Uses pre-trained neural networks to extract distance distributions from DEER data. See our paper (https://dx.doi.org/10.1126/sciadv.aat5218) for further details.
deer_trace - experimental DEER trace, a real column vec- tor, must be phased into pure absorption and cropped on the left to make the first point correspond to the echo modulation maximum time_axis - experimental time axis in seconds, a column vector; must start at zero and have uniform time step
The function displays a figure if no outputs are requested, or returns a data structure with the following fields.
dataset.input. time_axis - time axis, as supplied by the user deer_trace - DEER trace, as supplied by the user dataset.deernet. library - the parameters of the library with which current DEERNet had been trained resamp_axis - time axis after the data was resampled to match DEERNet inputdimension resamp_trace - DEER trace after the data was resampl- ed to match DEERNet input dimension deernet_rmin - left edge of the distance grid as com- puted from the user-supplied time trace duration and DEERNEt input point count deernet_rmax - right edge of the distance grid as com- puted from the user-supplied time trace duration and DEERNEt input point count input_rmin - left edge of the distance grid as compu- ted from the user-supplied time trace du- duration and point count input_rmax - right edge of the distance grid as compu- ted from the user-supplied time trace du- duration and point count dataset.output. background_mean - arithmetic mean of the background sig- nal over the netset background_lb - 95% confidence interval lower bound for the background signal obtained from net- set statistics background_ub - 95% confidence interval upper bound for the background signal obtained from net- set statistics dist_distr_mean - arithmetic mean of the distance dist- ributions over the netset dist_distr_lb - 95% confidence interval lower bound for the distance distribution obtained from netset statistics dist_distr_ub - 95% confidence interval upper bound for the distance distribution obtained from netset statistics dist_axis - the distance axis matching the distance dis- ribution and satisfying the constraints im- posed by both the timing of the input signal or the limits of DEERNet, whichever is more stringent mdepth - modulation depth estimate
First, the experimental data is loaded:
% Load experimental data [deer_trace,time_axis]=elexsys2deernet('data_deeran/CT_DEER_mix_28_36/CT_DEER_mix_28_36');
The data is then sent to DEERNet:
% Run deernet analysis deernet(deer_trace,time_axis);
With no output arguments requested in the call to DEERNet, the following plot is produced.
Further examples are available under examples/deernet/
- example_set_paper.m - The six examples used in the paper on DEERNet.
- example_set_deeran.m - Selection of the examples included with the DeerAnalysis package.
- Do not supply excessively long baseline tails in the input data - unlike Tikhonov tools, DEERNet does not need those, it works better if you cut off baseline tails.
- The input DEER trace must be cropped on the left to make the first point of the trace correspond to the maximum.
This is done automatically in elexsys2deernet.m, and this is the only preprocessing required by DEERNet - it can handle background and noise.