Image_9_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_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.DOC by Zackary J. Delisle (10200560)

Table_1_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.docx by Zackary J. Delisle (10200560)

Table_6_Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation.XLSX by Zackary J. Delisle (10200560)

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). …”