FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

Photobleaching-Corrected FRET Efficiency Imaging of Live Cells

Acceptor-sensitised 3-cube fluorescence resonance energy transfer (FRET) imaging (also termed as E-FRET imaging) is a popular fluorescence intensity-based FRET quantification method. Here, an automated E-FRET microscope with user-friendly interfaces was set up for dynamical online quantitative live-cell FRET imaging. This microscope reduces the time of a quantitative E-FRET imaging from 12 to 3 s. After locating cells, calibration of the microscope and E-FRET imaging of the cells can be performed automatically by clicking 'Capture' button on interfaces. E-FRET imaging was performed on the microscope for living cells expressing different FRET tandem constructs. Dynamical E-FRET imaging on the microscope for live cells coexpressing CFP-Bax and YFP-Bax treated by staurosporine (STS) revealed three Bax redistribution stages: Bax translocation from cytosol to mitochondria within 10 min, membrane insertion with conformational change on mitochondrial membrane within about 30 min, and subsequent oligomerisation within about 10 min. Because of excellent user-friendly interface and stability, the automated E-FRET microscope is a convenient tool for quantitative FRET imaging of living cell. LAY DESCRIPTION: Acceptor-sensitised 3-cube fluorescence resonance energy transfer (FRET) imaging (also termed as E-FRET) is a popular fluorescence intensity-based FRET quantification methods. E-FRET measurements are currently performed manually, and a complete FRET measurement takes about 12 s. E-FRET measurement necessitates not only a skilled operator and specialised equipment but also expertise in the interpretation of FRET signals, a considerable challenge in the application of FRET technology in living cells. Furthermore, manual E-FRET microscope is hard to perform dynamical quantitative FRET measurement, the ever-increasing applications in mapping the biochemical signal transduction within cells. Here, an automated E-FRET microscope with user-friendly interfaces was set up for dynamical online quantitative live-cell FRET imaging. This microscope reduces the time of a quantitative E-FRET imaging from 12 to 3 s. After locating cells, calibration of the microscope and E-FRET imaging of the cells can be performed automatically by clicking 'Capture' button on interfaces. Because of excellent user-friendly interface and stability, the automated E-FRET microscope is a convenient tool for quantitative FRET imaging of living cell.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study

Flp-In(TM) T-REx(TM) 293 cells expressing a wild type human M(3) muscarinic acetylcholine receptor construct constitutively and able to express a receptor activated solely by synthetic ligand (RASSL) form of this receptor on demand maintained response to the muscarinic agonist carbachol but developed response to clozapine N-oxide only upon induction of the RASSL. The two constructs co-localized at the plasma membrane and generated strong ratiometric fluorescence resonance energy transfer (FRET) signals consistent with direct physical interactions. Increasing levels of induction of the FRET donor RASSL did not alter wild type receptor FRET-acceptor levels substantially. However, ratiometric FRET was modulated in a bell-shaped fashion with maximal levels of the donor resulting in decreased FRET. Carbachol, but not the antagonist atropine, significantly reduced the FRET signal. Cell surface homogeneous time-resolved FRET, based on SNAP-tag technology and employing wild type and RASSL forms of the human M(3) receptor expressed stably in Flp-In(TM) TREx(TM) 293 cells, also identified cell surface dimeric/oligomeric complexes. Now, however, signals were enhanced by appropriate selective agonists. At the wild type receptor, large increases in FRET signal to carbachol and acetylcholine were concentration-dependent with EC(50) values consistent with the relative affinities of the two ligands. These studies confirm the capacity of the human M(3) muscarinic acetylcholine receptor to exist as dimeric/oligomeric complexes at the surface of cells and demonstrate that the organization of such complexes can be modified by ligand binding. However, conclusions as to the effect of ligands on such complexes may depend on the approach used.

Binding of the human immunodeficiency virus (HIV) envelope gp120 glycoprotein to CD4 and CCR5 receptors on the plasma membrane initiates the viral entry process. Although plasma membrane cholesterol plays an important role in HIV entry, its modulating effect on the viral entry process is unclear. Using fluorescence resonance energy transfer imaging, we have provided evidence here that CD4 and CCR5 localize in different microenvironments on the surface of resting cells. Binding of the third variable region V3-containing gp120 core to CD4 and CCR5 induced association between these receptors, which could be directly monitored by fluorescence resonance energy transfer on the plasma membrane of live cells. Depletion of cholesterol from the plasma membrane abolished the gp120 core-induced associations between CD4 and CCR5, and reloading cholesterol restored the associations in live cells. Our studies suggest that, during the first step of the HIV entry process, gp120 binding alters the microenvironments of unbound CD4 and CCR5, with plasma membrane cholesterol required for the formation of the HIV entry complex.

Highlights A semantic segmentation model is proposed for paddy field images that uses a lightweight backbone network and a boundary enhancement module to enable end-to-end training with annotated images. Morphological operations are employed to achieve field ridge closure. Abstract. The precise positioning data for farmland boundaries serves as crucial support for creating high-precision farmland maps, guiding intelligent agricultural machinery in autonomous field operations, and accurately measuring the coverage areas. This study proposes a method for the automatic extraction of farmland boundaries based on low-altitude remote sensing images with Unmanned Aerial Vehicles (UAVs). To effectively segment the boundaries of paddy fields in UAV low-altitude remote sensing images, a semantic segmentation model is constructed, which is based on an improved version of the Mask R-CNN model. The network architecture incorporates MobileNet V2 and Feature Pyramid Networks (FPN) as the backbone network, while the PointRend boundary enhancement module is introduced. Experimental results demonstrate the effectiveness of the proposed model. The experiments were trained and tested on paddy fields in the dataset; the results showed that the mean values of MPA, MIoU, the Average Inference Time per Single Image, and the Quantity of Model Parameters reached 0.9308, 0.8996, 2.6 s, and 6.12 M. When compared to the original model, the improved model brought about an increase in the measures of performance of 0.015 (MPA) and 0.019 (MIoU), while the performance measures decreased by 0.847 (the Average Inference Time per Single Image) and 0.202 (the Quantity of Model Parameters). To address the issue of incomplete paddy field boundary segmentation, a series of morphological operations, including erosion operations and dilation operations, is iteratively applied. The results indicate that the mean pixel accuracy (MPA) of the morphologically processed farmland image is 93.14%, and the mean intersection over union (MIoU) is 89.98%. This iterative process aids in achieving field ridge closure, thereby facilitating the acquisition of boundary information for narrow field ridges and incomplete ridges caused by agricultural machinery. Keywords: Deep learning, Morphological operations, Paddy field boundaries, Semantic segmentation.

To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network’s output to ensure the consistency of each channel’s output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification.

Calpains are Ca(2+)-dependent cysteine proteases known to be important for the regulation of cell functions and which aberrant activation causes cell death in a number of degenerative disorders. To provide a tool for monitoring the status of calpain activity in vivo under physiological and pathological conditions, we created a mouse model that expresses ubiquitously a fluorescent reporter consisting of eCFP and eYFP separated by a linker cleavable by the ubiquitous calpains. We named this mouse CAFI for calpain activity monitored by FRET imaging. Our validation studies demonstrated that the level of calpain activity correlates with a decrease in FRET (fluorescence resonance energy transfer) between the two fluorescent proteins. Using this model, we observed a small level of activity after denervation and fasting, a high level of activity during muscle regeneration and ischemia, and local activity in damaged myofibers after exercise. Finally, we crossed the CAFI mouse with the alpha-sarcoglycan-deficient model, demonstrating an increase of calpain activity at the steady state. Altogether, our results present evidence that CAFI mice could be a valuable tool in which to follow calpain activity at physiological levels and in disease states.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Fibroadenoma is a common benign breast disease that affects women of all ages. Early diagnosis can greatly improve the treatment outcomes and reduce the associated pain. Computer-aided diagnosis (CAD) has great potential to improve diagnosis accuracy and efficiency. However, its application in sonography is limited. A network that utilizes expansive receptive fields and local information learning was proposed for the accurate segmentation of breast fibroadenomas in sonography. The architecture comprises the Hierarchical Attentive Fusion module, which conducts local information learning through channel-wise and pixel-wise perspectives, and the Residual Large-Kernel module, which utilizes multiscale large kernel convolution for global information learning. Additionally, multiscale feature fusion in both modules was included to enhance the stability of our network. Finally, an energy function and a data augmentation method were incorporated to fine-tune low-level features of medical images and improve data enhancement. The performance of our model is evaluated using both our local clinical dataset and a public dataset. Mean pixel accuracy (MPA) of 93.93% and 86.06% and mean intersection over union (MIOU) of 88.16% and 73.19% were achieved on the clinical and public datasets, respectively. They are significantly improved over state-of-the-art methods such as SegFormer (89.75% and 78.45% in MPA and 83.26% and 71.85% in MIOU, respectively). The proposed feature extraction strategy, combining local pixel-wise learning with an expansive receptive field for global information perception, demonstrates excellent feature learning capabilities. Due to this powerful and unique local-global feature extraction capability, our deep network achieves superior segmentation of breast fibroadenoma in sonography, which may be valuable in early diagnosis.

Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.

Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.

Cloud and snow identification in remote sensing images is critical for snow mapping and snow hydrology research. Aimed at the problem that the semantic segmentation model is prone to producing blurred boundaries, slicing traces and isolated small patches for cloud and snow identification in high-resolution remote sensing images, the feasibility of combining DeepLab v3+ and conditional random field (CRF) models for cloud and snow identification based on GF-1 WFV images is studied. For GF-1 WFV images, the model training and testing experiments under the conditions of different sample numbers, sample sizes and loss functions are compared. The results show that, firstly, when the number of samples is 10,000, the sample size is 256 × 256, and the loss function is the Focal function, the model accuracy is the optimal and the Mean Intersection over Union (MIoU) and the Mean Pixel Accuracy (MPA) reach 0.816 and 0.918, respectively. Secondly, after post-processing with the CRF model, the MIoU and the MPA are improved to 0.836 and 0.941, respectively, compared with those without post-processing. Moreover, the misclassifications such as blurred boundaries, slicing traces and isolated small patches are significantly reduced, which indicates that the combination of the DeepLab v3+ and CRF models has high accuracy and strong feasibility for cloud and snow identification in high-resolution remote sensing images. The conclusions can provide a reference for high-resolution snow mapping and hydrology applications using deep learning models.

As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment.

CvT-UNet: A weld pool segmentation method integrating a CNN and a transformer