The new specimen is housed at the Museum of Palaeontology of the ‘Sapienza’ University of Rome (MPUR, Museo Paleontologico dell'Università di Roma), Lazio, Italy. Natural light photos were taken using a Canon EOS 1000D digital single-lens reflex camera. A Nikon D3100 digital single-lens reflex camera was used for UV light photography. A Nikon Coolpix S3600 compact digital camera was used for dissecting scope photomicrography. Line drawings were made by hand using photographs of the material at both natural and ultraviolet light, and by direct observation of the specimen. Digitizing and figure construction were accomplished using Adobe® Photoshop® (outlines and colouring) and Adobe® Illustrator® (labelling and final production), both version CC 17 (2013 release).
Eos 1000d
Manufactured by Canon
Sourced in Japan
The Canon EOS 1000D is a digital single-lens reflex (DSLR) camera. It features a 10.1-megapixel CMOS sensor, DIGIC III image processor, and a 2.5-inch LCD display. The camera supports JPEG and RAW image formats, and can record video in Motion JPEG format.
Automatically generated - may contain errors
Lab products found in correlation
A plan, by Canon (5 mentions)
Axiovision software, by Zeiss (5 mentions)
Eclipse e800, by Nikon (5 mentions)
Sigma 50 mm f2.8 dg macro, by Merck Group (2 mentions)
D3100, by Nikon (1 mentions)
Pmod software, by PMOD Technologies (1 mentions)
Methylene blue, by Merck Group (1 mentions)
A1 microscope, by Zeiss (1 mentions)
Minolta, by Konica Minolta (1 mentions)
Cr 410, by Konica Minolta (1 mentions)
Eos utility software, by Canon (1 mentions)
Tissue tek oct compound, by Sakura Finetek (1 mentions)
Infinite m200, by Tecan (1 mentions)
Axiovert 25, by Zeiss (1 mentions)
Axiocam 105, by Zeiss (1 mentions)
Efficient navigation zen , by Zeiss (1 mentions)
Axiovision, by Zeiss (1 mentions)
Matlab, by MathWorks (1 mentions)
Axiostar microscope, by Zeiss (1 mentions)
Envision flex hrp, by Agilent Technologies (1 mentions)
36 protocols using eos 1000d
1
Palaeontological Specimen Imaging Protocol
2
Quantifying Alveolar Bone Loss via μCT
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To quantify alveolar bone loss, we used microcomputed tomography (MILabs, The Netherlands) on live animals. The measurement was carried out with the following settings: voltage, 50 kV; tube current, 0.21 mA; exposure time, 75 ms; and step angle, 0.1°. Briefly, the linear measurements of the distances between cementoenamel junction (CEJ) and alveolar bone crest (ABC) were taken at two sites of second molars in the right and left dental arches (50 (link)). Data were quantified using PMOD software (PMOD Technologies Ltd., Switzerland). Additionally, bone loss was visualized by methylene blue and eosin staining after euthanasia, as described previously (51 (link)). Soft tissue was removed from bones, and the jaws were immersed overnight in 3% hydrogen peroxide. The next day, jaws were incubated in 1% bleach and then washed and air dried. Alveolar bone and teeth were stained with 0.5% eosin for 5 min and 1% methylene blue (Merck Millipore) for 1 min, followed by several washes in distilled water. Bone loss was visualized with a dissecting microscope (SZX-9; Olympus) connected to a digital camera (EOS 1000D; Canon).
3
Microscopic Imaging of Biological Samples
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Images of H&E and IHC were acquired on an Axio Lab. A1 microscope using 10x and 40x Zeiss A-Plan objectives with a Canon EOS 1000D camera and using Axiovision software (Carl Zeiss). Images of immunofluorescence staining were acquired on a Nikon ECLIPSE E800 epi-fluorescence microscope using 20x and 40x Nikon Plan Fluor objectives with an QImaging RETIGA EXi camera and using QCapture software (QImaging).
4
Color Analysis of GC Samples
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The color of GCs was indicated by image analysis, referring to color factors; L* (lightness/brightness), a* (redness/greenness), b* (yellowness/blueness). The samples were put in a box (50 × 50 × 100 cm) which was prepared with eight fluorescent lamps with a radiation angle of 45° to each sample. The samples were photographed using a digital camera (Canon, EOS 1000D) with a distance of 20 cm and then analyzed by ImageJ (1.52v, NIH, Bethesda, MD, USA) software.
5
Evaluating Fish Sauce Color Characteristics
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The color characteristics of fish sauce were evaluated using L*, a*, and b* components. For imaging, fish sauce samples were located in a dark room at four 60 cm long, 10‐W fluorescent lamps. The lamps were fixed at a 45° angle and a distance of 45 cm from the sample. The distance between the camera and the sample was considered 25 cm. The pictures were taken with a digital camera (EOS 1000D, Canon, Tokyo, Japan). All of the images were taken using a 55‐mm zoom at a shutter speed of 1/3 of a second. Finally, they were saved in JPEG format. The images were then transformed into L*, a*, and b* space using ImageJ software version 1.48 v (Ghaitaranpour et al., 2018 (link)).
6
Microscopic Imaging and Statistical Analysis
7
Image Analysis of Rice Bran
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Image acquisition and processing of processed and untreated rice bran was performed based on the published literature (Abdollahi Moghaddam, Rafe, & Taghizadeh, 2015 ). Briefly, the images were captured by a color digital camera (Canon EOS 1000D, Taiwan) with resolution (2272 × 1704 pixels) in a wooden black box, and they were saved on a computer with software (Canon Utilities Zoom Browser EX version 6.1.1) in JPEG format. The image processing was accomplished by the Image J software (National Institutes Health, Bethesda, MD) after improving the image quality. Then, RGB images were converted into L*a*b* units in which L* is lightness (0 (black) to 100 (white)), a* is varied from red (+60) to green (−60) index, and b* is ranging from yellow (+60) to blue (−60). The total color change (ΔE) was also determined by the following equation:
where, subscribes 1 and 2 are before and after processing with extrusion cooking, respectively.
where, subscribes 1 and 2 are before and after processing with extrusion cooking, respectively.
8
Evaluating Bread Appearance Using Digital Imaging
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For color evaluation and image analysis of the bread, the images were captured using a digital camera (Canon EOS 1000D, Japan) at a lens aperture of 5.6, ISO of 100, and a shutter speed of 1/80 s to achieve high uniformity and reproducibility. Then, they were saved as the JPG format. The digital images were taken under five fluorescent lights (Opple, 8 W, model: MX396‐Y82; 60 cm in length). Image analysis was conducted using the ImageJ software (version 1.46r, National Institutes of Health, the U.S.) on a 30 × 50 mm2 area of the center of the bread loaf on the third day after production.
9
Color Analysis of Powder Samples
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A colorimeter (Konica Minolta, CR‐410, Japan) was used to determine the color of the powder samples. On the basis of our previous studies, we captured images and processed samples (Hesarinejad, Lorenzo, et al., 2021 ; Rezagholi & Hesarinejad, 2017 (link)). The photographs were taken in a wooden black box using a color digital camera (Canon EOS 1000D, Taiwan) with a resolution of 2272 × 1704 pixels. Image J (National Institutes Health) was used to process the photographs. The RGB images were converted into L*a*b* units, where L*, a*, and b* correspond to lightness (from black to white (0 to 100)), red‐green index (from +60 to −60), and yellow‐blue index (from +60 to −60), respectively.
10
High-Resolution Imaging and Analysis of Plant Shoots
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All the images were captured using a SLR camera (Canon EOS 1000D) with 53 mm focal length and 1/15 s exposure time. Two 60 W incandescent light bulbs were used to illuminate the samples from each side. The distance between the lens and the samples was 35 cm. Due to the high resolution of the imagery device (3888 × 2592 pixels), three shoots were placed on a spectralon white platform (SphereOptics) and imaged together in order to enhance the contrast between the foreground and background. The images were captured using EOS utility software (Canon) and saved as JPG files. Individual shoots were cropped from each image and due to small variations in the size of shoots, the resolution of the images varied from 43 × 754 to 282 × 839 pixels. All the images were saved and processed on a Dell desktop computer (Intel® Xeon(R) CPU X5560 @ 2.80 GHz × 16). The automated image analysis software was written in C++ [51 ] utilising the OpenCV Library [52 ] on an Ubuntu 14.04 operating system.
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