Coptidis Rhizoma binds to the membrane receptors on hPDLSC/CMC, and the active ingredient Berberine (BER) that can be extracted from it may promote the proliferation and osteogenesis of periodontal ligament stem cells (hPDLSC). The membrane receptor that binds with BER on the cell surface of hPDLSC, the mechanism of direct interaction between BER and hPDLSC, and the related signal pathway are not yet clear. In this research, EGFR was screened as the affinity membrane receptor between BER and hPDLSC, through retention on CMC, competition with BER and by using a molecular docking simulation score. At the same time, the MAPK PCR Array was selected to screen the target genes that changed when hPDLSC was simulated by BER. In conclusion, BER may bind to EGFR on the cell membrane of hPDLSC so the intracellular ERK signalling pathways activate, and nuclear-related genes of FOS change, resulting in the effect of osteogenesis on PDLSC.

Electron impact ionization of helium nanodroplets containing a dopant, M, can lead to the detection of both M(+) and helium-solvated cations of the type M(+)·He(n) in the gas phase. The observation of helium-doped ions, He(n)M(+), has the potential to provide information on the aftermath of the charge transfer process that leads to ion production from the helium droplet. Here we report on helium attachment to the ions from four common diatomic dopants, M = N(2), O(2), CO, and NO. For experiments carried out with droplets with an average size of 7500 helium atoms, the monomer cations show little tendency to attach and retain helium atoms on their journey out of the droplet. By way of contrast, the corresponding cluster cations, M(n)(+), where n ≥ 2, all show a clear affinity for helium and form He(m)M(n)(+) cluster ions. The stark difference between the monomer and cluster ions is attributed to more effective cooling of the latter in the aftermath of the ionization event.

Background. Chronic obstructive pulmonary disease (COPD) is a common chronic disease. Progression is further exacerbated by the coexistence of cardiovascular disease (CVD). We aim to construct a diagnostic nomogram for predicting the risk of coexisting CVD and a prognostic nomogram for predicting long-term survival in COPD. Methods. The 540 eligible participants selected from the NHANES 2005–2010 were included in this study. Logistic regression analysis was used to construct a diagnostic nomogram for the diagnosis of coexisting CVD in COPD. Cox regression analyses were used to construct a prognostic nomogram for COPD. A risk stratification system was developed based on the total score generated from the prognostic nomogram. We used C-index and ROC curves to evaluate the discriminant ability of the newly built nomograms. The models were also validated utilizing calibration curves. Survival curves were made using the Kaplan–Meier method and compared by the Log-rank test. Results. Logistic regression analysis showed that gender, age, neutrophil, RDW, LDH, and HbA1c were independent predictors of coexisting CVD and were included in the diagnostic model. Cox regression analysis indicated that CVD, gender, age, BMI, RDW, albumin, LDH, creatinine, and NLR were independent predictors of COPD prognosis and were incorporated into the prognostic model. The C-index and ROC curves revealed the good discrimination abilities of the models. And the calibration curves implied that the predicted values by the nomograms were in good agreement with the actual observed values. In addition, we found that coexisting with CVD had a worse prognosis compared to those without CVD, and the prognosis of the low-risk group was better than that of the high-risk group in COPD. Conclusions. The nomograms we developed can help clinicians and patients to identify COPD coexisting CVD early and predict the 5-year and 10-year survival rates of COPD patients, which has some clinical practical values.

Background Epigallocatechin-3-gallate (EGCG) was recently proposed to have the potential to regulate bone metabolism, however, its influence on osteogenesis remains controversial. The present study aimed to investigate the effects of EGCG on the proliferation and osteogenesis of human periodontal ligament cells (hPDLCs). Methods Cells were cultured in osteogenic medium and treated with EGCG at various concentrations. Cell proliferation was analyzed using a CCK-8 assay and acridine orange (AO)/ethidium bromide (EB) staining. Flow cytometry was used to measure the intracellular reactive oxygen species (ROS) potential of hPDLCs. The expression levels of osteogenic marker genes and proteins in hPDLCs, including type I collagen (COL1), runt-related transcription factor 2 (RUNX2), osteopontin (OPN), and osterix (OSX), were determined by quantitative real-time polymerase chain reaction (qRT-PCR) and western blot analysis. In addition, alkaline phosphatase (ALP) activity was monitored both quantitatively and qualitatively. Extracellular matrix mineralization was further analyzed by alizarin red S staining. Results The results showed that EGCG concentrations from 6 to 10 μM increased the ROS level and inhibited the cell proliferation of hPDLCs. EGCG concentrations from 2 to 8 μM effectively increased extracellular matrix mineralization, in which 4 and 6 μM EGCG generated the most mineralizing nodules. The ALP activity and the mRNA and protein expression levels of the tested osteogenic markers were most strongly up-regulated by treatment with 4 and 6 μM EGCG. Conclusions The present study demonstrated that EGCG might promote the osteogenesis of hPDLCs in a dose-dependent manner, with concentrations of 4 and 6 μM EGCG showing the strongest osteogenic enhancement without cytotoxicity, indicating a promising role for EGCG in periodontal regeneration in patients with deficient alveolar bone in the future.

Kernel methods are powerful for developing a nonlinear learning algorithm in a high-dimensional linear space. The least mean square (LMS) and the least absolute deviation (LAD) are two well-known linear adaptive filtering algorithms. The former performs very well when the noise is Gaussian, while the later possesses desirable performance when the noise has a long-tailed distribution (e.g. alpha-stable distribution). The combination of the LMS and LAD yields a robust mixed-norm (RMN) algorithm. In this paper, we combine the popular kernel methods and the RMN algorithm to develop a new kernel adaptive filtering algorithm, namely the kernel RMN (KRMN) algorithm, which is a robust adaptive algorithm in reproducing kernel Hilbert space (RKHS). The mean square convergence is analyzed, and the excellent and robust performance of the new algorithm is demonstrated by the simulation results of nonlinear time series prediction.

In order to improve the dynamic optimization of fleet size and standardized management of dockless bike-sharing, this paper focuses on using the Markov stochastic process and linear programming method to solve the problem of bike-sharing fleet size and rebalancing. Based on the analysis of characters of bike-sharing, which are irreducible, aperiodic and positive-recurrence, we prove that the probability limits the state (steady-state) of bike-sharing Markov chain only exists and is independent of the initial probability distribution. Then a new "Markov chain dockless bike-sharing fleet size solution" algorithm is proposed. The process includes three parts. Firstly, the irreducibility of the bike-sharing transition probability matrix is analyzed. Secondly, the rank-one updating method is used to construct the transition probability random prime matrix. Finally, an iterative method for solving the steady-state probability vector is therefore given and the convergence speed of the method is analyzed. Furthermore, we discuss the dynamic solution of the bike-sharing steady-state fleet size according to the time period, so as improving the practicality of the algorithm. To verify the efficiency of this algorithm, we adopt the linear programming method for bicycle rebalancing analysis. Experiment results show that the algorithm could be used to solve the disordered deployment of dockless bike-sharing.The bike-sharing deployed by merchants since 2016 are mainly dockless bicycles [10]. Compared with traditional docked bicycles, it could allow users to pick up and drop off bikes freely instead of at the designated station [11]. The dockless bike-sharing adopts the operation mode of "smart dockless", "GPS positioning", "network convenient payment" and "requesting on demand". It has become a popular way for short-distance green travel in China.The emergence of dockless bike-sharing is an innovative model of the sharing economy in the Internet era, which facilitates the daily short-distance travel of the public [12]. However, on the other hand, the disordered deployment of dockless bike-sharing has become an increasingly serious problem, because some bike-sharing companies try to keep the competition by blind expansions. The problems of fleet size and rebalancing [13][14][15] of the dockless bike-sharing stations are mainly illustrated as follows:•There are too many bicycles in some stations, but the problems of idleness are prominent, which even hinders the passage of pedestrians and cars. In contrast, the number of bicycles in some other stations is not enough to meet the needs of users. •The response of the bike-sharing replenishment or repositioning transport is not so efficiently, and the rationality of the rebalancing is still a concern.Therefore, the fleet size and rebalancing of dockless bike-sharing stations are in urgent need of scientific analysis. It is necessary to propose an algorithm to determine the number of shared bicycles rationally and quickly at each station to achieve the goal of standardized management....

No abstract

No abstract