Fig 6 - by Akiyoshi Shimatani (11547400)

DataSheet_2_Isoliquiritigenin Derivative Regulates miR-374a/BAX Axis to Suppress Triple-Negative Breast Cancer Tumorigenesis and Development.pdf by Fu Peng (1426402)

“…<p>Triple-negative breast cancer (TNBC) is a subtype of breast cancer that accounts for the largest proportion of breast cancer-related deaths. …”

DataSheet_1_Isoliquiritigenin Derivative Regulates miR-374a/BAX Axis to Suppress Triple-Negative Breast Cancer Tumorigenesis and Development.zip by Fu Peng (1426402)

“…<p>Triple-negative breast cancer (TNBC) is a subtype of breast cancer that accounts for the largest proportion of breast cancer-related deaths. …”

DataSheet1_Macleayins A From Macleaya Promotes Cell Apoptosis Through Wnt/β-Catenin Signaling Pathway and Inhibits Proliferation, Migration, and Invasion in Cervical Cancer HeLa Ce... by Chunmei Sai (11240097)

“…We found that MA inhibited the growth of HeLa cells at 72 h (IC₅₀ = 26.88 µM) via inducing apoptotic process, reduced the proliferation rate by 29.89%, and decreased the cells migration and invasion as compared to the untreated group. …”

TKI increases parkin ubiquitination and interaction with Beclin-1. by Irina Lonskaya (429729)

Trends in late HIV diagnosis among men who have sex with men in Jiangsu province, China: Results from four consecutive community-based surveys, 2011-2014 by Haiyang Hu (140624)

“…MSM who were older than 24 years (aOR = 1.748, <i>p</i> = 0.020 for 25–39 years old; aOR = 3.148, <i>p</i><0.001 for 40 years old or older), were recruited via internet (aOR = 1.596, <i>p</i> = 0.024), and did not have an HIV test in the past 12 months (aOR = 3.385, <i>p</i><0.001) were more likely to be late diagnosed.…”

Pharmacological Inhibition of Dynamin II Reduces Constitutive Protein Secretion from Primary Human Macrophages by Maaike Kockx (154125)

“…Inhibition of dynamin also altered the constitutive secretion of other proteins, decreasing the secretion of fibronectin, matrix metalloproteinase 9, Chitinase-3-like protein 1 and lysozyme but unexpectedly increasing the secretion of the inflammatory mediator cyclophilin A. …”

Mobilized teams grew to a variety of sizes at a variety of rates. by Jeff Alstott (3282036)

“…This example team was the 4^th largest in the contest. (<b>B</b>–<b>C</b>) Using a similar social mobilization incentive system to that used in the present study, previous research suggested the distributions of team sizes and of recruiters' number of recruits followed power laws, with α of 1.96 and 1.69, respectively <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095140#pone.0095140-Pickard1" target="_blank">[12]</a>. …”

Testing a contribution from a self-reflective MB planner. by Rani Moran (3315630)

“…Note that additionally, High, Low and Unrelated rewards all increase the tendency to repeat the reward setting via a non-displayed MF pathway (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008552#pcbi.1008552.g004" target="_blank">Fig 4D and 4E</a>). …”

Information Sampling (Beads) Task and examples of agent performance in high and low certainty environments. by Diana C. Burk (14522613)

“…Because the agents always select the largest action value on each time step, the agents only guess a color when the action value for guessing blue or orange surpasses the action value to draw another bead. …”

Efficient Direct H₂O₂ Synthesis Enabled by PdPb Nanorings via Inhibiting the O–O Bond Cleavage in O₂ and H₂O₂ by Kailei Cao (9962668)

Experimental environment and parameters. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

Results of ablation experiments. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

ECA structural model diagram. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

YOLOv5s general structure diagram. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

Heat maps for different models. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

Defect category statistics. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

CARAFE general structure. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

ECN-YOLOv5s structure diagram. by Haisong Xu (141729)

“…First, an efficient channel attention mechanism (ECA) is inserted in the layer before the SPPF in the backbone network, which realizes efficient computation of channel attention and reduces redundant computation while decreasing the model complication. Second, using the Content-Aware ReAssembly of Features (CARAFE) module instead of the original nearest-neighbor up-sampling module achieves light weighting while allowing for better aggregation of contextual information within a larger sensory field, which effectively improves the diversity and effectiveness of the model. …”

Physical properties of LNPs and LNPs-Lac 99 μg. by Hossein Alishah Aratboni (11032083)

“…When the LNPs synthesis was performed in the presence of laccase, biocatalytically active nanoparticles with a 1.25-fold larger diameter (85 nm) were obtained, and a maximum load of 243 μg laccase per g of nanoparticle was achieved. …”