Ultraviolet vision in larval Neogonodactylus oerstedii

Stomatopod crustaceans have among the most complex eyes in the animal kingdom, with up to twelve different color detection channels. The capabilities of these unique eyes include photoreception of ultraviolet (UV) wavelengths (<400 nm). UV vision has been well characterized in adult stomatopods but has not been previously demonstrated in the comparatively simpler larval eye. Larval stomatopod eyes are developmentally distinct from their adult counterpart and have been described as lacking the visual pigment diversity and morphological specializations found in adult eyes. However, recent studies have provided evidence that larval stomatopod eyes are more complex than previously thought and warrant closer investigation. Using electroretinogram recordings in live animals we found physiological evidence of blue and UV sensitive photoreceptors in larvae of the Caribbean stomatopod species Neogonodactylus oerstedii. Transcriptomes of individual larvae were used to identify the expression of three distinct UV opsins transcripts, which may indicate the presence of multiple UV spectral channels. This is the first paper to document UV vision in any larval stomatopod, expanding our understanding of the importance of UV sensitivity in plankton. Similar to adults, larval stomatopod eyes are more complex than expected and contain previously uncharacterized molecular diversity and physiological functions.

Deficits in color detection in patients with Alzheimer disease

Deficits in color vision and related retinal changes hold promise as early screening biomarkers in patients with Alzheimer’s disease. This study aimed to determine a cut-off score that can screen for Alzheimer’s dementia using a novel color vision threshold test named the red, green, and blue (RGB) modified color vision plate test (RGB-vision plate). We developed the RGB-vision plate consisting of 30 plates in which the red and green hues of Ishihara Plate No.22 were sequentially adjusted. A total of 108 older people participated in the mini-mental state examination (MMSE), Ishihara plate, and RGB-vision plate. For the analyses, the participants were divided into two groups: Alzheimer’s dementia (n = 42) and healthy controls (n = 38). K-means cluster analysis and ROC curve analysis were performed to identify the most appropriate cut-off score. As a result, the cut-off screening score for Alzheimer’s dementia on the RGB-vision plate was set at 25, with an area under the curve of 0.773 (p<0.001). Moreover, there was a negative correlation between the RGB-vision plate thresholds and MMSE scores (r = -0.36, p = 0.02). In conclusion, patients with Alzheimer’s dementia had a deficit in color vision. The RGB-vision plate is a potential early biomarker that may adequately detect Alzheimer’s dementia.

Establishing in vitro and in vivo Co-culture Models of Staphylococcus epidermidis and Enterococcus faecalis to Evaluate the Effect of Topical Fluoroquinolone on Ocular Microbes

Purpose: To establish in vitro and in vivo ocular co-culture models of Staphylococcus epidermidis and Enterococcus faecalis and to study how various concentrations of moxifloxacin affect the survival of these two endophthalmitis-causing bacteria.Methods: Standard strains of S. epidermidis and E. faecalis were used. Color detection agar plates were employed to distinguish their colonies. To establish the in vitro and in vivo co-culture models, S. epidermidis and E. faecalis were co-cultivated at different ratios for various periods. For the in vivo model, various volumes and concentrations of either a mono-culture or co-culture were inoculated into the lower conjunctival sac of rabbits. Finally, the newly developed in vitro and in vivo co-culture models were subjected to the moxifloxacin treatment to access its effect on S. epidermidis and E. faecalis.Results: When S. epidermidis and E. faecalis were cultured separately in tryptic soy broth, their growth peaked and plateaued at approximately 16 and 6 h, respectively. When they were co-cultured, the growth peak of S. epidermidis got delayed, whereas the growth peak of E. faecalis did not change. The number of E. faecalis was significantly higher in the co-culture than that in the mono-culture. Treatment with moxifloxacin in the in vitro co-culture model rapidly decreased the number of S. epidermidis cells at doses ≥ 0.125 μg/ml. In contrast, the number of E. faecalis did not change significantly up to 16 μg/ml moxifloxacin. In in vivo co-culture (at 1:1), the S. epidermidis count decreased in a pattern similar to that seen in in vivo mono-culture and was barely detectable at 24 h after inoculation. In contrast, the of E. faecalis count increased up to 16 h and then decreased. When moxifloxacin was applied (zero, one, or two times) to this model, the S. epidermidis count decreased in proportion to the number of treatments. In contrast, the E. faecalis count increased with moxifloxacin treatment.Conclusions: The in vitro and in vivo co-culture models of S. epidermidis and E. faecalis were established to determine the influence of moxifloxacin eye drops on these bacteria. The results clearly show that the moxifloxacin eye drops can make E. faecalis dominant on the ocular surface.

Color detection algorithm for early fire diagnostics

In the paper as a problem domain was chosen oil mining and its peculiarities related to early fire diagnostics. Main feature of the described method of early fire diagnostics is application of color detection algorithm together with video sequence acquired from survey cameras. Drawback of the known algorithms of fire diagnostics that also use video streams is selection of the only one color of visible spectrum. Proposed algorithm makes frames preprocessing with purpose of white noise and Gaussian noise suppression. Main feature is complex registration of color components of fire images that are specific to chosen problem domain. Described obtained results of practical application of proposed color detection approach. Experiments were carried out using test video sequences from Bilkent University and Dyntex database. It is shown that advantage of the proposed approach is an ability to select different color components and process them in complex during color detection.

Automated color detection in orchids using color labels and deep learning

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

Plasmonic Refractive Index Sensor Optimized for Color Detection

In this paper, a six cavity-based metal-insulator-metal plasmonic sensor is proposed. The designed sensor can detect six primary colors in the visible wavelength. Moreover, the proposed sensor can also sense the change in the refractive index. An initial sensitivity of 648.41 nm/RIU and figure of merit of (FOM) 141.29 are found based on the transmittance profile extracted through the two-dimensional (2D) finite element method (FEM). The structural parameters are optimized to maximize the performance of the modeled device both as a color filter and a refractive index sensor. The optimized FOM, FOM* and sensitivity are recorded as 218.80, 4.771 × 10⁴, and 865.31 nm/RIU, respectively. Due to high FOM and FOM*, this sensor is expected to be utilized as a color filter in various sectors, such as medical, industrial, and forensic, where the light of a particular wavelength is mandatory.

Color Detection of RGB Images Using Python and OPENCV

This application intent is to accomplish the explicit strategy to recognize the miscellaneous shades of colors precisely. According to study of sciences, a normal healthy human can identify and differentiate nearly one million shades of color. But it is impossible for an individual having “enchroma”. It is indispensable for a painter to recognize different color patterns precisely to make realistic images Keywords: Enchroma, RGB value, OpenCv, pandas