Principal component analysis of soil parameters. by Wasihun Mengiste (22470304)

“…P, TN, CEC, OM, extractable micronutrients like Fe, Mn, Cu, and Zn decreased with increasing soil depth, while the clay content, BD, pH, and exchangeable bases like Ca, Mg, Na, and K were increased with increasing soil depth. …”

LC_MS/MS analysis for OGG1 interactomes. by Lang Pan (5145221)

“…Importantly, inhibiting OGG1’s ability to recognize 8-oxoGua significantly decreases RSV progeny production. Our results underscore the viral replication machinery’s adaptation to oxidative challenges, suggesting that inhibiting OGG1’s reading function could be a novel strategy for antiviral intervention.…”

Primer sequences for RT q-PCR. by Lang Pan (5145221)

“…Importantly, inhibiting OGG1’s ability to recognize 8-oxoGua significantly decreases RSV progeny production. Our results underscore the viral replication machinery’s adaptation to oxidative challenges, suggesting that inhibiting OGG1’s reading function could be a novel strategy for antiviral intervention.…”

Oligo sequences for EMSA and excision assay. by Lang Pan (5145221)

“…Importantly, inhibiting OGG1’s ability to recognize 8-oxoGua significantly decreases RSV progeny production. Our results underscore the viral replication machinery’s adaptation to oxidative challenges, suggesting that inhibiting OGG1’s reading function could be a novel strategy for antiviral intervention.…”

Disruption of OGG1-RNA interaction reduces RSV progeny production. by Lang Pan (5145221)

“…<p>(A) OGG1 depletion, but not NEIL2 or MTH1, reduces RSV progeny yield. …”

Data Sheet 1_A novel short-course, low-intensity blood-flow-restricted exercise (BFRE) regimen to study satellite cell function in critical illness survivors with sustained muscle... by Sunita Mathur (759075)

“…In controls, satellite cell content and MuRF1 transcript expression were significantly higher (1.53 ± 0.30- and 1.34 ± 0.31-fold difference, respectively) and myostatin transcript expression was significantly lower (0.58 ± 0.31-fold difference) in BFRE-trained versus untrained VL. …”

Different agronomic traits associated with pod dehiscence. by JaeBuhm Chun (20958047)

Random forest analysis. by Emir Matpan (21655811)

Effect of the replacement parameter . by Fynn Bachmann (21409831)

The analysis procedure. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Rank of mutation fitness. by Zhong-yi Lei (22552944)

“…Furthermore, we found that accounting for epistasis was critical, as ignoring epistasis led to a 43% decrease in forecasting accuracy. Case studies showed that CoVPF delivered more accurate and timely forecasts for lineage expansions and inflections such as EG.5.1 and XBB.1.5. …”

Soil mechanical parameters. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Parameters of the contact surface. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

The schematic diagram of free-field boundary. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Residual deformation parameters of soil. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Soil fluid and liquefaction parameters. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

PL-Finn model development procedure diagram. by Jie Zhao (49409)

“…Results demonstrate that unreinforced foundations exhibit systematic residual deformation due to liquefaction-induced sand flow, which is significantly reduced by gravel pile reinforcement. Both excess pore water pressure and pore pressure ratio decrease markedly after reinforcement. …”

Key modeling details for CoVPF and controls. by Zhong-yi Lei (22552944)

“…Furthermore, we found that accounting for epistasis was critical, as ignoring epistasis led to a 43% decrease in forecasting accuracy. Case studies showed that CoVPF delivered more accurate and timely forecasts for lineage expansions and inflections such as EG.5.1 and XBB.1.5. …”

RBM39 is methylated by PRMT6 at arginine 92. by Tongjia Zhang (16496661)

Overall model framework. by Ke Yan (331581)

“…On the other hand, taking English writing teaching as an example, the proposed method is further verified by designing a comparative experiment in groups. The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”