Image_4_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.TIFF by Zackary J. Delisle (10200560)

Image_3_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.TIFF by Zackary J. Delisle (10200560)

Image_6_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.TIFF by Zackary J. Delisle (10200560)

Image_8_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.TIFF by Zackary J. Delisle (10200560)

Table_4_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Data_Sheet_1_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.ZIP by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Table_3_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Table_1_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Table_7_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Table_6_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Image_1_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.TIF by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Table_5_CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.docx by Omneya Attallah (770722)

“…Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. …”

Mutation in <i>mtor</i> impairs RPE regeneration. by Fangfang Lu (664493)

“…(C) Quantification of the p-S6 signal in the RPE layer revealed a significant decrease in MTZ⁺ <i>mtor</i>^-/- larvae, when compared to MTZ⁺ <i>mtor</i>^+/+ siblings. …”

Evaluating Strategies to Normalise Biological Replicates of Western Blot Data by Andrea Degasperi (515010)

Effect of plant extracts from Cerrado on Alternaria leaf spot control in tomato plants by Bruna Fukumoto Kobayashi (5378267)

“…The experiment was performed in two steps: 1- in vivo (in a greenhouse under hydroponic system) and 2- in vitro (in the laboratory). …”

Table_2_Habenular Involvement in Response to Subcallosal Cingulate Deep Brain Stimulation for Depression.DOCX by Gavin J. B. Elias (12044813)

“…Clinical response to treatment (≥50% decrease in HAMD-17 from baseline to 12 months post-operation) was significantly associated with longitudinal Hb volume change: responders tended to have increased Hb volume over time, while non-responders showed decreased Hb volume (t = 2.4, p = 0.021). …”

Src Family Kinases Modulate the Loss of Endothelial Barrier Function in Response to TNF-α: Crosstalk with p38 Signaling by Alejandro P. Adam (55143)

“…Treatment of DN-Csk-expressing cells with 50 pg/ml TNF-α induced a loss of TEER as well as drastic changes in the actin cytoskeleton and focal adhesion proteins. …”

Paraquat-induced cell loss is strain dependent. by Yun Jiao (115357)

“…<p>(A) After chronic PQ administration, we find that C57BL/6J have a ≈50% decrease in SNpc DA neurons, while SWR have a ≈10% loss of SNpc neurons. …”

Time course of <i>K</i>. <i>pneumoniae</i> susceptibility to CHX. by Stephen Buxser (11302550)

“…<p>The results are from a single representative linear regression analysis from among 50 permutation runs. …”

Dynamic modeling of rapid calcium signaling in spines and dendrites. by L. Niels Cornelisse (67656)

“…Quantification of fluorescence step sizes (middle panel) and fluorescence decreases after APs (right panel) during the AP train calculated by the model cf <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001073#pone-0001073-g002" target="_blank">Figure 2F</a>.…”