3 research outputs found
Identification of Neural Outgrowth Genes using Genome-Wide RNAi
While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system
Data reduction algorithms to enable long-term monitoring from low-power miniaturised wireless EEG systems
Objectives: The weight and volume of battery-powered wireless electroencephalography
(EEG) systems are dominated by the batteries. Battery dimensions are in
turn determined by the required energy capacity, which is derived from the system
power consumption and required monitoring time. Data reduction may be carried
out to reduce the amount of data transmitted and thus proportionally reduce
the power consumption of the wireless transmitter, which dominates system power
consumption. This thesis presents two new data selection algorithms that, in addition
to achieving data reduction, also select EEG containing epileptic seizures and
spikes that are important in diagnosis.
Methods: The algorithms analyse short EEG sections, during monitoring, to
determine the presence of candidate seizures or spikes. Phase information from
different frequency components of the signal are used to detect spikes. For seizure
detection, frequencies below 10 Hz are investigated for a relative increase in frequency
and/or amplitude.
Significant attention has also been given to metrics in order to accurately evaluate
the performance of these algorithms for practical use in the proposed system.
Additionally, signal processing techniques to emphasize seizures within the EEG
and techniques to correct for broad-level amplitude variation in the EEG have been
investigated.
Results: The spike detection algorithm detected 80% of spikes whilst achieving
50% data reduction, when tested on 992 spikes from 105 hours of 10-channel scalp
EEG data obtained from 25 adults. The seizure detection algorithm identified 94%
of seizures selecting 80% of their duration for transmission and achieving 79% data
reduction. It was tested on 34 seizures with a total duration of 4158 s in a database
of over 168 hours of 16-channel scalp EEG obtained from 21 adults. These algorithms
show great potential for longer monitoring times from miniaturised wireless
EEG systems that would improve electroclinical diagnosis of patients