The effect of Menoprogen (MPG) on ovarian granulosa cell (GC) apoptosis was investigated in vitro and in vivo in an aged rat model of menopause. Intragastric administration of Menoprogen or estradiol valerate to 14-month-old senile female rats for eight weeks increased plasma E2 levels, as well as the weight of both ovarian and uterine tissues. Flow cytometric (FCM) analysis of isolated GCs from MPG-treated aged rats showed reductions in the G0/G1 ratio and apoptotic peaks. Isolated GCs also exhibited an increase in cell size and the number of cytoplastic organelles and intracellular gap junctions, the reappearance of secretory granules, and a lack of apoptotic bodies as determined by TEM. Results from a TdT-mediated dUTP nick end-labeling (TUNEL) assay revealed a reduction in TUNEL-positive GCs after MPG treatment. Immunohistochemical analysis showed a downregulation of proapoptotic Bax proteins and an upregulation of antiapoptotic Bcl-2 proteins. The addition of MPG-medicated serum to the media of cultured GCs also reduced cadmium chloride-induced apoptosis and downregulated caspase-3 protein expression. This work demonstrates that Menoprogen inhibits GC apoptosis in aged female rats and thereby increases E2 production. This represents a novel mechanism of action for this herbal medicine in the treatment of menopausal symptoms.
Oil spill incidents threaten the marine ecological environment. Detecting sea surface oil slicks by remote sensing images provides support for the efficient treatment of oil spills. This is important for sustainable marine development. However, traditional methods based on field analysis are time-consuming. Spectral indices lack applicability. In addition, traditional machine learning methods strictly rely on training and testing samples which are in short supply in oil spill images. Inspired by the spectral DNA encoding method, a spectral gene extraction (SGE) method was proposed to detect oil spills in hyperspectral images (HSI) and multispectral images (MSI). The SGE method contained a parameter and two strategies. The parameter of elimination was designed based on the population genetic frequency. It was used to control the number of spectral genes. The spectral gene extraction strategies, named largest in-class similarity (LIS) strategy and largest inter-class difference (LID) strategy, were proposed to mine the spectral genes by oil spill samples. The oil spills would be determined by calculating the similarity of the extracted spectral genes to the DNA encoded images. In this research, the SGE method was validated by two AVIRIS images of the Gulf of Mexico oil spill, one MODIS image of the Gulf of Mexico oil spill, and one Landsat 8 image of a Persian Gulf oil spill. The oil spills in different remote sensing images could be detected accurately by the proposed method in a small set of samples. Experimental results indicated that the proposed method was suitable for detecting marine oil spills in AVIRIS, MODIS, and Landsat 8 images. In addition, the SGE method with the LIS strategy was more suitable for detecting oil spills in HSI. Its proper elimination rates were 0.8~1.0. The SGE method with the LID strategy was more suitable for detecting oil spills in MSI. Its proper elimination rates were 0.5~0.7.
Remote sensing technologies are suitable for detecting marine oil-gas leakages on a large scale. It is important to structure an accurate method for detecting marine oil-gas leakages in varied remote sensing images. However, traditional spectral indexes have limited applicability. Machine learning methods need plenty of training and testing samples to establish the optimized models, which is too rigorous for satellite images. Thus, we proposed a multi-scale encoding (MSE) method with spectral shape information (SSI) to detect the oil-gas leakages in multi-source remote sensing data. First, the spectral amplitude information (SAI) and SSI of the original spectra were encoded into a series of code words according to the scales. Then, the differential code words of the marine oil-gas leakage objects were extracted from the SAI and SSI code words. Finally, the pixels of the encoded hyperspectral image (HSI) and multispectral image (MSI) would be determined by the differential code words. Seven images captured by different platforms/sensors (Landsat 7, Landsat 8, MODIS, Sentinel 2, Zhuhai-1, and AVIRIS) were used to validate the performance of the proposed method. The experimental results indicated that the MSE method with SSI was convergent and could detect the oil-gas leakages accurately in different images using a small set of samples.
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