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

“…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. …”

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

“…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.…”

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

“…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.…”

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

“…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.…”

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

“…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.…”

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

“…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.…”

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

“…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.…”

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

“…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. …”

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

“…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. …”

Image 1_Psittacosis chlamydia pneumonia complicated with organizing pneumonia: a case report and literature review.jpeg 著者: Qiao Li (571252)

“…</p>Results<p>A 66-year-old male with a history of poultry farming presented with fever, cough, sputum production, and hemoptysis. …”

Supplementary file 1_Psittacosis chlamydia pneumonia complicated with organizing pneumonia: a case report and literature review.xlsx 著者: Qiao Li (571252)

“…</p>Results<p>A 66-year-old male with a history of poultry farming presented with fever, cough, sputum production, and hemoptysis. …”

Assessing the Effect of Undirected Forest Restoration and Flooding on the Soil Quality in an Agricultural Floodplain 著者: Clayton Williams (10873518)

“…Two recently abandoned farm field sites (3.4 and 4.1 acres), now a mixture of young trees and prairie species, were selected near two forest sites.…”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”

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

“…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. …”