Search Results (12)

Far-red fluorescent proteins (FPs) have emerged as indispensable tools in in vivo imaging, playing a pivotal role in elucidating fundamental mechanisms and addressing application issues in biotechnology and biomedical fields. Their ability for deep penetration, coupled with reduced light scattering and absorption, robust resistance to autofluorescence, and diminished phototoxicity, has positioned far-red biosensors at the forefront of non-invasive visualization techniques for observing intracellular activities and intercellular behaviors. In this review, far-red FPs and their applications in living systems are mainly discussed. Firstly, various far-red FPs, characterized by emission peaks spanning from 600 nm to 650 nm, are introduced. This is followed by a detailed presentation of the fundamental principles enabling far-red biosensors to detect biomolecules and environmental changes. Furthermore, the review accentuates the superiority of far-red FPs in multi-color imaging. In addition, significant emphasis is placed on the value of far-red FPs in improving imaging resolution, highlighting their great contribution to the advancement of in vivo imaging. Full article

Underwater vision technology is crucial for marine exploration, aquaculture, and environmental monitoring. However, the challenging underwater conditions, including light attenuation, color distortion, reduced contrast, and blurring, pose difficulties. Current deep learning models and traditional image enhancement techniques are limited in addressing these challenges, making it challenging to acquire high-quality underwater image signals. To overcome these limitations, this study proposes an approach called adaptive multi-scale multi-color space underwater image enhancement with GAN-physics fusion (AMSMC-UGAN). AMSMC-UGAN leverages multiple color spaces (RGB, HSV, and Lab) for feature extraction, compensating for RGB’s limitations in underwater environments and enhancing the use of image information. By integrating a membership degree function to guide deep learning based on physical models, the model’s performance is improved across different underwater scenes. In addition, the introduction of a multi-scale feature extraction module deepens the granularity of image information, learns the degradation distribution of different image information of the same image content more comprehensively, and provides useful guidance for more comprehensive data for image enhancement. AMSMC-UGAN achieved maximum scores of 26.04 dB, 0.87, and 3.2004 for PSNR, SSIM, and UIQM metrics, respectively, on real and synthetic underwater image datasets. Additionally, it obtained gains of at least 6.5%, 6%, and 1% for these metrics. Empirical evaluations on real and artificially distorted underwater image datasets demonstrate that AMSMC-GAN outperforms existing techniques, showcasing superior performance with enhanced quantitative metrics and strong generalization capabilities. Full article

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%. Full article

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products. Full article

Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning. Full article

Natural disturbances like hurricanes can cause extensive disorder in forest structure, composition, and succession. Consequently, ecological, social, and economic alterations may occur. Terrestrial laser scanning (TLS) and deep learning have been used for estimating forest attributes with high accuracy, but to date, no study has combined both TLS and deep learning for assessing the impact of hurricane disturbance at the individual tree level. Here, we aim to assess the capability of TLS and convolutional neural networks (CNNs) combined for classifying post-Hurricane Michael damage severity at the individual tree level in a pine-dominated forest ecosystem in the Florida Panhandle, Southern U.S. We assessed the combined impact of using either binary-color or multicolored-by-height TLS-derived 2D images along with six CNN architectures (Densenet201, EfficientNet_b7, Inception_v3, Res-net152v2, VGG16, and a simple CNN). The confusion matrices used for assessing the overall accuracy were symmetric in all six CNNs and 2D image variants tested with overall accuracy ranging from 73% to 92%. We found higher F-1 scores when classifying trees with damage severity varying from extremely leaning, trunk snapped, stem breakage, and uprooted compared to trees that were undamaged or slightly leaning (<45°). Moreover, we found higher accuracies when using VGG16 combined with multicolored-by-height TLS-derived 2D images compared with other methods. Our findings demonstrate the high capability of combining TLS with CNNs for classifying post-hurricane damage severity at the individual tree level in pine forest ecosystems. As part of this work, we developed a new open-source R package (rTLsDeep) and implemented all methods tested herein. We hope that the promising results and the rTLsDeep R package developed in this study for classifying post-hurricane damage severity at the individual tree level will stimulate further research and applications not just in pine forests but in other forest types in hurricane-prone regions. Full article

Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these descriptors are used with a predefined setting of their parameters and computed from images coded in a chosen color space. The prior choice of a color space, a descriptor and its setting suited to a given application is a crucial but difficult problem that strongly impacts the classification results. To overcome this problem, this paper proposes a color texture representation that simultaneously takes into account the properties of several settings from different descriptors computed from images coded in multiple color spaces. Since the number of color texture features generated from this representation is high, a dimensionality reduction scheme by clustering-based sequential feature selection is applied to provide a compact hybrid multi-color space (CHMCS) descriptor. The experimental results carried out on five benchmark color texture databases with five color spaces and manifold settings of two texture descriptors show that combining different configurations always improves the accuracy compared to a predetermined configuration. On average, the CHMCS representation achieves 94.16% accuracy and outperforms deep learning networks and handcrafted color texture descriptors by over 5%, especially when the dataset is small. Full article

With the development of computer hardware and deep learning, face manipulation videos represented by Deepfake have been widely spread on social media. From the perspective of symmetry, many forensics methods have been raised, while most detection performance might drop under compression attacks. To solve this robustness issue, this paper proposes a Deepfake video detection method based on MesoNet with preprocessing module. First, the preprocessing module is established to preprocess the cropped face images, which increases the discrimination among multi-color channels. Next, the preprocessed images are fed into the classic MesoNet. The detection performance of proposed method is verified on two datasets; the AUC on FaceForensics++ can reach 0.974, and it can reach 0.943 on Celeb-DF which is better than the current methods. More importantly, even in the case of heavy compression, the detection rate can still be more than 88%. Full article

Gene therapy is expected to be used for the treatment of peritoneal fibrosis, which is a serious problem associated with long-term peritoneal dialysis. Hepatocyte growth factor (HGF) is a well-known anti-fibrotic gene. We developed an ultrasound and nanobubble-mediated (sonoporation) gene transfection system, which selectively targets peritoneal tissues. Thus, we attempted to treat peritoneal fibrosis by sonoporation-based human HGF (hHGF) gene transfection in mice. To prepare a model of peritoneal fibrosis, mice were intraperitoneally injected with chlorhexidine digluconate. We evaluated the preventive and curative effects of sonoporation-based hHGF transfection by analyzing the following factors: hydroxyproline level, peritoneum thickness, and the peritoneal equilibration test. The transgene expression characteristics of sonoporation were also evaluated using multicolor deep imaging. In early-stage fibrosis in mice, transgene expression by sonoporation was observed in the submesothelial layer. Sonoporation-based hHGF transfection showed not only a preventive effect but also a curative effect for early-stage peritoneal fibrosis. Sonoporation-based hHGF transfection may be suitable for the treatment of peritoneal fibrosis regarding the transfection characteristics of transgene expression in the peritoneum under fibrosis. Full article

The present study investigated a pulmonary delivery system of plasmid DNA (pDNA) and its application to melanoma DNA vaccines. pCMV-Luc, pEGFP-C1, and pZsGreen were used as a model pDNA to evaluate transfection efficacy after inhalation in mice. Naked pDNA and a ternary complex, consisting of pDNA, dendrigraft poly-l-lysine (DGL), and γ-polyglutamic acid (γ-PGA), both showed strong gene expression in the lungs after inhalation. The transgene expression was detected in alveolar macrophage-rich sites by observation using multi-color deep imaging. On the basis of these results, we used pUb-M, which expresses melanoma-related antigens (ubiquitinated murine melanoma gp100 and tyrosinase-related protein 2 (TRP2) peptide epitopes), as DNA vaccine for melanoma. The inhalation of naked pUb-M and its ternary complex significantly inhibited the metastasis of B16-F10 cells, a melanoma cell line, in mice. The levels of the inflammatory cytokines, such as TNF-α, IFN-γ, and IL-6, which enhance Th1 responses, were higher with the pUb-M ternary complex than with naked pUb-M and pEGFP-C1 ternary complex as control. In conclusion, we clarified that the inhalation of naked pDNA as well as its ternary complex are a useful technique for cancer vaccination. Full article

Simultaneous imaging of multiple targets (SIMT) in opaque biological tissues is an important goal for molecular imaging in the future. Multi-color fluorescence imaging in deep tissues is a promising technology to reach this goal. In this work, we developed a dual-modality imaging system by combining our recently developed ultrasound-switchable fluorescence (USF) imaging technology with the conventional ultrasound (US) B-mode imaging. This dual-modality system can simultaneously image tissue acoustic structure information and multi-color fluorophores in centimeter-deep tissue with comparable spatial resolutions. To conduct USF imaging on the same plane (i.e., x-z plane) as US imaging, we adopted two 90°-crossed ultrasound transducers with an overlapped focal region, while the US transducer (the third one) was positioned at the center of these two USF transducers. Thus, the axial resolution of USF is close to the lateral resolution, which allows a point-by-point USF scanning on the same plane as the US imaging. Both multi-color USF and ultrasound imaging of a tissue phantom were demonstrated. Full article

Lanthanide-doped upconverting nanoparticles (UCNPs) possess uniqueanti-Stokes optical properties, in which low energy near-infrared (NIR) photons can beconverted into high energy UV, visible, shorter NIR emission via multiphoton upconversionprocesses. Due to the rapid development of synthesis chemistry, lanthanide-doped UCNPscan be fabricated with narrow distribution and tunable multi-color optical properties. Theseunique attributes grant them unique NIR-driven imaging/drug delivery/therapeuticapplications, especially in the cases of deep tissue environments. In this brief review, weintroduce both the basic concepts of and recent progress with UCNPs in material engineeringand theranostic applications in imaging, molecular delivery, and tumor therapeutics. The aimof this brief review is to address the most typical progress in basic mechanism, materialdesign as bioimaging tools. Full article