Achieving an economically viable transformation of plant cell walls into bioproducts requires a comprehensive understanding of enzymatic deconstruction. Microscale quantitative analysis offers a relevant approach to e...
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Achieving an economically viable transformation of plant cell walls into bioproducts requires a comprehensive understanding of enzymatic deconstruction. Microscale quantitative analysis offers a relevant approach to enhance our understanding of cell wall hydrolysis, but becomes challenging under high deconstruction conditions. This study comprehensively addresses the challenges of quantifying the impact of extensive enzymatic deconstruction on plant cell wall at microscale. Investigation of highly deconstructed spruce wood provided spatial profiles of cell walls during hydrolysis with remarkable precision. A distinct cell wall autofluorescence distribution pattern marking enzymatic hydrolysis along with an asynchronous impact of hydrolysis on cell wall structure, with cell wall volume reduction preceding cell wall accessible surface area decrease, were revealed. This study provides novel insights into enzymatic deconstruction of cell wall at under-investigated cell scale, and a robust computational pipeline applicable to diverse biomass species and pretreatment types for assessing hydrolysis impact and efficiency.
Pancreatic beta cells biosynthesize and package insulin in insulin granules, whose secretion is regulated to maintain blood glucose homeostasis. The detailed knowledge of the dynamics of insulin granules could reveal ...
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ISBN:
(纸本)9781538654880
Pancreatic beta cells biosynthesize and package insulin in insulin granules, whose secretion is regulated to maintain blood glucose homeostasis. The detailed knowledge of the dynamics of insulin granules could reveal defects in the intracellular handling and secretion of these granules, leading to impaired insulin secretion and consequently to the development of several metabolic diseases, including type-2 diabetes and the metabolic syndrome. The use of spinning disk confocal and light sheet microscopy with fast sequential scanning that enable rapid volumetric imaging, allows to monitor at high spatial and temporal resolution the dynamics of insulin granules. However, obtaining all the information for accurate 3d imaging and particle tracking within a single cell is complex and challenging, and extracting information from the particle tracking data requires to analyse the segments of motion trajectories. To this aim, we present in this study a quantitative analysis of the 4d motion of insulin granules in glucose-stimulated INS-1E beta cells. First, we tracked each granule inside the cells via a computer-based automatic approach relying on a two-step iterative process. Next, we removed the artifacts and introduced a set of quantitative cinematic features describing granule dynamics. Finally, we implemented an unsupervised machine learning based exploratory data analysis, which allows to distinguish two sets of granules marked by distinct dynamics: a first pool is characterized by a diffusive dynamic behavior, and a second pool that is characterized by a more directed and targeted movement. These pools may have distinct functional roles and/or interactions with other structures and organelles in beta cells that could be selectively impaired in pathological settings.
Medical imaging scanners now exist that can generate 4d cardiac images, Since the heart moves, cardiac anatomy and physiology can be studied using 4dimage sequences. Interactive manual 4dimage analysis Can be time-c...
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Medical imaging scanners now exist that can generate 4d cardiac images, Since the heart moves, cardiac anatomy and physiology can be studied using 4dimage sequences. Interactive manual 4dimage analysis Can be time-consuming and error prone-automatic and semi-automatic methods have many advantages over manual segmentation. This paper describes a procedure for performing semi-automatic image segmentation on 4dimage sequences, Our procedure is based on a small set of user definedimage-segmentation cues specified at certain time points in the sequence. These cues are then automatically interpolated or extrapolated for the remaining time points, The complete set of cues is interpreted and used to generate a sequence of imageprocessing operations (such as operators for image enhancement, morphological processing, and region segmentation) that can subsequently segment the 4dimage, This procedure permits 4d cardiac image segmentation with only a small amount of user interaction. The proposed approach compares favorably to results generated by defining cues on each individual volume and to results generated completely manually. The 4d approach also requires significantly less interaction time than pure manual analysis. (C) 2000 Academic Press.
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