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Abstract The increasing incidence of hyperlipidemia is a serious threat to public health. The development of effective and safe lipid‐lowering drugs with few side effects is necessary. The purpose of this study was to assess the lipid‐lowering activity of Sanghuangporus vaninii extract (SVE) in rat experiments and reveal the molecular mechanism by transcriptome analysis. Hyperlipidemia was induced in the animals using a high‐fat diet for 4 weeks. At the end of the 4th week, hyperlipidemic rats were assigned into two control groups (model and positive simvastatin control) and three treatment groups that received SVE at 200, 400, or 800 mg kg−1 day−1 for another 4 weeks. A last control group comprised normal chow‐fed rats. At the end of the 8th week, rats were sacrificed and lipid serum levels, histopathology, and liver transcriptome profiles were determined. SVE was demonstrated to relieve the lipid disorder and improve histopathological liver changes in a dose‐dependent manner. The transcriptomic analysis identified changes in hepatocyte gene activity for major pathways including steroid biosynthesis, bile secretion, cholesterol metabolism, AMPK signaling, thyroid hormone signaling, and glucagon signaling. The changed expression of crucial genes in the different groups was confirmed by qPCR. Collectively, this study revealed that SVE could relieve hyperlipidemia in rats, the molecular mechanism might be to promote the metabolism of lipids and the excretion of cholesterol, inhibit the biosynthesis of cholesterol by activating the AMPK signaling pathway, the thyroid hormone signaling pathway, and the glucagon signaling pathway.

Saikosaponin D (SSd) is a naturally active product with strong pharmacological activity found in Bupleurum scorzonerifolium Willd. Studies have shown that endophytic fungi have great potential as sources of natural medicines. Fusarium acuminatum (CHS3), an SSd-producing endophytic fungus, was isolated from B. scorzonerifolium. To elucidate the effect of host plants on the production of SSd in CHS3, CHS3 was co-cultured with suspension cells of B. scorzonerifolium and SSd was detected using high-performance liquid chromatography (HPLC). Transcriptome sequencing (RNA-Seq) of CHS3 before and after co-culture was performed using an Illumina HiSeq 2500 platform. The results indicated that the content of SSd synthesised by CHS3 increased after co-culture with suspension cells of B. scorzonerifolium. Transcriptome analysis of CHS3 with differentially expressed genes (DEGs) showed that 1202 and 1049 genes were upregulated and downregulated, respectively, after co-culture. Thirty genes associated with SSd synthesis and 11 genes related to terpene backbone biosynthesis were annotated to the Kyoto Encyclopaedia of Genes and Genomes (KEGG). Combined with transcriptome data, it was speculated that the mevalonate (MVA) pathway is a possible pathway for SSd synthesis in CHS3, and the expression of key enzyme genes (HMGR, HMGCS, GGPS1, MVK, FDFT1, FNTB) was validated by qRT-PCR. In conclusion, the endophytic fungus CHS3 can form an interactive relationship with its host, thereby promoting SSd biosynthesis and accumulation by upregulating the expression of key enzyme genes in the biosynthesis pathway.

Abstract Traumatic brain injury (TBI) is a common trauma with high mortality and disability rates worldwide. However, the current management of this disease is still unsatisfactory. Therefore, it is necessary to investigate the pathophysiological mechanisms of TBI in depth to improve the treatment options. In recent decades, abundant evidence has highlighted the significance of endoplasmic reticulum stress (ERS) in advancing central nervous system (CNS) disorders, including TBI. ERS following TBI leads to the accumulation of unfolded proteins, initiating the unfolded protein response (UPR). Protein kinase RNA-like ER kinase (PERK), inositol-requiring protein 1 (IRE1), and activating transcription factor 6 (ATF6) are the three major pathways of UPR initiation that determine whether a cell survives or dies. This review focuses on the dual effects of ERS on TBI and discusses the underlying mechanisms. It is suggested that ERS may crosstalk with a series of molecular cascade responses, such as mitochondrial dysfunction, oxidative stress, neuroinflammation, autophagy, and cell death, and is thus involved in the progression of secondary injury after TBI. Hence, ERS is a promising candidate for the management of TBI.

In the rapidly evolving landscape of smart transportation, the passenger volume in urban rail transit consistently demonstrates an upward trajectory. In this context, precise and scientifically grounded short-term passenger flow prediction methods are essential for optimizing operational scheduling and ensuring safety in urban rail transit. Consequently, this paper introduces Temporal Graph Attention Long Short-Term Memory (TGALSTM), a spatiotemporal integrated prediction network model that incorporates the surrounding environment of the station. Initially, the paper enhanced the Temporal Convolutional Network (TCN) model to capture temporal features more accurately. Subsequently, the paper utilizes the Graph Attention Network (GAT) network module specifically to extract the topological structure and surrounding environmental features of the station. Lastly, the prediction task is accomplished by weighted fusion of various features, inputting them into the Attention Long Short-Term Memory (LSTM) network. Experiments were conducted on two authentic datasets, revealing that the TGALSTM model outperforms the baseline model in both single-step and double-step predictions, showcasing the model’s exceptional performance and robustness. This research offers a robust method and support to enhance the operational efficiency and passenger flow management of urban rail transit systems.

Adversarial examples have been shown to easily mislead neural networks, and many strategies have been proposed to defend them. To address the problem that most transformation-based defense strategies will degrade the accuracy of clean images, we proposed an Enhanced Image Transformation Generative Adversarial Network (EITGAN). Positive perturbations were employed in the EITGAN to counteract adversarial effects while enhancing the classified performance of the samples. We also used the image super-resolution method to mitigate the effect of adversarial perturbations. The proposed method does not require modification or retraining of the classifier. Extensive experiments demonstrated that the enhanced samples generated by the EITGAN effectively defended against adversarial attacks without compromising human visual recognition, and their classification performance was superior to that of clean images.

Abstract Inhibin subunit beta A (INHBA) is a member of the transforming growth factor-beta (TGF-β) superfamily that plays a fundamental role in various cancers. However, a systematic analysis of the exact role of INHBA in patients with gastric cancer (GC) has not yet been conducted. We evaluated the expression levels of INHBA and the correlation between INHBA and GC prognosis in GC. The relationship between INHBA expression, immune infiltration levels, and type markers of immune cells in GC was also explored. In addition, we studied INHBA mutations, promoter methylation, and functional enrichment analysis. Besides, high expression levels of INHBA in GC were significantly related to unfavorable prognosis. INHBA was negatively correlated with B cell infiltration, but positively correlated with macrophage and most anticancer immunity steps. INHBA expression was positively correlated with the type markers of CD8+ T cells, neutrophils, macrophages, and dendritic cells. INHBA has a weak significant methylation level change between tumor and normal tissues and mainly enriched in cancer-related signaling pathways. The present study implies that INHBA may serve as a potential biomarker for predicting the prognosis of patients with GC. INHBA is a promising predictor of immunotherapy response, with higher levels of INHBA indicating greater sensitivity.

Abstract The evergrowing diversity of encrypted and anonymous network traffic makes network management more formidable to manage the network traffic. An intelligent system is essential to analyse and identify network traffic accurately. Network management needs such techniques to improve the Quality of Service and ensure the flow of secure network traffic. However, due to the usage of non‐standard ports and encryption of data payloads, the classical port‐based and payload‐based classification techniques fail to classify the secured network traffic. To solve the above‐mentioned problems, this paper proposed an effective deep learning‐based framework employed with flow‐time‐based features to predict heterogeneous secure network traffic best. The state‐of‐the‐art machine learning strategies (C4.5, random forest, and K‐nearest neighbour) are investigated for comparison. The proposed 1D‐CNN model achieved higher accuracy in classifying the heterogeneous secure network traffic. In the next step, the proposed deep learning model characterises the major categories (virtual private network traffic, the onion router network traffic, and plain encrypted network traffic) into several application types. The experimental results show the effectiveness and feasibility of the proposed deep learning framework, which yields improved predictive power compared to the state‐of‐the‐art machine learning techniques employed for secure network traffic analysis.

Ensuring mitochondrial quality is essential for maintaining neuronal homeostasis, and mitochondrial transport plays a vital role in mitochondrial quality control. In this review, we first provide an overview of neuronal mitochondrial transport, followed by a detailed description of the various motors and adaptors associated with the anterograde and retrograde transport of mitochondria. Subsequently, we review the modest evidence involving mitochondrial transport mechanisms that has surfaced in acute neurological disorders, including traumatic brain injury, spinal cord injury, spontaneous intracerebral hemorrhage, and ischemic stroke. An in-depth study of this area will help deepen our understanding of the mechanisms underlying the development of various acute neurological disorders and ultimately improve therapeutic options.

Abstract Background Macrophage infiltration in the tumor microenvironment participates in the regulation of tumor progression. Previous studies have found that Notch signaling pathway is involved in regulating the progression of colorectal cancer (CRC), however, the specific mechanism is still unclear. Methods The correlation between Notch signaling pathway and macrophage infiltration was investigated in TCGA database and verified in clinical samples of patients with CRC using immunohistochemistry. Gene Set Enrichment Analysis was used to find out genes related to Notch3 expression. Colony formation assay, and flow cytometry were utilized to test tumor growth and immune cell infiltration in vitro and in vivo. Results Using bioinformatics analysis and clinical sample validation, we found that Notch3 was highly expressed in colon tumor tissues compared to adjacent normal tissues, and it participated in regulating the recruitment of macrophages to the tumor microenvironment. Furthermore, we found that the Notch3 expression was positively correlated with the expression of macrophage recruitment-related cytokines in colon tumor tissues. Finally, we demonstrated that depletion of Notch3 had no significant effect on the growth of colon tumor cells in vitro, while, attenuated the growth of colon cancer tumors in vivo. Simultaneous, immunosuppressive cells, macrophages and myeloid-derived suppressor cell (MDSC) infiltration were dramatically reduced in the tumor microenvironment. Conclusion Our study illustrated that Notch3 could facilitate the progression of CRC by increasing the infiltration of macrophages and MDSCs to promote the immunosuppressive tumor microenvironment. Targeting Notch3 specifically is a potentially effective treatment for CRC.

This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the Taylor series expansion method, we simplify the complex integral calculations of mutual information and KL divergence into summation operations. This simplification not only reduces computational complexity but also ensures precise calculations. Theoretical derivations establish the necessary conditions for the covert transmission of surveillance videos, optimizing modulation probabilities for constellation points. Experimental results and simulation validations demonstrate the superior performance of our proposed method in terms of both information transfer and precision.

Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs.

Armcx1 is highly expressed in the brain and is located in the mitochondrial outer membrane of neurons, where it mediates mitochondrial transport. Mitochondrial transport promotes the removal of damaged mitochondria and the replenishment of healthy mitochondria, which is essential for neuronal survival after traumatic brain injury (TBI). This study investigated the role of Armcx1 and its potential regulator(s) in secondary brain injury (SBI) after TBI. An in vivo TBI model was established in male C57BL/6 mice via controlled cortical impact (CCI). Adeno-associated viruses (AAVs) with Armcx1 overexpression and knockdown were constructed and administered to mice via stereotactic cortical injection. Exogenous miR-223-3p mimic or inhibitor was transfected into cultured cortical neurons, which were then scratched to simulate TBI in vitro. It was found that Armcx1 expression decreased significantly, while miR-223-3p levels increased markedly in peri-lesion tissues after TBI. The overexpression of Armcx1 significantly reduced TBI-induced neurological dysfunction, neuronal cell death, mitochondrial dysfunction, and axonal injury, while the knockdown of Armcx1 had the opposite effect. Armcx1 was potentially a direct target of miR-223-3p. The miR-223-3p mimic obviously reduced the Armcx1 protein level, while the miR-223-3p inhibitor had the opposite effect. Finally, the miR-223-3p inhibitor dramatically improved mitochondrial membrane potential (MMP) and increased the total length of the neurites without affecting branching numbers. In summary, our results suggest that the decreased expression of Armcx1 protein in neurons after experimental TBI aggravates secondary brain injury, which may be regulated by miR-223-3p. Therefore, this study provides a potential therapeutic approach for treating TBI.

The elastic modulus is a basic parameter reflecting the deformation resistance and energy concentration characteristics of rock. The traditional testing method of the rock elastic modulus needs to core the surrounding rock and testing it in the laboratory, which has difficulty reflecting the rock mechanical properties under the engineering site environment, and uncontrolled broken surrounding rock is difficult to core for testing. Digital drilling technology provides a new idea for in situ testing of the rock’s elastic modulus. The key is to establish a quantitative relationship between the elastic modulus and drilling data. In this paper, the relationship between rock cutting energy density and drilling data is established, and a series of rock digital drilling tests are carried out. The response law of the drilling data and cutting energy density to the rock elastic modulus is clarified. The inversion model of the rock elastic modulus while drilling (DP-E model) is established. The verification test results indicate that the average difference rate of the test results based on the DP-E model is 7.06% compared with the compression test method, which verifies the effectiveness of the model. This study provides a theoretical basis for in situ testing of the rock elastic modulus.

Abstract Background Coinfection with HIV and Plasmodium parasites is fairly common, but the sequence of infection with these two pathogens and their impact on disease progression are poorly understood. Methods A Chinese rhesus macaque HIV and Plasmodium coinfection model was established to compare the impact of pre-existing and subsequent malaria on the progression of SIV infection. Results We found that a pre-existing malaria caused animals to produce a greater number of CD4+CCR5+ T cells for SIV replication, resulting in higher viral loads. Conversely, subsequent malaria induced a substantially larger proportion of CD4+CD28highCD95high central memory T cells and a stronger SIV-specific T cell response, maintained the repertoire diversity of SIV-specific T cell receptors, and generated new SIV-specific T cell clonotypes to trace SIV antigenic variation, resulting in improved survival of SIV-infected animals. Conclusion The complex outcomes of this study may have important implications for research on human HIV and malaria coinfection. The infection order of the two pathogens (HIV and malaria parasites) should be emphasized. Video abstract

Abstract The low response rate to immunosuppressant is mainly due to the lack of adequate knowledge about the tumor microenvironment (TME) and screening biomarkers for gliomas. We aimed to identify the promising immune biomarkers and new immune classification of glioma. In this study, multiple‐immune algorithms were used to calculate immune‐infiltration scores. Unsupervised and supervised machine learning methods were used to perform the classification. We observed that OLFML3 overexpression was indicated in gliomas and linked to poor prognosis. OLFML3 knockdown inhibited proliferation, invasion and increased the sensitivity of glioma cells to temozolomide. OLFML3 expression could also reflect the aberrant immune status. Based on the immune‐related signature, patients were divided into three immune subtypes via consensus clustering. Patients with C2 subtype presented poorer prognosis and shorter progression free survival than patients with other two subtypes. The TME patterns among subtypes were different. C2 and C3 subtypes are the immune‐inflamed and immune‐desert tumors, respectively. Additionally, compared with C3 subtype, patients with C1/C2 subtypes were more likely to respond to immunotherapy. The pRRophetic algorithm indicated patients with C1/C2 subtypes were more resistant to temozolomide, but sensitive to paclitaxel and cisplatin. To conclude, OLFML3 overexpression affects glioma cell proliferation, invasion, and TMZ sensitivity and has been proved to be an independent prognostic‐ and immune‐related biomarker. Additionally, the novel immune subtype's classification could provide the prognostic and predictive predictors for glioma patients and may guide physicians in selecting potential responders.

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

BackgroundMicroglia have been identified for a century. In this period, their ontogeny and functions have come to light thanks to the tireless efforts of scientists. However, numerous documents are being produced, making it challenging for scholars, especially those new to the field, to understand them thoroughly. Therefore, having a reliable method for quickly grasping a field is crucial.MethodsWe searched and downloaded articles from the Web of Science Core Collection with “microglia” or “microglial” in the title from 2002 to 2021. Eventually, 12,813 articles were located and, using CiteSpace and VOSviewer, the fundamental data, knowledge domains, hot spots, and emerging trends, as well as the influential literature in the field of microglia research, were analyzed.ResultsFollowing 2011, microglia publications grew significantly. The two prominent journals are Glia and J Neuroinflamm. The United States and Germany dominated the microglia study. The primary research institutions are Harvard Univ and Univ Freiburg, and the leading authors are Prinz Marco and Kettenmann Helmut. The knowledge domains of microglia include eight directions, namely neuroinflammation, lipopolysaccharide, aging, neuropathic pain, macrophages, Alzheimer’s disease, retina, and apoptosis. Microglial phenotype is the focus of research; while RNA-seq, exosome, and glycolysis are emerging topics, a microglial-specific marker is still a hard stone. We also identified 19 influential articles that contributed to the study of microglial origin (Mildner A 2007; Ginhoux F 2010), identity (Butovsky O 2014), homeostasis (Cardona AE 2006; Elmore MRP 2014); microglial function such as surveillance (Nimmerjahn A 2005), movement (Davalos D 2005; Haynes SE 2006), phagocytosis (Simard AR 2006), and synapse pruning (Wake H 2009; Paolicelli RC 2011; Schafer DP 2012; Parkhurst CN 2013); and microglial state/phenotype associated with disease (Keren-Shaul H 2017), as well as 5 review articles represented by Kettenmann H 2011.ConclusionUsing bibliometrics, we have investigated the fundamental data, knowledge structure, and dynamic evolution of microglia research over the previous 20 years. We hope this study can provide some inspiration and a reference for researchers studying microglia in neuroscience.

Colchicine is a bioactive alkaloid originally from Colchicum autumnale and possesses excellent antiproliferative activity. However, colchicine-associated severe toxicity, gastrointestinal side effects in particular, limits its further therapeutic use. In the current study, we thus designed and synthesized a novel hybrid (CMH) by splicing colchicine and magnolol, a multifunctional polyphenol showing favorable gastrointestinal protection. The antitumor activity of CMH in Lewis lung carcinoma (LLC) was then evaluated in vitro and in vivo. Biologically, CMH inhibited the growth of LLC cells with an IC50 of 0.26 μM, 100 times more potently than cisplatin (26.05 μM) did. Meanwhile, the cytotoxicity of CMH was 10-fold lower than that of colchicine in normal human lung cells (BEAS-2B). In C57BL/6 mice xenograft model, CMH (0.5 mg/kg) worked as efficacious as colchicine (0.5 mg/kg) to inhibit tumor growth and 2 times more potently than cisplatin (1 mg/kg). In terms of mortality, 7 out of 10 mice died in colchicine group (0.75 mg/kg), while no death was observed in groups receiving CMH or cisplatin at 0.75 mg/kg. Mechanistic studies using Western blot revealed that CMH dose-dependently suppressed the protein expression of phosphorylated ERK. Molecular docking analysis further indicated that CMH was well fitted in the colchicine binding site of tubulin and formed several hydrogen bonds with tubulin protein. These results enable our novel hybrid CMH as a potential antineoplastic agent with lower toxicity, and provide perquisites for further investigation to confirm the therapeutic potentiality of this novel hybrid.

Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

BackgroundIt is still unclear whether stage I lung adenocarcinoma patients with tumour spread through air spaces (STAS) can benefit from postoperative adjuvant chemotherapy (ACT) after lobectomy. This study investigated the effect of ACT on the postoperative survival of patients with stage I (STAS+) lung adenocarcinoma.MethodsWe retrospectively analysed the clinical data of stage I (STAS+) invasive lung adenocarcinoma patients who underwent lobectomy in the Department of Thoracic Surgery of our hospital from January 1, 2013 to January 1, 2016. Propensity score matching (PSM) was performed to group patients to investigate whether ACT could lead to better prognosis of patients.ResultsA total of 593 patients with stage I (STAS+) lung adenocarcinoma were enrolled. The study after PSM included 406 patients. Kaplan–Meier survival analysis showed the experimental group had a better 3-year recurrence-free survival (RFS) rate (p = 0.037) and the 5-year RFS rate (p = 0.022) than the control group. It also had higher 5-year overall survival (p = 0.017). The multivariate analysis by Cox proportional hazard regression model showed that stage I STAS+ lung adenocarcinoma patients with lymphatic vessel invasion (HR: 1.711, 95% CI: 1.052-2.784; p = 0.045), vascular invasion (HR: 5.014, 95% CI: 3.154-7.969; p < 0.001), and visceral pleural invasion (HR: 2.086, 95% CI: 1.162-3.743; p = 0.014), and without ACT (HR: 1.675, 95% CI: 1.043-2.689; p = 0.033) had a significant survival disadvantage.ConclusionACT can boost the postoperative survival of patients with stage I (STAS+) lung adenocarcinoma.

Abstract Background Migraine is one of the most common neurological diseases around the world and calcitonin gene-related peptide (CGRP) plays an important role in its pathophysiology. Therefore, in the present study, we evaluated the efficacy of monoclonal antibodies blocking the CGRP ligand or receptor in episodic and chronic migraine. Objective The objective of our study is implementing a meta-analysis to systematically evaluate the efficacy and safety of eptinezumab for the treatment of migraine compared with placebo. Method We searched the Medline, Embase, Cochrane Library and Clinicaltrials.gov for randomized controlled trials (RCTs) which were performed to evaluate eptinezumab versus placebo for migraine up to September 2020. The data was assessed by Review Manager 5.3 software. The risk ratio (RR) and standard mean difference (SMD) were analyzed using dichotomous outcomes and continuous outcomes respectively with a random effect model. Result We collected 2739 patients from 4 RCTs: the primary endpoint of efficacy was the change from baseline to week 12 in mean monthly migraine days (MMDs). We found that eptinezumab (30 mg, 100 mg, 300 mg) led to a significant reduction in MMDs (P = 0.0001,P

Abstract Neuronal apoptosis has an important role in early brain injury (EBI) following subarachnoid hemorrhage (SAH). TRAF3 was reported as a promising therapeutic target for stroke management, which covered several neuronal apoptosis signaling cascades. Hence, the present study is aimed to determine whether downregulation of TRAF3 could be neuroprotective in SAH-induced EBI. An in vivo SAH model in mice was established by endovascular perforation. Meanwhile, primary cultured cortical neurons of mice treated with oxygen hemoglobin were applied to mimic SAH in vitro. Our results demonstrated that TRAF3 protein expression increased and expressed in neurons both in vivo and in vitro SAH models. TRAF3 siRNA reversed neuronal loss and improved neurological deficits in SAH mice, and reduced cell death in SAH primary neurons. Mechanistically, we found that TRAF3 directly binds to TAK1 and potentiates phosphorylation and activation of TAK1, which further enhances the activation of NF-κB and MAPKs pathways to induce neuronal apoptosis. Importantly, TRAF3 expression was elevated following SAH in human brain tissue and was mainly expressed in neurons. Taken together, our study demonstrates that TRAF3 is an upstream regulator of MAPKs and NF-κB pathways in SAH-induced EBI via its interaction with and activation of TAK1. Furthermore, the TRAF3 may serve as a novel therapeutic target in SAH-induced EBI.

Network management is facing a great challenge to analyze and identify encrypted network traffic with specific applications and protocols. A significant number of network users applying different encryption techniques to network applications and services to hide the true nature of the network communication. These challenges attract the network community to improve network security and enhance network service quality. Network managers need novel techniques to cope with the failure and shortcomings of the port-based and payload-based classification methods of encrypted network traffic due to emergent security technologies. Mainly, the famous network hopping mechanisms used to make network traffic unknown and anonymous are VPN (virtual private network) and TOR (Onion Router). This paper presents a novel scheme to unveil encrypted network traffic and easily identify the tunneled and anonymous network traffic. The proposed identification scheme uses the highly desirable deep learning techniques to easily and efficiently identify the anonymous network traffic and extract the Voice over IP (VoIP) and Non VoIP ones within encrypted traffic flows. Finally, the captured traffic has been classified into four different categories, i-e., VPN VoIP, VPN Non-VoIP, TOR VoIP, and TOR Non-VoIP. The experimental results show that our identification engine is extremely robust to VPN and TOR network traffic.

Due to the capability of passing through heavily censored networks or gateway equipped with the traffic-monitoring module, DNS tunnel has been the dominant covert communication technique for command and control between the victim and the attacker in network attack events. Although the discovery of DNS tunnel has been intensively studied, the internal application behavior identification for DNS tunnels still remains a challenging problem. The fine-gained identification can help to reveal more behavior information wrapped in DNS tunnels. In this study, we investigate the spatial-temporal information from the raw packets to identify the internal application behaviors in DNS tunnels. Multi-dimensional features on packet length and timing for DNS tunnels with different internal application behaviors are incorporated with a machine-learning algorithm to identify the internal application behaviors in DNS tunnels. We consider 4 common types of application behaviors in our research, including browsing webpages, emailing, downloading data, and controlling the remote servers. The experimental results show that the proposed scheme can achieve higher identification accuracy with a much lower packet consuming rate when compared with the state-of-the-art internal protocol identification scheme. The experiment results depict that our proposed scheme is better in terms of F-score, which can reach 99% with only 100 packets.

BackgroundSynchronous multiple ground-glass nodules (SMGGNs) in synchronous multiple lung cancers are associated with specific imaging findings. It is difficult to distinguish whether multiple nodules are primary tumors or metastatic lesions in the lungs. The need for PET/CT and contrast-enhanced brain MRI for these patients remains unclear. This study investigated the necessity of these two imaging examinations for SMGGN patients by means of retrospective analysis.MethodsSMGGN patients who were diagnosed and treated in our hospital from October 2017 to May 2020 and underwent whole-body PET/CT(Cranial excepted) and/or contrast-enhanced brain MRI+DWI were enrolled in this study. We analyzed the imaging and clinical characteristics of these patients to evaluate SMGGN patients’ need to undergo whole-body PET/CT and brain MRI examination.ResultsA total of 87 SMGGN patients were enrolled. 51 patients underwent whole-body PET/CT examinations and did not show signs of primary tumors in other organs, metastatic foci in other organs, or metastasis to surrounding lymph nodes. 87 patients underwent whole-brain MRI, which did not reveal brain metastases but did detect an old cerebral infarction in 23 patients and a new cerebral infarction in one patient. 87 patients underwent surgical treatment in which 219 nodules were removed. All nodules were diagnosed as adenocarcinoma or atypical adenomatous hyperplasia. No lymph node metastasis was noted.ConclusionFor SMGGN patients, PET/CT and enhanced cranial MRI are unnecessary for SMGGNs patients, but from the perspective of perioperative patient safety, preoperative MRI+DWI examination is recommended for SMGGNs patients.

Although the era of big data has brought convenience to daily life, it has also caused many problems. In the field of scenic tourism, it is increasingly difficult for people to choose the scenic spot that meets their needs from mass information. To provide high-quality services to users, a recommended tourism model is introduced in this paper. On the one hand, the tourism system utilises the users' historical interactions with different scenic spots to infer their short- and long-term favorites. Among them, the users' short-term demands are modelled through self-attention mechanism, and the proportion of short- and long-term favorites is calculated using the Euclidean distance. On the other hand, the system models the relationship between multiple scenic spots to strengthen the item relationship and further form the most relevant tourist recommendations.

The persistent emergence of new network applications, along with encrypted network communication, has make traffic analysis become a challenging issue in network management and cyberspace security. Currently, virtual private network (VPNs) has become one of the most popular encrypted communication services for bypassing censorship and guarantee remote access to geographically locked services. In this paper, a novel identification scheme of VoIP traffic tunneled through VPN is proposed. We employed a set of Flow Spatio-Temporal Features (FSTF) to six well-known classifiers, including decision trees, K-Nearest Neighbor (KNN), Bagging and Boosting via C4.5, and Multi-Layer perceptron (MLP). The overall accuracy, precision, sensitivity, and F-measure verify that the proposed scheme can effectively distinguish between the VoIP flows and Non-VoIP ones in VPN traffic.

DNS (Domain Name System) tunnels almost obscure the true network activities of users, which makes it challenging for the gateway or censorship equipment to identify malicious or unpermitted network behaviors. An efficient way to address this problem is to conduct a temporal‐spatial analysis on the tunnel traffic. Nevertheless, current studies on this topic limit the DNS tunnel to those with a single protocol, whereas more than one protocol may be used simultaneously. In this paper, we concentrate on the refined identification of two protocols mixed in a DNS tunnel. A feature set is first derived from DNS query and response flows, which is incorporated with deep neural networks to construct a regression model. We benchmark the proposed method with captured DNS tunnel traffic, the experimental results show that the proposed scheme can achieve identification accuracy of more than 90%. To the best of our knowledge, the proposed scheme is the first to estimate the ratios of two mixed protocols in DNS tunnels.

As one of the most critical infrastructure, the power grid has been increasingly threatened by network attacks, especially advanced persistent threats (APTs). APT in the power grid is a continual and stealthy attack that analyzes the interaction between the cyber layer and the physical layer. The existing offensive and defensive processes for power grid using honeypots against APTs are modeled based on full rationality. Therefore, both the attacker and the defender make decisions to maximize their payoffs under full rationality. However, fully rational decisions made by end-users are not always conformed with the real cases, and prospect theory is a typical boundedly rational method to model these deviations. In this study, we propose a subjective APT-honeypot game model to study the offensive and defensive interactions between the attacker and the defender based on the prospect theory. In this model, we protect the power grid bus nodes by deploying honeypots, which consider both low- and high-interaction honeypot modes. We prove the existence of Bayesian-Nash equilibrium strategies in defense and attack strategies under bounded rationality. In addition, we used IEEE-30 Bus system to verify the proposed model in this paper. Experiment results show that bounded rationality affects strategy selection and reduces attacker's payoffs.

Distinguishing malicious domain names generated by various domain generation algorithms (DGA) is critical for defending a network against sophisticated network attacks. In recent years, stealthy domain generation algorithms (SDGA) have been proposed and revealed significantly stronger stealthiness comparing to the traditional character-based DGA. Existing state-of-the-art detection schemes are not effective enough for detecting SDGA. In this paper, we exploit the character-level characteristics of the SDGA domain names and propose a heterogeneous deep neural network framework (HDNN) for detecting SDGA. HDNN employs a proposed improved parallel CNN (IPCNN) architecture with multi-sizes of convolution kernel for extracting multi-scale local features from a domain name. The framework also contains a proposed self-attention based bidirectional long short term memory (SA-Bi-LSTM) architecture which can extract the bidirectional global features with attention mechanism from a domain name. Besides that, the focal loss function is introduced to mitigate the imbalance of the sample quantity in the training phase. The benchmark experiments are carried out based on the database composed of the collected benign domain names, real-world DGA and SDGA ones. Compared to the 6 influential deep-learning-based DGA detection schemes, the proposed scheme has achieved state-of-the-art detection results on SDGAs, and also achieved state-of-the-art results on binary and multiclass classification for traditional DGAs.

Soil temperature (Ts) is a vital meteorological parameter for ecological, physical and biological research. Ts estimation is very important for a variety of fields and presents great challenges because the relevant areas have complex characteristics, human activities, and a nonlinear nature between Ts and its environmental factors. Hence, a novel model based on long short-term memory (LSTM), is proposed here as an alternative data-intelligence tool. The proposed model designs a novel function that combines LSTM loss with an adversarial term for enhancing the correlation between Ts and its environmental factors. For this purpose, we designed a novel function that combines LSTM loss with an adversarial term. The adversarial term encourages the LSTM model to estimate Ts, which cannot be distinguished from the observed Ts by an adversarial model. In this study, the proposed model is trained using meteorological information from two stations in China (Changbai Mountain and Haibei). The model is constructed using hourly input variables, including air temperature (Ta), wind speed (W), relative humidity (RH), solar radiation (SR), and vapor pressure deficit(VPD), while the objective variable is the Ts measurement at a 5 cm depth for the period 2003-2005. Different statistical evaluation criteria, including the root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NS), Willmott index of agreement(WI) and the Legates and McCabe index (LMI), have been employed to assess the model performance. Through experimentation, the proposed model generally performs superior to the other seven state-of-the-art estimation models (autoregressive integrated moving average model, linear regression, backpropagation neural networks, support vector regression, extreme learning machine, eXtreme gradient boosting and long short-term memory) in Ts estimation at a 5 cm depth over the Changbai Mountain and Haibei stations. For this case, the most accurate performance is attained for Ts estimation at a 5 cm depth, with the highest values of NS = 0.915, WI = 0.978, and LMI = 0.686, and the lowest values of the relative RMSE and MAE being 2.276 and 1.796, respectively. In accordance with the present results, it is concluded that the proposed model can serve as an alternative approach for estimating Ts, while ensuring that an appropriate combination of meteorological inputs is applied to yield an optimal model.

With the increasingly fierce competition for marine resources, underwater acoustic communication as the main form of underwater communication, its security has received more and more attention. The traditional underwater acoustic communication technology with fixed frequency and modulation may cause information leakage and location exposure of the communicating platform. The communication based on ocean noise is helpful to improve the communication security. This article proposes a new covert underwater acoustic communication scheme based on ship-radiated noise and chaos signal. Firstly, the characteristics of chaos signal is studied, which is generated by the Chebyshev sequence, and the reconstruction process of ship-radiated noise is analyzed. Secondly, we generate mimic ship-radiated noise containing the secret message based on the time and frequency feature of the chaos signal and ship-radiated noise. Finally, resampling technology is used to equalize the Doppler frequency shift, and time inversion mirror technology is used to perform multipath channel equalization. Simulation and sea experiment verify the effectiveness and feasibility of this scheme. In the sea experiments, the proposed scheme can reliably transmit over 40 per second when the communication distance is about 10km.

Abstract Background The co-occurrence of human immunodeficiency virus (HIV) infection and malaria in humans in endemic areas raises the question of whether one of these infections affects the course of the other. Although epidemiological studies have shown the impact of HIV infection on malaria, the mechanism(s) are not yet fully understood. Using a Chinese rhesus macaque coinfection model with simian immunodeficiency virus (SIV) and Plasmodium cynomolgi (Pc) malaria, we investigated the effect of concurrent SIV infection on the course of malaria and the underlying immunological mechanism(s). Methods We randomly assigned ten Chinese rhesus monkeys to two groups based on body weight and age. The SIV-Pc coinfection animals (S + P group) were infected intravenously with SIVmac251 eight weeks prior to malaria infection, and the control animals (P group) were infected intravenously with only Pc-infected red blood cells. After malaria was cured with chloroquine phosphate, we also initiated a secondary malaria infection that lasted 4 weeks. We monitored body weight, body temperature and parasitemia, measured SIV viral loads, hemoglobin and neopterin, and tracked the CD4+, CD8+, and CD4+ memory subpopulations, Ki67 and apoptosis by flow cytometry. Then, we compared these parameters between the two groups. Results The animals infected with SIV prior to Pc infection exhibited more severe malaria symptoms characterized by longer episodes, higher parasitemia, more severe anemia, greater body weight loss and higher body temperature than the animals infected with Pc alone. Concurrent SIV infection also impaired immune protection against the secondary Pc challenge infection. The coinfected animals showed a reduced B cell response to Pc malaria and produced lower levels of Pc-specific antibodies. In addition, compared to the animals subjected to Pc infection alone, the animals coinfected with SIV and Pc had suppressed total CD4+ T cells, CD4+CD28highCD95high central memory T cells, and CD4+CD28lowCD95− naïve T cells, which may result from the imbalanced immune activation and faster CD4+ T cell turnover in coinfected animals. Conclusions SIV infection aggravates malaria physiologically and immunologically in Chinese rhesus monkeys. This nonhuman primate SIV and Pc malaria coinfection model might be a useful tool for investigating human HIV and malaria coinfection and developing effective therapeutics.

AbstractA cyber‐physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well‐funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game‐theoretical model considering both low‐ and high‐interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.

BackgroundPogostemon cablin is a traditional Chinese medicine (TCM) that is frequently used to treat various gastrointestinal diseases. Patchouli alcohol (PA), a compound extracted from the Pogostemon cablin, has been shown to have anti-tumor efficacy in human colorectal cancer. However, the mechanism of PA’s anticancer effect on gastric cancer (GC) remains unknown.MethodsWe used the public database to obtain the potential targets of PA and genes related to GC. Bioinformatic analyses, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein-protein interactions (PPI), were used for analyzing the potential signal pathways and targets. Cell experiments were also conducted to further explain the impact and molecular mechanism of PA on GC, as well as to confirm the findings of network pharmacology.ResultsUsing network pharmacological analysis, 161 possible targets were identified for the treatment of GC. Network analysis and functional enrichment analysis show that PA produced a marked effect in the treatment of GC through multi-targets and multi-pathways, especially the MAPK and PI3K/AKT signal pathways. In addition, PA showed the inhibition of GC cell proliferation, migration and invasion in cell experiments. According to our findings, PA could also cause G0/G1 phase arrest and apoptosis in GC cells.ConclusionUsing network pharmacology, we aim to uncover the possible molecular mechanism of PA on GC treatment in this research. Cell experiments were also conducted to confirm the therapeutic effect of PA on GC.

BackgroundCraniopharyngioma (CP) is a challenging intracranial tumor due to its special hypothalamus-pituitary location. Each patient with CP should be evaluated and treated separately. Exploring novel methods of automatized analysis of data for gaining knowledge on any medical field is an encouraging task, particularly in such an extremely challenging tumor as CP. We aim to summary the situations, investigate the research trends and evaluate research hotspots using bibliometric analysis for the CP research.MethodsWe extracted all the CP-related literatures from 2011 to 2020 from the Web of Science database. An Online analysis platform of literature metrology (Bibliometric), BICOMB, gCLUTO and CiteSpace softwares were used to do bibliometric analysis. As a supplement, we also analyzed the top 100 cited case reports with particular and certainly infrequent information to improve the analysis.ResultsAccording to our retrieval strategy, we found a total of 1262 CP-related literatures. The United States has maintained a leading position in global CP research, followed by China and Germany. Among institutions, Capital Med Univ, St Jude Childrens Res Hosp and Southern Med Univ rank in the top 3 in terms of the number of articles published. “WORLD NEUROSURGERY” is the most popular journal for CP-related research. Moreover, MULLER HL, MERCHANT TE, QI ST and others have made great achievements in the study of CP. Finally, we did biclustering analysis on keywords and identified 4 CP research hotspot clusters.ConclusionsOur research provides a comprehensive analysis of the scientific progress of CP in the past 10 years, and insight into the development of CP research field, highlight research trends over time, and help identify valuable future directions.

The accurate classification of traffic data is challenging for network management and security, especially in imbalanced situations. The limitation of the existing convolutional neural networks is that they have problems such as overfitting, instability, and poor generalization when used to classify imbalanced datasets. In this paper, we propose a new imbalanced encrypted traffic classification model. The proposed model is based on the improved convolutional block attention module (CBAM) and re-weighted cross-entropy focal loss (CEFL) function. The model exploits the redefined imbalance degree to construct a weight function, which is used to reassign the weights of the categories. The improved CBAM based on the redefined imbalance degree can make the model pay more attention to the characteristics of the minority samples, and increase the representation ability of these samples. The re-weighted CEFL loss function can be used to expand the effective loss gap between minority and majority samples. The method is validated on the public ISCX Tor 2016 dataset. The experimental results show that the performance of the new classification model is better than the baseline methods, and the proposed method can remarkably push the precision of the minority categories to 93.28% (14.63%↑), recall to 91.71% (16.98%↑), and F1 score to 92.49% (16.23%↑).

Early brain injury is considered to be a primary reason for the poor prognosis of patients suffering from subarachnoid hemorrhage (SAH). Due to its pro-inflammatory activity, cold-inducible RNA-binding protein (CIRP) has been implicated in the ischemic brain insult, but its possible interplay with hypothermia in SAH treatment remains to be evaluated. One-hundred and thirty-eight Sprague-Dawley rats (300–350 g males) were randomly allocated into the following groups: sham-operated (Sham); SAH; and SAH + hypothermia (SAH + H), each comprised of 46 animals. After treatments, the brain tissues of the three groups were randomly collected after 12 h, 1 d, 3 d, and 7 d, and the expression levels of the CIRP and mitochondrial apoptosis pathway-related proteins Bax, Bcl-2, caspase-9, caspase-3, and cytochrome c measured using Western blotting and real-time PCR. Brain damage was assessed by TUNEL and Nissl staining, the electron microscopy of brain tissue slices as well as functional rotarod tests. Expression of CIRP, Bax, caspase-9, caspase-3, and cytochrome c as well as reduced motor function incidence were higher in the SAH group, particularly during the first 3 d after SAH induction. Hypothermia blunted these SAH responses and apoptosis, thereby indicating reduced inflammatory signaling and less brain cell injury in the early period after SAH. Hypothermia treatment was found to effectively protect the brain tissue from early SAH injury in a rat model and its further evaluation as a therapeutic modality in SAH patients requires further study.

BackgroundLung cancer is a malignant tumor that has the highest morbidity and mortality rate among all cancers. Early diagnosis of lung cancer is a key factor in reducing mortality and improving prognosis.MethodsIn this study, we performed CTC next-generation sequencing (NGS) in early-stage lung cancer patients to identify lung cancer-related gene mutations. Meanwhile, a serum liquid chromatography-tandem mass spectrometry (LC-MS) untargeted metabolomics analysis was performed in the CTC-positive patients. To screen potential diagnostic markers for early lung cancer.Results62.5% (30/48) of lung cancer patients had ≥1 CTC. By CTC NGS, we found that > 50% of patients had 4 commonly mutated genes, namely, NOTCH1, IGF2, EGFR, and PTCH1. 47.37% (9/19) patients had ARIDH1 mutations. Additionally, 30 CTC-positive patients and 30 healthy volunteers were subjected to LC-MS untargeted metabolomics analysis. We found 100 different metabolites, and 10 different metabolites were identified through analysis, which may have potential clinical application value in the diagnosis of CTC-positive early-stage lung cancer (AUC >0.9).ConclusionsOur results indicate that NGS of CTC and metabolomics may provide new tumor markers for the early diagnosis of lung cancer.

Background and Purpose: Fluoxetine is a drug commonly used to treat mental disorders, such as depression and obsessive–compulsive disorder, and some studies have shown that fluoxetine can improve motor and function recovery after stroke. Therefore, we performed a meta-analysis to investigate the efficacy and safety of fluoxetine in the treatment of post-stroke neurological recovery.Methods: PubMed, Embase, and Cochrane Library were searched for randomized controlled trials (RCTs) that were performed to assess the efficacy and safety of fluoxetine for functional and motor recovery in subacute stroke patients up to October 2020. Review Manager 5.3 software was used to assess the data. The risk ratio (RR) and standardized mean difference (SMD) were analyzed and calculated with a fixed effects model.Results: We pooled 6,788 patients from nine RCTs. The primary endpoint was modified Rankin Scale (mRS). Fluoxetine did not change the proportion of mRS ≤ 2 (P = 0.47). The secondary endpoints were Fugl-Meyer Motor Scale (FMMS), Barthel Index (BI), and National Institutes of Health Stroke Scale (NIHSS). Fluoxetine improved the FMMS (P < 0.00001) and BI(P < 0.0001) and showed a tendency of improving NIHSS (P = 0.08). In addition, we found that fluoxetine reduced the rate of new-onset depression (P < 0.0001) and new antidepressants (P < 0.0001).Conclusion: In post-stroke treatment, fluoxetine did not improve participants' mRS and NIHSS but improved FMMS and BI. This difference could result from heterogeneities between the trials: different treatment duration, clinical scales sensitivity, patient age, delay of inclusion, and severity of the deficit.

With the rapid development of mobile internet and cloud computing, numerous digital me-dia files in mobile social networking and media sharing software have become the important carriers of steganography. However, these digital media files may be resampled by the media server when being pushed to the intelligent mobile terminals. The resampling of digital media files is a transfor-mation which enlarges or shrinks objects by a scale factor that is the same in all dimensions. In order to reduce embedding distortion while ensuring the correct extraction of secret messages under resam-pling mechanism, a steganographic coding scheme based on dither convolutional trellis is proposed in this paper. The resampling mapping is estimated with finite sample pairs. The resampling stego media files with secret messages embedded are generated from the estimated resampling cover media files by syndrome-trellis codes (STCs). According to the estimated resampling mapping, the dither convolutional trellis for one dimensional resampling is constructed to generate the source stego me-dia files from source cover media files and resampling stego media files. The steganographic coding scheme is also extended to the circumstance of two dimensional resampling such as image scaling. The experimental results show that the proposed steganographic scheme can achieve less embedding dis-tortion while ensuring the accuracy of secret messages extraction under multi-dimensional resampling mechanism.

Upregulation of human epididymis protein 4 (HE4) is often observed in different types of cancers, including gastric cancer (GC), but the association of elevated HE4 level with radiation resistance in GC remains unclear. The expression of HE4 and hypoxia-inducible factor 1α subunit (HIF1α) was assessed in GC patient samples and cell lines. Chromatin immunoprecipitation (ChIP) and luciferase reporter assays were performed to reveal the regulation between HE4 and HIF1α. Stable HE4 knockdown and HIF1α overexpression were introduced into GC cell lines to study the role of HE4 in the resistance of GC to radiation therapy. Colony formation assay and the xenograft mouse model were used to investigate the effects of radiation on GC cells. HE4 and HIF1α were upregulated in both GC patient tissues and GC cells. Hypoxia and HIF1α upregulated HE4 by directly targeting the hypoxia response element in its promoter region. Stable HE4 knockdown significantly sensitized GC cells and xenograft tumors to radiation. HIF1α overexpression markedly elevated the radiation resistance of GC cells, which was almost completely abolished by HE4 knockdown. Hypoxia-induced upregulation of HE4 is responsible for resistance to radiation therapy of GC, suggesting that HE4 knockdown or inhibition, combined with radiation therapy, holds great potential in the clinical treatment of GC. Keywords: hypoxia, human epididymis protein 4, WFDC2, radiation therapy, gastric cancer, hypoxia-inducible factor 1 α subunit, HIF1α, resistance

Abstract Background Pulmonary arterial smooth muscle cell (PASMC) proliferation in response to hypoxia plays an important role in the vascular remodelling that occurs in hypoxic pulmonary hypertension. MicroRNAs (miRs) are emerging as important regulators in the progression of pulmonary hypertension. In this study, we investigated whether the expression of miR-17-5p is modulated by hypoxia and is involved in the hypoxia-induced proliferation of PASMCs. Methods Human PASMCs were cultured under hypoxic conditions. miR-17-5p expression was determined by real-time RT-PCR. A BrdU incorporation assay and time-lapse recording were utilized to determine cell proliferation and migration. Results PASMC proliferation was increased by moderate hypoxia (3% oxygen) but was reduced by severe hypoxia (0.1% oxygen) after 48 h. Moderate hypoxia induced miR-17-5p expression. Overexpression of miR-17-5p by transfection with miR-17-5p enhanced cell proliferation and migration in normoxia, whereas knockdown of miR-17-5p with anti-miR-17-5p inhibitors significantly reduced cell proliferation and migration. The expression of miR-17-5p target genes, specifically phosphatase and tensin homologue (PTEN) and cyclin-dependent kinase inhibitor 1 (p21WAF1/Cip1, p21), was reduced under moderate hypoxia in PASMCs. Under normoxia, overexpression of miR-17-5p in PASMCs reduced the expression of PTEN and p21. Conclusion Our data indicate that miR-17-5p might play a significant role in hypoxia-induced pulmonary vascular smooth muscle cell proliferation by regulating multiple gene targets, including PTEN and p21, and that miR-17-5p could be a novel therapeutic target for the management of hypoxia-induced PH.

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

Subarachnoid hemorrhage (SAH) elicits destruction of neuronal cells and neurological function, which is exacerbated by neuro-inflammation in EBI, and toll-like receptor 4 (TLR4) plays an important role in inflammatory cascade via modulation microglia polarization. Curcumin (Cur), as a natural phytochemical compound, has the potential characteristics on anti-inflammatory and microglia phenotype transformation. In this study, we verified the hypothesis curcumin promotes M2 polarization to inhibiting neuro-inflammation, which through suppressing TLR4 signaling pathway after SAH. In tlr4–/– mice and wild type (WT) subjected to prechiasmatic cistern blood injection, Western blotting, brain water content, neurological score, enzyme-linked immunosorbent assay (ELISA) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were performed to investigate the role of TLR4 on neuro-inflammation response and microglia polarization. Curcumin with three different concentrations (50 mg/kg, 100 mg/kg and 200 mg/kg) were injected intraperitoneally (i.p.) at 15 min after SAH. The levels of TLR4, myeloid differentiation factor 88 (MyD88), nuclear factor- κB (NF-κB), Iba-1, CD86, CD206 and pro/anti-inflammation cytokines were measured by Western blotting and immunofluorescence staining at 24 h after SAH. SAH induction increased the protein levels of TLR4, pro-inflammation cytokines and proportion of M1 phenotype. Curcumin with 100 mg/kg treatment dramatically inhibited the release of pro-inflammatory mediators, and elevated the protein levels of anti-inflammatory cytokines and promoted microglia switch to M2. Meanwhile, curcumin treatment also decreased the expressions of TLR4, Myd88 and NF-κB at 24 h post SAH. TLR4 deficiency ameliorated brain water content, neurological deficit and reduced pro-inflammation cytokines after SAH. Moreover, curcumin treatment in tlr4–/– mice further induced M2 polarization, while had no statistic difference on brain water content and neurological score at 24 h post SAH. Our results indicated that curcumin treatment alleviated neuro-inflammation response through promoting microglia phenotype shift toward M2, and which might inhibiting TLR4/MyD88/NF-κB signaling pathway after SAH.

Rainstorms, insect swarms, and galloping horses produce “sound textures,”which are the resulting natural sounds of many similar acoustic events. With new achievements emerging regularly for generative models, the deep convolutional neural network (CNN) has proven to be a tremendously successful approach for image and sound synthesis. Existing state-of-the-art sound texture generative models simply treat sound texture signals as 1-D images while discarding the difference between the human vision and auditory systems. This paper considers mel-frequency statistical features, which are designed according to the human auditory system and have been viewed as the dominant features for sound identification. We first construct a CNN structure for extracting mel-frequency features from sounds losslessly. This structure is called mel-frequency CNN (MF-CNN). Next, we investigate a novel sound texture generative model by incorporating the MF-CNN into a convolutional generative network composed of cascading upsampling groups. A jointly alternating back propagation algorithm is proposed to train the overall network. The feedback of the MF-CNN is used to advise the gradients in the inferential and learning back propagation to make the mel-frequency features of the synthesized sounds more similar to the natural ones. Moreover, the proposed generative model can also be extended to other sound synthesis tasks.

In highly sophisticated network attacks, command-and-control (C&C) servers always use domain generation algorithms (DGAs) to dynamically produce several candidate domains instead of static hard-coded lists of IP addresses or domain names. Distinguishing the domains generated by DGAs from the legitimate ones is critical for finding out the existence of malware or further locating the hidden attackers. The word-based DGAs disclosed in recent network attack events have shown significantly stronger stealthiness when compared with traditional character-based DGAs. In word-based DGAs, two or more words are randomly chosen from one or more specific dictionaries to form a dynamic domain, these regularly generated domains aim to mimic the characteristics of a legitimate domain. Existing DGA detection schemes, including the state-of-the-art one based on deep learning, still cannot find out these domains accurately while maintaining an acceptable false alarm rate. In this study, we exploit the inter-word and inter-domain correlations using semantic analysis approaches, word embedding and the part-of-speech are taken into consideration. Next, we propose a detection framework for word-based DGAs by incorporating the frequency distribution of the words and that of part-of-speech into the design of the feature set. Using an ensemble classifier constructed from Naive Bayes, Extra-Trees, and Logistic Regression, we benchmark the proposed scheme with malicious and legitimate domain samples extracted from public datasets. The experimental results show that the proposed scheme can achieve significantly higher detection accuracy for word-based DGAs when compared with three state-of-the-art DGA detection schemes.

In this article, we study oscillatory properties of solutions to neutral nonlinear impulsive hyperbolic partial differential equations with several delays. We establish sufficient conditions for oscillation of all solutions.