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The driving cycle is a speed-to-time curve, a fundamental technique in the automotive industry, and also a basis to set standards for fuel consumption and emissions of vehicles. A driving cycle is developed based on firsthand driving data collected from fieldwork. First, bad data in the original dataset are preprocessed, the time-series standard smoothing algorithm is used to smoothen the data, and Lagrange's interpolation is used to realize data interpolation. Next, the rules for kinematic fragment extraction are set to divide the data into kinematic fragments. Last, an evaluation system of kinematic fragment feature parameters is built. On that basis, the K-means clustering method is used to cluster the dimensionally reduced data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm is employed to select the optimal fragments from candidate fragments to develop a driving cycle. The experiment result shows that the developed driving cycle can represent the kinematic features of the experiment car and provides a basis for the development of a driving cycle for Fuzhou., Author(s): Minrui Zhao [1]; Hongni Gao [1]; Qi Han [1]; Jiaang Ge [1]; Wei Wang (corresponding author) [1]; Jue Qu [1] 1. Introduction Energy consumption and exhaust emissions are two [...]

MRI is often influenced by many factors, and single image super-resolution (SISR) based on a neural network is an effective and cost-effective alternative technique for the high-resolution restoration of low-resolution images. However, deep neural networks can easily lead to overfitting and make the test results worse. The network with a shallow training network is difficult to fit quickly and cannot completely learn training samples. To solve the above problems, a new end-to-end super-resolution (SR) method is proposed for magnetic resonance (MR) images. Firstly, in order to better fuse features, a parameter-free chunking fusion block (PCFB) is proposed, which can divide the feature map into n branches by splitting channels to obtain parameter-free attention. Secondly, the proposed training strategy including perceptual loss, gradient loss, and L1 loss has significantly improved the accuracy of model fitting and prediction. Finally, the proposed model and training strategy take the super-resolution IXISR dataset (PD, T1, and T2) as an example to compare with the existing excellent methods and obtain advanced performance. A large number of experiments have proved that the proposed method performs better than the advanced methods in highly reliable measurement. [ABSTRACT FROM AUTHOR]

In this study, on the basis of basket CDS, we take into account the complex correlations among market participants and choose to use the contagion model to price basket CDS. This study assumes that a basket of reference assets is defaultable and that defaults between two counterparties are contagious. Without taking into account the default of the CDS buyer, we assume that both the interest rate and the default intensity of the reference assets follow the Vasicek model. We obtain the related probability density function by the PDE method. Under the no-arbitrage principle, we set up several scenarios to get the basket CDS pricing formula. In the numerical analysis part, we verify the validity of the formula by Monte Carlo simulation, and discuss the influence of the main parameters on the formula. [ABSTRACT FROM AUTHOR]

Objective. To evaluate whether traditional Chinese medicine compound preparations (TCMCPs) are associated with rheumatoid arthritis- (RA-) related complications (including readmission, Sjogren's syndrome, surgical treatment, and all-cause death) in patients with RA. Methods. Clinical outcome data were retrospectively collected from patients with RA discharged from the Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2009 to June 2021. The propensity score matching method was used to match baseline data. Multivariate analysis was conducted to analyze sex, age, the incidence of hypertension, diabetes, and hyperlipidemia and identify the risk of readmission, Sjogren's syndrome, surgical treatment, and all-cause death. Users of TCMCP and nonusers of TCMCP were defined as the TCMCP and non-TCMCP groups, respectively. Results. A total of 11,074 patients with RA were included in the study. The median follow-up time was 54.85 months. After propensity score matching, the baseline data of TCMCP users corresponded with those of non-TCMCP users, with 3517 cases in each group. Retrospective analysis revealed that TCMCP significantly reduced clinical, immune, and inflammatory indices in patients with RA, and these indices were highly correlated. Notably, the composite endpoint prognosis for treatment failure in TCMCP users was better than that in non-TCMCP users (HR = 0.75 (0.71-0.80)). The risk of RA-related complications in TCMCP users with high-exposure intensity (HR = 0.669 (0.650-0.751)) and medium-exposure intensity (HR = 0.796 (0.691-0.918)) was significantly lower than those in non-TCMCP users. An increase in exposure intensity was associated with a concomitant decrease in the risk of RA-related complications. Conclusion. The use of TCMCPs, as well as long-term exposure to TCMCPs, may lower RA-related complications, including readmission, Sjogren's syndrome, surgical treatment, and all-cause death, in patients with RA. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario. [ABSTRACT FROM AUTHOR]

Background. Scutellaria baicalensis Georgi (SBG) has significant anti-inflammatory and immune-modulating activities and is widely used in the treatment of inflammatory and autoimmune diseases. However, the mechanism of SBG in the treatment of ankylosing spondylitis (AS) remains to be elucidated. Methods. Differentially expressed genes (DEGs) related to AS were analyzed based on two GEO gene chips. The DEGs were merged with the data derived from OMIM, GeneCards, and PharmGKB databases to ascertain AS-related targets. Active components of SBG and their targets were acquired from the TCMSP database. After overlapping the targets of AS and SBG, the action targets were acquired. Subsequently, protein-protein interaction (PPI) network and core target screening were conducted using the STRING database and Cytoscape software. Moreover, the DAVID platform was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of action targets. Finally, the affinity of major active components and core targets was validated with molecular docking. Results. A total of 36 active components of SBG were acquired from TCMSP database. Among these, the main active components were baicalein, wogonin, and oroxylin A. The PPI network and screening showed TNF, IL-6, CXCL8, PTGS2, and VEGFA as core targets associated SBG against AS. GO and KEGG analyses indicated that SBG participated in various biological processes, via regulating IL-17, TNF, and NF-κB signaling pathways. Molecular docking results confirmed a strong binding activity between the main active components and the core targets. Conclusion. The therapeutic mechanism of SBG associated with AS can be characterized as a multicomponent, multitarget, and multipathway mechanism. SBG may be a promising therapeutic candidate for AS. [ABSTRACT FROM AUTHOR]

Radix Salvia miltiorrhiza (RSM) is widely used for the clinical improvement of inflammatory diseases. However, the actions of RSM in the treatment of ankylosing spondylitis (AS) have not been fully explored. Therefore, this study was designed to use retrospective clinical data mining approach to understand the effects of RSM on AS-related immuno-inflammatory processes, use network pharmacology to predict therapeutic targets of RSM, and to further investigate the pharmacological molecular mechanism in vitro. RSM treatment has a long-term correlation with the improvement of AS-related immuno-inflammatory indicators through computational models. We established protein-protein interaction networks, conducted KEGG analysis to enrich significant TNF pathways, and finally obtained three core targets of RSM in the treatment of AS, namely, prostaglandin endoperoxide synthase 2 (PTGS2), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-alpha). Screening of RSM active ingredients with node degree greater than 20 yielded cryptotanshinone and tanshinone IIA, and previous studies have reported their anti-inflammatory effects. In vitro, both cryptotanshinone and tanshinone IIA significantly inhibited the expressions of PTGS2, IL-6, and TNF-α in peripheral blood mononuclear cells in AS patients. In conclusion, cryptotanshinone and tanshinone IIA, which are the active components of RSM, may inhibit the activation of TNF signaling pathway in AS patients by downregulating the expression of PTGS2, IL-6, and TNF-α. These findings illustrate that RSM may be a promising therapeutic candidate for AS, but further validation is required. [ABSTRACT FROM AUTHOR]

In order to further explore the therapeutic effects of high-frequency and low-frequency repetitive transcranial magnetic stimulation on depression and cognitive function in the elderly, this paper proposed a study on cortical plasticity mechanism and efficacy prediction of repetitive transcranial magnetic stimulation based on continuous short pulse fast pulse stimulation (CTBS). This paper selected 92 patients with depression in a hospital from January to December 2020 as the research object and divided them into control group, low-frequency group, and high-frequency group, 31 cases, 29 cases, and 32 cases, respectively. The continuous short pulse rapid pulse stimulation (CTBS) mode was used to explore the effect of brain network on patients' emotional processing. After clinical treatment contrast, there was no significant difference in HAMD-24 scores and RBANS scores before treatment (P > 0.05), and there was a significant negative correlation between factors of cognitive impairment in patients and RBANS scores (P < 0.01 or P < 0.05), so it was proved that the repeated transcranial magnetic stimulation (cTBS) could be used as an effective treatment for depression. [ABSTRACT FROM AUTHOR]

The classification method of steel surface defects based on deep learning provides a basis for quality control of industrial steel manufacturing. Due to a large number of interference in the steel production area and the limited computing resources of the edge equipment deployed in the production area, it is a challenge to develop a lightweight model to achieve rapid and accurate classification in the case of limited computing resources. In this article, an improved lightweight convolution structure (LCS) is proposed, which combines the separable structure of convolution and introduces depth convolution and point direction convolution instead of the traditional convolutional module, so as to realize the lightweight of the model. In order to ensure the classification accuracy, spatial attention and channel attention are combined to compensate for the accuracy loss after deep convolution and point direction convolution respectively. Further, in order to improve the classification accuracy, a mixed interactive attention module (MIAM) is proposed to enhance the extracted feature information after LCS. The experimental results show that the recognition accuracy of our method exceeds that of the traditional model, and the number of parameters and the amount of calculation are greatly reduced, which realizes the lightweight of the steel surface defect classification model. [ABSTRACT FROM AUTHOR]

Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose of melanoma segmentation. Specifically, based on the popular encoder-decoder structure, we designed three functional modules, namely MSF, asymmetric skip connection structure (ASCS), and calibration decoder (Decoder). In addition, a weighted cross-entropy loss and two-stage learning rate optimization strategy are designed to train the network more effectively. Compared qualitatively and quantitatively with the representative neural network methods with encoder-decoder structure, such as U-Net, the proposed method can achieve advanced performance. [ABSTRACT FROM AUTHOR]

This article considers the load frequency control of multi-area power system-based multi-agent system method under false data injection attacks. The research can provide better solutions for multi-area power system load frequency control under false data injection attacks. First, an event-triggered mechanism is introduced to decide which data should be transmitted in the controller to save the limited network bandwidth. Besides, a model of cyberattacks is built using the Bernoulli random variables. Then, conditions are given for maintaining the system asymptotic stability under attack. Finally, simulations are performed to demonstrate the validity of the theory proposed in this article. [ABSTRACT FROM AUTHOR]

Background. Ankylosing spondylitis (AS) is a rheumatism that mainly affects the axial bones and joints. Xinfeng capsule (XFC) is a preparation with a remarkable clinical effect that is used in our hospital. And it has definite curative effect and less side effects in the treatment of AS. Objective. Data mining and network pharmacology were used to analyze the efficacy of Chinese medicine Xinfeng capsule on treating the hypercoagulable state of ankylosing spondylitis and the underlying mechanism behind it. Methods. Clinical data were collected and compiled from the Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui University of Chinese Medicine. Cluster analysis was used to investigate herbs that frequently used to treat AS, Apriori module was used to analyze the association rules between herbs and laboratory indexes, and the random walk model was used to reveal the therapeutic efficacy of XFC against AS. The TCMSP database was used to acquire the active components and targets of XFC, and the GeneCards and OMIM database were used to obtain the targets of AS. Afterward, an active ingredient-target network was established and core targets were screened for; overlapping targets were screened for the protein-protein interaction (PPI) network analysis, the Gene Ontology (GO) enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Molecular docking was adopted to investigate the interactions between main active components and core targets. Results. Frequently used herbs could be divided into three groups, and according to the analysis of Apriori module, there is a strong correlation between XFC and the improvement of ESR and hs-CRP, and the results of the random walk model demonstrated that the effect of XFC on improving PLT, ESR, and hs-CRP was superior to the use of traditional Chinese medicine alone. In total, 103 active compounds of XFC and 59 overlapping targets were obtained. The PPI relationships were obtained through the STRING database, and 13 core targets were identified. 1786 GO enrichment results and 205 KEGG enrichment results were obtained, including NF-kappa B signaling pathway, TNF signaling pathway, and IL17 signaling pathway. The outcomes of molecular docking revealed a close relationship between the active compounds of XFC and core targets. Conclusion. This study demonstrated that XFC can effectively improve the hypercoagulable state and the inflammatory indices of AS patients through data mining, and it has a strong correlation with the clinical improvement of inflammation. The active compounds of formononetin, triptolide, quercetin, and kaempferol may be the key active components of XFC in regulating AS, possibly through inhibiting the activation of NF-kappa B signaling pathway to improve hypercoagulable state. [ABSTRACT FROM AUTHOR]

Compared with previous studies, this paper focuses on the time delay phenomenon in the consensus state between the leader and followers and considers that the DoS attack occurs nonperiodically. First, a new event-triggered mechanism and lag consensus control strategy are proposed. Then, through the Lyapunov stability theory, algebraic knowledge, and graph theory, it is proved that followers and leader can achieve lag consistent under the DoS attack, and the trigger interval is designed to ensure that Zeno behavior does not occur. Finally, the correctness and effectiveness of the proposed theory and method are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

External fixation is a long-standing but well-established method, which has been widely used for the treatment of fractures. To obtain the maximum benefit from the mechanical stimulus, the stiffness of the external fixator should be adjusted properly throughout the treatment phase. Nevertheless, the lack of a valid dynamic adjustable fixation device impedes this possibility. Based on the stiffness adjustment tolerance of the healing callus, this paper proposes an active-dynamic stiffness adjustable external fixator design method to meet stiffness requirements at different stages of the tibial fracture healing process. A novel external fixator with an adjustable stiffness configuration was designed, and the finite element method was used to simulate the stress distribution between fixator and fracture gap. The stiffness adjustment tolerance was determined based on previous studies. According to this tolerance, the optimal block structure dismantling sequence was sought and the corresponding stiffness was calculated through topology optimization for the entire external fixator model. The appropriate amount of variable stiffness at the fracture gap was applied by dismantling the configuration of the block structure external fixator during the healing process. A novel patient-specific adjustable stiffness external fixator for mechanically stimulated tibial fracture reduction and therapy was proposed. This enables surgeons to tailor the construction of the external fixator frame to the clinical needs of each patient. The presented dismantling approach of the block structure to produce conformable stiffness provides a new clinical treatment strategy for tibial fractures. [ABSTRACT FROM AUTHOR]

In the research on the construction of cold chain logistics intelligent system based on 5G ubiquitous Internet of Things as an emerging technology, the Internet of Things is penetrating into the logistics field with its technical advantages and changing the original industry form. According to the development status of cold chain logistics, this paper constructs a set of cold chain logistics intelligent system by using RFID technology, sensing equipment, GPS system, fuzzy PID hierarchical control, and other Internet of Things technologies and analyzes the impact of the cost sharing ratio of Internet of Things on the income of wholesalers, retailers, and supply chain. The example analysis shows that when retailers share all the adoption costs of the Internet of Things, the benefit of the whole fresh agricultural product cold chain logistics is the largest. This study provides a scientific basis for logistics decision-making for wholesalers and retailers and has important application value for improving the logistics efficiency of the whole fresh agricultural product cold chain. [ABSTRACT FROM AUTHOR]

The surface topographies of artificial implants including surface roughness, surface groove size and orientation, and surface pore size and distribution have a great influence on the adhesion, migration, proliferation, and differentiation of nerve cells in the nerve regeneration process. Optimizing the surface topographies of biomaterials can be a key strategy for achieving excellent cell performance in various applications such as nerve tissue engineering. In this review, we offer a comprehensive summary of the surface topographies of nerve implants and their effects on nerve cell behavior. This review also emphasizes the latest work progress of the layered structure of the natural extracellular matrix that can be imitated by the material surface topology. Finally, the future development of surface topographies on nerve regeneration was prospectively remarked. [ABSTRACT FROM AUTHOR]

The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data. [ABSTRACT FROM AUTHOR]

Viscoelastic polymer solution shows shear thinning behavior at low shear rates and shear thickening behavior at high shear rates in reservoirs. However, models that ignored shear thickening behavior were commonly employed to interpret transient pressure data derived from tested wells in viscoelastic polymer flooding systems; although, viscoelastic polymer solutions show shear thickening behavior in the near-wellbore region due to high shear rate. To better characterize the oilfield with pressure transient analysis in viscoelastic polymer flooding systems, we developed a numerical model that takes into account both shear thinning behavior and shear thickening behavior. A finite volume method was employed to discretize partially differential flow equations in a hybrid grid system including PEBI mesh and Cartesian grid, and the Newton-Raphson method was used to solve the fully implicit nonlinear system. To illustrate the significance of our model, we compared our model with a model that ignores the shear thickening behavior by graphing their solutions on log-log plots. In the flow regime of near-wellbore damage, the pressure derivative computed by our model is distinctly larger than that computed by the model ignoring shear thickening behavior. Furthermore, the effect of shear thickening behavior on pressure derivative differs from that of near-wellbore damage. We then investigated the influence of shear thickening behavior on pressure derivative with different polymer injection rates, injection rates, and permeabilities. The results can provide a benchmark to better estimate near-wellbore damage in viscoelastic polymer flooding systems. Besides, we demonstrated the applicability and accuracy of our model by interpreting transient pressure data from a field case in an oilfield with viscoelastic polymer flooding treatments. [ABSTRACT FROM AUTHOR]

Epidemiological dynamics is a vital method in studying the spread of computer network viruses. In this paper, an optimal control measure is proposed based on the SEIR virus propagation model in point-to-group information networks. First, considering the need for antivirus measures in reality, an optimal control problem is introduced, and then a controlled computer virus spread model in point-to-group information networks is established. Second, the optimal control measure is formulated by making a tradeoff between control cost and network loss caused by virus intrusion. Third, optimal control strategies are theoretically investigated by Pontryagin's maximum principle and the Hamiltonian function. Finally, through numerical simulations, effective measures for controlling virus spread in point-to-group information networks are proposed. [ABSTRACT FROM AUTHOR]

As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive. [ABSTRACT FROM AUTHOR]

Even with an improved understanding of pain mechanisms and advances in perioperative pain management, inadequately controlled postoperative pain remains. Predicting acute postoperative pain based on presurgery physiological measures could provide valuable insights into individualized, effective analgesic strategies, thus helping improve the analgesic efficacy. Considering the strong correlation between pain perception and neural oscillations, we hypothesize that acute postoperative pain could be predicted by neural oscillations measured shortly before the surgery. Here, we explored the relationship between neural oscillations 2 hours before the thoracoscopic surgery and the subjective intensity of acute postoperative pain. The spectral power density of resting-state beta and gamma band oscillations at the frontocentral region was significantly different between patients with different levels of acute postoperative pain (i.e., low pain vs. moderate/high pain). A positive correlation was also observed between the spectral power density of resting-state beta and gamma band oscillations and subjective reports of postoperative pain. Then, we predicted the level of acute postoperative pain based on features of neural oscillations using machine learning techniques, which achieved a prediction accuracy of 92.54% and a correlation coefficient between the real pain intensities and the predicted pain intensities of 0.84. Altogether, the prediction of acute postoperative pain based on neural oscillations measured before the surgery is feasible and could meet the clinical needs in the future for better control of postoperative pain and other unwanted negative effects. The study was registered on the Clinical Trial Registry (https://clinicaltrials.gov/ct2/show/NCT03761576?term=NCT03761576&draw=2&rank=1) with the registration number NCT03761576. [ABSTRACT FROM AUTHOR]

The underlying mechanism of shale gas migration behavior is of great importance to understanding the flow behavior and the prediction of shale gas flux. The slippage of the methane molecules on the surface is generally emphasized in nanopores in most predicted methods currently. In this work, we use molecular dynamic (MD) simulations to study the methane flow behavior in organic (graphene) and inorganic (quartz) nanopores with various pore size. It is observed that the slippage is obvious only on the graphene nanopores and disappeared on the quartz surface. Compared with the traditional Navier-Stokes equation combined with the no-slip boundary, the enhancement of the gas flux is nonnegligible in the graphene nanopores and could be neglected in the quartz nanopores. In addition, the flux contribution ratios of the adsorption layer, Knudsen layer, and the bulk gas are analyzed. In quartz nanopores, the contributions of the adsorption layer and the Knudsen layer are slight when the pore size is larger than 10 nm. It is also noted that even if the Knudsen number is the same, the flow mode may be various with the effect of the pore surface type. Our work should give molecular insights into gas migration mechanisms in organic and inorganic nanopores and provide important reference to the prediction of the gas flow in various types of shale nanopores. [ABSTRACT FROM AUTHOR]

Background. The microliposome maintenance (MCM) complex, MCM2-7, is revealed to be involved in multiple cellular processes and plays a key role in the development and progression of human cancers. However, the MCM complex remains poorly elaborated in hepatic carcinoma (HCC). Methods. In the study, we found the mRNA and protein level by bioinformatics. We also explored the prognostic value, genetic alteration, interaction network, and functional enrichment of MCM2-7. The MCM expression and correlation among these MCMs in HCC cell lines were identified by western blot. Results. MCM2-7 was significantly increased in HCC tissues compared to normal liver tissues. The high level of MCM2-7 had a positive correlation with poor prognosis. However, MCM2-7 alterations were not correlated with poor OS. MCMs were both increased in HCC cell lines compared to the normal hepatocyte cell line. Furthermore, the positive correlation was found among MCMs in HCC cell lines. Conclusions. The MCM complex was increased in HCC tissues and cell lines and negatively correlated with prognosis, which might be important biomarkers for HCC. [ABSTRACT FROM AUTHOR]

A BOC signal has been widely used in the Global Navigation Satellite System (GNSS). Although the BOC signal has many advantages, the autocorrelation function (ACF) of the BOC signal has lots of peaks, which makes signal acquisition difficult. This paper proposes a similar enfoldment acquisition (SEA) algorithm, which can be applied to sin-BOC and cos-BOC signals, with even and odd modulation orders. The SEA algorithm utilizes the similarity between the main peak and the secondary peak to construct a new ACF to eliminate the ambiguity of the BOC signal. This paper simulates the acquisition performance of the SEA algorithm, Martin algorithm, and SCPC algorithm, while the simulation result shows that the SEA algorithm is superior to the Martin algorithm and SCPC algorithm. Since the SEA algorithm does not need filters or auxiliary signals, its complexity is lower than that of the Martin algorithm and SCPC algorithm. [ABSTRACT FROM AUTHOR]

Oral lichen planus (OLP) is a chronic inflammatory oral mucosal disorder mediated by T cells, with a multifactorial etiology. Hashimoto's thyroiditis (HT) is a common autoimmune disease characterized by hypothyroidism. Although many clinical studies conducted over the past several decades have reported the cooccurrence of OLP and HT, the underlying mechanism remains unclear. This review summarizes potential mechanisms that might be involved in the cooccurrence of OLP and HT. We find that OLP and HT share a common or overlapping pathogenesis in terms of immune, heredity, environmental, and hormonal factors, which might cause cooccurrence. Furthermore, considering the latency of HT, a routine screen for thyroid diseases, particularly HT, is suggested for confirmed OLP patients. [ABSTRACT FROM AUTHOR]

Nanoparticles could enhance the drug targeted to the cancer cell by the enrichment of the drug levels, which leads to the improvement of the codelivery of both drugs for an antitumor effect. In the current study, we reported an efficient, local drug-loaded delivery strategy with a nanoparticle-loaded system. After the synthesis of Rituximab/MTX-PEG-PLL-PLGA, the transferrin receptor monoantibody (TfR mAb) was subsequently cross-linked to the nanoparticles. Compared to the traditional drug, the nanoparticle-loaded system can precisely and efficiently transport the Rituximab and Methotrexate (MTX) drug into SU-DHL-4 cells, a typical kind of B lymphocytes, which can significantly increase the cell apoptosis in the SU-DHL-4 cells. Thus, the multifunctional drug-loaded nanoparticles displayed the persistent stability and precise targeting properties, which enhanced the efficiency of anticancer efficiency in B lymphocytes. [ABSTRACT FROM AUTHOR]

Quaternary ammonium compounds constitute a large group of antibacterial chemicals with a potential for inhibiting dental plaque. The aims of this study were to evaluate short-time antibacterial and regulating effects of dimethylaminododecyl methacrylate (DMADDM) on multispecies biofilm viability, reformation, and bacterial composition in vitro. DMADDM, chlorhexidine (CHX), and sodium fluoride (NaF) were chosen in the present study. Streptococcus mutans, Streptococcus sanguinis, and Streptococcus gordonii were used to form multispecies biofilm. Cytotoxicity assay was used to determine the optimal tested concentration. 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and resazurin test of biofilm were conducted to study the biomass changes and metabolic changes of controlled multispecies biofilm. Scanning electron microscopy (SEM) was used to observe biofilm images. TaqMan real-time polymerase chain reaction was performed to evaluate the proportion change in multispecies biofilm of different groups. Cytotoxicity assay showed that there existed a certain concentration application range for DMADDM, CHX, and NaF. MTT assay and resazurin test results showed that DMADDM and CHX groups decreased multispecies biofilm growth and metabolic activity (p < 0.05), no matter after 1 min or 5 min direct contact killing or after 24 h regrowth. The proportion of S. mutans decreased steadily in DMADDM and CHX groups after 1 min and 5 min direct contact killing and 24 h regrowth, compared to control groups. A novel DMADDM-containing solution was developed, achieving effective short-time antibacterial effects and regulation ability of biofilm formation. [ABSTRACT FROM AUTHOR]

FTY720, a new chemical substance derived from the ascomycete Isaria sinclairii, is used for treating multiple sclerosis, renal cancer, and asthma. Sphingosine 1-phosphate (S1P) is a bioactive sphingolipid metabolite and exists in red blood cells. FTY720 is a synthetic S1P analog which can block S1P evoking physiological effects. Recently studies show that S1P was participating in activated inflammation cells induced renal injury. The objective of this study was to assess the protective effect of FTY720 on kidney damage and the potential mechanism of FTY720 which alleviate podocyte injury in chronic kidney disease. In this study, we selected 40 patients with IgA nephropathy and examined their clinical characteristics. Ang II-infusion rat renal injury model was established to evaluate the glomeruli and tubulointerstitial lesion. The result showed that the concentration of S1P in serum and urine was positively correlated with IgA nephropathy patients’ renal injury. FTY720 could reduce renal histological lesions induced by Ang II-infusion in rats. Moreover, FTY720 decreased S1P synthesis in Ang II-infusion rats via downregulation of inflammatory cytokines including TNF-α and IL-6. In addition, FTY720 alleviated exogenous S1P-induced podocyte damage. In conclusion, FTY720 is able to attenuate S1P-induced podocyte damage via reducing inflammatory cytokines. [ABSTRACT FROM AUTHOR]

In order to solve the node localization problem in wireless sensor networks, we propose a novel distributed weighted search localization algorithm (WSLA) in this paper. The WSLA adopts a modified received signal strength indicator-based range model to estimate the distances between nodes, utilizes the results of a centroid localization algorithm as the search initial point, and employs a new weighted search method to compute the positions of nodes in a distributed and recursive manner. The key ideas of the WSLA include a node localization precision classification scheme, a processing scheme for special nodes, and weight-based searches. Compared with three state-of-art localization algorithms—namely, maximum likelihood estimation (MLE), edge-based second-order cone programming + nonconvex sequential greedy (ESOCP + NCSG), and particle swarm optimization (PSO)—the simulation results show that localization performance of the WSLA is superior to that of MLE, ESOCP + NCSG, and PSO. [ABSTRACT FROM AUTHOR]

Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications. [ABSTRACT FROM AUTHOR]

Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K-nearest neighbors (WKNN), which calculates K-nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems that there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close to the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio signal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering based on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the proposed approach does outperform existing approaches. [ABSTRACT FROM AUTHOR]

With ZnO nanorods hydrothermally synthesized on manually assembled arrays of optical fiber cores, 3D arrays of ZnO nanorod-based cylindrically hierarchical structures with nominal pitch 250 µm or 375 µm were constructed. Based on micrographs of scanning electron microscopy and image processing operators of MATLAB software, the 3D arrays of cylindrically hierarchical structures were quantitatively characterized. The values of the actual diameters, the actual pitches, and the parallelism errors suggest that the process capability of the manual assembling is sufficient and the quality of the 3D arrays of cylindrically hierarchical structures is acceptable. The values of the characteristic parameters such as roughness, skewness, kurtosis, correlation length, and power spectrum density show that the surface morphologies of the cylindrically hierarchical structures not only were affected significantly by Zn2+ concentration of the growth solution but also were anisotropic due to different curvature radii of the optical fiber core at side and front view. [ABSTRACT FROM AUTHOR]

We performed a retrospective study of 856 breast cancer patients in our hospital, to compare the therapeutic effect of pirarubicin with cyclophosphamide and 5-fluorouracil (CPF) with the standard epirubicin-based regimen (CEF) in adjuvant treatment of breast cancer. Patients were given cyclophosphamide and 5-fluorouracil 500 mg/m2 each, and either pirarubicin 40 mg/m2 or epirubicin 75-100 mg/m2, every 3 weeks, six cycles. A total of 233 patients used CPF and 623 patients used CEF regimen. The clinical and pathologic characteristics were well balanced between the two groups. The median follow-up time was 41 months, relapse-free survival (RFS) and overall survival (OS) were similar in both groups, p = 0.561 and p = 0.783, respectively. No treatment-related congestive heart failure or death was observed in either group. Regardless of chemotherapy regimens, only tumor size, lymph node status, and ER status were predictive factors in multivariate survival analysis. In stratified analysis, the total hazard ratio estimate for RFS was 0.876 (95% CI 0.561-1.369; p = 0.562), not in favor of either regimen, and no significant difference was observed in any subgroups between the two treatment arms. Our study verified that 3 weekly CPF gives the same efficacy and safety as the standard CEF; both CPF and CEF are the effective regimens that can be used in adjuvant chemotherapy of breast cancer. [ABSTRACT FROM AUTHOR]

We consider the problem of evaluating continuous selection queries over sensor-generated values in the presence of faults. Small sensors are fragile, have finite energy and memory, and communicate over a lossy medium; hence, tuples produced by them may not reach the querying node, resulting in an incomplete and ambiguous answer, as any of the non-reporting sensors may have produced a tuple which was lost. We develop a protocol, FAult Tolerant Evaluation of Continuous Selection Queries (FATE-CSQ), which guarantees a user-requested level of quality in an efficient manner. When many faults occur, this may not be achievable; in that case, we aim for the best possible answer, under the query's time constraints. FATE-CSQ is designed to be resilient to different kinds of failures. Our design decisions are based on an analytical model of different fault tolerance strategies based on feedback and retransmission. Additionally, we evaluate FATE-CSQ and competing protocols with realistic simulation parameters under a variety of conditions, demonstrating its good performance. [ABSTRACT FROM AUTHOR]

Apoptosis and expression of apoptosis-regulating proteins, Bcl-2 and Bax, have been observed in human breast carcinomas. The authors investigated whether expression of Bcl-2 and Bax proteins and apoptotic index (AI) had significance in cases of primary breast cancer. The authors evaluated Bcl-2 and Bax immunoreactivity and AI in primary breast cancers with the ApopTag method in 91 breast cancer patients retrospectively with long-term follow-up (median 60 months). Bcl-2 expression was seen in 60 (65.9%) cases and Bax expression was observed in 59 (64.8%) cases. Increased Bcl-2 and absence or low Bax immunoreactivity were significantly associated with low AI, high tumor grade, axillary lymph node involvement, postoperative recurrence, and metastasis. Thirty-five (38.5%) samples expressed high AI, which correlated with low tumor grade, absent axillary lymph node metastasis, and low levels of Bcl-2 with Bax overexpression. In univariate analysis, the variables associated with short relapse-free survival (RFS) and overall survival (OS) were large tumor size, axillary lymph node involvement, high histologic grade, low AI, high Bcl-2 expression, and absence or low Bax expression. In multivariate analysis, only Bcl-2 expression, lymph node status, and histologic grade were of independent prognostic value with respect to RFS and OS. Because the vast majority of the patients in this study received chemotherapy, it can be concluded that these apoptotic markers were also predictive of response to chemotherapy. Immunostaining of apoptosis-related genes, Bcl-2 and Bax, together with AI, may stratify high- versus low-risk breast cancer patients. [ABSTRACT FROM AUTHOR]