<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</li></ul><p dir="ltr">The scripts rely on the following Python packages. Where available, repository links are provided:</p><ol><li><b>NumPy</b>, version 1.22.1</li><li><b>SciPy</b>, version 1.7.3</li><li><b>PyGAD</b>, version 3.0.1 — https://pygad.readthedocs.io/en/latest/#</li><li><b>bees-algorithm</b>, version 1.0.2 — https://pypi.org/project/bees-algorithm</li><li><b>KrakenOS</b>, version 1.0.0.19 — https://github.com/Garchupiter/Kraken-Optical-Simulator</li><li><b>matplotlib</b>, version 3.5.2</li></ol><p dir="ltr">All scripts are modular and organized to reflect the design stages described in the manuscript.…”

EFGs: A Complete and Accurate Implementation of Ertl’s Functional Group Detection Algorithm in RDKit by Gonzalo Colmenarejo (650249)

“…For a RDKit molecule, it provides (i) a PNG binary string with an image of the molecule with color-highlighted functional groups; (ii) a list of sets of atom indices (idx), each set corresponding to a functional group; (iii) a list of pseudo-SMILES canonicalized strings for the full functional groups; and (iv) a list of RDKit labeled mol objects, one for each full functional group. …”

FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Comparison of algorithm performance in ZDT1 and ZDT2 function tests. by Tao Dong (15551)

“…<p>Comparison of algorithm performance in ZDT1 and ZDT2 function tests.…”

Performance comparison of algorithms in ZDT5 and ZDT6 function tests. by Tao Dong (15551)

Comparison of algorithm performance in ZDT3 and ZDT4 function tests. by Tao Dong (15551)

Expression and functional analysis of MRGs. by Xuefen Lin (17785367)

Algorithm framework. by Zongjin Li (38031)

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Swarm-averaged relative link counts <i>U</i>_<i>r</i> as a function of simulation tick-count <i>t</i> for the different transmission tactics. by Paul Kinsler (3975620)

Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

Functional examination of the 6 key significant genes found in modules 1 and 2. by Ravinder Sharma (13154079)

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Flowchart of the general algorithm for one trial. by Junying Wang (509009)

Functional pathways and evaluation of mutation between the two MRG-related risk groups. by Xuefen Lin (17785367)

Visualization for the module 1. by Zongjin Li (38031)

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

GO analysis bubble plot for module 1. by Zongjin Li (38031)

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Pathway analysis bubble plot for module 1. by Zongjin Li (38031)

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Functional enrichment of DEGs. by Man Wang (110466)

“…</p><p>Methods</p><p>The study employed a combination of differential expression analysis, weighted gene co-expression network analysis (WGCNA), and various machine learning algorithms to screen for characteristic genes. Gene set enrichment analysis (GSEA), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to evaluate relevant biological functions and pathways. …”

Table 1_Extracellular microRNAs modulate human microglial function through TLR8.docx by Hannah Weidling (14422749)

“…Using ELISA, scratch assay, and FACS, we investigated the miRNAs’ potential to modulate iMGL function, including cytokine release, motility, and phagocytosis, respectively. …”