Data Sheet 3_Identification of telomere-related gene subtypes and prognostic signatures in osteosarcoma.xlsx by Zhaoguang Song (20778602)

“…Finally, we discovered that expression of PDK2, PPARG, MORC4, SP110, TERT and MAP3K5 was significantly decreased in OS cells.</p>Conclusion<p>In conclusion, our study has uncovered the importance of TRGs in defining distinct subtypes of OS with different survival outcomes and immune contexts. …”

Image 1_Identification of telomere-related gene subtypes and prognostic signatures in osteosarcoma.jpeg by Zhaoguang Song (20778602)

“…Finally, we discovered that expression of PDK2, PPARG, MORC4, SP110, TERT and MAP3K5 was significantly decreased in OS cells.</p>Conclusion<p>In conclusion, our study has uncovered the importance of TRGs in defining distinct subtypes of OS with different survival outcomes and immune contexts. …”

Data Sheet 2_Identification of telomere-related gene subtypes and prognostic signatures in osteosarcoma.xlsx by Zhaoguang Song (20778602)

“…Finally, we discovered that expression of PDK2, PPARG, MORC4, SP110, TERT and MAP3K5 was significantly decreased in OS cells.</p>Conclusion<p>In conclusion, our study has uncovered the importance of TRGs in defining distinct subtypes of OS with different survival outcomes and immune contexts. …”

Data Sheet 1_Identification of telomere-related gene subtypes and prognostic signatures in osteosarcoma.xlsx by Zhaoguang Song (20778602)

“…Finally, we discovered that expression of PDK2, PPARG, MORC4, SP110, TERT and MAP3K5 was significantly decreased in OS cells.</p>Conclusion<p>In conclusion, our study has uncovered the importance of TRGs in defining distinct subtypes of OS with different survival outcomes and immune contexts. …”

Table 1_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.docx by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Image 1_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.tif by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Image 4_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.tif by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Image 3_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.tif by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Image 2_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.tif by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Table 2_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.docx by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Table 3_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.docx by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

Table 4_Comparative microbiome analysis of paired mucosal and fecal samples in Korean colorectal cancer patients.docx by Younggwang Kim (21562529)

“…Fusobacterium, Prevotella 9, Parvimonas, and Holdemanella were significantly enriched in pre-surgical samples and declined after surgery (p < 0.01). …”

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

“…Vertical lines show the peak-to-trough heights extracted for each significant peak, in order of decreasing value; <b>e.…”

Nyamsuren, I., Mitchell, E., and Tsay, J. (April 27 - May 2, 2025), <i>Learning Sign Language with Real-Time Kinematic Feedback</i> [Poster Presentation]. The Annual Meeting of the... by Indranil Nyamsuren (21498833)

“…This growing demand emphasizes the significance of sign language—not just as a communication tool, but as a key to ensuring equal participation in society.…”

Data Sheet 1_Analysis of pharmacotherapeutic approaches for multiple myeloma and correlated renal and pulmonary impairments: a retrospective real-world registry study in the Greate... by Ayman Alhejazi (21092054)

“…Background<p>Multiple myeloma (MM) is a plasma cell malignancy with significant unmet medical needs, particularly in the treatment of relapsed and refractory disease. …”

<i>Eggerthella lenta</i> down regulated flavone and flavonol biosynthesis promoted Kawasaki disease by Yao-Tsung Yeh (2009278)

“…Alpha (<i>p</i> < 0.042) and beta (<i>p</i> < 0.001) diversity in KD were significantly decreased when compared to the control group. …”

Coherence of SCN metabolic pathways changes during development. by Martin Sládek (130663)

“…<b>(C)</b> Number of all significant polar metabolite and lipid correlations (positive in red, negative in blue) across all analyzed ages. …”

Image 1_Population pharmacokinetics of polymyxin B in critically ill patients with carbapenem-resistant organisms infections: insights from steady-state trough and peak plasma conc... by Jun Yang (2084)

“…Bland-Altman analysis revealed a mean bias of 12.98 mg·h·L^-1 between the two AUC_ss,24h estimation methods. …”

Internal states adjust the Brp compartmental heterogeneity through octopamine. by Hongyang Wu (8856740)

“…Pseudo color in the images represents the value of log₂ (pixel intensity/mean pixel intensity in the γ lobe). Pseudo color range: −1.2 to 1.2. …”

Table 1_The interaction between temperature and rainfall determines the probability of tropical forest fire occurrence in Hainan Island.docx by Xiaohua Chen (257762)

“…The results show that: (1) climatic factors contribute most significantly to the tropical forest fire probability, followed by human activities and topography, while vegetation factors (i.e., normalized difference vegetation index) made no significant contribution; (2) temperature and rainfall are the dominant factors influencing fire probability, with rising temperatures and decreasing rainfall substantially increasing the forest fire hazard; and (3) both logistic regression and random forest are reliable for predicting tropical forest fire hazards, but random forest demonstrates greater adaptability. …”