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Nie L, Fang Y, Xia Z, Wei X, Wu Z, Yan Y, Wang F. Relationships within Bolbitis sinensis Species Complex Using RAD Sequencing. PLANTS (BASEL, SWITZERLAND) 2024; 13:1987. [PMID: 39065514 PMCID: PMC11280518 DOI: 10.3390/plants13141987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/18/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
Species identification and phylogenetic relationship clarification are fundamental goals in species delimitation. However, these tasks pose challenges when based on morphologies, geographic distribution, and genomic data. Previously, two species of the fern genus Bolbitis, B. × multipinna and B. longiaurita were described based on morphological traits; they are phylogenetically intertwined with B. sinensis and fail to form monophyletic groups. To address the unclear phylogenetic relationships within the B. sinensis species complex, RAD sequencing was performed on 65 individuals from five populations. Our integrated analysis of phylogenetic trees, neighbor nets, and genetic structures indicate that the B. sinensis species complex should not be considered as separate species. Moreover, our findings reveal differences in the degree of genetic differentiation among the five populations, ranging from low to moderate, which might be influenced by geographical distance and gene flow. The Fst values also confirmed that genetic differentiation intensifies with increasing geographic distance. Collectively, this study clarifies the complex phylogenetic relationships within the B. sinensis species complex, elucidates the genetic diversity and differentiation across the studied populations, and offers valuable genetic insights that contribute to the broader study of evolutionary relationships and population genetics within the Bolbitis species.
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Affiliation(s)
- Liyun Nie
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; (L.N.); (Y.F.); (Z.X.); (X.W.)
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China;
- School of Medical, Molecular and Forensic Sciences, Murdoch University, Murdoch, WA 6149, Australia
| | - Yuhan Fang
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; (L.N.); (Y.F.); (Z.X.); (X.W.)
| | - Zengqiang Xia
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; (L.N.); (Y.F.); (Z.X.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xueying Wei
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; (L.N.); (Y.F.); (Z.X.); (X.W.)
- Shenzhen Key Laboratory for Orchid Conservation and Utilization, The National Orchid Conservation Center of China and the Orchid Conservation & Research Center of Shenzhen, Shenzhen 518114, China;
| | - Zhiqiang Wu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China;
| | - Yuehong Yan
- Shenzhen Key Laboratory for Orchid Conservation and Utilization, The National Orchid Conservation Center of China and the Orchid Conservation & Research Center of Shenzhen, Shenzhen 518114, China;
| | - Faguo Wang
- Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; (L.N.); (Y.F.); (Z.X.); (X.W.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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Chen G, Hou Y, Cui T, Li H, Shangguan F, Cao L. YOLOv8-CML: a lightweight target detection method for color-changing melon ripening in intelligent agriculture. Sci Rep 2024; 14:14400. [PMID: 38909076 PMCID: PMC11193782 DOI: 10.1038/s41598-024-65293-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/18/2024] [Indexed: 06/24/2024] Open
Abstract
Color-changing melon is an ornamental and edible fruit. Aiming at the problems of slow detection speed and high deployment cost for Color-changing melon in intelligent agriculture equipment, this study proposes a lightweight detection model YOLOv8-CML.Firstly, a lightweight Faster-Block is introduced to reduce the number of memory accesses while reducing redundant computation, and a lighter C2f structure is obtained. Then, the lightweight C2f module fusing EMA module is constructed in Backbone to collect multi-scale spatial information more efficiently and reduce the interference of complex background on the recognition effect. Next, the idea of shared parameters is utilized to redesign the detection head to simplify the model further. Finally, the α-IoU loss function is adopted better to measure the overlap between the predicted and real frames using the α hyperparameter, improving the recognition accuracy. The experimental results show that compared to the YOLOv8n model, the parametric and computational ratios of the improved YOLOv8-CML model decreased by 42.9% and 51.8%, respectively. In addition, the model size is only 3.7 MB, and the inference speed is improved by 6.9%, while mAP@0.5, accuracy, and FPS are also improved. Our proposed model provides a vital reference for deploying Color-changing melon picking robots.
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Affiliation(s)
- Guojun Chen
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Yongjie Hou
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China.
| | - Tao Cui
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Huihui Li
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China
| | - Fengyang Shangguan
- College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266100, China
| | - Lei Cao
- Faculty of Light Industry, Qilu University of Technology, Jinan, 250300, China
- State Key Laboratory of Biobased Material and Green Papermaking, Shandong Academy of Sciences, Jinan, 250300, China
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Koshiishi Y, Wada K. Genetic structure and origin of emu populations in Japanese farms inferred from large-scale SNP genotyping based on double-digest RAD-seq. Sci Rep 2024; 14:6982. [PMID: 38523157 PMCID: PMC10961305 DOI: 10.1038/s41598-024-57032-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/13/2024] [Indexed: 03/26/2024] Open
Abstract
The emu is a novel poultry species in Japan. However, Japanese farmed emu populations have reduced genetic diversity owing to inbreeding. We have previously suggested that there are genetic resources in the Tohoku Safari Park (TSP) and Fuji/Kakegawa Kachoen Garden Park (FGP/KGP) to extend the genetic diversity of commercial emu farms based on microsatellite (SSR) and mitochondrial DNA. However, those markers provide relatively poor information. Thus, we investigated the genetic structure of farmed Japanese populations based on a large-scale genotyping system using RAD-seq and verified the usefulness of TSP and FGP/KGP as genetic resources for expanding genetic diversity. Admixture, phylogenetic, and principal component analyses based on 28,676 SNPs showed that TSP individuals were ancestors in the Okhotsk Emu Farm (OEF). FGP/KGP individuals showed a unique genetic component that differed from that of the others. We have previously reported that the mitochondrial haplotypes of FGP/KGP were shared with an isolated wild population in eastern Australia. These results suggest that FGP/KGP individuals originated from an eastern Australia isolated population different from other populations including ancestral of OEF/TSP. Our results would provide information for the development of Japanese emu farms and industry and for the conservation of genetic resources in the Australian wild emu.
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Affiliation(s)
- Yuichi Koshiishi
- NODAI Genome Research Center, Tokyo University of Agriculture, Setagaya, Tokyo, 156-8502, Japan.
| | - Kenta Wada
- Faculty of Bioindustry, Tokyo University of Agriculture, Abashiri, Hokkaido, 099-2493, Japan.
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Bai Q, Shi L, Li K, Xu F, Zhang W. The Construction of lncRNA/circRNA-miRNA-mRNA Networks Reveals Functional Genes Related to Growth Traits in Schima superba. Int J Mol Sci 2024; 25:2171. [PMID: 38396847 PMCID: PMC10888550 DOI: 10.3390/ijms25042171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 02/05/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Schima superba is a precious timber and fire-resistant tree species widely distributed in southern China. Currently, there is little knowledge related to its growth traits, especially with respect to molecular breeding. The lack of relevant information has delayed the development of modern breeding. The purpose is to identify probable functional genes involved in S. superba growth through whole transcriptome sequencing. In this study, a total of 32,711 mRNAs, 525 miRNAs, 54,312 lncRNAs, and 1522 circRNAs were identified from 10 S. superba individuals containing different volumes of wood. Four possible regulators, comprising three lncRNAs, one circRNA, and eleven key miRNAs, were identified from the regulatory networks of lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA to supply information on ncRNAs. Several candidate genes involved in phenylpropane and cellulose biosynthesis pathways, including Ss4CL2, SsCSL1, and SsCSL2, and transcription factors, including SsDELLA2 (SsSLR), SsDELLA3 (SsSLN), SsDELLA5 (SsGAI-like2), and SsNAM1, were identified to reveal the molecular regulatory mechanisms regulating the growth traits of S. superba. The results not merely provide candidate functional genes related to S. superba growth trait and will be useful to carry out molecular breeding, but the strategy and method also provide scientists with an effective approach to revealing mechanisms behind important economic traits in other species.
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Affiliation(s)
- Qingsong Bai
- Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou 510520, China
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El-Kassaby YA, Cappa EP, Chen C, Ratcliffe B, Porth IM. Efficient genomics-based 'end-to-end' selective tree breeding framework. Heredity (Edinb) 2024; 132:98-105. [PMID: 38172577 PMCID: PMC10844606 DOI: 10.1038/s41437-023-00667-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/07/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingencies and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: (1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, (2) generates unprecedented resolution of genealogical relationships among tested individuals, and (3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.
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Affiliation(s)
- Yousry A El-Kassaby
- Faculty of Forestry, The University of British Columbia, Vancouver, BC, Canada.
| | - Eduardo P Cappa
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Oklahoma, OK, USA
| | - Blaise Ratcliffe
- Faculty of Forestry, The University of British Columbia, Vancouver, BC, Canada
| | - Ilga M Porth
- Department of Wood and Forest Sciences, Université Laval, Quebec, QC, Canada
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Hua J, Zhong C, Chen W, Fu J, Wang J, Wang Q, Zhu G, Li Y, Tao Y, Zhang M, Dong Y, Lu S, Liu W, Qiang J. Single nucleotide polymorphism SNP19140160 A > C is a potential breeding locus for fast-growth largemouth bass (Micropterus salmoides). BMC Genomics 2024; 25:64. [PMID: 38229016 DOI: 10.1186/s12864-024-09962-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Largemouth bass (Micropterus salmoides) has significant economic value as a high-yielding fish species in China's freshwater aquaculture industry. Determining the major genes related to growth traits and identifying molecular markers associated with these traits serve as the foundation for breeding strategies involving gene pyramiding. In this study, we screened restriction-site associated DNA sequencing (RAD-seq) data to identify single nucleotide polymorphism (SNP) loci potentially associated with extreme growth differences between fast-growth and slow-growth groups in the F1 generation of a largemouth bass population. RESULTS We subsequently identified associations between these loci and specific candidate genes related to four key growth traits (body weight, body length, body height, and body thickness) based on SNP genotyping. In total, 4,196,486 high-quality SNPs were distributed across 23 chromosomes. Using a population-specific genotype frequency threshold of 0.7, we identified 30 potential SNPs associated with growth traits. Among the 30 SNPs, SNP19140160, SNP9639603, SNP9639605, and SNP23355498 showed significant associations; three of them (SNP9639603, SNP9639605, and SNP23355498) were significantly associated with one trait, body length, in the F1 generation, and one (SNP19140160) was significantly linked with four traits (body weight, height, length, and thickness) in the F1 generation. The markers SNP19140160 and SNP23355498 were located near two growth candidate genes, fam174b and ppip5k1b, respectively, and these candidate genes were closely linked with growth, development, and feeding. The average body weight of the group with four dominant genotypes at these SNP loci in the F1 generation population (703.86 g) was 19.63% higher than that of the group without dominant genotypes at these loci (588.36 g). CONCLUSIONS Thus, these four markers could be used to construct a population with dominant genotypes at loci related to fast growth. These findings demonstrate how markers can be used to identify genes related to fast growth, and will be useful for molecular marker-assisted selection in the breeding of high-quality largemouth bass.
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Grants
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- No. JBGS [2021] 130 Project of Seed Industry Revitalization in Jiangsu Province, China
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- 2022-ZYXT-07 Major Technology Collaborative Promotion Plan for Largemouth bass Industry in Jiangsu Province
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- NO. 2023JBFR02 the central public-interest scientific institution basal research fund, freshwater fisheries research center, CAFS
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
- No. SNG2021009 the Suzhou Science and Technology Program
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Affiliation(s)
- Jixiang Hua
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Chunyi Zhong
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Wenhua Chen
- Suzhou Aquatic Technology Extension Station, Suzhou, 215004, China
| | - Jianjun Fu
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Jian Wang
- Guangxi Xinjian Investment Group Limited Company, Hechi, 530201, China
| | - Qingchun Wang
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China
| | - Geyan Zhu
- Suzhou Aquatic Technology Extension Station, Suzhou, 215004, China
| | - Yan Li
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Yifan Tao
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Maoyou Zhang
- Suzhou Aquatic Technology Extension Station, Suzhou, 215004, China
| | - Yalun Dong
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Siqi Lu
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Wenting Liu
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China
| | - Jun Qiang
- Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China.
- Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China.
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