Search results for " CLASSIFICATION"

showing 10 items of 1043 documents

Image-Evoked Affect and its Impact on Eeg-Based Biometrics

2019

Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…

021110 strategic defence & security studiesmedicine.diagnostic_testBiometricsComputer scienceSpeech recognition0211 other engineering and technologies02 engineering and technologyElectroencephalographyStimulus (physiology)Statistical classification0202 electrical engineering electronic engineering information engineeringTask analysismedicine020201 artificial intelligence & image processingMel-frequency cepstrum2019 IEEE International Conference on Image Processing (ICIP)
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Biowaiver Monograph for Immediate-Release Solid Oral Dosage Forms: Amoxicillin Trihydrate

2018

Literature and experimental data relevant to waiver of in vivo bioequivalence (BE) testing for the approval of immediate-release solid oral dosage forms containing amoxicillin trihydrate are reviewed. Solubility and permeability characteristics according to the Biopharmaceutics Classification System (BCS), therapeutic uses, therapeutic index, excipient interactions, as well as dissolution and BE and bioavailability studies were taken into consideration. Solubility and permeability studies indicate that amoxicillin doses up to 875 mg belong to BCS class I, whereas 1000 mg belongs to BCS class II and doses of more than 1000 mg belong to BCS class IV. Considering all aspects, the biowaiver pro…

0301 basic medicine030106 microbiologyAdministration OralBiological AvailabilityPharmaceutical ScienceExcipientPharmacologyBioequivalence030226 pharmacology & pharmacyPermeabilityDosage formBiopharmaceuticsExcipients03 medical and health sciences0302 clinical medicinemedicineAnimalsHumansDosage FormsActive ingredientChemistryBiopharmaceuticsAmoxicillinAmoxicillinBiopharmaceutics Classification SystemBioavailabilitySolubilityTherapeutic Equivalencymedicine.drugJournal of Pharmaceutical Sciences
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The tumour microenvironment as an integrated framework to understand cancer biology

2019

Cancer cells all share the feature of being immersed in a complex environment with altered cell-cell/cell-extracellular element communication, physicochemical information, and tissue functions. The so-called tumour microenvironment (TME) is becoming recognised as a key factor in the genesis, progression and treatment of cancer lesions. Beyond genetic mutations, the existence of a malignant microenvironment forms the basis for a new perspective in cancer biology where connections at the system level are fundamental. From this standpoint, different aspects of tumour lesions such as morphology, aggressiveness, prognosis and treatment response can be considered under an integrated vision, givin…

0301 basic medicineCancer ResearchStromal cellBiophysicsDiseaseBiologyExtracellular matrix03 medical and health sciences0302 clinical medicineGermline mutationImmune systemNeoplasmsTumor MicroenvironmentmedicineStromal classificationAnimalsHumansCompartment (development)CancerExtracellular matrixmedicine.diseaseBioelectricExtracellular MatrixMetabolism030104 developmental biologyOncologyCancer treatment030220 oncology & carcinogenesisCancer cellStromal CellsNeuroscienceCancer Letters
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Molecular subtyping of colon cancer (CC) based on mutational status of RAS, BRAF, and DNA mismatch repair (MMR) proteins. Prognostic value.

2016

e15094Background: CC is a heterogeneuous disease with clinical, pathological and biological variability. Molecular classification could indentify prognostic subtypes. Methods: 105 patients with sta...

0301 basic medicineCancer Researchbusiness.industryColorectal cancerDiseasemedicine.diseaseSubtyping03 medical and health sciences030104 developmental biology0302 clinical medicineMolecular classificationOncology030220 oncology & carcinogenesisCancer researchMedicineMutational statusDNA mismatch repairbusinessPathologicalValue (mathematics)Journal of Clinical Oncology
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ICTV Virus Taxonomy Profile: Dicistroviridae

2017

Dicistroviridae is a family of small non-enveloped viruses with monopartite, linear, positive-sense RNA genomes of approximately 8–10 kb. Viruses of all classified species infect arthropod hosts, with some having devastating economic consequences, such as acute bee paralysis virus in domesticated honeybees and taura syndrome virus in shrimp farming. Conversely, the host specificity and other desirable traits exhibited by several members of this group make them potential natural enemies for intentional use against arthropod pests, such as triatoma virus against triatomine bugs that vector Chagas disease. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on…

0301 basic medicineChagas diseasevirusesInsect VirusesGenome ViralDisease VectorsVirus ReplicationGenome03 medical and health sciencestaxonomyVirologymedicineICTV ReportAnimalsNatural enemiesTriatomaVirus classificationEconomic consequencesDicistroviridaebiologyVirus AssemblyfungiVirionBeesbiology.organism_classificationmedicine.diseaseVirology3. Good healthICTV Virus Taxonomy Profiles030104 developmental biologyDicistroviridaeRNATaxonomy (biology)ArthropodThe Journal of General Virology
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Deep learning architectures for prediction of nucleosome positioning from sequences data

2018

Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…

0301 basic medicineComputer scienceCellBiochemistrychemistry.chemical_compound0302 clinical medicineStructural Biologylcsh:QH301-705.5Nucleosome classificationSequenceSettore INF/01 - InformaticabiologyApplied MathematicsEpigeneticComputer Science ApplicationsChromatinNucleosomesmedicine.anatomical_structurelcsh:R858-859.7EukaryoteDNA microarrayDatabases Nucleic AcidComputational biologySaccharomyces cerevisiaelcsh:Computer applications to medicine. Medical informatics03 medical and health sciencesDeep LearningmedicineNucleosomeAnimalsHumansEpigeneticsMolecular BiologyGeneBase Sequencebusiness.industryDeep learningResearchReproducibility of Resultsbiology.organism_classificationYeastNucleosome classification Epigenetic Deep learning networks Recurrent neural networks030104 developmental biologylcsh:Biology (General)chemistryRecurrent neural networksROC CurveDeep learning networksArtificial intelligenceNeural Networks Computerbusiness030217 neurology & neurosurgeryDNABMC Bioinformatics
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Network-Wide Adaptive Burst Detection Depicts Neuronal Activity with Improved Accuracy

2017

Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introducedan adaptive burst analysis methodwhich enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules. Here, we test how using multiple channels and measurement time points affect adaptive b…

0301 basic medicineComputer scienceNeuroscience (miscellaneous)Interval (mathematics)Machine learningcomputer.software_genreta3112lcsh:RC321-57103 medical and health sciencesCellular and Molecular NeuroscienceBursting0302 clinical medicineMoving averageHistogramMethodsCluster analysislcsh:Neurosciences. Biological psychiatry. Neuropsychiatryta113network classificationbusiness.industryEmphasis (telecommunications)Pattern recognition217 Medical engineeringlaskennallinen neurotiede113 Computer and information sciencesPower (physics)030104 developmental biologymicroelectrode arraysburst detectionburst synchronySpike (software development)Artificial intelligenceneuronal networksbusinesscomputer030217 neurology & neurosurgeryNeurosciencecomputational neuroscienceFrontiers in Computational Neuroscience
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Automatic detection of hemangiomas using unsupervised segmentation of regions of interest

2016

In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…

0301 basic medicineComputer scienceScale-space segmentation02 engineering and technologyOtsu's methodHemangioma03 medical and health sciencessymbols.namesakeMinimum spanning tree-based segmentationRegion of interestHistogram0202 electrical engineering electronic engineering information engineeringmedicineComputer visionSegmentation-based object categorizationbusiness.industryPattern recognitionImage segmentationmedicine.diseaseStatistical classification030104 developmental biologyRegion growingsymbols020201 artificial intelligence & image processingArtificial intelligencebusiness2016 International Conference on Communications (COMM)
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Deep learning network for exploiting positional information in nucleosome related sequences

2017

A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…

0301 basic medicineComputer scienceSpeech recognitionCell02 engineering and technologyComputational biologyGenomeDNA sequencing03 medical and health scienceschemistry.chemical_compoundDeep Learning0202 electrical engineering electronic engineering information engineeringmedicineNucleosomeNucleotideGeneSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionichemistry.chemical_classificationSequenceSettore INF/01 - Informaticabiologybusiness.industryDeep learningnucleosomebiology.organism_classificationSubstringChromatinIdentification (information)030104 developmental biologymedicine.anatomical_structurechemistry020201 artificial intelligence & image processingEukaryoteNucleosome classification Epigenetic Deep learning networks Recurrent Neural NetworksArtificial intelligencebusinessDNA
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Membrane chaperoning by members of the PspA/IM30 protein family

2017

ABSTRACTPspA, IM30 (Vipp1) and LiaH, which all belong to the PspA/IM30 protein family, form high molecular weight oligomeric structures. For all proteins membrane binding and protection of the membrane structure and integrity has been shown or postulated. Here we discuss the possible membrane chaperoning activity of PspA, IM30 and LiaH and propose that larger oligomeric structures bind to stressed membrane regions, followed by oligomer disassembly and membrane stabilization by protein monomers or smaller/different oligomeric scaffolds.

0301 basic medicineDewey Decimal Classification::500 | Naturwissenschaften::570 | Biowissenschaften BiologieProtein familyPspA030106 microbiologyProtein familyBiologyBiochemistryOligomerVipp103 medical and health scienceschemistry.chemical_compoundddc:570membrane stressLiaHlcsh:QH301-705.5BiologyYjfJMembrane stressMembraneMembrane structuremembrane chaperoneMonomerMembrane structureMonomerMembranelcsh:Biology (General)chemistryBiochemistryOligomerMembrane bindingGeneral Agricultural and Biological SciencesIM30PspA/IM30 familyCommunicative & Integrative Biology
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