Lymnae stagnalis (pond snail) is emerging as a preferable invertebrate model in understanding neurological mechanisms because of its simple nervous system. A three-cell network mediates behaviours such as aerial respiration and research has shown that small, subtle changes occurring across the network might result in a disruption of natural behaviour (Lukowiak et al. 1995). It is also known that Lymnae features a more developed eye than other molluscs and studies have shown that various wavelengths of light can activate photoreceptors producing distinct electrophysiological responses (Sakakibara et al. 2004). However, no studies have looked beyond the electrophysiological response. The purpose of this project was to determine if coloured light would firstly, elicit a behavioural response as observed in its movement and secondly, affect learning and memory through the operant conditioning of its aerial respiration.

Pedicle probes are medical devices used by surgeons during spinal fusions for patients with conditions such as scoliosis and spinal fractures. The probe creates pilot holes to guide the placement of pedicle screws in vertebrae. The screws are then connected with a metal rod to stabilize the spine. Twenty-nine percent of patients who undergo spinal fusions suffer from vertebral breaches – accidental damage to the spinal cord – which cause complications such as infection, motor defects, and in many cases paralysis. My goal was to make spinal fusions safer by redesigning the pedicle probe to provide surgeons with instantaneous feedback on the probe’s location, enabling them to more accurately place pedicle screws. The pedicle probe I developed takes advantage of the difference in density between the inner cancellous (spongy) bone and the outer cortical (compact) bone found in vertebrae. Cortical bone is avoided by monitoring the cannulation force – the force required to insert the probe. When the probe contacts denser cortical tissue, it warns the user by providing tactile and visual feedback through a vibration motor and an LED. This enables the surgeon to redirect the probe and advance down the optimum path, preventing a possible breach. It proved successful in preventing breaches on lamb vertebrae, which closely resemble human vertebrae. This novel device improves feedback to the surgeon and eliminates the need for costly and potentially harmful ionizing radiation exposure. Furthermore, it does not depend on, or require, any preoperative imaging. The cost of manufacturing the improved probe is less than $42 USD (NT$1297). Results of patent searches for 加拿大, the 美國, and Europe suggest that the redesigned probe is unique in predicting and preventing breaches in spinal fusions based on predetermined force threshold values. The probe is also unique in enabling personalized procedures in spinal fusions for those with complications, through calibrating a control (force) limit based on tissue samples prior to the procedure. Enhancing a surgeon’s ability to determine an appropriate path for pedicle screws through a sensor-enabled probe has the potential to significantly reduce the incidence of vertebral breaches during spinal fusion surgery.

This study demonstrates that microbial film power generation is a potentially viable source of alternative energy. This research occurred over a period of two years. In the first year (2016) I tested a new method of generating renewable energy, referred to as microbial film power generation. I showed that electricity could be captured from microbial decomposition using solid graphite plates (29cm x 20cm) placed in lightly decomposed muskeg (collected in northern British Columbia). In the second year (2017) the purpose was to increase the power output of the fuel cell, while also compacting the setup. Certain changes were made to the experimental set up, namely the use of spongy graphite felt in place of solid graphite plates, thus providing a larger surface area for microbial activity to occur. The new fuel cells made produced about twice as much power. Not only was the power output greater, but it was produced from a much smaller area: 7.82 mWh/cm2 on graphite felt, compared to 0.21 mWh/cm2 on graphite plates. In other words, graphite felt produced 37 times more power per unit area than graphite plates. Furthermore, it would appear that by removing the load from the fuel cell for approximately 24 hours, the fuel cell could essentially recharge. This may be due to microbial activity releasing more electrons onto the anode permitting a new cycle to take place. This would suggest that the system could naturally recharge itself.

Microalgae-fungi biofuel has significantly less CO2 emissions than fossil fuels, making it much more environmentally friendly. As well, unlike traditional biofuel, microalgae-fungi does not require large masses of agricultural land for production. Thus, microalgae-fungi is an optimal option for biofuel production. This is a cost-effective renewable energy source that can be used in place of regular gas in cars and other means of transportation. By determining the most effective fungi for biofuel production, the threat of the impending environmental damage from pollution can be diminished. This novel experiment determines which fungi: Aspergillus niger, Rhizopus stolonifer or Saccharomyces cerevisiae, is the most effective bioflocculant in the microalgae-fungi co-pelletization process for biofuel production. We hypothesize that when paired with the microalgae Chlorella vulgaris, Rhizopus stolonifer will be the most effective. It has a high lipid content which could enhance the overall production of biofuel. Furthermore, its negative charge will aid with attracting and neutralizing the C. vulgaris colloidal particles resulting in an easier and more efficient removal of microalgae particles. Through the process of bioflocculation, pelletization, esterification and transesterification, the most effective fungi paired with C. vulgaris was determined. This experiment was carried out thoroughly and precisely resulting in a cost-effective solution for the world's current pollution crisis.

As our planet suffers the effects of climate change, it is only a matter of time before society will have to centre all aspects of development around sustainability. In the past, clean solutions for transportation have been dismissed due to the higher cost, and lower efficiency than fossil fuels. However, in the past few decades, there has been a steep decline in solar module cost, and and a steady climb towards higher efficiency. From my findings in this project, I have concluded that we are now at a point where we can embrace the clean, renewable potential which our sun offers. I have created and tested a proof-of concept electric vehicle (Solar EV), which can run indefinitely during daylight hours, provided sunny conditions. There are several mechanical features of my project which highlight the potential that renewable energy in transportation can have. Firstly the vehicle’s 500W motor is powered by 3 100W solar modules, and 3 50W modules, for a total of 450W or power generation. This means that when driving at anything less than 90% throttle, the Solar EV can run continuously without needing to stop to charge or refuel. Another design mechanism installed in the vehicle are three 12V lead acid batteries. These batteries allow the Solar EV to be powered for over 1.5 hours, which is useful during cloudy conditions, night, and most importantly, when driving through areas of shade. A unique efficiency component designed into my vehicle is the linear actuator I installed into the module racking system. This design element allows the tilt of the modules to be altered, to maximize the efficiency of the solar module array. At early or late hours of the day, it can be heavily tilted with the press toggle switch, or kept at a relatively flat level when the sun is the highest in the sky. I ran a series of trials to figure out whether or not the theoretical data matches up with the experimental results. After my series of trials, the bike was yet to run out of power. The solar vehicle reaches speeds up to 32 km/h, however comfortably glides at around 25 km/h. The linear actuator I installed allows the solar modules’ tilt to change . During different times of day or year, the sun is at different heights in the sky, however it is very important to maximize the solar potential. With the press of a switch, the module can be actuated to account for this. Lastly, regenerative braking captures the energy from braking. Using the reversible nature of a DC motor with a specialized motor controller responding to feedback from the brake actuators allows the vehicle to reuse energy that would otherwise be wasted as heat.

In all experiments for detection of WIMP dark matter, it is essential to develop a classifier that can distinguish potential WIMP events from background radiation. Most often, clas- sifiers are developed manually, via physical modeling and empirical optimization. This is problematic for two reasons: it takes a great deal of time and effort away from developing the experiment, and the resulting classifiers often perform suboptimally (which means that a greater amount of expensive run time is required to obtain a confident experimental result). Machine learning has the potential to automate this and accelerate experimentation, and also to detect patterns that humans cannot. However, two major challenges, which are shared among several dark matter experiments, stand in the way: impure calibration data, which hinders training of models, and unpredictable physical dynamics within the detector itself. My objective was to develop a set of machine learning techniques that address these two problems, and thus more efficiently generate highly accurate classifiers. I was able to obtain raw data for two dark matter experiments which exhibit these challenges: the PICO-60 bubble chamber [2], and the DEAP-3600 liquid argon scintillator [1]. For each experiment, I developed and compared three general-purpose algorithms intended to resolve its inherent challenge (impurity and unpredictable dynamics, respectively). In PICO-60, background alpha and WIMP-like neutron calibration datasets are used for training; however, there is an impurity of 10% alphas in the neutron set. While a conventional classifier was developed (and is believed to be 100% accurate), machine learning in the form of a supervised neural network (NN) has also been previously explored, because of the benefits of automation. Unfortunately, it achieved a mean accuracy of only 80.2% – not usable as a practical replacement for conventional methods in future iterations of the experiment. In DEAP-3600, photons are absorbed by a wavelength shifting medium and re-emitted in an unpredictable direction, before being detected by one of 255 photomultiplier tubes (PMTs) around the spherical detector. The randomness severely limits the accuracy of conventional classifiers; in a simulation, the best so far removes 99.6% of alpha background, while also (undesirably) removing 91.0% of WIMP events. Because of physical limitations, simulated data is used for calibration, with 30 real-world experimental events available for testing. I have written a research paper [11] about my work on PICO-60, which has been approved by the PICO collaboration and pre-published at https://arxiv.org/abs/1811.11308. It is currently undergoing peer review for publication in Computer Physics Communications. All PICO researchers are listed on my paper for their work on the original PICO-60 experi- ment. They did not contribute to this study; I completed and documented it independently.

This research project focuses on developing a web-based multi-platform solution for augmenting prognostic strategies to diagnose breast cancer (BC), from a variety of different tests, including histology, mammography, cytopathology, and fine-needle aspiration cytology, all in an automated fashion. The respective application utilizes tensor-based data representations and deep learning architectural algorithms, to produce optimized models for the prediction of novel instances against each of these medical tests. This system has been designed in a way that all of its computation can be integrated seamlessly into a clinical setting, without posing any disruption to a clinician’s productivity or workflow, but rather an enhancement of their capabilities. This software can make the diagnostic process automated, standardized, faster, and even more accurate than current benchmarks achieved by both pathologists, and radiologists, which makes it invaluable from a clinical standpoint to make well-informed diagnostic decisions with nominal resources.

Alzheimer’s disease (AD) is a neurodegenerative disease in which current diagnostic tools are invasive and lack the ability to diagnose early-onset dementia. Current antibody-based diagnostic tests for neurodegenerative diseases require invasive measures such as a lumbar puncture, and lack specificity to biomarkers that are found in both healthy individuals and patients with AD. In this project, a design for a carbon dot(CD)-bound bispecific antibody is developed for the minimally- invasive diagnosis of AD. The molecular probe can be easily synthesized with a specificity to amyloid- beta (Aβ) oligomers as it distribution and abundance in the brain suggest they are better predictors of disease progression and are present in the early-onset of the dementia. The bispecific antibody conjugated to the CD displays a low affinity to transferrin receptors (TfRs) which allows the probe to cross the blood-brain barrier via receptor mediated transcytosis leading to a minimally invasive diagnosis. A synthesis technique was developed to conjugate the bispecific antibody to the CD. As a proof of concept, this technique was used to couple bovine serum albumin (BSA) to CDs. The structural and optical properties of the CDs were observed. By synthesizing a novel carbon dot conjugated specific antibody that emits light at a specific wavelength in the near-infra red region, the molecular probe displays optical properties suitable for the minimally-invasive diagnosis using fNIR- spectroscopy.

Due to widespread water shortages, there is an increasing need for innovative water conservation strategies, such as reusing greywater from laundry. The World Health Organization (WHO) recognizes greywater as suitable for plant irrigation, but commercial laundry detergents contain synthetic chemicals that can harm both the environment and plant health. Soap nuts, derived from the Sapindus mukorossi tree, offer a natural alternative. Their pericarp is rich in triterpenoid saponins, amphiphilic compounds, composed of hydrophilic sugar group and hydrophobic triterpenoid sapogenins. These saponins mimic the chemical structure of surfactants in detergents, allowing soap nuts to act as natural foaming and surface-active agents in water. As a result, soap nuts have long been used as a sustainable option for shampoo and laundry detergent in many Asian countries (Sochacki & Vogt, 2022). Greywater, an often overlooked resource, is generated from household activities like laundry, showers, and basins. Unlike blackwater, it contains lower levels of pathogens and bacteria. However, due to a lack of awareness, greywater is frequently mixed with blackwater and directed to the same sewage treatment systems (Greywater Systems: From Recycling to Filtration, n.d.). Greywater accounts for 50-80% of a household’s daily wastewater (Wong, 2011). Repurposing greywater offers a promising and sustainable solution to address water conservation challenges.

1.1. Cystic Fibrosis Biofilms Biofilms are bacterial aggregates in a matrix of polysaccharides, proteins and nucleic acids (Donlan, 2002). They account for 80% of all chronic infections and cause over 500,000 deaths annually. Cystic fibrosis (CF) is a genetic disorder characterized by mucus accumulation in the respiratory tracts (Morrison et al., 2020). This impairs mucociliary clearance, allowing chronic colonization by bacterial biofilms, leading to fatal respiratory failure, lung scarring, and necrosis of pulmonary epithelial tissues (Martin et al., 2021). 1.2. Obstacles in Current Treatments Three major therapies are used against CF biofilms: (1) aminoglycoside antibiotics like tobramycin, (2)non-aminoglycoside antibiotics such as ciprofloxacin and vancomycin, and (3) non-antibiotic therapies including flushing, chlorination, and ultraviolet disinfection. These have two major flaws. First, they are cytotoxic; 30% of patients experience acute kidney injury after three days of intravenous aminoglycoside therapy (Joyce et al., 2017). Furthermore, non-aminoglycoside therapies can cause phospholipid buildup in lysosomes of proximal tubule epithelial cells, accounting for 10-20% of acute renal failure cases. Second, antibiotic resistance due to horizontal gene transfer and mutations has significantly reduced treatment effectiveness. Therefore, cystic fibrosis biofilms remain a critical threat with few effective treatments. 1.3. Salicylate Derivatives This project tackled this issue using an innovative non-antibiotic approach with salicylate derivatives. Salicylates, a class of benzoic acids—benzene-based carboxylic acids (Figure 1)—used in painkillers and blood thinners, were investigated for their antibiofilm potential through a 3-step process: 1. Literature review: Identified three key biofilm therapeutic targets: quorum sensing, bacterial adhesion, and cell motility. Disrupting these pathways would result in biofilm eradication. 2. Molecule Identification: Recognized key molecules in each pathway: LasR, adhesins, and flagellin. Inhibiting these molecules would disrupt the pathways. 3. Screening: Found that salicylates could inhibit the identified molecules, though they had never been tested against cystic fibrosis biofilms.