Multivariable analysis between 2006 and 2018. by Hiroaki Yoshikawa (4615801)

“…</p><p>Results</p><p>Our analysis revealed a significantly older age of onset in the 2018 cohort compared to 2006. …”

Comparison of dot gain across paper types and screen frequency. by Xiaoli Liu (165371)

“…Dot gain curves for 133 lpi and 175 lpi show a consistent non-linear trend, rising to a maximum in the mid-tone range (40%–60% dot area) before decreasing. …”

Estimates of recent population sizes for eight populations sampled in the 1000 Genomes Project. by Zhendong Huang (390367)

NADC30-like PRRSV activates the FAK-PI3K-AKT pathway through integrin <i>αV.</i> by Chunlin Li (180545)

Dot gain curves for three types of paper at different screen frequency. by Xiaoli Liu (165371)

“…<p>(a) 133 lpi and (b) 175 lpi. Dot gain initially increases and then decreases with rising dot area percentage, showing the most significant expansion in the mid-tone range (approximately 40%–60%) across coated, offset, and newsprint papers.…”

Top Panel: Representation of the input voltage signal in the time domain as applied to the proteinoid sample. by Panagiotis Mougkogiannis (17002143)

Frequency distribution of depths of coverage (DP) and allele balances (AB). by Eileen Marie Hanna (9693992)

Fig 2 - by Kieran L. Quinn (18127273)

Raw data set for Table 3. by Smriti Shrestha (21212910)

“…<div><p>A significant reduction in influenza incidence during the early days of COVID-19 pandemic was reported worldwide. …”

FluMart Data from 2018 to 2022. by Smriti Shrestha (21212910)

“…<div><p>A significant reduction in influenza incidence during the early days of COVID-19 pandemic was reported worldwide. …”

Sensitivity of modeled cell trajectories to parameter perturbations. by Renad Al-Ghazawi (22411258)

Dataset. by Nathan Mubiru (21627195)

The distribution of age-related resting-state functional connectivity (RSFC) based on Euclidean distance. by Lanxin Ji (20290942)

“…The figure does not exhibit a discernible contrast between the positively and negatively age-related RSFC, suggesting that distance may not be a significant factor influencing the increase or decrease of RSFC with age.…”

Identifying optimal cluster numbers in hierarchical cluster analysis. by Dolores Bernenko (16528426)

Genes in the top 10 up- and down-regulated. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”

Differential analysis between OEG and CG groups. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”

Overview of Sequencing Results. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”

PCA plot of OEG group and CG group. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”

siRNA sequences designed for this study. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”

Statistics of reads aligned to gene structure. by Chaoyun Yang (11523358)

“…The results of transcriptome sequencing demonstrated that the overexpression of the HADHB gene markedly altered the transcriptional profile of preadipocytes, with 170 genes exhibiting a significant increase in expression and 113 genes displaying a decrease. …”