Subtle Color Changes Research Articles

Nitrogen and Sulfur Co-Doped Graphene-Quantum-Dot-Based Fluorescent Sensor for Rapid Visual Detection of Water Content in Organic Solvents.

Accurate water content detection is crucial for optimizing chemical reactions, ensuring product quality in pharmaceutical manufacturing, and maintaining food safety. In this study, nitrogen and sulfur co-doped graphene quantum dots (R-GQDs) were synthesized via a one-step hydrothermal method using o-phenylenediamine as the carbon source. The synthesis conditions, including reaction time, temperature, o-phenylenediamine concentration, and H2SO4/water ratio, were optimized using the Box-Behnken response surface methodology. The R-GQDs exhibited excellent fluorescence stability and distinct solvent-dependent characteristics, alongside a broad linear detection range and high sensitivity, making them highly suitable for dual-mode water content detection (colorimetric and fluorescent). To enhance the accuracy of visual detection, R-GQDs were incorporated into portable test strips with smartphone-assisted analysis, compensating for the human eye's limitations in distinguishing subtle color changes. The sensor's practical utility was validated through spiked recovery experiments in food samples, and the R-GQDs demonstrated good biocompatibility for in vivo imaging in shrimp. These findings highlight a novel strategy for developing portable, real-time water content sensors with potential applications in both portable detection systems and biological imaging.

Carbon dots-based dual-mode sensor for highly selective detection of nitrite in food substrates through diazo coupling reaction

As an antioxidant and preservative agent, nitrite (NO2−) plays an essential role in the food industry to maintain freshness or inhibit microbial growth. However, excessive addition of NO2− is detrimental to health, so accurate and portable detection of NO2− is critical for food quality control. Notably, the selectivity of most carbon dots (CDs)-based fluorescence sensors was not enough due to the nonspecific interaction mechanism of hydrogen bond, electrostatic interaction and inner filter effect etc. Herein, a novel fluorescence/UV–vis absorption (FL/UV–vis) dual-mode sensor was developed on basis of mC-CDs, which were prepared by simple solvothermal treatment of m-Phenylenediamine (m-PDA) and cyanidin cation (CC). The fluorescence of these mC-CDs could be selectively responded by NO2− through the specific diazo coupling reaction between NO2− and amino groups on the surface of mC-CDs, thus effectively improving the selectivity of NO2− detection. The CDs-based fluorescence sensor possessed a low detection limit of 0.091 μM and 0.143 μM for FL and UV–vis methods and the excellent linear range of 0.0–60.0 μM. Furthermore, the mC-CDs sensor was employed to detect NO2− in real samples with a recovery rate of 97.11 %–104.15 % for quantitative addition. Moreover, the smartphone-assisted fluorescence sensing platform developed could identify the subtle color changes that could not be distinguished by the naked eye, and had the advantages of fast detection speed and intelligence. More importantly, the portable solid phase sensor based on mC-CDs had been successfully applied to the specific fluorescence identification and concentration monitoring of NO2−. Accordingly, the designed sensor provided a new strategy for the highly selective and convenient sensing of NO2− in food substrates, and paved the way for the wide application of CDs-based nanomaterials in the detection of food safety.

A Fast FPGA Hardware Accelerator for Remote Heart Rate Detection Based on RGB Vision.

A fast hardware accelerator is created by this work via field programmable gate array (FPGA) to estimate heart rate (HR) through the video recorded by a RGB camera based on the technology of remote photoplethysmography (rPPG). The method of rPPG acquires physiological signals of a human body by analyzing the subtle color changes on the surface of the human skin. The hardware implementation of rPPG to estimate HR is proposed herein to aim for a much faster calculation speed than software for a number of applications, like heart failure pre-warning of an in-action athlete and drowsiness detection of a driver. In this accelerator, ICA (Independent Component Analysis) is used to recover the blood volume pulse from the raw signals of remote PPG, and then obtain the heart rate value. The architecture of the hardware circuit is described in Verilog HDL and verified by Quartus II, and also implemented in an Altera DE10-Standard FPGA board, which consists of image capture, heart rate algorithm and image display. A TRDB-D5M camera is utilized for image capture. Two experiments were conducted with image collecting duration of 16 seconds and 8 seconds respectively, and the commercial device Omron HEM-6111 was used as the golden value. The proposed system achieves an accuracy in (ME ± 1.96SD) of -0.76 ± 5.09 and -0.70 ± 8.71 bpm in the short periods of 16-second and 8-second versions, respectively, which outperforms all the reported prior works in combined computation time and accuracy.

Novel colorimetric indicators based on alginate hydrogel beads containing anthocyanin for visual freshness monitoring of shrimp and minced chicken

Novel colorimetric indicators were developed by incorporating anthocyanins into alginate hydrogel beads through electrostatic extrusion for intelligent packaging. Two different sizes of hydrogel beads were produced and applied into shrimp and minced chicken packaging to visually detect freshness status during 4 °C storage. To assess the freshness of the products, the viable count of various types of bacteria (psychrotrophic, H2S-producing, mesophilic, and Pseudomonas spp.), total volatile basic nitrogen (TVB-N) content, pH, K-value, H-value, and biogenic amines level were analyzed. The indicators' color change (ΔE) was then correlated with these parameters. Color analysis of the indicators during storage indicated ΔE values below 10 and over 30 in the hydrogels incorporated into minced chicken and shrimp packaging, respectively. The color change of both indicators integrated into the shrimp package was strongly correlated with the change in viable counts, TVB-N, pH, K-value, and H-value (r ≥ 0.8, p < 0.016), as well as biogenic amines (r = 0.7, p < 0.05). The indicators applied to minced chicken packaging showed subtle color changes during storage, while those in shrimp packaging consistently reflected shelf-life variations. This innovative approach shows potential for improving food safety and quality monitoring, particularly in seafood packaging, where indicators have been effective in visually signaling product freshness.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Unlocking ion Storage Chemistry in Aqueous Batteries via Operando Smartphone Multispectral Techniques

AbstractReal‐time monitoring of the dynamic charging process and state‐of‐charge inside energy storage systems is crucial for ensuring their sustainable operation. Here, a novel computational multispectral imaging reconstruction (MSIRC) strategy is presented using a smartphone for in situ monitoring of the optical states of aqueous batteries via transparent monitoring window, allowing analysis of ion storage chemistry and real‐time capacity variations. Prussian blue and MnO2 that exhibit significant and subtle color changes during the ion storage process are selected to validate this approach. Time‐domain reconstructed reflectance spectra are capable of selecting the proper wavelength with the largest variation in reconstructed reflectance and precisely monitor the ion storage process of these two materials. This indicates MSIRC's rapidly distinguishing capability between electrodes with unique ion storage mechanisms and great potential in recognizing new materials with unknown mechanisms. Furthermore, the MSIRC technique can perform operando monitoring for several kinds of Zn ion batteries using cathodes with different ion storage mechanisms. These imply the ability of MSIRC to detect the real‐time healthy state of batteries at the device level and judge the inside varying ion storage mechanism.

Remote heart rate estimation via convolutional neural networks with transformers

Remote photoplethysmography (rPPG) uses subtle color changes in facial videos to estimate heart rate(HR). However, recent methods face challenges in solving remote heart rate estimation tasks, because the color changes in facial skin are very subtle and the pseudo-periodicity of rPPG requires long-distance temporal detection. To address these issues of the rPPG estimation task, we propose a convolution neural network with transformers for rPPG estimation which takes the advantages of convolutions in the locality and transformers in long-range dependencies. Specifically, we first proposed a local feed-forward module following the multi-head self-attention to compensate for the difficulties of transformers in capturing neighboring feature information. Then we add the relative and absolute position encoding to obtain the ordering of the tokens which is the key to capturing the pseudo-periodicity of rPPG. Furthermore, the temporal multi-scale module is proposed to learn the temporal information from different scales. Extensive experimental results demonstrate that our method is superior to the state-of-the-art results on the COHFACE, UBFC-rPPG, PURE, and VIPL-HR databases compared with traditional methods and deep learning-based methods. We also evaluated our method under different conditions on the VIPL-HR database, and the results show that our method is robust to various conditions.

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity.

Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact-printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights into surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles.

Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring

Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.

The effect of color vision resolution on intra-oral shade-selection accuracy.

Dentists must be able to identify subtle color changes as shade-matching is crucial in aesthetic dentistry. To determine whether color discrimination ability is related to shade-matching accuracy among dentists. The sensitivity of the normal-color vision population to different colors was investigated using Farnsworth Munsell 100 Hue (FM-100) test results. The FM-100 test was administered to 37 dentists at the Hospital of Stomatology, Jilin University. Sensitivity of dentists with normal-color vision to different colors was investigated using the FM-100 test. Participants were instructed to arrange color caps of various colors according to the gradual change in colors and the results were scored. Visual shade matching test using Vita 3D-MASTER shade guide was performed to determine shade-matching accuracy. The relationship between color discrimination ability and shade-matching accuracy was analyzed. The number of misplaced color caps in the FM-100 test was also calculated. The FM-100 test revealed that 16 and 21 participants had excellent and average color discrimination ability, respectively, and their shade-matching accuracies were 68.75% and 66.67%, respectively. No significant difference was observed in the shade-matching accuracy between the two groups. No significant correlation was observed between the color discrimination ability and shade-matching accuracy. In addition, the number of wrong color caps on the 43-63 color tray with the transition from blue-green to blue-purple was the highest according to Friedman's test. Color discrimination ability of dentists does not affect their visual shade-matching accuracy. Additionally, people with normal color vision are not sensitive to the transition from blue-green to blue-purple.

Robust Heart Rate Variability Measurement from Facial Videos.

Remote Photoplethysmography (rPPG) is a contactless method that enables the detection of various physiological signals from facial videos. rPPG utilizes a digital camera to detect subtle changes in skin color to measure vital signs such as heart rate variability (HRV), an important biomarker related to the autonomous nervous system. This paper presents a novel contactless HRV extraction algorithm, WaveHRV, based on the Wavelet Scattering Transform technique, followed by adaptive bandpass filtering and inter-beat-interval (IBI) analysis. Furthermore, a novel method is introduced to preprocess noisy contact-based PPG signals. WaveHRV is bench-marked against existing algorithms and public datasets. Our results show that WaveHRV is promising and achieves the lowest mean absolute error (MAE) of 10.5 ms and 6.15 ms for RMSSD and SDNN on the UBFCrPPG dataset.

Role of linked color imaging for upper gastrointestinal disease: present and future.

Techniques for upper gastrointestinal endoscopy are advancing to facilitate lesion detection and improve prognosis. However, most early tumors in the upper gastrointestinal tract exhibit subtle color changes or morphological features that are difficult to detect using white light imaging. Linked color imaging (LCI) has been developed to overcome these shortcomings; it expands or reduces color information to clarify color differences, thereby facilitating the detection and observation of lesions. This article summarizes the characteristics of LCI and advances in LCI-related research in the upper gastrointestinal tract field.

Applying Quaternions to Recognize Hidden Details in Images: Rothko as a Case Study

Images or paintings with homogeneous colors may appear dull to the naked eye; however, there may be numerous details in the image that are expressed through subtle changes in color. This manuscript introduces a novel approach that can uncover these concealed details via a transformation that increases the distance between adjacent pixels, ultimately leading to a newly modified version of the input image. We chose the artworks of Mark Rothko—famous for their simplicity and limited color palette—as a case study. Our approach offers a different perspective, leading to the discovery of either accidental or deliberate clusters of colors. Our method is based on the quaternion ring, wherein a suitable multiplication can be used to boost the color difference between neighboring pixels, thereby unveiling new details in the image. The quality of the transformation between the original image and the resultant versions can be measured by the ratio between the number of connected components in the original image (m) and the number of connected components in the output versions (n), which usually satisfies nm≫1. Although this procedure has been employed as a case study for artworks, it can be applied to any type of image with a similar simplicity and limited color palette.

ECG signal reconstruction based on facial videos via combined explicit and implicit supervision

Accurate and continuous electrocardiogram (ECG) signal measurements are of paramount importance for the prevention of cardiovascular disease. Existing physiological measurement methods require the placement of sensors on the human body, which proves uncomfortable and inconvenient during long-term monitoring. Recent research has found that heart rates can be extracted from noncontact RGB facial videos by measuring subtle color changes in the skin region. However, designing an accurate feature representation structure for physiological measurements remain a challenge. This study presents a novel end-to-end ECG signal synthesis network using short facial video sequences. We combined explicit and implicit supervision structures, which allows the network to perceive physiological-related features from unlabeled feature sets and adopts a channel and frame aggregation attention mechanism to boost the network acquisition of periodic spatiotemporally correlated features. Finally, the generated features were fed into a physiological estimator consisting of a serially connected “Generative Adversarial” module for ECG signal reconstruction. Experimental results on publicly available datasets and a new dataset collected by us demonstrate that the proposed network outperforms state-of-the-art methods in ECG signal measurements. This is a powerful tool for use in telemedicine and health monitoring.

Visual perceptual load and processing of somatosensory stimuli in primary and secondary somatosensory cortices

Load theory assumes that neural activation to distractors in early sensory cortices is modulated by the perceptual load of a main task, regardless of whether task and distractor share the same sensory modality or not. While several studies have investigated the question of load effects on distractor processing in early sensory areas, there is no functional magnetic resonance imaging (fMRI) study regarding load effects on somatosensory stimuli. Here, we used fMRI to investigate effects of visual perceptual load on neural responses to somatosensory stimuli applied to the wrist in a study with 44 participants. Perceptual load was manipulated by an established sustained visual detection task, which avoided simultaneous target and distractor presentations. Load was operationalized by detection difficulty of subtle or clear color changes of one of 12 rotating dots. While all somatosensory stimuli led to activation in somatosensory areas SI and SII, we found no statistically significant difference in brain activation to these stimuli under high compared to low sustained visual load. Moreover, exploratory Bayesian analyses supported the absence of differences. Thus, our findings suggest a resistance of somatosensory processing to at least some forms of visual perceptual load, possibly due to behavioural relevance of discrete somatosensory stimuli and separable attentional resources for the somatosensory and visual modality.

Endoscopic diagnosis of Helicobacter pylori gastritis using white light imaging and texture and color enhancement imaging.

Background and study aims Texture and color enhancement imaging (TXI) is a new image-enhanced endoscopy that augments subtle tissue differences and color changes in gastric mucosa. This study aimed to compare the usefulness of TXI and white light imaging (WLI) for diagnosing Helicobacter pylori gastritis. Patients and methods We retrospectively prepared one image set with 22 endoscopic images acquired by WLI and TXI from 60 consecutive patients individually. Five endoscopists independently reviewed the randomly displayed image sets and assessed the H. pylori infection status on endoscopy according to the Kyoto Classification of Gastritis. The primary endpoints were the accuracies of WLI and TXI in diagnosing H. pylori -active gastritis. The correlation of the endoscopic features with the three H. pylori infection statuses (current infection, past infection, and noninfection) was also evaluated. Results Diagnostic accuracy for active gastritis was significantly higher in TXI than in WLI (85.3 % vs. 78.7 %; P = 0.034). All the specific endoscopic features associated with H. pylori infection statuses had a higher odds ratio with TXI than with WLI. Additionally, interobserver agreement among the five reviewers was higher in TXI than in WLI, except for one pair. Conclusions TXI may improve the endoscopic diagnosis accuracy for H. pylori infection.

X-iPPGNet: A novel one stage deep learning architecture based on depthwise separable convolutions for video-based pulse rate estimation

Pulse rate (PR) is one of the most important markers for assessing a person’s health. With the increasing demand for long-term health monitoring, much attention is being paid to contactless PR estimation using imaging photoplethysmography (iPPG). This non-invasive technique is based on the analysis of subtle changes in skin color. Despite efforts to improve iPPG, the existing algorithms are vulnerable to less-constrained scenarios (i.e., head movements, facial expressions, and environmental conditions). In this article, we propose a novel end-to-end spatio-temporal network, namely X-iPPGNet, for instantaneous PR estimation directly from facial video recordings. Unlike most existing systems, our model learns the iPPG concept from scratch without incorporating any prior knowledge or going through the extraction of blood volume pulse signals. Inspired by the Xception network architecture, color channel decoupling is used to learn additional photoplethysmographic information and to effectively reduce the computational cost and memory requirements. Moreover, X-iPPGNet predicts the pulse rate from a short time window (2 s), which has advantages with high and sharply fluctuating pulse rates. The experimental results revealed high performance under all conditions including head motions, facial expressions, and skin tone. Our approach significantly outperforms all current state-of-the-art methods on three benchmark datasets: MMSE-HR (MAE = 4.10 ; RMSE = 5.32 ; r = 0.85), UBFC-rPPG (MAE = 4.99 ; RMSE = 6.26 ; r = 0.67), MAHNOB-HCI (MAE = 3.17 ; RMSE = 3.93 ; r = 0.88).

Iris Melanoma: A Review of the Literature

Abstract Iris melanoma is a rare form of ocular malignancy which presents with unique challenges in the diagnosis and management. This review includes current knowledge and recent advancements in the understanding of iris melanoma, focusing mainly on epidemiology, clinical presentation, diagnostic modalities, and treatment options. The incidence of iris melanoma is low. However, it has potential for metastasis therefore it is important to diagnose and intervene in the right time. Clinical manifestations vary widely, often presenting with subtle changes in iris color or structure, necessitating a high index of suspicion for early detection. Diagnostic tools such as slit-lamp biomicroscopy, ultrasound biomicroscopy, and advanced imaging techniques play important roles in establishing a diagnosis and assessing tumor characteristics. Treatment strategies range from conservative observation to surgical excision or radiotherapy, with individualized approaches based on tumor size, location, and patient factors. Finally, the review also discusses prognostic factors and outcomes.

Fantasie nègre: The Piano Music of Florence Price, by Samantha Ege

<i>Fantasie nègre: The Piano Music of Florence Price</i>, by Samantha Ege

Texture and Color Enhancement Imaging with Indigo Carmine Dye Accentuates Slightly Depressed Early Gastric Cancer Lesions

A 74-year-old man visited our hospital for follow-up esophagogastroduodenoscopy after eradication of Helicobacter pylori. Transnasal endoscopy (GIF-1200N with the EVIS X1 video system, Olympus Medical Systems, Tokyo, Japan) revealed a reddish lesion located in the anterior wall of the prepyloric region on white light imaging (Figure A) using indigo-carmine dye (Figure B). On texture and color enhancement imaging (TXI) mode 1 with indigo-carmine dye (chromo-TXI), the lesion was highlighted as being reddish and slightly depressed (Figure C). Endoscopic submucosal dissection was performed on the suspicion of a well-differentiated tubular adenocarcinoma for biopsy and endoscopic diagnosis. Histologic examination showed the lesion to be adenocarcinoma, well-differentiated, Paris type 0-IIc, measuring 5 × 5 mm, pT1a (M), no lymphovascular invasion (Figure D). TXI is a recently developed technique that enhances brightness, surface irregularities, and subtle color changes. Chromo-TXI facilitated the endoscopic diagnosis of early gastric cancer even with transnasal endoscopy, highlighting slightly depressed lesions, where reddish depressed lesions observed following H pylori eradication are often shown to be indistinguishable from gastric cancer lesions. Thus, chromo-TXI seems to be a more useful modality than white light imaging and may set a new standard for the detection of depressed early gastric cancer lesions.