Search

Objective Nervous system damage in patients with SSc has recently attracted attention. In this study, we aimed to explore mild cognitive impairment (MCI) in SSc patients and the characteristics of these patients. Methods A total of 103 SSc patients were consecutively enrolled from July 2018 to May 2019, and 97 matched healthy individuals were also included as controls. Brief cognitive tests, such as the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), were used to assess the cognitive function of all subjects. We compared the differences in MCI between SSc patients and healthy controls, as well as the differences in demographic and clinical features between SSc patients with and without MCI. Associations of quantitative demographic and clinical features with MoCA-BJ scores in the SSc patients were also evaluated. Results The score of MoCA-BJ was lower in the SSc group compared with those in the healthy group [24 (9–30) vs 26 (15–30), P Conclusion MCI is common in patients with SSc and should be drawn to the attention of rheumatologists. Lower education level, malnutrition and higher ANA positivity were closely related to the cognitive dysfunctions in SSc patients, providing directions for further interventions.

Enzyme-powered micro/nanomotors propelled by biocompatible fuels generally show a weak propulsive force, which greatly limits their applications in complex biological environments. Herein, we have developed a novel and versatile approach to significantly enhance the propulsion of enzyme-powered micromotors by multilayered assembly of enzymes. As an example, multilayers of biotinylated ureases (BU) were asymmetrically immobilized on biotinylated Janus Au/magnetic microparticles (MMPs) with the assistance of streptavidin (SA). When the mass ratio of BU into SA and the amount of BU used in the assembly process are increased, the amount of urease immobilized on the biotinylated Janus Au/MMPs increased monotonously while the migration speed of the micromotor was augmented gradually until a saturated value. The as-optimized micromotors can be self-propelled with an average speed up to about 21.5 ± 0.8 μm/s at physiological urea concentrations (10 mM), which is five times faster than that of the monolayered counterparts and two times faster than that of the previously reported values. Owing to the enhanced thrust, the micromotors can move in liquids with viscosities similar to that of blood. In addition, with the inherent magnetic property of MMPs, the micromotors can exhibit fast magnetic separation and controllable motion direction by external magnetic fields. Our results provide a new pathway for designing high-efficient enzyme-powered micro/nanomotors and thereby promote their biomedical applications.

Entity resolution identifies and removes duplicate entities in large, noisy databases and has grown in both usage and new developments as a result of increased data availability. Nevertheless, entity resolution has tradeoffs regarding assumptions of the data generation process, error rates, and computational scalability that make it a difficult task for real applications. In this paper, we focus on a related problem of unique entity estimation, which is the task of estimating the unique number of entities and associated standard errors in a data set with duplicate entities. Unique entity estimation shares many fundamental challenges of entity resolution, namely, that the computational cost of all-to-all entity comparisons is intractable for large databases. To circumvent this computational barrier, we propose an efficient (near-linear time) estimation algorithm based on locality sensitive hashing. Our estimator, under realistic assumptions, is unbiased and has provably low variance compared to existing random sampling based approaches. In addition, we empirically show its superiority over the state-of-the-art estimators on three real applications. The motivation for our work is to derive an accurate estimate of the documented, identifiable deaths in the ongoing Syrian conflict. Our methodology, when applied to the Syrian data set, provides an estimate of $191,874 \pm 1772$ documented, identifiable deaths, which is very close to the Human Rights Data Analysis Group (HRDAG) estimate of 191,369. Our work provides an example of challenges and efforts involved in solving a real, noisy challenging problem where modeling assumptions may not hold., 35 pages, 6 figures, 2 tables

Many indoor localization algorithms have been proposed to enable location-based applications in indoor environments. However, these systems are monolithic and not component-based. We present BearLoc, a distributed modular framework for indoor localization systems that provides (1) natural development abstractions for sensor, algorithm, and application components, and (2) easy and flexible component composition. We demonstrate the merits of BearLoc with an example use case. Our evaluation shows we can reduce developer lines of code by 60% while introducing acceptable network delay overhead.