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

Dysregulated miR-183 inhibits migration in breast cancer cells

Background: The involvement of miRNAs in the regulation of fundamental cellular functions has placed them at the fore of ongoing investigations into the processes underlying carcinogenesis. MiRNA expression patterns have been shown to be dysregulated in numerous human malignancies, including breast cancer, suggesting their probable involvement as novel classes of oncogenes or tumour suppressor genes. The identification of differentially expressed miRNAs and elucidation of their functional roles may provide insight into the complex and diverse molecular mechanisms of tumorigenesis. MiR-183 is located on chromosome 7q32 and is part of a miRNA family which are dysregulated in numerous cancers. The aims of this study were to further examine the expression and functional role of miR-183 in breast cancer. Methods: MiR-183 expression was quantitated in primary breast tumours, tumour associated normal tissue and breast cancer cell lines using RQ-PCR. Gain of function analysis was performed in breast cancer cells using pre-miR- 183 and the effect of miR-183 overexpression on cell viability, proliferation, apoptosis and migration was examined. Customized Taqman Low Density Arrays (TLDA) were used to identify dysregulated genes in breast cancer cells transfected with pre-miR-183. Results: We demonstrate that miR-183 is dysregulated in breast cancer and expression correlates with estrogen receptor and HER2/neu receptor expression. Induced overexpression of miR-183 inhibited migration of breast cancer cells. This finding was substantiated by RQ-PCR of mRNA from cells overexpressing miR-183 which showed dysregulation of several migration and invasion related genes. Specifically, the VIL2-coding protein Ezrin was confirmed as a target of miR-183 and downregulation of this protein was confirmed with immunocytochemistry. Conclusions: These findings indicate that miR-183 targets VIL2 and may play a central role in the regulation of migration and metastasis in breast cancer. Consequently, this miRNA may present an attractive target for therapeutic intervention

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

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 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

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

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