| 1 | High rumen degradable starch decreased goat milk fat via trans-10,cis-12 conjugated linoleic acid-mediated downregulation of lipogenesis genes,particularly,INSIG1显示文摘Background:Starch is an important substance that supplies energy to ruminants.To provide sufficient energy for high-yielding dairy ruminants,they are typically fed starch-enriched diets.However,starch-enriched diets have been proven to increase the risk of milk fat depression(MFD)in dairy cows.The starch present in ruminant diets could be divided into rumen-degradable starch(RDS)and rumen escaped starch(RES)according to their different degradation sites(rumen or intestine).Goats and cows have different sensitivities to MFD.Data regarding the potential roles of RDS in milk fat synthesis in the mammary tissue of dairy goats and in regulating the occurrence of MFD are limited.Results:Eighteen Guanzhong dairy goats(day in milk=185±12 d)with similar parity,weight,and milk yield were selected and randomly assigned to one of three groups(n=6),which were fed an LRDS diet(Low RDS=20.52%),MRDS diet(Medium RDS=22.15%),or HRDS diet(High RDS=24.88%)for 5 weeks.Compared with that of the LRDS group,the milk fat contents in the MRDS and HRDS groups significantly decreased.The yields of short-,mediumand long-chain fatty acids decreased in the HRDS group.Furthermore,increased RDS significantly decreased ruminal B.fibrisolvens and Pseudobutyrivibrio abundances and increased the trans-10,cis-12 conjugated linoleic acid(CLA)and trans-10 C18:1 contents in the rumen fluid.A multiomics study revealed that the HRDS diet affected mammary lipid metabolism down-regulation of ACSS2,MVD,AGPS,SCD5,FADS2,CERCAM,SC5D,HSD17B7,HSD17B12,ATM,TP53RK,GDF1 and LOC102177400.Remarkably,the significant decrease of INSIG1,whose expression was depressed by trans-10,cis-12 CLA,could reduce the activity of SREBP and,consequently,downregulate the downstream gene expression of SREBF1.Conclusions:HRDS-induced goat MFD resulted from the downregulation of genes involved in lipogenesis,particularly,INSIG1.Specifically,even though the total starch content and the concentrate-to-fiber ratio were the same as those of the high-RDS diet,the low and medium RDS diets did not cause MFD in lactating goats. | Lixin Zheng Shengru Wu Jing Shen Xiaoying Han Chunjia Jin Xiaodong Chen Shengguo Zhao Yangchun Cao Junhu Yao | 2020 | Journal of Animal Science and Biotechnology2020，11，3： | 4 |
| 2 | Classification of weed seeds based on visual images and deep learning显示文摘Weeds are mainly spread by weed seeds being mixed with agricultural and forestry crop seeds,grain,animal hair,and other plant products,and disturb the growing environment of target plants such as crops and wild native plants.The accurate and efficient classification of weed seeds is important for the effective management and control of weeds.However,classification remains mainly dependent on destructive sampling-based manual inspection,which has a high cost and rather low flux.We considered that this problem could be solved using a nondestructive intelligent image recognition method.First,on the basis of the establishment of the image acquisition system for weed seeds,images of single weed seeds were rapidly and completely segmented,and a total of 47696 samples of 140 species of weed seeds and foreign materials remained.Then,six popular and novel deep Convolutional Neural Network(CNN)models are compared to identify the best method for intelligently identifying 140 species of weed seeds.Of these samples,33600 samples are randomly selected as the training dataset for model training,and the remaining 14096 samples are used as the testing dataset for model testing.AlexNet and GoogLeNet emerged from the quantitative evaluation as the best methods.AlexNet has strong classification accuracy and efficiency(low time consumption),and GoogLeNet has the best classification accuracy.A suitable CNN model for weed seed classification could be selected according to specific identification accuracy requirements and time costs of applications.This research is beneficial for developing a detection system for weed seeds in various applications.The resolution of taxonomic issues and problems associated with the identi-fication of these weed seeds may allow for more effective management and control. | Tongyun Luo Jianye Zhao Yujuan Gu Shuo Zhang Xi Qiao Wen Tian Yangchun Han | 2023 | Information Processing in Agriculture2023，10，1： | 1 |
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