Identification of induced polarization of submarine hydrocarbons in marine controllable source electromagnetic exploration

The identification of hydrocarbons buried on the seafloor is highly dependent on geophysical exploration capabilities. Seismic exploration has been an important tool in providing information on submarine stratigraphy before offshore drilling, but it is a challenge to identify the nature and saturation of the fluid in the structure by seismic exploration. Of all the physical properties, electrical parameters are the most sensitive to the fluids in the reservoir and would be able to be combined with seismic data to improve the identification of hydrocarbons at depth. However, the marine controlled-source electromagnetic method usually only considers the effect of electromagnetic induction and ignores the induced polarization (IP) effects. The IP effects can occur in the stratum where the reservoir is located due to a variety of factors, so considering the IP effects will make the modeling more reasonable and thus give more accurate results when interpreting and processing the data. We have used the integral equation method for modeling, adopted the scattering and superposition methods to calculate the dyadic Green’s function required in the study, replaced the real resistivity with a complex resistivity that takes into account the IP effects, investigated the response patterns of different ion polarization models, and analyzed the influence patterns of various model parameters. These investigations will provide important contributions to the study of submarine hydrocarbon detection. The field data also show the amplitude, and phase response results of polarizability show that it gradually increases from the offset

Effect of Noni on Growth and Immunity Performance of Hainan Black Goat

【Objective】In order to improve the growth and immune performance of Hainan black goat, a new type of immune synergist was developed with Noni (Morinda citrigolia L.) as the material to promote the healthy breeding and sustainable industry development of Hainan black goat.【Method】Fifty-four healthy Hainan black goats were selected and randomly divided into 6 groups. The experimental period was 60 d, and the growth and immune performance of each group were measured on the 0 d, 30 d and 60 d of the experiment, respectively.【Result】On the 60th day of the experiment, the average body weight and daily gain of the high-dose Noni group were significantly different from those of the Astragalus polysaccharide and other groups, and increased with the increase of the supplemental dose. In addition, there was no significant difference in serum total protein and globulin between Astragalus p. group and Noni group. On the 30th day and 60th day of the test, the serum levels of IgG, IL-2 and IFN-γ in the high-dose Noni group were slightly higher than those in the other treatment groups, which were significantly different from those in the control group, and they were increased with the increase of the additive dose.【Conclusion】Adding 20 g Noni powder every day and feeding for 60 days is the ideal feeding scheme, and the daily gain of Hainan black goat can reach 97.27 (±20.87) g. The levels of IgG, IL-2 and IFN-γ are 49.17 (±3.52) g/L, 136.24 (±12.77) ng/L and 112.72 (±10.94) ng/L, respectively. Noni can significantly improve the growth and immune performance of Hainan black goat. Therefore, Noni has a broad application prospect in the green and healthy breeding and industrial development of Hainan black goat

血糖控制较好的2型糖尿病患者β细胞葡萄糖激酶表达增加

Abstract Background Type 2 diabetes (T2D) is characterized by a progressive deterioration of β‐cell function with a continuous decline in insulin secretion. Glucokinase (GCK) facilitates the rate‐limiting step of glycolysis in pancreatic β‐cells, to acquire the proper glucose‐stimulated insulin secretion. Multiple glucokinase activators (GKAs) have been developed and clinically tested. However, the dynamic change of human pancreatic GCK expression during T2D progression has not been investigated. Methods We evaluated GCK expression by measuring the average immunoreactivity of GCK in insulin+ or glucagon+ cells from pancreatic sections of 11 nondiabetic subjects (ND), 10 subjects with impaired fasting glucose (IFG), 9 with well‐controlled T2D (wT2D), and 5 individuals with poorly controlled T2D (uT2D). We also assessed the relationship between GCK expression and adaptive unfolded protein response (UPR) in human diabetic β‐cells. Results We did not detect changes of GCK expression in IFG islets. However, we found β‐cell GCK levels were significantly increased in T2D with adequate glucose control (wT2D) but not in T2D with poor glucose control (uT2D). Furthermore, there was a strong positive correlation between GCK expression and adaptive UPR (spliced X‐box binding protein 1 [XBP1s] and activating transcription factor 4 [ATF4]), as well as functional maturity marker (urocortin‐3 [UCN3]) in human diabetic β‐cells. Conclusions Our study demonstrates that inductions of GCK enhanced adaptive UPR and UCN3 in human β‐cells, which might be an adaptive mechanism during T2D progression. This finding provides a rationale for exploring novel molecules that activate β‐cell GCK and thereby improve pharmacological treatment of T2D

Uncertainty assessment in aboveground biomass estimation at the regional scale using a new method considering both sampling error and model error

Uncertainty associated with multiple sources of error exists in biomass estimation over large areas. This uncertainty affects the accuracy of the resultant biomass estimates. A new method that introduces Taylor series principles into a Monte Carlo simulation procedure was proposed and developed for estimating regional-scale aboveground biomass, along with quantifying the corresponding uncertainty arising from both sampling and model predictions. Additionally, the effect of sample size on estimates during model fitting was studied based on the new method in order to determine whether the effect of the size of the calibration data set can be neglected when the number of simulations is sufficiently large. The results revealed that the proposed method not only produces more reliable estimates of both biomass and uncertainty but also effectively and separately quantifies the uncertainties associated with different sources of error. The new method also reduced the effect of model uncertainty on final estimates. The uncertainty that was associated with model error increased significantly with decreasing sample sizes during model fitting, and the error was not reduced by increasing the number of Monte Carlo simulations.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author