Journal: Toxicology Reports

Article Title: Introducing an interesting and novel strategy based on exploiting first-order advantage from spectrofluorimetric data for monitoring three toxic metals in living cells

doi: 10.1016/j.toxrep.2022.03.049

Figure Lengend Snippet: Predicted concentrations of the validation set by different algorithms.

Article Snippet: First-order multivariate calibration algorithms including PLS, PCR, OSC-PLS, CPR, RCR, PRM, smoothing of the data and elliptical joint confidence region (EJCR) were run in MATLAB (Version 7.5) by the use of a series of m-files.

Techniques: Biomarker Discovery

Journal: Toxicology Reports

Article Title: Introducing an interesting and novel strategy based on exploiting first-order advantage from spectrofluorimetric data for monitoring three toxic metals in living cells

doi: 10.1016/j.toxrep.2022.03.049

Figure Lengend Snippet: The REP and RMSEP values related to the prediction of the validation set by different algorithms.

Article Snippet: First-order multivariate calibration algorithms including PLS, PCR, OSC-PLS, CPR, RCR, PRM, smoothing of the data and elliptical joint confidence region (EJCR) were run in MATLAB (Version 7.5) by the use of a series of m-files.

Techniques: Biomarker Discovery

Journal: Toxicology Reports

Article Title: Introducing an interesting and novel strategy based on exploiting first-order advantage from spectrofluorimetric data for monitoring three toxic metals in living cells

doi: 10.1016/j.toxrep.2022.03.049

Figure Lengend Snippet: (A), (B) and (C) Ellipses obtained by EJCR related to the prediction of the concentration of Pb, Zn and Cd, respectively. Blue ellipse, pink ellipse, green ellipse, yellow ellipse, black ellipse and red ellipse are related to OSC-PLS, PLS, PRM, CPR, RCR and RCR, respectively. The black point shows the ideal point.

Article Snippet: First-order multivariate calibration algorithms including PLS, PCR, OSC-PLS, CPR, RCR, PRM, smoothing of the data and elliptical joint confidence region (EJCR) were run in MATLAB (Version 7.5) by the use of a series of m-files.

Techniques: Concentration Assay

Journal: eLife

Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

doi: 10.7554/eLife.98739

Figure Lengend Snippet: ( A ) Participants slept inside a scanner from ~23:00 to ~07:00 for two consecutive nights, with concurrent EEG-fMRI recording. During each night, the fMRI experiments were intermittently disrupted by either acoustical arousals (eight random arousals) or spontaneous awakenings. Sleep stages and slow wave density were derived from EEG signals alone. ( B ) Hidden Markov model (HMM) was trained on the principal components of fMRI signals of night 2. Then the identified HMM states were generalized to night 1 fMRI signals. Finally, we studied the state-related variations in fMRI activation, FC patterns, and EEG measures. Notes: EEG, electroencephalographic; TR: repetition time; FC, functional connectivity; ROI, region of interest; PCA, principal component analysis.

Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

Techniques: Derivative Assay, Activation Assay, Functional Assay

Journal: eLife

Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

doi: 10.7554/eLife.98739

Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of an example run.

Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

Techniques:

Journal: eLife

Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

doi: 10.7554/eLife.98739

Figure Lengend Snippet: State timecourse of Hidden Markov model (HMM) states and its associations with polysomnography (PSG) stages, variation in photoplethysmography (PPG) amplitude, and variations in RespRVT signals of a second example run.

Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

Techniques:

Journal: eLife

Article Title: Reproducible, data-driven characterization of sleep based on brain dynamics and transitions from whole-night fMRI

doi: 10.7554/eLife.98739

Figure Lengend Snippet: The error bars represent the standard error of the mean. Panel ( A ) free energy; Panel ( B ) maximum Occupancy (percentage); Panel ( C ) median Occupancy (percentage); Panel ( D ) Wilk’s Λ; Panel ( E ) mean Hidden Markov model (HMM) state Lifetime (TR, 3 s).

Article Snippet: We employed a Gaussian HMM using the Matlab toolbox HMM-MAR v1.0 ( https://github.com/OHBA-analysis/HMM-MAR , copy archived at ), where each state was modeled as a multivariate normal distribution encompassing both first-order statistics (mean activity) and second-order statistics (covariance matrix).

Techniques: