No abstract

In this article, a new nonlinear robust contouring control scheme is proposed for a planar manipulator. Compared with the end effector tracking error, contouring error is a more reasonable description of the minimum distance between actual position and desired contour when tracking a complex trajectory. Thus, the task coordinate contour error is selected to evaluate the tracking performance. However, most of the contouring control schemes are designed for biaxial gantry, the planar manipulator system is subjected to payload change, and the conventional contouring controller cannot solve this problem faultlessly. Here, the continuous nonsingular terminal sliding mode control and time delay estimation are integrated to develop a new contouring controller for large curvature trajectory high-speed tracking. The time delay estimation is adopted to estimate the manipulator dynamic and payload change. The tracking precision can be guaranteed even parameter uncertainty exsits. Furthermore, continuous nonsingular terminal sliding mode control is integrated with the contouring control to obtain faster convergence performance and robustness of the overall system. The proposed controller possesses obvious advantages, such as robust to payload change and a better performance when tracking a large curvature ellipse in high speed. The effectiveness of the proposed method is verified through simulation and experiment on a planar manipulator.

There is an urgent need to improve the standard of care for PWE in Guinea. Several missed opportunities were identified, including low use of AEDs and high use of traditional medicines, particularly in children. Targeted programs should be developed to prevent unintentional injury and improve seizure control.

Background: Pragmatic trials provide the opportunity to study the effectiveness of health interventions to improve care in real-world settings. However, use of open-cohort designs with patients becoming eligible after randomization and reliance on electronic health records (EHRs) to identify participants may lead to a form of selection bias referred to as identification bias. This bias can occur when individuals identified as a result of the treatment group assignment are included in analyses. Methods: To demonstrate the importance of identification bias and how it can be addressed, we consider a motivating case study, the PRimary care Opioid Use Disorders treatment (PROUD) Trial. PROUD is an ongoing pragmatic, cluster-randomized implementation trial in six health systems to evaluate a program for increasing medication treatment of opioid use disorders (OUDs). A main study objective is to evaluate whether the PROUD intervention decreases acute care utilization among patients with OUD (effectiveness aim). Identification bias is a particular concern, because OUD is underdiagnosed in the EHR at baseline, and because the intervention is expected to increase OUD diagnosis among current patients and attract new patients with OUD to the intervention site. We propose a framework for addressing this source of bias in the statistical design and analysis. Results: The statistical design sought to balance the competing goals of fully capturing intervention effects and mitigating identification bias, while maximizing power. For the primary analysis of the effectiveness aim, identification bias was avoided by defining the study sample using pre-randomization data (pre-trial modeling demonstrated that the optimal approach was to use individuals with a prior OUD diagnosis). To expand generalizability of study findings, secondary analyses were planned that also included patients newly diagnosed post-randomization, with analytic methods to account for identification bias. Conclusion: As more studies seek to leverage existing data sources, such as EHRs, to make clinical trials more affordable and generalizable and to apply novel open-cohort study designs, the potential for identification bias is likely to become increasingly common. This case study highlights how this bias can be addressed in the statistical study design and analysis.

Recent migrations and inter-ethnic mating of long isolated populations have resulted in genetically admixed populations. To understand the complex population admixture process, which is critical to both evolutionary and medical studies, here we used admixture induced linkage disequilibrium (LD) to infer continuous admixture events, which is common for most existing admixed populations. Unlike previous studies, we expanded the typical continuous admixture model to a more general scenario with isolation after a certain duration of continuous gene flow. Based on the new models, we developed a method, CAMer, to infer the admixture history considering continuous and complex demographic process of gene flow between populations. We evaluated the performance of CAMer by computer simulation and further applied our method to real data analysis of a few well-known admixed populations.Human migrations involve gene flow among previously isolated populations, resulting in admixed populations. In both evolutionary and medical studies of admixed populations, it is essential to understand admixture history and accurately estimate the time since population admixture because genetic architecture at both population and individual levels are determined by admixture history, especially the admixture time. However, the estimation of admixture time depends largely on the precision of the applied admixture models. Several methods have been developed to estimate admixture time based on the hybrid isolation (HI) model 1-4 or intermixture admixture model (IA) 5 , which assume that the admixed population is formed by one wave of admixture at a certain time. However, the one-wave assumption often leads to under-estimation when the progress of the true admixture cannot be well modeled by the HI model. Jin et al. showed earlier that under the assumption of HI, the estimated time is half of the true time when the true model is a modified gradual admixture (GA) model 6 . Admixture models can be theoretically distinguished by comparing the length distribution of continuous ancestral tracts (CAT) [7][8][9] , which refers to continuous haplotype tracts that were deviated from the same ancestral population. CAT inherently represents admixture history as it accumulates recombination events. Short CAT always indicates long admixture history of the same admixture proportion, whereas long CAT may indicate a recent gene flow from the ancestral population to which the CAT belongs. Based on the information it provides, CAT can be used to distinguish different admixture models and estimate corresponding admixture time. However, accurately estimating the length of CAT is often very difficult.Weighted linkage disequilibrium (LD) is an alternative type of information that can be used to infer admixture 1,10 . Previous studies have indicated that it is more efficient than CAT because it requires neither ancestry inference nor haplotype phasing, which often introduces false recombination thus decreasing the power of estimation. Weighted LD has already been use...