The automatic detection of aircrafts from SAR images is widely applied in both military and civil fields, but there are still considerable challenges. To address the high variety of aircraft sizes and complex background information in SAR images, a new fast detection framework based on convolution neural networks is proposed, which achieves automatic and rapid detection of aircraft with high accuracy. First, the airport runway areas are detected to generate the airport runway mask and rectangular contour of the whole airport are generated. Then, a new deep neural network proposed in this paper, named Efficient Weighted Feature Fusion and Attention Network (EWFAN), is used to detect aircrafts. EWFAN integrates the weighted feature fusion module, the spatial attention mechanism, and the CIF loss function. EWFAN can effectively reduce the interference of negative samples and enhance feature extraction, thereby significantly improving the detection accuracy. Finally, the airport runway mask is applied to the detected results to reduce false alarms and produce the final aircraft detection results. To evaluate the performance of the proposed framework, large-scale Gaofen-3 SAR images with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EWFAN algorithm are 95.4% and 3.3%, respectively, which outperforms Efficientdet and YOLOv4. In addition, the average test time with the proposed framework is only 15.40 s, indicating satisfying efficiency of automatic aircraft detection.

In aircraft detection from synthetic aperture radar (SAR) images, there are several major challenges: the shattered features of the aircraft, the size heterogeneity and the interference of a complex background. To address these problems, an Efficient Bidirectional Path Aggregation Attention Network (EBPA2N) is proposed. In EBPA2N, YOLOv5s is used as the base network and then the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module are proposed and systematically integrated to improve the detection accuracy of the aircraft. The IEPA module aims to effectively extract advanced semantic and spatial information to better capture multi-scale scattering features of aircraft. Then, the lightweight ERSA module further enhances the extracted features to overcome the interference of complex background and speckle noise, so as to reduce false alarms. To verify the effectiveness of the proposed network, Gaofen-3 airports SAR data with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EBPA2N algorithm are 93.05% and 4.49%, respectively, which is superior to the latest networks of EfficientDet-D0 and YOLOv5s, and it also has an advantage of detection speed.

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This study investigated the effect of magnesium sulfate (MgSO 4) on the viability of the human gastric adenocarcinoma (AGS) epithelial cell line. AGS cells were exposed to various concentrations of MgSO 4 for different time-periods and the effects on cell viability, DNA fragmentation, apoptotic gene expression, and caspase activity were investigated. A range of tests were employed in this study, including a lactate dehydrogenase cell viability assay, annexin V staining, reverse transcription polymerase chain reaction (RT-PCR), terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL), and several caspase activity assays. Expression of tumor necrosis factor-related apoptosisinducing ligand (TRAIL), Fas ligand (FasL) and Fas receptor (FasR) were analyzed using RT-PCR. These analyses revealed that MgSO 4 induced a dose-dependent reduction in AGS cell viability. Annexin V staining did not differ between negative control and treated cells, indicating that no apoptotic cells were detected. The TUNEL assay data were consistent with the annexin V staining results. FasL mRNA expression was increased and FasR mRNA expression was decreased in a dose-dependent manner by MgSO 4. MgSO 4 treatment tended to cause an initial (1 h) dose-dependent increase in the activities of caspase-3,-6,-8, and-9; however, these activities were subsequently inhibited (24 h). We deduced that MgSO 4 exerted an anti-apoptotic effect in AGS cells via caspase activation.

Although deep learning has achieved great success in aircraft detection from SAR imagery, its blackbox behavior has been criticized for low comprehensibility and interpretability. Such challenges have impeded the trustworthiness and wide application of deep learning techniques in SAR image analytics. In this paper, we propose an innovative eXplainable Artificial Intelligence (XAI) framework to glassbox deep neural networks (DNN) by using aircraft detection as a case study. This framework is composed of three parts: hybrid global attribution mapping (HGAM) for backbone network selection, path aggregation network (PANet), and class-specific confidence scores mapping (CCSM) for visualization of the detector. HGAM integrates the local and global XAI techniques to evaluate the effectiveness of DNN feature extraction; PANet provides advanced feature fusion to generate multi-scale prediction feature maps; while CCSM relies on visualization methods to examine the detection performance with given DNN and input SAR images. This framework can select the optimal backbone DNN for aircraft detection and map the detection performance for better understanding of the DNN. We verify its effectiveness with experiments using Gaofen-3 imagery. Our XAI framework offers an explainable approach to design, develop, and deploy DNN for SAR image analytics.

Objective. The aim of this study is to explore the clinical effect of emergency dermabrasion combined with biological dressing A on wound microcirculation and preventing sepsis in deep degree-II burns. Methods. A total of 90 patients with deep degree-II burns admitted to the hospital were retrospectively enrolled between January 2020 and January 2022. According to different treatment methods, they were divided into the control group (42 cases, biological dressing A) and the observation group (48 cases, emergency dermabrasion combined with biological dressing A). The clinical curative effect in both groups was observed. The wound repair rate and wound healing quality, and changes in levels of wound microcirculation-related indexes (serum epidermal growth factor (EGF), wound blood flow, and partial pressure of transcutaneous oxygen) and inflammatory cytokines (C-reactive protein (CPR), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR), and procalcitonin (PCT)) before treatment, at 3d and 7d after treatment were compared between the two groups. The incidence of wound infection and sepsis in both groups was recorded. Results. The wound healing time in the observation group was significantly shorter than that in the control group, and wound healing quality in the observation group was better than that in the control group ( P < 0.05 ). At 3 d and 7d after treatment, the levels of serum EGF, wound blood flow and partial pressure of transcutaneous oxygen in both groups were all increased ( P < 0.05 ), which were higher in the observation group than those in the control group ( P < 0.05 ). The levels of CRP, IL-6, ESR, and PCT in both groups were all decreased ( P < 0.05 ), which were lower in the observation group than those in the control group ( P < 0.05 ). There was no significant difference in incidence of sepsis between observation group and control group (4.17% (2/48) vs. 7.14% (3/42)) (Fisher = 0.539). Conclusion. Emergency dermabrasion combined with biological dressing A can effectively improve wound microcirculation in patients with deep degree-II burns, promote wound healing, shorten wound healing time, improve wound healing quality, effectively control inflammatory response, and prevent sepsis.