简介：Denovosequencingisoneofthemostpromisingproteomicstechniquesforidentificationofproteinposttranslationmodifications(PTMs)instudyingproteinregulationsandfunctions.WehavedevelopedacomputertoolPRIMEforidentificationofbandyionsintandemmassspectra,akeychallengingproblemindenovosequencing.PRIMEutilizesafeaturethationsofthesameanddifferenttypesfollowdifferentmass-differencedistributionstoseparatebfromyionscorrectly.Wehaveformulatedtheproblemasagraphpartitionproblem.Alinearinteger-programmingalgorithmhasbeenimplementedtosolvethegraphpartitionproblemrigorouslyandefficiently.TheperformanceofPRIMEhasbeendemonstratedonalargeamountofsimulatedtandemmassspectraderivedfromYeastgenomeanditspowerofdetectingPTMshasbeentestedon216simulatedphosphopeptides.

简介：Thispaperpresentsafunctionallanguagefortheunambiguousdescriptionofdigitalcircuits,amethodandalgorithmstoobtainastandard-celllayout,andacomparativeevaluationofthedevelopedfunctionalstandard-cellplacementtechnique.Thepresentedplacementschemeisdifferentfromtraditionalmethodsbecausethecompletelayoutgrometryisspecifiedandconstructedautomaticallyfromafunctionaldescription.Theconstructionreliesonatranslationthatcombinesthesimplicityofstandard-cellswiththeeleganceoffunctionalprogramming.Anevaluationofthemethodintroducedshowsthatthequalityoftheresultingplacementisclosetotheresultsachievedwithsimulatedannealingwhilethecomputationtimeissignificantlyless.Furthermore,theevaluationsuggeststoemploythefunctionalplacementmethodinconjunctionwithlow-temperaturesimulatedannealingforrunning-timereductionandimprovedresults.

简介：Ensuringahighmanufacturingtestqualityofanintegratedelectroniccircuitmandatestheapplicationofalargevolumetestset.Evenifthetestdatacanbefitintothememoryofanexternaltester,theconsequentincreaseintestapplicationtimereflectsintoelevatedproductioncosts.Testdatacompressionsolutionshavebeenproposedtoaddressthetesttimeanddatavolumeproblembystoringanddeliveringthetestdatainacompressedformat,andsubsequentlybyexpandingthedataon-chip.Inthispaper,weproposeascancellpositioningmethodologythataccompaniesacompressiontechniqueinordertoboostthecompressionratio,andsquashthetestdataevenfurther.Whilewepresenttheapplicationoftheproposedapproachinconjunctionwiththefan-outbaseddecompressionarchitecture,thisapproachcanbeextendedforapplicationalongwithothercompressionsolutionsaswell.Theexperimentalresultsalsoconfirmthecompressionenhancementoftheproposedmethodology.

简介：Anincreasingnumberofstructuralhomologysearchtools,mostlybasedonprofilestochasticcontext-freegrammars(SCFGs)havebeenrecentlydevelopedforthenon-codingRNAgeneidentification.SCFGscanincludestatisticalbiasesthatoftenoccurinRNAsequences,necessarytoprofilespecificRNAstructuresforstructuralhomologysearch.Inthispaper,asuccinctstochasticgrammarmodelisintroducedforRNAthathascompetitivesearcheffectiveness.Moreimportantly,theprofilingmodelcanbeeasilyextendedtoincludepseudoknots,structuresthatarebeyondthecapabilityofprofileSCFGs.Inaddition,themodelallowsheuristicstobeexploited,resultinginasignificantspeed-upfortheCYKalgorithm-basedsearch.