Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

“…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”

Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

DataSheet_1_Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.xlsx by Wenbo Chen (519725)

“…A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.…”

DataSheet_2_Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.docx by Wenbo Chen (519725)

“…A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.…”

Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

Comparison of the Edge Betweenness and Fast Greedy methods for analysing dialysis network activities in France. by Cécile Couchoud (682106)

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Results of the Fast Greedy clustering in the Aquitaine region. by Cécile Couchoud (682106)

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Description of the networks (Fast Greedy method). by Cécile Couchoud (682106)

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Illustration of the modularity index. by Cécile Couchoud (682106)

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<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. by Riley McGlasson (13876874)

“…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…”

Table_1_Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-... by Jiyue Wu (3814717)

“…Background<p>Ischemia reperfusion injury (IRI) is an inevitable process in renal transplantation, which is closely related to serious postoperative complications such as delayed graft function (DGF), acute rejection and graft failure. …”

Table_4_Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-... by Jiyue Wu (3814717)

“…Background<p>Ischemia reperfusion injury (IRI) is an inevitable process in renal transplantation, which is closely related to serious postoperative complications such as delayed graft function (DGF), acute rejection and graft failure. …”

Table_2_Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-... by Jiyue Wu (3814717)

“…Background<p>Ischemia reperfusion injury (IRI) is an inevitable process in renal transplantation, which is closely related to serious postoperative complications such as delayed graft function (DGF), acute rejection and graft failure. …”

Table_3_Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-... by Jiyue Wu (3814717)

“…Background<p>Ischemia reperfusion injury (IRI) is an inevitable process in renal transplantation, which is closely related to serious postoperative complications such as delayed graft function (DGF), acute rejection and graft failure. …”

Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)