Search results for "85"

showing 10 items of 1612 documents

The Effect of Paddle Stroke Variables Measured by Trainesense SmartPaddle® on the Velocity of the Kayak

2022

(1) Background: This study aimed to compare key variables of paddle stroke measured by a commercial Trainesense SmartPaddle® against the strain-gauge shaft and investigate how these variables are associated with the velocity of the boat among national-level canoe polo players. (2) Methods: This study involved 14 Finnish national-level canoe polo players. The measurement protocol consisted of three different paddling velocities, which were performed in indoor swimming pools. The velocity of the boat was calculated based on the performance time measured with the laser photocell gate. Canoe polo equipment was used in the study and a SmartPaddle sensor was attached to the paddle blade. A strain…

Chemical technologyinertial measurement unitTP1-1185kayaking; biomechanics; canoe polo; inertial measurement unitBiochemistryAtomic and Molecular Physics and OpticsbiomechanicsAnalytical Chemistrymelontakayakingcanoe polomittauslaitteetvoimantuotto (fysiologia)biomekaniikkaElectrical and Electronic EngineeringInstrumentation
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Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content

2011

ESA’s upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the…

ChlorophyllChlorophyll contentMean squared errorRed edgelcsh:Chemical technologyBiochemistrySentinel-2; chlorophyll; LAI; NAOC; NDI; red-edgeGreen leafArticleNDIAnalytical Chemistryred-edgelcsh:TP1-1185Electrical and Electronic EngineeringSpacecraftInstrumentationRemote sensingNAOCHyperspectral imagingSpectral bandsReflectivityAtomic and Molecular Physics and OpticsLAIPlant LeavesSpectrophotometryTemporal resolutionEnvironmental scienceSentinel-2Sensors (Basel, Switzerland)
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The Short-Access Mother–Baby-(SAMBA) Cholangioscopy System

2014

Conventional mother–baby cholangioscopy systems have significant limitations including poor image resolution, limited maneuverability and fragile cholangioscopes. We propose the novel short-access mother–baby-(SAMBA) cholangioscopy system that involves a very short and flexible cholangioscope that is introduced in a dedicated mother duodenoscope with an extra distal side port. Clinical feasibility of this device is demonstrated in three complex cases with cholangiopathies. The strengths of SAMBA cholangioscopy are improved imaging quality and excellent maneuverability of the baby that allows access of small intrahepatic ducts. Evaluation of suspected intrahepatic biliary disease is an excel…

Cholangiopathymedicine.medical_specialtylcsh:Medical technologyNeoplasiabusiness.industryGastroenterologyVideoSecondary sclerosing cholangitisCholangiocellular carcinomamedicine.diseaseBiliary diseasePort (medical)lcsh:R855-855.5Cholangiocellular carcinomaLaser lithoripsyImaging qualitymedicineSecondary sclerosing cholangitisCholangioscopyMother–baby cholangioscopyRadiology Nuclear Medicine and imagingRadiologybusinessVideo Journal and Encyclopedia of GI Endoscopy
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RIP-Chip analysis supports different roles for AGO2 and GW182 proteins in recruiting and processing microRNA targets.

2019

Background MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. To discern further RISC functions, we analyzed the activities of two RISC proteins, AGO2 and GW182, in the MCF-7 human breast cancer cell line. Methods We performed three RIP-Chip experiments using either anti-AGO2 or anti-GW182 antibodies and compiled a data set made up of the miRNA and mRNA expression profiles of three samples for each experiment. Specifically, we analyzed the input sample, the immunoprecipita…

Chromatin ImmunoprecipitationSupport Vector MachineRIP-Chip data analysisMiRNA bindingComputational biologyBiologylcsh:Computer applications to medicine. Medical informaticsBiochemistryAutoantigens03 medical and health sciencesOpen Reading Frames0302 clinical medicineStructural BiologymicroRNARIP-Chip data analysiCoding regionGene silencingHumansRNA MessengerMolecular BiologyGenelcsh:QH301-705.5030304 developmental biology0303 health sciencesBinding SitesApplied MathematicsGene Expression ProfilingResearchRNARNA-Binding ProteinsmicroRNA target predictionRISC proteins AGO2 and GW182Computer Science ApplicationsSettore BIO/18 - GeneticaMicroRNAslcsh:Biology (General)Gene Expression Regulation030220 oncology & carcinogenesismicroRNA regulatory activityArgonaute ProteinsMCF-7 Cellslcsh:R858-859.7DNA microarrayRIP-ChipBMC bioinformatics
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Topological structure analysis of chromatin interaction networks.

2019

Abstract Background Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. Results It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, …

Chromatin interaction networksFunctionally related modulesComputer scienceCellStructure (category theory)Topologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeChromosome conformation capture03 medical and health sciences0302 clinical medicineGraph topologyStructural BiologyComponent (UML)medicineHumansGene Regulatory NetworksCell type specificityPromoter Regions GeneticMolecular Biologylcsh:QH301-705.5030304 developmental biologyConnected component0303 health sciencesApplied MathematicsResearchChromatinComputer Science ApplicationsChromatinHematopoiesisIdentification (information)medicine.anatomical_structurelcsh:Biology (General)Gene Expression RegulationTopological graph theorylcsh:R858-859.7DNA microarray030217 neurology & neurosurgeryAlgorithmsBMC bioinformatics
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GenClust: A genetic algorithm for clustering gene expression data

2005

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …

Clustering high-dimensional dataDNA ComplementaryComputer scienceRand indexCorrelation clusteringOligonucleotidesEvolutionary algorithmlcsh:Computer applications to medicine. Medical informaticscomputer.software_genreBiochemistryPattern Recognition AutomatedBiclusteringOpen Reading FramesStructural BiologyCURE data clustering algorithmConsensus clusteringGenetic algorithmCluster AnalysisCluster analysislcsh:QH301-705.5Molecular BiologyGene expression data Clustering Evolutionary algorithmsOligonucleotide Array Sequence AnalysisModels StatisticalBrown clusteringHeuristicGene Expression ProfilingApplied MathematicsComputational BiologyComputer Science Applicationslcsh:Biology (General)Gene Expression RegulationMutationlcsh:R858-859.7Data miningSequence AlignmentcomputerSoftwareAlgorithmsBMC Bioinformatics
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Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia

2021

Highlights • Social/non-social information processing in three modalities was investigated in SZ. • SZ showed reduced activation for social information only in gesture modality. • Reduced activation in SZ was observed for non-social information only in speech. • Neural Neural processing in bimodal condition is not different between patients and controls.

Cognitive NeuroscienceSchizoaffective disorderDysfunctional familylcsh:Computer applications to medicine. Medical informaticsmPFC050105 experimental psychologylcsh:RC346-42903 medical and health sciencesGesture0302 clinical medicineSocialmedicineImage Processing Computer-AssistedHumansSpeech0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imaging10. No inequalityPrefrontal cortexlcsh:Neurology. Diseases of the nervous systemBrain MappingModality (human–computer interaction)medicine.diagnostic_testGestures05 social sciencesRegular ArticleMultimodal processingmedicine.diseaseMagnetic Resonance ImagingNeurologySchizophreniaNeural processingSchizophrenialcsh:R858-859.7Neurology (clinical)PsychologyFunctional magnetic resonance imaging030217 neurology & neurosurgeryCognitive psychologyGestureNeuroImage: Clinical
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On James Hyde's example of non-orderable subgroup of $\mathrm{Homeo}(D,\partial D)$

2020

In [Ann. Math. 190 (2019), 657-661], James Hyde presented the first example of non-left-orderable, finitely generated subgroup of $\mathrm{Homeo}(D,\partial D)$, the group of homeomorphisms of the disk fixing the boundary. This implies that the group $\mathrm{Homeo}(D,\partial D)$ itself is not left-orderable. We revisit the construction, and present a slightly different proof of purely dynamical flavor, avoiding direct references to properties of left-orders. Our approach allows to solve the analogue problem for actions on the circle.

CombinatoricsGroup (mathematics)Primary 37C85. Secondary 37E05 37E10 37E20[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS]FOS: MathematicsBoundary (topology)Finitely-generated abelian groupGroup Theory (math.GR)Dynamical Systems (math.DS)Mathematics - Dynamical SystemsMathematics - Group Theory[MATH.MATH-GR]Mathematics [math]/Group Theory [math.GR]Mathematics
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JANE: efficient mapping of prokaryotic ESTs and variable length sequence reads on related template genomes

2009

Abstract Background ESTs or variable sequence reads can be available in prokaryotic studies well before a complete genome is known. Use cases include (i) transcriptome studies or (ii) single cell sequencing of bacteria. Without suitable software their further analysis and mapping would have to await finalization of the corresponding genome. Results The tool JANE rapidly maps ESTs or variable sequence reads in prokaryotic sequencing and transcriptome efforts to related template genomes. It provides an easy-to-use graphics interface for information retrieval and a toolkit for EST or nucleotide sequence function prediction. Furthermore, we developed for rapid mapping an enhanced sequence align…

Computational biologyBiologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeUser-Computer InterfaceStructural BiologyDatabases Geneticlcsh:QH301-705.5Molecular BiologySequence (medicine)Expressed Sequence TagsWhole genome sequencingGeneticsInternetExpressed sequence tagGenomeBase SequencePhylumApplied MathematicsNucleic acid sequenceComputational BiologySequence Analysis DNAComputer Science Applicationslcsh:Biology (General)Single cell sequencinglcsh:R858-859.7DNA microarraySoftwareBMC Bioinformatics
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RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

2021

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…

Computational complexity theoryProcess (engineering)Computer sciencesulfur recovery unit02 engineering and technologytransfer learningMachine learningcomputer.software_genrelcsh:Chemical technologyBiochemistryRNNField (computer science)ArticleAnalytical ChemistryDomain (software engineering)0202 electrical engineering electronic engineering information engineeringlcsh:TP1-1185Electrical and Electronic EngineeringInstrumentationsystem identificationHyperparameterbusiness.industry020208 electrical & electronic engineeringdynamical modelsSystem identificationAtomic and Molecular Physics and OpticsNonlinear systemRecurrent neural networksoft sensors020201 artificial intelligence & image processingArtificial intelligenceTransfer of learningbusinessLSTMcomputerDynamical models; LSTM; RNN; Soft sensors; Sulfur recovery unit; System identification; Transfer learningSensors
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