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Kulkarni PM, Barton E, Savelonas M, Padmanabhan R, Lu Y, Trett K, Shain W, Leasure JL, Roysam B. Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images. J Neurosci Methods 2015; 246:38-51. [DOI: 10.1016/j.jneumeth.2015.02.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 02/13/2015] [Accepted: 02/14/2015] [Indexed: 12/31/2022]
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Zhao T, Xie J, Amat F, Clack N, Ahammad P, Peng H, Long F, Myers E. Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics 2012; 9:247-61. [PMID: 21547564 PMCID: PMC3104133 DOI: 10.1007/s12021-011-9120-3] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.
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Affiliation(s)
- Ting Zhao
- Qiushi Academy for Advanced Studies, Zhejiang University, 38 ZheDa Road, Hangzhou, 310027 China
| | - Jun Xie
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Fernando Amat
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Nathan Clack
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Parvez Ahammad
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Hanchuan Peng
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Fuhui Long
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
| | - Eugene Myers
- HHMI Janelia Farm Research Campus, 19700 Helix Dr., Ashburn, VA 20147 USA
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Abstract
This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.
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Abstract
Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable. Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly). Availability: The software is available upon request. We plan to eventually release the software as a plugin of the V3D-Neuron package at http://penglab.janelia.org/proj/v3d. Contact:pengh@janelia.hhmi.org
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Affiliation(s)
- Hanchuan Peng
- Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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Abstract
The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. (c) 2010 International Society for Advancement of Cytometry.
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Affiliation(s)
- Erik Meijering
- Biomedical Imaging Group Rotterdam, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
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Narayanaswamy A, Dwarakapuram S, Bjornsson CS, Cutler BM, Shain W, Roysam B. Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2010; 29:583-97. [PMID: 20199906 PMCID: PMC2852140 DOI: 10.1109/tmi.2009.2022086] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.
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Affiliation(s)
- Arunachalam Narayanaswamy
- Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Saritha Dwarakapuram
- Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy 12180 NY. She is now with the U.S. Research Center, Sony Electronics, Inc., San Jose, CA 95131 USA
| | - Christopher S. Bjornsson
- Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Barbara M. Cutler
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - William Shain
- Center for Neural Communication Technology, Wadsworth Center, New York State Department of Health, Albany, NY 12201 USA
| | - Badrinath Roysam
- Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
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Yuan X, Trachtenberg JT, Potter SM, Roysam B. MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images. Neuroinformatics 2009; 7:213-32. [PMID: 20012509 DOI: 10.1007/s12021-009-9057-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2008] [Accepted: 10/30/2009] [Indexed: 11/27/2022]
Abstract
This paper presents a method for improved automatic delineation of dendrites and spines from three-dimensional (3-D) images of neurons acquired by confocal or multi-photon fluorescence microscopy. The core advance presented here is a direct grayscale skeletonization algorithm that is constrained by a structural complexity penalty using the minimum description length (MDL) principle, and additional neuroanatomy-specific constraints. The 3-D skeleton is extracted directly from the grayscale image data, avoiding errors introduced by image binarization. The MDL method achieves a practical tradeoff between the complexity of the skeleton and its coverage of the fluorescence signal. Additional advances include the use of 3-D spline smoothing of dendrites to improve spine detection, and graph-theoretic algorithms to explore and extract the dendritic structure from the grayscale skeleton using an intensity-weighted minimum spanning tree (IW-MST) algorithm. This algorithm was evaluated on 30 datasets organized in 8 groups from multiple laboratories. Spines were detected with false negative rates less than 10% on most datasets (the average is 7.1%), and the average false positive rate was 11.8%. The software is available in open source form.
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Affiliation(s)
- Xiaosong Yuan
- Jonsson Engineering Center, Center for Subsurface Sensing & Imaging Systems, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
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Jhaveri SJ, Hynd MR, Dowell-Mesfin N, Turner JN, Shain W, Ober CK. Release of Nerve Growth Factor from HEMA Hydrogel-Coated Substrates and Its Effect on the Differentiation of Neural Cells. Biomacromolecules 2008; 10:174-83. [DOI: 10.1021/bm801101e] [Citation(s) in RCA: 98] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shalin J. Jhaveri
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
| | - Matthew R. Hynd
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
| | - Natalie Dowell-Mesfin
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
| | - James N. Turner
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
| | - William Shain
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
| | - Christopher K. Ober
- Department of Materials Science and Engineering and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853 and Wadsworth Center, NYS Department of Health, Albany, New York 12201
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Kim KH, Ragan T, Previte MJR, Bahlmann K, Harley BA, Wiktor-Brown DM, Stitt MS, Hendricks CA, Almeida KH, Engelward BP, So PTC. Three-dimensional tissue cytometer based on high-speed multiphoton microscopy. Cytometry A 2008; 71:991-1002. [PMID: 17929292 DOI: 10.1002/cyto.a.20470] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Image cytometry technology has been extended to 3D based on high-speed multiphoton microscopy. This technique allows in situ study of tissue specimens preserving important cell-cell and cell-extracellular matrix interactions. The imaging system was based on high-speed multiphoton microscopy (HSMPM) for 3D deep tissue imaging with minimal photodamage. Using appropriate fluorescent labels and a specimen translation stage, we could quantify cellular and biochemical states of tissues in a high throughput manner. This approach could assay tissue structures with subcellular resolution down to a few hundred micrometers deep. Its throughput could be quantified by the rate of volume imaging: 1.45 mm(3)/h with high resolution. For a tissue containing tightly packed, stratified cellular layers, this rate corresponded to sampling about 200 cells/s. We characterized the performance of 3D tissue cytometer by quantifying rare cell populations in 2D and 3D specimens in vitro. The measured population ratios, which were obtained by image analysis, agreed well with the expected ratios down to the ratio of 1/10(5). This technology was also applied to the detection of rare skin structures based on endogenous fluorophores. Sebaceous glands and a cell cluster at the base of a hair follicle were identified. Finally, the 3D tissue cytometer was applied to detect rare cells that had undergone homologous mitotic recombination in a novel transgenic mouse model, where recombination events could result in the expression of enhanced yellow fluorescent protein in the cells. 3D tissue cytometry based on HSMPM demonstrated its screening capability with high sensitivity and showed the possibility of studying cellular and biochemical states in tissues in situ. This technique will significantly expand the scope of cytometric studies to the biomedical problems where spatial and chemical relationships between cells and their tissue environments are important.
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Affiliation(s)
- Ki Hean Kim
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Al-Kofahi Y, Dowell-Mesfin N, Pace C, Shain W, Turner JN, Roysam B. Improved detection of branching points in algorithms for automated neuron tracing from 3D confocal images. Cytometry A 2008; 73:36-43. [DOI: 10.1002/cyto.a.20499] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Four-dimensional vascular tree reconstruction using moving grid deformation. Acad Radiol 2007; 14:1540-53. [PMID: 18035283 DOI: 10.1016/j.acra.2007.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2006] [Revised: 07/02/2007] [Accepted: 07/04/2007] [Indexed: 11/24/2022]
Abstract
RATIONALE AND OBJECTIVES Thinned perforator flaps have been widely used in plastic surgery for greater survivability and decreased morbidity. However, quantitative analysis of three-dimensional (3D) blood flow direction and location has not been examined yet. Such information will benefit and guide the surgical thinning and dissection process. Toward this goal, this study was performed for 3D vascular tree reconstruction with the incorporation of temporal contrast-agent propagation information (three spatial dimensions plus one temporal dimension; ie, 4D). MATERIALS AND METHODS A novel computational framework by adopting a moving grid deformation method is presented. To take advantage of temporal information of the bolus propagating, a sequential segmentation procedure is proposed. Moreover, the temporal evolution of the vascular tree (4D vascular tree) is reconstructed during the procedure. RESULTS Eight anterolateral thigh perforator flaps from eight cadavers were used for this study. The age range is 60-80 years old and the gender includes four males and four females. The 3D nature of the vascular structure and 4D vascular tree evolving process are showed in comparison with maximum intensity projection images. CONCLUSION The proposed computational framework demonstrates effectiveness in the modeling of 4D vascular tree. Furthermore, it reveals the ability to detect small vessel tree structures that are beyond the limit of image resolution.
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Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Assist Radiol Surg 2007. [DOI: 10.1007/s11548-007-0125-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wang YP, Ragib H, Huang CM. A Wavelet Approach for the Identification of Axonal Synaptic Varicosities from Microscope Images. ACTA ACUST UNITED AC 2007; 11:296-304. [PMID: 17521079 DOI: 10.1109/titb.2006.884370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Direct visualization of synapses is a prerequisite to the analysis of the spatial distribution patterns of synaptic systems. Such an analysis is essential to the understanding of synaptic circuitry. In order to facilitate the visualization of individual synapses at the subcellular level from microscope images, we have introduced a wavelet-based approach for the semiautomated recognition of axonal synaptic varicosities. The proposed approach to image analysis employs a family of redundant wavelet representations. They are specifically designed for the recognition of signal peaks, which correspond to the presence of axonal synaptic varicosities. In this paper, the two-dimensional image of an axon together with its synaptic varicosities is first transformed into a one-dimensional (1-D) profile in which the axonal varicosities are represented by peaks in the signal. Next, by decomposing the 1-D profile in the differential wavelet domain, we employ the multi-scale point-wise product to distinguish between peaks and noises. The ability to separate the true signals (due to synaptic varicosities) from noise makes possible a reliable and accurate recognition of axonal synaptic varicosities. The proposed algorithms are also designed with a variable threshold that effectively allows variable sensitivities in varicosity detection. The algorithm has been systematically validated using images containing varicosities (< or =30) that have been consistently identified by seven human observers. The proposed algorithm can give high sensitivity and specificity with appropriate threshold. The results have indicated that the semiautomatic approach is satisfactory for processing a variety of microscopic images of axons under different conditions.
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Affiliation(s)
- Yu-Ping Wang
- School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA.
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Narro ML, Yang F, Kraft R, Wenk C, Efrat A, Restifo LL. NeuronMetrics: software for semi-automated processing of cultured neuron images. Brain Res 2007; 1138:57-75. [PMID: 17270152 PMCID: PMC1945162 DOI: 10.1016/j.brainres.2006.10.094] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2006] [Revised: 10/04/2006] [Accepted: 10/30/2006] [Indexed: 12/28/2022]
Abstract
Using primary cell culture to screen for changes in neuronal morphology requires specialized analysis software. We developed NeuronMetrics for semi-automated, quantitative analysis of two-dimensional (2D) images of fluorescently labeled cultured neurons. It skeletonizes the neuron image using two complementary image-processing techniques, capturing fine terminal neurites with high fidelity. An algorithm was devised to span wide gaps in the skeleton. NeuronMetrics uses a novel strategy based on geometric features called faces to extract a branch number estimate from complex arbors with numerous neurite-to-neurite contacts, without creating a precise, contact-free representation of the neurite arbor. It estimates total neurite length, branch number, primary neurite number, territory (the area of the convex polygon bounding the skeleton and cell body), and Polarity Index (a measure of neuronal polarity). These parameters provide fundamental information about the size and shape of neurite arbors, which are critical factors for neuronal function. NeuronMetrics streamlines optional manual tasks such as removing noise, isolating the largest primary neurite, and correcting length for self-fasciculating neurites. Numeric data are output in a single text file, readily imported into other applications for further analysis. Written as modules for ImageJ, NeuronMetrics provides practical analysis tools that are easy to use and support batch processing. Depending on the need for manual intervention, processing time for a batch of approximately 60 2D images is 1.0-2.5 h, from a folder of images to a table of numeric data. NeuronMetrics' output accelerates the quantitative detection of mutations and chemical compounds that alter neurite morphology in vitro, and will contribute to the use of cultured neurons for drug discovery.
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Affiliation(s)
- Martha L. Narro
- ARL Division of Neurobiology, University of Arizona, Tucson, AZ 85721
| | - Fan Yang
- ARL Division of Neurobiology, University of Arizona, Tucson, AZ 85721
| | - Robert Kraft
- ARL Division of Neurobiology, University of Arizona, Tucson, AZ 85721
| | - Carola Wenk
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249
| | - Alon Efrat
- Department of Computer Science, University of Arizona, Tucson, AZ 85721
| | - Linda L. Restifo
- ARL Division of Neurobiology, University of Arizona, Tucson, AZ 85721
- Interdisciplinary Programs in Neuroscience, Genetics and Cognitive Science, University of Arizona, Tucson, AZ 85721
- BIO5 Institute for Collaborative Bioresearch, University of Arizona, Tucson, AZ 85721
- Department of Neurology, Arizona Health Sciences Center, Tucson, AZ 85724
- * Author for correspondence: Linda L. Restifo, 611 Gould-Simpson Bldg., University of Arizona, Tucson, AZ 85721-0077, phone: (520) 621-9821, FAX: (520) 621-8282,
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