Data_Sheet_1_Dissect Relationships Between Gene Co-expression and Functional Connectivity in Human Brain.pdf by Xue Zhang (166886)

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Reward function related parameters. by Honglei Pang (22693724)

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Penalty functions used in different methods. by Souvik Seal (12437166)

Application of the algorithm to unseen data. Presynaptic neurons labelled by rabies viral injection into layer 2/3 primary visual cortex in Penk-Cre mice. by Adam L. Tyson (10892822)

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Co-simulation architecture. by Honglei Pang (22693724)

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Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Data_Sheet_1_Abnormal Brain Functional Network Dynamics in Acute CO Poisoning.docx by Hongyi Zheng (816158)

“…</p><p>Methods: Combining the sliding window method and k-means algorithm, we identified four recurrent dynamic functional cognitive impairment states from resting-state functional magnetic resonance imaging data from 29 patients in the acute phase of carbon monoxide poisoning and 29 healthy controls. …”

Data_Sheet_1_Application of Machine Learning and Weighted Gene Co-expression Network Algorithm to Explore the Hub Genes in the Aging Brain.DOCX by Keping Chai (11574214)

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Functional profiling of GS height <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression. by Eugenia Radulescu (319733)

Functional profiling of GS-SCZ <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression. by Eugenia Radulescu (319733)

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). …”

Data_Sheet_1_Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.DOCX by Eric Maltbie (445150)

“…The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. …”

Video_1_Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics.MP4 by Eric Maltbie (445150)

“…The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. …”

<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.…”

Functional profiling of GS height <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression; PITT sample. by Eugenia Radulescu (319733)

Functional profiling of GS-SCZ <i>preserved</i> modules with cumulative evidence for genomic risk effect on co-expression- PITT sample. by Eugenia Radulescu (319733)

Main parameters of braking system. by Honglei Pang (22693724)

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Simulation Results Plot of the SBW System Response: (a) Step response curve of the SBW system; (b) Sine response curve of the SBW system; (c) Steady state error curve of the SBW sy... by Honglei Pang (22693724)

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EMB and SBW system structure. by Honglei Pang (22693724)

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