Academic literature on the topic 'TIRF Deconvolution'

Introduction: Activation of the T cell Receptor (TCR) by antigen on the surface of an Antigen Presenting Cell (APC) leads to a dramatic change in T cell morphology and formation of a specialized signaling interface with the APC known as the Immune Synapse (IS). The IS allows for controlled cytokine secretion and is an important regulator of T cell activation. IS formation has been found to be dysfunctional in many immune disorders including T cell acute lymphoblastic leukemia (ALL).The actin and microtubule (MT) cytoskeletons have individually been implicated in regulating both IS formation and TCR signaling, but the crosstalk between actin and MTs in T cells has not been well explored. We hypothesized that MACF1 (Microtubule Actin Crosslinking Factor 1), a giant cytoskeleton crosslinker from the spectraplakin family, is present at the IS following Jurkat T cell activation and coordinates actin and MTs. To investigate this, we used TIRFM (Total Internal Reflection Fluorescence Microscopy) and confocal microscopy to image MACF1 organization at the IS. Methods: Jurkat E6-1 cells were cultured in RPMI 1640 supplemented with 10% FBS and 1% penicillin/streptomycin. Glass coverslips were coated with Poly-L-Lysine 0.01% and then with 10 µg/mL anti-CD3 for 2 hours at 37 °C to create an activating surface. Cells were dropped carefully and allowed to spread on these coverslips at 37 °C for the indicated time. In the inhibition experiments,5 µM SB415286 or 5 µM PP2 was added to cells at 8 minutes following the initiation of activation and allowed to incubate for an additional 8 minutes. Cells were fixed with 3.7% paraformaldehyde following activation and then permeabilized with 0.1% Triton X-100. Cells were stained for anti-MACF1, anti-beta tubulin clone KMX-1, respective secondary antibodies, actin-stain 488 phalloidin, and DAPI (4',6-diamidino-2-phenylindole). Fluorescence of labeled cells was imaged using TIRFM with a Nikon Ti microscope and Andor Zyla CMOS camera with a 100x magnification objective (1.49 NA). TIRFM images were analyzed for Corrected Total Cell Fluorescence, which is the total fluorescence of the cell corrected for the background. Mann-Whitney U tests were used for all statistical comparisons. Cells were imaged on Leica SP5X laser confocal microscope with 63x objective for localization of MACF1 with actin and tubulin. Confocal images were deconvolved using the deconvolution lab2 plugin in FIJI (FIJI Is Just ImageJ). Results: Corrected Total Cell Fluorescence (CTCF) of MACF1 was higher at 4, 8 and 16 minutes following activation compared to the PLL control (Figures 1A and 1B). There was no significant difference in CTCF across the different activation times. To test whether MACF1 is dis-inhibited in Jurkat T cells during activation, we used SB415286 to inhibit Glycogen Synthase Kinase 3-Beta (GSK3B), a protein that inhibits MACF1 function, on PLL-only coverslips. We found MACF1 CTCF to be higher following SB415286 treatment compared to the control, indicating that MACF1 levels increase with GSK3B inhibition (Figure 1C). Thus, SB415286 inhibition of GSK3B mimics T cell activation where Akt upregulation downstream of TCR signaling inhibits GSK3B. To further test whether CD3 signaling activates MACF1, we stimulated Jurkat cells on anti-CD3 coated glass and then removed the activation signal with the src-kinase inhibitor PP2, which inhibits lymphocyte-specific protein tyrosine kinase (Lck) - an important regulator of early T cell signaling. MACF1 CTCF was lower after PP2 inhibition compared with the control, suggesting that continued CD3 stimulation is necessary for MACF1 accumulation at the IS (Figure 1D). We then used confocal microscopy to image the spatial distribution of MACF1 and assess whether it localizes with actin and MTs at the IS. We found that MACF1 puncta are present at the F-actin rich lamellipodial region of the IS and appear to localize with MT filaments, suggesting that MACF1 coordinates the two cytoskeleton components at the IS (Figure 2). Conclusion: Using high resolution microscopy, we show that MACF1 is upregulated at the IS during Jurkat T cell activation downstream of CD3 signaling through a GSK3B dependent pathway. At the IS, we found the novel localization of MACF1 with actin and MTs. This upregulation and expression of MACF1 during activation warrants more studies of the role MACF1 plays in T cell activation, cytoskeletal dynamics and IS function. Disclosures No relevant conflicts of interest to declare.

In order to optimize gold prospecting in the Meiganga zone located in the Adamaoua region of Cameroon, aeromagnetic and remote sensing prospecting was carried out in the eastern and southern parts. The remote sensing approach on a Landsat 8 OLI/TIRS image highlighted areas of maximum gold concentration. Thus, ferric ion bearing minerals are located in the North-West, silicate minerals bearing ferrous ions are in the Centre while clay minerals are in the North-East and East. The principal component analysis revealed important structural information. The PCA Spatial Map (PC1, PC2, PC3) showed the plutonic formations composed of anatexis and anatexis granites, vegetation cover (at the date of image acquisition: February 22, 2019), areas of permanent water circulation or accumulation, and metamorphic and sedimentary formations namely gneisses, quartzites, schists and superficial clay formations. A Landsat SRTM (Shuttle Radar Topography Mission) image was also used to enhance the lineaments through the Sobel filter to highlight the geomorphological (cliffs, valleys, ...) and topographic (river network, ridge and drainage segment) structures. The aeromagnetic approach was also important. The study of the modified magnetic field (CM) showed 4 ranges: very high, high, medium and low. The Total Magnetic Anomalies (TMI) of the area are subdivided into 2 ranges large positive anomalies (221.1-103.0 nT) located in the lower part of NE-SW orientation, small positive anomalies (103.0-(-)89.7 nT) located in the upper part of NE-SW orientation. The reduced total magnetic anomaly at the equator shows a fairly similar distribution to the total magnetic anomaly with the large positive anomalies in almost the entire lower part. Superimposed on the geological map, Neoproterozoic pre- to syn-tectonic granitoids (C) are superimposed on the large positive anomalies and Neoproterozoic conglomerates, quartzites, sedimentary shales and volcanosedimentary rocks (A) and Neoproterozoic syn-tectonic granitoids (B) are superimposed on the large and small positive anomalies. The grid of the reduced residual equatorial anomaly (ARRE) confirms that the local geology is strongly magnetic (gneiss and quartzite). The filters of the derivatives allowed to establish a map of magnetic lineaments of major orientation N045° and minor orientation N130°. The horizontal gadient superimposed on the local maxima showed the presence of deep structures oriented NE-SW. The analytical signal superimposed on the local maxima highlights the metamorphic basement consisting of rocks with strong magnetism. The application of Euler deconvolution localizes the depth of the sources of linear anomalies.

Abstract In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty – on the number of fluorophores rather than on their overall brightness – we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm-2 and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

ABSTRACT Two proposed methods to determine the adhesion friction coefficient were validated by experimental results of two types of rubber compounds at different sliding velocities under dry conditions. The experimental results were measured from a linear friction tester, while the viscoelastic friction coefficient was estimated using the Persson's contact theory. Adhesive friction (model 1) was derived from the deconvolution of dry friction coefficient in two Gaussian-like curves. Interesting results were obtained using the deconvoluted method in the range of intermediate sliding velocities where preponderant contribution to the adhesion friction is replaced by the viscoelastic friction. Fitting parameter results were in good general agreement with values derived from the literature, confirming the influence of the mechanical properties of the compound and substrate texture on the proposed adhesion frictional method. The second adhesive friction model (model 2) was based on the confinement rheology of rubber chains on the contact with the asperities of the road surface. We demonstrated that acceptable adhesion friction results were achieved from a dynamic viscosity test at low frequencies, confirming the applicability of the proposed rheological model. Moreover, the relationship between the rubber composition and the modified contact layer along with the likely interphase reaction are also discussed.

Image restoration/reconstruction refers to the estimation of an underlying image from measurements generated by imaging devices. This problem is generally ill-posed since the measurements are corrupted by the physical limitations of the imaging device, and the inherent noise involved in the measurement process. There are three main classes of methods in the current literature. The first class of methods is based on regularization framework that enforces an ad-hoc prior on the restored image. The second class of methods uses regression-based learning paradigms, where a training set of clean images and the corresponding distorted measurements are used to generate a trained prior. The third class of methods adopts trained priors similar to the ones utilized in the second class of methods but within the regularization framework. This third class of methods, the trained regularization methods, are getting increasing attention because of their versatility as regularization methods, while also encompassing natural priors obtained from training. However, the need for training data can limit their applicability. In this thesis, we propose spatially adaptive regularization methods where the adaptation information is retrieved from the measured data that undergoes reconstruction. Due to adaptation, the enforced prior is more natural than the existing regularization methods. At the same time, our methods do not require training data. We summarize our contribution in three parts. In the first part, we propose a novel regularization method that adaptively combines the well-known second-order regularization, called Hessian-Schatten (HSN) norm regularization, and first-order TV (TV-1) functionals with spatially varying weights. The relative weight involved in combining the first- and second-order terms becomes an image, and this weight is determined through the minimization of a composite cost function, without user intervention. Our contributions in this part can be summarized as follows: • We construct a composite regularization functional containing two parts: (i) the first part is constructed as the sum of TV-1 and HSN with spatially varying relative weights; (ii) the second part is an additional regularization term for preventing rapid spurious variations in the relative weights. The total composite cost functional is convex with respect to either the required image or the relative weight, but it is non-convex jointly. • We construct a block coordinate descent method involving minimizations w.r.t. the required image and the relative weight alternatively with the following structure: the minimization w.r.t. the required image is carried out using Alternating Direction Method of Multipliers (ADMM), and the minimization w.r.t. the relative weight is carried out as a single step exact minimization using a formula that we derive. • Since the total cost is non-convex, the reconstruction results are highly dependent on the initialization for the block-coordinate descent method. We handle this problem using a multiresolution approach, where, a series of coarse-to-fine reconstructions are performed by the minimization of cost functionals defined through upsampling operators. Here, minimization w.r.t. the relative weight and the required image is carried out alternatively, as we progress from coarse to final resolution levels. At the final resolution level, the above-mentioned block coordinate descent method is applied. • Note that the sub-problem of minimization w.r.t. the required image involves spatially varying relative weights. Further, this sub-minimization problem in the abovementioned multiresolution loop involves upsampling operators. Hence, the original ADMM method proposed by Papafitsoros et al. turns out to be unsuitable. We propose an improved variable splitting method and computational formulas to handle this issue. • We prove that the overall block coordinate descent method converges to a local minimum of the total cost function using Zangwill’s convergence theorem. We name our method Combined Order Regularization with Optimal Spatial Adaptation (COROSA). We provide restoration examples involving deconvolution of TIRF images and reconstruction of Magnetic Resonance Imaging (MRI) images from under-sampled Fourier data. We demonstrate that COROSA outperforms existing regularization methods, and selected learning-based methods. In the second part, we make COROSA more adaptive by replacing the HSN with a spatially varying weighted combination of Eigenvalues of the Hessian. This means that the resulting regularization will be in the form of a spatially varying weighted sum of three terms involving the gradient and two Eigenvalues of Hessian. This allows the functional to restore fine image structures through directional weighting, in terms of the local Eigenvalues. We again adopt a BCD scheme that alternates between the spatially varying weight estimation and image computation, as done in the first part. However, both steps are more complex with the new form. The first task of weight estimation is more complex as it involves three terms. The second task of image computation is more complex because there is no known proximal operator for regularization involving unequally weighted Hessian Eigenvalues. We solve the first problem by constructing a novel iterative method, and the second problem by deriving a novel proximal formula. Here too, we adopt a multiresolution approach to initialize the BCD method. We call our method the Hessian based Combined Order Regularization with Optimal Spatial Adaptation (H-COROSA). We experimentally compare H-COROSA with well known regularization methods and selected deep learning based methods for MRI reconstruction from undersampled Fourier data. Compressive Sensing based methods have shown the advantage of ℓ0-based sparsity enforcing functionals in restoration. For practical applications, ℓp, 0 < p ≤ 1 functionals have been found to perform better than ℓ1 functionals. In the last part, we propose an ℓp-based generalization of the previous COROSA and H-COROSA formulations. We replace the corresponding ℓ1-based functionals with ℓp norm enforced on the combined multi-order functionals. Additionally for H-COROSA, we also consider two forms of penalty for the spatial weights. We construct an iteration scheme that is a merging of the majorization-minimization method for ℓp norm and BCD method used in the first two parts of the thesis. Again, we use a similar multiresolution method for initialization. We demonstrate the advantage of using ℓp norm with MRI reconstruction examples involving severe undersampling in the Fourier domain.

Abstract Blind deconvolution techniques were used to enhance scanning electron microscope (SEM) images in the range of 200,000x to 500,000x magnification. Typical SEM samples were imaged including a gold island reference standard, a plasma delayered integrated circuit, and an integrated circuit cross section. Image resolution improvement up to 40% was observed. However, it was necessary to use 16-bit TIFF images with greater than 120:1 signal to noise ratio, which required 10 minute frame times.

We introduce a lensless high-throughput fluorescent detection modality that can simultaneously image micro-objects and labeled cells over an ultra-wide field-of-view (FOV) of ∼8cm2 without the use of any lenses, thin-film filters and mechanical scanners. This lensfree platform utilizes total-internal-reflection (TIR) to block the excitation light, and an inexpensive absorption filter to remove the weakly scattered light that does not obey TIR. The emitted fluorescent light from the objects is then detected on the same chip without the use of any lenses. A digital deconvolution algorithm is used to resolve overlapping fluorescent spots, enabling a resolution of ∼40–50 μm over the entire field-of-view. Such an ultra wide field-of-view lensfree fluorescent imaging modality might be very valuable for high-throughput screening applications as well as quantification of rare cells such as circulating tumor cells using ultra-large microfluidic devices.