Green Channel Research Articles

NIR-excited imaging and in vivo visualization of β-galactosidase activity using a pyranonitrile-modified upconversion nanoprobe

β-galactosidase (β-gal) is a diagnostic biomarker of primary ovarian cancers. The development of effective fluorescent probes for investigating the activity of β-gal will be beneficial to cancer diagnosis. Herein, a near-infrared (NIR) excited ratiometric nanoprobe (DCM-β-gal-UCNPs) by assembling pyranonitrile dye (DCM-β-gal) on the surface of upconversion nanophosphors (UCNPs) was designed for the evaluation of β-gal activity in vivo. Upon the interaction with β-gal, a marked decrease of upconversion luminescence (UCL) signal in the green channel was observed owing to the luminescence resonance energy transfer from the UCNPs to pyranonitrile chromophore, whereas the NIR UCL emission at 800 nm was almost no influence. Thus, the β-gal activity could be quantitatively detected by the UCL intensity ratio of UCL543 nm/UCL800 nm with the limit of detection of 3.1 × 10−4 U/mL. Moreover, DCM-β-gal-UCNPs was effectively applied for monitoring β-gal fluctuation in living cells and zebrafish by a ratiometric UCL signal excited by 980 nm laser. We envision that nanoprobe DCM-β-gal-UCNPs might be used as a potential bioimaging tool to disclose more biological information of β-gal in β-gal-associated diseases in the future.

The effects of the coronavirus disease pandemic on intravenous thrombolytic therapy among patients with acute ischemic stroke in Dalian, China

BackgroundWe investigated the influence of the coronavirus disease 2019 (COVID-19) pandemic on the number of patients with acute ischemic stroke who received intravenous thrombolytic therapy (ITT) in Dalian, China, in 2020.MethodsThis retrospective descriptive study, conducted from February 1, 2020, to August 31, 2020, examined 13 hospitals in Dalian that participated in the “stroke emergency map”. To use this “stroke emergency map” of China, patients followed the official “Stroke Map” WeChat account and dialed 120 for emergency medical services. We analyzed the number of patients with acute ischemic stroke who underwent ITT. In particular, we examined the onset-to-door time (ODT), door-to-needle time (DNT), onset-to-needle time (ONT), mode of transportation to the hospital, and National Institutes of Health Stroke Scale (NIHSS) scores before and after ITT. Data were collected for the aforementioned period and compared with the 2021 baseline data from the same time of year. The Mann‒Whitney U test was performed for data analysis.ResultsCompared with the data from 2020, the number of patients with acute ischemic stroke who underwent ITT increased (from 735 to 1719 cases) in 2021, but the DNT decreased (from 59 to 45 min; P = 0.002). Moreover, 83.9% of patients in 2020 presented to the hospital without ambulance transport, compared to 81.1% of patients in the 2021 non-COVID-19 pandemic period. Patients with NIHSS scores of 6–14 were more likely to call an ambulance for transport to the hospital than to transport themselves to the emergency department.ConclusionsDuring the 2020 COVID-19 pandemic, the DNT was prolonged as a result of strengthened fever surveillance. In 2021, the number of patients with acute ischemic stroke who underwent ITT increased compared to the previous year. Notably, the growth in the number of patients with acute ischemic stroke who underwent ITT benefited from both the “stroke emergency map” of China and the “green channel,” a novel treatment approach that focuses on the rational design of the rescue process.Trial registration.Our study was a retrospective descriptive study, not a clinical trial, thus we did not have to register for clinical trials.

Remote Photoplethysmography with a High-Speed Camera Reveals Temporal and Amplitude Differences between Glabrous and Non-Glabrous Skin.

Photoplethysmography (PPG) is a noninvasive optical technology with applications including vital sign extraction and patient monitoring. However, its current use is primarily limited to heart rate and oxygenation monitoring. This study aims to demonstrate the utility of PPG for physiological investigations. In particular, we sought to demonstrate the utility of simultaneous data acquisition from several regions of tissue using remote/contactless PPG (rPPG). Specifically, using a high-speed scientific-grade camera, we collected rPPG from the hands (palmar/dorsal) of 22 healthy volunteers. Data collected through the red and green channels of the RGB CMOS sensor were analyzed. We found a statistically significant difference in the amplitude of the glabrous skin signal over the non-glabrous skin signal (1.41 ± 0.85 in the red channel and 2.27 ± 0.88 in the green channel). In addition, we found a statistically significant lead of the red channel over the green channel, which is consistent between glabrous (17.13 ± 10.69 ms) and non-glabrous (19.31 ± 12.66 ms) skin. We also found a statistically significant lead time (32.69 ± 55.26 ms in the red channel and 40.56 ± 26.97 ms in the green channel) of the glabrous PPG signal over the non-glabrous, which cannot be explained by bilateral variability. These results demonstrate the utility of rPPG imaging as a tool for fundamental physiological studies and can be used to inform the development of PPG-based devices.

Differentiation of HSA and BSA and Instantaneous Detection of HSO3 - Using Confined Space of Serum Albumins and Live Cell Imaging of Exogenous/Endogenous HSO3.

The limitations of prevailing probes for the detection of human serum albumin (HSA) and HSO3 - make it challenging to apprehend the cooperative effect of both HSA and HSO3 - in biological systems. Herein, we present a multi-responsive fluorescent probe MGTP, which distinguishes HSA from bovine serum albumin (BSA) through an ∼104-fold fluorescence enhancement at an emission maximum of 595 nm with HSA and only an ∼10-fold increase at an emission maximum of 615 nm with a shoulder at 680 nm with BSA. The absorbance spectrum of MGTP also discriminates HSA and BSA with the respective absorption maxima at 543 nm and at 580 nm. MGTP in the confined space of HSA or BSA undergoes instantaneous conjugate addition of HSO3 - and results in a ratiometric change in fluorescence intensity with diminishing of red fluorescence (600 nm) and emergence of green fluorescence (515 nm). MGTP in the absence of SAs does not react with HSO3 - in phosphate-buffered saline buffer and reacts sluggishly in the dimethyl sulfoxide-water 1:1 mixture. The limit of detection values for the detection of HSA and HSO3 - are 4 and 6.88 nM, respectively. The drug binding studies reveal that MGTP preferably confines itself at the bilirubin site of HSA. In MCF-7 cancer cells, MGTP is localized into mitochondria and reveals both exogenous and endogenous visualization of HSO3 - through a change in fluorescence from the red to green channel.

Heart rate estimation from color video sequences with high SNR.

We propose an absorption intensity heartbeat modulation-averaged shifted histogram (AIHM-ASH) method for estimating human heart rate (HR) using color videos of lip image sequences. When heartbeat occurs, AIHM is generated. Based on the AIHM, HR signals can be demodulated by computing the instantaneous HR modulation depth that presents the relative red blood cell (RBC) concentration from the green channel image of the RGB color video. In addition, the ASH algorithm further suppresses the background tissue and vein signals, and increases the signal-to-noise ratio (SNR). The experimental results for flow phantoms, chicken embryos, and human lips validated the proposed method's optimal estimation conditions and effectiveness, where the accuracy and root mean square error (RMSE) were 99.23% and 0.8 bpm, respectively. The proposed HR estimation method has significant potential to advance health monitoring and disease prevention via conventional color video cameras installed in public places.

Is it worth promoting battery swapping? A social welfare perspective on provider- and consumer-side incentives

In countries that promote further EV diffusion, battery swapping opens up a market outlet for time-sensitive consumers as well as another green policy channel. To better understand this phenomenon from the perspective of policy-making, this study establishes a Hotelling model of a competitive market comprising a battery swapping service provider and a charging service provider. The analyses of three scenarios, no subsidization, consumer subsidization, and provider subsidization, reveal how to maximize social welfare. Considering subsidy amount, consumer time preferences, service time, and operating costs, the findings provide valuable insights for policymakers. Whereas incentive programs are generally helpful for battery swapping development and social welfare improvement, it is more effective to subsidize service providers than end consumers. Considering subsidy opportunity cost, it is necessary to control incentive amount and make continuous adjustments to promote battery swapping services without harming charging services.

Retracted: Acute Thrombolytic Therapy Combined with the Green Channel Can Reduce the Thrombolytic Time and Improve Neurological Function in Acute Stroke Patients.

[This retracts the article DOI: 10.1155/2022/1127159.].

Retracted] Enhance‐Net: An Approach to Boost the Performance of Deep Learning Model Based on Real‐Time Medical Images

Real‐time medical image classification is a complex problem in the world. Using IoT technology in medical applications assures that the healthcare sectors improve the quality of treatment while lowering costs via automation and resource optimization. Deep learning is critical in categorizing medical images, which is accomplished by artificial intelligence. Deep learning algorithms allow radiologists and orthopaedic surgeons to make their life easier by providing them with quicker and more accurate findings in real time. Despite this, the classic deep learning technique has hit its performance limits. For these reasons, in this research, we examine alternative enhancement strategies to raise the performance of deep neural networks to provide an optimal solution known as Enhance‐Net. It is possible to classify the experiment into six distinct stages. Champion‐Net was chosen as a deep learning model from a pool of benchmark deep learning models (EfficientNet: B0, MobileNet, ResNet‐18, and VGG‐19). This stage helps choose the optimal model. In the second step, Champion‐Net was tested with various resolutions. This stage helps conclude dataset resolution and improves Champion‐Net performance. The next stage extracts green channel data. In the fourth step, Champion‐Net combines with image enhancement algorithms CLAHE, HEF, and UM. This phase serves to improve Enhance‐performance. The next stage compares the Enhance‐Net findings to the lightness order error (LoE). In Enhance‐Net models, the current study combines image enhancement and green channel with Champion‐Net. In the final step, radiologists and orthopaedic surgeons use the trained model for real‐time medical image prediction. The study effort uses the musculoskeletal radiograph‐bone classification (MURA‐BC) dataset. Classification accuracy of Enhance‐Net was determined for the train and test datasets. These models obtained 98.02 percent, 94.79 percent, and 94.61 percent accuracy, respectively. The 96.74% accuracy was achieved during real‐time testing with the unseen dataset.

WISE Green Objects (WGOs): The Massive Star Candidates in the Whole Galactic Plane (∣b∣ < 2°)

Massive young stellar objects (MYSOs) play a crucial role in star formation. Given that MYSOs were previously identified based on the extended structure and the observational data for them is limited, screening the Wide-field Infrared Survey Explorer (WISE) objects showing green features (for the common coding of the 4.6 μm band as the green channel in three-color composite WISE images) will yield more MYSO candidates. Using WISE images in the whole Galactic plane (0° < l < 360° and ∣b∣ < 2°), we identified sources with strong emissions at the 4.6 μm band, then according to morphological features divided them into three groups. We present a catalog of 2135 WISE Green Objects (WGOs). 264 WGOs have an extended structure. 1366 WGOs show compact green features but without extended structure. 505 WGOs have neither extended structure nor green features, but the intensity at 4.6 μm is numerically at least 4.5 times that of 3.4 μm. According to the analysis of the coordinates of WGOs, we find WGOs are mainly distributed in ∣l∣ < 60°, coincident with the position of the giant molecular clouds in ∣l∣ > 60°. Matching results with various masers show that those three groups of WGOs are at different evolutionary stages. After crossmatching WGOs with published YSO survey catalogs, we infer that ∼50% of WGOs are samples of newly discovered YSOs. In addition, 1260 WGOs are associated with Hi-GAL sources, according to physical parameters estimated by spectral energy distribution fitting, of which 231 are classified as robust MYSOs and 172 as candidate MYSOs.

Enhancing Disease Classification in Abdominal CT Scans through RGB Superposition Methods and 2D Convolutional Neural Networks: A Study of Appendicitis and Diverticulitis.

The primary symptom of both appendicitis and diverticulitis is a pain in the right lower abdomen; it is almost impossible to diagnose these conditions through symptoms alone. However, there will be misdiagnoses happening when using abdominal computed tomography (CT) scans. Most previous studies have used a 3D convolutional neural network (CNN) suitable for processing sequences of images. However, 3D CNN models can be difficult to implement in typical computing systems because they require large amounts of data, GPU memory, and extensive training times. We propose a deep learning method, utilizing red, green, and blue (RGB) channel superposition images reconstructed from three slices of sequence images. Using the RGB superposition image as the input image of the model, the average accuracy was shown as 90.98% in EfficietNetB0, 91.27% in EfficietNetB2, and 91.98% in EfficietNetB4. The AUC score using the RGB superposition image was higher than the original image of the single channel for EfficientNetB4 (0.967 vs. 0.959, p = 0.0087). The comparison in performance between the model architectures using the RGB superposition method showed the highest learning performance in the EfficientNetB4 model among all indicators; accuracy was 91.98% and recall was 95.35%. EfficientNetB4 using the RGB superposition method had a 0.011 (p value = 0.0001) AUC score higher than EfficientNetB0 using the same method. The superposition of sequential slice images in CT scans was used to enhance the distinction in features like shape, size of the target, and spatial information used to classify disease. The proposed method has fewer constraints than the 3D CNN method and is suitable for an environment using 2D CNN; thus, we can achieve performance improvement with limited resources.

Pricing and carbon emission reduction decision in dual-channel supply chain with carbon tax and consumer preference

With the development of society and economy, the production methods that consuming a lot of resources are gradually unable to meet the needs of modern life. And with the rise of consumers' awareness of environmental protection, more and more low-carbon production of manufacturers have been put forward. In order to encourage manufacturers to product with low carbon, the carbon tax policy is proposed. Under the pressure of the new policy and consumers' environmental awareness, manufacturers' production decisions will face many challenges. In addition, the rapid development of the Internet and the popularity of online shopping have also enabled manufacturers to engage in online direct sales, which has many impacts on traditional retail. Under such circumstances, consumers' channel preferences will also have many influences on manufacturers' production decisions. Therefore, it is necessary to study the influence of carbon tax, consumer green preference and channel preference on supply chain members. Therefore, this study constructs a dual-channel supply chain model consisting of a single manufacturer and a retailer to determine the optimal pricing of supply chain members and minimum carbon emissions of the manufacturer in the two scenarios of centralized decision-making and decentralized decision-making, respectively. The impact of consumers' environmental awareness, carbon tax rates and consumers' channel preferences on the carbon emissions of products and the profits of manufacturers and retailers are analyzed. The research results show that: when the carbon tax rate is low, with the rising of consumers' awareness of environmental protection and carbon tax, it is necessary to increase the carbon tax on clean manufacturers to enable them to take more market share, and increase the carbon tax on low carbon emission manufacturers to force them to make more efforts on clean manufacturing. Besides, low-carbon taxes should be imposed on medium carbon emission manufacturers to avoid them from going back to high-polluting production, and on high carbon emission manufacturers, so that they will have more funds to carry out cleaner production reforms. Besides, shopping online should be encouraged.

"Not everyone deserves the Green Channel": Self-declaration in the publication is only for the truly mature and honest.

"Not everyone deserves the Green Channel": Self-declaration in the publication is only for the truly mature and honest.

MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network for Single-Wavelength LiDAR Bathymetry

Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shallow channel waveforms based on the analysis of CZMIL waveform data.

Signal priority control method for emergency vehicles at intersections based on wireless communication technology

In order to give priority to control emergency vehicle signals at intersections, a priority control method for emergency vehicle signals at intersections is proposed based on wireless communication technology. The setting method of vehicle detector and emergency vehicle detector is analysed, and the non-delay model of emergency vehicle passing through the intersection is established to solve the phase and green time problems of emergency vehicle. Combined with wireless communication technology, the determination process of priority signal of emergency vehicle under fixed cycle conditions is elaborated in detail. The experimental results show that the queuing time of the designed French ordinary cars is significantly shortened, the average number of stops can be ignored, and the average delay time is shorter than that of the traditional method. This provides theoretical and practical basis for further guaranteeing the implementation of urban emergency rescue green channel and enriching the content of urban emergency rescue decision support method.

IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

In recent years, there has been a significant increase in the number of people suffering from eye illnesses, which should be treated as soon as possible in order to avoid blindness. Retinal Fundus images are employed for this purpose, as well as for analysing eye abnormalities and diagnosing eye illnesses. Exudates can be recognised as bright lesions in fundus pictures, which can be the first indicator of diabetic retinopathy. With that in mind, the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis (IM-EDRD) with multi-level classifications. The model uses Support Vector Machine (SVM)-based classification to separate normal and abnormal fundus images at the first level. The input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix (GLCM). Furthermore, the presence of Exudate and Diabetic Retinopathy (DR) in fundus images is detected using the Adaptive Neuro Fuzzy Inference System (ANFIS) classifier at the second level of classification. Exudate detection, blood vessel extraction, and Optic Disc (OD) detection are all processed to achieve suitable results. Furthermore, the second level processing comprises Morphological Component Analysis (MCA) based image enhancement and object segmentation processes, as well as feature extraction for training the ANFIS classifier, to reliably diagnose DR. Furthermore, the findings reveal that the proposed model surpasses existing models in terms of accuracy, time efficiency, and precision rate with the lowest possible error rate.

Monitoring of Chlorophylls during the Maturation Stage of Plums by Multivariate Calibration of RGB Data from Digital Images

The methodology developed in this study was based on digital imaging processing of plums harvested in eight different weeks during their ripening process. Mean RGB data, histograms, and matrices of RGB data were used to characterise the ripening stage of the plums, in both qualitative and quantitative approaches, by using classification and quantification chemometric methods. An exploratory analysis of data was performed using principal component analysis (PCA) and parallel factor analysis (PARAFAC) in RGB histograms and matrices data, respectively, showing differences in the colour features since the fourth week of harvesting. In the case of the quantitative approach, high correlation was achieved between the histogram data, using partial least squares (PLS), and total chlorophyll content. In addition, between three-way matrixes and total chlorophyll content, good correlations were obtained applying unfolded-PLS (U-PLS) and N-way-PLS (N-PLS). The most accurate results were obtained on the green channel. Analytical parameters obtained were good, with determination coefficients (R2) higher than 0.91 for all models in the first and second-order multivariate analysis. In addition, relative errors of prediction (REPs) were lower than 12% in all models for the green channel. Therefore, the proposed method was a satisfactory alternative to destructive physiological and biochemical methods in the determination of total chlorophylls in plum samples. In the routine analysis, first-order multivariate calibration with PLS analysis is a good option due to the simplicity of data processing.

Retinal haemorrhages on ultra-widefield red channel images and perfusion status in central retinal vein occlusion.

To evaluate the relationship between retinal haemorrhages detected on Ultra-widefield (UWF) red channel images and perfusion status in eyes with acute central retinal vein occlusion (CRVO). UWF fundus images were split into green and red channels using ImageJ software. The retinal haemorrhages were calculated quantitatively with both the green and red channel images, resulting in green channel haemorrhages (GCH) and red channel haemorrhages (RCH). The nonperfusion area (NPA) was also calculated from fluorescein angiography in each eye. The relationships between both the GCH and RCH with the NPA were investigated. Thirty-two eyes of 32 patients with acute CRVO (18 men, 14 women) were included. The mean GCH and RCH values were 10.4% ± 8.2% and 1.7% ± 1.7%, respectively. The mean NPA was 39.2% ± 28.8%. Significant correlations were seen between the GCH and NPA (r = 0.38; P = 0.022) and RCH and NPA (r = 0.44; P = 0.010, linear regression analysis). Multivariate analysis suggested that only the RCHs were correlated significantly with the NPA. Retinal haemorrhages detected by UWF red channel imaging were less compared to green channel imaging and associated closely with retinal NPAs in eyes with acute CRVO. UWF red channel imaging allowed us to identify ischaemia-related haemorrhage.

Contrast Enhancement of RGB Retinal Fundus Images for Improved Segmentation of Blood Vessels Using Convolutional Neural Networks.

Retinal fundus images are non-invasively acquired and faced with low contrast, noise, and uneven illumination. The low-contrast problem makes objects in the retinal fundus image indistinguishable and the segmentation of blood vessels very challenging. Retinal blood vessels are significant because of their diagnostic importance in ophthalmologic diseases. This paper proposes improved retinal fundus images for optimal segmentation of blood vessels using convolutional neural networks (CNNs). This study explores some robust contrast enhancement tools on the RGB and the green channel of the retinal fundus images. The improved images undergo quality evaluation using mean square error (MSE), peak signal to noise ratio (PSNR), Similar Structure Index Matrix (SSIM), histogram, correlation, and intersection distance measures for histogram comparison before segmentation in the CNN-based model. The simulation results analysis reveals that the improved RGB quality outperforms the improved green channel. This revelation implies that the choice of RGB to the green channel for contrast enhancement is adequate and effectively improves the quality of the fundus images. This improved contrast will, in turn, boost the predictive accuracy of the CNN-based model during the segmentation process. The evaluation of the proposed method on the DRIVE dataset achieves an accuracy of 94.47, sensitivity of 70.92, specificity of 98.20, and AUC (ROC) of 97.56.

A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques

The effect of the carbon emission trading scheme on a firm’s total factor productivity: An analysis of corporate green innovation and resource allocation efficiency

This study investigates the effect of the carbon emission trading scheme on a firm’s total factor productivity in China. With a sample from 2008 to 2019, applying the time-varying DID method, our empirical results reveal that the carbon emission trading scheme significantly improves a firm’s total factor productivity, which provides evidence for Porter’s hypothesis. Moreover, there are two channels through which the total factor productivity is impacted: the corporate green innovation channel and the resource allocation efficiency channel. Furthermore, the impact of the carbon emission trading scheme is more pronounced for private firms, and firms in the provinces with higher institutional development, lower environmental quality, and greater law enforcement of environmental protection tend to have larger total factor productivity. Our models survive numerous robustness checks.