Inhibition effects of paeonol on mice bearing EMT6 breast cancer through inducing rumor cell apoptosis

Paeonol, a phenolic component from the root bark of Paeonia moutan, has been identified to possess antitumor effects on mice bearing EMT6 breast cancer in our previous studies. However, the underlying mechanisms remain unknown. In the present study the molecular mechanisms of paeonol were further investigated in EMT6 mice model. The results showed that treatment of mice with 175 and 350 mg/kg/day of paeonol significantly inhibited the growth of the EMT6 tumor in mice, and induced tumor cell apoptosis which were demonstrated by light microscopy after hematoxylin and eosin staining and apoptosis analysis by flow cytometry. In addition, compared with the control group, paeonol increased the number of tumor cells in G0/G1 phase but decreased the number of cells in S and G2/M phase. Paeonol treatment (350 mg/kg body weight) also resulted in a decrease of Bcl-2 and an increase in Bax and caspase-3 expressions, which were demonstrated by immunohistochemical and western blot analysis. These results indicate that the antitumor effects of paeonol might be associated with arresting tumor cells in the G0/G1 phase, inducing cell apoptosis and regulation of the expression of Bcl-2, Bax and activation of caspase-3

The sensitivity of satellite solar‐induced chlorophyll fluorescence (SIF) to meteorological drought

Solar‐induced chlorophyll fluorescence (SIF) could provide information on plant physiological response to water stress (e.g., drought). There are growing interests to study the effect of drought on SIF. However, to what extent SIF responds to drought and how the responses vary under different precipitation, temperature and potential evapotranspiration conditions are not clear. In this regard, we evaluated the relationship between satellite‐based SIF product and four commonly used meteorological drought indices (Standardized Precipitation‐Evapotranspiration Index, SPEI; Standardized Precipitation Index, SPI; Temperature Condition Index, TCI; and Palmer Drought Severity Index, PDSI) under diverse climate regions in the continental United States. The four drought indices were used because they estimate meteorological drought conditions from either single or combined meteorological factors such as precipitation, temperature, and potential evapotranspiration, representing different perspectives of drought. The relationship between SIF and meteorological drought varied spatially and differed for different ecosystem types. The high sensitivity occurred in dry areas characterized by a high mean annual growing season temperature and low vegetation productivity. Through random forest regression analyses, we found that temperature, gross primary production, precipitation, and land cover are the major factors affecting the relationships between SIF and meteorological drought indices. Taken together, satellite SIF is highly sensitive to meteorological drought but the high sensitivity is constrained to dry regions

A new station-enabled multi-sensor integrated index for drought monitoring

Remote sensing data are frequently incorporated into drought indices used widely by research and management communities to assess and diagnose current and historic drought events. The integrated drought indices combine multiple indicators and reflect drought conditions from a range of perspectives (i.e., hydrological, agricultural, meteorological). However, the success of most remote sensing based drought indices is constrained by geographic regions since their performance strongly depends on environmental factors such as land cover type, temperature, and soil moisture. To address this limitation, we propose a framework for a new integrated drought index that performs well across diverse climate regions. Our framework uses a geographically weighted regression model and principal component analysis to composite a range of vegetation and meteorological indices derived from multiple remote sensing platforms and in-situ drought indices developed from meteorological station data. Our new index, which we call the station-enabled Geographically Independent Integrated Drought Index (GIIDI_station), compared favorably with other common drought indices such as Microwave Integrated Drought Index (MIDI), Optimized Meteorological Drought Index (OMDI), Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Soil Moisture Condition Index (SMCI), and Vegetation Condition Index (VCI). Using Pearson correlation analyses between remote sensing and in-situ drought indices during the growing season (April to October) from 2002 to 2011, we show that GIIDI_station had the best correlations with in-situ drought indices. Across the entire study region of the continental United States, the performance of GIIDI_station was not affected by common environmental factors such as precipitation, temperature, land cover and soil conditions. Taken together, our results suggest that GIIDI_station has considerable potential to improve our ability of monitoring drought at regional scales, provided local meteorological station data are available