Advertisement · 728 × 90
#
Hashtag
#Electroantennograms
Advertisement · 728 × 90
SICB journals
SICB journals
@sicbjournals.bsky.social
4 months ago
Post image

Meet more of IOB's co authors of
Classification of #Odor -Derived #Electroantennograms with Machine Learning
doi.org/10.1093/iob/...
Marissa Dominguez
sites.uw.edu/wlaursen/peo...
Tom Daniel
formerly of Univ of Washington
www.wrfseattle.org/news/washing...

#insects #entomology #sensory

2 1 0 0
SICB journals
SICB journals
@sicbjournals.bsky.social
4 months ago
Post image

Meet IOB coauthors from the
riffelllab.org
who co-authored
Classification of Odor-Derived #Electroantennograms with Machine Learning O
Joshua Swore, Melanie Anderson, Marissa Dominguez, Tom Daniel, Jeff Riffell
doi.org/10.1093/iob/...

#insects #sensory #entomology #neurons #science

0 0 0 0
SICB journals
SICB journals
@sicbjournals.bsky.social
4 months ago
fig 1 Experimental set-up, data processing, and Pipeline. (A) Experimental set-up. An excised M. sexta antenna is suspended between electrodes and exposed to volatile organic compounds (VOCs). The local field response of the entire antenna is recorded. (B) Signal normalization. Antennae responses were calibrated with ylang-ylang; responses to VOCs were normalized to the maximum and minimum of the ylang-ylang response. This technique resulted in reduced variance, indicated by the narrower shaded region in the normalized response plot, across experiments for each VOC tested. (C) Workflow. Data were collected, normalized, and passed through quality control where waves containing irrelevant frequencies were removed. A genetic algorithm (GA) was used to tune the parameters of a butterworth filter for signal filtering. The filtered time-series data was used in the SVM while principal components analysis (PCA) was used to produce dimensionally reduced components for the RF classifier.

fig 1 Experimental set-up, data processing, and Pipeline. (A) Experimental set-up. An excised M. sexta antenna is suspended between electrodes and exposed to volatile organic compounds (VOCs). The local field response of the entire antenna is recorded. (B) Signal normalization. Antennae responses were calibrated with ylang-ylang; responses to VOCs were normalized to the maximum and minimum of the ylang-ylang response. This technique resulted in reduced variance, indicated by the narrower shaded region in the normalized response plot, across experiments for each VOC tested. (C) Workflow. Data were collected, normalized, and passed through quality control where waves containing irrelevant frequencies were removed. A genetic algorithm (GA) was used to tune the parameters of a butterworth filter for signal filtering. The filtered time-series data was used in the SVM while principal components analysis (PCA) was used to produce dimensionally reduced components for the RF classifier.

IOB
"We use antennae of the Manduca sexta #moth to record LFPs generated in response to Floral and #disease associated VOC’s..."
from

Classification of Odor-Derived #Electroantennograms with Machine Learning

Joshua Swore et al

doi.org/10.1093/iob/...

#insects #entomology #olfaction

1 0 0 0
SICB journals
SICB journals
@sicbjournals.bsky.social
4 months ago
Post image

IOB's latest!
Classification of Odor-Derived #Electroantennograms with Machine Learning

Joshua Swore, Melanie Anderson, Marissa Dominguez, Tom Daniel, Jeff Riffell

doi.org/10.1093/iob/...

#insects #olfactory #disease #science #biology #research

2 0 0 0