People who suffer a mild traumatic brain injury (mTBI) have heterogeneous symptoms and disease trajectories, which make it difficult to precisely assess long-term complications. This pilot study assessed and compared deficits in cognitive, psychosocial, visual functions, and balance performance between college students with and without histories of mTBI. Global DNA methylation ratio (5-mC%) in blood was also compared as a peripheral epigenetic marker. Twenty-five volunteers participated, including 14 healthy controls (64.3% females; mean age of 22.0) and 11 mTBI cases (27.3% females; mean age of 28.7 years) who self-reported mTBI history (63.6% multiple; 2.5 ± 1.29 injuries) with 7.1 years on average elapsed following the last injury. Every participant was assessed for cognitive (executive function, memory, and processing speed), psychological (depression, anxiety, and sleep disturbances), and visual function (by King–Devick and binocular accommodative tests); force-plate postural balance performance; and blood 5-mC% levels. Students with mTBI showed poorer episodic memory, severe anxiety, and higher blood 5-mC% ratio, compared to controls (all p’s < 0.05), which were still significant after adjusting for age. No differences were detected in sleep problems (after adjusting for age), visual function, and postural balance. These findings identified changes in multiple functions and peripheral epigenetics long after mTBI.

Background: Most individuals with mild traumatic brain injury (mTBI) experience post-injury deficits in postural control. Currently available measures of postural control are lab-based or supervised, which may hinder timely symptom assessment for individuals with mTBI, including Asian populations, who do not seek initial screening post-injury. In this proof-of-concept testing study, we introduce a real-time mobile health (mHealth) system to measure postural control during walking. The proposed mHealth system can be used for home-based symptom assessment and management of mTBI. Methods: In our proposed mHealth system, a smartwatch, a smartphone, and a cloud server communicate to measure, collect, and store body balance data in real time. Specifically, we focus on the rotation vector data that have been reported to be the most effective in terms of differentiating balance control during walking across different participants. Results: Constant motion change in four participants (two females and two males; three healthy participants, and one individual with reduced physical mobility) was collected and analyzed. The results of our data analysis show that, compared to healthy participants, the individual was reduced physical mobility had a wider range of motion between right and left, up and down, and forward and backward while walking. We also found that female participants had narrower ranges of right-to-left and up-and-down motions than their male counterparts. Conclusions: Our results highlight the potential of the proposed real-time mHealth system for home-based symptom assessment and management of mTBI, which may benefit Asian and other nonwhite racial minority groups that appear to be more reluctant to access post-acute rehabilitation services.

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Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS.

People who suffer a mild traumatic brain injury (mTBI) have heterogeneous symptoms and disease trajectories, which make it difficult to precisely diagnose and assess complications long-term. Insufficient information is available regarding how to precisely diagnose and assess mTBI. This study identified and compared deficits in cognitive, psychosocial, visual functions, and balance performance between college students with and without histories of mTBI. Global DNA methylation ratio (5-mC%) in blood was also compared as a peripheral epigenetic marker. Twenty-five volunteers participated in this pilot study, including 11 mTBI cases (27.3% females; mean age of 28.7 years, SD=5.92) and 14 healthy controls (64.3% females; mean age of 22.0, SD=4.13). All the participants were assessed for cognitive (by NIH toolbox&mdash;executive function, memory, and processing speed), psychological (by PROMIS&mdash;depression, anxiety, and sleep disturbances), visual function (by King-Devick and binocular accommodative tests), postural balance performance (by a force plate), and blood 5-mC% (global methylation) levels. Students with mTBI reported significantly poorer episodic memory, severe anxiety, and more sleep disturbance problems. They also had higher blood 5-mC% level (all p&rsquo;s&lt;.05). No significant differences were found in visual function and postural balance. These findings validate changes in cognitive, psychosocial, and global DNA methylation long after mTBI.