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RISS 인기검색어
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- 저자 : Zhao Guiqin (검색결과 3 건)
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The Effects of Seeding Rate and N Fertilizer on Dry Matter Accumulation and Grain Yield in Oats
Zhao Guiqin,Gong Jianjun,Liu Huan 한국초지조사료학회 2009 한국초지조사료학회 학술대회논문집 Vol.2009 No.08
The effects of seeding rate and N fertilizer on leaf area index, dry matter accumulation, grain yield and yield components in oats were studied using split experimental design. The results indicated t㏊t seeding rate and N fertilizer ㏊d significant influence on leaf area index, dry matter accumulation and grain yield. With the increasing seeding and fertilizer rates, leaf area index, aboveground dry matter increased significantly. Oat reached the highest grain yield under 112.5㎏/h㎡ seeding rate, N2 was the best N fertilizer treatment for oat.
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Yang, Guiqin,Zhao, Fei,Tian, He,Li, Jiantao,Guo, Dongxin Asian Australasian Association of Animal Productio 2020 Animal Bioscience Vol.33 No.4
Objective: Substituting starch with digestible fiber (dF) can improve digestive health of rabbits and reduce costs. Therefore, it is necessary to develop a criterion for dF and starch supply. Effects of the dietary dF-to-starch ratio on pellet quality, growth and cecal microbiota of Angora rabbits were evaluated. Methods: Five isoenergetic and isoproteic diets with increasing dF/starch ratios (0.59, 0.66, 0.71, 1.05, and 1.44) were formulated. A total of 120 Angora rabbits with an average live weight of 2.19 kg were randomly divided into five groups with four replicates. At the end of 40 day feeding trial, cecal digesta were collected to analyse microbiota. Results: The results showed that the dF/starch ratio had linear effects on pellet variables (p<0.01). When the dF/starch ratio was 1.44, the pellets had the lowest powder and highest durability. The dF/starch ratio had unfavorable linear effects on growth variables (p<0.001). When analyzed by quadratic regression, the optimal dF/starch ratios for average weight gain and feed/gain were 0.59 and 0.74, respectively. There were differences in wool yield, fiber length and fiber diameter caused by the dF/starch ratio (p<0.05), and the dF/starch ratios that ranged from 0.66 to 1.06 were appropriate for good results. The cecal microbiota operational taxonomic unit (OTU) number index in the 1.05 dF/starch treatment was higher than that in the 0.66 and 0.71 dF/starch treatments. The higher dF/starch ratio resulted in a higher cecal microbiota OTU number index (p<0.05). The proportion of Ruminococcus in the 0.71 dF/starch treatment was higher than that in the 0.59 dF/starch treatment (p<0.05) Conclusion: The most suitable dF/starch ratio for feed pellet quality is 1.44, and for rabbit growth the optimal range of ratios is from 0.59 to 0.74. With combination of the wool growth, output cost, and cecal microbiota, we suggest that a dietary dF/starch ratio ranging from 0.74 to 1.06 is optimal.
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Jianjian Zhang,Shiteng Suo,Guiqin Liu,Shan Zhang,Zizhou Zhao,Jianrong Xu,Guangyu Wu 대한영상의학회 2019 Korean Journal of Radiology Vol.20 No.5
Objective: To compare various models of diffusion-weighted imaging including monoexponential apparent diffusion coefficient (ADC), biexponential (fast diffusion coefficient [Df], slow diffusion coefficient [Ds], and fraction of fast diffusion), stretched-exponential (distributed diffusion coefficient and anomalous exponent term [α]), and kurtosis (mean diffusivity and mean kurtosis [MK]) models in the differentiation of renal solid masses. Materials and Methods: A total of 81 patients (56 men and 25 women; mean age, 57 years; age range, 30–69 years) with 18 benign and 63 malignant lesions were imaged using 3T diffusion-weighted MRI. Diffusion model selection was investigated in each lesion using the Akaike information criteria. Mann–Whitney U test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. Results: Goodness-of-fit analysis showed that the stretched-exponential model had the highest voxel percentages in benign and malignant lesions (90.7% and 51.4%, respectively). ADC, Ds, and MK showed significant differences between benign and malignant lesions (p < 0.05) and between low- and high-grade clear cell renal cell carcinoma (ccRCC) (p < 0.05). α was significantly lower in the benign group than in the malignant group (p < 0.05). All diffusion measures showed significant differences between ccRCC and non-ccRCC (p < 0.05) except Df and α (p = 0.143 and 0.112, respectively). α showed the highest diagnostic accuracy in differentiating benign and malignant lesions with an area under the ROC curve of 0.923, but none of the parameters from these advanced models revealed significantly better performance over ADC in discriminating subtypes or grades of renal cell carcinoma (RCC) (p > 0.05). Conclusion: Compared with conventional diffusion parameters, α may provide additional information for differentiating benign and malignant renal masses, while ADC remains the most valuable parameter for differentiation of RCC subtypes and for ccRCC grading.
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