Spinal cord injury therapy through active learning

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.

Engineered underdominance as a method of insect population replacement and reproductive isolation

Insect vector-borne diseases, such as malaria and dengue fever (both spread by mosquito vectors), continue to significantly impact health worldwide, despite the efforts put forth to eradicate them. Suppression strategies utilizing genetically modified disease-refractory insects have surfaced as an attractive means of disease control, and progress has been made on engineering disease-resistant insect vectors. However, laboratory-engineered disease refractory genes would probably not spread in the wild, and would most likely need to be linked to a gene drive system in order to proliferate in native insect populations. Underdominant systems like translocations and engineered underdominance have been proposed as potential mechanisms for spreading disease refractory genes. Not only do these threshold-dependent systems have certain advantages over other potential gene drive mechanisms, such as localization of gene drive and removability, extreme engineered underdominance can also be used to bring about reproductive isolation, which may be of interest in controlling the spread of GMO crops. Proof-of-principle establishment of such drive mechanisms in a well-understood and studied insect, such as Drosophila melanogaster, is essential before more applied systems can be developed for the less characterized vector species of interest, such as mosquitoes. This work details the development of several distinct types of engineered underdominance and of translocations in Drosophila, including ones capable of bringing about reproductive isolation and population replacement, as a proof of concept study that can inform efforts to construct such systems in insect disease vectors.