Adult neural stem cells and multiciliated ependymal cells share a common lineage regulated by the Geminin family members

Adult neural stem cells and multiciliated ependymalcells are glial cells essential for neurological func-tions. Together, they make up the adult neurogenicniche. Using both high-throughput clonal analysisand single-cell resolution of progenitor division pat-terns and fate, we show that these two componentsof the neurogenic niche are lineally related: adult neu-ral stem cells are sister cells to ependymal cells,whereas most ependymal cells arise from the termi-nal symmetric divisions of the lineage. Unexpectedly,we found that the antagonist regulators of DNA repli-cation, GemC1 and Geminin, can tune the proportionof neural stem cells and ependymal cells. Our find-ings reveal the controlled dynamic of the neurogenicniche ontogeny and identify the Geminin familymembers as key regulators of the initial pool of adultneural stem cells

FastSME dataset

FastSME dataset EPENDYMAL CELLS Postnatal day 1 Centrin2GFP transgenic mouse, Ependymal cell junctions stained with βCatenin; nascent centrioles with mouse IgG2b anti-Sas-6 Confocal Apotome2 (Zeiss) CCD 40x 1024X1024 X58 0.18x0.18 x0.28 DENDRITES 14 days cerebellar mixed culture, Purkinje cells immunolabeled with calbindin, granule cells with VGLUT1 Confocal Leica SP5 PMT 63x 255X700 X32 0.06x0.06 x0.19 MEMBRANE1 Ependymal cell junctions stained with rab- bit anti-ZO1 (Life Technologies) Confocal Leica SP8 Hybrid 40x 407X421 X19 0.18x0.18 x0.28 NEURON1 GFP labeled Purkinje cell in 8 days cerebellar mixed culture Widefield Leica DMiRBE CCD 10x 168X201 X8 0.06x0.06 x0.19 NEURON2 CaPB labeled Purkinje cell in 7 days cerebellar mixed culture Confocal Leica SP5 PMT 63x 1024X1024 X31 0.06x0.06 x0.19 TUBULIN Primary cycling ependymal progenitor in vitro immunostained for tyrosinated tubulin Widefield Apotome 2 (Zeiss) CCD 100x 556X610 X21 0.065x0.065 x0.23 CANCER CELL Study of colocalization of huntingtin phosphorylation at serine 421 (S421-P-HTT) with cellcell junction at MCF10 Healthy cells Confocal Leica SP5 Hybrid 63x 512X512 X34 0.12x0.12 x0.125 NUCLEI Centrin2GFP transgenic mouse at P45, Ependymal cell necleus stained with DAPI, junctions stained with rabbit anti-ZO1 (Life Technologies) Confocal Leica SP8 Hybrid 63x 370X335 X24 0.121x0.121 x0.21 7 5 SYNTHETIC TISSUE Cell junctions and centrioles on a complex synthetic manifold Confocal 400X400 X128 - 0

Coral reef dataset

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Coral reef dataset

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

DTU - Drone inspection images of wind turbine

This dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

A Human-Cyber-Physical System toward Intelligent Wind Turbine Operation and Maintenance

This work proposes a novel concept for an intelligent and semi-autonomous human-cyber-physical system (HCPS) to operate future wind turbines in the context of Industry 5.0 technologies. The exponential increase in the complexity of next-generation wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving the current Industry 4.0 digital twin technology beyond a sole aid for the human decision-making process, the digital twin in the proposed system is used for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, in which high-level decisions made through a human–machine interface break the autonomy, when needed. This paper also identifies and elaborates key enabling technologies (KETs) that are essential for realizing the proposed HCPS

Automated classification and thematic mapping of bacterial mats in the North Sea

International audienc

A study on local binary pattern for automated weed classification using template matching and support vector machine

Concerns regarding the environmental and economic impacts of excessive herbicide applications in agriculture have promoted interests in seeking alternative weed control strategies. In this context, an automated machine vision system that has the ability to differentiate between broadleaf and grass weeds in digital images to optimize the selection and dosage of herbicides can enhance the profitability and lessen environmental degradation. This paper presents an efficient and effective texture-based weed classification method using local binary pattern (LBP). The objective was to evaluate the feasibility of using micro-level texture patterns to classify weed images into broadleaf and grass categories for real-time selective herbicide applications. Two well-known machine learning methods, template matching and support vector machine, are used for classification. Experiments on 200 sample field images with 100 samples from each category show that, the proposed method is capable of classifying weed images with high accuracy and computational efficiency