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Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Azimuth ambiguities appear widely throughout spaceborne Synthetic Aperture Radar (SAR) images. If the ambiguous energy is relatively strong, a large number of brilliant areas or points will emerge, which may be erroneously judged as actual targets. This is a disadvantage in image interpretation. Due to the fact that ambiguous energy is mixed with energy from the main zone in the frequency and time domains, it is difficult to suppress azimuth ambiguity to a reasonable level using the existing approach without loss of resolution. This study proposes an innovative approach for suppressing azimuth ambiguity based on the compressive sensing recovery framework, in which the original image acts as prior information and the corresponding frequency spectrum truncated in a proper ratio acts as measurement information. With the proposed approach, highresolution low-ambiguity images can be obtained by iteration. We used simulation and satellite data to validate the effectiveness of this proposed approach in suppressing azimuth ambiguity.

The resolution degradation poses a huge challenge for object detection (OD) in aerial imagery. The existing methods use super-resolution (SR) based on generative adversarial network (GAN) to restore texture details in degraded images. However, constrained detection results are still acquired due to object feature difference between restored and clear images. Therefore, we propose a simple yet effective learning method called mutual-feed learning (MFL) to solve the problem in this article. A closed-loop structure is designed via building the feedback connection based on the feedforward connection between the two tasks. It effectively delivers the object spatial and feature information from OD to SR and provides restoration-enhanced images from SR to OD. Specifically, a feedback of region of interest (FROI) module is introduced to realize a region-level discrimination under the guidance of object information. It guides the discrimination process of SR. Furthermore, a multiscale object information (MSOI) module is developed to implement a feature-level restoration by narrowing the differences in object-related features. It improves the generation process of SR. Then OD can be performed in restoration-enhanced images to obtain more accurate results. Extensive experiments over Northwestern Polytechnical University (NWPU) Very High Resolution (VHR)-10, Cars Overhead With Context (COWC), and Fine-grained Object Recognition (FAIR)1M datasets show that the method can achieve state-of-the-art results. [ABSTRACT FROM AUTHOR]

Depression is a serious public health problem and an important factor leading to disease-related disability. Influenced by many factors, such as psychological, hormonal, and genetic factors, the incidence rate of depression in females is approximately two times that in males. However, in preclinical neuroscience research, the selection of the animals' sex for use in depression models has been controversial. At present, in most preclinical studies, the animals generally chosen in depression models have been male rodents rather than female rodents. It remains doubtful whether the data obtained from male animals can be generalized to female animals. The performance of female animals in preclinical studies of depression has been inconclusive. Based on a review of a large number of original studies in the PubMed database, it was found that although male rodents are more commonly used in the study of depression, the use of female animals also shows good modeling of depression and has its advantages. The influence of the animals' sex in the chronic unpredictable mild stress (CUMS) model needs further research. [ABSTRACT FROM AUTHOR]

Boundary information is essential for the semantic segmentation of remote sensing images. However, most existing methods were designed to establish strong contextual information while losing detailed information, making it challenging to extract and recover boundaries accurately. In this article, a boundary-sensitive network (BSNet) is proposed to address this problem via dynamic hybrid gradient convolution (DHGC) and coordinate sensitive attention (CSA). Specifically, in the feature extraction stage, we propose DHGC to replace vanilla convolution (VC), which adaptively aggregates one VC kernel and two gradient convolution kernels (GCKs) into a new operator to enhance boundary information extraction. The GCKs are proposed to explicitly encode boundary information, which is inspired by traditional Sobel operators. In the feature recovery stage, the CSA is introduced. This module is used to reconstruct the sharp and detailed segmentation results by adaptively modeling the boundary information and long-range dependencies in the low-level features as the assistance of high-level features. Note that DHGC and CSA are plug-and-play modules. We evaluate the proposed BSNet on three public datasets: the ISPRS 2-D semantic labeling Vaihingen, the Potsdam benchmark, and the iSAID dataset. The experimental results indicate that BSNet is a highly effective architecture that produces sharper predictions around object boundaries and significantly improves the segmentation accuracy. Our method demonstrates superior performance on the Vaihingen, the Potsdam benchmark, and the iSAID dataset in terms of the mean $F_{1}$ , with improvements of 4.6%, 2.3%, and 2.4% over strong baselines, respectively. The code and models will be made publicly available. [ABSTRACT FROM AUTHOR]

Lightweight object detector is currently gaining more and more popularity in remote sensing. In general, it is hard for lightweight detectors to achieve competitive performance compared with the traditional deep models, while knowledge distillation (KD) is a promising training method to tackle the issue. Since the background is more complicated and the object size varies extremely in remote sensing images, it will deliver lots of noise and affect the training performance when directly applying the existing KD methods. To tackle the above problems, we propose an adaptive reinforcement supervision distillation (ARSD) framework to promote the detection capability of the lightweight model. First, we put forward a multiscale core features imitation (MCFI) module for transferring the knowledge of features, which can adaptively select the multiscale core features of objects for distillation and focus more on the features of small objects by an area-weighted strategy. In addition, a strict supervision regression distillation (SSRD) module is designed to select the optimal regression results for distillation, which facilitates the student to effectively imitate the more precise regression output of the teacher network. Massive experiments on a large-scale Dataset for Object deTection in Aerial images (DOTA), object DetectIon in Optical Remote sensing images (DIOR), and Northwestern Polytechnical University Very-High-Resolution 10-class (NWPU VHR) datasets prove that ARSD outperforms the existing distillation state-of-the-art (SOTA) methods. Moreover, the performance of the lightweight model trained with our method transcends other classic heavy and lightweight detectors, which beneficiates the development of lightweight models. [ABSTRACT FROM AUTHOR]

Range sidelobe is very common in synthetic aperture radar (SAR) images, particularly when imaging scene includes strongly scattering targets such as ships or complex buildings. As a kind of interference, it may reduce the image quality and hinder the image interpretation. Hence, range sidelobe suppression is an important mission for SAR images. The main task of mitigating the sidelobe is how to achieve the most effective suppression with the minimal resolution loss and signal-to-noise ratio (SNR) loss. However, the widely recognized classic method, spatially variant apodization (SVA), still has a lot of residual sidelobe energy and other problems. This article proposes a novel suppression approach based on time-variant transmission of chaotic frequency modulation (CFM) signals. The key is to build an appropriate transmitted signal set, where the signals are generated by various chaotic initial states and the same special map with low mixing rate and uniform invariant probability density (IPD). Due to their beneficial autocorrelation properties, the proposed approach achieves superior performance in range sidelobe suppression and resolution preservation. More importantly, it maintains the energy of the signals and overcomes the SNR loss that occurs in some classic methods, such as spectral weighting (SW) and SVA. In addition, it is suitable for both vertical and squint side-looking mode and can well reconstruct the weakly scattering targets which are severely disturbed by range sidelobe. All of them are validated by comparative experiments. [ABSTRACT FROM AUTHOR]

Myeloid‐derived suppressor cells (MDSCs) play an important role in tumor immune escape. Recent studies have shown that MDSCs contribute to tumor progression under psychological stress, but the underlying mechanism of MDSCs mobilization and recruitment remains largely unknown. In the present study, a chronic restraint stress paradigm was applied to the H22 hepatocellular carcinoma (HCC) bearing mice to mimic the psychological stress. We observed that chronic restraint stress significantly promoted HCC growth, as well as the mobilization of MDSCs to spleen and tumor sites from bone marrow. Meanwhile, chronic restraint stress enhanced the expression of C‐X‐C motif chemokine receptor 2 (CXCR2) and pErk1/2 in bone marrow MDSCs, together with elevated chemokine (C‐X‐C motif) ligand 5 (CXCL5) expression in tumor tissues. In vitro, the treatments of MDSCs with epinephrine (EPI) and norepinephrine (NE) but not corticosterone (CORT)‐treated H22 conditioned medium obviously inhibited T‐cell proliferation, as well as enhanced CXCR2 expression and extracellular signal‐regulated kinase (Erk) phosphorylation. In vivo, β‐adrenergic blockade with propranolol almost completely reversed the accelerated tumor growth induced by chronic restraint stress and inactivated CXCL5‐CXCR2‐Erk signaling pathway. Our findings support the crucial role of β‐adrenergic signaling cascade in the mobilization and recruitment of MDSCs under chronic restraint stress. What's new? Myeloid derived suppressor cells (MDSCs) contribute to tumor progression under psychological stress, but the underlying mechanism of MDSCs mobilization and recruitment remains largely unknown. Using the chronic restraint stress paradigm in a mouse hepatocellular carcinoma model, here the authors reveal that stress‐induced β‐adrenergic signaling enhances the mobilization and immunosuppressive function of MDSCs via the CXCL5‐CXCR2 axis and Erk signaling activation. These findings support the crucial role of the β‐adrenergic signaling cascade in the harnessing of MDSCs under chronic stress conditions and highlight adrenergic receptor signaling antagonism as a potentially beneficial strategy in cancer therapy. [ABSTRACT FROM AUTHOR]

The terrain segmentation in polarimetric synthetic aperture radar (PolSAR) images is an important task for image interpretation. Since the speckle noise and complex scattering mechanism exist in SAR images, the classification results achieved by traditional methods appear fragmented. Gradually, deep-learning-based methods are proposed to solve this problem. However, only the amplitude data in the SAR image is utilized, which limits the classification precision. In this letter, a novel method based on a dual-attention fusion network (DAFN) is presented. DAFN is mainly composed of a two-way structure encoder for feature extraction and the attention-based fusion module. Considering the terrain characteristic and the SAR imaging mechanism, the introduction of the polarization information in DAFN increases the discrimination of different categories, which contributes to the consistent and accurate fine-grained classification results. To demonstrate the effectiveness of the proposed method, the corresponding experiments are done based on a GaoFen-3 satellite full-polarization SAR data set, in which the superior performance in terrain segmentation is obtained. [ABSTRACT FROM AUTHOR]

Azimuth ambiguity may introduce false targets into synthetic aperture radar images, particularly likely in inshore and oceanic observation. To suppress the azimuth ambiguities for any acquisition mode, a new model is developed to describe the impact of spatially variant azimuth antenna pattern weighting on azimuth ambiguities. By accurately estimating the ratio of ambiguous to main zone energy based on the model, the proposed algorithm selects the subspectra with less ambiguous disturbance, and adopts extrapolation with weighted energy measure to obtain a full spectrum. Due to spectral selection and extrapolation, the novel algorithm achieves superior performance in azimuth ambiguity suppression and resolution preservation, which is compared with the classical algorithm, and validated by applying TerraSAR-X and RADARSAT-2 images. [ABSTRACT FROM AUTHOR]

The article presents research on the synthesis and properties of polyaniline (PANI) nanolayers as shown in the presence of retinol in aqueous ethanol. In the study, the two-probe method was used to examine the effects of retinol, the volume fraction of ethanol and the acidity of the polymerization medium on the direct circuit conductivity of PANI. It was observed that the PANI nanolayers showed an electromagnetic loss at the microwave frequency.

Background: Serum CA-125 has been used as a biomarker of gynecological tumors. In this study, we investigated the CA-125 levels in cervical and vaginal secretions from Chinese patients with endometrial polyps, hyperplasia and carcinoma in comparison with those in endometrium and serum., Material/methods: An electro-chemiluminescent immunoassay was utilized to determine the levels of CA-125 in 51 healthy Chinese women and 97 patients with polyps, hyperplasia or endometrial cancer. An immunohistochemistry method was used to detect endometrial CA-125 expression in 242 subjects., Results: Our study demonstrated that serum CA-125 levels were much lower than those in cervical and vaginal secretions in healthy and diseased women. The levels of CA-125 in serum, and cervical and vaginal secretions were significantly increased in complex hyperplasia and endometrial cancer. The increase of CA-125 content in serum, cervical and vaginal secretions was lesser significant in grade 3 cancer than that in grade 1 and 2 cancer. Generally, serum CA-125 levels correlated with those in cervical and vaginal secretions and CA-125 content in cervical secretion correlated with that in vaginal secretion. There was only a weak CA-125 expression in normal endometrium and simple endometrial hyperplasia. There was a significant difference in CA-125 expression among patients with pathological grade 1, 2 and 3 of endometrial carcinoma., Conclusions: Endo.metrial CA-125 expression together with its levels in the serum and cervical and vaginal secretions can be used as a potential biomarker in the diagnosis of precancerous diseases and endometrial carcinoma.