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Summary: Osteophyte formation, a key indicator of osteoarthritis (OA) severity, remains poorly understood in its relation to gut microbiota and metabolites in knee osteoarthritis (KOA). We conducted 16S rDNA sequencing and untargeted metabolomics on fecal and serum samples from 20 healthy volunteers, 80 KOA patients in Guangdong, and 100 in Inner Mongolia, respectively. Through bioinformatics analysis, we identified 3 genera and 5 serum metabolites associated with KOA osteophyte formation. Blautia abundance negatively correlated with meat, cheese, and bean consumption. The 5 serum metabolites negatively correlated with dairy, beef, cheese, sugar, and salt intake, yet positively with age and oil consumption. Higher Blautia levels in the gut may contribute to KOA osteophyte formation, with serum metabolites LTB4 and PGD2 potentially serving as biomarkers. KOA patients in Inner Mongolia exhibited lower Blautia levels and reduced expression of 5 serum metabolites, possibly due to cheese consumption habits, resulting in less osteophyte formation.

Abstract Background Metabolomics is a tool to study the pathogenesis of diseases and their associated metabolites, but there are still insufficient metabolomic studies on severe knee osteoarthritis.To investigate the differences in serum metabolites between healthy populations and knee osteoarthritis (KOA) patients in Southern China using widely targeted metabolomics, and to explore biomarkers and their metabolic pathways that could be associated with the severity of KOA. Methods There were 10 healthy individuals in the control group and 32 patients with KOA. According to the Kellgren–Lawrence (KL) grading system, KOA was further divided into mild (n = 13, KL grade 1 and 2) and severe (n = 19, KL grade 3 and 4). Serum samples from all participants were collected and analyzed metabolomics based on ultra-performance liquid chromatography/electrospray ionization/tandem mass spectrometry. We screened for differential metabolites between patients and controls, and between mild and severe KOA. We explored the metabolic pathways involved in differential metabolism using the Kyoto Encyclopedia of Genes and Genomes database. Results Sixty-one metabolites were differentially expressed in the sera of the patient group compared with the control group (45 upregulated and 16 downregulated). Analysis of the mild and severe KOA groups showed a total of 12 differential metabolites. Receiver operating characteristic curve analysis showed N-alpha-acetyl-L-asparagine was a good predictor of advanced osteoarthritis(OA).Differential metabolites are enriched in multiple pathways such as arachidonic acid metabolism. Conclusion Widely targeted metabolomics found that upregulation of the amino acid metabolite N-α-acetyl-L-asparagine was significantly associated with severe KOA and could be a biomarker for predicting severity of KOA. Arachidonic acid metabolism may play an important role in patients with severe KOA.

Abstract In addition to the classical role as a serum effector system of innate immunity, accumulating evidence suggests that intracellular complement components have indispensable functions in immune defense, T cell homeostasis, and tumor cell proliferation and metastasis. Here, we revealed that complement component 3 (C3) is remarkably upregulated in paclitaxel (PTX)-resistant non-small cell lung cancer (NSCLC) cells and that knockdown of C3 promoted PTX-induced cell apoptosis, sensitizing resistant cells to PTX therapy. Ectopic C3 decreased PTX-induced apoptosis and induced resistance to PTX treatment in original NSCLC cells. Interestingly, C3b, the activated fragment of C3, was found to translocate into the nucleus and physically associate with the HDAC1/2-containing SIN3A complex to repress the expression of GADD45A, which plays an important role in cell growth inhibition and apoptosis induction. Importantly, C3 downregulated GADD45A by enhancing the binding of the SIN3A complex with the promoter of GADD45A, thus decreasing the H3Ac level to compress chromatin around the GADD45A locus. Subsequently, ectopic GADD45A promoted PTX-induced cell apoptosis, sensitizing resistant cells to PTX therapy, and insufficiency of GADD45A in original cancer cells induced resistance to PTX treatment. These findings identify a previously unknown nucleus location and oncogenic property for C3 in chemotherapy and provide a potential therapeutic opportunity to overcome PTX resistance.

Frequency modulation (FM)-to-amplitude modulation (AM) conversion is an important factor that affects the time–power curve of inertial confinement fusion (ICF) high-power laser facilities. This conversion can impact uniform compression and increase the risk of damage to optics. However, the dispersive grating used in the smoothing by spectral dispersion technology will introduce a temporal delay and can spatially smooth the target. The combined effect of the dispersive grating and the focusing lens is equivalent to a Gaussian low-pass filter, which is equivalent to 8 GHz bandwidth and can reduce the intensity modulation on the target to below 5% with 0.3 nm @ 3 GHz + 20 GHz spectrum phase modulation. The results play an important role in the testing and evaluating of the FM-to-AM on the final optics and the target, which is beneficial for comprehensively evaluating the load capacity of the facility and isentropic compression experiment for ICF.

Abstract Background Osteoarthritis (OA) is a chronic degenerative joint disease characterized by loss of joint function, which seriously reduces the quality of life of the elderly and imposes a heavy socioeconomic burden worldwide. Monotropein (MON), the main active ingredient of Morinda officinalis F.C. How, has exhibited therapeutic effects in different disease models. However, its potential effects on chondrocytes in an arthritic model remain unclear. This study aimed to evaluate the effects of MON in chondrocytes and a mouse model of OA, and explore the potential mechanisms. Materials and methods Murine primary chondrocytes were pretreated with 10 ng/ml interleukin (IL)-1β for 24 h to establish an in vitro model of OA, and then treated with different concentrations of MON (0, 25, 50 and 100 μM) for 24 h. The proliferation of the chondrocytes was assayed using ethynyl-deoxyuridine (EdU) staining. Immunofluorescence staining, western blotting and TUNEL staining were performed to assess the effects of MON on cartilage matrix degradation, apoptosis and pyroptosis. The mouse model of OA was constructed by surgical destabilization of the medial meniscus (DMM), and the animals were randomly divided into the sham-operated, OA and OA + MON groups. Following OA induction, the mice were given intraarticular injection of 100 μM MON or equal volume of normal saline twice a week for 8 weeks. The effects of MON on cartilage matrix degradation, apoptosis and pyroptosis were assessed as indicated. Results MON significantly accelerated the proliferation of chondrocytes, and inhibited cartilage matrix degradation, apoptosis and pyroptosis in the IL-1β-stimulated cells by blocking the nuclear factor-kappa B (NF-κB) signaling pathway. In the mouse model as well, MON treatment alleviated OA progression and promoted cartilage repair by inhibiting cartilage matrix degradation, and chondrocyte apoptosis and pyroptosis through the inactivation of the NF-κB signaling pathway. Furthermore, the MON-treated arthritic mice exhibited better articular tissue morphology and lower OARSI scores. Conclusions MON alleviated OA progression by inhibiting cartilage matrix degradation, and the apoptosis and pyroptosis of chondrocytes via NF-κB pathway inactivation, and is a promising alternative for the treatment of OA.

Sufficient evidence has linked many different types of cancers and T2D through shared risk factors; however, the underlying mechanisms are not fully understood. α-Hydroxybutyrate (α-HB), a byproduct metabolite increased in diabetes and cancer, including colorectal cancer (CRC), triggers lactate dehydrogenase A (LDHA) nuclear translocation. Nuclear LDHA markedly extends NF-κB nuclear retention by interacting with phosphorylated p65, leading to an increase in TNF-α production, impaired insulin secretion and the exacerbation of azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced CRC and high-fat diet (HFD)-induced type 2 diabetes. Furthermore, metformin interrupted this process by inhibiting the transcription of FOXM1 and c-MYC, the resultant downregulation of LDHA expression and α-HB-induced LDHA nuclear translocation. Thus, the results reveal the elevated α-HB level could be a novel shared risk factor of linking CRC, diabetes and the use of metformin treatment, as well as highlight the importance of preventing NF-κB activation for protecting against cancer and diabetes.

Osteoarthritis is non-inflammatory degenerative joint arthritis, which exacerbates disability in elder persons. The molecular mechanisms of osteoarthritis are elusive. Ubiquitination, one type of post-translational modifications, has been demonstrated to accelerate or ameliorate the development and progression of osteoarthritis via targeting specific proteins for ubiquitination and determining protein stability and localization. Ubiquitination process can be reversed by a class of deubiquitinases via deubiquitination. In this review, we summarize the current knowledge regarding the multifaceted role of E3 ubiquitin ligases in the pathogenesis of osteoarthritis. We also describe the molecular insight of deubiquitinases into osteoarthritis processes. Moreover, we highlight the multiple compounds that target E3 ubiquitin ligases or deubiquitinases to influence osteoarthritis progression. We discuss the challenge and future perspectives via modulation of E3 ubiquitin ligases and deubiquitinases expression for enhancement of the therapeutic efficacy in osteoarthritis patients. We conclude that modulating ubiquitination and deubiquitination could alleviate the osteoarthritis pathogenesis to achieve the better treatment outcomes in osteoarthritis patients.

Colitis is a chronic disease associated with alterations in the composition of gut microbiota. Schisandra chinensis bee pollen extract (SCPE) has been proved to be rich in phenolic compounds and effective in modulating gut microbiota, but its effect on colitis and the underlying mechanism remains unclear. This study investigates the relationship between colitis amelioration and the gut microbiota regulation of SCPE via fecal microbial transplantation (FMT). The results showed that administration of 20.4 g/kg BW of SCPE could primely ameliorate colitis induced by dextran sulfate sodium (DSS) in mice, showing as more integration of colon tissue structure and the colonic epithelial barrier, as well as lower oxidative stress and inflammation levels compared with colitis mice. Moreover, SCPE supplement restored the balance of T regulatory (Treg) cells and T helper 17 (Th17) cells. Gut microbiota analysis showed SCPE treatment could reshape the gut microbiota balance and improve the abundance of gut microbiota, especially the beneficial bacteria (Akkermansia and Lactobacillus) related to the production of short-chain fatty acids and the regulation of immunity. Most importantly, the protection of 20.4 g/kg BW of SCPE on colitis can be perfectly transmitted by fecal microbiota. Therefore, the gut microbiota–SCFAS–Treg/Th17 axis can be the main mechanism for SCPE to ameliorate colitis. This study suggests that SCPE can be a new promising functional food for prevention and treatment of colitis by reshaping gut microbiota and regulating gut immunity.

Natural gas is an eco-friendly energy source with low carbon emissions, making it attractive globally. Understanding gas reservoirs is crucial for sustainable extraction and optimizing potential. However, the complicated fluid flow and production dynamics within intricate gas reservoirs, particularly those characterized by abnormally high pressures and tight porous media, remain partially understood and demand further investigation. In a tight porous medium subjected to high pressure, the assumption of constant permeability is no longer valid. Consequently, a novel composite seepage model has been developed in this study, which considers the responsiveness of permeability to stress. Perturbation theory is employed to address the inherent non-linearity demonstrated by the permeability modulus. The solution of dimensionless pressure responses under constant production conditions is accomplished in the Laplace domain by implementing integral transformation methods. Overall, a comprehensive model is provided to understand the production behaviors of tight gas reservoirs. Moreover, in order to comprehend the transient flow characteristics of tight gas reservoirs, log–log plots are generated through the Stehfest numerical inversion approach, with the flow regimes categorized based on the normalized time phases of the pressure curves. Parametric investigations reveal that stress sensitivity detrimentally affects permeability, resulting in more pronounced pressure declines during the intermediate and late flow phases. The transient seepage model elaborated in this study is able to consider the pertinent formation and well parameters. These interpreted parameters bear significance in designing hydraulic fracturing operations, assessing the potential of tight gas reservoirs, and ultimately enhancing gas production. The presented model not only enhances our understanding of the behavior of horizontal wells in stress-sensitive tight gas reservoirs but also makes a valuable contribution to the broader discussion on transient flow phenomena in petroleum engineering.

Background: Spinal cord injury (SCI) is a common disorder of the central nervous system with considerable socio-economic burden. Andrographolide (Andro), the main active component of Andrographis paniculata, has exhibited neuroprotective effects in different models of neurological diseases. The aim of this study was to evaluate the neuroprotective effects of Andro against SCI and explore the related mechanisms.Methods: SCI was induced in rats by the Allen method, and the modeled animals were randomly divided into sham-operated, SCI, SCI + normal saline (NS) and SCI + Andro groups. The rats were injected intraperitoneally with Andro (1 mg/kg) or the same volume of NS starting day one after the establishment of the SCI model for 28 consecutive days. Post-SCI tissue repair and functional recovery were evaluated by measuring the spinal cord water content, footprint tests, Basso-Beattie-Bresnahan (BBB) scores, hematoxylin-eosin (HE) staining and Nissl staining. Apoptosis, oxidative stress and inflammation, as well as axonal regeneration and remyelination were analyzed using suitable markers. The in vitro model of SCI was established by treating cortical neurons with H2O2. The effects of Andro on apoptosis, oxidative stress and inflammation were evaluated as indicated.Results: Andro treatment significantly improved tissue repair and functional recovery after SCI by reducing apoptosis, oxidative stress and inflammation through the nuclear factor E2-related factor 2/heme oxygenase-1 (Nrf-2/HO-1) and nuclear factor-kappa B (NF-κB) signaling pathways. Furthermore, Andro treatment promoted M2 polarization of the microglial cells and contributed to axonal regeneration and remyelination to improve functional recovery after SCI. In addition, Andro also attenuated apoptosis, oxidative stress and inflammation in H2O2-stimulated cortical neurons in vitro.Conclusion: Andro treatment alleviated SCI by reducing apoptosis, oxidative stress and inflammation in the injured tissues and cortical neurons, and promoted axonal regeneration and remyelination for functional recovery.

Laser–plasma interactions (LPIs) hinder the interaction of high-energy laser pulses with targets. The use of broadband low-coherence light has been proposed to reduce the impact of LPIs. In this study, to improve the time–frequency characteristics of broadband low-coherence optical seeds, we proposed an arbitrary time-shaping technique scheme based on optical parametric amplification (OPA) that differs from traditional arbitrary time shaping. The shaping process and output characteristics were analyzed in detail. The theoretical and experimental results show that an arbitrary time-shaping pulse output with a large time-shaping contrast, fast-rising edge, and wide spectral width can be obtained. The time shaping contrast of the shaped pulse can be >300:1, and the spectral width is ~40 nm. The output time waveform is smoother than in traditional schemes, and the noise-like modulation is approximately 4% (approximately equal to the unshaped initial amplified spontaneous emission source). The arbitrary time-shaping scheme based on OPA provides a viable solution for the temporal waveform shaping of broadband low-coherence light.

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.

BackgroundC5AR2 (GPR77, C5L2) is the second receptor for C5a that is a potent protein generated by complement activation. C5AR2 can mediate its own signaling events and exert significant immunomodulatory effects through those events. However, research of C5AR2 in cancer is limited, and its function remains unclear in breast cancer.MethodsThe expression of C5AR2 and its correlations with prognosis, immune infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI) in more than thirty types of cancers were described through GTEx, TCGA, PrognoScan, TIMER2.0, CCLE, HPA, and TISIDB database. C5AR2 showed strong relationships to those immune marker sets in breast cancer. Otherwise, CCK8 assay and Transwell assay were conducted to illustrate the role of C5AR2 in migration, invasion, and proliferation of breast cancer cells.ResultsGenerally, C5AR2 expression differed across most cancerous and noncancerous tissues, and high C5AR2 expression significantly related to poor prognosis in BRCA, GBM, KICH, LAML, LGG, LIHC, PAAD, and STAD. Moreover, C5AR2 expression levels were dramatically correlated with recognized immune infiltration, especially the polarization of macrophages in breast cancer. Gene set enrichment analysis confirmed that C5AR2 participates in regulating multiple signaling pathways involved in tumorigenesis as well as tumor immunity. C5AR2 overexpression facilitated the functions such as migration, invasion, and proliferation in breast cancer cells, which is consistent with bioinformatics analysis.ConclusionsC5AR2 is involved in immune infiltration and malignant characteristics of breast cancer, which may be a prospective biomarker for breast cancer.

The 1.7 μm band eye-safe laser sources have recently received lots of attention thanks to the development of various applications. Although a variety of lasing configurations operating in this band have been demonstrated, one still needs to seek a good candidate for particular applications with a reasonable compromise between the relative performance targets (e.g., stability, output power, and spectral purity) and the construction cost. Here, we demonstrate a high-power 1694 nm random fiber laser (RFL) in a cost-effective structure pumped by a high-powered 1565 nm RFL. The maximum output power reached the 10 W level, and the output showed extremely low-intensity fluctuations for both the short-time and long-time regimes. Meanwhile, an excellent spectral purity as high as 26.9 dB was also realized. This work provides one of the most attractive approaches for constructing high-performance 1.7 μm band laser sources for practical applications.

The noise and gain characteristics of pulsed double-pass (DP) amplifiers operating at low repetition rates are analyzed theoretically and experimentally. The Frantz-Nodvik equations are extended to realize rapid calculation of signal gain in DP fiber amplifiers. At the same time, the rate equations are used to compute the evolution of amplified spontaneous emission (ASE) noise. The effects of the pump scheme, the input energy, and the insertion loss on the gain and noise characteristics are discussed. Based on the simulation results, a new structure Yb-doped DP amplifier is proposed. The experimental results show that the small-signal gain of the DP amplifier can exceed 50 dB. In particular, the average ASE background of the DP amplifier at high-energy output is lower than that of the single-pass amplifier at the same output. This result is quite different from continuous-wave fiber amplifiers.

High-power laser pulse transmitted by phase modulation with certain spectrum distribution can suppress the buildup of transverse stimulated Brillouin scattering (TSBS) in large aperture laser optics and smooth the speckle pattern illuminating the target by spectral smoothing dispersion (SSD). In this paper, based on the requirements of the double-cone ignition scheme including simultaneously realizing that the focal spot is variable at different times in size and the spatial intensity distribution is uniform, we propose a novel phase modulation technology with a rapid variable modulation index in the nanosecond scale instead of utilizing conventional constant amplitude sinusoidal curve. The relevant simulation results indicate that the proposed technology can realize the dynamic nanosecond spectral distribution and the trend correlates with the variety of modulation index. Particularly, we indirectly measure this rapid changeable spectral distribution based on the mapping relationship between frequency and time domain. We believe that the new technology is expected to meet the requirements of SSD and the dynamic focus simultaneously.

The group velocity dispersion (GVD) occurring in the front end of high-power lasers is one of the primary factors leading to the conversion of frequency modulation (FM) to amplitude modulation (AM). In this paper, we propose a modified, active, closed-loop feedback compensation device for GVD-induced FM–AM conversion, using a two-dimensional, electric, adjustable mirror mount and parallel grating pair to improve the long-term stability, efficiency of adjustment, and accuracy of compensation. Experimental results of a 12 h FM–AM depth test revealed that the depth varied between 2.28% and 5.22%. Moreover, we formulated a mathematical relationship between the dispersion parameters and temperature in optical fibers to analyze the intrinsic effect of temperature on FM–AM. The related simulation and experimental results consistently validated the quantitative relationship between the temperature and FM–AM depth.

Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method.

The fault response signals of an axle-box bearing of a rail vehicle have strongly non-linear and non-stationary characteristics, which can reflect the operating state of the running gears. This paper proposes a novel method for bearing fault diagnosis based on frequency-domain energy feature reconstruction (EFR) and composite multiscale permutation entropy (CMPE). First, a wavelet packet transform (WPT) is applied to decompose the vibration signals into multiple frequency bands. Then, considering that the bearing-localized defects cause the axle-box bearing system to resonate at a high frequency, which will lead to uneven energy distribution of the signal in the frequency domain, the energy factors of each frequency band are calculated by an energy feature extraction algorithm, from which the frequency band with maximum energy factor (which contains abundant fault information) is reconstructed to the time-domain signal. Next, the complexity of the reconstructed signals is calculated by CMPE as fault feature vectors. Finally, the feature vectors are input into a medium Gaussian support vector machine (MG-SVM) for bearing condition classification. The proposed method is validated by a public bearing data set and a wheelset-bearing system test bench data set. The experimental results indicate that the proposed method can effectively extract bearing fault features and provides a new solution for condition monitoring and fault diagnosis of rail vehicle axle-box bearings.

Cu-xNi-5Sn (wt %) alloys with a different Ni content were prepared by a powder metallurgy method. The effect of Ni content on the hardness and yield strength of Cu-xNi-5Sn (wt %) alloys was investigated. The microstructure, composition, and morphology of Cu-xNi-5Sn (wt %) alloys were observed by X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and cold field emission scanning electron microscope (FESEM), respectively. Results indicate that the hardness and yield strength firstly increase and then decrease with the increase of Ni content and reach up to a maximum when Ni content is 12.5 wt %. Furthermore, the formation of the sandwich structure and needle-like phase is found in the grain, the grain boundary and intragranular precipitates are rich in both the Ni and Sn phase. The formation of the inerratic and suitable lamellar precipitates of sandwich structure and needle-like phase can be responsible for the good mechanical properties of the Cu-12.5Ni-5Sn alloy after aging treatment. The sandwich structure and need-like phase that were observed by FESEM can contribute to clarify the morphology of Cu-Ni-Sn alloys.

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]