ABSTRACT
Triethylamine (TEA) is an effective medium for inhibiting dye aggregation and improving the luminescence of dye-sensitized lanthanide-doped upconversion nanoparticles (UCNPs). However, excessive TEA will cause quenching of upconversion luminescence. In this paper, the possible mechanism of TEA affecting upconversion luminescence is discussed. It is found that TEA can enhance the nucleophilicity of the solvent, leading to dye shedding from the nanoparticles. Reducing the dielectric constant of the solvent can make TEA play a more positive role in upconversion luminescence and photostability of dye-sensitized UCNPs. When heptanol is selected as the solvent for CyBSO-sensitized ß-NaYF4:20%Yb3+,2%Er3+ (UNs), TEA can increase the upconversion luminescence by 6.0 times relative to that in methanol. More importantly, the optimal content of TEA in heptanol is 3700 times more than that in methanol. Under the action of large amounts of TEA in heptanol, a novel upconversion nanoprobe for detecting ascorbic acid is developed with a limit of detection of 0.103 µM and high selectivity over potential interfering species. Meanwhile, the high concentration of TEA in heptanol can improve the photostability of CyBSO-sensitized UNs by 10.4 times, which is of paramount importance for the practical application of dye-sensitized UCNPs.
ABSTRACT
AIM: To compare the outcomes of retrograde intrarenal surgery (RIRS) and miniaturized percutaneous nephrolithotomy (mini-PCNL) in treating lower pole (LP) renal stones with a diameter of 1.5-2.5 cm. METHODS: A total of 216 patients who underwent mini-PCNL (n = 103) or RIRS n = 113) for LP stones with a diameter of 1.5-2.5 cm were enrolled between December 2015 and April 2017 at the Urology Department of Ningbo Urology and Nephrology Hospital. RESULTS: Significant differences were found in the hospital stay (9.39 ± 4.01 vs 14.08 ± 5.26, P < 0.0001) and hospitalization costs (2624.5 ± 513.36 vs 3255.2 ± 976.5, P < 0.0001) between the RIRS and mini-PCNL groups. The mean operation time was not significantly different between the RIRS group (56.48 ± 24.77) and the mini-PCNL group (60.04 ± 30.38, P = 0.345). The stone-free rates at the first postoperative day (RIRS vs mini-PCNL: 90.2% vs 93.2%, P = 0.822) and the second month postoperatively (RIRS vs mini-PCNL: 93.8% vs 95.1%, P = 0.986) were not significantly different. CONCLUSION: RIRS and mini-PCNL are both safe and effective methods for treating LP stones with a diameter of 1.5-2.5 cm. RIRS can be considered as an alternative to PCNL for the treatment for LP stones of 1.5-2.5 cm.
ABSTRACT
OBJECTIVE: To investigate the lipid metabolic effect and mechanism of water extract of lotus leaves(Traditional Chinese Medicine). METHODS: Isolated SD rat lipid tissues were suspended in organ baths containing Krebs solution, and the effect of lotus leaf water extract on free fatty acids (FFA) release was observed; The experimental obesity rat model was established by feeding them high glucose and fat diets, then the changes of body weight and blood lipid were measured in the model rats after intragastric administration with water extract of lotus leaves for four weeks, and the expressions of peroxisome proliferator-activated receptor gamma (PPAR-γ) and leptin were examined by RT-PCR and immunohistochemical. RESULTS: The ex vivo experiment showed that water extract of lotus leaves effectively promoted the FFA release from isolated lipid tissues. In vivo experiment, similarly to Orlistat, water extract of lotus leaves(60 mg/kg)markedly decreased the body weight and blood lipid of experimental obesity rats(P<0.05), and obviously reduced the expressions of PPAR-γ and leptin(P<0.05). CONCLUSIONS: Water extract of lotus leaves greatly improves the expression of PPAR-γ and leptin, which can promote the lipid mobilization and dissolution, reduce the body weight and blood lipid of adult rats with experimental obesity, therefore is expected to be developed into lipid-lowering diet pills.
Subject(s)
Lipid Metabolism/drug effects , Lotus/chemistry , Obesity , Plant Extracts/pharmacology , Animals , Leptin/metabolism , PPAR gamma/metabolism , Plant Leaves/chemistry , Rats , Rats, Sprague-Dawley , WaterABSTRACT
This paper studies the finite-horizon optimal guaranteed cost control (GCC) problem for a class of time-varying uncertain nonlinear systems. The aim of this problem is to find a robust state feedback controller such that the closed-loop system has not only a bounded response in a finite duration of time for all admissible uncertainties but also a minimal guaranteed cost. A neural network (NN) based approximate optimal GCC design is developed. Initially, by modifying the cost function to account for the nonlinear perturbation of system, the optimal GCC problem is transformed into a finite-horizon optimal control problem of the nominal system. Subsequently, with the help of the modified cost function together with a parametrized bounding function for all admissible uncertainties, the solution to the optimal GCC problem is given in terms of a parametrized Hamilton-Jacobi-Bellman (PHJB) equation. Then, a NN method is developed to solve offline the PHJB equation approximately and thus obtain the nearly optimal GCC policy. Furthermore, the convergence of approximate PHJB equation and the robust admissibility of nearly optimal GCC policy are also analyzed. Finally, by applying the proposed design method to the entry guidance problem of the Mars lander, the achieved simulation results show the effectiveness of the proposed controller.
ABSTRACT
In order to classify and set different prices on basis of difference of beet sugar content in the acquisition process and promote the development of beet sugar industry healthily, a fast, nondestructive, accurate method to detect sugar content of beet was determined by applying near infrared spectroscopy technology. Eight hundred twenty samples from 28 representative varieties of beet were collected as calibration set and 70 samples were chosen as prediction set. Then near infrared spectra of calibration set samples were collected by scanning, effective information was extracted from NIR spectroscopy, and the original spectroscopy data was optimized by data preprocessing methods appropriately. Then partial least square(PLS)regression was used to establish beet sugar quantitative prediction mathematical model. The performances of the models were evaluated by the root mean square of cross-validation (RMSECV), the coefficient of determination (R2) of the calibration model and the standard error of prediction (SEP), and the predicted results of these models were compared. Results show that the established mathematical model by using first derivative (FD) and standard normal variate transformation (SNV) coupled with partial least squares has good predictive ability. The R2 of calibration models of sugar content of beet is 0.908 3, and the RMSECV is 0.376 7. Using this model to forecast the prediction set including 70 samples, the correlation coefficient is 0.921 4 between predicted values and measured values, and the standard error of prediction (SEP) is 0.439, without significant difference (p > 0.05) between predicted values and measured values. These results demonstrated that NIRS can take advantage of simple, rapid, nondestructive and environmental detection method and could be applied to predict beet sugar content. This model owned high accuracy and can meet the precision need of determination of beet sugar content. This detection method could be used to classify and set different prices on basis of difference of beet sugar content in the acquisition process.