2024
Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features
Lanino L, D'Amico S, Maggioni G, Al Ali N, Wang Y, Gurnari C, Gagelmann N, Bewersdorf J, Ball S, Guglielmelli P, Meggendorfer M, Hunter A, Kubasch A, Travaglino E, Campagna A, Ubezio M, Russo A, Todisco G, Tentori C, Buizza A, Sauta E, Zampini M, Riva E, Asti G, Delleani M, Ficara F, Santoro A, Sala C, Dall'Olio D, Dall'Olio L, Kewan T, Casetti I, Awada H, Xicoy B, Vucinic V, Hou H, Chou W, Yao C, Lin C, Tien H, Consagra A, Sallman D, Kern W, Bernardi M, Chiusolo P, Borin L, Voso M, Pleyer L, Palomo L, Quintela D, Jerez A, Cornejo E, Martin P, Díaz-Beyá M, Pita A, Roldan V, Suarez D, Velasco E, Calabuig M, Garcia-Manero G, Loghavi S, Platzbecker U, Sole F, Diez-Campelo M, Maciejewski J, Kröger N, Fenaux P, Fontenay M, Santini V, Haferlach T, Germing U, Padron E, Robin M, Passamonti F, Solary E, Vannucchi A, Castellani G, Zeidan A, Komrokji R, Della Porta M. Artificial-Intelligence, Data-Driven, Comprehensive Classification of Myeloid Neoplasms Based on Genomic, Morphological and Histological Features. Blood 2024, 144: 1005. DOI: 10.1182/blood-2024-204826.Peer-Reviewed Original ResearchGenomic featuresSplicing mutationBiallelic inactivationAnalysis of genomic profilesBiallelic inactivation of TP53Clinical phenotypeGene expression profilesCNV analysisMorphological featuresInactivation of TP53Myeloid neoplasmsGenomic characterizationRNAseq dataMorphological dataMutation screeningExpression profilesMutationsJAK/STATGenomic profilingGenomeHierarchical importanceHeterogeneous phenotypesIntegrated analysisPhenotypeHematological phenotypeEnhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA)
Sauta E, Sartori F, Lanino L, Asti G, D'Amico S, Delleani M, Riva E, Zampini M, Zazzetti E, Bicchieri M, Maggioni G, Campagna A, Todisco G, Tentori C, Ubezio M, Russo A, Buizza A, Ficara F, Crisafulli L, Brindisi M, Ventura D, Pinocchio N, Rahal D, Lancellotti C, Bonometti A, Di Tommaso L, Savevski V, Santoro A, Derus N, Dall'Olio D, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Garcia-Manero G, Haferlach T, Kordasti S, Zeidan A, Castellani G, Sanavia T, Fariselli P, Della Porta M. Enhancing Personalized Prognostic Assessment of Myelodysplastic Syndromes through a Multimodal and Explainable Deep Data Fusion Approach (MAGAERA). Blood 2024, 144: 105-105. DOI: 10.1182/blood-2024-205413.Peer-Reviewed Original ResearchPersonalized medicine programsMyelodysplastic syndrome patientsMyelodysplastic syndromeOverall survivalConcordance indexClinical outcomesMay-Grunwald-GiemsaHypomethylating agentsBone marrowAnalysis of hematological malignanciesSomatic mutation screeningEvaluation of T lymphocytesResponse to hypomethylating agentsCD34+ bone marrowStudies of myelodysplastic syndromesGenomic featuresMDS populationRNA-seqPrediction of patient outcomeGenomic characterizationHarrell's concordance indexPredicting Clinical OutcomesHematoxylin and eosin (H&EMorphological dataMulti-Omics
2012
Recovery of a nearly extinct Galápagos tortoise despite minimal genetic variation
Milinkovitch MC, Kanitz R, Tiedemann R, Tapia W, Llerena F, Caccone A, Gibbs JP, Powell JR. Recovery of a nearly extinct Galápagos tortoise despite minimal genetic variation. Evolutionary Applications 2012, 6: 377-383. PMID: 23467700, PMCID: PMC3586625, DOI: 10.1111/eva.12014.Peer-Reviewed Original ResearchEspañola IslandGenetic variationEffective population sizeUnequal reproductive successMinimal genetic variationMolecular genetic analysisGalápagos tortoisesReproductive successCaptive populationsNonrandom matingBreeding regimesParental populationsGenetic analysisMorphological dataPopulation sizeTortoise sizeSpeciesCaptivityNumber of animalsTortoisesIslandsInbreedingGalápagosMatingRepatriation efforts
2009
Evolution of petal identity
Irish VF. Evolution of petal identity. Journal Of Experimental Botany 2009, 60: 2517-2527. PMID: 19443615, DOI: 10.1093/jxb/erp159.Peer-Reviewed Original ResearchConceptsPetal identityDifferent lineagesDifferent angiosperm lineagesDevelopmental control genesAncestral developmental pathwaysEvolution of petalsSize of petalsSimilar genetic pathwaysGene regulatory networksMolecular genetic analysisAngiosperm lineagesPlant evolutionDeep homologyPhylogenetic reconstructionDevelopmental programRegulatory networksAngiosperm taxaGenetic pathwaysPetal morphologyGenetic analysisControl genesMorphological dataDevelopmental pathwaysLineagesPetalsMore is better: the uses of developmental genetic data to reconstruct perianth evolution
Hileman LC, Irish VF. More is better: the uses of developmental genetic data to reconstruct perianth evolution. American Journal Of Botany 2009, 96: 83-95. PMID: 21628177, DOI: 10.3732/ajb.0800066.Peer-Reviewed Original ResearchDevelopmental genetic dataGenetic dataPerianth evolutionState reconstructionCharacter state reconstructionAncestral state reconstructionAngiosperm cladesAngiosperm evolutionExtant speciesEvolutionary historyPhylogenetic reconstructionDifferentiated perianthMorphological dataCharacter statesExpression dataDistinct organsMaximum likelihood frameworkPerianthNew insightsFunctional dataMultiple linesLikelihood frameworkAngiospermsCladeSepals
2005
Molecular Phylogenetics of the Siphonophora (Cnidaria), with Implications for the Evolution of Functional Specialization
Dunn C, Pugh P, Haddock S. Molecular Phylogenetics of the Siphonophora (Cnidaria), with Implications for the Evolution of Functional Specialization. Systematic Biology 2005, 54: 916-935. PMID: 16338764, DOI: 10.1080/10635150500354837.Peer-Reviewed Original ResearchConceptsRibosomal RNA genesRNA genesNuclear small subunit ribosomal RNA geneLarge subunit ribosomal RNA geneFunctional specializationSmall subunit ribosomal RNA geneMitochondrial large subunit ribosomal RNA geneFree-living animalsMolecular phylogeneticsOutgroup taxaColonial hydrozoansParsimony reconstructionsMolecular dataBudding processMorphological dataTaxaFunctional typesSiphonophoresMajor groupsPhylogenyDeep seaMedusaeGenesLiving animalsScuba diving
1975
Morphology of spinal electromotor neurons and presynaptic coupling in the gymnotidSternarchus albifrons
Pappas G, Waxman S, Bennett M. Morphology of spinal electromotor neurons and presynaptic coupling in the gymnotidSternarchus albifrons. Brain Cell Biology 1975, 4: 469-478. PMID: 1151441, DOI: 10.1007/bf01261376.Peer-Reviewed Original ResearchConceptsSpinal electromotor neuronsElectromotor neuronsCell bodiesGap junctionsAxosomatic synapsesSpinal cordPresynaptic fibersGlial lamellaeAdjacent neuronsElectrotonic couplingSingle axonsMyelin segmentsNeuronsMedial regionMorphological correlatesMorphological dataElectric organ dischargeAbsence of dendritesAxonsSynapsesAdjacent cellsAlbifronsDendritesLarge majorityCord
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