906094379814101911056810945-169521695211328117051208212459128361321313590139671434414721-169521695215104154811585816235166121698917366177431812018497-1695216952da +f -s 16data.lsp.syn5Speech Analysis /Synthesis Based on Mel-Generalized Cepstral Repre-sentation5.1Mel-generalized cepstral analysis of speechFiles:speech data:data (short integer,10kHz sampling)data.mgcep:mel-generalized cepstrum (ﬂoat)14Conditions:frame length:400points(25ms)frame period:80points(5ms)window:Blackman windowanalysis order:20frequency warping parameter:α=0.42power parameter:γ=−1/2x2x+sf<data.short|frame+f-l400-p80|window-l400-L512|\ mgcep-m20-a0.42-g2-l512>data.mgcep5.2Plotting spectral estimates from mel-generalized cepstrumFiles:data.mgcep:mel-generalize cepstrum(ﬂoat)Conditions:analysis order:20frequency warping parameter:α=0.42power parameter:γ=−1/2plotted frames:from10-th to135-thsampling frequency:16kHzbcut+f-n20-s10-e135<data.mgcep|\mgc2sp-m20-a0.42-g2-l512|grlogsp-l512-x8|xgr1502468Frequency (kHz)5.3Plotting the spectral estimate with the FFT spectrum Files:data.mgcep:mel-generalized cepstrum(ﬂoat)Conditions:analysis order:20frequency warping parameter:α=0.42power parameter:γ=−1/2FFT size:512pointsplotted frame:65-thsampling frequency:16kHz(x2x+sf<data.short|frame+f-l400-p80|\bcut+f-l400-s65-e65|\window-l400-L512|spec-l512|\glogsp-l512-x8-p2;\\bcut+f-n20-s65-e65<data.mgcep|\mgc2sp-m20-a0.42-g2-l512|glogsp-l512-x8)|xgr1620468Frequency (kHz)020406080100L o g m a g n i t u d e (d B )5.4Speech synthesis from mel-generalized cepstrum Files:data.pitch:pitch data extracted from speech data ”data”data.mgcep:mel-generalized cepstrum (ﬂoat)data.mgcep.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:20frequency warping parameter:α=0.42power parameter:γ=−1/2excite -p 80data.pitch |\mglsadf -m 20-a 0.42-c 2-p 80data.mgcep >data.mgcep.syn gwave +f data.mgcep.syn |xgr1703777541131150818852262263930163393-15982159823776415345304907528456616038641567927169-15982159827552792983068683906094379814101911056810945-159821598211328117051208212459128361321313590139671434414721-159821598215104154811585816235166121698917366177431812018497-1598215982da +f -s 16data.mgcep.syn6Vector Quantization of Mel-Cepstrum6.1Train a (very small)CodebookFiles:data.mcep:mel-cepstrum for training (ﬂoat)codebook.mcep:codebook (ﬂoat)Conditions:vector size:21(analysis order:20)codebook size:32lbg -n 20-e 32<data.mcep >codebook.mcep6.2Encode (training vectors)Files:codebook.mcep:codebook (ﬂoat)data.mcep.index:index (int)18Conditions:vector size:21(analysis order:20)codebook size:32vq-n20codebook.mcep<data.mcep>data.mcep.index6.3Decode(training vectors)Files:codebook.mcep:codebook(ﬂoat)data.mcep.index:index(int)data.mcep.vq:quantized mel-cepstrum(ﬂoat)Conditions:vector size:21(analysis order:20)codebook size:32ivq-n20codebook.mcep<data.mcep.index>data.mcep.vq6.4Plotting original and quantized spectraFiles:data.mcep:original mel-cepstrum(ﬂoat)data.mcep.vq:quantized mel-cepstrum(ﬂoat)Conditions:analysis order:20frequency warping parameter:α=0.42plotted frames:from10-th to135-thsampling frequency:16kHz(bcut+f-n20-s10-e135<data.mcep|\mgc2sp-m20-a0.42-g0-l512|\grlogsp-l512-x8-O1-c"(a)original";\\bcut+f-n20-s10-e135<data.mcep.vq|\mgc2sp-m20-a0.42-g0-l512|\grlogsp-l512-x8-O2-c"(b)quantized")|xgr19(a) original 02468Frequency (kHz)(b) quantized 02468Frequency (kHz)6.5Performance evaluation on the training dataFiles:codebook.mcep:codebook (ﬂoat)data.mcep.index:index (int)data.mcep.vq:quantized vectors (ﬂoat)data.mcep.vq.cdist:cepstrum distortion in dB (ﬂoat)Conditions:vector size:21(analysis order:20)codebook size:32freqt -a 0.42-m 20-A 0-M 255<data.mcep >data.mcep.cep freqt -a 0.42-m 20-A 0-M 255<data.mcep.vq |\cdist data.mcep.cep -m 255>data.mcep.vq.cdist\rm data.mcep.cep206.6Speech synthesis from quantized mel-cepstrumFiles:data.pitch:pitch data extracted from speech data ”data.short”data.mcep.vq:quantized mel-cepstrum (ﬂoat)data.mcep.vq.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:20frequency warping parameter:α=0.42excite -p 80data.pitch |\mlsadf -m 20-a 0.42-p 80data.mcep.vq >data.mcep.vq.syngwave +f data.mcep.vq.syn |xgr 03777541131150818852262263930163393-12989129893776415345304907528456616038641567927169-12989129897552792983068683906094379814101911056810945-129891298911328117051208212459128361321313590139671434414721-129891298915104154811585816235166121698917366177431812018497-1298912989da +f -s 16data.mcep.vq.syn217Preparation of Speech Parameter for Speech Recognition 7.1Cepstrum derived from LPC analysis(LPC cepstrum)Files:data.short:speech data included in this example(short integer,16kHz sampling) Conditions:frame length:400points(25ms)frame period:80points(5ms)window:Blackman windowanalysis order:12order of LPC cepstrum:12x2x+sf<data.short|frame+f-l400-p80|window-l400|\lpc-l400-m12|lpc2c-m12-M12>data.lpc.cep7.2Mel-cepstrum derived from LPC analysis(LPC mel-cepstrum) Files:data.short:speech data included in this example(short integer,16kHz sampling) Conditions:frame length:400points(25ms)frame period:80points(5ms)window:Blackman windowanalysis order:12order of LPC mel-cepstrum:12x2x+sf<data.short|frame+f-l400-p80|window-l400|\lpc-l400-m12|\lpc2c-m12-M256|\freqt-m256-a0-M12-A0.42>data.lpc.mceporx2x+sf<data.short|frame+f-l400-p80|window-l400|\lpc-l400-m12|\mgc2mgc-m12-a0-g-1-n-u-M12-A0.42-G0>data.lpc.mcep7.3Mel-cepstrum obtained by mel-cepstral analysisFiles:data.short:speech data included in this example(short integer,16kHz sampling) data.mcep:mel-cepstrum(ﬂoat)Conditions:frame length:400points(25ms)frame period:80points(5ms)window:Blackman windowanalysis order:20frequency warping parameter:α=0.42FFT size:512pointsx2x+sf<data.short|frame+f-l400-p80|window-l400-L512|\ mcep-l512-m12-a0.42>data.mcep.mcep227.4Mel-cepstrum derived from mel-generalized cepstral analysis Files:data.short:speech data included in this example(short integer,10kHz sampling)Conditions:frame length:400points(25ms)frame period:80points(5ms)Blackman windowFFT size:512points(α,γ)for analysis:(0.42,-0.5)analysis order:12order of mel-cepstrum:12x2x+sf<data.short|frame+f-l400-p80|window-l400-L512|\ mgcep-m12-a0.42-g2-l512|\mgc2mgc-m12-a0.42-g2-M12-A0.42-G0>data.mgcep.mcep7.5Plotting spectra for each speech recognition parameterFiles:data.lpc.cep:LPC cepstrum(ﬂoat)data.lpc.mcep:LPC mel-cepstrum(ﬂoat)data.mcep.mcep:mel-cepstrum(ﬂoat)data.mgcep.mcep:mel-cepstrum derived from mel-generalized cepstrum(ﬂoat) Conditions:plotted frames:from10-th to135-th(\bcut+f-n12-s10-e135<data.lpc.cep|\mgc2sp-m12-a0-g0-l512|\grlogsp-l512-x8-O1-c"(a)LPC-CEP";\\bcut+f-n12-s10-e135<data.lpc.mcep|\mgc2sp-m12-a0.42-g0-l512|\grlogsp-l512-x8-O2-c"(b)LPC-MCEP";\\bcut+f-n12-s10-e135<data.mcep.mcep|\mgc2sp-m12-a0.42-g0-l512|\grlogsp-l512-x8-O3-c"(c)MCEP";\\bcut+f-n12-s10-e135<data.mgcep.mcep|\mgc2sp-m12-a0.42-g0-l512|\grlogsp-l512-x8-O4-c"(d)MGCEP-MCEP")|xgr23(a) LPC-CEP 02468Frequency (kHz)(b) LPC-MCEP 02468Frequency (kHz)(c) MCEP 02468Frequency (kHz)(d) MGCEP-MCEP 02468Frequency (kHz)8Playing with the Vocoder Based on Mel-Cepstrum8.1High-or low-pitched voiceFiles:data.mcep.high.syn :synthesized speech (ﬂoat)data.mcep.low.syn :synthesized speech (ﬂoat)sopr -m 0.4data.pitch |\excite -p 80|mlsadf -m 20-a 0.42-p 80data.mcep |\tee data.mcep.high.syn |da +f -s 16sopr -m 2data.pitch |\excite -p 80|mlsadf -m 20-a 0.42-p 80data.mcep |\tee data.mcep.low.syn |da +f -s 16248.2Fast-or slow-speaking voiceFiles:data.mcep.fast.syn:synthesized speech(ﬂoat)data.mcep.slow.syn:synthesized speech(ﬂoat)sopr-m1data.pitch|\excite-p40|mlsadf-m20-a0.42-p40data.mcep|\tee data.mcep.fast.syn|da+f-s16sopr-m1data.pitch|\excite-p160|mlsadf-m20-a0.42-p160data.mcep|\tee data.mcep.slow.syn|da+f-s168.3Hoarse voiceFiles:data.mcep.hoarse.syn:synthesized speech(ﬂoat)sopr-m0data.pitch|\excite-p80|mlsadf-m20-a0.42-p80data.mcep|\tee data.mcep.hoarse.syn|da+f-s168.4Robotic voiceFiles:data.mcep.robot.syn:synthesized speech(ﬂoat)train-p200-l-1|mlsadf-m20-a0.42-p80data.mcep|\tee data.mcep.robot.syn|da+f-s168.5Child-like or deep voiceFiles:data.mcep.child.syn:synthesized speech(ﬂoat)data.mcep.deep.syn:synthesized speech(ﬂoat)sopr-m0.4data.pitch|\excite-p80|mlsadf-m20-a0.1-p80data.mcep|\tee data.mcep.child.syn|da+f-s16sopr-m2data.pitch|\excite-p80|mlsadf-m20-a0.6-p80data.mcep|\tee data.mcep.deep.syn|da+f-s168.6Various voicesFiles:data.ﬂoat:original speech(ﬂoat)data.mcep.syn:synthesized speech(ﬂoat)data.mcep.{high,low,fast,slow,hoarse,robot,child,deep}.syn:synthesized speech(ﬂoat) da+f-v-s16data.float data.mcep.syn\data.mcep.{high,low,fast,slow,hoarse,robot,child,deep}.syn259Speech Synthesis Based on HMM9.1Speech parameter generation from a sequence of HMMsFiles:sample.pdf:sequence of mean and variance corresponding to a state sequence included in this example(ﬂoat,little endian)3sample.mcep:mel-cepstrum generated from a sequence of HMMs (ﬂoat)Conditions:analysis order:24weight coe ﬃcients for calculating delta:w (−1)=−0.5,w (0)=0,w (1)=0.5weight coe ﬃcients for calculating delta-delta:w (−1)=0.25,w (0)=−0.5,w (1)=0.25Note:The state sequence is determined according to the state duration densities of the HMMs.The algorithm isnot included in SPTK-3.2.mlpg -m 24-i 1-d -0.500.5-d 0.25-0.50.25sample.pdf >sample.mcep9.2Plotting spectra calculated from generated mel-cepstrumFiles:sample.mcep:mel-cepstral coe ﬃcients (ﬂoat)Conditions:analysis order:24frequency warping parameter:α=0.42FFT size:512pointsplotted frames:from 100-th to 250-thsampling frequency:16kHzbcut +f -n 24-s 100-e 250<sample.mcep |\mgc2sp -m 24-a 0.42-g 0-l 512|grlogsp -l 512-x 8-t |xgr2468F r e q u e n c y (k H z )9.3Speech synthesis from the generated mel-cepstrumFiles:sample.pitch:pitch data generated from a sequence of MSD-HMMs included in this example (ﬂoat,littleendian)4sample.mcep:mel-cepstrum (ﬂoat)sample.mcep.syn :synthesized speech (ﬂoat)Conditions:frame period:80points (5ms)analysis order:24frequency warping parameter:α=0.423Ifyou compiled SPTK with "--enable-double"option,please ﬁrst convert this ﬁle into double format:x2x +sd sample.pdf >sample.pdf.double4If you compiled SPTK with "--enable-double"option,please ﬁrst convert this ﬁle into double format:x2x +sd sample.pitch >sample.pitch.double 26Note:The pitch pattern generation algorithm is not included in SPTK-3.2.excite -p 80sample.pitch |\mlsadf -p 80-a 0.42-m 24sample.mcep >sample.mcep.syngwave +f sample.mcep.syn |xgr 0108621723258434454306516760286889774-161681616810864119501303614122152081629417380184661955220638-161681616821728228142390024986260722715828244293303041631502-161681616832592336783476435850369363802239108401944128042366-161681616843456445424562846714478004888649972510585214453230-1616816168da +f -s 16sample.mcep.syn9.4Check the given mean and variance vectorsFiles:sample.pdf:sequence of mean and variance corresponding to a state sequence (ﬂoat)Conditions:analysis order:249.4.1Dump static feature vectorsbcp +f -l 150-s 0-e 24sample.pdf |dmp -n 24|less279.4.2Dump variance vectors of static feature vectorsbcp +f -l 150-s 75-e 99sample.pdf |sopr -INV |dmp -n 24|less9.4.3Dump dynamic feature vectors (delta)bcp +f -l 150-s 25-e 49sample.pdf |dmp -n 24|less9.4.4Dump variance vectors of dynamic feature vectors (delta)bcp +f -l 150-s 100-e 124sample.pdf |sopr -INV |dmp -n 24|less9.5Speech synthesis without dynamic featureFiles:sample.pitch:pitch data generated from a sequence of MSD-HMMs (ﬂoat)sample.mcep.wo-dyn:mel-cepstrum generated without dynamic feature (ﬂoat)sample.mcep.wo-dyn.syn :synthesized speech without dynamic feature (ﬂoat)Conditions:frame period:80points (5ms)analysis order:24frequency warping parameter:α=0.42bcp +f -l 150-s 0-e 24sample.pdf >sample.mcep.wo-dynbcut +f -n 24-s 100-e 250<sample.mcep.wo-dyn |\mgc2sp -m 24-a 0.42-g 0-l 512|grlogsp -l 512-x 8-t |xgr 02468F r e q u e n c y (k H z )excite -p 80sample.pitch |\mlsadf -p 80-a 0.42-m 24sample.mcep.wo-dyn >sample.mcep.wo-dyn.syn gwave +f sample.mcep.wo-dyn.syn |xgr280108621723258434454306516760286889774-197301973010864119501303614122152081629417380184661955220638-197301973021728228142390024986260722715828244293303041631502-197301973032592336783476435850369363802239108401944128042366-197301973043456445424562846714478004888649972510585214453230-1973019730da +f -s 16sample.mcep.wo-dyn.syn sample.mcep.syn29。