Table1_TFRC–RNA interactions show the regulation of gene expression and alternative splicing associated with IgAN in human renal tubule mesangial cells.pdf

Introduction: IgA nephropathy (IgAN) is the most common primary glomerular disease (PGD) which could progress to renal failure and is characterized by aberrant IgA immune complex deposition. Transferrin receptor1 (TFRC), an IgA receptor, is a potential RNA binding protein (RBP) which regulates expression of genes positively associated with the cell cycle and proliferation and is involved in IgAN. Molecular mechanisms by which TFRC affects IgAN development remain unclear.Methods: In this study, TFRC was overexpressed in human renal tubular mesangial cells (HRMCs) and RNA-sequencing (RNA-seq) and improved RNA immunoprecipitation sequencing (iRIP-seq) were performed. The aim was to identify potential RNA targets of TFRC at transcriptional and alternative splicing (AS) levels.Results: TFRC-regulated AS genes were enriched in mRNA splicing and DNA repair, consistent with global changes due to TFRC overexpression (TFRC-OE). Expression of TFRC-regulated genes potentially associated with IgAN, including CENPH, FOXM1, KIFC1, TOP2A, FABP4, ID1, KIF20A, ATF3, H19, IRF7, and H1-2, and with AS, CYGB, MCM7 and HNRNPH1, were investigated by RT-qPCR and iRIP-seq data analyzed to identify TFRC-bound RNA targets. RCC1 and RPPH1 were found to be TFRC-bound RNA targets involved in cell proliferation.Discussion: In conclusion, molecular TFRC targets were identified in HRMCs and TFRC found to regulate gene transcription and AS. TFRC is considered to have potential as a clinical therapeutic target.</p

Comparison of recognition accuracy of proposed method on MSRDailyActivity 3D.

<p>Comparison of recognition accuracy of proposed method on MSRDailyActivity 3D.</p

The confusion matrix on MSR Action 3D dataset on UTD-MHAD.

<p>Average accuracy: 90.47%.</p

An example of the structure of an action sequence.

<p>An example of the structure of an action sequence.</p

Comparison of recognition accuracy of proposed method on UTD-MHAD.

<p>Comparison of recognition accuracy of proposed method on UTD-MHAD.</p

Frames in the range of the time window near <i>t</i>_<i>i</i> can vote for classification.

<p>Frames in the range of the time window near <i>t</i>_<i>i</i> can vote for classification.</p

The confusion matrix on MSR Action 3D dataset with unsegmented sequence.

<p>Average accuracy: 92.34%.</p

An example of locating the true action start frame.

<p>The action ‘high arm wave’ (a01s01e01) in MSR Action 3D was chosen. From frames 1 to 27 (as a result of Gaussian smoothing influence, the sequence number of this frame actually is 30), the subject remains standing. The body starts to raise his hand and has substantial movement.</p

The overview of the suggested approach.

<p>The method consists of two stages: Step 1 is the preprocessing of data, and the second step is action recognition. The blue arrow lines show the process of data in training, and the green arrow lines show the recognition of testing data.</p

Steady-State Motion Visual Evoked Potential (SSMVEP) Based on Equal Luminance Colored Enhancement

<div><p>Steady-state visual evoked potential (SSVEP) is one of the typical stimulation paradigms of brain-computer interface (BCI). It has become a research approach to improve the performance of human-computer interaction, because of its advantages including multiple objectives, less recording electrodes for electroencephalogram (EEG) signals, and strong anti-interference capacity. Traditional SSVEP using light flicker stimulation may cause visual fatigue with a consequent reduction of recognition accuracy. To avoid the negative impacts on the brain response caused by prolonged strong visual stimulation for SSVEP, steady-state motion visual evoked potential (SSMVEP) stimulation method was used in this study by an equal-luminance colored ring-shaped checkerboard paradigm. The movement patterns of the checkerboard included contraction and expansion, which produced less discomfort to subjects. Feature recognition algorithms based on power spectrum density (PSD) peak was used to identify the peak frequency on PSD in response to visual stimuli. Results demonstrated that the equal-luminance red-green stimulating paradigm within the low frequency spectrum (lower than 15 Hz) produced higher power of SSMVEP and recognition accuracy than black-white stimulating paradigm. PSD-based SSMVEP recognition accuracy was 88.15±6.56%. There was no statistical difference between canonical correlation analysis (CCA) (86.57±5.37%) and PSD on recognition accuracy. This study demonstrated that equal-luminance colored ring-shaped checkerboard visual stimulation evoked SSMVEP with better SNR on low frequency spectrum of power density and improved the interactive performance of BCI.</p></div