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Abstract Background Migraine is a neurological disorder characterized by complex, widespread, and sudden attacks with an unclear pathogenesis, particularly in chronic migraine (CM). Specific brain regions, including the insula, amygdala, thalamus, and cingulate, medial prefrontal, and anterior cingulate cortex, are commonly activated by pain stimuli in patients with CM and animal models. This study employs fluorescence microscopy optical sectioning tomography (fMOST) technology and AAV-PHP.eB whole-brain expression to map activation patterns of brain regions in CM mice, thus enhancing the understanding of CM pathogenesis and suggesting potential treatment targets. Methods By repeatedly administering nitroglycerin (NTG) to induce migraine-like pain in mice, a chronic migraine model (CMM) was established. Olcegepant (OLC) was then used as treatment and its effects on mechanical pain hypersensitivity and brain region activation were observed. All mice underwent mechanical withdrawal threshold, light-aversive, and elevated plus maze tests. Viral injections were administered to the mice one month prior to modelling, and brain samples were collected 2 h after the final NTG/vehicle control injection for whole-brain imaging using fMOST. Results In the NTG-induced CMM, mechanical pain threshold decreased, photophobia, and anxiety-like behavior were observed, and OLC was found to improve these manifestations. fMOST whole-brain imaging results suggest that the isocortex-cerebral cortex plate region, including somatomotor areas (MO), somatosensory areas (SS), and main olfactory bulb (MOB), appears to be the most sensitive area of activation in CM (P

Abstract Background Energy metabolism disorders and neurogenic inflammation play important roles in the central sensitization to chronic migraine (CM). AMP-activated protein kinase (AMPK) is an intracellular energy sensor, and its activation regulates inflammation and reduces neuropathic pain. However, studies on the involvement of AMPK in the regulation of CM are currently lacking. Therefore, this study aimed to explore the mechanism underlying the involvement of AMPK in the central sensitization to CM. Methods Mice with recurrent nitroglycerin (NTG)-induced CM were used to detect the expression of AMPK protein in the trigeminal nucleus caudalis (TNC). Following intraperitoneal injection of the AMPK activator 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR) and inhibitor compound C, the mechanical pain threshold, activity level, and pain-like behaviors in the mice were measured. The expression of calcitonin gene-related peptide (CGRP) and cytokines, M1/M2 microglia, and NF-κB pathway activation were detected after the intervention. Results Repeated NTG injections resulted in a gradual decrease in AMPK protein expression, and the negative regulation of AMPK by increased ubiquitin-like plant homeodomain and RING finger domain 1 (UHRF1) expression may counteract AMPK activation by increasing ADP/ATP. AICAR can reduce the hyperalgesia and pain-like behaviors of CM mice, improve the activity of mice, reduce the expression of CGRP, IL-1β, IL-6, and TNF-α in the TNC region, and increase the expression of IL-4 and IL-10. Moreover, AMPK in TNC was mainly located in microglia. AICAR could reduce the expression of inducible NO synthase (iNOS) in M1 microglia and increase the expression of Arginase 1 (Arg1) in M2 microglia by inhibiting the activation of NF-κB pathway. Conclusions AMPK was involved in the central sensitization of CM, and the activation of AMPK reduced neuroinflammation in NTG-induced CM mice. AMPK may provide new insights into interventions for energy metabolism disorders and neurogenic inflammation in migraine.

iWith the rapid development and the increasing security threats of D2D communication, the D2D communication security has become more and more important. Device recognition is the foundation of communication security. Traditional device authentication is the cryptographic mechanism and the security protocol that are not enough for the new security threat. RF fingerprint of wireless device has physical characteristics, which is difficult to clone. Therefore, it can be used for D2D device recognition. In this paper, a recognition algorithm of D2D device based on RF fingerprint is proposed. First, Hilbert transform and principal component analysis is used to generate the RF fingerprint of D2D device. Second, support vector machine (SVM) is introduced and CV-SVM is used as the classifier. Finally, simulation results show that the recognition rate is more than 90% under SNR = 5 dB, which proves that the proposed method is effective for D2D device recognition. Therefore, it can be used as the trust management of D2D communication.

With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

Multi-node and multi-feature fusion is an important approach for digital modulations signal recognition in modern communication field. Information obtained from multi-node and multi-feature needs to be fused because of incompleteness of single feature and uncertainty of single node. As a powerful method for data fusion and conclusion reasoning in uncertain environments, evidence theory is widely used. Establishing reliable BPA is the prerequisite for evidence fusion. In this paper, in order to improve the coincidence of basic probability assignment (BPA) with real probability, the notion of bell function (Bell-F) based similarity evaluation model (SEM) is introduced. Through comparative experiments, it is proved that the new method based on Bell-F is effective for BPA acquisition. Furthermore, a new information fused based digital signal modulation recognition scheme is described. Finally, a case study is given to illustrate the performance of the proposed model. Through test and calculations under the digital modulation signal data set, under 0 ~ 10dB, the recognition rate based on the Bell-F fusion method is above 90%, which is 20% higher than methods without fusion. Under 0dB or less, the integrated recognition rate of the Bell-F is 40% higher than the Gray relation method. [ABSTRACT FROM AUTHOR]