Earn 6 research credits over 6 weeks of study at the University of Queensland. You will participate in weekly research group meetings, and attend departmental seminars that relate to the discipline of your project. Available research projects will focus on math, engineering, biology, physics and chemistry. You will complete a minimum of 240 hours of research.
|AUUQ RSLW 392S||International Independent Research in STEM Fields||6|
Today, the threat of extensive bacterial resistance to antibiotics has reignited interest in alternative strategies to treat infectious diseases, with silver regaining well-deserved renewed attention. Silver ions are highly disruptive to bacterial integrity and biochemical function, with comparatively minimal toxicity to mammalian cells. The project is about applying silver alone or in the synergistic combination therapy with traditional antibiotics or other known antimicrobial agents to improve the drug's antimicrobial potency.
Background that the students need to have: Some interest in working with silver/metal ions and skills in analytical chemistry (be able to prepare percentage and molar concentrations), microbiology (be able to streak bacteria on agar plates).
HPV causes approx 5% of human cancers. Vaccine co-developed by the head of our lab (Prof Ian Frazer) is effective, however does not provide therapeutic benefit to individuals already infected with HPV. To develop therapies, we are on a mission to understand how HPV causes cancers. We know that E7 is one of the oncoproteins from HPV which causes immune suppression, however the exact mechanism is unclear. We have recently identified a few cytokines to be of interest and would like to understand their involvement in E7 driven immune suppression. This project will involve characterizing a few immune populations in mouse skin and lymph nodes when our cytokines of interest are absent or over-induced.
The work content moderators do for social media platforms is not easy. They often need to spend 8 hours a day looking at explicit material such as drugs, porn, and terrorism. Even if AI can help, given the scale of digital content that needs to be manually checked for compliance with platform guidelines, there is an increasing need for a human workforce doing this type of job. In this project, we will validate the assumption that a crowdsourcing approach to content moderation is possible. The goal is to perform experiments using an online crowdsourcing platform like Amazon MTurk to crowdsource content moderation tasks after training crowd workers about content moderation guidelines such as, for example, Facebook community standards. The expected work involves the design, validation, and deployment of content moderation tasks as well as the subsequent data collection and analysis to perform data-driven observations (e.g., using appropriate evaluation measures such as assessor agreement metrics) on the feasibility of crowdsourcing as a methodology for online content moderation.
In this project, students will work with a large data set that has already been collected. The goal of the project is to analyze the data set, which consists of Likert scale data and open text responses. We want to understand the roles and career pathways of faculty members who are employed as teaching specialists in the sciences in Australian universities.
Background that the students need to have: An interest in learning to analyze non-traditional "science" data is important. This is a social science project. The supervisor, Prof Rowland, is the coordinator of the for-credit course offered to Arcadia students. My group published actively, and most of my publications are co-authored with student researchers. You can read more about Prof Rowland here.
Nitrogen fertilizer use in agriculture is inefficient, costly and can be environmentally damaging. Legume crops represent an economically and environmentally sound alternative, as their relationship with nitrogen-fixing soil bacteria enables them to thrive in the absence of nitrogen fertilizer. The bacteria (commonly referred to as rhizobia) are housed in specialized root organs, called nodules. Identifying critical components in the development and control of legume nodules is now needed to optimize the process and improve agriculture sustainability. The project aims to discover and functionally characterize novel factors that are regulated by acid soils to inhibit nodule formation. Findings could enhance the current nodulation model and could help to underpin future strategies to reduce the over-reliance on nitrogen fertilizer use in agriculture.
Interplant movement in caterpillars is often suspected, but difficult to study. We will use fluorescent dust to mark and track late instar larval movements (III-V instar) in a field population of Danaus plexippus in southeast Queensland, Australia. In preliminary studies, the degree of movement appears to be high and we suspect has been underestimated in previous work. Caterpillar movement between plants questions the relevance of “mother-knows-best” interpretations of oviposition behavior in relation to immature performance. The latter has as much to do with larval movement behavior as it does with female host plant choice.
This project will aim to understand the links between environmental toxicants, micronutrients and neurodevelopmental disorders.
High caffeine intake has been associated with adverse pregnancy outcomes, such as low birth weight. However, observational studies are prone to confounding and conclusions regarding causality cannot easily be drawn. Mendelian randomization (MR) is a method that uses genetic data to provide information on causality in observational studies. We will use MR to explore if the previously observed relationship between maternal caffeine intake and low birthweight is causal. If the student is interested, other maternal exposures could be added to the project.
Background that the students need to have: Interested students should be familiar with the software R. It would also be preferred if the students have some background in bioinformatics, genetics or epidemiology.
Despite advances in treatment and earlier detection, cancer is still the main cause of cancer death worldwide. Natural killer (NK) cells are circulating innate lymphocytes that naturally protect against tumor spread (metastasis), and recently shown by our group as dysfunctional in the environment (niche) established by cancers at distant organs for future metastatic spread. Yet, despite knowing that NK cells do control cancer metastasis, our knowledge of how cancer cells evade NK cell control is still very poor. This project aims to examine several immune-suppressive pathways that cancers likely manipulate to avoid NK cells and spread. These include factors like adenosine, transforming growth factor (TGF)-β superfamily, among others, that are elevated in the tumor environment. These molecules have great potential to suppress the normally high killing and anti-metastatic activity mediated by NK cells, but to date, we don’t fully understand the molecular mechanisms of suppression each pathway might be.
The aim of this project is to map the spatial and temporal characteristics of dust transport from Australia’s east coast that occurred from August 2019 to December 2019 in response to widespread severe drought. This knowledge will inform the debate on the impact of the dust on the Southern Hemisphere climate and biogeochemical processes.
Our work centers on the world’s most deadly pathogen, Mycobacterium tuberculosis, causing TB. The TB bacterium is difficult to treat and it’s becoming harder, with many strains infecting people that are resistant to many of our existing antibiotics. We would like to harness the survival skills of the bacterial world and force one bacteria to try and kill another. We will set up a mixed culture and force evolution to produce bacteria that can make antibiotics that can kill TB. We will monitor progress with exciting, modern molecular biology techniques.
Interested students should have a knowledge of and experience with microbiology, specifically, bacteriology exposure/skills. General molecular biology knowledge and interest in combating superbug drug resistance are ideal.
The teaching team for MICR2000 is looking to expand upon an existing series of videos featuring laboratory techniques. This will include editing video footage, creating molecular animations, and analyzing video viewing analytics through YouTube. The student can be involved in one or multiple aspects of online resource creation, with the finished product(s) delivered to students in semester 2 of 2020.
Interacting particle systems arise in many areas including population biology, sociology and statistical physics. These systems are finite-state Markov chains so they can in principle be simulating using the Doob-Gillespie algorithm. The aim of the project is to study fast methods of generating (approximate) realizations of interacting particle systems that incorporate heterogeneity and spatial structure.
Follicular helper T (TFH) cells are a specialized CD4+ T cell subset that critically controls antibody responses by supporting antibody affinity maturation and memory formation. The normal function of TFH cells is essential to support antibody responses underlying most successful vaccination while continuous stimulations from autoantigens and pro-inflammatory cytokines drive excessive TFH differentiation in autoimmune diseases. This project will adopt a newly developed in vitro culture system to understand how to control the function of TFH cells to boost vaccination and treat autoimmune diseases.
Optical methods are well suited to study bulk properties of materials and devices but are typically insensitive to surface phenomena. Recently, reflectance difference spectroscopy (RDS) has emerged as a powerful tool to study adsorption, growth, and the electronic states of very thin molecular layers. In this project, the student will help construct an RDS system for a precision deposition instrument.
Precision studies in heavy atoms may be used to test the standard model of particle physics and search for new physics beyond, complementing studies at high-energy colliders. Several projects are available to develop state-of-the-art theoretical methods and perform high-precision calculations of effects in atoms of current interest.
Short peptides (sPEPs)that are encoded by short Open Reading Frames (sORFs) are surprisingly common in eukaryote genomes. Recently, a mutation in a sPEP has been associated with a genetic disorder. Recent bioinformatic and ribosomal footprinting studies have identified several thousand sORFs with coding potential and several sPEPs have been identified by mass spectrometry. However, their role in cellular functions remains to be determined. You will identify and characterize sPEPs using bioinformatic tools, proteomics (mass spec) and cell biology. You will help to determine the contribution of sPEPs to the human proteome, and provide insights into their roles. This project will involve analyzing raw proteomic (mass spec) data.
This project will involve calculations on materials, electrolytes and/or catalysts to assist in the selection of new materials for energy storage systems. It will focus on potential applications in supercapacitors, fuel cells and rechargeable batteries.
The ATB is a web-based molecular topology builder and repository used by 10,000's of researchers worldwide involved in materials research and computational drug design. The site currently contains parameters for over 286,000 compounds (see: https://atb.uq.edu.au). The student on this project will be part of a team focused on further improving the reliability of the parameters generated.
They will assist by either a) testing the predictive power of the parameters for a range of compounds by calculating properties such as their free energy of solvation in different environments or b) by helping to further develop the algorithms and code underlying the ATB web site.
We will be extracting Australian plants that have been identified by Indigenous collaborators as those used medicinally by Indigenous groups. We will be using column chromatography, HPLC, GCMS, NMR and other state of the art techniques to isolate and identify the potentially active compounds in these plants.
The project will focus on using computer simulation (QU-GENE and R) to simulate the genomic selection in perennial ryegrass breeding program.
Machine learning has become a widely used tool to solve problems and model outcomes for a multitude of applications. However, the “black box problem” - where inputs and outputs to the machine learning algorithm can be assessed, but the internal workings of the model cannot - is a significant drawback to its for modeling physical processes. The inability to accurately describe the model being used by machine learning methods has resulted in a reluctance to accept and use these models by the larger scientific community. Identifying potential physical signals within machine learning models would be a significant step towards addressing the “black box problem” and may result in greater acceptance of these tools by the larger community. This project will focus on the modeling of 4-dimensional heat transport within a mountain snowpack. Several different machine learning methods will be used to develop models based on snowpack temperature data that was collected during a 2018-2019 field campaign in Australia’s Snowy Mountains. These machine learning models will then need to be compared to the established physical snowpack models that are accepted by the cryosphere community to determine whether physical signals can be isolated within the models. Students interested in this project will need to have a strong foundation in mathematics and be proficient in programming with either the Python or R languages.
Textural sensory attributes of food fluid can be predicted by analyzing its lubrication behavior between oral-modeled surfaces. This project aims to gain a fundamental understanding of this behavior by studying friction properties of colloidal solutions on surfaces with controlled morphologies.
Students will have a chance to develop instrumental skills such as a microscope, particle size analysis, rheology and tribology measurements. In the meantime, students will develop knowledge and skills in the area of food science and technology, chemical engineering and data analysis.
In this project, students will be examining wild banana plants to look for resistance to Fusarium wilt. Identified genes may be deployed against this important agricultural disease in commercial banana cultivars. Flexible research activities could involve glasshouse screening with the fungal pathogen, molecular analysis including PCR, RNA seq, and bioinformatics to identify genes associated with resistance traits.
Soils in Papua New Guinea are traditionally managed by fire-stick farming and these soils have high concentrations of charcoal and organic matter. Mineral fertilizer is not applied to these soils and their phosphorus content is low, yet these soils manage to sustain yields of many types of crops. We speculate that the availability and cycling of phosphorus in these soils is higher than in conventionally farmed soils. Therefore, we aim to compare various pools of phosphorus in soils from Papua New Guinea and Australia to establish the availability of phosphorous.
This is a project for a student with chemistry knowledge and interest. It involves various wet chemical analyses. A background in agriculture is beneficial. It is a lab-based project at St Lucia, so no travel to Papua New Guinea will be involved.