The impact of different nanoparticle surface chemistry and size on uptake and toxicity in a murine macrophage cell line

This study investigated the uptake, kinetics and cellular distribution of different surface coated quantum dots (QDs) before relating this to their toxicity. J774.A1 cells were treated with organic, COOH and NH2 (PEG) surface coated QDs (40 nM). Model 20 nm and 200 nm COOH-modified coated polystyrene beads (PBs) were also examined (50 microg ml(-1)). The potential for uptake of QDs was examined by both fixed and live cell confocal microscopy as well as by flow cytometry over 2 h. Both the COOH 20 nm and 200 nm PBs were clearly and rapidly taken up by the J774.A1 cells, with uptake of 20 nm PBs being relatively quicker and more extensive. Similarly, COOH QDs were clearly taken up by the macrophages. Uptake of NH2 (PEG) QDs was not detectable by live cell imaging however, was observed following 3D reconstruction of fixed cells, as well as by flow cytometry. Cells treated with organic QDs, monitored by live cell imaging, showed only a small amount of uptake in a relatively small number of cells. This uptake was insufficient to be detected by flow cytometry. Imaging of fixed cells was not possible due to a loss in cell integrity related to cytotoxicity. A significant reduction (p<0.05) in the fluorescent intensity in a cell-free environment was found with organic QDs, NH2 (PEG) QDs, 20 nm and 200 nm PBs at pH 4.0 (indicative of an endosome) after 2 h, suggesting reduced stability. No evidence of exocytosis was found over 2 h. These findings confirm that surface coating has a significant influence on the mode of NP interaction with cells, as well as the subsequent consequences of that interaction.

The role of 3D fusion imaging in sentinel lymphadenectomy for vulvar cancer

OBJECTIVES: To evaluate the feasibility of fusion imaging compound tomography (FICT) of CT/MRI and single photon emission tomography (SPECT) versus planar scintigraphy only (plSc) in pre-surgical staging for vulvar cancer. MATERIALS AND METHODS: Analysis of consecutive patients with vulvar cancer who preoperatively underwent sentinel scintigraphy (planar and 3D-SPECT imaging) and CT or MRI. Body markers were used for exact anatomical co-registration and fusion datasets were reconstructed using SPECT and CT/MRI. The number and localisation of all intraoperatively identified and resected sentinel lymph nodes (SLN) were compared between planar and 3D fusion imaging. RESULTS: Twenty six SLN were localized on planar scintigraphy. Twelve additional SLN were identified after SPECT and CT/MRI reconstruction, all of them were confirmed intraoperatively. In seven cases where single foci were identified at plSc, fusion imaging revealed grouped individual nodes and five additional localisations were discovered at fusion imaging. In seven patients both methods identified SLN contra lateral to the primary tumor site, but only fusion imaging allowed to localise iliac SLN in four patients. All SLN predicted on fusion imaging could be localised and resected during surgery. CONCLUSIONS: Fusion imaging using SPECT and CT/MRI can detect SLN in vulvar cancer more precisely than planar imaging regarding number and anatomical localisation. FICT revealed additional information in seven out of ten cases (70%).

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

Application of MFBD Algorithms to Image Reconstruction Under Anisoplanatic Conditions

All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun-sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. My dissertation explores the performance of a multi-frame-blind-deconvolution technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios and compared to other speckle imaging techniques. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate and severe turbulence conditions. Each set consisted of 1000 simulated, turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. I will compare the mean-square-error (MSE) performance of speckle imaging methods and a maximum-likelihood, multi-frame blind deconvolution (MFBD) method applied to long-path horizontal imaging scenarios. Both methods are used to reconstruct a scene from simulated imagery featuring anisoplanatic turbulence induced aberrations. This comparison is performed over three sets of 1000 simulated images each for low, moderate and severe turbulence-induced image degradation. The comparison shows that speckle-imaging techniques reduce the MSE 46 percent, 42 percent and 47 percent on average for low, moderate, and severe cases, respectively using 15 input frames under daytime conditions and moderate frame rates. Similarly, the MFBD method provides, 40 percent, 29 percent, and 36 percent improvements in MSE on average under the same conditions. The comparison is repeated under low light conditions (less than 100 photons per pixel) where improvements of 39 percent, 29 percent and 27 percent are available using speckle imaging methods and 25 input frames and 38 percent, 34 percent and 33 percent respectively for the MFBD method and 150 input frames. The MFBD estimator is applied to three sets of field data and the results presented. Finally, a combined Bispectrum-MFBD Hybrid estimator is proposed and investigated. This technique consistently provides a lower MSE and smaller variance in the estimate under all three simulated turbulence conditions.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

3D Ultrasound imaging--a useful non-invasive tool to detect AV fistulas in transplanted kidneys

BACKGROUND: A precise, non-invasive, non-toxic, repeatable, convenient and inexpensive follow-up of renal transplants, especially following biopsies, is in the interest of nephrologists. Formerly, the rate of biopsies leading to AV fistulas had been underestimated. Imaging procedures suited to a detailed judgement of these vascular malformations are to be assessed. METHODS: Three-dimensional (3D) reconstruction techniques of ultrasound flow-directed and non-flow-directed energy mode pictures were compared with a standard procedure, gadolinium-enhanced nuclear magnetic resonance imaging angiography (MRA) using the phase contrast technique. RESULTS: Using B-mode and conventional duplex information, AV fistulas were localized in the upper pole of the kidney transplant of the index patient. The 3D reconstruction provided information about the exact localization and orientation of the fistula in relation to other vascular structures, and the flow along the fistula. The MRA provided localization and orientation information, but less functional information. Flow-directed and non-flow-directed energy mode pictures could be reconstructed to provide 3D information about vascular malformations in transplanted kidneys. CONCLUSION: In transplanted kidneys, 3D-ultrasound angiography may be equally as effective as MRA in localizing and identifying AV malformations. Advantages of the ultrasound method are that it is cheaper, non-toxic, non-invasive, more widely availability and that it even provides more functional information. Future prospective studies will be necessary to evaluate the two techniques further.

Comparison of climate field reconstruction techniques: Application to Europe

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable “a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future applications.