Ensemble-Empirical-Mode-Decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles

Micro RNA expression counts and variation between breast cancer patient and control samples.

<p>Expression values for eight miRNAs in breast cancer patients (15) and controls (26) that showed some evidence of differential expression. FC stands for the RPKM fold expression change (control/condition). Difference is Cohen’s d: the mean change in miRNA expression between groups scaled by the pooled SD (standard deviation), which reflects the effect size. Power indicates the probability of detecting true differential expression for that miRNA given the difference observed, alpha value (0.05) and sample sizes.</p><p>Micro RNA expression counts and variation between breast cancer patient and control samples.</p

Sample cohort set analysed using small RNA sequencing and RT-qPCR.

<p>^<b>a</b> NA = Not applicable</p><p>Sample cohort set analysed using small RNA sequencing and RT-qPCR.</p

Differential analysis of miRNA isolated from total and enriched RNA from complete sample set.

<p>(A) Differential expression of miRNAs between healthy and cancer samples measured in total RNA from the complete sample set measured using RT-qPCR A 2.38 fold decrease is observed (with a P value of 0.111), in miR-320a from Healthy (1.58E +08) to Cancer (6.63E +07) state. Log 10 box-plots indicating changes in expression of miR-195, miR-16 and Let-7b from healthy to cancer state measured in total RNA using RT-qPCR. (B) Differential expression of selected miRNAs between healthy and cancer samples measured in enriched small RNA from the complete sample set measured using RT-qPCR An 8.89 fold decrease is observed (with a P value of 0.006), in miR-320a from Healthy (4.20E +07) to Cancer (4.72E +06) state. Log 10 box-plots indicating changes in expression of miR-195, miR-16 and Let-7b from healthy to cancer state measured in enriched small RNA using RT-qPCR.</p

Analysis of precursor and mature miR-16 sequence using real-time PCR.

<p>Real-time PCR amplification curves detecting the mature miR16 standard curve 10*8–10*4 (circles- decreasing in concentration from left to right) with detection of precursor miR-16 sequence from 10*8–10*6 (triangles- decreasing in concentration from left to right) with the no template control, highlighted with diamonds in the FAM channel (465 to 510 nm).</p

Impact of small RNA enrichment on miRNA measurement.

<p>Impact of small RNA enrichment on miRNA measurement.</p

Analysis of miRNA dysregulation between healthy and cancer patient samples for the complete sample set.

<p>Analysis of miRNA dysregulation between healthy and cancer patient samples for the complete sample set.</p

Differential analysis of miRNA between breast cancer and control groups.

<p>The distribution of miR-320a and miR-140 upper quantile normalised expression levels between the breast cancer and control groups from Illumina reads. Note that the y-axes are different for each miRNA because of the differing scales.</p

Differential analysis of miRNA isolated from total and enriched RNA from initial sample set.

<p>(A) Differential expression of miRNAs between healthy and cancer samples measured in Total RNA from the initial sample set measured using RT-qPCR A 2.3 fold decrease is observed (with a P value of 0.008), in miR-320a from Healthy (1.53E +08) to Cancer (6.63E +07) state. Log 10 box-plots indicating changes in expression of miR-195, miR-16 and Let-7b from healthy to cancer state measured in total RNA using RT-qPCR. (B) Differential expression of miRNAs between healthy and cancer samples measured in enriched small RNA from the initial sample set measured using RT-qPCR. A 5.45 fold decrease is observed (with a P value of 0.0001), in miR-320a from Healthy (4.24E +07) to Cancer (7.78E +06) state. Log 10 box-plots indicating changes in expression of miR-195, miR-16 and Let-7b from healthy to cancer state measured in total RNA using RT-qPCR. *Indicates an outlying value.</p

Differential expression patterns separating breast cancer patient from control samples.

<p>Differential expression testing for eight miRNAs in breast cancer patients (15) and controls (26) that showed some evidence of differential expression across quantile, RPKM and TMM normalisation methods. Welch t-tests for differential expression using RPKM values, upper quantile normalised rates and TMM normalised levels all supported lower miR-320a and miR-140 expression in breast cancer patients. The two-tailed p-values were Benjamini-Hochberg corrected to adjust for multiple testing and those with p<0.05 are marked *.</p><p>Differential expression patterns separating breast cancer patient from control samples.</p