PubMed · 7657320
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H K Thomsen. 1995. Dysplastic naevi.. https://doi.org/10.1111/j.1365-2559.1995.tb00263.x
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MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.
An artificial neural network model is proposed that combines several aspects taken from physiological observations (oscillations, synchronizations) with a visual latency mechanism in order to achieve an improved analysis of visual scenes. The network consists of two parts. In the lower layers that contain no lateral connections the propagation velocity of the activity of the units depends on the contrast of the individual objects in the scene. In the upper layers lateral connections are used to achieve synchronization between corresponding image parts. This architecture assures that the activity that arises in response to a scene containing objects with different contrast is spread out over several layers in the network. Thereby adjacent objects with different contrast will be separated and synchronization occurs in the upper layers without mutual disturbance between different objects. A comparison with a one-layer network shows that synchronization occurs in the upper layers without mutual disturbance between different objects. A comparison with a one-layer network shows that synchronization in the latency dependent multilayer net is indeed achieved much faster as soon as more than five objects have to be recognized. In addition, it is shown that the network is highly robust against noise in the stimuli or variations in the propagation delays (latencies), respectively. For a consistent analysis of a visual scene the different features of an individual object have to be recognized as belonging together and separated from other objects. This study shows that temporal differences, naturally introduced by stimulus latencies in every biological sensory system, can strongly improve the performance and allow for an analysis of more complex scenes.
Magnetic deflection forces and imaging artifact were assessed for 13 pediatric interventional cardiology devices. The magnetic deflection forces incurred by these devices are small compared with physiologic forces experienced in the cardiovascular system.