Table 1_Phylogenetic analysis and genetic evolution of porcine respiratory coronavirus in Guangxi province, Southern China from 2022 to 2024.docx Tekijä 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. …”

Data Sheet 1_Phylogenetic analysis and genetic evolution of porcine respiratory coronavirus in Guangxi province, Southern China from 2022 to 2024.docx Tekijä 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. …”

Image 1_Toxicological effects of sublethal microcystin-LR exposure in Labeo rohita: histopathological, ultrastructural, immunological, and biochemical impairments.jpeg Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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. …”

Table 8_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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 Tekijä 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 4_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Tekijä 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 1_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.png Tekijä 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_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Tekijä Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Table 1_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Tekijä Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Table 2_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Tekijä Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Assessing contract solutions for agricultural public goods in the Netherlands Tekijä Kina S. Harmanny (21629360)

“…<p>Farming significantly influences public goods (PGs) like water quality and biodiversity, both positively and negatively. …”