Color Distribution Research Articles

Col-OSSOS: Evidence for a Compositional Gradient Inherited from the Protoplanetary Disk?

In the present-day Kuiper Belt, the number of compositional classes and the orbital distributions of these classes hold important cosmogonic implications for the solar system. The Colours of the Outer Solar System Origins Survey (Col-OSSOS) recently showed that the observed color distribution of small (H ⪆ 6) trans-Neptunian objects (TNOs) can be accounted for by the existence of only two composition classes, named BrightIR and FaintIR, where the range of colors in each class can be modeled as mixtures of two material end-members. Here, we combine the high-precision photometric measurements of Col-OSSOS with those of previous surveys to investigate the orbital distribution of the two color classes and reinterpret the known color–inclination correlation of TNOs in the context of the BrightIR/FaintIR taxonomy. In addition to confirming the previously reported distinct orbital distributions of these classes, we identify a trend of increasing orbital inclinations toward the bluer end of the optical and near-infrared color distribution of BrightIR objects. Using the output of numerical simulations investigating the orbital evolution of TNOs during their scattering phase with Neptune, we show that this trend could reflect a composition gradient in the early protoplanetary disk, in the range of heliocentric distances over which TNOs from the BrightIR class accreted. However, tensions between this interpretation and the existence of blue contaminants among cold classical TNOs, and possible alternative origins for the detected correlation, currently bear uncertainty on our proposed interpretation.

The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching

We use subhalo abundance and age distribution matching to create magnitude-limited mock galaxy catalogs at z ∼ 0.43, 0.52, and 0.63 with z-band and 3.4 μm W1-band absolute magnitudes and r − z and r − W1 colors. From these magnitude-limited mocks, we select mock luminous red galaxy (LRG) samples according to the (r − z)-based (optical) and (r − W1)-based (infrared) selection criteria for the LRG sample of the Dark Energy Spectroscopic Instrument (DESI) survey. Our models reproduce the number densities, luminosity functions, color distributions, and projected clustering of the DESI Legacy Surveys that are the basis for DESI LRG target selection. We predict the halo occupation statistics of both optical and IR DESI LRGs at fixed cosmology and assess the differences between the two LRG samples. We find that IR-based SHAM modeling represents the differences between the optical and IR LRG populations better than using the z band and that age distribution matching overpredicts the clustering of LRGs, implying that galaxy color is uncorrelated with halo age in the LRG regime. Both the optical and IR DESI LRG target selections exclude some of the most luminous galaxies that would appear to be LRGs based on their position on the red sequence in optical color–magnitude space. Both selections also yield populations with a nontrivial LRG–halo connection that does not reach unity for the most massive halos. We find that the IR selection achieves greater completeness (≳90%) than the optical selection across all redshift bins studied.

First light and reionisation epoch simulations (FLARES) – VIII. The emergence of passive galaxies at z ≥ 5

ABSTRACT Passive galaxies are ubiquitous in the local universe, and various physical channels have been proposed that lead to this passivity. To date, robust passive galaxy candidates have been detected up to z ≤ 5, but it is still unknown if they exist at higher redshifts, what their relative abundances are, and what causes them to stop forming stars. We present predictions from the first light and reionisation epoch simulations (flares), a series of zoom simulations of a range of overdensities using the eagle code. Passive galaxies occur naturally in the eagle model at high redshift, and are in good agreement with number density estimates from Hubble Space Telescope (HST) and early JWST results at 3 ≤ z ≤ 5. Due to the unique flares approach, we extend these predictions to higher redshifts, finding passive galaxy populations up to z ∼ 8. Feedback from supermassive black holes is the main driver of passivity, leading to reduced gas fractions and star forming gas reservoirs. We find that passive galaxies at z ≥ 5 are not identified in the typical UVJ selection space due to their still relatively young stellar populations, and present new rest-frame selection regions. We also produce mock NIRCam and MIRI fluxes, and find that significant numbers of passive galaxies at z ≥ 5 should be detectable in upcoming wide surveys with JWST. Finally, we present JWST colour distributions, with new selection regions in the observer–frame for identifying these early passive populations.

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

Peperomia mangalbaria (Piperaceae), a new species from Sikkim Himalaya, India

Piperaceae is one of the most diverse plant families. Peperomia, the second largest genus of this family, has about 10 species in India which are distributed from the Northern Himalayas to the Southern Peninsula. Here we describe a new species of Peperomia from Sikkim Himalaya in northeastern India. A color plate, line drawing, distribution map, description, and taxonomic key to current species of Peperomia in Sikkim are provided.

The Experimental Development of Emulsions Enriched and Stabilized by Recovering Matter from Spirulina Biomass: Valorization of Residue into a Sustainable Protein Source.

Spirulina consists of a cluster of green-colored cyanobacteria; it is commonly consumed as a food or food supplement rich in bioactive compounds with antioxidant activity, predominantly C-phycocyanin (C-PC), which is related to anti-inflammatory action and anticancer potential when consumed frequently. After C-PC extraction, the Spirulina residual biomass (RB) is rich in proteins and fatty acids with the potential for developing food products, which is interesting from the circular economy perspective. The present work aimed to develop a vegan oil-in-water emulsion containing different contents of Spirulina RB, obtaining a product aligned with current food trends. Emulsions with 3.0% (w/w) of proteins were prepared with different chickpea and Spirulina RB ratios. Emulsifying properties were evaluated regarding texture and rheological properties, color, antioxidant activity, and droplet size distribution. The results showed that it was possible to formulate stable protein-rich emulsions using recovering matter rich in protein from Spirulina as an innovative food ingredient. All the concentrations used of the RB promoted the formulation of emulsions presenting interesting rheological parameters compared with a more traditional protein source such as chickpea. The emulsions were also a source of antioxidant compounds and maintained the color for at least 30 days after production.

Formation of asymmetric body color in the caudal fin of common carp (Cyprinus carpio)

Color pattern is one of the most important characteristics of species, which plays essential roles in reproduction, adaptability, and even survival. The adult caudal fin of common carp (Cyprinus carpio) has an asymmetrical distribution of body color, with the dorsal lobe (DL) being dark-gray and the ventral lobe (VL) being red. This study first investigates the cytological basis of forming such an asymmetric distribution by observing the component of pigment cells and exploring the early development of the body color, combined with adult caudal fin regeneration experiments in the common carp. In terms of the distribution feature and the number of pigment cells, differences between the DL and the VL are demonstrated, especially with respect to their erythrophores. Abundant lipid droplets and carotenoid droplets are observed in the VL, conforming to the distribution of erythrophores. Analysis of the gene expression profiles indicates that there are highly expressed levels of carotenoid deposition and lipid droplets transport genes, such as scarb and plin6. In contrast, the gene expression levels of the β-carotene dioxygenase (bco) family are significantly down-regulated in the VL. In addition, knocking down of the bco2a gene expression level causes a significant increment of erythrophores. It is concluded that the directed accumulation of lipid droplets and carotenoid particles is the reason for the asymmetric distribution of red color in the caudal fin of common carp.

Star cluster classification using deep transfer learning with PHANGS-HST

ABSTRACT Currently available star cluster catalogues from the Hubble Space Telescope (HST) imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models that accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST ultraviolet (UV)–optical imaging of over 20 000 sources in 23 galaxies from the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: (i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12, 14–18, and 18–24 Mpc), and (ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80 per cent) and shows improvement by a factor of 2 in classifying asymmetric and multipeaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and spectral energy distribution (SED)-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ∼ 200 000) for public release.

Lip color measurement: A new hyperspectral imaging device.

The lips are of significant interest among the facial features for self-perception and social interactions. Such perceptions could rely in part upon the contrast in color between the lips and the surrounding skin. In contrast to skin color, lip color ethnic diversity measurement provides a challenge and is less documented in the literature, most likely due primarily to challenges in measurement based upon the lip characteristics of size and contour. Accurate measurement and reproduction of lip color are essential to build strong cross-sectional knowledge about lip color. This preliminary experiment of a large-scale project on lip and skin color was designed to assess accuracy in lip color measurement and recommend instruments and protocols for good measurement practice. The study involved 19 healthy French Caucasian women. Their inferior and superior natural lip color was measured twice with three devices in order to extract the standard colorimetric parameters of lightness (L*), chroma (C*), and hue (h): the VISIA-CR® (high-resolution digital camera, Canfield Scientific Inc.); the spectrophotometer VS3200 (45°:0° spectrophotometer, MetaVue™ VS3200, X-Rite Inc.); and the SpectraFace® (new hyperspectral imaging system, Newtone Technologies). The intercomparison and color distribution between the different instruments and the measurement repeatability of each one were assessed. Our results showed a reliable and equivalent repeatability for the three tested instruments. Moreover, the three tools led to the same conclusion regarding lip color heterogeneity (lighter, redder, less yellow and more saturated inferior lip versus superior lip). Agreement between the SpectraFace® and the spectrophotometer measurements was higher, and the SpectraFace® allowed to identify three separated lip color typologies. In conclusion, the combination of imaging technology and hyperspectral measurement makes the SpectraFace® a useful tool to investigate lip color characterization and establish a large-scale cross-sectional knowledge study about lip color.

Photometric Survey of Neptune's Trojan Asteroids. I. The Color Distribution

In 2018, Jewitt identified the “The Trojan Color Conundrum,” namely that Neptune's Trojan asteroids (NTs) had no ultrared members, unlike the the nearby Kuiper Belt. Since then, numerous ultrared NTs have been discovered, seemingly resolving this conundrum. However, it is still unclear whether or not the Kuiper Belt has a color distribution consistent with the NT population, as would be expected if it were the source population. In this work, we present a new photometric survey of 15 out of 31 NTs. We utilized the Sloan filters on the IMACS f/4 instrument, which is mounted on the 6.5 m Baade telescope. In this survey, we identify four NTs as being ultrared using a principal component analysis. This result brings the ratio of red to ultrared NTs to 7.75:1, more consistent with the corresponding trans-Neptunian object ratio of 4–11:1. We also identify three targets as being blue (nearly solar) in color. Such objects may be C-type surfaces, but we see more of these blue NTs than has been observed in the Kuiper Belt. Finally, we show that there are hints of a color-absolute magnitude (H) correlation, with larger H (smaller sized, lower albedo) tending to be more red, but more data are needed to confirm this result. The origin of such a correlation remains an open question that will be addressed by future observations of the surface composition of these targets and their rotational properties.

PEARLS: Low Stellar Density Galaxies in the El Gordo Cluster Observed with JWST

A full understanding of how unusually large ultradiffuse galaxies (UDGs) fit into our conventional theory of galaxy formation remains elusive, despite the large number of objects identified locally. A natural extension of UDG research is the study of similar galaxies at higher redshift to establish how their properties may evolve over time. However, this has been a challenging task given how severely systematic effects and cosmological surface brightness dimming inhibit our ability to analyze low surface brightness galaxies at high z. Here, we present a sample of low stellar surface density galaxies (LDGs) at moderate redshift, likely the progenitors of local UDGs, identified using deep near-IR observations of the El Gordo cluster at z = 0.87 with JWST. By stacking eight NIRCAM filters, we reach an apparent surface brightness sensitivity of 24.59 mag arcsec−2, which is faint enough to be complete to the bright end of the LDG population. Our analysis identifies significant differences between this population and UDGs observed locally, such as their color and size distributions, which suggest that the UDG progenitors at high z are bluer and more extended than UDGs at z = 0. This suggests that multiple mechanisms are responsible for the UDG formation and that prolonged transformation of cluster dwarfs is not a primary UDG formation mechanism at high z. Furthermore, we find a slight overabundance of LDGs in El Gordo, and, in contrast to findings in local clusters, our analysis does not show a deficit of LDGs in the center of El Gordo, implying that tidal destruction of LDGs is significant between z = 0.87 and z = 0.

How does color distribution learning affect oculomotor selection?

How does color distribution learning affect oculomotor selection?

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color. With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques.

Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing.

The present study has investigated the effect of the removal of artifacts in cone beam computed tomography (CBCT) images with image processing techniques to dental implant planning. The aim of this study has been to benefit from the novel image processing techniques and additive manufacturing technologies in order to change the existing approach in the usage of the 3D model in the orthogonal surgery, traumatic cases, and tumor operations and to solve the restrictions in surgical operations. In the study, firstly, 3 × 3, 5 × 5, and 7 × 7 kernel values were determined on the CBCT image data of the patient. The determined kernel values were applied on CBCT images by choosing median, median-mean-Gaussian (MMG), and bilateral filters, which are quite successful in removing noise in medical images. A thresholding process to separate teeth and bones from soft tissue regions on CBCT images, histogram normalization for a balanced color distribution, morphology operations to reduce noise areas, and tooth and bone boundaries were determined as closely as possible to patient anatomy. The original image and the images obtained from image enhancement techniques were compared. Results showed that the 3 × 3 median filtering method from three different kernel values out of three different image processing methods used in the study greatly improved the artifacts. It has also been shown that the availability of image processing and additive manufacturing methods on CBCT images has been shown to be a highly important factor before dental surgery planning.

A new species of Atriplex (Amaranthaceae) from the Indian subcontinent.

A new subshrubby C4-species from the lowlands and foothills of India, Pakistan and SE Afghanistan, Atriplexpseudotatarica, is described and illustrated. Previously, it was incorrectly identified as A.crassifolia auct. non C.A.Mey. belonging to a distant C3-group of the genus. A phylogenetic analysis based on nrITS and nrETS revealed its position as sister to A.schugnanica (sect. Obionopsis). Both species share aphyllous inflorescence and smooth bract-like cover, but differ in life form, leaves, seed colour, and geographical distribution. We revised native Indian Atriplex species and excluded some of them from the flora of the country. An improved checklist of the native Atriplex species in India with their corrected synonymy and nomenclature is given, and a new diagnostic key is provided.

Automated Tongue Diagnosis: A Deep Autoencoder Neural Network and Clustering-Based Image Segmentation Approach

Automated tongue diagnosis has gained significant attention in recent years as a non-invasive and cost-effective method for disease detection and monitoring. In this project, propose a novel approach for automated tongue diagnosis using a combination of Deep Autoencoder Neural Network (DAENN) and clustering-based image segmentation techniques. The objective is to develop a reliable and efficient system that can accurately analyze tongue images and provide diagnostic information. The proposed methodology involves several key steps. First, high-resolution images of the tongue are acquired using a digital camera. Preprocessing techniques are then applied to enhance image quality and remove noise. Next, a clustering-based image segmentation algorithm is employed to extract the tongue region from the background. This step ensures that subsequent analysis is focused on the relevant area of interest. After tongue region extraction, relevant features are extracted from the segmented image. These features capture important visual characteristics such as color distribution, texture, and shape. The DAENN is then trained using these features to learn an efficient representation of the input data. The network's encoder-decoder architecture enables it to compress the features into a lower-dimensional latent space and reconstruct the original features. By leveraging advanced machine learning techniques, the system can accurately analyze tongue images and assist healthcare professionals in making informed decisions. The evaluation of the proposed system on a diverse dataset demonstrates promising results, showcasing its potential as an effective tool for automated tongue diagnosis. Keywords—deep autoencoder, Tongue segmentation, Pattern recognition, colour intensity

Occurrence and dynamics of microplastics and emerging concern microparticles in coastal sediments: Impact of stormwater upgrade and port-associated facilities

Urban runoff is a significant source of microplastic pollution in aquatic environments, especially in coastal areas. Despite urban stormwater runoff being considered a major pathway of anthropogenic particles there's no studies about the impact of stormwater upgrades on microparticle transport. Moreover, due to the influence of anthropogenic activities, including maritime traffic and maintenance, on coastal environments, it is crucial to identify plastic debris from both inland and in-shore sources. This study evaluates characteristics, abundance, and distribution of microplastics in subtidal sediments from the southwestern Atlantic region, influenced by a recently upgraded stormwater outfall and port facilities. Herein, we have analyzed temporal trends, including seasonal dynamics and their relation with the pre- and post-upgrade of the stormwater outfall. Three main types of anthropogenic microparticles were observed: common plastic (MPs), paints (Pps), and tire wear particles (TWPs). Microparticle groups varied in morphology, color, and size distribution, including uncommon microparticle debris. Analysis by FTIR and Raman spectroscopies allows the identification of polyethylene, polypropylene, polyethylene terephthalate, polyvinyl chloride, polystyrene, polyamide, and polyacrylonitrile polymers for MPs mainly. Pigments such as Naphthol AS, phthalocyanine, and quinacridone have been identified in Pps. SEM-EDS and FTIR analysis of collected TWPs revealed similar trace metals constituents and infrared signals to those observed in tire road samples. Spatial and temporal abundances of microparticle groups were significantly different (p < 0.05), mainly related to the distance from the stormwater outfall and seasons. TWPs showed the most significant seasonal increment rate among pre and post-upgrade periods. Furthermore, the upgrades made to the stormwater system have been observed to intensify the transport and increase the presence of specific anthropogenic microparticles in subtidal sediments. Based on these findings, the occurrence of TWPs emerges as a reliable urban runoff indicator to differentiate ex and in-situ sources in multipolluted coastal environments.

A new species of the genus Paraphyllina (Scyphozoa: Coronatae): Paraphyllina kubanci sp. nov. from the Sea of Marmara, Turkey.

On August 9, 2008, during the MAREM (Marmara Environmental Monitoring) project survey, a single specimen of an unknown Scyphozoa was caught with a deep-water vertical plankton haul, below the thermo-halocline interface, at maximum 1042 m hauling depth, in the Sea of Marmara (40°44'00″N, 08°00'03″E), Turkey. In the following years, 22 specimens, which ranged in diameter from 18 to 26 mm and had the same characteristics, were caught in the same water mass of the Sea of Marmara during MAREM monitoring surveys. The specimens seem to correspond most closely with the medusae described by Maas (1903), Neppi (1915), and Russell (1956). All these specimens were similar in their essential characteristics, but differed in some details. Likewise, Paraphyllina kubanci sp. nov. differs from other species of the genus in terms of some characteristics, especially in the S-shaped gonads and in the distribution of coloration. It is identified as a new species of the genus Paraphyllina and described here.

Continuous Filament Network of the Local Universe

Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ridge filter used within the framework of image analysis. We considered galaxies from the HyperLeda database with redshifts 0.02 ≤ z ≤ 0.1, and in the solid angle 120° ≤ R.A. ≤ 240°, 0° ≤ decl. ≤ 60°. We divided the sample into 16 two-dimensional celestial projections with redshift bin Δz = 0.005, and compared our continuous filament network with a similar recent catalog covering the same region of the sky. We tested our catalog on two application scenarios. First, we compared the distributions of the distances to the nearest filament of various astrophysical sources (Seyfert galaxies and other active galactic nuclei, radio galaxies, low-surface-brightness galaxies, and dwarf galaxies), and found that all source types trace the filaments well, with no systematic differences. Next, among the HyperLeda galaxies, we investigated the dependence of the g − r color distribution on the distance to the nearest filament, and confirmed that early-type galaxies are located on average further from the filaments than late-type ones.

The profiles of bars in spiral galaxies

ABSTRACT We present an analysis of major-axis surface-brightness profiles of bars in a volume-limited sample of 182 barred spiral galaxies, using Spitzer 3.6 $\mu$m images. Unlike most previous studies, we use the entire bar profile, and we classify profiles into four categories. These are ‘Peak+Shoulders’ (P+Sh) – updating the classic ‘flat bar’ profile – and three subtypes of the classic ‘exponential’ profile: (true) Exponential, ‘Two-Slope’ (shallow inner slope + steeper outer slope), and ‘Flat-Top’ (constant inner region, steep outer slope). P+Sh profiles are preferentially found in galaxies with high stellar masses, early Hubble types, red colours, and low gas fractions; the most significant factor is stellar mass, and previous correlations with Hubble type can be explained by the tendency of higher-mass galaxies to have earlier Hubble types. The most common type of non-P+Sh profile is Exponential, followed by Flat-Top profiles; all non-P+Sh profiles appear to have similar distributions of stellar mass, Hubble type, colour, and gas fraction. We also morphologically classify the bars of an inclined subsample into those with and without boxy/peanut-shaped (B/P) bulges; as previously reported, the presence of a B/P bulge is very strong function of stellar mass. Essentially all bars with B/P bulges have P+Sh profiles; we associate the profile shoulders with the outer, vertically thin part of the bar. We find a small number of P+Sh profiles in bars without clear B/P bulges, which may indicate that P+Sh formation precedes the formation of B/P bulges.