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Based on the 3-UU parallel mechanism, a prototype robot for wrist joint rehabilitation is developed to assist stroke patients in wrist joint rehabilitation training. Based on the constraint relation and geometric characteristics of the 3-UU mechanism, the inverse kinematics of 3-UU mechanism is analyzed by spherical coordinate method and roll-pitch-yaw (RPY) method， and the relation between the platform and the driver is obtained. Compared with the classic 3-RRR wrist joint rehabilitation mechanism, the developed prototype has no advantages such as multi-solution and singular value. The motion performance of the prototype and the electromyographic signals of the two major muscle groups in the forearm are measured. The experimental results show that the maximum roll angle is -90° to 90°, the pitch angle is -90° to 90°， and the virtual yaw angle is -180° to 180°. The maximum electromyogram (EMG) signal can be generated at 950 mV. The results show that the developed model can meet the requirements of the wrist motion and train the forearm muscle group.

Abstract Before a wide range of enhanced oil recovery (EOR) techniques were implemented for an oilfield, the EOR potential and economic evaluation of the techniques should be evaluated in advance for each reservoir to determine which EOR technique was proper. In an oilfield developed with fluvial delta reservoirs, the complicated distribution of scattered small reservoirs in vertical and horizontal directions brought trouble for evaluation work. A rapid and reliable evaluation model for EOR techniques applicability was necessary to deal with the evaluation simulation for many small reservoirs of an oilfield. Combining fraction theory model with auxiliary equations, which describe the effect of formation heterogeneity and mechanism of different EOR technique on fractional flow rate, an evaluation analysis method for EOR techniques applicability of gas flooding, foam flooding and surfactant flooding was proposed. In the gas flooding model, the minimum miscible pressure of impure gas was introduced, and the relative permeability was modified by the minimum miscible factor. In the foam flooding model, changes in the mobility ratio and chemical adsorption were considered and a reduction factor of mobility ratio was introduced. In the surfactant flooding model, calculation formulas of viscosity and interfacial tension as well as the relative permeability were introduced. Finally, the model was simulated for a low permeability reservoir, and the simulation results were compared with that from Eclipse software. The similar results, little calculation time and feasibility of predicting optimal injection parameter had shown the reliability of the rapid evaluation model.

The long noncoding RNA DARS-AS1 was aberrantly expressed and participated in several human cancer progressions, whereas whether DARS-AS1 is involved in human gastric cancer remains unclear. This study aimed to investigate the influence of DARS-AS1 on gastric cancer progression and explore the potential regulatory network of DARS-AS1/miR-330-3p/NAT10. The expression levels of DARS-AS1, miR-330-3p, and NAT10 were measured by quantitative real-time polymerase chain reaction. The CCK-8 assay and Transwell assay were used to determine the cell viability, migration, and invasion capacities, respectively. The target association between miR-330-3p and DARS-AS1 or NAT10 was confirmed using a luciferase reporter assay. In result, DARS-AS1 levels were elevated in tumor tissues and associated with shorter overall survival in patients with gastric cancer. Knockdown of DARS-AS1 could hamper cell viability, migration, and invasion in gastric cancer cells. DARS-AS1 acts as a competitive endogenous RNA to regulate the NAT10 expression by sponging miR-330-3p in gastric cancer cells. In conclusion, DARS-AS1 was elevated in gastric cancer, and DARS-AS1/miR-330-3p/NAT10 signaling offered some new horizons for predicting prognosis and a novel therapeutic method for the treatment of gastric cancer.

BACKGROUND:The application of high-throughput sequencing in a broad range of quantitative genomic assays (e.g., DNA-seq, ChIP-seq) has created a high demand for the analysis of large-scale read-count data. Typically, the genome is divided into tiling windows and windowed read-count data is generated for the entire genome from which genomic signals are detected (e.g. copy number changes in DNA-seq, enrichment peaks in ChIP-seq). For accurate analysis of read-count data, many state-of-the-art statistical methods use generalized linear models (GLM) coupled with the negative-binomial (NB) distribution by leveraging its ability for simultaneous bias correction and signal detection. However, although statistically powerful, the GLM+NB method has a quadratic computational complexity and therefore suffers from slow running time when applied to large-scale windowed read-count data. In this study, we aimed to speed up substantially the GLM+NB method by using a randomized algorithm and we demonstrate here the utility of our approach in the application of detecting copy number variants (CNVs) using a real example. RESULTS:We propose an efficient estimator, the randomized GLM+NB coefficients estimator (RGE), for speeding up the GLM+NB method. RGE samples the read-count data and solves the estimation problem on a smaller scale. We first theoretically validated the consistency and the variance properties of RGE. We then applied RGE to GENSENG, a GLM+NB based method for detecting CNVs. We named the resulting method as "R-GENSENG". Based on extensive evaluation using both simulated and empirical data, we concluded that R-GENSENG is ten times faster than the original GENSENG while maintaining GENSENG's accuracy in CNV detection. CONCLUSIONS:Our results suggest that RGE strategy developed here could be applied to other GLM+NB based read-count analyses, i.e. ChIP-seq data analysis, to substantially improve their computational efficiency while preserving the analytic power.