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Abstract Panax japonicus, an endangered species in China, is usually used as a traditional medicine with functions of hemostasis, pain relief, and detoxify. However, the seeds of P. japonicus are hard to germinate in natural conditions, and the molecular events and systematic changes occurring in seed germination are still largely unknown. In this study, we compared the seeds in different germination stages in terms of morphological features, antioxidant enzyme activities, and transcriptomics. The results indicated that sand storage at 25℃ for 120 d effectively released the seed dormancy of P. japonicus and promoted the seed germination. Moreover, sand storage treatment increased the antioxidant capacity of P. japonicus seeds through increasing the activities of SOD, POD, and CAT. The RNA-seq identified 28,908 differentially expressed genes (DEGs) between different germination stages, of which 1697 DEGs significantly changed throughout the whole germination process. Functional annotations showed that the seed germination of P. japonicus was mainly regulated by the DEGs related to pathways of ROS-scavenging metabolism, plant hormonal signal transduction, starch and sucrose metabolism, energy supply (glycolysis, pyruvate metabolism, and oxidative phosphorylation), and phenylpropanoid biosynthesis, as well as the transcription factors such as bHLHs, MYBs, WRKYs, and bZIPs. This study provides a foundation for unveiling molecular mechanisms underlying the seed germination and is beneficial for accelerating the development of P. japonicus industry.

Abstract For the clinical diagnosis of diseases and for basic biological research, it is crucial to develop a trustworthy and efficient method for detecting small extracellular vesicles (sEVs) in multiple experimental conditions. Here, we create a colorimetric assay that enables sensitive and precise sEVs identification without the need for pricey equipment. In this assay, the exonuclease III (Exo III)-assisted signal recycle is activated by the released single-strand DNA (ssDNA) from SMBs (streptavidin magnetic beads)-aptamer-ssDNA complex after identification of sEVs. By integrating with the strand displacement amplification (SDA) process, a significant amount of double-strand DNA products with G-rich tails is produced. The G-rich tails fold to G-quadruplex under the assistance of hemin to catalyze the oxidation of TMB, yielding a color change. The approach offers a broad detection range of 5 orders of magnitudes based on the signal recycles and SDA. In addition, single-stranded DNA binding protein (SSB) is exploited in this method to minimize the background signal from non-specific digestion of Exo-III, making the method a robust tool for sEVs detection and disease diagnosis.

Abstract Background Understanding resident physicians’ perceptions of competency-based medical education(CBME) may help improve approaches for implementing such education in standardized resident training (SRT). We conducted surveys of residents in China to identify their perceptions of CBME and determine the degree to which such education impacts their career plans. Methods Questionnaire contained a total of 24 questions, which were answered using multiple choice or yes/no, was distributed to residents who were undergoing SRT, regardless of specialty, at 7 accredited training bases located across six provinces of China. The survey aimed to investigate residents’ reasons for participating in SRT, perceptions of CBME, interest in receiving CBME-associated courses, and attitudes towards CBME. Results Overall, 441 residents completed the questionnaire.17.7% (78/441) responded “no clear objective” before the participated in SRT. Only 3.9% (17/441) fully understood the objectives, training contents, and assessment system of the current “competency-based” standardized training program for residents in China. Residents ranked clinical skills and patient care, interpersonal communication, and professionalism, as the three most important competencies. Most were interested in the CBME residency programs. 90.7% felt that implementing CBME could help them clarify their professional direction and improve their career planning. Conclusion Residents had positive perceptions of the incorporation of CBME into SRT. Administrators, educational leaders, and clinical faculty should seek to further publicize and increase the popularity of CBME.

This paper uses the physical information neural network (PINN) model to solve a 3D anisotropic steady-state heat conduction problem based on deep learning techniques. The model embeds the problem’s governing equations and boundary conditions into the neural network and treats the neural network’s output as the numerical solution of the partial differential equation. Then, the network is trained using the Adam optimizer on the training set. The output progressively converges toward the accurate solution of the equation. In the first numerical example, we demonstrate the convergence of the PINN by discussing the effect of the neural network’s number of layers, each hidden layer’s number of neurons, the initial learning rate and decay rate, the size of the training set, the mini-batch size, the amount of training points on the boundary, and the training steps on the relative error of the numerical solution, respectively. The numerical solutions are presented for three different examples. Thus, the effectiveness of the method is verified.

Due to the low computational efficiency of the Improved Element-Free Galerkin (IEFG) method, efficiently solving three-dimensional (3D) Laplace problems using meshless methods has been a longstanding research direction. In this study, we propose the Dimension Coupling Method (DCM) as a promising alternative approach to address this challenge. Based on the Dimensional Splitting Method (DSM), the DCM divides the 3D problem domain into a coupling of multiple two-dimensional (2D) problems which are handled via the IEFG method. We use the Finite Element Method (FEM) in the third direction to combine the 2D discretized equations, which has advantages over the Finite Difference Method (FDM) used in traditional methods. Our numerical verification demonstrates the DCM’s convergence and enhancement of computational speed without losing computational accuracy compared to the IEFG method. Therefore, this proposed method significantly reduces computational time and costs when solving 3D Laplace equations with natural or mixed boundary conditions in a dimensional splitting direction, and expands the applicability of the dimension splitting EFG method.

Aiming at the vibration problem of traditional parallel manipulator trajectory planning， two-degree-of-freedom parallel mechanism is studied， and a trajectory planning method based on high-order differentiable compound polynomial is proposed. Firstly， a planar two-degree-of-freedom parallel manipulator for product packaging is designed， and the inverse kinematics and forward kinematics are analyzed. The model is built by Solidworks software， and the correctness of kinematics analysis is verified. According to the physical boundaries and industry needs， the appropriate workspace is selected. Then， a new compound polynomial is generated by combining cubic polynomial with Bessel curve. Finally， the prototype of the manipulator is made and tested in practice. The research results show that， compared with cubic polynomial， the position， velocity and acceleration curves of the trajectory generated by the new polynomial are smoother in the same duration， which effectively reduces the vibration of the mechanism， reduces the maximum velocity and acceleration by about 40%， and effectively prolongs the mechanical life.

Objective Hypertension predicts the development of diabetes. However, there are still lacking high-quality studies on the correlation between mean arterial pressure (MAP) and incident diabetes. We aimed to explore the relationship between MAP and diabetes in Chinese adults.Design This is a secondary retrospective cohort study and the data were downloaded from the ‘DATADRYAD’ database (www.Datadryad.org).Participants The study included 210 418 adults without diabetes at baseline between 2010 and 2016 across 32 sites and 11 cities in China.Setting The target-independent and dependent variables were MAP measured at baseline and diabetes occurred during follow-up. Cox proportional hazards regression was used to explore the relationship between MAP and diabetes.Primary outcome measures The outcome was incident diabetes, which was defined as fasting blood glucose ≥7.00 mmol/L and/or self-reported diabetes during follow-up. Patients were censored either at the time of the diagnosis or at the last visit, whichever comes first.Results 3927 participants developed diabetes during a 5-year follow-up. After adjusting covariates, MAP positively correlated with diabetes (HR=1.008, 95% CI 1.005 to 1.011, p

The study on stationary dynamic response of a gear transmission system with time-varying mesh stiffness subjected to stationary stochastic external excitation is carried out. A spur gear pair model with periodical time-varying mesh stiffness is built to include stochastic external force excitations. In order to compute the stationary response of gear system， a very efficient method， called the iterative spectral method is used. This method is derived in the frequency domain and provides the explicit power spectral density（PSD） of the response. The PSD response is expressed as a function of the bispectrum which is the bilinear Fourier transform of the bitemporal impulse response of the parametric system.

Abstract Background Reliable quantification of the relationship between hypertension and diabetes risk is limited, especially among Chinese people. We aimed to investigate the association between hypertension and the risk of diabetes in a large cohort of the Chinese population. Methods This was a retrospective propensity score-matched cohort study among 211,809 Chinese adults without diabetes at baseline between 2010 and 2016. The target independent and dependent variable were hypertension at baseline and incident diabetes during follow-up respectively. The propensity score matching using a non-parsimonious multivariable logistic regression was conducted to balance the confounders between 28,711 hypertensive patients and 28,711 non-hypertensive participants. The doubly robust estimation method was used to investigate the association between hypertension and diabetes. Results In the propensity-score matching cohort, diabetes risk increased by 11.0% among hypertensive patients (HR = 1.110, 95% confidence interval (CI): 1.031–1.195, P = 0.00539). And diabetes risk dropped to 8.3% among hypertensive subjects after adjusting for the propensity score (HR = 1.083, 95%CI: 1.006–1.166, P = 0.03367). Compared to non-hypertensive participants with low propensity score, the risk of incident diabetes increased by 2.646 times among hypertensive patients with high propensity score (HR = 3.646, 95%CI: 2.635–5.045, P

Abstract Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887–0.9364) and 0.9030 (95% CI, 0.8747–0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126–0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

Introduction: Renal cancer is one of the most common cancers in the world, but the effect of therapies on advanced renal cancer has not improved for decades. Ferroptosis is an emerging type of programmed cell death and has been proved to play a vital role in many kinds of cancers. However, the mechanisms of ferroptosis regulated by long noncoding RNA (lncRNA) in the context of renal cancer was still unknown. Methods: We used bioinformation analysis to identify SLC16A1-AS1 as a survival-related lncRNA in renal cancer. The expression levels of SLC16A1-AS1 and microRNA-143-3p (miR-143-3p) were detected by quantitative reverse transcription–polymerase chain reaction. Cell counting kit-8 assay, 5-bromo-2′-deoxyuridine proliferation assay, and colony-formation assay were performed to evaluate cell viability and proliferation. Wound-healing assay and transwell assay were used to examine cell invasive and migration capacity. Dual-luciferase reporter assay and RNA-binding protein immunoprecipitation were used to identify the interaction among SLC16A1-AS1, miR-143-3p, and the target protein solute carrier family 7 membrane 11 (SLC7A11). Reduced glutathione and glutathione and lipid peroxidation measurements were carried out to evaluate the level of ferroptosis, and the expression levels of ferroptosis-related proteins were analyzed by western blot. Results: Our study revealed that SLC16A1-AS1 has high expression and was associated with overall survival in renal cancer. Knockdown SLC16A1-AS1 inhibited cell viability, proliferation, and migration of renal cancer cells. Furthermore, it was demonstrated that SLC16A1-AS1 served as a sponge of miR-143-3p, and knockdown SLC16A1-AS1 significantly increased the enrichment of miR-143-3p. And then, SLC7A11 was identified as the target protein of miR-143-3p, and overexpression miR-143-3p remarkably inhibited the expression of SLC7A11. Moreover, knockdown SLC16A1-AS1 could aggravate this effect. Finally, through inhibiting SLC7A11 expression, silencing SLC16A1-AS1 induced ferroptosis via increasing miR-143-3p. Conclusion: The present results suggest that silencing lncRNA SLC16A1-AS1 can induce ferroptosis through miR-143-3p/SLC7A11 signaling in renal cancer. Our study provided a novel view into the pathogenesis and treatment strategy of RCC.

In order to obtain the numerical results of 3D convection-diffusion-reaction problems with variable coefficients efficiently, we select the improved element-free Galerkin (IEFG) method instead of the traditional element-free Galerkin (EFG) method by using the improved moving least-squares (MLS) approximation to obtain the shape function. For the governing equation of 3D convection-diffusion-reaction problems, we can derive the corresponding equivalent functional; then, the essential boundary conditions are imposed by applying the penalty method; thus, the equivalent integral weak form is obtained. By introducing the IMLS approximation, we can derive the final solved linear equations of the convection-diffusion-reaction problem. In numerical examples, the scale parameter and the penalty factor of the IEFG method for such problems are discussed, the convergence is proved numerically, and the calculation efficiency of the IEFG method are verified by four numerical examples.

Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults.Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants.Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824).Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.

The improved element-free Galerkin (IEFG) method is proposed in this paper for solving 3D Helmholtz equations. The improved moving least-squares (IMLS) approximation is used to establish the trial function, and the penalty technique is used to enforce the essential boundary conditions. Thus, the final discretized equations of the IEFG method for 3D Helmholtz equations can be derived by using the corresponding Galerkin weak form. The influences of the node distribution, the weight functions, the scale parameters of the influence domain, and the penalty factors on the computational accuracy of the solutions are analyzed, and the numerical results of three examples show that the proposed method in this paper can not only enhance the computational speed of the element-free Galerkin (EFG) method but also eliminate the phenomenon of the singular matrix.