A novel method to identify and grade DNA damage on comet images

Turan, Muhammed Kamil; Sehirli, Eftal

doi:10.1016/j.cmpb.2017.06.002

Cited by 16 publications

(5 citation statements)

References 10 publications

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“…Figure shows the SEM–EDX micrographs and elemental maps for some representative samples which are used to determine the silica distribution in the PEG matrix. The red line in the micrographs indicates the scan for the Si element using PixelProfile analysis . Because Si can be represented by a certain color in the images, we can draw the silica distribution in the PEG matrix (right plots) and analyze the dispersibility index ( I dispersibility ) using;

I_{dispersibility} = \frac{S_{intensity}}{{\overset{x}{true}}_{intensity}}

…”

Section: Resultsmentioning

confidence: 99%

Dynamic tensile and shear storage moduli of PEG/silica‐polymorph composites

Fauziyah

Hilmi

Fadly

et al. 2018

J of Applied Polymer Sci

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The effect of silica polymorphs on the thermomechanical properties of 0, 5, 10, and 20 wt % silica particles-reinforced-based poly(ethylene glycol) (PEG) composites have been studied as a function of temperature using dynamic mechanical analysis (DMA). The silica polymorphs exhibited quartz (Q), cristobalite (C), and amorphous (A) phases, which were obtained by processing natural silica sand. The DMA thermomechanical properties were determined in tensile (E) and shear (G) modes. The maximum storage moduli (E 0 and G 0 ) were achieved by samples with 20 wt % silica for all type of fillers. These values increased approximately 12 times for PEG/Q, 10 times for PEG/A, and 11 times for PEG/C composites compared to the pure PEG. Furthermore, the Poisson's ratio values of the composites were filler phase dependent, that is, 0.39-0.47 for PEG/Q, 0.15-0.18 for PEG/A, and somewhat anomalous for PEG/C composites.

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I_{dispersibility} = \frac{S_{intensity}}{{\overset{x}{true}}_{intensity}}

…”

Section: Resultsmentioning

confidence: 99%

Dynamic tensile and shear storage moduli of PEG/silica‐polymorph composites

Fauziyah

Hilmi

Fadly

et al. 2018

J of Applied Polymer Sci

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“…Next is the availability of large, well-organized, and publicly accessible image datasets, such as MNIST 29 , ImageNet 30 , and COCO 31 . Although there are a few reports applying deep learning on visual scoring automatically 32 – 34 , still none of the open-source software tools for comet assay analysis deployed DL methods to detect and segment comets so far. We believe that one of the primary reasons is the lack of suitable datasets to train a deep neural network.…”

Section: Introductionmentioning

confidence: 99%

Deep learning method for comet segmentation and comet assay image analysis

Hong¹,

Han

Lee

et al. 2020

Sci Rep

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Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.

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“…A plot that shows a more regular intensity with small fluctuations corresponds to higher homogeneity. Furthermore, to determine the quantitative distribution of Zr, further analysis was conducted using the dispersibility index equation 19,20 :

I_{d} = \frac{S_{italicintensity}}{{\overset{x}{true}}_{italicintensity}}

where S intensity is the standard deviation and

{\overset{false¯}{x}}_{intensity}

is the average intensity. A lower I d corresponds to a higher homogeneity of the element Zr.…”

Section: Resultsmentioning

confidence: 99%

Zirconia‐dispersedpolyethylene glycol composites with a lowtemperature‐degradationrate

Hilmi

Muwwaqor

Fauziyah

et al. 2020

J of Applied Polymer Sci

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The effect of tetragonal‐zirconia nanoparticle inclusion on the temperature‐dependent storage modulus, or the temperature‐degradation rate, of polyethylene glycol (PEG)‐based composites was investigated using shear‐mode dynamic mechanical analysis (DMA) at temperatures ranging from room temperature up to 75°C. We carried out further investigations in the rubbery area in terms of the characteristic of the degradation rate with temperature and compared it with silica‐quartz‐filled PEG composites, which exhibited a potential as phase‐change materials (PCMs). The investigation began by plotting the shear storage modulus (G′) of the composites and observing the slopes of the curves in the range of the rubbery area, which were assumed to follow a straight‐line equation. Then, we completed the investigation by developing and introducing a temperature‐dependent storage modulus model in the rubbery area for describing the storage modulus of such filler‐dispersed PEG/inorganic composites, to yield the degradation rates of the composites. The new model includes parameters k1 and C that are associated with the degradation rate and the amount of a filler, respectively. The model shows a satisfactory agreement with the experimental data of G′ in the rubbery area, being parameter k1 is associated with a linear degradation rate.

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A novel method to identify and grade DNA damage on comet images

Cited by 16 publications

References 10 publications

Dynamic tensile and shear storage moduli of PEG/silica‐polymorph composites

Dynamic tensile and shear storage moduli of PEG/silica‐polymorph composites

Deep learning method for comet segmentation and comet assay image analysis

Zirconia‐dispersedpolyethylene glycol composites with a lowtemperature‐degradationrate

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