生物技术通报 ›› 2023, Vol. 39 ›› Issue (1): 295-304.doi: 10.13560/j.cnki.biotech.bull.1985.2022-0752
张岩峰(), 丁燕玲, 马应, 周小南, 杨朝云, 史远刚, 康晓龙()
收稿日期:
2022-06-21
出版日期:
2023-01-26
发布日期:
2023-02-02
作者简介:
张岩峰,男,硕士,研究方向:动物遗传育种与繁殖;E-mail: 基金资助:
ZHANG Yan-feng(), DING Yan-ling, MA Ying, ZHOU Xiao-nan, YANG Chao-yun, SHI Yuan-gang, KANG Xiao-long()
Received:
2022-06-21
Published:
2023-01-26
Online:
2023-02-02
摘要:
本试验旨在研究不同剩余采食量(residual feed intake, RFI)相关肉牛胃肠道微生物物种组成及相对丰度、微生物基因功能注释与富集特征。选取30头牛进行81 d饲喂试验,试验结束后选取极端RFI个体各5头屠宰并采集瘤胃液及肠道末端粪便样用于宏基因组测序。结果表明:共获得259 045.47 Mb有效数据,组装得到 4 318 393条Scaftigs,基因预测得到7 008 053个开放阅读框(ORFs)。进行物种注释后发现粪便样中Top10物种相对丰度在高剩余采食量(high residual feed intake, HRFI)、低剩余采食量(low residual feed intake, LRFI)组间无差异(P>0.05),瘤胃液中的Top10微生物在LRFI组的相对丰度均低于HRFI组;粪便中、瘤胃液中优势菌门均为拟杆菌门和厚壁菌门;粪便中的优势属为拟杆菌属,瘤胃液中的优势属为普雷沃菌属。LefSe分析显示,在粪便中LRFI组的丹毒丝菌纲(Erysipelotrichia)显著富集(P<0.05),瘤胃液中差异最显著的是甲烷杆菌纲(Methanobacteria),且该菌在HRFI组的相对丰度显著高于LRFI组(P<0.05)。使用KEGG、eggNOG和CAZy数据库进行功能注释分析表明,在胃肠道中微生物的一些功能基因的丰度与RFI的分组有关。不同RFI肉牛瘤胃液、粪便中的微生物结构存在显著差异,丹毒丝菌纲、甲烷杆菌纲可能是区分肉牛饲料效率的潜在生物标志物之一。
张岩峰, 丁燕玲, 马应, 周小南, 杨朝云, 史远刚, 康晓龙. 肉牛剩余采食量相关瘤胃及粪便微生物特征比较分析[J]. 生物技术通报, 2023, 39(1): 295-304.
ZHANG Yan-feng, DING Yan-ling, MA Ying, ZHOU Xiao-nan, YANG Chao-yun, SHI Yuan-gang, KANG Xiao-long. Comparative Analysis of Rumen and Fecal Microbial Characteristics Associated with Residual Feed Intake in Beef Cattle[J]. Biotechnology Bulletin, 2023, 39(1): 295-304.
样本名称Sample ID | 有效数据Valid data/Mb | Q20/% | Scaftigs总长Scaftigs total/bp | 开放阅读框ORFs | 完整基因Integrity:All |
---|---|---|---|---|---|
FB.H1 | 12 509.32 | 98.06 | 188 879 887 | 297 809 | 84 910(28.51%) |
FB.H2 | 13 567.54 | 97.81 | 276 330 484 | 405 700 | 139 819(34.46%) |
FB.H3 | 12 159.56 | 97.22 | 189 874 035 | 293 866 | 80 445(27.37%) |
FB.H4 | 12 053.01 | 97.03 | 135 845 081 | 208 630 | 59 756(28.64%) |
FB.H5 | 13 225.16 | 97.81 | 171 198 747 | 264 485 | 73 853(27.92%) |
FB.L1 | 13 375.02 | 97.98 | 290 986 051 | 436 592 | 131 731(30.17%) |
FB.L2 | 12 739.79 | 98.10 | 265 213 212 | 412 889 | 128 852(31.21%) |
FB.L3 | 12 652.81 | 98.06 | 228 899 989 | 359 316 | 103 408(28.78%) |
FB.L4 | 12 870.73 | 97.34 | 262 012 715 | 390 833 | 119 371(30.54%) |
FB.L5 | 12 315.37 | 97.47 | 234 495 499 | 351 948 | 109 337(31.07%) |
LWY.H1 | 13 319.20 | 97.90 | 148 596 432 | 243 354 | 60 663(24.93%) |
LWY.H2 | 13 041.11 | 97.72 | 292 975 636 | 396 572 | 107 715(27.16%) |
LWY.H3 | 13 532.42 | 97.85 | 319 093 131 | 480 760 | 147 723(30.73%) |
LWY.H4 | 13 226.23 | 98.02 | 272 607 966 | 428 244 | 131 496(30.71%) |
LWY.H5 | 12 721.76 | 97.44 | 173 570 140 | 274 033 | 70 778(25.83%) |
LWY.L1 | 12 771.32 | 97.99 | 282 351 096 | 394 302 | 154 391(39.16%) |
LWY.L2 | 12 488.25 | 98.00 | 210 931 091 | 321 579 | 87 924(27.34%) |
LWY.L3 | 13 391.11 | 97.64 | 245 419 502 | 300 276 | 66 510(22.15%) |
LWY.L4 | 13 351.27 | 97.03 | 296 978 984 | 387 232 | 119 832(30.95%) |
LWY.L5 | 13 734.49 | 97.00 | 275 938 988 | 359 633 | 95 278(26.49%) |
表1 各样本基本信息统计表
Table 1 Basic information statistics of each sample
样本名称Sample ID | 有效数据Valid data/Mb | Q20/% | Scaftigs总长Scaftigs total/bp | 开放阅读框ORFs | 完整基因Integrity:All |
---|---|---|---|---|---|
FB.H1 | 12 509.32 | 98.06 | 188 879 887 | 297 809 | 84 910(28.51%) |
FB.H2 | 13 567.54 | 97.81 | 276 330 484 | 405 700 | 139 819(34.46%) |
FB.H3 | 12 159.56 | 97.22 | 189 874 035 | 293 866 | 80 445(27.37%) |
FB.H4 | 12 053.01 | 97.03 | 135 845 081 | 208 630 | 59 756(28.64%) |
FB.H5 | 13 225.16 | 97.81 | 171 198 747 | 264 485 | 73 853(27.92%) |
FB.L1 | 13 375.02 | 97.98 | 290 986 051 | 436 592 | 131 731(30.17%) |
FB.L2 | 12 739.79 | 98.10 | 265 213 212 | 412 889 | 128 852(31.21%) |
FB.L3 | 12 652.81 | 98.06 | 228 899 989 | 359 316 | 103 408(28.78%) |
FB.L4 | 12 870.73 | 97.34 | 262 012 715 | 390 833 | 119 371(30.54%) |
FB.L5 | 12 315.37 | 97.47 | 234 495 499 | 351 948 | 109 337(31.07%) |
LWY.H1 | 13 319.20 | 97.90 | 148 596 432 | 243 354 | 60 663(24.93%) |
LWY.H2 | 13 041.11 | 97.72 | 292 975 636 | 396 572 | 107 715(27.16%) |
LWY.H3 | 13 532.42 | 97.85 | 319 093 131 | 480 760 | 147 723(30.73%) |
LWY.H4 | 13 226.23 | 98.02 | 272 607 966 | 428 244 | 131 496(30.71%) |
LWY.H5 | 12 721.76 | 97.44 | 173 570 140 | 274 033 | 70 778(25.83%) |
LWY.L1 | 12 771.32 | 97.99 | 282 351 096 | 394 302 | 154 391(39.16%) |
LWY.L2 | 12 488.25 | 98.00 | 210 931 091 | 321 579 | 87 924(27.34%) |
LWY.L3 | 13 391.11 | 97.64 | 245 419 502 | 300 276 | 66 510(22.15%) |
LWY.L4 | 13 351.27 | 97.03 | 296 978 984 | 387 232 | 119 832(30.95%) |
LWY.L5 | 13 734.49 | 97.00 | 275 938 988 | 359 633 | 95 278(26.49%) |
[1] |
Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine[J]. Cell, 2006, 124(4): 837-848.
doi: 10.1016/j.cell.2006.02.017 pmid: 16497592 |
[2] |
Ross EM, Moate PJ, Marett LC, et al. Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle[J]. PLoS One, 2013, 8(9): e73056.
doi: 10.1371/journal.pone.0073056 URL |
[3] |
Barlow GM, Yu A, Mathur R. Role of the gut microbiome in obesity and diabetes mellitus[J]. Nutr Clin Pract, 2015, 30(6): 787-797.
doi: 10.1177/0884533615609896 pmid: 26452391 |
[4] |
Wang J, Jia HJ. Metagenome-wide association studies: fine-mining the microbiome[J]. Nat Rev Microbiol, 2016, 14(8): 508-522.
doi: 10.1038/nrmicro.2016.83 pmid: 27396567 |
[5] | Roehe R, Dewhurst RJ, Duthie CA, et al. Bovine host genetic variation influences rumen microbial methane production with best selection criterion for low methane emitting and efficiently feed converting hosts based on metagenomic gene abundance[J]. PLoS Genet, 2016, 12(2): e1005846. |
[6] |
Camarinha-Silva A, Maushammer M, Wellmann R, et al. Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs[J]. Genetics, 2017, 206(3): 1637-1644.
doi: 10.1534/genetics.117.200782 pmid: 28468904 |
[7] | Jami E, White BA, Mizrahi I. Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency[J]. PLoS One, 2014, 9(1): e85423. |
[8] |
Jewell KA, McCormick CA, Odt CL, et al. Ruminal bacterial community composition in dairy cows is dynamic over the course of two lactations and correlates with feed efficiency[J]. Appl Environ Microbiol, 2015, 81(14): 4697-4710.
doi: 10.1128/AEM.00720-15 URL |
[9] |
Nielsen MK, MacNeil MD, Dekkers JCM, et al. Review: Life-cycle, total-industry genetic improvement of feed efficiency in beef cattle: blueprint for the Beef Improvement Federation[J]. Prof Animal Sci, 2013, 29(6): 559-565.
doi: 10.15232/S1080-7446(15)30285-0 URL |
[10] |
VandeHaar MJ, Armentano LE, Weigel K, et al. Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency[J]. J Dairy Sci, 2016, 99(6): 4941-4954.
doi: S0022-0302(16)30165-5 pmid: 27085407 |
[11] |
Koch RM, Swiger LA, Chambers D, et al. Efficiency of feed use in beef cattle[J]. J Anim Sci, 1963, 22(2): 486-494.
doi: 10.2527/jas1963.222486x URL |
[12] |
Herd RM, Arthur PF. Physiological basis for residual feed intake[J]. J Anim Sci, 2009, 87(S14): E64-E71.
doi: 10.2527/jas.2008-1345 URL |
[13] |
Crews DHD Jr. Genetics of efficient feed utilization and national cattle evaluation: a review[J]. Genet Mol Res, 2005, 4(2): 152-165.
pmid: 16110437 |
[14] |
Trevizan N, Canesin RC, Branco RH, et al. Growth, ruminal and metabolic parameters and feeding behavior of Nellore cattle with different residual feed intake phenotypes[J]. Livest Sci, 2021, 244: 104393.
doi: 10.1016/j.livsci.2021.104393 URL |
[15] | Jubb KVF, Kennedy PC, Palmer N. Pathology of domestic animals[M]. Elsevier, 2015. |
[16] |
Berry DP, Crowley JJ. Cell Biology Symposium: genetics of feed efficiency in dairy and beef cattle[J]. J Anim Sci, 2013, 91(4): 1594-1613.
doi: 10.2527/jas.2012-5862 pmid: 23345557 |
[17] | Richardson E, Herd R. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection[J]. Animal Prod Sci, 2004, 44: 431-440. |
[18] |
Kayser W, Hill RA. Relationship between feed intake, feeding behaviors, performance, and ultrasound carcass measurements in growing purebred Angus and Hereford bulls[J]. J Anim Sci, 2013, 91(11): 5492-5499.
doi: 10.2527/jas.2013-6611 pmid: 23989865 |
[19] | Richardson EC. Possible physiological indicators for net feed conversion efficiency in beef cattle, 1996[C]. Sydney: Pergamon Press, 1996. |
[20] |
Guan LL, Nkrumah JD, et al. Linkage of microbial ecology to phenotype: correlation of rumen microbial ecology to cattle's feed efficiency[J]. FEMS Microbiol Lett, 2008, 288(1): 85-91.
doi: 10.1111/j.1574-6968.2008.01343.x pmid: 18785930 |
[21] | 杨朝云, 康晓龙, 淡新刚, 等. 不同剩余采食量水平的安格斯牛生长性状差异性分析[J]. 西南大学学报: 自然科学版, 2020, 42(2): 8-14. |
Yang CY, Kang XL, Dan XG, et al. Differential analysis of growth traits in Angus cattle on different levels of residual feed intake[J]. J Southwest Univ Nat Sci Ed, 2020, 42(2): 8-14. | |
[22] |
Sanford RA, Lloyd KG, et al. Microbial taxonomy Run amok[J]. Trends Microbiol, 2021, 29(5): 394-404.
doi: 10.1016/j.tim.2020.12.010 pmid: 33546975 |
[23] |
Terry SA, Badhan A, Wang Y, et al. Fibre digestion by rumen microbiota—a review of recent metagenomic and metatranscriptomic studies[J]. Canadian Journal of Animal Science, 2019, 99(4): 678-692.
doi: 10.1139/cjas-2019-0024 URL |
[24] |
Kenny DA, Fitzsimons C, Waters SM, et al. Invited review: improving feed efficiency of beef cattle - the current state of the art and future challenges[J]. Animal, 2018, 12(9): 1815-1826.
doi: 10.1017/S1751731118000976 pmid: 29779496 |
[25] |
Frey JC, Pell AN, et al. Comparative studies of microbial populations in the rumen, duodenum, ileum and faeces of lactating dairy cows[J]. J Appl Microbiol, 2010, 108(6): 1982-1993.
doi: 10.1111/j.1365-2672.2009.04602.x pmid: 19863686 |
[26] | Noel SJ, Olijhoek DW, McLean F, et al. Rumen and fecal microbial community structure of Holstein and jersey dairy cows as affected by breed, diet, and residual feed intake[J]. Animals(Basel), 2019, 9(8): 498. |
[27] |
Shabat SKB, Sasson G, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants[J]. ISME J, 2016, 10(12): 2958-2972.
doi: 10.1038/ismej.2016.62 pmid: 27152936 |
[28] |
Mu YY, Lin XY, Wang ZH, et al. High-production dairy cattle exhibit different rumen and fecal bacterial community and rumen metabolite profile than low-production cattle[J]. MicrobiologyOpen, 2019, 8(4): e00673.
doi: 10.1002/mbo3.673 URL |
[29] |
Duncan SH, Louis P, Thomson JM, et al. The role of pH in determining the species composition of the human colonic microbiota[J]. Environ Microbiol, 2009, 11(8): 2112-2122.
doi: 10.1111/j.1462-2920.2009.01931.x pmid: 19397676 |
[30] |
Thomas F, Hehemann JH, et al. Environmental and gut bacteroidetes: the food connection[J]. Front Microbiol, 2011, 2: 93.
doi: 10.3389/fmicb.2011.00093 pmid: 21747801 |
[31] |
Jami E, Mizrahi I. Composition and similarity of bovine rumen microbiota across individual animals[J]. PLoS One, 2012, 7(3): e33306.
doi: 10.1371/journal.pone.0033306 URL |
[32] |
Bekele AZ, Koike S, Kobayashi Y. Genetic diversity and diet specificity of ruminal Prevotella revealed by 16S rRNA gene-based analysis[J]. FEMS Microbiol Lett, 2010, 305(1): 49-57.
doi: 10.1111/j.1574-6968.2010.01911.x URL |
[33] |
McGovern E, McGee M, Byrne CJ, et al. Investigation into the effect of divergent feed efficiency phenotype on the bovine rumen microbiota across diet and breed[J]. Sci Rep, 2020, 10(1): 15317.
doi: 10.1038/s41598-020-71458-0 pmid: 32948787 |
[34] | 吴宇佳, 迟晓培, 等. 肥胖者唾液微生物宏基因组学特点[J]. 北京大学学报: 医学版, 2018, 50(1): 5-12. |
Wu YJ, Chi XP, et al. Sal Ivary microbiome in people with obesity: a pilot study[J]. J Peking Univ Heal Sci, 2018, 50(1): 5-12. | |
[35] |
Lam S, Munro JC, Zhou M, et al. Associations of rumen parameters with feed efficiency and sampling routine in beef cattle[J]. Animal, 2018, 12(7): 1442-1450.
doi: 10.1017/S1751731117002750 pmid: 29122053 |
[36] |
Henderson G, Cox F, et al. Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range[J]. Sci Rep, 2015, 5: 14567.
doi: 10.1038/srep14567 pmid: 26449758 |
[37] |
Hegarty RS, Goopy JP, Herd RM, et al. Cattle selected for lower residual feed intake have reduced daily methane production[J]. J Anim Sci, 2007, 85(6): 1479-1486.
pmid: 17296777 |
[38] |
Nkrumah JD, Okine EK, Mathison GW, et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle[J]. J Anim Sci, 2006, 84(1): 145-153.
doi: 10.2527/2006.841145x pmid: 16361501 |
[39] |
Johnson KA, Johnson DE. Methane emissions from cattle[J]. J Anim Sci, 1995, 73(8): 2483-2492.
doi: 10.2527/1995.7382483x pmid: 8567486 |
[40] |
Reinhold P, Sachse K, Kaltenboeck B. Chlamydiaceae in cattle: commensals, trigger organisms, or pathogens?[J]. Vet J, 2011, 189(3): 257-267.
doi: 10.1016/j.tvjl.2010.09.003 pmid: 20980178 |
[41] |
Luton PE, Wayne JM, Sharp RJ, et al. The mcrA gene as an alternative to 16S rRNA in the phylogenetic analysis of methanogen populations in landfillbbThe GenBank accession numbers for the mcrA sequences reported in this paper are AF414034-AF414051 and AF414007-AF414033.[J]. Microbiology, 2002, 148(11): 3521-3530.
doi: 10.1099/00221287-148-11-3521 URL |
[42] |
Chouchane L, Danguir J, Beji C, et al. Genetic variation in the stress protein hsp70-2 gene is highly associated with obesity[J]. Int J Obes, 2001, 25(4): 462-466.
doi: 10.1038/sj.ijo.0801545 URL |
[43] | Hardie DG, Ross FA, Hawley SA. AMPK: a nutrient and energy sensor that maintains energy homeostasis[J]. Nat Rev Mol Cell Biol, 2012, 13(4): 251-262. |
[44] |
Marshall MS. Ras target proteins in eukaryotic cells[J]. FASEB J, 1995, 9(13): 1311-1318.
doi: 10.1096/fasebj.9.13.7557021 pmid: 7557021 |
[45] |
Gadsden MH, McIntosh EM, Game JC, et al. dUTP pyrophosphatase is an essential enzyme in Saccharomyces cerevisiae[J]. EMBO J, 1993, 12(11): 4425-4431.
doi: 10.1002/j.1460-2075.1993.tb06127.x pmid: 8223452 |
[46] |
Wang W, Zheng SS, Li LX, et al. Comparative metagenomics of the gut microbiota in wild greylag geese(Anser anser)and ruddy shelducks(Tadorna ferruginea)[J]. MicrobiologyOpen, 2019, 8(5): e00725.
doi: 10.1002/mbo3.725 URL |
[47] | 张慧敏, 夏海磊, 黄强, 等. 海子水牛瘤胃微生物的宏基因组学分析[J]. 动物营养学报, 2017, 29(11): 4151-4161. |
Zhang HM, Xia HL, Huang Q, et al. Metagenomic analysis of microorganisms in rumen of haizi buffalo[J]. Chin J Animal Nutr, 2017, 29(11): 4151-4161. | |
[48] |
Jose VL, Appoothy T, More RP, et al. Metagenomic insights into the rumen microbial fibrolytic enzymes in Indian crossbred cattle fed finger millet straw[J]. AMB Express, 2017, 7(1): 13.
doi: 10.1186/s13568-016-0310-0 pmid: 28050853 |
[49] |
Stewart RD, Auffret MD, Warr A, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen[J]. Nat Commun, 2018, 9(1): 870.
doi: 10.1038/s41467-018-03317-6 pmid: 29491419 |
[1] | 张坤, 闫畅, 田新朋. 微生物单细胞分离方法研究进展[J]. 生物技术通报, 2023, 39(9): 1-11. |
[2] | 赵志祥, 王殿东, 周亚林, 王培, 严婉荣, 严蓓, 罗路云, 张卓. 枯草芽孢杆菌Ya-1对辣椒枯萎病的防治及其对根际真菌群落的影响[J]. 生物技术通报, 2023, 39(9): 213-224. |
[3] | 程亚楠, 张文聪, 周圆, 孙雪, 李玉, 李庆刚. 乳酸乳球菌生产2'-岩藻糖基乳糖的途径构建及发酵培养基优化[J]. 生物技术通报, 2023, 39(9): 84-96. |
[4] | 江润海, 姜冉冉, 朱城强, 侯秀丽. 微生物强化植物修复铅污染土壤的机制研究进展[J]. 生物技术通报, 2023, 39(8): 114-125. |
[5] | 李焕敏, 高峰涛, 李伟忠, 王金庆, 封佳丽. 天然生物质材料作为固定化载体的研究应用进展[J]. 生物技术通报, 2023, 39(7): 105-112. |
[6] | 赵林艳, 徐武美, 王豪吉, 王昆艳, 魏富刚, 杨绍周, 官会林. 施用生物炭对连作三七根际真菌群落与存活率的影响[J]. 生物技术通报, 2023, 39(7): 219-227. |
[7] | 徐汝悦, 王子霄, 沈禄, 吴蓉蓉, 姚芳婷, 谭中原, 刘恒蔚, 张文超. Cr(VI)的生物修复技术研究进展[J]. 生物技术通报, 2023, 39(6): 49-60. |
[8] | 张晶, 张浩睿, 曹云, 黄红英, 曲萍, 张志萍. 嗜热纤维素降解菌研究进展[J]. 生物技术通报, 2023, 39(6): 73-87. |
[9] | 余洋, 刘天海, 刘理旭, 唐杰, 彭卫红, 陈阳, 谭昊. 羊肚菌菌种生产车间气溶胶微生物群落研究[J]. 生物技术通报, 2023, 39(5): 267-275. |
[10] | 雷彩荣, 郭晓鹏, 柴冉, 张苗苗, 任军乐, 陆栋. 组学技术在重离子辐射微生物诱变育种中的应用[J]. 生物技术通报, 2023, 39(5): 54-62. |
[11] | 张华香, 徐晓婷, 郑云婷, 肖春桥. 溶磷微生物在钝化和植物修复重金属污染土壤中的作用[J]. 生物技术通报, 2023, 39(3): 52-58. |
[12] | 李凯航, 王浩臣, 程可心, 杨艳, 金一, 何晓青. 全基因组关联分析研究植物与微生物组的互作遗传机制[J]. 生物技术通报, 2023, 39(2): 24-34. |
[13] | 李昕悦, 周明海, 樊亚超, 廖莎, 张风丽, 刘晨光, 孙悦, 张霖, 赵心清. 基于转运蛋白工程提升微生物菌株耐受性和生物制造效率的研究进展[J]. 生物技术通报, 2023, 39(11): 123-136. |
[14] | 胡锦超, 沈文琦, 徐超业, 樊雅祺, 卢浩宇, 蒋雯杰, 李世龙, 晋洪晨, 骆健美, 王敏. 微生物酸胁迫耐受性能强化的研究进展[J]. 生物技术通报, 2023, 39(11): 137-149. |
[15] | 王晨宇, 周楚源, 何堤, 樊梓豪, 王梦梦, 杨柳燕. 多聚磷酸盐在微生物抗环境胁迫中的作用及机制[J]. 生物技术通报, 2023, 39(11): 168-181. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||