Preprocessing and simulation software Hardware implementation.docx
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Preprocessing and simulation software Hardware implementation.docx
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PreprocessingandsimulationsoftwareHardwareimplementation
869
Determinationofgeometricparametersoffracturenetworksusing1Ddata OriginalResearchArticle
JournalofStructuralGeology,Volume32,Issue7,July2010,Pages878-885
TivadarM.Tóth
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Abstract
Tosimulateasuitablefracturenetworkforhydrogeologicalmodelling,inputstatisticaldataoftheindividualfaults,aswellasfracturesets,shouldbedeterminedfirstusingeither2Dsectionsor1Dscanlines.Althoughtheaccuracyofthismeasurementisfundamental,exactdeterminationisratherproblematicandisusuallypossibleonlyataparticularscale.Thispaperintroducesacoupledmethodforcomputinglengthexponent(E)andspatialdensity(Dc),thetwomostessentialparametersformodellingfracturenetworks.Tocalculatethelengthexponent,datasetsofatleasttwoindependentimagingprocessesareneeded.Utilizingdifferentsensitivitythresholdsofthetwomethodsandthewell-knownanalyticalformofafracturelengthdistributionfunction,itsparameterscanbecalculated.Toestimatethespatialdensityoffracturecentresin3D,theseriesofintersectionsshouldbeanalysedasafractionalBrownianmotionandthencalibratedwithvirtualwellssimulatedwithoptionalmodellingsoftware.Themethodmakesfractureintensityloggingpossiblealongscanlines.Basedontheseapproaches,thereisnoneedtoimportfractureparametersfromtheoutcropsurveyorfromotherpartsofthereservoir,becauseallgeometricinformationofthefracturesystemreferstotherockbodyunderexamination.Usingsite-specificparametersmakesfracturenetworkmodellingmorereliable.
ArticleOutline
1.Introduction
2.Geometricparametersoffracturesystems
3.Themodelused-RepSim
4.Determinationofgeometricparametersoffractures
4.1.Evaluationof1Dsections
4.1.1.Estimationoffractaldimension(D3c)
4.1.2.Determinationoflengthexponent(E)
5.Casestudy—Mórágygranitebody
5.1.2DSections
5.2.1DSections
6.Conclusions
Acknowledgements
References
870
Postchallengeplasmaglucoseexcursions,carotidintima-mediathickness,andriskfactorsforatherosclerosisinChinesepopulationwithtype2diabetes OriginalResearchArticle
Atherosclerosis,Volume210,Issue1,May2010,Pages302-306
YaominHu,WeiLiu,RongHuang,XiaoyingZhang
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Abstract
Aims
Isolatedhyperglycemiaisassociatedwithatherosclerosisinindividualswithtype2diabetes,buttherelationshipbetweenpostchallengeglucoseexcursionandatherosclerosisislessclear.Thisstudyexaminestherelationshipsbetweenpostchallengeglucosespikes(PGS),carotidintima-mediathickness(IMT),andtraditionalriskfactorsforatherosclerosisinindividualswithtype2diabetes.
Methods
Atotalof474individualswithtype2diabeteswhowerewithinthehighestorlowestIMTdistributionquartilewereincluded.TheStudent'st-test,one-wayanalysisofvariance(ANOVA),singlevariateandmultivariateanalyseswereimplementedtostudythedata.Anadditionalhealthycontrolgroup(n = 896)wasselectedduringroutinehealthexamination.TheywereHannationalityandunrelatedtothediabeticpatients.
Results
(1)Comparedwithsubjectsofhealthycontrolgroup,thesubjectswithtype2diabeteshadsignificantlyhigherlevelsofbodymassindex(BMI),waist-to-hipratio(WHR),systolicbloodpressure,triglyceride,totalcholesterol,lowdensitylipoproteincholesterol(LDL-C),fastingplasmaglucose(FPG),120 minpostchallengeglucose(PG120),hemoglobinA1c(HbA1c)andIMT(P ≤ 0.01)andrelativelylowerlevelsofhighdensitylipoproteincholesterol(HDL-C)(P ≤ 0.05).
(2)AccordingtotheIMTwhichwasmeasuredbyB-modeultrasonography,thepatientsoftype2diabetescouldbedividedintotwosubgroups:
onewasthesubgroupofIMT ≥ P75andanotherwasthesubgroupofIMT ≤ P25.ComparedwithsubjectsofIMT ≥ P25subgroup,subjectsbeingintheIMT ≥ P75subgroupexhibitedsignificantlyincreasedage,WHR,diabetesduration,systolicbloodpressure,totalcholesterol,triglyceride,LDL-C,andsignificantlydecreasedHDL-Clevels.Andamongalltheplasmaglucosevariables,exceptforFPGandPG30,alltheothervariables(includePG60,PG120,PG180,PGS,HbA1C,underareacurveofglucose)showedasignificantincreaseintheIMT ≥ P75subgroup.(3)AmultivariatelogisticregressionanalysiswasperformedtoestablishwhichwereindependentlyrelatedwithcarotidIMT,andtheresultsshowedthePGSwasidentifiedasthestrongestdeterminantofIMTfromalltheatherosclerosisriskfactors.(4)PGSissignificantlycorrelatedtoavarietyofatherosclerosisriskfactors.
Conclusions
ThisstudyidentifiedseveralimportantassociationsbetweenPGSandknownriskfactorsforatherosclerosisandsuggestedthatPGSisindependentlyrelatedtocarotidIMT.Widepostchallengeglucoseexcursionsmaycontributetothedevelopmentofatherosclerosisinindividualswithtype2diabetes,independentofotherriskfactors.
ArticleOutline
1.Introduction
2.Subjectsandmethods
2.1.Subjects
2.2.Laboratorymeasurements
2.3.IMTmeasurement
2.4.Statisticalanalysis
3.Results
3.1.Clinicalandbiologicalcharacteristicsbetweentype2DMandcontrolgroup(Table1)
3.2.ClinicalandbiologicalcharacteristicsbetweenIMT ≤ P25andIMT ≥ P75subgroupintype2diabetes(Table2)
3.3.LogisticregressionanalysisonthevariousriskfactorsforIMTinpatientsoftype2diabetes(Table3)
3.4.RelationshipsbetweenPGSandtheotheratherosclerosisriskfactors(Fig.1)
4.Discussion
Acknowledgements
References
871
Invivomicrocartographyandsubcellularimagingoftumorangiogenesis:
Anovelplatformfortranslationalangiogenesisresearch OriginalResearchArticle
MicrovascularResearch,Volume78,Issue1,June2009,Pages51-56
MarkP.S.Dunphy,DavidEntenberg,RicardoToledo-Crow,StevenM.Larson
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Abstract
Purpose
Toeliminatethevariableoftumorheterogeneityfromanovelinvivomodeloftumorangiogenesis.
Experimentaldesign
Wedevelopedamethodtonavigatetumorneovasculatureinalivingtissuemicroenvironment,enablingrelocationofacell-ormicroregion-of-interest,forserialinvivoimaging.Orthotopicmelanomawasgrown,inimmunocompetentTie2GFPmice.Intravitalmultiphotonfluorescenceandconfocalreflectanceimagingwasperformed,onacustommicroscopewithmotorizedstageandcoordinatenavigationsoftware.ApointwithinaTie2GFP+microvesselwasselectedforrelocation.Customsoftwarepredictedtargetcoordinatesbaseduponreferencepoints(tissue-embeddedpolystyrenebeads)andbaselinetargetcoordinates.Micewereremovedfromthestagetomakepreviously-obtainedtargetcoordinatesinvalidinsubsequentimaging.
Results
Coordinatepredictionsalwaysrelocatedtargetpoints,invivo,towithin10–200 μm(withinasingle40×field-of-view).Themodelsystemprovidedavirtuallivinghistologyoftumorneovascularizationandmicroenvironment,withsubcellularspatialresolutionandhemodynamicinformation.
Conclusions
Thenavigationprocedure,termedinvivomicrocartography,permitscontroloftissueheterogeneity,asavariable.Tie2maybethebestreportergeneidentified,to-date,forintravitalmicroscopyoftumorangiogenesis.Thisnovelmodelsystemshouldstrengthenintravitalmicroscopyinitshistoricalroleasavitaltoolinoncology,angiogenesisresearch,andangiotherapeuticdrugdevelopment.
ArticleOutline
Introduction
Materialsandmethods
Animalmodel
Intravitalmicroscopy
Invivomicrocartography
Imageprocessing
Externalbeamradiotherapy
Results
Animalmodelandintravitalmicroscopy
Invivomicrocartography
Radiationvasculopathy
Discussion
Summary
Acknowledgements
AppendixA.Supplementarydata
References
872
Asystematicandcomprehensiveinvestigationofmethodstobuildandevaluatefaultpredictionmodels OriginalResearchArticle
JournalofSystemsandSoftware,Volume83,Issue1,January2010,Pages2-17
ErikArisholm,LionelC.Briand,EivindB.Johannessen
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Abstract
ThispaperdescribesastudyperformedinanindustrialsettingthatattemptstobuildpredictivemodelstoidentifypartsofaJavasystemwithahighfaultprobability.Thesystemunderconsiderationisconstantlyevolvingasseveralreleasesayearareshippedtocustomers.Developersusuallyhavelimitedresourcesfortheirtestingandwouldliketodevoteextraresourcestofaultysystemparts.Themainresearchfocusofthispaperistosystematicallyassessthreeaspectsonhowtobuildandevaluatefault-pronenessmodelsinthecontextofthislargeJavalegacysystemdevelopmentproject:
(1)comparemanydataminingandmachinelearningtechniquestobuildfault-pronenessmodels,
(2)assesstheimpactofusingdifferentmetricsetssuchassourcecodestructuralmeasuresandchange/faulthistory(processmeasures),and(3)compareseveralalternativewaysofassessingtheperformanceofthemodels,intermsof(i)confusio
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