Background The normal RPE sheet in the C57BL/6J mouse is subclassified into two major tiling patterns: a regular generally hexagonal array covering most of the surface and a “soft network” near the ciliary body made of irregularly shaped cells. Physics models predict these two patterns based on contractility and elasticity of the RPE cell, and strength of cellular adhesion between cells. Hypothesis We hypothesized and identified major changes in RPE regular hexagonal tiling pattern in rd10 compared to C57BL/6J mice. Results In rd10 mice, RPE sheet damage was extensive but occurred later than expected, after most retinal degeneration was complete. RPE sheet changes occur in zones with a bullseye pattern. In the posterior zone, around the optic nerve, RPE cells take on larger irregular and varied shapes to maintain an intact monolayer. In mid periphery, RPE cells have a compressed or convoluted morphology that progress into ingrown layers of RPE under the retina. Cells in the periphery maintain their shape and size until the late stages of the RPE reorganization. The number of neighboring cells varies widely depending on zone and progression. RPE morphology continues to deteriorate after the photoreceptors have degenerated. Conclusions The RPE cells are bystanders to photoreceptor degeneration in the rd10 model, and the collateral damage to the RPE results in changes in morphology as early as 30 days old. Quantitative measures of the tiling patterns and histopathology detected here were scripted in a pipeline written in Perl and Cell Profiler (an open source MatLab plugin) and are directly applicable to RPE sheet images from noninvasive fundus autofluorescence (FAF), adaptive optics confocal scanning laser ophthalmoscope (AO-cSLO), and spectral domain optical coherence tomography (SD-OCT) of patients with early stage AMD or RP.
Quantitative differences in the RPE sheet morphology allowed discrimination of rd10 from C57BL/6J strains despite the confounding effect of aging. This has implications for RPE sheet morphology as a potential early biomarker for diagnosis of eye disease and prognosis of the eye at early stages when disease is subtle. We conclude that an RPE cell's area and aspect ratio are very early quantitative indicators that predict progression to advanced RPE disease as manifested in rd10.
Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features. Recent work, however, has shown that deep learning models can be used to automatically learn feature representations directly from the raw, unstructured bytes of the binaries themselves. In this paper, we explore what these models are learning about malware. To do so, we examine the learned features at multiple levels of resolution, from individual byte embeddings to end-to-end analysis of the model. At each step, we connect these byte-oriented activations to their original semantics through parsing and disassembly of the binary to arrive at humanunderstandable features. Through our results, we identify several interesting features learned by the model and their connection to manually-derived features typically used by traditional machine learning models. Additionally, we explore the impact of training data volume and regularization on the quality of the learned features and the efficacy of the classifiers, revealing the somewhat paradoxical insight that better generalization does not necessarily result in better performance for byte-based malware classifiers.
We are interested in developing quantitative tools to study RPE morphology. We want to detect changes in the RPE by strain, disease, genotype, and age. Ultimately these tools should be useful in predicting retinal disease progression. The morphometric data will also help us to understand RPE sheet formation and barrier functions. A clear disruption of the regular cell size and shape appeared in mouse mutants. Aspect ratio and cell area together gave rise to principal components that predicted age and genotype accurately and well before visually obvious damage could be seen.
As the drive to improve the cost, performance characteristics and safety of lithium-ion batteries increases with adoption, one area where significant value could be added is that of battery diagnostics. This paper documents an investigation into the use of plasmonic-based optical fibre sensors, inserted internally into 1.4 Ah lithium-ion pouch cells, as a real time and in-situ diagnostic technique. The successful implementation of the fibres inside pouch cells is detailed and promising correlation with battery state is reported, while having negligible impact on cell performance in terms of capacity and columbic efficiency. The testing carried out includes standard cycling and galvanostatic intermittent titration technique (GITT) tests, and the use of a reference electrode to correlate with the anode and cathode readings separately. Further observations are made around the sensor and analyte interaction mechanisms, robustness of sensors and suggested further developments. These finding show that a plasmonic-based optical fibre sensor may have potential as an opto-electrochemical diagnostic technique for lithium-ion batteries, offering an unprecedented view into internal cell phenomena.
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