Inhibition of Necroptosis Rescues SAH-Induced Synaptic Impairments in Hippocampus via CREB-BDNF Pathway

Subarachnoid hemorrhage (SAH) is a devastating form of stroke that leads to incurable outcomes. Increasing evidence has proved that early brain injury (EBI) contributes mostly to unfavorable outcomes after SAH. A previously unknown mechanism of regulated cell death known as necroptosis has recently been reported. Necrostatin-1 (nec-1), a specific and potent inhibitor of necroptosis, can attenuate brain impairments after SAH. However, the effect of nec-1 on the hippocampus and its neuroprotective impact on synapses after SAH is not well understood. Our present study was designed to investigate the potential effects of nec-1 administration on synapses and its relevant signal pathway in EBI after SAH. Nec-1 was administrated in a rat model via intracerebroventricular injection after SAH. Neurobehavior scores and brain edema were detected at 24 h after SAH occurred. The expression of the receptor-interacting proteins 1 and 3 (RIP1and3) was examined as a marker of necroptosis. We used hematoxylin and eosin staining, Nissl staining, silver staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) to observe the morphological changes in hippocampus. The protective effect of nec-1 on synapses was evaluated using western blotting and electron microscopy and Western blotting was used to detect the cAMP responsive element binding (CREB) protein and brain-derived neurotrophic factor (BDNF), and we used transmission electron microscopy and TUNEL to detect the protective effects of nec-1 when a specific inhibitor of CREB, known as 666-15, was used. Our results showed that in the SAH group, RIP1, and RIP3 significantly increased in the hippocampus. Additionally, injection of nec-1 alleviated brain edema and improved neurobehavior scores, compared with those in the SAH group. The damage to neurons was attenuated, and synaptic structure also improved in the Sham+nec-1 group. Furthermore, nec-1 treatment significantly enhanced the levels of phospho-CREB and BDNF compared with those in the SAH group. The protective effect of nec-1 could hindered by 666-15. Thus, nec-1 mitigated SAH-induced synaptic impairments in the hippocampus through the inhibition of necroptosis in connection with the CREB-BDNF pathway. This study may provide a new strategy for SAH patients in clinical practice

A Method of SAR Image Automatic Target Recognition Based on Convolution Auto-Encode and Support Vector Machine

In this paper, a method of Synthetic Aperture Radar (SAR) image Automatic Target Recognition (ATR) based on Convolution Auto-encode (CAE) and Support Vector Machine (SVM) is proposed. Using SVM replaces the traditional softmax as the classifier of the CAE model to classify the feature vectors extracted by the CAE model, which solves the problem that the softmax classifier is less effective in the nonlinear case. Since the SVM can only solve the binary classification problem, and in order to realize the classification of the class objectives, the SVM were designed to achieve the classification of the input samples. After unsupervised training for CAE, the coding layer is connected with SVM to form a classification network. CAE can extract the features of the data by an unsupervised method, and the nonlinear classification advantage of SVM can classify the features extracted by CAE and improve the accuracy of the object recognition. At the same time, the high-accuracy identification of key targets is required in some special cases. A new initialization method is proposed, which initializes the network parameters by pretraining the key targets and changes the weights of different targets in the loss function to obtain better feature extraction, so it can ensure good multitarget recognition ability while realizing the high recognition accuracy of the key targets

Two-Step Matching Approach to Obtain More Control Points for SIFT-like Very-High-Resolution SAR Image Registration

Airborne VHR SAR image registration is a challenging task. The number of CPs is a key factor for complex CP-based image registration. This paper presents a two-step matching approach to obtain more CPs for VHR SAR image registration. In the past decade, SIFT and other modifications have been widely used for remote sensing image registration. By incorporating feature point location affine transformation, a two-step matching scheme, which includes global and local matching, is proposed to allow for the determination of a much larger number of CPs. The proposed approach was validated by 0.5 m resolution C-band airborne SAR data acquired in Sichuan after the 2008 Wenchuan earthquake via a SAR system designed by the IECAS. With the proposed matching scheme, even the original SIFT, which is widely known to be unsuitable for SAR images, can achieve a much larger number of high-quality CPs than the one-step SIFT–OCT, which is tailored for SAR images. Compared with the classic one-step matching approach using both the SIFT and SITF–OCT algorithms, the proposed approach can obtain a larger number of CPs with improved precision

A Method of SAR Image Automatic Target Recognition Based on Convolution Auto-Encode and Support Vector Machine

In this paper, a method of Synthetic Aperture Radar (SAR) image Automatic Target Recognition (ATR) based on Convolution Auto-encode (CAE) and Support Vector Machine (SVM) is proposed. Using SVM replaces the traditional softmax as the classifier of the CAE model to classify the feature vectors extracted by the CAE model, which solves the problem that the softmax classifier is less effective in the nonlinear case. Since the SVM can only solve the binary classification problem, and in order to realize the classification of the class objectives, the SVM were designed to achieve the classification of the input samples. After unsupervised training for CAE, the coding layer is connected with SVM to form a classification network. CAE can extract the features of the data by an unsupervised method, and the nonlinear classification advantage of SVM can classify the features extracted by CAE and improve the accuracy of the object recognition. At the same time, the high-accuracy identification of key targets is required in some special cases. A new initialization method is proposed, which initializes the network parameters by pretraining the key targets and changes the weights of different targets in the loss function to obtain better feature extraction, so it can ensure good multitarget recognition ability while realizing the high recognition accuracy of the key targets

Channel Phase Error Compensation for MIMO-SAR

Multi-input multioutput (MIMO) is a novel technique to achieve high-resolution as well as wide swath in synthetic aperture radar (SAR) systems. Channel imbalance is inevitable in multichannel systems that it declines the imaging quality. Generally, the imbalance cannot be fully compensated by simple internal calibration in a MIMO-SAR system. In this paper, a new algorithm based on raw data is presented to remove the channel phase error. Based on the error source, this approach models the phase error as two parts: the transmit phase error and the receive phase error. The receive phase error is removed using cost function at the azimuth processing stage, whereas the transmit phase error is estimated with correlation. Point target simulations confirm the influence of channel phase error and the validation of the proposed approach. Besides, the performance is also investigated

The SAR Payload Design and Performance for the GF-3 Mission

This paper describes the C-band multi-polarization Synthetic Aperture Radar (SAR) sensor for the Gaofen-3 (GF-3) mission. Based on the requirement analysis, the design of working modes and SAR payload are given. An accurate antenna model is introduced for the pattern optimization and SAR performance calculation. The paper concludes with an overview of predicted performance which was verified by in-orbit tests

Distributed Target Detection in Unknown Interference

Interference can degrade the detection performance of a radar system. To overcome the difficulty of target detection in unknown interference, in this paper we model the interference belonging to a subspace orthogonal to the signal subspace. We design three effective detectors for distributed target detection in unknown interference by adopting the criteria of the generalized likelihood ratio test (GLRT), the Rao test, and the Wald test. At the stage of performance evaluation, we illustrate the detection performance of the proposed detectors in the presence of completely unknown interference (not constrained to lie in the above subspace). Numerical examples indicate that the proposed GLRT and Wald test can provide better detection performance than the existing detectors