Image Superresolution Reconstruction via Granular Computing Clustering

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso

Towards quantitative high-throughput 3D localization microscopy

Advances in light microscopy have allowed circumventing the diffraction barrier, once thought to be the ultimate resolution limit in optical microscopy, and given rise to various superresolution microscopy techniques. Among them, localization microscopy exploits the blinking of fluorescent molecules to precisely pinpoint the positions of many emitters individually, and subsequently reconstruct a superresolved image from these positions. While localization microscopy enables the study of cellular structures and protein complexes with unprecedented details, severe technical bottlenecks still reduce the scope of possible applications. In my PhD work, I developed several technical improvements at the level of the microscope to overcome limitations related to the photophysical behaviour of fluorescent molecules, slow acquisition rates and three-dimensional imaging. I built an illumination system that achieves uniform intensity across the field-of view using a multi-mode fiber and a commercial speckle-reducer. I showed that it provides uniform photophysics within the illuminated area and is far superior to the common illumination system. It is easy to build and to add to any microscope, and thus greatly facilitates quantitative approaches in localization microscopy. Furthermore, I developed a fully automated superresolution microscope using an open-source software framework. I developed advanced electronics and user friendly software solutions to enable the design and unsupervised acquisition of complex experimental series. Optimized for long-term stability, the automated microscope is able to image hundreds to thousands of regions over the course of days to weeks. First applied in a system-wide study of clathrin-mediated endocytosis in yeast, the automated microscope allowed the collection of a data set of a size and scope unprecedented in localization microscopy. Finally, I established a fundamentally new approach to obtain three-dimensional superresolution images. Supercritical angle localization microscopy (SALM) exploits the phenomenon of surface-generated fluorescence arising from fluorophores close to the coverslip. SALM has the theoretical prospect of an isotropic spatial resolution with simple instrumentation. Following a first proof-of-concept implementation, I re-engineered the microscope to include adaptive optics in order to reach the full potential of the method. Taken together, I established simple, yet powerful, solutions for three fundamental technical limitations in localization microscopy regarding illumination, throughput and resolution. All of them can be combined within the same instrument, and can dramatically improve every cutting-edge microscope. This will help to push the limit of the most challenging applications of localization microscopy, including system-wide imaging experiments and structural studies

STED Nanoscopy to Illuminate New Avenues in Cancer Research – From Live Cell Staining and Direct Imaging to Decisive Preclinical Insights for Diagnosis and Therapy

Molecular imaging is established as an indispensable tool in various areas of cancer research, ranging from basic cancer biology and preclinical research to clinical trials and medical practice. In particular, the field of fluorescence imaging has experienced exceptional progress during the last three decades with the development of various in vivo technologies. Within this field, fluorescence microscopy is primarily of experimental use since it is especially qualified for addressing the fundamental questions of molecular oncology. As stimulated emission depletion (STED) nanoscopy combines the highest spatial and temporal resolutions with live specimen compatibility, it is best-suited for real-time investigations of the differences in the molecular machineries of malignant and normal cells to eventually translate the acquired knowledge into increased diagnostic and therapeutic efficacy. This thesis presents the application of STED nanoscopy to two acute topics in cancer research of direct or indirect clinical interest. The first project has investigated the structure of telomeres, the ends of the linear eukaryotic chromosomes, in intact human cells at the nanoscale. To protect genome integrity, a telomere can mask the chromosome end by folding back and sequestering its single-stranded 3’-overhang in an upstream part of the double-stranded DNA repeat region. The formed t-loop structure has so far only been visualized by electron microscopy and fluorescence nanoscopy with cross-linked mammalian telomeric DNA after disruption of cell nuclei and spreading. For the first time, this work demonstrates the existence of t-loops within their endogenous nuclear environment in intact human cells. The identification of further telomere conformations has laid the groundwork for distinguishing cancerous cells that use different telomere maintenance mechanisms based on their individual telomere populations by a combined STED nanoscopy and deep learning approach. The population difference was essentially attributed to the promyelocytic leukemia (PML) protein that significantly perturbs the organization of a subpopulation of telomeres towards an open conformation in cancer cells that employ a telomerase-independent, alternative telomere lengthening mechanism. Elucidating the nanoscale topology of telomeres and associated proteins within the nucleus has provided new insight into telomere structure-function relationships relevant for understanding the deregulation of telomere maintenance in cancer cells. After understanding the molecular foundations, this newly gained knowledge can be exploited to develop novel or refined diagnostic and treatment strategies. The second project has characterized the intracellular distribution of recently developed prostate cancer tracers. These novel prostate-specific membrane antigen (PSMA) inhibitors have revolutionized the treatment regimen of prostate cancer by enabling targeted imaging and therapy approaches. However, the exact internalization mechanism and the subcellular fate of these tracers have remained elusive. By combining STED nanoscopy with a newly developed non-standard live cell staining protocol, this work confirmed cell surface clustering of the targeted membrane antigen upon PSMA inhibitor binding, subsequent clathrin-dependent endocytosis and endosomal trafficking of the antigen-inhibitor complex. PSMA inhibitors accumulate in prostate cancer cells at clinically relevant time points, but strikingly and in contrast to the targeted antigen itself, they eventually distribute homogenously in the cytosol. This project has revealed the subcellular fate of PSMA/PSMA inhibitor complexes for the first time and provides crucial knowledge for the future application of these tracers including the development of new strategies in the field of prostate cancer diagnostics and therapeutics. Relying on the photostability and biocompatibility of the applied fluorophores, the performance of live cell STED nanoscopy in the field of cancer research is boosted by the development of improved fluorophores. The third project in this thesis introduces a biocompatible, small molecule near-infrared dye suitable for live cell STED imaging. By the application of a halogen dance rearrangement, a dihalogenated fluorinatable pyridinyl rhodamine could be synthesized at high yield. The option of subsequent radiolabeling combined with excellent optical properties and a non-toxic profile renders this dye an appropriate candidate for medical and bioimaging applications. Providing an intrinsic and highly specific mitochondrial targeting ability, the radiolabeled analogue is suggested as a vehicle for multimodal (positron emission tomography and optical imaging) medical imaging of mitochondria for cancer diagnosis and therapeutic approaches in patients and biopsy tissue. The absence of cytotoxicity is not only a crucial prerequisite for clinically used fluorophores. To guarantee the generation of meaningful data mirroring biological reality, the absence of cytotoxicity is likewise a decisive property of dyes applied in live cell STED nanoscopy. The fourth project in this thesis proposes a universal approach for cytotoxicity testing based on characterizing the influence of the compound of interest on the proliferation behavior of human cell lines using digital holographic cytometry. By applying this approach to recently developed live cell STED compatible dyes, pronounced cytotoxic effects could be excluded. Looking more closely, some of the tested dyes slightly altered cell proliferation, so this project provides guidance on the right choice of dye for the least invasive live cell STED experiments. Ultimately, live cell STED data should be exploited to extract as much biological information as possible. However, some information might be partially hidden by image degradation due the dynamics of living samples and the deliberate choice of rather conservative imaging parameters in order to preserve sample viability. The fifth project in this thesis presents a novel image restoration method in a Bayesian framework that simultaneously performs deconvolution, denoising as well as super-resolution, to restore images suffering from noise with mixed Poisson-Gaussian statistics. Established deconvolution or denoising methods that consider only one type of noise generally do not perform well on images degraded significantly by mixed noise. The newly introduced method was validated with live cell STED telomere data proving that the method can compete with state-of-the-art approaches. Taken together, this thesis demonstrates the value of an integrated approach for STED nanoscopy imaging studies. A coordinated workflow including sample preparation, image acquisition and data analysis provided a reliable platform for deriving meaningful conclusions for current questions in the field of cancer research. Moreover, this thesis emphasizes the strength of iteratively adapting the individual components in the operational chain and it particularly points towards those components that, if further improved, optimize the significance of the final results rendering live cell STED nanoscopy even more powerful

Spatiotemporal localization of proteins in microorganisms via photoactivated localization microscopy

Photoactivated localization microscopy (PALM) is a single molecule fluorescence microscopy technique (SMLM) that relies on the controlled activation and imaging of photo-activatable/convertible fluorescent proteins to determine their position with nanometer scale precision. The analysis of SMLM data is composed of two sequential aspects: the generation of a super-resolution table/image and the subsequent analysis. In recent years, several data analysis packages dedicated to the generation of super-resolved images have been developed. These packages have been extensively characterized and compared in a community-wide effort, therefore allowing researchers to identify optimal solutions for their experiments and providing software developers with a gold standard. On the contrary, the development of data analysis packages dedicated to the study of protein coordinates has been lagging behind, and no comprehensive approach has been developed to date. Here, I present a combination of Fiji and R based scripts for the characterization, filtering and quality assurance of SMLM derived localizations. Furthermore, I demonstrate that specific conventional image analysis techniques can be applied, both quantitatively and qualitatively, to super resolution images. I then apply these analysis tools exemplarily on the characterization of the spatio-temporal localization of a novel DNA repair system in Corynebacterium glutamicum, termed Dip (DNA damage induced protein) C. Finally, I combine the multiple data analysis packages that I developed and/or adapted for the study of specific biological scenarios into a single cohesive pipeline, therefore providing a generalized and comprehensive approach toward the coordinate based analysis of the spatio-temporal localization of proteins in PALM and, in general, in SMLM. Each of the data analysis packages that comprise the pipeline is here presented together with the biological scenario that prompted its development. These include the study of magnetosome formation in Magnetospirillum gryphiswaldense, the study of the chromosome segregation machinery in C. glutamicum and the study of flagellar organization in Trypanosoma brucei.Die photoaktivierte Lokalisationsmikroskopie (PALM) ist eine Einzelmolekül-Fluoreszenzmikroskopie Technik (SMLM), die auf der kontrollierten Aktivierung und Aufnahme von photoaktivierbaren / konvertierbaren fluoreszierenden Proteinen beruht, um ihre Position mit einer Präzision im Nanometerbereich zu bestimmen. Die Analyse von SMLM-Daten besteht aus zwei aufeinander folgenden Aspekten: der Erzeugung einer Tabelle / eines hochauflösenden Bildes und der anschließenden Analyse. In den letzten Jahren wurden mehrere Datenanalysepakete entwickelt, die sich der Berechnung der hochaufgelösten Bilder widmen. Diese Pakete wurden in gemeinschaftsweiten Anstrengungen umfassend charakterisiert und verglichen, sodass Forscher eine optimale Lösung für eigene Experimente wählen können, während Softwareentwicklern einen Goldstandard zur Hand haben. Gegensätzlich wurde jedoch die Entwicklung von Datenanalysepaketen zur spezifischen Untersuchung von Proteinkoordinaten vernachlässigt, so dass in diesem Bereich keine umfassenden Instrumente existieren. In dieser Arbeit präsentiere ich eine Kombination aus Fiji- und R basierten Skripten zur Charakterisierung, Filterung und Qualitätssicherung von SMLM Proteinkoordinaten. Darüber hinaus zeige ich, dass bestimmte konventionelle Bildanalysetechniken sowohl quantitativ als auch qualitativ auf „Superresolution“ Bilder angewandt werden können. Im Folgenden verwende Ich diese Analysewerkzeuge dann beispielhaft zur Charakterisierung der räumlich-zeitlichen Lokalisierung eines neuartigen DNA-Reparatursystems in Corynebacterium glutamicum, welches ich DipC (DNA-Schaden-induziertes Protein) genannt habe. Schließlich kombiniere ich die genannten Datenanalysepakete, die ich für die Untersuchung spezifischer biologischer Szenarien entwickelt und / oder angepasst habe, zu einer einzigen zusammenhängenden Arbeitsroutine. Diese bietet einen allgemeinen und umfassenden Ansatz für die koordinatenbasierte Analyse der räumlich-zeitlichen Lokalisierung von Proteinen aus PALM- und im Allgemeinen aus SMLM-Experimenten. Jedes der Datenanalysepakete, die in beschriebener Routine enthalten sind, wird hier zusammen mit dem biologischen Szenario vorgestellt, das zu ihrer Entwicklung geführt hat. Dazu gehören die Untersuchung der Magnetosomenbildung in Magnetospirillum gryphiswaldense, die Untersuchung der Chromosomensegregationsmaschinerie in C. glutamicum und die Untersuchung der Flagellenorganisation in Trypanosoma brucei

Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

THE PERIODIC AND DYNAMIC STRUCTURE OF CHROMATIN

The organisation of chromatin is non-random and shows a broad diversity across cell types, developmental stages, and cell cycle stages. During G0 and G1 phase of interphase, chromatin displays a bivalent status. The condensed chromatin (heterochromatin) at the nuclear periphery is mostly associated with low levels of gene expression, while the loosened chromatin (euchromatin) towards the interior of the nucleus is associated with higher gene expression. This quiescent picture of interphase radically changes when the cell cycle progresses toward cell division. Firstly, during S phase, DNA is replicated, and chromatin progressively condenses. This is followed by the G2 phase that shows a compact heterochromatin recruited towards the centre of the nucleus. At the beginning of mitosis, the chromosomes condense with a significant topological change in their organisation and are segregated during the next stages of the cell division. Meiotic chromosomes are also highly condensed as mitotic chromosomes but show a particular functional structure, which prepares germ cells to exchange DNA sequences between their homologous chromosomes to generate diversity. To summarise, chromatin experiences dramatic organisational changes during mitosis and meiosis. These changes in chromatin organisation during the lifetime of a cell show that chromatin is not a static entity but highly dynamic in nature. For a variety of reasons, conventional light and electron microscopy have not been able to fully capture the finer details of chromatin organisation and dynamics. For a long time, description of the interphase nucleus was limited to delineate the euchromatin-heterochromatin dichotomy or describe some specific nuclear elements such as the nucleolus. Advancements in molecular biology during the last thirty years have brought an immense amount of information about how chromatin is organised and genes are regulated. As a classical example, the globin gene has been shown to display a highly constrained shape forced by chromatin looping that brings the regulatory regions to the promoter of the gene. Nowadays, genomic studies can acquire an immense amount of information regarding chromatin organisation and gene regulation, leaving one with the expectation that structure of individual genes could potentially be described visually if sufficient specificity and resolution were reached. With the advent of various super-resolution methods, in particular, single molecule localisation microscopy (SMLM) based methods and recently developed strategies for labelling DNA, it is now possible to study chromatin organisation and underlying gene regulatory mechanisms at the nanoscale. During my PhD, I have analysed a broad range of nuclear phenotypes using SMLM. My analyses contribute to the description of a periodic and dynamic structure of chromatin. Moreover, I have described several elementary chromatin structures that I call chromatin domains, both in interphase and meiosis, that are potentially associated with a local function such as gene activation or silencing. Firstly with colleagues, I established an experimental setup to study chromatin organisation with single molecule localisation microscopy. I investigated how UV-induced photo-conversion of conventional DNA dyes allows increasing sufficiently the labelling density such that it is possible to study various organisational aspects of chromatin in basal interphase. An adequate imaging protocol has been established to bring DNA minor groove binding dyes such as Hoechst 33258, Hoechst 33342 and DAPI (4’,6-diamidino-2-phenylindole) into an efficient blinking state necessary to record single molecule locations with high precision. This method was applied to several cell types to investigate the chromatin organisation during different stages of the cell cycle at the highest resolution currently achievable with light microscopy. The results show that the method can capture several hierarchical levels of chromatin organisation. In reverse hierarchical order, I could describe previously known chromatin territories of 1000 nm, subchromosomal domains of 500 nm, chromatin domains of 100 to 400 nm (and further sub-categories of active or repressed domains) and chromatin fibres below 100 nm, mostly between 30 to 60 nm. Individual nucleosomal domains are also described, which tend to cluster in batches of 10-15 nucleosomes, a number close to one found in genomic studies upstream to promoter regions. Next, with colleagues, I studied the dynamics of chromatin using stress as a model system. It was found that short-term oxygen and nutrient deprivation provokes chromatin to shrink to a hollow, condensed ring and rod-like configuration, which reverses back to the initial structure when the stress conditions cease. The condensed network of rods and rings interspersed with large, chromatin-sparse nuclear voids were 40-700 nm in dimension, capturing another level of chromatin organisation not described before. Finally, I explored the unique properties of chromatin during meiosis, which has escaped analysis at the single-molecule level until now. Single molecule analysis revealed unexpected highly recognisable periodic patterns of chromatin. Firstly, I observed that meiotic chromatin show unique clusters of 250 nm diameter along the synaptonemal complex, extended laterally by chromatin fibres forming loops. These clusters show a remarkable periodicity of 500 nm, a pattern possible to spot because of the highly deterministic nature of pachytene chromosomes and the resolution of the experimental setup. Furthermore, guided by genomic data, I selected histone modifications associated with different chromatin states to dissect the morphology of meiotic chromosomes. I could examine the morphology of these chromosomes into three spatially distinct nanoscale sub-compartments. Histone mark H3K4me3 associated with active chromatin was found in a lateral position, potentially located at the places of \textit{de novo} double-strand breaks. Repressive histone mark H3K27me3 was shown to display a surprising medial symmetrical and periodic pattern, putatively associated with recombination. Finally, centromeric histone mark H3K9me3 locates at one of meiotic chromosome ends and is potentially associated with repression of repeated regions and pairing of homologous chromosomes at early stages. I summarise these findings in a comprehensive final model. Overall, I have used new information brought by super-resolution technologies to show the dynamics of chromatin in various processes and novel orders of chromatin compaction, which were not reported previously. Among these new levels of chromatin compaction are the interphase hierarchical chromatin domains, the stress pattern of cells upon oxygen and nutrients deprivation and the novel epigenetic domains found at pachytene stage of meiosis. These architectures show that the organisation of chromatin is more complex than thought before, dynamic in nature and shows a high order of periodicity. Further investigation is, therefore, necessary to understand how chromatin transits from a ’beads-on-string’ model to the intermediary chromatin domains and finally to the commonly observed X-shaped chromosomes

Denoising, deconvolving and decomposing multi-domain photon observations- The D4PO algorithm

Astronomical imaging based on photon count data is a non-trivial task. In this context we show how to denoise, deconvolve, and decompose multi-domain photon observations. The primary objective is to incorporate accurate and well motivated likelihood and prior models in order to give reliable estimates about morphologically different but superimposed photon flux components present in the data set. Thereby we denoise and deconvolve photon counts, while simultaneously decomposing them into diffuse, point-like and uninteresting background radiation fluxes. The decomposition is based on a probabilistic hierarchical Bayesian parameter model within the framework of information field theory (IFT). In contrast to its predecessor D$^3$PO, D$^4$PO reconstructs multi-domain components. Thereby each component is defined over its own direct product of multiple independent domains, for example location and energy. D$^4$PO has the capability to reconstruct correlation structures over each of the sub-domains of a component separately. Thereby the inferred correlations implicitly define the morphologically different source components, except for the spatial correlations of the point-like flux. Point-like source fluxes are spatially uncorrelated by definition. The capabilities of the algorithm are demonstrated by means of a synthetic, but realistic, mock data set, providing spectral and spatial information about each detected photon. D$^4$PO successfully denoised, deconvolved, and decomposed a photon count image into diffuse, point-like and background flux, each being functions of location as well as energy. Moreover, uncertainty estimates of the reconstructed fields as well as of their correlation structure are provided employing their posterior density function and accounting for the manifolds the domains reside on

Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease

This is the peer reviewed version of the following article: Xie, L, Wisse, LEM, Pluta, J, et al. Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. Hum Brain Mapp. 2019; 40: 3431 3451, which has been published in final form at https://doi.org/10.1002/hbm.24607. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.Northern California Institute for Research and Education; Foundation for the National Institutes of Health; Canadian Institutes of Health Research; Transition Therapeutics; Takeda Pharmaceutical Company; Servier; Piramal Imaging; Pfizer Inc.; Novartis Pharmaceuticals Corporation; Neurotrack Technologies; NeuroRx Research; Meso Scale Diagnostics, LLC.; Lundbeck and Merck & Co., Inc.; Lumosity; Johnson & Johnson Pharmaceutical Research & Development LLC.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; IXICO Ltd.; GE Healthcare; Fujirebio; Genentech, Inc.; F. Hoffmann-La Roche Ltd.; EuroImmun; Eli Lilly and Company; Elan Pharmaceuticals, Inc.; Cogstate and Eisai Inc.; CereSpir, Inc.; Bristol-Myers Squibb Company; Biogen; BioClinica, Inc.; Araclon Biotech; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; AbbVie; National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Department of Defense ADNI, Grant/Award Number: W81XWH-12-2-0012; Alzheimer's Disease Neuroimaging Initiative, Grant/Award Number: U01 AG024904; Spain Ministry of Economy, Industry and Competitiveness, Grant/Award Number: DPI2017-87743-R; Foundation Philippe Chatrier; BrightFocus Foundation; National Institutes of Health, Grant/Award Numbers: R01-AG055005, R01-EB017255, P30-AG010124, R01-AG040271, R01-AG056014Xie, L.; Wisse, LEM.; Pluta, J.; De Flores, R.; Piskin, V.; Manjón Herrera, JV.; Wang, H.... (2019). 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