Color Information Research Articles

Discovery of an Apparent Red, High-velocity Type Ia Supernova at z = 2.9 with JWST

We present the James Webb Space Telescope (JWST) discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS+53.13485−27.82088 with a host spectroscopic redshift of 2.903 ± 0.007. The transient was identified in deep (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic follow-up with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (c ∼ 0.9) despite a host galaxy with low extinction and has a high Ca ii velocity (19,000 ± 2000 km s−1) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-z Ca-rich population. Although such an object is too red for any low-z cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (≲1σ) with ΛCDM. Therefore unlike low-z Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high z truly diverge from their low-z counterparts and to confirm that standardized luminosities nevertheless remain constant with redshift.

Non-local feature aggregation quaternion network for single image deraining

The existing deraining methods are based on convolutional neural networks (CNN) learning the mapping relationship between rainy and clean images. However, the real-valued CNN processes the color images as three independent channels separately, which fails to fully leverage color information. Additionally, sliding-window-based neural networks cannot effectively model the non-local characteristics of an image. In this work, we proposed a non-local feature aggregation quaternion network (NLAQNet), which is composed of two concurrent sub-networks: the Quaternion Local Detail Repair Network (QLDRNet) and the Multi-Level Feature Aggregation Network (MLFANet). Furthermore, in the subnetwork of QLDRNet, the Local Detail Repair Block (LDRB) is proposed to repair the backdrop of an image that has not been damaged by rain streaks. Finally, within the MLFANet subnetwork, we have introduced two specialized blocks, namely the Non-Local Feature Aggregation Block (NLAB) and the Feature Aggregation Block (Mix), specifically designed to address the restoration of rain-streak-damaged image backgrounds. Extensive experiments demonstrate that the proposed network delivers strong performance in both qualitative and quantitative evaluations on existing datasets. The code is available at https://github.com/xionggonghe/NLAQNet.

Taming Reversible Halftoning Via Predictive Luminance.

Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color image into a binary halftone with full restorability to its original version. Our novel base halftoning technique consists of two convolutional neural networks (CNNs) to produce the reversible halftone patterns, and a noise incentive block (NIB) to mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the conflicts between the blue-noise quality and restoration accuracy in our novel base method, we proposed a predictor-embedded approach to offload predictable information from the network, which in our case is the luminance information resembling from the halftone pattern. Such an approach allows the network to gain more flexibility to produce halftones with better blue-noise quality without compromising the restoration quality. Detailed studies on the multiple-stage training method and loss weightings have been conducted. We have compared our predictor-embedded method and our novel method regarding spectrum analysis on halftone, halftone accuracy, restoration accuracy, and the data embedding studies. Our entropy evaluation evidences our halftone contains less encoding information than our novel base method. The experiments show our predictor-embedded method gains more flexibility to improve the blue-noise quality of halftones and maintains a comparable restoration quality with a higher tolerance for disturbances.

BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-Based Video Colorization.

How to effectively explore the colors of exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this article, we present a BiSTNet to explore colors of exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the exemplars in deep feature space to explore color information from exemplars. Then, we develop a simple yet effective bidirectional temporal feature fusion module to propagate the colors of exemplars into each frame and avoid inaccurate alignment. We note that there usually exist color-bleeding artifacts around the boundaries of important objects in videos. To overcome this problem, we develop a mixed expert block to extract semantic information for modeling the object boundaries of frames so that the semantic image prior can better guide the colorization process. In addition, we develop a multi-scale refinement block to progressively colorize frames in a coarse-to-fine manner. Extensive experimental results demonstrate that the proposed BiSTNet performs favorably against state-of-the-art methods on the benchmark datasets and real-world scenes. Moreover, the BiSTNet obtains one champion in NTIRE 2023 video colorization challenge (Kang et al. 2023).

Deep Learning-Based Real-Time Organ Localization and Transit Time Estimation in Wireless Capsule Endoscopy.

Wireless capsule endoscopy (WCE) has significantly advanced the diagnosis of gastrointestinal (GI) diseases by allowing for the non-invasive visualization of the entire small intestine. However, machine learning-based methods for organ classification in WCE often rely on color information, leading to decreased performance when obstacles such as food debris are present. This study proposes a novel model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze multiple frames and incorporate temporal information, ensuring that it performs well even when visual information is limited. We collected data from 126 patients using PillCam™ SB3 (Medtronic, Minneapolis, MN, USA), which comprised 2,395,932 images. Our deep learning model was trained to identify organs (stomach, small intestine, and colon) using data from 44 training and 10 validation cases. We applied calibration using a Gaussian filter to enhance the accuracy of detecting organ boundaries. Additionally, we estimated the transit time of the capsule in the gastric and small intestine regions using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) designed to be aware of the sequence information of continuous videos. Finally, we evaluated the model's performance using WCE videos from 72 patients. Our model demonstrated high performance in organ classification, achieving an accuracy, sensitivity, and specificity of over 95% for each organ (stomach, small intestine, and colon), with an overall accuracy and F1-score of 97.1%. The Matthews Correlation Coefficient (MCC) and Geometric Mean (G-mean) were used to evaluate the model's performance on imbalanced datasets, achieving MCC values of 0.93 for the stomach, 0.91 for the small intestine, and 0.94 for the colon, and G-mean values of 0.96 for the stomach, 0.95 for the small intestine, and 0.97 for the colon. Regarding the estimation of gastric and small intestine transit times, the mean time differences between the model predictions and ground truth were 4.3 ± 9.7 min for the stomach and 24.7 ± 33.8 min for the small intestine. Notably, the model's predictions for gastric transit times were within 15 min of the ground truth for 95.8% of the test dataset (69 out of 72 cases). The proposed model shows overall superior performance compared to a model using only CNN. The combination of CNN and LSTM proves to be both accurate and clinically effective for organ classification and transit time estimation in WCE. Our model's ability to integrate temporal information allows it to maintain high performance even in challenging conditions where color information alone is insufficient. Including MCC and G-mean metrics further validates the robustness of our approach in handling imbalanced datasets. These findings suggest that the proposed method can significantly improve the diagnostic accuracy and efficiency of WCE, making it a valuable tool in clinical practice for diagnosing and managing GI diseases.

Explicit Incorporation of Spatial Autocorrelation in 3D Deep Learning for Geospatial Object Detection

Three-dimensional (3D) geospatial object detection has become essential for 3D geospatial studies driven by explosive growth in 3D data. It is extremely labor- and cost-intensive, though, as it often requires manual detection. Deep learning has been recently used to automate object detection within 3D context. Yet, addressing spatial dependency in 3D data and how it might inform deep learning for 3D geospatial object detection remains a significant challenge. Traditional models focus on the use of spatial properties, often overlooking color and contextual information. Exploiting these nonspatial attributes for 3D geospatial object detection thus becomes crucial. Our study pioneers explicit incorporation of spatial autocorrelation of color information into 3D deep learning for object detection. We introduce an innovative framework to estimate spatial autocorrelation, addressing challenges in unstructured 3D data sets. Our experiments suggest the effectiveness of incorporating spatial autocorrelation features in enhancing the accuracy of 3D deep learning models for geospatial object detection. We further investigate the uncertainty of such contextual information brought by diverse configurations, exemplified by the number of nearest neighbors. This study advances 3D geospatial object detection via using spatial autocorrelation to inform deep learning algorithms, strengthening the connection between GIScience and artificial intelligence and, thus, holding implications for diverse GeoAI applications.

Integrated metabolome and transcriptome analysis revealed color formation in purple leaf mustard (Brassica juncea)

Purple leaf mustard has garnered attention owing to its abundant anthocyanin content. This study investigated the phenolic metabolome and transcriptome of purple variety ZC-Red and green variety ZC-2. Each of the two varieties had three biological replicates, and each replicate consisted of ten leaves from five plants. Phenolic metabolome was analyzed using an ultra-high performance liquid chromatography system and tandem mass spectrometry, and the transcriptome sequencing platform was the Illumina NovaSeq 6000 platform. The green variety ZC-2 was used as the control for differential analysis. All differential metabolites and genes were significant. Findings revealed the accumulation of anthocyanins in ZC-Red, whereas ZC-2 exhibited higher levels of phenolic acids and flavonoids, excluding anthocyanins. This could be attributed to the directional preference of the phenylpropanoid metabolic flow. Transcriptome analysis highlighted the pivotal roles of phenylpropanoid metabolism and photosynthesis in color formation. In ZC-Red, nearly all anthocyanin biosynthesis genes exhibited up-regulation, with multiple genes in late biosynthesis showing increases thousands of times greater than ZC-2. Conversely, the expression of genes associated with photosystem subunits and light-harvesting complexes was down-regulated. Key candidate transcription factors, including MYB113, TT8, WRKY44, and MYB30, were identified as regulators of anthocyanin and photosynthesis. Findings contribute valuable insights into the molecular mechanisms underlying anthocyanin accumulation. Core differential metabolites such as anthocyanins and phenolic acids are of great significance to the utilization of diversified functional compounds and targeted breeding of leaf mustard.

A dynamic object removing 3D reconstruction system based on multi-sensor fusion

Abstract Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.

A double-layer film based on the strategy of tannic acid-anthocyanin co-pigmentation and tannic-crosslinked-gelatin/−reduced Ag nanoparticles for beef preservation and monitoring

A double-layer film was developed with tannic acid (TA) co-pigmented purple potato anthocyanin extract (PAE)-agar as the inner layer, and K-carrageenan-oregano essential oil Pickering emulsion (OPE)/silver nanoparticles (TA-AgNPs) as the outer layer. Molecular docking and FT-IR results elucidated that intermolecular hydrogen bond was the main interaction between components in the agar-carrageenan matrix, with TA and PAE contributing to intensified anthocyanin color through π-π stacking. The incorporation of OPE/TA-AgNPs enhanced the film's hydrophobicity (WCA > 100°) and UV–vis barrier (close to 0% at 200-320 nm, effectively impeding UVA, UVB, and UVC) properties and exhibited outstanding antioxidant (DPPH scavenging rate > 88%) and antimicrobial activities. This film showed a significant color change in the pH range of 2–12 (from pink to yellow) and a considerable sensitivity to volatile amines within 2 min. The films effectively alleviated beef spoilage (extending the shelf life of beef for 1d) and reflected the freshness of beef during storage. Additionally, the digital color information of the film was obtained by a smartphone combined with RGB values analysis to quantify the freshness of beef rapidly. Therefore, this study expands the application of food packaging films with freshness preservation and monitoring in the field of animal-derived food.

Towards Robust Colour Texture Analysis with Limited Training Data

Texture analysis plays an important role in different domains of healthcare, agriculture, and industry, where multi-channel sensors are gaining more attention. This contribution presents an interpretable and efficient framework for texture classification and segmentation that exploits colour or channel information and does not require much data to produce accurate results. This makes such a framework well-suited for medical applications and resource-limited hardware. Our approach builds upon a distance-based generalized matrix learning vector quantization (GMLVQ) algorithm. We extend it with parametrized angle-based dissimilarity and introduce a special matrix format for multi-channel images. Classification accuracy evaluation of various model designs was performed on VisTex and ALOT data, and the segmentation application was demonstrated on an agricultural data set. Our extension of parametrized angle dissimilarity measure leads to better model generalization and robustness against varying lighting conditions than its Euclidean counterpart. The proposed matrix format for multichannel images enhances classification accuracy while reducing the number of parameters. Regarding segmentation, our method shows promising results, provided with a small class-imbalanced training data set. Proposed methodology achieves higher accuracy than prior work benchmarks and a small-scale CNN while maintaining a significantly lower parameter count. Notably, it is interpretable and accurate in scenarios where limited and unbalanced training data are available.

Effect of label elements in bottled water: Impact on consumer preferences, purchase intentions and health perception through affective sensory tests

Bottled water has become a popular beverage choice worldwide, with consumers increasingly seeking healthier options. However, label elements can significantly influence consumer perception and purchasing decisions. The research aimed to assess how label elements affect the liking, purchase intention, preference and concept of healthy bottled water. Two stages involved 180 and 100 participants aged between 18 and 40, provided sociodemographic information. The first stage used a hedonic scale and ranking test to perception of nine labels with different elements. The second stage selected a consensus label from prior tests. Four labels were designed, differing in brand color and nutritional information placement. In this last stage, the acceptability, preference ranking and concept of healthy were re-evaluated and eye tracking via the Pupil Lab program. Findings showed varied responses in acceptability and purchase intention among consumers. However, significant differences were observed in preferences and healthiness perceptions based on label characteristics. The label with the highest preference and perceived healthiness featured a sky-blue design with nutritional information on the right side. Combining sensory testing and eye tracking offers valuable insights for designing labels that positively impact consumer perception. The results provide important implications for bottled water manufacturers and marketers in developing effective labeling strategies to meet consumer preferences and promote healthier choices.

Ethanol-responsive structural colors with multi-level information encryption based on the patterned three-layer inverse opal photonic crystal

Stimulus-responsive inverse opal photonic crystals (IOPCs) with tunable structural colors show significant promise in information security. To improve upon the traditional bilayer structure with limited color information and single decoding mode, this work developed an ethanol-responsive structure with multi-level information encryption ability by inserting a functional layer into two shielding layers (red Layer A with a photonic stop band (PSB) at 640 nm and green Layer C with a PSB at 530 nm). The functional layer was composed of colorless Layer B, a quick response (QR) code pattern made of TiO2 nanoparticles, and a dense polymer. Due to the isolation of distinct layers, different reflectance values, and different PSB positions of the three-layer IOPC, the structural color of Layer B could only be “turned on” by wetting the entire structure when its PSB redshifted from 360 nm to 460 nm. Specifically, when either side was individually wetted, the PSB of Layer A or C redshifted to 825 nm or 685 nm, and the color of the QR code was dominated by the unwetted red or green layer. After the entire structure had been soaked, the blue QR code was decoded. Meanwhile, when the detecting angle increased from 5° to 60°, the PSBs of Layers B and C in the wetted three-layer IOPC blueshifted from 460 nm to 365 nm and from 685 nm to 540 nm, respectively, which resulted in a cascade decoding process with a single- or mixed-color output. This structure provides a good foundation for multi-level information encryption.

Multi-omics analysis revealed the difference in pigment accumulation profiles between Syringa oblata and S. oblata var. alba

Syringa oblata (So) and its variety S. oblata var. alba (Soa) are famous flowering shrubs with high ornamental value due to their beautiful inflorescence. The flower colors of So (purple) and Soa (white) differ significantly. In this study, pigment contents were detected and flavonoid and anthocyanin metabolites were determined by UPLC-ESI-MS/MS methods. Combined with transcriptome analysis, the key genes related to anthocyanin accumulation were obtained to explore the reasons for the difference in flower colors. The results showed that the total anthocyanin content was obviously higher in So than in Soa. The differential metabolites annotated in anthocyanin biosynthesis were significantly up-regulated, and conversely, the differential metabolites in biosynthesis of secondary metabolites and flavone and flavonol biosynthesis were down-regulated in So compared by Soa, with similar results revealed by the analysis of the top 20 fold change metabolites. It is explained that the anthocyanins and chlorophylls were the key pigment for the difference in flower color between So and Soa. Furthermore, the quantitative analysis of anthocyanins showed that delphinidin-3-O-rutinoside and cyanidin-3-O-rutinoside were the major anthocyanins in So, and their contents were significantly higher than other anthocyanins. However, the levels of major anthocyanins in Soa were very low, resulting in its white flower. In addition, high levels of kaempferol-3-O-rutinoside, rutin, quercetin-3-O-glucoside and naringenin were detected in flowers. These metabolites could play the roles of co-pigments in So and major pigments in Soa, respectively. Cyanidin-3-O-rutinoside were markedly elevated from initial flower bud (IFB) to flower bud (FB), which contributed to the anthocyanin accumulation and the flower color change in So, while the reduction of chlorophyll content was the main reason for the change in flower color from green to white in Soa. The integrated analysis showed that the genes (F3GT, FLS and F3H) might play the key roles in flower color formation. The study revealed the reason for the flower color variation between So and Soa and the flower color change during the development. It will provide the metabolites fundamental for research on flower color breeding in Syringa.

Mutation of YFT3, an isomerase in the isoprenoid biosynthetic pathway, impairs its catalytic activity and carotenoid accumulation in tomato fruit.

Tomato fruit colors are directly associated with their appearance quality and nutritional value. However, tomato fruit color formation is an intricate biological process that remains elusive. In this work we characterized a tomato yellow fruited tomato 3 (yft3, e9292, Solanum lycopersicum) mutant with yellow fruits. By the map-based cloning approach, we identified a transversion mutation (A2117C) in the YFT3 gene encoding a putative isopentenyl diphosphate isomerase (SlIDI1) enzyme, which may function in the isoprenoid biosynthetic pathway by catalyzing conversion between isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP). The mutated YFT3 (A2117C) (designated YFT3 allele) and the YFT3 genes did not show expression difference at protein level, and their encoded YFT3 allelic (S126R) and YFT3 proteins were both localized in plastids. However, the transcript levels of eight genes (DXR, DXS, HDR, PSY1, CRTISO, CYCB, CYP97A, and NCED) associated with carotenoid synthesis were upregulated in fruits of both yft3 and YFT3 knockout (YFT3-KO) lines at 35 and 47days post-anthesis compared with the red-fruit tomato cultivar (M82). In vitro and in vivo biochemical analyses indicated that YFT3 (S126R) possessed much lower enzymatic activities than the YFT3 protein, indicating that the S126R mutation can impair YFT3 activity. Molecular docking analysis showed that the YFT3 allele has higher ability to recruit isopentenyl pyrophosphate (IPP), but abolishes attachment of the Mg2+ cofactor to IPP, suggesting that Ser126 is a critical residue for YTF3 biochemical and physiological functions. As a result, the yft3 mutant tomato line has low carotenoid accumulation and abnormal chromoplast development, which results in yellow ripe fruits. This study provides new insights into molecular mechanisms of tomato fruit color formation and development.

Phytochemical Analysis and Antioxidant Activity of Pandila Tree Leaves (Saurauia Tristlyla Dc) From Tibo Village

Introduction: The Pandila tree known as pandila (Saurauia tristlyla DC) is one of the plants that has been traditionally used as a medicinal material. Pandila has been traditionally used by the community as a medicine for external and internal diseases, for example, pandila leaves are believed by the Tibo Village community to absorb congealed blood due to accidents. This study aims to determine the secondary metabolite content and antioxidant activity of pandila leaves. Method: The research method used in this study is a descriptive method by looking at changes in color and liquid form in alkaloids, Flavonoid, Tannin, Saponin, Steroid, Terpenoid, and Carotenoid compounds. Phytochemical analysis was carried out on 96% ethanol extract of pandila leaves. The DPPH method was used to determine the antioxidant activity. The results of phytochemical analysis obtained on the leaves of Pandila tree leaves (Saurauia Tristlyla DC) using 96% ethanol solvent are old leaf extracts and young leaves of pandila positively contain 3 compounds namely Tannins, Steroids, and Carotenoids. Results and Discussion: The results of the antioxidant activity test of Pandila leaf extract using the DPPH method on old leaves have an IC50 of 101, 2844 ppm including in the moderate category, and on young leaves have an IC50 of 76.688 ppm which is included in the strong category. Conclusion: Pandila tree leaves are still safe to be used as traditional medicine, by looking at the tannin, steroid, and carotenoid compounds contained in pandila leaves. In addition, the results of antioxidant activity analysis are also a supporting factor where the antioxidant content in old leaves and young leaves of Pandila trees is classified as strong and moderate, so this plant is still safe to be used as a traditional medicinal plant.

Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution

In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.

Deciphering the microbial succession and color formation mechanism of "green-covering and red-heart" Guanyin Tuqu.

"Green-covering and red-heart" Guanyin Tuqu (GRTQ), as a type of special fermentation starter, is characterized by the "green-covering" formed on the surface of Guanyin Tuqu (SQ) and the "red-heart" in the center of Guanyin Tuqu (CQ). However, the mechanisms that promote temporal succession in the GRTQ microbial ecology and the formation of "green-covering and red-heart" characteristics remain unclear. Herein, we correlated the temporal profiles of microbial community succession with the main environmental variables (temperature, moisture, and acidity) and spatial position (center and surface) in GRTQ throughout fermentation. According to the results of high-throughput sequencing and culture-dependent methods, the microbial communities in the CQ and SQ demonstrated functional complementarity. For instance, the bacterial richness index of the CQ was greater than that of SQ, and the fungal richness index of the SQ was greater than that of CQ at the later stage of fermentation. Furthermore, Saccharomycopsis, Saccharomyces, Aspergillus, Monascus, Lactobacillus, Bacillus, Rhodanobacter, and Chitinophaga were identified as the dominant microorganisms in the center, while the surface was represented by Saccharomycopsis, Aspergillus, Monascus, Lactobacillus, Acetobacter, and Weissella. By revealing the physiological characteristics of core microorganisms at different spatial positions of GRTQ, such as Aspergillus clavatus and Monascus purpureus, as well as their interactions with environmental factors, we elucidated the color formation mechanism behind the phenomenon of "green" outside and "red" inside. This study provides fundamental information support for optimizing the production process of GRTQ.

De novo transcriptome analysis identifies RpMYB1 as an activator of anthocyanin biosynthesis in Rehmannia piasezkii

Rehmannia piasezkii is a kind of medicinal plants, of the Orobanchaceae family, and well known for its large pink or purple corolla. However, no research on the molecular mechanism of flower color formation in R. piasezkii has been conducted so far. In this study, we investigated the transcriptome of root, stem, leaf and corollas of R. piasezkii using transcriptome sequencing technology and assembled 144,582 unigenes. A total of 58 anthocyanin biosynthetic genes were identified in the R. piasezkii transcriptome, fourteen of which were highly correlated with anthocyanin content, especially RpF3H2, RpDFR2, RpANS1, RpANS2 and RpUFGT. Totally, 35 MYB genes with FPKM values greater than 5 were identified in the R. piasezkii transcriptome, including an R2R3 MYB transcriptional factor RpMYB1, which belongs to subgroup 6 of the R2R3 MYB family. Agrobacterium-mediated transient expression of Nicotiana benthamiana revealed that overexpression of RpMYB1 could activate the expression of structural genes in anthocyanin synthesis pathway and promote the accumulation of anthocyanins in N. benthamiana leaves, indicating that RpMYB1 is a positive regulator of anthocyanin synthesis. Furthermore, combined transient overexpression of RpMYB1 with RpANS1, RpMYB1+RpANS1 with other structural genes all could further enhance the accumulation of anthocyanins in N. benthamiana leaves. Permanent overexpression of RpMYB1 in R. glutinosa promoted anthocyanin accumulation and expression levels of RgCHS, RgF3H, RgDFR and RgANS. Further evidence from dual-luciferase assay suggested that RpMYB1 could bind to the promoter of RpDFR2 and hence activating its expression. These findings provide insight into the molecular regulation in anthocyanin biosynthesis in R. piasezkii and provide valuable genetic resources for the genetic improvement of flower color.

A cross-fusion of non-local attention network for infrared small target tracking

Infrared small target tracking plays important roles in many applications, such as infrared surveillance and reconnaissance. However, achieving promising performance with existing trackers is difficult due to the following challenges: (1) Infrared small targets lack color and texture information, making it difficult for the network to capture effective features; (2) A slight centroid-shift of the target can lead to tracking failure. To overcome these challenges, this paper proposes the CFNLA network, which consists of two parts: (1) The cross-fusion module is designed to capture the relationship between the target and its surroundings, including a non-local block to enhance the feature information of the target at the current scale and reduce interference such as background; (2) The confidence loss function is proposed to improve tracking stability by forcing the network to concentrate on centroid shift. Furthermore, a self-designed infrared small target tracking dataset, IRSTT, is constructed to evaluate the proposed algorithm. Finally, the experimental results on various open-source datasets and the IRSTT dataset verify the superior performance of the CFNLA network.

The influence of color vision deficiency on vessel visibility during colorectal endoscopic submucosal dissection and the potential advantage of red dichromatic imaging to achieve color vision barrier-free.

Although color information is important in gastrointestinal endoscopy, there are limited studies on how endoscopic images are viewed by people with color vision deficiency. We aimed to investigate the differences in the visibility of blood vessels during endoscopic submucosal dissection (ESD) among people with different color vision characteristics and to examine the effect of red dichromatic imaging (RDI) on blood vessel visibility. Seventy-seven pairs of endoscopic images of white light imaging (WLI) and RDI of the same site were obtained during colorectal ESD. The original images were set as type C (WLI-C and RDI-C), a common color vision. These images were computationally converted to simulate images perceived by people with color vision deficiency protanope (Type P) or deutanope (Type D) and denoted as WLI-P and RDI-P or WLI-D and RDI-D. Blood vessels and background submucosa that needed to be identified during ESD were selected in each image, and the color differences between these two objects were measured using the color difference (ΔE 00) to assess the visibility of blood vessels. ΔE 00 between a blood vessel and the submucosa was greater under RDI (RDI-C/P/D: 24.05 ± 0.64/22.85 ± 0.66/22.61 ± 0.64) than under WLI (WLI-C/P/D: 22.26 ± 0.60/5.19 ± 0.30/8.62 ± 0.42), regardless of color vision characteristics. This improvement was more pronounced in Type P and Type D and approached Type C in RDI. Color vision characteristics affect the visibility of blood vessels during ESD, and RDI improves blood vessel visibility regardless of color vision characteristics.