Sign language continues to be the preferred method of communication among the deaf and the hearing-impaired. Advances in information technology have prompted the development of systems that can facilitate automatic translation between sign language and spoken language. More recently, systems translating between Arabic sign and spoken language have become popular. This paper reviews systems and methods for the automatic recognition of Arabic sign language. Additionally, this paper highlights the main challenges characterizing Arabic sign language as well as potential future research directions.Index Terms-Arabic sign language recognition (ArSLR), continuous sign recognition, image-based, isolated word recognition, sensor-based.

Background Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic disease with pathophysiological characteristics of transforming growth factor-β (TGF-β), and reactive oxygen species (ROS)-induced excessive fibroblast-to-myofibroblast transition and extracellular matrix deposition. Macrophages are closely involved in the development of fibrosis. Nuclear factor erythroid 2 related factor 2 (Nrf2) is a key molecule regulating ROS and TGF-β expression. Therefore, Nrf2 signaling modulation might be a promising therapy for fibrosis. The inhalation-based drug delivery can reduce systemic side effects and improve therapeutic effects, and is currently receiving increasing attention, but direct inhaled drugs are easily cleared and difficult to exert their efficacy. Therefore, we aimed to design a ROS-responsive liposome for the Nrf2 agonist dimethyl fumarate (DMF) delivery in the fibrotic lung. Moreover, we explored its therapeutic effect on pulmonary fibrosis and macrophage activation. Results We synthesized DMF-loaded ROS-responsive DSPE-TK-PEG@DMF liposomes (DTP@DMF NPs). DTP@DMF NPs had suitable size and negative zeta potential and excellent capability to rapidly release DMF in a high-ROS environment. We found that macrophage accumulation and polarization were closely related to fibrosis development, while DTP@DMF NPs could attenuate macrophage activity and fibrosis in mice. RAW264.7 and NIH-3T3 cells coculture revealed that DTP@DMF NPs could promote Nrf2 and downstream heme oxygenase-1 (HO-1) expression and suppress TGF-β and ROS production in macrophages, thereby reducing fibroblast-to-myofibroblast transition and collagen production by NIH-3T3 cells. In vivo experiments confirmed the above findings. Compared with direct DMF instillation, DTP@DMF NPs treatment presented enhanced antifibrotic effect. DTP@DMF NPs also had a prolonged residence time in the lung as well as excellent biocompatibility. Conclusions DTP@DMF NPs can reduce macrophage-mediated fibroblast-to-myofibroblast transition and extracellular matrix deposition to attenuate lung fibrosis by upregulating Nrf2 signaling. This ROS-responsive liposome is clinically promising as an ideal delivery system for inhaled drug delivery. Graphical Abstract

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We read with great interest on the study by Fernando et al., 1 The authors indicated that frailty was directly associated with mortality in patients with in-hospital cardiac arrest (IHCA). We appreciated the author's efforts, and fully agreed with the author's insight that the presence of pre-admission frailty evaluated by Clinical Frailty Scale (CFS) was associated with increased in-hospital mortality, but there was a limitation that independent risk factors in subgroups with and without frailty were not analyzed separately. Therefore, we supplemented that study with our retrospective cohort to determine whether CFS was an independent risk factor for in-hospital mortality of IHCA in subgroups with and without frailty. We retrospectively collected the data of consecutive patients !18 years of age, admitted to the hospital wards at the Zigong fourth people's hospital, from January 1, 2017 to December 31, 2018, who experienced IHCA. All data were extracted from hospital information system (HIS) and laboratory informationsystem (LIS), which were listed in Table 1. All statistical analyses were performed with R (Version 3.6.2

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.