Surrogate data was generated by randomly switching condition labels (ripple events vs. peri-ripple periods).
The data was generated by the Division of Youth and Family Services' own data analysis and reporting unit last October as part of an internal audit.
Consequently, data was generated by a Monte Carlo technique under the assumption of heterotrophic inhibition to nitrification.
To determine SMX and SMT with a high degree of overlapping, second-order electrochemical data was generated by changing the pulse height as an instrumental parameter.
The LFP data was generated by simulating 16-site electrode array with the help of 'efield' objects arranged at the predetermined positions with respect to the surface of the simulated network.
For two particular cases of growth and agglomeration including size-dependent nuclei formation, simulation data was generated by continuous feeding of nuclei in a certain range to demonstrate the capability of parameter extraction of the model.
Several encoding time windows showed higher replay levels for remote vs. recent items (100 200 ms, 500 700 ms, and 1100 1200 ms; Fig. 3e; all p < 0.035, cluster-corrected for multiple comparisons; surrogate data was generated by switching remote and recent labels).
During waking state and nREM sleep, replay levels of remote items were higher than those of recent items for late windows (waking stage, 1000 1200 ms; p = 0.022; nREM sleep, 500 700 ms; p = 0.021; p values were corrected for multiple comparisons; surrogate data was generated by switching remote and recent labels; Supplementary Fig. 4d, e, left panels).
Data was generated by the duplex qPCR assay on 138 enriched cattle lymph node samples without the internal control, and compared with data on the same samples tested by the triplex qPCR assay that has the 18S rRNA gene as internal control.
Plausible data was generated by the majority of the sensors.
Data was generated by author's own field sampling and laboratory analysis.
