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Deep learning (DL)-based multi-user physical layer authentication (PLA) in the Industrial Internet of Things (IIoT) requires frequent updates as new users join. Class incremental learning (CIL) addresses this challenge, but existing generative replay approaches depend on heavy parameterized models, causing high computational overhead and limiting deployment in resource-constrained environments. To address these challenges, we propose a parameter-free statistical generator-based CIL framework, PSG-CIL, for DL-based multi-user PLA in the IIoT. The parameter-free statistical generator (PSG) produces Gaussian sampling on user-specific means and variances to generate pseudo-data without training extra models, greatly reducing computational overhead. A confidence-based pseudo-data selection ensures pseudo-data reliability, while a dynamic adjustment mechanism for the loss weight balances the retention of old users’ knowledge and the adaptation to new users. Experiments on real industrial datasets show that PSG-CIL consistently achieves superior accuracy while maintaining a lightweight scale; for example, in the AAP outer loop scenario, PSG-CIL reaches 70.68%, outperforming retraining from scratch (58.57%) and other CIL methods.

The growing digitalization of Industrial Control Systems (ICSs) presents both significant benefits and security challenges, especially for small and medium-sized factories with limited resources. Effective anomaly detection is essential to safeguard these facilities and prevent costly disruptions. Although current research has advanced anomaly detection, it is still challenging for algorithms to be capable of effectively balancing the interplay between training speed, computational cost, and accuracy while simultaneously exhibiting robust stability and adaptability. This gap often leaves small and medium-sized factories without efficient solutions. To address these issues, this work introduces a deep belief network-based boosting adversarial autoencoder termed DBN-BAAE, a novel lightweight anomaly detection mechanism based on boosting adversarial learning. The proposed lightweight mechanism saves computational overhead, enhances autoencoder training stability with an improved deep belief network (DBN) for pre-training, boosts encoder expression through ensemble learning, achieves high detection accuracy via an adversarial decoder, and employs a dynamic threshold to enhance adaptability and reduce the need for retraining. Experiments reveal that the mechanism not only achieves an F1 score of 0.82, surpassing the best baseline by 1%, but also accelerates training speed by 2.2 times, demonstrating its effectiveness and efficiency in ICS environments, particularly for small and medium-sized factories.

IoT-based applications require effective anonymous authentication and key agreement (AKA) protocols to secure data and protect user privacy due to open communication channels and sensitive data. While AKA protocols for these applications have been extensively studied, achieving anonymity remains a challenge. AKA schemes using one-time pseudonyms face resynchronization issues after desynchronization attacks, and the high computational overhead of bilinear pairing and public key encryption limits its applicability. Existing schemes also lack essential security features, causing issues such as vulnerability to ephemeral secret leakage attacks and key compromise impersonation. To address these issues, we propose two novel AKA schemes, PUAKA and RCAKA, designed for different IoT traffic patterns. PUAKA improves end device anonymity in the periodic update pattern by updating one-time pseudonyms with authenticated session keys. RCAKA, for the remote control pattern, ensures anonymity while reducing communication and computation costs using shared signatures and temporary random numbers. A key contribution of RCAKA is its ability to resynchronize end devices with incomplete data in the periodic update pattern, supporting continued authentication. Both protocols’ security is proven under the Real-or-Random model. The performance comparison results show that the proposed protocols exceed existing solutions in security features and communication costs while reducing computational overhead by 32% to 50%.

Internet of Things (IoT) user authentication protocols enable secure authentication and session key negotiation between users and IoT devices via an intermediate server, allowing users to access sensor data or control devices remotely. However, the existing IoT user authentication schemes often assume that the servers (registration center and intermediate servers) are fully trusted, overlooking the potential risk of insider attackers. Moreover, most of the existing schemes lack critical security properties, such as resistance to ephemeral secret leakage attacks and offline password guessing attacks, and they are unable to provide perfect forward security. Furthermore, with the rapid growth regarding IoT devices, the servers must manage a large number of users and device connections, making the performance of the authentication scheme heavily reliant on the server’s computational capacity, thereby impacting the system’s scalability and efficiency. The design of security protocols is based on the underlying security model, and the current IoT user authentication models fail to cover crucial threats like insider attacks and ephemeral secret leakage. To overcome these limitations, we propose a new security model, IoT-3eCK, which assumes semi-trusted servers and strengthens the adversary model to better meet the IoT authentication requirements. Based on this model, we design an efficient protocol that ensures user passwords, biometric data, and long-term keys are protected from insider users during registration, mitigating insider attacks. The protocol also integrates dynamic pseudo-identity anonymous authentication and ECC key exchange to satisfy the security properties. The performance analysis shows that, compared to the existing schemes, the new protocol reduces the communication costs by over 23% and the computational overhead by more than 22%, with a particularly significant reduction of over 95% in the computational overhead at the intermediate server. Furthermore, the security of the protocol is rigorously demonstrated using the random oracle model and verified with automated tools, further confirming its security and reliability.

Accumulating evidence suggests cancer-derived exosomes play an important role in promoting angiogenesis. Long noncoding RNA small nucleolar RNA host gene 16 (SNHG16) is known to aggravate hepatocellular carcinoma (HCC) progression. However, the function of exosomal SNHG16 in HCC angiogenesis remains unclear. In this study, the expression of SNHG16 was significantly upregulated in HCC tissues and cell lines. The proliferative, migratory, and angiogenic abilities of HUVECs were enhanced after exposure to exosomes derived from HCC cells by transmitting SNHG16. In addition, SNHG16 was validated to promote the biological function of HUVECs directly. Exosomal SNHG16 increased GALNT1 expression to promote angiogenesis via sponging miR-4500. SNHG16/miR-4500/GALNT1 axis played an important role in exosome-mediated angiogenesis and tumor growth in vitro and vivo. Furthermore, SNHG16 activated PI3K/Akt/mTOR pathway via competing endogenous miR-4500 and GALNT1. Meanwhile, the expression of plasma exosomal SNHG16 upregulated in the plasma of HCC patients. These data elucidated the essential role of exosomal SNHG16 in communication between HCC cells and endothelial cells. Exosomal SNHG16 could be utilized as a therapeutic target for anti-angiogenesis in HCC progression.

Recent research on mast cell biology has turned its focus on MRGPRX2, a new member of the Mas-related G protein-coupled subfamily of receptors (Mrgprs), originally described in nociceptive neurons of the dorsal root ganglia. MRGPRX2, a member of this group, is present not only in neurons but also in mast cells (MCs), specifically, and potentially in other cells of the immune system, such as basophils and eosinophils. As emerging new functions for this receptor are studied, a variety of both natural and pharmacologic ligands are being uncovered, linked to the ability to induce receptor-mediated MC activation and degranulation. The diversity of these ligands, characterized in their human, mice, or rat homologues, seems to match that of the receptor’s interactions. Natural ligands include host defense peptides, basic molecules, and key neuropeptides such as substance P and vasointestinal peptide (known for their role in the transmission of pain and itch) as well as eosinophil granule-derived proteins. Exogenous ligands include MC secretagogues such as compound 48/80 and mastoparan, a component of bee wasp venom, and several peptidergic drugs, among which are members of the quinolone family, neuromuscular blocking agents, morphine, and vancomycin. These discoveries shed light on its capacity as a multifaceted participant in naturally occurring responses within immunity and neural stimulus perception, as in responses at the center of immune pathology. In host defense, the mice Mrgprb2 has been proven to aid mast cells in the detection of peptidic molecules from bacteria and in the release of peptides with antimicrobial activities and other immune mediators. There are several potential actions described for it in tissue homeostasis and repair. In the realm of pathologic response, there is evidence to suggest that this receptor is also involved in chronic inflammation. Furthermore, MRGPRX2 has been linked to the pathophysiology of non-IgE-mediated immediate hypersensitivity drug reactions. Different studies have shown its possible role in other allergic diseases as well, such as asthma, atopic dermatitis, contact dermatitis, and chronic spontaneous urticaria. In this review, we sought to cover its function in physiologic processes and responses, as well as in allergic and nonallergic immune disease.

Background SARS‐CoV‐2 has spread worldwide causing more than 400 million people with virus infections since early 2020. Currently, the existing vaccines targeting the spike glycoprotein (S protein) of SARS‐CoV‐2 are facing great challenge from the infection of SARS‐CoV‐2 virus and its multiple S protein variants. Thus, we need to develop a new generation of vaccines to prevent infection of the SARS‐CoV‐2 variants. Compared with the S protein, the nucleocapsid protein (N protein) of SARS‐CoV‐2 is more conservative and less mutations, which also plays a vital role in viral infection. Therefore, the N protein may have the great potential for developing new vaccines. Methods The N protein of SARS‐CoV‐2 was recombinantly expressed and purified in Escherichia coli. Western Blot and ELISA assays were used to demonstrate the immunoreactivity of the recombinant N protein with the serum of 22 COVID‐19 patients. We investigated further the response of the specific serum antibodies and cytokine production in BALB/c mice immunized with recombinant N protein by Western Blot and ELISA. Results The N protein had good immunoreactivity and the production of IgG antibody against N protein in COVID‐19 patients was tightly correlated with disease severity. Furthermore, the N protein was used to immunize BALB/c mice to have elicited strong immune responses. Not only high levels of IgG antibody, but also cytokine‐IFN‐γ were produced in the N protein‐immunized mice. Importantly, the N protein immunization induced a high level of IgM antibody produced in the mice. Conclusion SARS‐CoV‐2 N protein shows a great big bundle of potentiality for developing a new generation of vaccines in fighting infection of SARS‐CoV‐2 and its variants. The SARS‐CoV‐2 N protein shows a great big bundle of potentiality for developing a new generation of vaccines in fighting SARS‐CoV‐2 infection.

BackgroundWhole-body bone scan is the widely used tool for surveying bone metastases caused by various primary solid tumors including lung cancer. Scintigraphic images are characterized by low specificity, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. Convolutional neural network can be used to develop automated classification of images by automatically extracting hierarchal features and classifying high-level features into classes.ResultsUsing convolutional neural network, a multi-class classification model has been developed to detect skeletal metastasis caused by lung cancer using clinical whole-body scintigraphic images. The proposed method consisted of image aggregation, hierarchal feature extraction, and high-level feature classification. Experimental evaluations on a set of clinical scintigraphic images have shown that the proposed multi-class classification network is workable for automated detection of lung cancer-caused metastasis, with achieving average scores of 0.7782, 0.7799, 0.7823, 0.7764, and 0.8364 for accuracy, precision, recall, F-1 score, and AUC value, respectively.ConclusionsThe proposed multi-class classification model can not only predict whether an image contains lung cancer-caused metastasis, but also differentiate between subclasses of lung cancer (i.e., adenocarcinoma and non-adenocarcinoma). On the context of two-class (i.e., the metastatic and non-metastatic) classification, the proposed model obtained a higher score of 0.8310 for accuracy metric.

Adzuki bean (Vigna angularis (Willd.) Ohwi & H. Ohashi) is a significant crop for its applications in both traditional medicine and nutritional diets in China. However, there remains a paucity of exploration employing an RNA-seq approach to investigate the molecular response mechanisms of the species under salinity stress. In this study, Jin Xiao Dou 6 (JXD6) adzuki bean cultivar was subjected to 0 mmol/L (CK), 32.5 mmol/L, and 65.0 mmol/L NaCl treatments to preliminarily characterize salinity-induced alterations in plant height, chloroplast pigment contents, leaf surface humidity and temperature, H2O2 and O2− accumulation, activities of antioxidative enzymes, and transcriptome profiles. Under increasing NaCl concentrations, the plant height of JXD6 seedlings was progressively inhibited. Conversely, the unifoliate leaves exhibited elevated leaf surface temperature, increased contents of chlorophyll a, total chlorophyll and carotenoids, enhanced accumulation of O2−, as well as heightened activities of superoxide dismutase, peroxidase, and catalase. Transcriptome profile analyses suggested that a total of 363 and 858 differentially expressed genes were obtained in the unifoliate leaves of adzuki bean seedlings treated with 32.5 mmol/L and 65.0 mmol/L NaCl groups, respectively. The up-regulated genes were mainly enriched in the spliceosome pathway, while the down-regulated genes were mainly enriched in pathways of plant hormone signal transduction, plant–pathogen interaction, and the MAPK signaling pathway in plants. These results provide new insight into exploring the response mechanisms of adzuki beans to salinity stress via transcriptome analyses.

Background: Corydalis saxicola Bunting (CSB) is a perennial herb belonging to genus Corydalis (Papaveraceae), called “Yan-huang-lian” in the Chinese folk. Traditionally, it is used to treat acute conjunctivitis, corneal pannus, acute abdominal pain, hemorrhoidal bleeding, haematochezia, swelling, hepatitis, cirrhosis and liver cancer based on traditional Chinese medicine (TCM) concepts. Purpose: This review aims to summarize and analyze the pharmacokinetics, pharmacological and toxicological properties of CSB and its extracts; to highlight the relevance of modern pharmacology to traditional pharmacology; also to assess its therapeutic potential. Methods: CSB related literatures were searched and screened from databases including PubMed, Web of Science and CNKI. The selected literatures provided reliable source identification evidences. Results: In traditional medicine concepts, CSB has the effects of clearing away heat and detoxification, eliminating dampness, relieving pain, and stopping bleeding. Its modern pharmacology includes hepatoprotective, anticancer, anti-inflammatory, analgesic, antibacterial, anti-oxidative effects. Further, some pharmacological effects support its traditional uses. The CSB total alkaloids (CSBTA) are the main constituents isolated from this plant, and they exert the major of the pharmacological effects. Toxicological studies have shown that the toxicity of CSBTA is mild and reversible in rodents and beagle dogs. Conclusion: Although the present study summarizes the botany, phytochemistry, pharmacokinetics, pharmacology, toxicity, and applications of this plant, it is still necessary to systemically evaluate the chemistry, safety and parameters related to drug metabolism of the extracts or compounds from this plant before or in clinical trials in the future. Meanwhile, cancers and inflammatory-related diseases may be new research directions of this ethnomedicine.