Abbreviations: miR-33a/b, microRNA 33a/b; HIF-1a, hypoxia inducible factor 1, a subunit Background: Our previous findings showed that miR-33 expressed abnormally in clinical specimens of melanoma, but the exact molecular mechanism has not been elucidated. Object: To determine miR-33's roles in melanoma and confirm whether HIF-1a is a direct target gene of miR-33a. Methods: First miR-33a/b expression levels were detected in HM, WM35, WM451, A375 and SK-MEL-1. Then lentiviral vectors were constructed to intervene miR-33a expression in melanoma cells. Cell proliferation, invasion and metastasis were detected. A375 cells mice model was performed to test the tumorigenesis of melanoma in vivo. Finally the dual reporter gene assay was carried out to confirm whether HIF-1a is a direct target gene of miR-33a. Results: MiR-33a/b exhibited a lower expression in WM35, WM451, A375 and SK-MEL-1 of the metastatic skin melanoma cell lines than that in HM. Then inhibition of miR-33a expression in WM35 and WM451 cell lines could promote cell proliferation, invasion and metastasis. Conversely, increased expression of miR-33a in A375 cells could inhibit cellproliferation, invasion and metastasis. In vivo tests also confirmed that overexpression of miR-33a in A375 cells significantly inhibited melanoma tumorigenesis. Finally, we confirmed that HIF-1a is a direct target gene of miR-33a. Conclusion: The newly identified miR-33a/HIF-1a axis might provide a new strategy for the treatment of melanoma.

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms.

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

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We highlighted the flexibility of using unstructured mesh together with the local refinement by a resistivity model with complicated topography. The effect of topography is emphasized.Based on this, we calculated a specific class of layered models and found that the accuracy is not always satisfactory by utilizing the standard approach. As an improvement, we employed the layered earth as the reference model to calculate the wavenumbers. The comparison demonstrates that the accuracy is considerably improved by using this enhanced approach. KEY WORDS: 2-D topography, 2.5-D DC resistivity modeling, layered earth. INTRODUCTIONThe so-called 2.5-D DC simulation is usually adopted instead of directly solving the 3-D problems when the 3-D model has a 2-D structure. However, due to the complexity of geometrical model and attribute distribution in DC resistivity surveying, the limited analytical solutions for some simple models can never satisfy the requirement of data interpretations. Based on this, many numerical tools have been developed like integral-equation techniques (Lesur et al.

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Platycodon grandiflorum (Jacq.) A. DC., a Chinese food and medicine, has been used as expectorant traditionally. The present study aimed to investigate the effect of Platycodon grandiflorum extract (PGE) on SKOV3 ovarian cancer cells. 3-(4,5- dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay was used to monitor cell numbers, Annexin-V/propidium iodide (PI) staining, RT-PCR and Western blot were used to examine cell apoptosis, caspases activation. Bcl-2 and Bax expressions and mitochondrial cytochrome c release. Our result showed that PGE-induced apoptosis was associated with activation of caspase-3, -8 and -9, down-regulation of Bcl-2, up-regulation of Bax and release of mitochondrial cytochrome c to cytosol. The data indicate that PGE may have anti-tumor effect mainly via caspase-3 and caspase-9 dependent apoptotic pathway.