Quantitative results on MTDC-UAV dataset. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Supplementary file 1_Global burden of HIV, syphilis, and HBV infection among women of childbearing age and children under five: based on the Global Burden of Diseases Study 2021.do... by Jia Lian (9469818)

“…</p>Conclusion<p>In 2021, the global disease burden of HIV, syphilis, and HBV infection remained high among WCBA and children under five, with significant regional and demographic disparities. These findings indicate a persistent public health challenge, necessitating continued global efforts to mitigate the burden through addressing socioeconomic inequalities, strengthening health education, and expanding healthcare access.…”

Counting results on WEDU dataset. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Supplementary file 2_Exploring dupilumab for asthma: from mechanistic insights to clinical outcomes, safety, and cost-effectiveness.docx by Omar Z. Ameer (8900039)

“…Clinical trials have demonstrated significant reductions in asthma exacerbation rates, improvements in forced expiratory volume in one second (FEV₁), and decreased oral corticosteroid (OCS) dependence in adults and children with moderate-to-severe asthma. …”

Example images from four plant datasets. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Counting results on RFRB dataset. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Detection visualization results on WEDU dataset. by Dunlu Lu (19964225)

“…This is particularly true in the agricultural field, where there is a multitude of crop types and a complex and variable environment. …”

Table 4_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 5_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 2_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 1_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Image 1_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.jpeg by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 9_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 8_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 6_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 7_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table 3_Interactions between the intestinal microbiome and host genes in regulating vibriosis resistance in Cynoglossus semilaevis.xlsx by Weiwei Zheng (140828)

“…</p>Results<p>Obvious histopathological differences were observed between the resistant and susceptible groups in terms of inflammatory cells infiltration, and tissue dissociation of mucosal layer. 16S rRNA sequencing analysis indicated that Vibrio increased but Stenotrophomonas, Chryseobacterium, Delftia, and Salinivibrio decreased in the susceptible group. Compared to the control group, 1,986 differentially expressed genes (DEGs) were detected in the susceptible group, significantly more than the 310 DEGs found in the resistant group. …”

Table_1_Unraveling the key mechanisms of Gastrodia elata continuous cropping obstacles: soil bacteria Massilia, Burkholderia-Caballeronia-Paraburkholderia, and Dyella along with so... by Mingzheng Duan (11630632)

“…Background<p>Tian-ma (Gastrodia elata) is a traditional medicinal herb found in China. It is used in healthy food and to treat various diseases, therefore cultivated extensively in southwest China. …”

Image_1_Unraveling the key mechanisms of Gastrodia elata continuous cropping obstacles: soil bacteria Massilia, Burkholderia-Caballeronia-Paraburkholderia, and Dyella along with so... by Mingzheng Duan (11630632)

“…Background<p>Tian-ma (Gastrodia elata) is a traditional medicinal herb found in China. It is used in healthy food and to treat various diseases, therefore cultivated extensively in southwest China. …”

Data Sheet 1_Global burden of inflammatory bowel disease in the elderly: trends from 1990 to 2021 and projections to 2051.docx by Ying Liu (18461)

“…Estimated annual percentage changes (EAPCs) were computed to quantify temporal trends. A Bayesian Age-Period-Cohort model was employed to project future trends.…”