The <i>ENT2</i> knockout effects on CRC proliferation and survival. by Safaa M. Naes (22075434)

“…<p>(A) Cell viability of HT29/KO as measured by the MTT assay. It was significantly lower in both HKO1 and HKO2 than in the control NTC (B) Cell viability of DLD1/KO with no significant difference between the DKO clone and NTC cells. …”

Timeline for study enrollment. by Saeun Park (20410160)

“…</p><p>Conclusions</p><p>Negative COVID-19 experiences were significantly associated with more severe depression and anxiety in Ugandan caregivers, regardless of their children’s malaria status. …”

Dataset used in the manuscript. by Saeun Park (20410160)

“…</p><p>Conclusions</p><p>Negative COVID-19 experiences were significantly associated with more severe depression and anxiety in Ugandan caregivers, regardless of their children’s malaria status. …”

Reversible Adsorption and Interfacial Photoisomerization of Azobenzene Surfactants Studied by QCM by Maren Umlandt (9638121)

“…Using quartz crystal microbalance (QCM) measurements, we show that the interfacial mass change is governed by the isomeric composition in the bulk solution: the <i>trans</i> isomer exhibits strong adsorption, while the <i>cis</i> isomer is significantly less surface-active. We further quantify the photoisomerization kinetics at the interface, revealing that the isomerization rate constant decreases with a lower <i>trans</i> isomer concentration due to a transition from a diffuse multilayer to a confined double-layer structure. …”

Evidence of the Giant Barocaloric Effect in the PVA-Slime System by Molecular Dynamics Simulations by Richard Javier Caraballo-Vivas (22113727)

“…This, combined with the applied simulated pressure, decreases the mobility of the polymer chains, lowering their kinetic energy while favoring potential energy. …”

Evidence of the Giant Barocaloric Effect in the PVA-Slime System by Molecular Dynamics Simulations by Richard Javier Caraballo-Vivas (22113727)

“…This, combined with the applied simulated pressure, decreases the mobility of the polymer chains, lowering their kinetic energy while favoring potential energy. …”

The performance of eigenvectors of the covariance matrix in a model with more complex population structure. by Joshua G. Schraiber (8027060)

Classification of Anti-HlyE IgG Responses. by Jo Walker (22404069)

PERCOM, an unbiased and easily-accessible protein-centric analysis workflow. by Brian Foo (19273132)

Alternative representation of daunorubicin fluorescence testing of novel inhibitors. by Lauren A. McCormick (20540638)

The overall framework of CARAFE. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

KPD-YOLOv7-GD network structure diagram. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison of different pruning rates. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison of experimental results at ablation. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Result of comparison of different lightweight. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

The parameters of the training phase. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Structure of GSConv network. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”