Search results for "FOS: Biological sciences"

showing 10 items of 164 documents

sj-pdf-1-ueg-10.1177_2050640620964132 - Supplemental material for Activities related to inflammatory bowel disease management during and after the co…

2020

Supplemental material, sj-pdf-1-ueg-10.1177_2050640620964132 for Activities related to inflammatory bowel disease management during and after the coronavirus disease 2019 lockdown in Italy: How to maintain standards of care by Simone Saibeni, Ludovica Scucchi, Gabriele Dragoni, Cristina Bezzio, Agnese Miranda, Davide Giuseppe Ribaldone, Angela Bertani, Fabrizio Bossa, Mariangela Allocca, Andrea Buda, Gianmarco Mocci, Alessandra Soriano, Silvia Mazzuoli, Lorenzo Bertani, Flavia Baccini, Erika Loddo, Antonino Carlo Privitera, Alessandro Sartini, Angelo Viscido, Laurino Grossi, Valentina Casini, Viviana Gerardi, Marta Ascolani, Mirko Di Ruscio, Giovanni Casella, Edoardo Savarino, Davide Strade…

FOS: Clinical medicineFOS: Biological sciences111199 Nutrition and Dietetics not elsewhere classifiedFOS: Health sciences110308 Geriatrics and Gerontology69999 Biological Sciences not elsewhere classified111299 Oncology and Carcinogenesis not elsewhere classified
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UEG916681 Supplemental Material - Supplemental material for European guidelines on chronic mesenteric ischaemia – joint United European Gastroenterol…

2020

Supplemental material, UEG916681 Supplemental Material for European guidelines on chronic mesenteric ischaemia – joint United European Gastroenterology, European Association for Gastroenterology, Endoscopy and Nutrition, European Society of Gastrointestinal and Abdominal Radiology, Netherlands Association of Hepatogastroenterologists, Hellenic Society of Gastroenterology, Cardiovascular and Interventional Radiological Society of Europe, and Dutch Mesenteric Ischemia Study group clinical guidelines on the diagnosis and treatment of patients with chronic mesenteric ischaemia by Luke G Terlouw, Adriaan Moelker, Jan Abrahamsen, Stefan Acosta, Olaf J Bakker, Iris Baumgartner, Louis Boyer, Olivie…

FOS: Clinical medicineFOS: Biological sciences111199 Nutrition and Dietetics not elsewhere classifiedFOS: Health sciences110308 Geriatrics and Gerontology69999 Biological Sciences not elsewhere classified111299 Oncology and Carcinogenesis not elsewhere classified
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Supplemental material for Long-term observation of hepatocellular carcinoma recurrence after liver transplantation at a European transplantation cent…

2019

Supplemental Material for Long-term observation of hepatocellular carcinoma recurrence after liver transplantation at a European transplantation centre by Friedrich Foerster, Maria Hoppe-Lotichius, Johanna Vollmar, Jens U Marquardt, Arndt Weinmann, Marcus-Alexander Wörns, Gerd Otto, Tim Zimmermann and Peter R Galle in United European Gastroenterology Journal

FOS: Clinical medicineFOS: Biological sciences111199 Nutrition and Dietetics not elsewhere classifiedFOS: Health sciences110308 Geriatrics and Gerontology69999 Biological Sciences not elsewhere classified111299 Oncology and Carcinogenesis not elsewhere classified
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sj-pdf-1-ueg-10.1177_2050640620964132 - Supplemental material for Activities related to inflammatory bowel disease management during and after the co…

2020

Supplemental material, sj-pdf-1-ueg-10.1177_2050640620964132 for Activities related to inflammatory bowel disease management during and after the coronavirus disease 2019 lockdown in Italy: How to maintain standards of care by Simone Saibeni, Ludovica Scucchi, Gabriele Dragoni, Cristina Bezzio, Agnese Miranda, Davide Giuseppe Ribaldone, Angela Bertani, Fabrizio Bossa, Mariangela Allocca, Andrea Buda, Gianmarco Mocci, Alessandra Soriano, Silvia Mazzuoli, Lorenzo Bertani, Flavia Baccini, Erika Loddo, Antonino Carlo Privitera, Alessandro Sartini, Angelo Viscido, Laurino Grossi, Valentina Casini, Viviana Gerardi, Marta Ascolani, Mirko Di Ruscio, Giovanni Casella, Edoardo Savarino, Davide Strade…

FOS: Clinical medicineFOS: Biological sciences111199 Nutrition and Dietetics not elsewhere classifiedFOS: Health sciences110308 Geriatrics and Gerontology69999 Biological Sciences not elsewhere classified111299 Oncology and Carcinogenesis not elsewhere classified
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Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

2018

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perce…

FOS: Computer and information sciences0301 basic medicineBrightnessVisual perceptionVisionComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognitionlcsh:MedicineSocial SciencesColor Vision Defects01 natural sciencesMass SpectrometryAnalytical ChemistrySecondary Ion Mass SpectrometrySpectrum Analysis TechniquesMathematical and Statistical TechniquesPsychologyComputer visionlcsh:ScienceData ProcessingMultidisciplinaryPhysicsClassical MechanicsOther Quantitative Biology (q-bio.OT)Quantitative Biology - Other Quantitative BiologyChemistryPhysical SciencesRegression AnalysisSensory PerceptionInformation TechnologyStatistics (Mathematics)AlgorithmsColor PerceptionResearch ArticleComputer and Information SciencesColor visionColorFluid MechanicsLinear Regression AnalysisColor spaceResearch and Analysis MethodsContinuum Mechanics010309 optics03 medical and health sciencesSine Waves0103 physical sciencesHumansStatistical MethodsFluid FlowVision OcularHueColor Visionbusiness.industrylcsh:RBiology and Life SciencesFluid Dynamics030104 developmental biologyFOS: Biological scienceslcsh:QArtificial intelligencebusinessMathematical FunctionsMathematicsPhotic StimulationSoftwareNeurosciencePLOS ONE
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Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox

2017

International audience; In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics. In this specific optimisation problem, the state space is a set of words of specified length over a finite alphabet. To each word is associated a score. The overall objective is to find the words which have the lowest possible score. This type of general optimization problem is encountered in e.g 3D conformation optimisation for protein structure prediction, or largest core genes subset discovery based on best supported phylogenetic tree for a set of species. In order to solve this problem, we propose a toolbox that can be easily launched usin…

FOS: Computer and information sciences0301 basic medicineTheoretical computer scienceOptimization problemComputer Science - Artificial IntelligenceComputer science[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]Quantitative Biology - Quantitative MethodsSet (abstract data type)[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing03 medical and health sciences[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]State spaceMetaheuristicQuantitative Methods (q-bio.QM)Protein structure prediction[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationToolboxCore (game theory)Artificial Intelligence (cs.AI)030104 developmental biology[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]FOS: Biological sciences[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Word (computer architecture)
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Selectivity in Probabilistic Causality: Drawing Arrows from Inputs to Stochastic Outputs

2011

Given a set of several inputs into a system (e.g., independent variables characterizing stimuli) and a set of several stochastically non-independent outputs (e.g., random variables describing different aspects of responses), how can one determine, for each of the outputs, which of the inputs it is influenced by? The problem has applications ranging from modeling pairwise comparisons to reconstructing mental processing architectures to conjoint testing. A necessary and sufficient condition for a given pattern of selective influences is provided by the Joint Distribution Criterion, according to which the problem of "what influences what" is equivalent to that of the existence of a joint distr…

FOS: Computer and information sciencesArtificial Intelligence (cs.AI)91E45 (Primary) 60A05 (Secondary)Computer Science - Artificial IntelligencePhysics - Data Analysis Statistics and ProbabilityFOS: Biological sciencesProbability (math.PR)FOS: MathematicsFOS: Physical sciencesQuantitative Biology - Quantitative MethodsMathematics - ProbabilityData Analysis Statistics and Probability (physics.data-an)Quantitative Methods (q-bio.QM)
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Nonlinearities and Adaptation of Color Vision from Sequential Principal Curves Analysis

2016

Mechanisms of human color vision are characterized by two phenomenological aspects: the system is nonlinear and adaptive to changing environments. Conventional attempts to derive these features from statistics use separate arguments for each aspect. The few statistical explanations that do consider both phenomena simultaneously follow parametric formulations based on empirical models. Therefore, it may be argued that the behavior does not come directly from the color statistics but from the convenient functional form adopted. In addition, many times the whole statistical analysis is based on simplified databases that disregard relevant physical effects in the input signal, as, for instance…

FOS: Computer and information sciencesColor visionComputer scienceCognitive NeuroscienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONStandard illuminantMachine Learning (stat.ML)Models BiologicalArts and Humanities (miscellaneous)Statistics - Machine LearningPsychophysicsHumansLearningComputer SimulationChromatic scaleParametric statisticsPrincipal Component AnalysisColor VisionNonlinear dimensionality reductionAdaptation PhysiologicalNonlinear systemNonlinear DynamicsFOS: Biological sciencesQuantitative Biology - Neurons and CognitionMetric (mathematics)A priori and a posterioriNeurons and Cognition (q-bio.NC)AlgorithmColor PerceptionPhotic Stimulation
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Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

2018

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.

FOS: Computer and information sciencesComputer Science - Machine Learning010504 meteorology & atmospheric sciences0211 other engineering and technologiesFOS: Physical sciencesMachine Learning (stat.ML)02 engineering and technology01 natural sciencesQuantitative Biology - Quantitative MethodsMachine Learning (cs.LG)Data modelingsymbols.namesakeStatistics - Machine LearningApplied mathematicsTime seriesGaussian processQuantitative Methods (q-bio.QM)021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsSeries (mathematics)Linear modelProbability and statisticsMissing dataFOS: Biological sciencesPhysics - Data Analysis Statistics and ProbabilitysymbolsFocus (optics)Data Analysis Statistics and Probability (physics.data-an)
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Disentangling the Link Between Image Statistics and Human Perception

2023

In the 1950s Horace Barlow and Fred Attneave suggested a connection between sensory systems and how they are adapted to the environment: early vision evolved to maximise the information it conveys about incoming signals. Following Shannon's definition, this information was described using the probability of the images taken from natural scenes. Previously, direct accurate predictions of image probabilities were not possible due to computational limitations. Despite the exploration of this idea being indirect, mainly based on oversimplified models of the image density or on system design methods, these methods had success in reproducing a wide range of physiological and psychophysical phenom…

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Vision and Pattern Recognition (cs.CV)FOS: Biological sciencesQuantitative Biology - Neurons and CognitionComputer Science - Computer Vision and Pattern RecognitionNeurons and Cognition (q-bio.NC)ArticleMachine Learning (cs.LG)
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