| 1. |
- Klionsky, Daniel J., et al.
(författare)
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Guidelines for the use and interpretation of assays for monitoring autophagy in higher eukaryotes
- 2008
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Ingår i: Autophagy. - : Landes Bioscience. - 1554-8627 .- 1554-8635. ; 4:2, s. 151-175
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Forskningsöversikt (refereegranskat)abstract
- Research in autophagy continues to accelerate,1 and as a result many new scientists are entering the field. Accordingly, it is important to establish a standard set of criteria for monitoring macroautophagy in different organisms. Recent reviews have described the range of assays that have been used for this purpose.2,3 There are many useful and convenient methods that can be used to monitor macroautophagy in yeast, but relatively few in other model systems, and there is much confusion regarding acceptable methods to measure macroautophagy in higher eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers of autophagosomes versus those that measure flux through the autophagy pathway; thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from fully functional autophagy that includes delivery to, and degradation within, lysosomes (in most higher eukaryotes) or the vacuole (in plants and fungi). Here, we present a set of guidelines for the selection and interpretation of the methods that can be used by investigators who are attempting to examine macroautophagy and related processes, as well as by reviewers who need to provide realistic and reasonable critiques of papers that investigate these processes. This set of guidelines is not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to verify an autophagic response.
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| 2. |
- Korall, Petra, 1968-, et al.
(författare)
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A molecular phylogeny of scaly tree ferns (Cyatheaceae)
- 2007
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Ingår i: American Journal of Botany. - : Wiley. - 0002-9122 .- 1537-2197. ; 94:5, s. 873-886
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Tidskriftsartikel (refereegranskat)abstract
- Tree ferns recently were identified as the closest sister group to the hyperdiverse clade of ferns, the polypods. Although most of the 600 species of tree ferns are arborescent, the group encompasses a wide range of morphological variability, from diminutive members to the giant scaly tree ferns, Cyatheaceae. This well-known family comprises most of the tree fern diversity (similar to 500 species) and is widespread in tropical, subtropical, and south temperate regions of the world. Here we investigate the phylogenetic relationships of scaly tree ferns based on DNA sequence data from five plastid regions (rbcL, rbcL-accD IGS, rbcL-atpB IGS, trnG-trnR, and trnL-trnF). A basal dichotomy resolves Sphaeropteris as sister to all other taxa and scale features support these two clades: Sphaeropteris has conform scales, whereas all other taxa have marginate scales. The marginate-scaled clade consists of a basal trichotomy, with the three groups here termed (1) Cyathea (including Cnemidaria, Hymenophyllopsis, Trichipteris), (2) Alsophila sensu stricto, and (3) Gymnosphaera (previously recognized as a section within Alsophila) + A. capensis. Scaly tree ferns display a wide range of indusial structures, and although indusium shape is homoplastic it does contain useful phylogenetic information that supports some of the larger clades recognised.
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| 3. |
- Razifar, Pasha, et al.
(författare)
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A new application of pre-normalized principal component analysis for improvement of image quality and clinical diagnosis in human brain PET studies - Clinical brain studies using [C-11]-GR205171, [C-11]-L-deuterium-deprenyl, [C-11]-5-hydroxy-L-tryptophan, [C-11]-L-DOPA and Pittsburgh Compound-B
- 2006
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 33:2, s. 588-598
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Tidskriftsartikel (refereegranskat)abstract
- Principal component analysis (PCA) is one of the most applied multivariate image analysis tool on dynamic Positron Emission Tomography (PET). Independent of used reconstruction methodologies, PET images contain correlation in-between pixels, correlations in-between frame and errors caused by the reconstruction algorithm including different corrections, which can affect the performance of the PCA. In this study, we have investigated a new approach of application of PCA on pre-normalized, dynamic human PET images. A range of different tracers have been used for this purpose to explore the performance of the new method as a way to improve detection and visualization of significant changes in tracer kinetics and to enhance the discrimination between pathological and healthy regions in the brain. We compare the new results with the results obtained using other methods. Images generated using the new approach contain more detailed anatomical information with higher quality, precision and visualization, compared with images generated using other methods.
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| 4. |
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| 5. |
- Razifar, Pasha, et al.
(författare)
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Noise correlation in PET, CT, SPECT and PET/CT data evaluated using autocorrelation function: a phantom study on data, reconstructed using FBP and OSEM.
- 2005
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Ingår i: Bio Medical Central (BMC): Medical Imaging. - : Springer Science and Business Media LLC. ; 5:5
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Positron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone.Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT. METHODS: Experiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images. RESULTS: The most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images. CONCLUSION: The combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.
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| 6. |
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| 7. |
- Razifar, Pasha, et al.
(författare)
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Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function
- 2005
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Ingår i: Bio Medical Central (BMC): Medical Imaging. - : Springer Science and Business Media LLC. ; 5:3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known. METHODS: In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images. RESULTS: We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study. CONCLUSION: With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.
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| 8. |
- Razifar, Pasha, 1968-
(författare)
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Novel Approaches for Application of Principal Component Analysis on Dynamic PET Images for Improvement of Image Quality and Clinical Diagnosis
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Positron Emission Tomography, PET, can be used for dynamic studies in humans. In such studies a selected part of the body, often the whole brain, is imaged repeatedly after administration of a radiolabelled tracer. Such studies are performed to provide sequences of images reflecting the tracer’s kinetic behaviour, which may be related to physiological, biochemical and functional properties of tissues. This information can be obtained by analyzing the distribution and kinetic behaviour of the administered tracers in different regions, tissues and organs. Each image in the sequence thus contains part of the kinetic information about the administered tracer. Several factors make analysis of PET images difficult, such as a high noise magnitude and correlation between image elements in conjunction with a high level of non-specific binding to the target and a sometimes small difference in target expression between pathological and healthy regions. It is therefore important to understand how these factors affect the derived quantitative measurements when using different methods such as kinetic modelling and multivariate image analysis.In this thesis, a new method to explore the properties of the noise in dynamic PET images was introduced and implemented. The method is based on an analysis of the autocorrelation function of the images. This was followed by proposing and implementing three novel approaches for application of Principal Component Analysis, PCA, on dynamic human PET studies. The common underlying idea of these approaches was that the images need to be normalized before application of PCA to ensure that the PCA is signal driven, not noise driven. Different ways to estimate and correct for the noise variance were investigated. Normalizations were carried out Slice-Wise (SW), for the whole volume at once, and in both image domain and sinogram domain respectively. We also investigated the value of masking out and removing the area outside the brain for the analysis. The results were very encouraging. We could demonstrate that for phantoms as well as for real image data, the applied normalizations allow PCA to reveal the signal much more clearly than what can be seen in the original image data sets. Using our normalizations, PCA can thus be used as a multivariate analysis technique that without any modelling assumptions can separate important kinetic information into different component images. Furthermore, these images contained optimized signal to noise ratio (SNR), low levels of noise and thus showed improved quality and contrast. This should allow more accurate visualization and better precision in the discrimination between pathological and healthy regions. Hopefully this can in turn lead to improved clinical diagnosis.
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| 9. |
- Razifar, Pasha, et al.
(författare)
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Volume-Wise Application of Principal Component Analysis on Masked Dynamic PET Data in Sinogram Domain
- 2006
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Ingår i: IEEE Transactions on Nuclear Science. - 0018-9499 .- 1558-1578. ; 53:5, s. 2759-2768
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Tidskriftsartikel (refereegranskat)abstract
- Most of the methods used for analyzing PET data are applied in the spatial domain (image domain), in which reconstructed images contain all different types of effects and errors caused by the reconstruction algorithm such as correlation in-between pixels, correlations in-between frames, and streak-artifacts. In this paper, we have investigated a new, pixel wise, noise prenormalization method used for transformation of input data followed by volume-wise application of principal component analysis (PCA) on masked dynamic PET data in the sinogram domain. We are aiming to improve the performance of PCA and to provide images with improved quality and signal extraction. We compare the performance of PCA and the image quality obtained with the new method with previously published approaches. The results show improvement of performance of PCA with respect to, image quality, signal extraction, precision, and visualization.
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| 10. |
- Smith, Alan R., et al.
(författare)
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16. Fern classification
- 2008
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Ingår i: Biology and evolution of ferns and lycophytes. - : Cambridge University Press, Cambridge. - 9780521874113
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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