Labelling and detection of B-THP-T target(s). by Lindsay B. Tulloch (1266861)

Level of mucin secretion and water channel expression. by Ji Eun Kim (403026)

The effect of Gas6 recombinant protein on the expression of ZO-1 and occludin in Caco-2 cells without <i>K</i>. <i>pneumoniae</i> infection. by Hitoshi Tsugawa (296864)

Proliferative tissue degeneration is accompanied by a sustained decrease in proliferation, acute apoptotic responses, and progressive accumulation of DDR foci. by Catarina M. Henriques (102294)

“…Data are represented as mean +/− SEM. Scale bar = 50 µm.</p>…”

MVEC:AoAF interactions are dependent on signaling through ALK5. by Rebecca A. Scott (488956)

“…<p>(<b>A</b>) Representative images of microvascular tubules in 3:3 MVEC:AoAF co-cultures in degradable 7.5wt% hydrogels with 3 mM RGD after 14 days of culture with 1 μM A83-01 (ALK5 inhibitor) or DMSO (control). …”

Data cleaning and preparation algorithm. by Ayana Ablayeva (22103708)

“…</p><p>Results</p><p>Over the decade, age-standardized incidence rates decreased from 5.55 to 5.40 per 100,000, while mortality rates rose from 3.75 to 4.75 per 100,000. …”

Nucleotide sequences for hybrid striped bass piscidin gene transcripts. by Scott A. Salger (3072093)

B-THP-Ts bind to C-terminal GFP-PTP tagged F₁ α- and β- subunits. by Lindsay B. Tulloch (1266861)

Most important percentage changes in CoA scenarios. by Inmaculada Villanueva-Baxarias (21454246)

HCV-NS5A protein mediates TGFβ-induced downmodulation of NKG2D. by Damien Sène (237641)

“…B) NS5A-mediated decrease in NK cell lytic potential. …”

An illustration of the subnetwork containing F2, F5, F7, F9 and F11. by Noël Malod-Dognin (8677110)

Effect of D₂/D₃ antagonist sulpiride on GBR-induced recognition memory impairments. by Jill Rocchetti (16883936)

Definitions of social influence metrics (i.e., betweenness centrality or age category) network level indices (weighted (W) diameter, global efficiency and modularity, as well as un... by Maggie Wiśniewska (11965538)

Training set data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane recognition effect. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Structural plane classification. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Mixup data expansion. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Marking example. by Qingjun Yu (1649473)

“…Based on the PyTorch deep learning framework, the initial U²-Net network weights were set, the learning rate was set to 0.001, the training batch was 4, and the Adam optimizer adaptively adjusted the learning rate during the training process. A dedicated network model for segmenting structural planes was obtained, and the model achieved a maximum F-measure value of 0.749 when the confidence threshold was set to 0.7, with an accuracy rate of up to 0.85 within the range of recall rate greater than 0.5. …”

Phenotype of Ad5-specific CD8⁺ T-cells. by Natalie A. Hutnick (234410)

“…<p>Five seronegative (Ad5 nAb titer ≤18, gray circles) and five seropositive subjects (Ad5 nAb titer >18, white circles) received Merck Ad5 gag/pol/nef as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014385#s2" target="_blank">Methods</a>. …”

Description of results of SD-TOPSIS application (combination A) using selected moment characteristics. by Roman Vavrek (13200141)