Background: A valid, reliable, accessible, engaging, and affordable digital cognitive screen instrument for clinical use is in urgent demand. Objective: To assess the clinical utility of the MemTrax memory test for early detection of cognitive impairment in a Chinese cohort. Methods: The 2.5-minute MemTrax and the Montreal Cognitive Assessment (MoCA) were performed by 50 clinically diagnosed cognitively normal (CON), 50 mild cognitive impairment due to AD (MCI-AD), and 50 Alzheimer’s disease (AD) volunteer participants. The percentage of correct responses (MTx-% C), the mean response time (MTx-RT), and the composite scores (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed and receiver operating characteristic (ROC) curves generated. Results: Multivariate linear regression analyses indicated MTx-% C, MTx-Cp, and the MoCA score were significantly lower in MCI-AD versus CON and in AD versus MCI-AD groups (all with p≤0.001). For the differentiation of MCI-AD from CON, an optimized MTx-% C cutoff of 81% had 72% sensitivity and 84% specificity with an area under the curve (AUC) of 0.839, whereas the MoCA score of 23 had 54% sensitivity and 86% specificity with an AUC of 0.740. For the differentiation of AD from MCI-AD, MTx-Cp of 43.0 had 70% sensitivity and 82% specificity with an AUC of 0.799, whereas the MoCA score of 20 had 84% sensitivity and 62% specificity with an AUC of 0.767. Conclusion: MemTrax can effectively detect both clinically diagnosed MCI and AD with better accuracy as compared to the MoCA based on AUCs in a Chinese cohort.

Dysarthria is universal in Parkinson's disease (PD) during disease progression; however, the quality of vocalization changes is often ignored. Furthermore, the role of changes in the acoustic parameters of phonation in PD patients remains unclear. We recruited 35 PD patients and 26 healthy controls to perform single, double, and multiple syllable tests. A logistic regression was performed to differentiate between protective and risk factors among the acoustic parameters. The results indicated that the mean f0, max f0, min f0, jitter, duration of speech and median intensity of speaking for the PD patients were significantly different from those of the healthy controls. These results reveal some promising indicators of dysarthric symptoms consisting of acoustic parameters, and they strengthen our understanding about the significance of changes in phonation by PD patients, which may accelerate the discovery of novel PD biomarkers. Abbreviations PD Parkinson's disease HKD Hypokinetic dysarthria VHI-30 Voice Handicap Index H&Y Hoehn-Yahr scale UPDRS III Unified Parkinson's Disease Rating Scale Motor Score Parkinson's disease (PD), a chronic, progressive neurodegenerative disorder with an unknown etiology, is associated with a significant burden with regards to cost and use of societal resources 1,2. More than 90% of patients with PD suffer from hypokinetic dysarthria 3. Early in 1969, Darley et al. defined dysarthria as a collective term for related speech disorders. The classification of dysarthria includes flaccid dysarthria, spastic dysarthria, ataxic dysarthria, hypokinetic dysarthria, hyperkinetic dysarthria, unilateral upper motor neuron dysarthria and mixed dysarthria 4. The speech abnormalities of patients with PD are collectively termed hypokinetic dysarthria (HKD). These speech flaws are typically characterized by increased acoustic noise, a reduced intensity of voice, harsh and breathy voice quality, increased voice nasality, monopitch, monoloudness, speech rate disturbances, the imprecise articulation of consonants, the involuntary introduction of pauses, the rapid repetitions of words and syllables and sudden deceleration or acceleration in speech. Speech impairments are caused by impaired speech mechanisms during any of the basic motor processes involved in speech performance 5. The neuromotor speech sequence activates the muscles of the pharynx, tongue, larynx, chest and diaphragm through subthalamic secondary pathways. The anatomical substrate that could result in the abnormalities of PD phonetics may be reduced by the poor coordination of the sound-making muscles 6 .

Background: In recent years, with the development of medical science and artificial intelligence, research on rehabilitation robots has gained more and more attention, for nearly 10 years in the Web of Science database by journal of rehabilitation robot-related research literature analysis, to parse and track rehabilitation robot research hotspot and front, and provide some guidance for future research.Methods: This study employed computer retrieval of rehabilitation robot-related research published in the core data collection of the Web of Science database from 2010 to 2020, using CiteSpace 5.7 visualization software. The hotspots and frontiers of rehabilitation robot research are analyzed from the aspects of high-influence countries or regions, institutions, authors, high-frequency keywords, and emergent words.Results: A total of 3,194 articles were included. In recent years, the research on rehabilitation robots has been continuously hot, and the annual publication of relevant literature has shown a trend of steady growth. The United States ranked first with 819 papers, and China ranked second with 603 papers. Northwestern University ranked first with 161 publications. R. Riener, a professor at the University of Zurich, Switzerland, ranked as the first author with 48 articles. The Journal of Neural Engineering and Rehabilitation has the most published research, with 211 publications. In the past 10 years, research has focused on intelligent control, task analysis, and the learning, performance, and reliability of rehabilitation robots to realize the natural and precise interaction between humans and machines. Research on neural rehabilitation robots, brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeletons has attracted more and more attention.Conclusions: At present, the brain–computer interface, virtual reality, flexible wearables, task analysis, and exoskeleton rehabilitation robots are the research trends and hotspots. Future research should focus on the application of machine learning (ML), dimensionality reduction, and feature engineering technologies in the research and development of rehabilitation robots to improve the speed and accuracy of algorithms. To achieve wide application and commercialization, future rehabilitation robots should also develop toward mass production and low cost. We should pay attention to the functional needs of patients, strengthen multidisciplinary communication and cooperation, and promote rehabilitation robots to better serve the rehabilitation medical field.

Introduction: Parkinson's (PD) is a common degenerative disease of the central nervous system. It affects more than 6 million individuals worldwide. The typical clinical manifestations include static tremor, slow movement, and unstable posture. However, the correlation between head tremor and the severity of PD remains unclear. Methods:In the current study, 18 patients and 18 healthy subjects were recruited to undergo a phonation test. Noldus facereader 7.0 software was used to analyze the range of head trembling between the two groups. Results:The data revealed that patients with PD had significant differences in the x-, y-, and z-axis of head movement with respect to the specific pronunciation syllables compared with the normal group. Moreover, the head movement of the patients with PD was positively correlated with the severity of the disease in the single, double, and multiple syllable tests. In the phonetic test, the head displacement of patients with PD was significantly greater than that of healthy individuals, and the displacement range was positively correlated with the severity of the disease. Conclusion:These pieces of evidence suggested that the measurement of head displacement assists the early diagnosis and severity of the disease.

With the dramatic increase of data volume, automatic data distribution has been one of the key techniques and intractable problem for distributed systems. This work summarizes the problem of data distribution and abstracts it as a general triangle model called DaWN. Based on data and workload analysis, it presents a novel strategy called ADDS for automatic data distribution in OLTP applications. According to the results of a series of experiments over TPC-C datasets and transactions, the proposed approach shows effective improvements.