04694nam 2200745 450 991025274300332120230120033618.02-7606-3075-7979-1-03-650112-82-7606-2591-510.4000/books.pum.7948(CKB)2560000000054110(EBL)3272719(SSID)ssj0000827118(PQKBManifestationID)11452965(PQKBTitleCode)TC0000827118(PQKBWorkID)10821416(PQKB)10531454(CEL)434862(CaBNvSL)slc00226301(Au-PeEL)EBL4750404(CaPaEBR)ebr11330687(OCoLC)969637385(FrMaCLE)OB-pum-7948(oapen)https://directory.doabooks.org/handle/20.500.12854/52647(VaAlCD)20.500.12592/cs6d1d(MiAaPQ)EBC4750404(PPN)224386794(EXLCZ)99256000000005411020170125h20102010 uy 0freur|n|---|||||txtccrLa malréglementation une éthique de la recherche est-elle possible et à quelles conditions? /sous la direction de Pierre Trudel et Michèle S. JeanPresses de l’Université de Montréal2010Montréal, [Canada] :Les Presses de l'Université de Montréal,2010.©20101 online resource (173 p.)Description based upon print version of record.2-7606-2198-7 Includes bibliographical references at the end of each chapters.À la recherche d'un contrôle illusoire / Guy Bourgeault -- Perspectives d'une jeune chercheure / Marie-Chantal Fortin -- Les aventures d'un juriste en terre étrangère / Yann Joly -- Des enjeux éthiques à l'évaluation éthique / Bartha Maria Knoppers -- Une nouvelle affection du chercheur en génomique? / Claude Laberge -- Pour une réflexion sur l'état actuel de l'éthique de la recherche / Michel Bergeron -- L'éthique de la recherche : nature, obstacles et tensions / Guy Rocher -- Cultiver des attetes pragmatiques à l'égard des comités d'éthique / Michel T. Giroux -- Quand éthique et loi se confrontent / Sylvie Normandeau -- Intégrer l'éthique dans la recherche / Emmanuelle Lévesque, Karine Bédard, Denise Avard, Jacques Simard -- Les CER sont-ils en train d'étrangler la recherche en épidémiologie / Jack Siemiatycki.Cet ouvrage pose un regard critique sur la régulation de l'activité de recherche au nom de l'éthique. Il démontre qu'au-delà des principes qui font consensus, la régulation de la recherche souffre d'un grave problème de malréglementation. Il est ici question de malréglementation comme, en d'autres milieux, il est question de malbouffe. Il y a malréglementation lorsque, s'appuyant sur des principes admis de tous, l'on multiplie les contraintes sans justifications sérieuses et sans pour autant accroître les protections recherchées. À partir de différentes perspectives, les auteurs décrivent comment ce qui devait constituer une occasion de dialogue sur les valeurs et les enjeux de certains types de recherche s'est peu à peu transformé en un dispositif autoritaire, bureaucratique, marqué au coin de la procédure et de l'obsession du « formulaire de consentement ». Ce type de dispositif est imposé dans un vaste ensemble de situations qui ne présentent pas toutes les mêmes intensités de risque. Une grande partie de la littérature sur l'éthique de la recherche s'attache à démontrer la nécessité de respecter les règles de bonne conduite. Moins nombreuses sont les tentatives de porter un regard critique sur les mécanismes régulateurs et de faire des suggestions concrètes pour s'affranchir de l'arbitraire. C'est le défi qu'ont voulu relever les auteurs de ce livre.ResearchMoral and ethical aspectsLawResearchMoral and ethical aspectsMedicineResearchMoral and ethical aspectsphilosophieéthiquerechercheResearchMoral and ethical aspects.LawResearchMedicineResearch174.95Michèle S. Jean Pierre Trudel (dir.)auth1355958Trudel PierreJean Michèle S.MiAaPQMiAaPQMiAaPQBOOK9910252743003321La malréglementation3360104UNINA08655nam 22007335 450 991075138360332120231011213602.03-031-38949-210.1007/978-3-031-38949-8(MiAaPQ)EBC30784285(Au-PeEL)EBL30784285(OCoLC)1402816722(DE-He213)978-3-031-38949-8(PPN)272914665(EXLCZ)992849317420004120231011d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMagnetic Resonance Brain Imaging[electronic resource] Modelling and Data Analysis Using R /by Jörg Polzehl, Karsten Tabelow2nd ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (268 pages)Use R!,2197-5744Print version: Polzehl, Jörg Magnetic Resonance Brain Imaging Cham : Springer International Publishing AG,c2023 9783031389481 Intro -- Preface to the Second Edition -- Preface to First Edition -- Contents -- Acronyms -- 1 Introduction -- 2 Magnetic Resonance Imaging in a Nutshell -- 2.1 The Principles of Magnetic Resonance Imaging -- 2.1.1 The Zeeman effect for Atomic Nuclei -- 2.1.2 Macroscopic Magnetization Vector -- 2.1.3 Spin Excitation and Relaxation -- 2.1.4 Spatial Localization and Pulse Sequences -- 2.1.5 MR Image Formation and Parallel Imaging -- 2.2 Special MR Imaging Modalities -- 2.2.1 Functional Magnetic Resonance Imaging (fMRI) -- 2.2.2 Diffusion Weighted Magnetic Resonance Imaging(dMRI) -- 2.2.3 Multi-parameter Mapping (MPM) -- 2.2.4 Inversion Recovery Magnetic Resonance Imaging (IR-MRI) -- 3 Medical Imaging Data Formats -- 3.1 DICOM Format -- 3.2 ANALYZE and NIfTI format -- 3.3 The BIDS Standard for Neuroimaging Data -- 4 Functional Magnetic Resonance Imaging -- 4.1 Prerequisites for Running the Code in This Chapter -- 4.2 Pre-processing fMRI Data -- 4.2.1 Example Data -- Functional MRI Data on Visual Object Recognition (ds000105) -- Multi-subject and Multi-modal Neuroimaging Dataset on Face Processing (ds000117) -- Multi-modal Longitudinal Study of a Single Subject (ds000031) -- 4.2.2 Slice Time Correction -- 4.2.3 Motion Correction -- 4.2.4 Registration -- 4.2.5 Normalization -- 4.2.6 Brain Mask -- 4.2.7 Brain Tissue Segmentation -- 4.2.8 Using Brain Atlas Information -- 4.2.9 Spatial Smoothing -- 4.3 The General Linear Model (GLM) for fMRI -- 4.3.1 Modeling the BOLD Signal -- 4.3.2 The Linear Model -- 4.3.3 Simulated fMRI Data -- 4.4 Signal Detection in Single-Subject Experiments -- 4.4.1 Voxelwise Signal Detection and the Multiple Comparison Problem -- 4.4.2 Bonferroni Correction -- 4.4.3 Random Field Theory -- 4.4.4 False Discovery Rate (FDR) -- 4.4.5 Cluster Thresholds -- 4.4.6 Permutation Tests -- 4.5 Adaptive Smoothing in fMRI.4.5.1 Analyzing fMRI Experiments with Structural Adaptive Smoothing Procedures -- 4.5.2 Structural Adaptive Segmentation in fMRI -- 4.6 Other Approaches for fMRI Analysis Using R -- 4.6.1 Multivariate fMRI Analysis -- 4.6.2 Independent Component Analysis (ICA) -- 4.7 Functional Connectivity for Resting-State fMRI -- 5 Diffusion-Weighted Imaging -- 5.1 Prerequisites -- 5.2 Diffusion-Weighted MRI Data -- 5.2.1 The Diffusion Equation and MRI -- 5.2.2 Example Data -- 5.2.3 Data Pre-processing -- 5.2.4 Reading Pre-processed Data -- 5.2.5 Basic Data Properties -- 5.2.6 Definition of a Brain Mask -- 5.2.7 Characterization of Noise in Diffusion-Weighted MRI -- 5.3 Modeling Diffusion-Weighted MRI Data -- 5.3.1 The Apparent Diffusion Coefficient (ADC) -- 5.3.2 Diffusion Tensor Imaging (DTI) -- 5.3.3 Diffusion Kurtosis Imaging (DKI) -- 5.3.4 The Orientation Distribution Function -- 5.3.5 Tensor Mixture Models -- 5.4 Smoothing Diffusion-Weighted Data -- 5.4.1 Effects of Gaussian Filtering -- 5.4.2 Multi-shell Position-Orientation Adaptive Smoothing (msPOAS) -- 5.5 Fiber Tracking Methods -- 5.6 Structural Connectivity -- 6 Multiparameter Mapping -- 6.1 Prerequisites -- 6.2 Multiparameter Mapping -- 6.2.1 Signal Model in FLASH Sequences -- 6.2.2 Data from the Multiparameter Mapping (MPM) Protocol -- 6.2.3 Reparameterization of the Signal Model by ESTATICS -- 6.2.4 Correction for Instrumental B1-Bias -- 6.2.5 Correction for the Bias Induced by Low SNR -- 6.2.6 Structural Adaptive Smoothing of Relaxometry Data -- 7 Inversion Recovery Magnetic Resonance Imaging -- 7.1 Prerequisites -- 7.2 Tissue Porosity Estimation by Inversion Recovery MRI-based Experiments -- 7.3 Generating a Simulated Dataset -- 7.4 Estimation of Parameters from IR MRI Data in a Mixture Model -- A Smoothing Techniques for Imaging Problems -- A.1 Non-parametric Regression -- A.1.1 Kernel Smoothing.A.2 Adaptive Weigths Smoothing -- A.2.1 Local Constant Likelihood Models -- A.2.2 Patch-Wise Adaptive Weights Smoothing (PAWS) -- A.3 Special Settings in Neuroimaging Experiments -- A.3.1 Simultaneous Mean and Variance Estimation -- A.3.2 Vector Valued Data -- A.3.3 Diffusion Data -- A.3.4 Tensor-Valued Data -- A.3.5 Model-Driven Smoothing of Observed Images -- B Resources for Neuroimaging in R -- B.1 An Overview on Selected R Packages for Neuroimaging -- B.2 Open Neuroimaging Data Archives -- C Data, Software and Hardware Resources -- C.1 How to Get the Example Code -- C.2 Packages and Software to Install -- C.3 How to Acquire and Organize the Example Data -- C.3.1 Data from the `Kirby21' Reproducibility Study -- C.3.2 Data from OpenNeuro -- C.3.3 DICOM Example Data -- C.3.4 MPM Data Example -- C.3.5 Atlas Data -- C.4 How to Obtain Precomputed Results -- C.5 System Requirements -- References -- Index.This book discusses modelling and analysis of Magnetic Resonance Imaging (MRI) data of the human brain. For the data processing pipelines we rely on R, the software environment for statistical computing and graphics. The book is intended for readers from two communities: Statisticians, who are interested in neuroimaging and look for an introduction to the acquired data and typical scientific problems in the field and neuroimaging students, who want to learn about the statistical modeling and analysis of MRI data. Being a practical introduction, the book focuses on those problems in data analysis for which implementations within R are available. By providing full worked-out examples the book thus serves as a tutorial for MRI analysis with R, from which the reader can derive its own data processing scripts. The book starts with a short introduction into MRI. The next chapter considers the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters then cover four common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, Multi-Parameter Mapping and Inversion Recovery MRI. The book concludes with extended Appendices on details of the utilize non-parametric statistics and on resources for R and MRI data. The book also addresses the issues of reproducibility and topics like data organization and description, open data and open science. It completely relies on a dynamic report generation with knitr: The books R-code and intermediate results are available for reproducibility of the examples.Use R!,2197-5744BiometryRadiologyImage processingDigital techniquesComputer visionMathematical statisticsData processingSignal processingBiostatisticsRadiologyComputer Imaging, Vision, Pattern Recognition and GraphicsStatistics and ComputingSignal, Speech and Image Processing Biometry.Radiology.Image processingDigital techniques.Computer vision.Mathematical statisticsData processing.Signal processing.Biostatistics.Radiology.Computer Imaging, Vision, Pattern Recognition and Graphics.Statistics and Computing.Signal, Speech and Image Processing .616.8047548Polzehl Jörg781365Tabelow Karsten781366MiAaPQMiAaPQMiAaPQBOOK9910751383603321Magnetic Resonance Brain Imaging1732554UNINA03981nam 2200637Ia 450 991083055650332120170815120128.01-280-27108-697866102710850-470-29911-80-470-03526-90-470-85924-5(CKB)111087027094290(EBL)154955(OCoLC)53890426(SSID)ssj0000263929(PQKBManifestationID)11227561(PQKBTitleCode)TC0000263929(PQKBWorkID)10283376(PQKB)11617777(MiAaPQ)EBC154955(PPN)124570763(EXLCZ)9911108702709429020021025d2002 uy 0engur|n|---|||||txtccrUncertainty in remote sensing and GIS[electronic resource] /edited by Giles M. Foody and Peter M. AtkinsonChichester ;Hoboken, NJ Wileyc20021 online resource (327 p.)Description based upon print version of record.0-470-84408-6 Includes bibliographical references and index.Uncertainty in Remote Sensing and GIS; Contents; List of Contributors; Foreword; Preface; 1 Uncertainty in Remote Sensing and GIS: Fundamentals; 2 Uncertainty in Remote Sensing; 3 Toward a Comprehensive View of Uncertainty in Remote Sensing Analysis; 4 On the Ambiguity Induced by a Remote Sensor's PSF; 5 Pixel Unmixing at the Sub-pixel Scale Based on Land Cover Class Probabilities: Application to Urban Areas; 6 Super-resolution Land Cover Mapping from Remotely Sensed Imagery using a Hopfield Neural Network7 Uncertainty in Land Cover Mapping from Remotely Sensed Data using Textural Algorithm and Artificial Neural Networks8 Remote Monitoring of the Impact of ENSO-related Drought on Sabah Rainforest using NOAA AVHRR Middle Infrared Reflectance: Exploring Emissivity Uncertainty; 9 Land Cover Map 2000 and Meta-data at the Land Parcel Level; 10 Analysing Uncertainty Propagation in GIS: Why is it not that Simple?; 11 Managing Uncertainty in a Geospatial Model of Biodiversity12 The Effects of Uncertainty in Deposition Data on Predicting Exceedances of Acidity Critical Loads for Sensitive UK Ecosystems13 Vertical and Horizontal Spatial Variation of Geostatistical Prediction; 14 Geostatistical Prediction and Simulation of the Lateral and Vertical Extent of Soil Horizons; 15 Increasing the Accuracy of Predictions of Monthly Precipitation in Great Britain using Kriging with an External Drift; 16 Conditional Simulation Applied to Uncertainty Assessment in DTMs; 17 Current Status of Uncertainty Issues in Remote Sensing and GIS; IndexRemote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it.This book provides general overviews on uncertainty in remote sensing and GISRemote sensingGeographic information systemsUncertainty (Information theory)Remote sensing.Geographic information systems.Uncertainty (Information theory)550.28910/.285Foody Giles M1646634Atkinson Peter M1646635MiAaPQMiAaPQMiAaPQBOOK9910830556503321Uncertainty in remote sensing and GIS3993742UNINA