搜索替代词:
resolve. » resolved. (扩展搜索)
evolved. » resolved. (扩展搜索)
Showing 181 - 200 results of 238 for search 'farm is ((resolve. OR (((evolved. OR removes.) OR involved.) OR remove.)) OR involves.)', 查询时间: 0.10s Refine Results
  1. 181

    Table 1_Detection and risk factor analysis of avian colibacillosis associated with colistin-resistant Escherichia coli and Klebsiella pneumoniae.xlsx Muhammad Adnan Saeed (21780770)

    出版 2025
    “...In total, 230 visceral organ samples were collected from 13 different chicken farms located in Sargodha, Jhang and Toba Tek Singh in Pakistan. ...”
  2. 182

    Data Sheet 2_Phylogenetic analysis and genetic evolution of porcine respiratory coronavirus in Guangxi province, Southern China from 2022 to 2024.docx Yuwen Shi (11730508)

    出版 2025
    “...To analyze the genetic and evolutional characteristics of PRCV in Guangxi province, southern China, a total of 6,267 clinical samples were collected from different pig farms, harmless treatment plants and abattoirs in Guangxi province during 2022–2024. ...”
  3. 183

    Table 1_Phylogenetic analysis and genetic evolution of porcine respiratory coronavirus in Guangxi province, Southern China from 2022 to 2024.docx Yuwen Shi (11730508)

    出版 2025
    “...To analyze the genetic and evolutional characteristics of PRCV in Guangxi province, southern China, a total of 6,267 clinical samples were collected from different pig farms, harmless treatment plants and abattoirs in Guangxi province during 2022–2024. ...”
  4. 184

    Data Sheet 1_Phylogenetic analysis and genetic evolution of porcine respiratory coronavirus in Guangxi province, Southern China from 2022 to 2024.docx Yuwen Shi (11730508)

    出版 2025
    “...To analyze the genetic and evolutional characteristics of PRCV in Guangxi province, southern China, a total of 6,267 clinical samples were collected from different pig farms, harmless treatment plants and abattoirs in Guangxi province during 2022–2024. ...”
  5. 185

    Table 4_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.xlsx Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  6. 186

    Table 5_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.xlsx Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  7. 187

    Table 3_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.xlsx Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  8. 188

    Table 1_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.doc Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  9. 189

    Table 2_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.xlsx Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  10. 190

    Image 1_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.jpeg Vikesh Tanwar (22809038)

    出版 2025
    “...Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant....”
  11. 191

    Image 1_Toxicological effects of sublethal microcystin-LR exposure in Labeo rohita: histopathological, ultrastructural, immunological, and biochemical impairments.jpeg Snatashree Mohanty (22459801)

    出版 2025
    “...Interestingly, the modulation in the expression of SOD, catalase, GST, CYP1A and CYP3A genes in different organs indicated their involvement in the antioxidant and detoxification process. ...”
  12. 192

    Table 1_Strawberry-herb intercropping: a 2-year study toward sustainable intensification and diversification.docx Sebastian Soppelsa (5776868)

    出版 2025
    “...Despite its ecological benefits, its adoption in specialized farming systems—such as strawberry monocultures—remains limited, as these systems typically focus on maximizing income from a single crop. ...”
  13. 193

    Table 8_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  14. 194

    Table 6_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  15. 195

    Image 2_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.png Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  16. 196

    Table 7_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  17. 197

    Table 3_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  18. 198

    Table 5_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  19. 199

    Table 1_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”
  20. 200

    Table 2_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.docx Yuliya Genievskaya (4725192)

    出版 2025
    “...Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. ...”