LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.Comment: Accepted at ICASSP 202

Th17/Treg Cells Imbalance and GITRL Profile in Patients with Hashimoto’s Thyroiditis

published_or_final_versio

Increased Frequency of Circulating Follicular Helper T Cells in Patients with Rheumatoid Arthritis

Follicular helper T (Tfh) cells are recognized as a distinct CD4+ helper T-cell subset, which provides for B-cell activation and production of specific antibody responses, and play a critical role in the development of autoimmune disease. So far, only one study investigated the circulating Tfh cells increased in a subset of SLE patients. Since relatively little is known about the Tfh cells in rheumatoid arthritis (RA) patients, in this study, Tfh-cell frequency, related cytokine IL-21, and transcription factor Bcl-6 were investigated in 53 patients with RA and 31 health controls. Firstly, we found that the frequency of CD4+CXCR5+ICOShigh Tfh cells was increased significantly in the peripheral blood of RA patients, compared with that in healthy controls. It is known that Tfh cells are critical for directing the development of an antibody response by germinal centers B cells; secondly, we observed that the Tfh-cell frequency is accompanied by the level of anti-CCP antibody in RA patients. Furthermore, expression of Bcl-6 mRNA and plasma IL-21 concentrations in RA patients was increased. Taken together, these findings have shown that the increased frequency of circulating Tfh cells is correlated with elevated levels of anti-CCP antibody, indicating the possible involvement of Tfh cells in the disease progression of RA

Immunostimulatory Activity of Protein Hydrolysate from Oviductus Ranae on Macrophage In Vitro

Oviductus Ranae is the dry oviduct of Rana chensinensis, which is also called R. chensinensis oil. Oviductus Ranae is a valuable Chinese crude drug and is recorded in the Pharmacopoeia of the People’s Republic of China. The aim of this study was to investigate the immunostimulatory activity of protein hydrolysate of Oviductus Ranae (ORPH) and to assess its possible mechanism. Immunomodulatory activity of ORPH was examined in murine macrophage RAW 264.7 cells. The effect of ORPH on the phagocytic activity of macrophages was determined by the neutral red uptake assay. After treatment with ORPH, NO production levels in the culture supernatant were investigated by Griess assay. The mRNA and protein expressions of inducible nitric oxide synthase (iNOS) were detected by RT-PCR and Western blotting. The production of TNF-α, IL-1β, and IL-6 after treatment with ORPH was measured using ELISA assay. In addition, NF-κB levels were also investigated by Western blot. The results showed that ORPH enhanced the phagocytosis of macrophage, increased productions of TNF-α, IL-1β, IL-6, and NO in RAW 264.7 cells, and upregulated the mRNA and protein expression of iNOS. Besides, NF-κB, levels in RAW 264.7 cells were elevated after ORPH treatment. These findings suggested that ORPH might stimulate macrophage activities by activating the NF-κB pathway

Association between Long-Term Changes in Dietary Percentage of Energy from Fat and Obesity: Evidence from over 20 Years of Longitudinal Data

Objectives: This study assessed the associations between long-term trajectories of percentage of energy from fat (PEF) and obesity among Chinese adults. Methods: Longitudinal data collected by the China Health and Nutrition Survey from 1991 to 2015 were analyzed. A body mass index ≥28.0 was defined as general obesity. Participants’ baseline PEF levels were categorized as lower than the recommendation of the Chinese Dietary Guideline (20%), meeting the recommendation (20−30%), and higher than the recommendation (>30%). Patterns of PEF trajectories were identified by latent class trajectory analysis for overall participants and participants in different baseline PEF groups, respectively. Cox proportional hazards regression models with shared frailty were used to estimate associations between PEF and obesity. Results: Data on 13,025 participants with 72,191 visits were analyzed. Four patterns of PEF trajectory were identified for overall participants and participants in three different baseline PEF groups, respectively. Among overall participants, compared with “Baseline Low then Increase Pattern” (from 12% to 20%), participants with “Baseline Normal-Low then Increase-to-High Pattern” (from 20% to 32%) had a higher hazard of obesity (hazard ratio (HR) and 95% confident interval (CI) at 1.18 (1.01−1.37)). Compared with the “Stable Pattern” group (stable at around 18% and 22%, respectively), participants with “Sudden-Increase Pattern” (from 18% to 30%) in the baseline group whose PEF levels were lower than the recommendation and those with “Sudden-Increase then Decrease Pattern” (rapidly increased from 25% to 40%, and then decreased) in the baseline group who met the recommendation had higher hazards of obesity (HRs and 95% CIs being 1.65 (1.13−2.41) and 1.59 (1.03−2.46), respectively). Conclusions: Adults with a trajectory that involved a sudden increase to a high-level PEF had a higher risk of general obesity. People should avoid increasing PEF suddenly

Development of a novel three-dimensional deformable mirror with removable influence functions for high precision wavefront correction in adaptive optics system

Deformable mirror is a widely used wavefront corrector in adaptive optics system, especially in astronomical, image and laser optics. A new structure of DM-3D DM is proposed, which has removable actuators and can correct different aberrations with different actuator arrangements. A 3D DM consists of several reflection mirrors. Every mirror has a single actuator and is independent of each other. Two kinds of actuator arrangement algorithm are compared: random disturbance algorithm (RDA) and global arrangement algorithm (GAA). Correction effects of these two algorithms and comparison are analyzed through numerical simulation. The simulation results show that 3D DM with removable actuators can obviously improve the correction effects.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Rice Disease and Pest Recognition Method Integrating ECA Mechanism and DenseNet201

ObjectiveTo address the problems of low efficiency and high cost of traditional manual identification of pests and diseases, improve the automatic recognition of pests and diseases by introducing advanced technical means, and provide feasible technical solutions for agricultural pest and disease monitoring and prevention and control, a rice image recognition model GE-DenseNet (G-ECA DenseNet) based on improved ECA (Efficient Channel Attention) mechanism with DenseNet201 was proposed.MethodsThe leaf images of three pests and diseases, namely, brownspot, hispa, leafblast and healthy rice were selected as experimental materials. The images were captured at the Zhuanghe Rice Professional Cooperative in Yizheng, Jiangsu Province, and the camera was used to manually take pictures from multiple angles such as the top and side of rice every 2 h, thus acquiring 1250 images of rice leaves under different lighting conditions, different perspectives, and different shading environments. In addition, samples about pests and diseases were collected in the Kaggle database. There were 1488 healthy leaves, 523 images of brownspot, 565 images of hispa, and 779 images of leafblast in the dataset. Since the original features of the pest and disease data were relatively close, firstly, the dataset was divided into a training set and a test set according to the ratio of 9:1, and then data enhancement was performed on the training set. A region of interest (ROI) was randomly selected to achieve a local scale of 1.1 to 1.25 for the sample images of the dataset, thus simulating the situation that only part of the leaves were captured in the actual shooting process due to the different distance of the plants from the camera. In addition, a random rotation of a certain angle was used to crop the image to simulate the different angles of the leaves. Finally, the experimental training set contains 18,018 images and the test set contains 352 images. The GE-DenseNet model firstly introduces the idea of Ghost module on the ECA attention mechanism to constitute the G-ECA Layer structure, which replaces the convolution operation with linear transformation to perform efficient fusion of channel features while avoiding dimensionality reduction when learning channel attention information and effectively enhancing its ability to extract features. Secondly, since the original Dense Block only considered the correlation between different layers and ignores the extraction of important channel information in the image recognition process, introducing G-ECA Layer before the original Dense Block of DenseNet201 gives the model a better channel feature extraction capability and thus improved the recognition accuracy. Due to the small dataset used in the experiment, the weight parameters of DenseNet201 pre-trained on the ImageNet dataset were migrated to GE-DenseNet. During the training process, the BatchSize size was set to 32, the number of iterations (Epoch) was set to 50, and the Focal Loss function was used to solve the problem of unbalanced samples for each classification. Meanwhile, the adaptive moment estimation (Adam) optimizer was used to avoid the problem of drastic gradient changes in back propagation due to random initialization of some weights at the early stage of model training, which weakened the uncertainty of network training to a certain extent.Results and DiscussionsExperimental tests were conducted on a homemade dataset of rice pests and diseases, and the recognition accuracy reached 83.52%. Comparing the accuracy change graphs and loss rate change graphs of GE-DenseNet and DenseNet201, it could be found that the proposed method in this study was effective in training stability, which could accelerate the speed of model convergence and improve the stability of the model, making the network training process more stable. And observing the visualization results of GE-DenseNet and DenseNet201 corresponding feature layers, it could be found that the features were more densely reflected around the pests and diseases after adding the G-ECA Layer structure. From the ablation comparison experiments of the GE-DenseNet model, it could be obtained that the model accuracy increased by 2.27% after the introduction of the Focal Loss function with the G-ECA Layer layer. Comparing the proposed model with the classical NasNet (4@1056), VGG-16 and ResNet50 models, the classification accuracy increased by 6.53%, 4.83% and 3.69%, respectively. Compared with the original DenseNet201, the recognition accuracy of hispa improved 20.32%.ConclusionsThe experimental results showed that the addition of G-ECA Layer structure enables the model to more accurately capture feature information suitable for rice pest recognition, thus enabling the GE-DenseNet model to achieve more accurate recognition of different rice pest images. This provides reliable technical support for timely pest and disease control, reducing crop yield loss and pesticide use. Future research can lighten the model and reduce its size without significantly reducing the recognition accuracy, so that it can be deployed in UAVs, tractors and various distributed image detection edge devices to facilitate farmers to conduct real-time inspection of farmland and further enhance the intelligence of agricultural production

Activation of the ABA Signal Pathway Mediated by GABA Improves the Drought Resistance of Apple Seedlings

Drought seriously affects the yield and quality of apples. γ-aminobutyric acid (GABA) plays an important role in the responses of plants to various stresses. However, the role and possible mechanism of GABA in the drought response of apple seedlings remain unknown. To explore the effect of GABA on apple seedlings under drought stress, seedlings of Malus hupehensis were treated with seven concentrations of GABA, and the response of seedlings under 15-day drought stress was observed. The results showed that 0.5 mM GABA was the most effective at relieving drought stress. Treatment with GABA reduced the relative electrical conductivity and MDA content of leaves induced by drought stress and significantly increased the relative water content of leaves. Exogenous GABA significantly decreased the stomatal conductance and intercellular carbon dioxide concentration and transpiration rate, and it significantly increased the photosynthetic rate under drought. GABA also reduced the accumulation of superoxide anions and hydrogen peroxide in leaf tissues under drought and increased the activities of POD, SOD, and CAT and the content of GABA. Exogenous treatment with GABA acted through the accumulation of abscisic acid (ABA) in the leaves to significantly decrease stomatal conductance and increase the stomatal closure rate, and the levels of expression of ABA-related genes PYL4, ABI1, ABI2, HAB1, ABF3, and OST1 changed in response to drought. Taken together, exogenous GABA can enhance the drought tolerance of apple seedlings

Location-grouping algorithm based on limited actuators deformable mirror for high precision wavefront aberration correction in adaptive optics system

The aberration in the center position of wavefront can be corrected well when the deformable mirrors (DM) used in high-resolution adaptive optics system of telescope. However, for the defocus and spherical aberration of telescope, the four corners of the wavefront cannot be corrected well. A novel correction method with different levels and regions of deformable mirror is proposed to solve this problem. The control elements of wavefront in four corners are divided. And every four or five DM units in one corner is in a group. Compared with conventional correction method, the location-grouping method showed significant advantages in correction of different order aberrations.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]