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T cell–mediated immunotherapy represents a promising strategy for cancer treatment; however, it has achieved satisfactory clinical responses in only a limited population. Thus, a broader view of the T-cell immune response is required. The Ras/MAPK pathway operates in many important signaling cascades and regulates multiple cellular activities, including T-cell development, proliferation, and function. Herein, we found that the typical membrane-bound complement regulatory protein CD59 is located intracellularly in T cells and that the intracellular form is increased in the T cells of patients with cancer. When intracellular CD59 is abundant, it facilitates Ras transport to the inner plasma membrane via direct interaction; in contrast, when CD59 is insufficient or deficient, Ras is arrested in the Golgi, thus enhancing Ras/MAPK signaling and T-cell activation, proliferation, and function. mCd59ab deficiency almost completely abolished tumor growth and metastasis in tumor-bearing mice, in which CD4+ and CD8+ T cells were significantly increased compared with their proportions in wild-type littermates, and their proportions were inversely correlated with tumor growth. Using bone marrow transplantation and CD4+ and CD8+ T-cell depletion assays, we further demonstrated the critical roles of these cells in the potent antitumor activity induced by mCd59ab deficiency. Reducing CD59 expression also enhanced MAPK signaling and T-cell activation in human T cells. Therefore, the subcellular compartmentalization of Ras regulated by intracellular CD59 provides spatial selectivity for T-cell activation and a potential T cell–mediated immunotherapeutic strategy.

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.

Supplementary Figure from CD59-Regulated Ras Compartmentalization Orchestrates Antitumor T-cell Immunity

T cell–mediated immunotherapy represents a promising strategy for cancer treatment; however, it has achieved satisfactory clinical responses in only a limited population. Thus, a broader view of the T-cell immune response is required. The Ras/MAPK pathway operates in many important signaling cascades and regulates multiple cellular activities, including T-cell development, proliferation, and function. Herein, we found that the typical membrane-bound complement regulatory protein CD59 is located intracellularly in T cells and that the intracellular form is increased in the T cells of patients with cancer. When intracellular CD59 is abundant, it facilitates Ras transport to the inner plasma membrane via direct interaction; in contrast, when CD59 is insufficient or deficient, Ras is arrested in the Golgi, thus enhancing Ras/MAPK signaling and T-cell activation, proliferation, and function. mCd59ab deficiency almost completely abolished tumor growth and metastasis in tumor-bearing mice, in which CD4+ and CD8+ T cells were significantly increased compared with their proportions in wild-type littermates, and their proportions were inversely correlated with tumor growth. Using bone marrow transplantation and CD4+ and CD8+ T-cell depletion assays, we further demonstrated the critical roles of these cells in the potent antitumor activity induced by mCd59ab deficiency. Reducing CD59 expression also enhanced MAPK signaling and T-cell activation in human T cells. Therefore, the subcellular compartmentalization of Ras regulated by intracellular CD59 provides spatial selectivity for T-cell activation and a potential T cell–mediated immunotherapeutic strategy.

Supplementary Data from CD59-Regulated Ras Compartmentalization Orchestrates Antitumor T-cell Immunity

According to the characteristics of the metro vehicles running on the same track leading to a similar wheel wear, a clustering method based on k-means is proposed. The weighted wheel flange height of Sh and qR, and wheel flange thickness of Sd are used as clustering parameters. This paper classifies the treads of the wheels to be repaired and obtains a typical worn wheel profile to reprofile the wheels. Under the premise of ensuring the operation safety of vehicles, the wheel profile optimization model is established for a typical worn wheel profile based on the minimum repair volume. The optimized profile is used as the reprofiling template for all wheels in the same classification. Finally, based on the reprofiling standard for the wheel diameter difference between the left and right wheels, the optimization reprofiling of the wheel profile is achieved. The research results show that the proposed optimization strategy base on cluster analysis can significantly reduce the material removal.

In the past decades, bi-dimensional empirical mode decomposition (BEMD) algorithms have been developed for the image watermarking task with good robustness and imperceptibility; however, the non-negligible algorithm efficiency has received little attention at the same time. In this paper, we devise a robust and efficient image watermarking algorithm based on 1D empirical mode decomposition (EMD) and dimensional reduction via Hilbert curve. Converting the problem of two-dimensional image watermarking into the problem of one-dimensional signal watermarking promises to enhance the efficiency and the robustness of the proposed algorithm. Specifically, host image is first reduced into one-dimensional signal by the Hilbert curve. Our key insight is a dimensionality reduction strategy based on the Hilbert curve that preserves the spatial local relationship to the greatest extent possible. Second, the one-dimensional signal is segmented into several short signal intervals, and each of them is decomposed into several intrinsic mode functions (IMFs) and a residue by 1D EMD, which is much faster than BEMD. Third, the maximum or minimum points of the first IMF are chosen as the watermark embedding positions. For the watermark image, it is first encrypted by Arnold transform, which improves the security of the algorithm. And then, it is also transformed into one-dimensional signal correspondingly. A repeated embedding strategy is used in the embedding process to improve the algorithm’s robustness. The final watermarked image can be reconstructed by the inverse Hilbert curve transform after integrating the modified first IMF, the remaining IMFs, and the residual. To improve the security and reduce the length of the key, Arnold transform and Huffman coding are adopted. The watermark extraction is the inverse of the embedding process without using the host image and watermark image. Comprehensive experimental results confirm that our new algorithm exhibits good robustness, efficiency, and high imperceptibility. Compared with the actual watermarking algorithms, the newly proposed watermarking algorithm not only reduces the computational expense, but also shows better performance in combating the cropping attacks, Gaussian noise, median filter, image enhancement attacks, etc.

A novel robust zero-watermarking algorithm for medical images is presented in this paper. The multi-scale decomposition of bi-dimensional empirical mode decomposition (BEMD) has exhibited many attractive properties that enable the proposed algorithm to robustly detect the tampering regions and protect the copyright of medical images simultaneously. Given a medical image, we first decompose a medical image adaptively into a finite number of intrinsic mode functions (IMFs) and a residue, by taking a full advantage of BEMD. The first IMF starts with the finest scale retaining fragile information and is best suitable for tampering detection, while the residue includes robust information at the coarser scale and is applied to the protection of intellectual property rights of medical images. Next, the feature matrices are extracted from the first IMF and the residue via singular value decomposition, which achieves robust performance subject to most attacks. For a given watermark image, it is encrypted by Arnold transform to enhance the security of the watermark. Then, the feature images are constructed by performing the exclusive-or operation between the encrypted watermark image and the extracted feature matrices. Finally, the feature images are securely stored in the copyright authentication database to be further used for copyright authentication and tampering detection. A large number of experimental results and comparisons with existing watermarking algorithms confirm that the newly proposed watermarking algorithm not only has strong ability on tampering detection, but also has better performance in combating various attacks, including cropping, Gaussian noise, median filtering, image enhancement attacks, etc. The newly developed algorithm also shows great promise in processing natural images.

Introduction: Lupus nephritis (LN) is a serious complication of systemic lupus erythematosus (SLE), and its pathogenesis is not fully understood. There is an imbalance in the polarization of macrophages and an interplay between fractalkine (FKN) and its receptor, CX3CR1, in LN. Previously we showed that the expression levels of FKN were positively correlated with the severity of LN. Here we aimed to study the role of the Hippo signaling pathway (HSP) and its interaction with FKN in LN in an attempt to provide novel strategies for the treatment of this condition. Methods: 91 LN patients and 64 healthy controls were included in this study. In addition, FKN-knockout mice and RAW.267.4 cells were used for in vivo and in vitro experiments. The levels of YAP, TAZ, MST1, IL-6, and IL-10 were measured in LN patients by qRT-PCR and ELISAs. HE, Masson and PASM staining were used to assess the pathological changes in kidney tissues and western blots measured the protein content of FKN, MST1, YAP, p-YAP, p-TAZ, iNOS, TNF-α, IL-10 and Arg-1. IF was used to localize F4/80, F4/80, p-YAP, IL-10 and TNF- α proteins. Results: Up-regulation of FKN was associated with increased macrophage activation and increased YAP expression. When FKN was down-regulated the HSP was activated and the YAP expression was decreased. In addition, macrophages polarization switched to M1-phenotype. Activation of the HSP in vivo was associated with FKN deficiency and increased phosphorylation of YAP and TAZ, leading to reduced activation of macrophages. This could relieve the renal damage associated with LN. Conclusions: Our results confirmed a negative correlation between the FKN gene and the HSP. Down-regulation of FKN could activate the HSP to reduce the activation of macrophages in kidney tissues and attenuate renal damage. The HSP may provide a new molecular target for the treatment of LN patients.

The Cu-12.5Ni-5Sn-xFe alloys were prepared using powder metallurgy. The effect of the amount of Fe addition on the microstructure and mechanical characteristics of Cu-12.5Ni-5Sn-xFe alloys was investigated. The microstructure and morphology of alloys were examined by means of X-ray diffraction, scanning electron microscopy and cold field emission scanning electron microscope. Results indicate that the hardness and yield strength of the Cu-12.5Ni-5Sn alloy are improved by addition of trace amounts of Fe. The lamellar precipitates of Cu-12.5Ni-5Sn-xFe alloys are more and finer compared to those of Cu-12.5Ni-5Sn alloy. Fe can facilitate the aging process and strengthen the effect of aging-hardening.

Interference noise affects the ranging capability of terahertz radar signal detection. Aiming at the above problem, a method based on adaptive empirical mode decomposition and stochastic resonance ...

In this paper, a robust and blind image watermarking algorithm via circular embedding and bidimensional empirical mode decomposition (BEMD) is developed. First, the watermark image is scrambled by Arnold transform to increase the security of the algorithm. Second, the Hilbert curve is adopted to reduce the scrambled 2D watermark image to one-dimensional watermark signal. Third, the host image is decomposed by BEMD to obtain the multi-scale representation in the forms of intrinsic mode functions (IMFs) and a residue. Then, the extreme points of the IMFs are extracted as the embedding locations. Finally, the one-dimensional watermark signal is repeatedly and cyclically embedded in the extreme locations of the first IMF according to the texture masking characteristics of the human visual system, which greatly improves the ability of our algorithm against various attacks. The final watermarked image is reconstructed by combining the modified first IMF and the residual. The watermark can be successfully extracted without resorting to the original host image. Furthermore, image correction can be applied before image watermarking extraction if there are geometric attacks in watermarked image. A large number of experimental results and thorough evaluations confirm that our method can obtain higher imperceptibility and robustness under different types of attacks, and achieve better performance than the current state-of-the-art watermarking algorithms, especially in large-scale cropping attack, JPEG compression, Gaussian noise, sharpening, Gamma correction, scaling, histogram equalization, and rotation attacks.

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.

A distributed storage system (DSS) has the ad-vantages of low price and high throughput, but it also raises serious data confidentiality and privacy issues. Fine-grained access control (AC) is important for further applications. Although attribute-based encryption (ABE) schemes can achieve fine-grained AC, almost all schemes distribute a private key to a visitor by a private key generator that may leak data. Besides, attribute revocation functions of the ABE scheme will lead to collusion attacks. Blockchain-based ABE schemes rarely consider ciphertext-based search and attribute revocation functions which lead to a large communication overhead. In this paper, we propose a secure ABE scheme in which a data owner is the only person who has the right to distribute keys. Then we combine blockchain with the proposed ABE scheme to realize fine-grained AC over a distributed storage system, which supports the attribute change function and resists collusive attacks. In addition, we build a series of smart contracts to ensure the fairness of trade between the data owner and visitor. The auxiliary information of data stored on the blockchain can realize ciphertext-based search. Finally, we prove the scheme resists collusion attacks under the computational Diffie-Hellman assumption (CDH) and conduct multiple experiments based on Ethernet test Network. The experimental results demonstrate our scheme has the running time of algorithms reached a millisecond-level and the ET H cost is acceptable.

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is $O(\sqrt n )$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.

Cloud storage saves a lot of storage overheads for users to manage data. However, it also brings data integrity challenges. Relying only on message authentication codes (MAC) and hash functions introduces serious communication overheads. Existing integrity verification schemes usually rely on third-party auditors (TPAs) to reduce users’ audit burden. However, malicious TPAs not only extract raw data from repeated audit processes but also collude with a cloud server to launch collusive attacks. Recently, some integrity verification schemes based on blockchain are presented to solve challenges of raw data extraction and collusive attacks. These schemes cannot realize large-scale data processing on a blockchain as well as dynamic data operations. To address the above issues, we propose a blockchain-based cloud data integrity verification scheme (BCD-IV) that removes TPAs by using chain-code that solves collusive attacks. Furthermore, we construct a new homomorphic verification tag (HVT) based on the signature proposed by Zhang, Safavi, and Susilo (ZSS), which supports blockless verification. The proposed scheme realizes full dynamic operations with auxiliary information that stores on a blockchain. Mathematical analysis shows that our scheme has lower complexity in terms of communication and computation overhead, which are both O(c). Finally, we conduct experiments on Hyperledger Fabric V2.2 to demonstrate that the computation efficiency of audit is the highest compared to the state-of-the-art and the storage overhead is negligible.

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.

Mesenchymal stem cells (MSCs) play a critical role in tumor metastasis. However, the dynamic process of MSCs-mediated cancer cell invasion remains inconclusive. In breast cancer mouse models, we observed that MSCs promoted lung metastasis. We constructed a microfluidic-based 3D co-culture device to monitor MSCs-mediated cancer cell invasion in a nutrient-deficient hypoxic microenvironment. On biomimetic microfluidic devices, MSCs guided cancer cell migration in a "cluster-sprout-infiltrating" mode. Importantly, hypoxic conditions significantly promoted MSCs migration at the infiltration stage, leading to accelerated breast cancer cell invasion. Moreover, hypoxia related LncRNA analysis showed that H19 was dramatically upregulated in response to hypoxic conditions. Conversely, H19 depletion impaired MSCs-directed breast cancer cell invasion. Mechanistically, H19 functions as a competitive endogenous RNA (ceRNA) which sequesters miRNA let-7 to release its target matrix metalloproteinase-1 (MMP1). Intriguingly, aspirin dramatically suppressed H19 and MMP1 expression and blocked MSCs infiltration under hypoxic conditions, resulting in alleviated breast cancer cell invasion. These findings point to the metastatic promoting role of MSCs in tumor stroma and suggest that MSCs might be a therapeutic target for metastatic breast cancer.

A high-gain fiber amplifier at 1053 nm with a double-pass configuration is proposed. Compared to the Faraday-based double-pass structures, the pump efficiency is almost doubled, and the optical signal-to-noise ratio is improved as well. The effects of fiber length and the insertion loss of elements between two passes on the efficiency are studied experimentally, which is meaningful for the optimization of double-pass amplifiers. Using the fibered polarized beam splitters allow for the orthogonal propagation of forward and the backward signals on the same polarization-maintaining fiber, the polarization mode dispersion in the amplifier is hence totally avoided. With this structure, over 50-dB gain is obtained at pump power of 800 mW for a 1053-nm, 1-Hz nanosecond pulse.

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.

A Ni–P composite coating with MoS2 and CaF2 was prepared via electroless plating. The effect of temperature on the tribological behavior of the composite coatings was investigated. The results show that the tribological behavior of coatings largely depends on the temperature. The friction coefficient shows a decreasing trend with increasing temperature; the wear rate first decreases and then increases with increasing temperature. As the temperature increases from room temperature (RT) to 200 °C and 390 °C, the wear rate decreases by one order of magnitude compared to that at RT. The composite coating exhibits stable friction coefficient during the entire sliding in the temperature range of 200–570°C. Lubricating mechanisms were discussed.

This paper studies the physical layer security problem in the downlink two-tier femtocell network system, where an eavesdropper wiretaps the legitimate macrocell users. In this system, femtocell base stations (FBSs) can reduce the performance of eavesdropper through cochannel interference, which in turn improves the secure transmission rate of the entire system. The power allocation problem of the FBSs and macro base station (MBS) has an important influence on the cochannel interference and system performance in femtocell networks. In this paper, the power allocation problem is modeled as an optimization problem. We aim to maximize the secrecy rate and reduce energy consumption. Based on this, a joint subchannel and power allocation algorithm is proposed. Simulation results show that the proposed algorithm can effectively improve the network transmission performance and reduce the energy consumption, compared with existing algorithms.

Condition monitoring of wheel-rail contact forces has great significance for evaluating the dynamic performance of railway vehicles. Instrumented wheelset method has disadvantages that strain gages are complicated in arrangement, wheel-rail forces decoupling is difficult, and the accuracy is low. Combined with the digital image correlation (DIC) technology, this paper introduces a novel non-contact monitoring method based on space fixed-point strain on the surface of wheel web. Through the finite element modeling and simulation analysis of wheelset, it is found that the strain at some space fixed-points on the surface of wheel web is linear with lateral/vertical forces. Based on this conclusion, the numerical equation between single space fixed-point strain and the lateral/vertical forces is established. Two point numerical equations can be decoupled to obtain the lateral and vertical force. The space fixed-point strain can be obtained by non-contact using the mature DIC technology and then achieve the simple and accurate measurement method of wheel-rail contact forces. It is verified by FEM simulation results that under the premise of using DIC technology to get space fixed-point strain of wheel web; the force monitoring method introduced in this paper can obtain the wheel-rail contact forces accurately and simply, which has important theoretical guiding significance for wheel-rail contact forces monitoring.

Based on the pulse-shaping unit in the front end of high-power laser facilities, we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery, so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve, and enhance the calibration accuracy of the pulse shape in the feedback control system. The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter. The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB, and the filtered pulse is smooth without modulation, with smoothness of about 98.8%.

Actinidia arguta pollen owns abundant nutrients, such as vitamins, polyphenols, etc., however, little research on its antioxidant ability and biological function was conducted. In this study, we observed A. arguta pollen spore structure by SEM (Scanning electron microscope), analyzed the phenolic composition of A. arguta pollen extract (AAPE) obtained by four extraction methods (A: ultrasound-assisted extraction with water, B: heat reflux extraction with water, C: ultrasound-assisted extraction with ethanol, and D: heat reflux extraction with ethanol). Total phenolic content, total flavonoid content, antioxidant activities (ferric reducing/antioxidant power [FRAP], chelating activity, and DPPH⋅ scavenging activity) were also determined. Finally, we investigated its protective effect on DNA and lymphocytes damage response to oxidative stress. The results showed that the morphology of A. arguta pollen was similar to other pollen of the genus A. Lindl., but differs from them slightly in the specific morphology indicators. What is more, AAPE obtained by different extraction methods exhibited a protective effect against DNA oxidative damage, they also possessed a strong cytoprotection effect on mouse lymphocytes, especially the extraction obtained by method C, which had the highest total phenolic content (15.05 ± 0.34 mg GAE/g), strong ferrous ion-chelating ability (0.37 ± 0.023 mg Na2 EDTA/g), DPPH⋅ scavenging activity (IC50 = 0.14 ± 0.04 mg/ml), and FRAP (7.13 ± 0.33 mg Trolox/g). This paper provided a new edible natural antioxidant for human which will protect us from oxidative stress. PRACTICAL APPLICATIONS: This study was the first to report the morphology of Actinidia arguta pollen by SEM analysis, and studied the effects of different extraction methods on antioxidant activities and cytoprotective effect of A. arguta pollen extract. We believed that our research makes a significant contribution to the literature, because the results of this research provide a reference for the development of a new edible natural antioxidant.

Complement is operative in not only the extracellular but also the intracellular milieu. However, little is known about the role of complement activation inside tumor cells. Here, we report that intracellular C5 is cleaved by cathepsin D (CTSD) to produce C5a in lysosomes and endosomes of colonic cancer cells. After stimulation by C5a, intracellular C5aR1 assembles a complex with KCTD5/cullin3/Roc-1 and β-catenin to promote the switch of polyubiquitination of β-catenin from K48 to K63, which enhances β-catenin stability. Genetic loss or pharmacological blockade of C5aR1 dramatically impedes colorectal tumorigenesis at least by destabilizing β-catenin. In human colorectal cancer specimens, high levels of C5aR1, C5a, and CTSD are closely correlated with elevated β-catenin levels and a poor prognosis. Importantly, intracellular C5a/C5aR1-mediated β-catenin stabilization is also observed ubiquitously in other cell types. Collectively, we identify a machinery for β-catenin activation and provide a potential target for tumor prevention and treatment.

The ultra-high pressure and high temperature (HP/HT) unconventional offshore reservoir in Liaohe Oil Field in Bohai Bay Basin has characteristics of deep depth (4500m), high temperature (145°C), low permeability (1.2mD) and low porosity (9.4%), until now, a floating hydraulic fracturing has been successfully performed in this area and an integrated technology has been formed. In this paper, we firstly delivered a case history of the hydraulic fracturing treatment, in particularly describing the main problems we met and the main method we take to solve these problems. Then the case study was given in consideration of a geo-engineering integrated fracturing design workflow. (1) Fracturing vessel arrangement to full fill the fracturing demand. (2) Sweet spot prediction and well trajectory optimization were performed based on a fine geological model. (3) Perforation optimization were made in consideration of the ground stress, natural fracture development, well logging and hydrocarbon display. (4) A hybrid fracturing mode (linear gel and crosslinked gel) was applied to connect more natural fractures and build a more complex fracture network. (5) Multi-size sand combination to match different artificial cracks. (6) Mini-frac test to analysis the stress situation and optimize the stimulation schedule. (7) Acid pre-treatment to reduce fracture initiation pressure, proppant multi-stage slug to reduce pumping pressure. Integrated technologies in the floating hydraulic fracturing for unconventional offshore reservoir in Bohai Bay Basin can be good reference to the hydraulic fracturing treatment of similar reservoirs.

Under the development trend of power industry with energy Internet as the core, with the update of information technologies such as intelligent devices, mobile communication technology and cloud computing, the application of Internet of things (IOT) in power industry has developed from the concept of specific link service to the application of large-scale market. The core of the next power Internet of things construction is to build an open IOT management platform to realize the ubiquitous access of massive devices, the deep sharing of cross-professional data, business integration, and the provision of precise user services. Based on this, an industrial alliance will be formed and a power industry ecosystem with power grid enterprises as the core will be established. At present, the application of power Internet of things in China is still in the primary stage of research and application. The research on what the IOT management platform should manage and the interaction technology between the IOT management platform and edge agents is still in the stage of exploration and pilot. This paper mainly studies the cloud-edge interaction technology, including the interaction object, process and message format. Then it formulates a set of cloud-edge format specifications to meet the requirements of the platform’s own management and business system message transmission, so as to provide reference for the cloud-edge interaction of power Internet of things.

With rapid development of the Internet of things (IOT) in power field, equipment of various majors began to access the smart IOT system on a large scale. In this paper, a new data compression algorithm based on object model (CABM) is proposed to solve the problems of large-scale applications of power Internet of Things, such as high-frequency transmission of large quantities, real-time short data can not be effectively compressed, resulting in low efficiency of cloud edge information transmission and serious waste of bandwidth resources. The CABM adds a compression and decompression module between the IOT management platform and the edge agent; the IOT management platform automatically generates a compression dictionary based on the object model file and saves it synchronously in the edge agent. During data transmission, both the sender and receiver complete automatic compression and decompression according to the compression dictionary. The experimental results show that for real-time message data below 1KB, the compression rate using CABM algorithm is 3-5 times higher than that using GZIP compression algorithm; and the compression time is 10-20 times lower than that using GZIP; the efficiency using CABM algorithm is 4.5 times higher than that using GZIP algorithm; that is, CABM can reduce redundant data transmission between cloud edges and improve the efficiency of cloud edge transmission.

Power Internet of Things [1] is the application of Internet of Things in smart grid. By effectively integrating power system and communication infrastructure resources, the information level of power system and the utilization rate of existing resources are improved. Thus, important technical support is provided for power grid generation, transmission, transformation, distribution and electricity consumption. The Power Internet of Things contains a large number of terminal equipment, which involves the data interaction frequency of 100 million levels per second. Therefore, how to avoid the access of invalid data is an important topic in the construction process of power Internet of Things. In response to the detection requirements of abnormal devices, this paper, based on existing Intelligent IoT System architecture, designs a real-time detection framework that integrates a two-level control mechanism, which is universal, extensible, and supports multiple protocols, using the data interaction specifications formulated by the State Grid Corporation as the basic detection basis. As a result, the framework realizes the effective interception of abnormal devices in power Internet of Things.

This paper presents a robust active guidance controller that uses the µ-synthesis method to improve the ability of guidance and to reduce the wheel–rail wear under the consideration of system nonlinearity, parameter uncertainty, and measurement noise in tramcars with independently rotating wheelsets (IRWs). Each wheel of the tramcar is connected to a sider motor, and the active yaw moment is generated by the torque differences between the left and right wheels in the same axle. To achieve local control, the wheel–rail force and the primary suspension force are considered as external disturbances, and the IRW state–space model is modeled independent of the tramcar system. The effectiveness of the controller is mainly verified by the cosimulation method: the dynamic model of the tramcar is established in SIMPACK, and the controller model is established in MATLAB. The simulation results show that compared with the passive case, for the actual tramcar, the control method can improve the vehicle's running performance and can significantly reduce the wheel–rail wear in both straight and curved tracks. A 1:5 scaled tramcar model is used for the active guidance experiment to further verify the effectiveness of the algorithm.

In this paper, to recover the missing geometry details on 3D surfaces, we develop a novel geometry detail recovery algorithm for 3D surfaces based on Empirical Mode Decomposition (EMD). EMD is a powerful tool for processing non-linear and non-stationary signals and has been successfully used in 3D surface analysis and processing. Given a signal defined on 3D surface, EMD could represent the signal in a multi-scale fashion and decompose the signal into a number of Intrinsic Mode Functions (IMFs) and a residue, which usually encode the multi-level finer-scale details and the overall shape of the signal, respectively. Benefiting from the multi-scale representation of geometry details, the EMD-based multi-scale geometry detail recovery algorithm is developed. Our strategy starts from an initial smooth filling of a hole and then transfers the desirable details from the most similar region to the smoothly-filled surface within the framework of EMD. Taking the advantages of EMD, we first apply EMD on the whole completed surface to obtain the multi-scale representation of geometry details. Then, the most similar region corresponding to the hole region is located by the patch descriptor constructed from Heat Kernel Signature (HKS). Finally, the missing geometry details can be effectively recovered by transferring the geometry details from the found similar region to the smoothly-filled surface. Traditional methods, such as context-based methods or example-based methods, usually cut the similar patch and paste them onto the hole region, and they require to match with the hole boundary, are complex in general. In contrast, our method is simple and can transfer different scale details individually or in a concerted way, which makes our algorithm more flexible and can achieve versatile detail recovery results. Comprehensive experiments and quantitative comparisons on popular models have been used to demonstrate the effectiveness of our EMD-based multi-scale geometry detail recovery algorithm.

Point clouds have been extensively employed to represent 3D shapes with the increasing availability of various data acquisition devices/technologies. As a result, more novel techniques are urgently needed for point clouds' analysis and processing. To date, empirical mode decomposition (EMD) has become a powerful and effective analytical tool for non-stationary, non-linear signals, and has been widely applied to time series processing. Despite the fact that EMD has exhibited its potential in 3D geometry processing, extending the existing techniques of EMD to operate directly on point clouds remains to be extremely challenging. This is primarily because of imperfect point clouds, as well as their absence of topological information. In this paper, we develop a multi-scale mesh-free EMD algorithm for point clouds and their analysis and processing. The multi-scale mesh-free EMD is achieved by iteratively extracting the detail level from the input signal and leaving the overall shape in residue. Furthermore, in order to preserve sharp features during point-based EMD analysis/processing, we devise an anisotropic structure measurement assisted envelope computation scheme. The structure measurement is computed by the eigenvalue decomposition of voting tensor, which could faithfully characterize the structure of any input model. Under the guidance of the structure measurement, the envelope is computed in a structure-aware manner and the sharp features are well preserved. Unlike previous feature-preserving EMD methods for meshed models, our algorithm does not explicitly resort to sharp feature detection, which is more suitable for complex geometric models. With the well decomposed multi-scale representation, we could explore various applications of point clouds, such as detail enhancement and smoothing, feature points extraction, and feature-preserving denoising. We showcase comprehensive experimental results to demonstrate the utility of our novel multi-scale mesh-free EMD algorithm.

Traditional image watermarking algorithms directly modify the host image by watermark embedding, which is hard to balance the contradiction between the robustness and imperceptibility. Inspired by the human brain’s associative memory, this paper proposes a non-embedding and blind image watermarking algorithm via mapping based Residual Convolution Neural Network (Mapping-based RCNN). For preprocessing, median filter is applied on the host image to enhance the robustness of the algorithm to against various attacks. After that, Discrete Cosine Transform and Singular Value Decomposition are adopted to extract the corresponding image information matrix. To obtain the mapping relationship between host image and watermark image, the information matrix is input into the designed Mapping-based RCNN structure for network training. The Mapping-based RCNN is a non-embedding watermarking algorithm, which not only overcomes the imperceptibility shortcoming but also wins good robustness compared with traditional watermarking algorithms. Experimental results show that the proposed algorithm can successfully extract the watermark images under various attacks, and is more robust than existing watermarking algorithms.

The dispersive Fourier transform is suitable for characterizing short pulses. However, the traditional temporal Gerchberg–Saxton (GS) algorithm suffers from the timing error of measured dispersed waveforms, which limits the measurement performance of the temporal phase. An annealing modified GS algorithm that can simultaneously retrieve the temporal profile and phase is proposed. The inevitable timing error in the measurement can be accurately recovered by the algorithm, which significantly improves the retrieval performance. Based on the annealing modified GS algorithm, an experimental structure is proposed to achieve single-shot full-field measurement. The algorithm is analyzed numerically and the temporal waveform and phase of short pulses can be retrieved successfully in the experiment. The results indicate that this single-shot full-field measurement method is free from timing error, which is important for real applications. The simple single-shot method promises future applications to measure the temporal profile and temporal phase of short pulses with high accuracy simultaneously.