Courses

STEM Summer Research Dublin Courses

You will earn 6 research credits over 8 weeks, conducting faculty-supervised, hands-on, directed study research projects with results that will culminate in the preparation of a research paper. You will complete a minimum of 240 hours on research in and out of the laboratory.

Faculty mentors will work closely with you to direct your continued growth and knowledge development in the chosen research topic discipline.

Please review your project with your academic or study abroad advisor to ensure it will transfer back to your home school and that you are following your home school’s policies.

Choosing Your Research Project

Review Project titles and descriptions below.
List 3 (in order of preference) in your Academic Preferences Form, using DUBL as the course code.
Program is highly individualized, with limited enrollment.
You will need to complete a brief Literature Review in consultation with your research supervisor prior to departure before the start of the program. More details here.
We encourage you to contact Arcadia’s Associate Dean of Academic Access and Curricular Solutions, Rob Hallworth, to discuss your particular research interests further.

Animal Behavior, Artificial Intelligence, Biology, Biomedical Science, Biomedical Science, Biomolecular and Biomedical Science, Chemical Engineering, Chemistry, Chemistry/Biomedical Science, Computer Science, Environmental Science, Molecular Biology, Organic Chemistry, Physics, Plant Genetics

Course ID	Title	Credits	Syllabus
DUBI RSLW 392S	International Independent Research in STEM Fields	6	PDF

Summer 2023 Research Projects

Metaverse for Climate Awareness

Discipline: Computer Science

Supervisor: Dr. Abraham Campbell

The Metaverse concept is based on the idea that we could create an interconnected web of virtual worlds, e.g. taking the current internet and making it 3D. The closest project to achieving this has been VR Chat, which offers a plugin in UNITY to connect Virtual worlds together. This project is to explore if we can take our existing VR environments to help aid climate awareness and bring them into VR chat as well as future-proof them by porting them to the Universal Scene Description format that both NVIDIA and PIXAR are promoting as the future HTML of the Metaverse.

The student will work closely with our VR lab team to help port and improve upon our Climate awareness app for flooding off the Irish coast in an area called Portrane north of Dublin. The internship will include a field trip to the area to test the app in a Mixed-Reality context.

We have already published one paper on the project https://arxiv.org/pdf/2205.01583.pdf.

The participant will work with UCD VR lab, and the main deliverable will be a VR experience allowing a user to enter a custom world through VR CHAT. The participant will have access to the VR lab during the internship and access to a Quest Pro to complete their research.

The Role of the Environment in Learning to Program: Exploring Educational Programming Data with the Blackbox Dataset

Discipline: Computer Science

Supervisor: Dr. Brett Becker (https://www.brettbecker.com)

The Blackbox dataset contains programming activity and source code from > 250,000 students learning to program in Java using the BlueJ development environment. Blackbox is relatively new and largely underutilized. This project will explore this data working with a PhD student and along with their supervisor, seek to answer questions like the following: What role do compiler error messages play in learning to program? How can they be made better? How does the programming environment affect student learning? Believe it or not, these questions don’t have definitive answers! The answers to questions like these will be of practical use to the overall project and also to the computer science education community in general. The exact nature of the questions to be answered will depend on the progress of the overall project, but the intern student can have a part in shaping these questions.

In 2019 an earlier stage of this project resulted in a paper published at the ACM SIGCSE Technical Symposium (sigcse2020.sigcse.org) with the summer student and the UCD PhD student as co-authors.

This project is suitable for students who have a strong interest in databases and programming and has a good chance of resulting in a publication. Some knowledge of Java and SQL (or willingness to learn ahead of time) is advantageous. This project offers an opportunity to contribute to a much larger-scale project and to see how PhD projects work on a daily basis. The student will be an active member of the supervisor’s lab during their stay.

Mini Green Supercomputer: Reducing the Cost and Carbon Footprint of High Performance Computing Development

Discipline: Computer Science

Supervisor: Dr. Brett Becker (https://www.brettbecker.com)

For students who like: programming, hardware/gadgets, carbon reduction and environmental change.

Present-day high-performance computing platforms are expensive and consume vast quantities of electricity, often enough to power small cities. The resulting carbon cost of development on these platforms is huge. Currently the easiest way for students to write and test parallel code is on multicore computers. However, these are not realistic for testing code, particularly as they are typically homogeneous and messages are passed through memory, not over a network.

In this project, you will create a mini heterogeneous ‘supercomputer’. This will consist of several single board computers (such as Raspberry Pi) networked together. Alongside this you will build an application that monitors the status of the system remotely. You will study and gain experience with installing and running parallel computing software. You will gain theoretical and practical skills in system administration, networking, and programming. You will also test and benchmark the supercomputer with the same software that is used for current Top500 supercomputers. Machines like this can be used for upper-level parallel computing classes and have positive economic and environmental outcomes. This project is suitable for students with an interest in programming, networking and gadgets – some hands-on work will be needed. All hardware will be provided to make this a really fun project! The only required experience is some high-level programming – typically a first-year intro course will do (C, C++, C#, Java, or similar). The student will work with a PhD student and their supervisor and be an active part of the supervisor’s laboratory during their stay.

Computing Crossroads: A Social Computing Project to Help Improve Diversity, Equality and Success in Computer Science

Discipline: Computer Science

Supervisor: Dr. Brett Becker (https://www.brettbecker.com)

Computer Science is one of the most influential academic disciplines in terms of impact on society. However, CS has significant issues with equity, diversity and inclusion - almost every minoritized group is underrepresented in industry and academia. Many of these issues can be attributed to misconceptions about what CS is and what computer scientists do. These misconceptions are propagated by the media and many other channels.

This project will contribute towards an ongoing project called Computing Crossroads (computingcrossroads.org). This project features people trained in CS who have gone on to pursue positions in life that are outside traditional CS, and those who do traditional CS but don’t have a traditional CS training. These stories can be inspiring and help to promote better equity, diversity, and inclusion in CS by putting a spotlight on inspiring personal stories of CS folks from all walks of life. These stories can serve to break down misconceptions such as “if you major in CS you are stuck in CS”, “I can’t do CS”, and “it’s too late to do CS now”, as well as stereotypes like “all computer scientists are nerds”.

This provides a great opportunity to do primary qualitative research with real human participants that is aligned with other social sciences. This project will live beyond the internship and provides a great chance for a student to contribute to a larger project with a long lifespan. The student will be an active member of the supervisor’s lab, working with other postgraduates during their stay.

Explaining the Unexplainable: A Web App to Explain Java Error Messages

Discipline: Computer Science

Supervisor: Dr. Brett Becker (https://www.brettbecker.com)

Programming error messages (PEMs) are frequently perceived as cryptic and unhelpful. It is not uncommon for students to ask questions like: What does “illegal start of type actually mean” and how can that help me fix my code?

PEMs have been investigated as opaque phenomena; however, they result from deterministic open-source code, and thus can be read, dissected, and understood much better.

This project aims to create an interactive web application showcasing frequently occurring PEMs in Java. These will be explained by exploring the Java compiler* to understand precisely why and under what circumstances a given PEM is generated. Each PEM should have a minimal reproducible example that will explain how to obtain it, and the exact line(s) of code within the compiler that generates it. The goal is to understand the compiler's point of view for generating each PEM.

*We will provide the specific Java implementation, compiler, etc. These are not very difficult to interpret once you know where to find things!

The ideal student will understand the basics of Java and be curious about compiler development. Some knowledge of web development and SQL (or willingness to learn ahead of time) is advantageous. This project offers an opportunity to contribute to a much larger scale project and to see how PhD projects work on a daily basis and there is an opportunity for this work to potentially contribute to a scientific publication. The student will be an active member of the supervisor’s lab during their stay.

Exploring Sense of Belonging in Undergraduate Computing Students

Discipline: Computer Science/Artificial Intelligence

Project Supervisor: Dr Catherine Mooney https://people.ucd.ie/catherine.mooney

Sense of belonging or belongingness can be described as “one’s personal belief that one is an accepted member of an academic community whose presence and contributions are valued”. Student sense of belonging impacts retention, progression, well-being, and academic achievement. Since 2017 we have been conducting surveys of undergraduate students which has allowed us to track changes in their sense of belonging, before and during the COVID-19 pandemic, and after their return to campus. This has allowed us to understand the issues that affect their belongingness comprehensively. The student who chooses this project will develop an Explainable Artificial Intelligence (XAI) interface which will allow us to explore how different features interact with each other to positively or negatively impact their sense of belonging, for example, student identity (gender, race/ethnicity, membership of the LGBTQIA+ community, etc.), how much they interact socially with other students, having a growth versus a fixed mindset, etc. This research will be conducted in collaboration with a PhD student and/or postdoc under the supervision of Dr Mooney as part of the Life Science Data Analytics Research Group (https://lisda.ucd.ie/).

The student should have an interest in machine learning and Python programming. Participating in this project will allow the student to contribute to an ambitious project that will improve our understanding of sense of belonging, and may contribute to a research publication.

Developing a prototyping XAI-enabled web interface for the classification of metabolic syndrome

Discipline: Computer Science/Artificial Intelligence

Project Supervisor: Dr Catherine Mooney https://people.ucd.ie/catherine.mooney

With the global prevalence of obesity, metabolic syndrome has raised increasing concern, as it puts people at risk of type 2 diabetes, heart disease, strokes, and other metabolic diseases. Artificial Intelligence (AI) and machine learning techniques have been extensively explored for the classification of metabolic syndrome, however, research has shown that for healthcare providers, an AI model with good performance is not enough; they also need explanations of the model's decisions. This calls for the incorporation of Explainable Artificial Intelligence (XAI), a research field that aims to make the outputs of AI models more understandable to humans. The goal of this project is to develop a web interface for AI models that can be accessible by healthcare providers, along with advanced XAI techniques to explain the behavior of the models.

The student who chooses this project will be working with a PhD student and/or postdoc under the supervision of Dr Mooney as part of the Life Science Data Analytics Research Group (https://lisda.ucd.ie/). This will provide a great opportunity for the student to be an active member of a research group and to see how PhD research is conducted on a daily basis. By working on this project, students have the potential to contribute to a large-scale project that may eventually improve clinical metabolic healthcare. This project is suitable for students with a strong interest in the adoption of AI in healthcare. Some knowledge of web development, Python or R is preferable.

An XAI-enabled Web Interface for pediatric EEG Analysis

Discipline: Computer Science/Artificial Intelligence

Project Supervisor: Dr Catherine Mooney https://people.ucd.ie/catherine.mooney

Approximately 1 in every 150 children is diagnosed with epilepsy during the first ten years of life. These children experience seizures, which disrupt their lives and directly harm the developing brain. Electroencephalography (EEG) is one of the tools used clinically in the diagnosis of epilepsy. However, the interpretation of EEGs requires time-consuming expert analysis. Automated detection systems are a powerful tool that can help address this issue by reducing experts’ annotation time. We are developing a pediatric-specific automatic seizure detection method on a large number of pediatric EEG recordings to assist clinicians in analyzing seizure events in children. The limitation of the work is that as machine learning is a “black box” method, clinicians may have difficulty trusting a machine learning based method. Explainable AI (XAI) can be described as aiming to make machine learning algorithms more understandable to humans. During this project you will use XAI to gain users’ trust in automatic seizure detection methods and assist experts’ analysis of EEG by developing a user-friendly XAI web interface that explains the machine learning-based EEG predictions to users. This interface will enhance the probability of these machine learning based approaches being used in clinical practice and facilitate more extensive validation in a clinical setting.

This proposed project has significant publication potential that can contribute to the body of knowledge in XAI and EEG analysis. Further, it offers an opportunity for an ambitious student to solve real-world UI/UX design and programming challenges. Specifically, Java/Python, JavaScript, and HTML knowledge will be helpful.

Making Conversation to Make Better Decisions

Discipline: Computer Science/Artificial Intelligence

Project Supervisor: Dr Vivek Nallur

Nudge theory has arisen from the Kahneman and Tversky's seminal work on behavioral Economics. It shows that people can make repeated, predictably irrational decisions, due to cognitive biases that exist among all humans. Nudges are behavioral interventions that arise primarily from human decision-making frailties (e.g., loss-aversion, inertia, conformity). However, not all humans are affected by the same biases, i.e., a nudge that works on one person may not work on another. BigData, when combined with Machine Learning, promises to create a potentially infinitely flexible nudging machine. A software could learn the specific set of biases that affect a particular person, and then adapt its nudges in a highly tailored manner. This is a HyperNudge. Conversational AI (or chatbots, as they are commonly known) have become an acceptable part of online interaction. These agents exist in conversation-enabled devices such as Alexa, and in devices such as smartphones, smartwatches, FitBit, etc. This project aims to utilize conversational agents (such as chatbots) to detect biases in human beings in an automated manner. The more confident the chatbot is, about a particular bias, the more confidently it can nudge the human to better decision-making. This project will require students to be self-starters, able to read papers, come up with hypotheses, and design experiments to provide evidence for/against the hypotheses. An ability to program in python, and comfort with using multiple open-source Git repositories would be highly recommended.

Implementing Ethical Preferences for Human-Centric AI

Discipline: Computer Science/Artificial Intelligence

Project Supervisor: Dr Vivek Nallur

To build AI systems that can be trusted to work with human beings we need some confidence that they will act ethically. Apart from the current concerns about bias in AI systems, we also need to embed some ethical principles in AI. This is currently an open research area. Extant ethical agent implementations are mainly two-fold; those designed to follow a set of rules bestowed upon them by the designers, and those focused on increasing the utility of the world. However, these ethical agent models are not competent enough to perform in relatively large and open environments, even though high impact AI agents like autonomous cars or censoring agents operate in such environments. Humans, on the other hand, manage to work effortlessly in these types of environments even with incomplete rule-sets or without a clear understanding of the correct utility models of the world. Humans achieve this by breaking rules when they think it is necessary for pro-social reasons. We believe that a human-centric AI agent would be able to deal with uncertainty in preferences, fuzziness in utility functions, and still be able to make decisions that are in general agreement with the humans they are interacting with. The prospective student must have some experience in python programming, data structures and algorithms, and be willing to read/learn quickly about knowledge representation and reasoning, and machine learning. The student needs to be a self-starter, and willing to experiment with code-bases from multiple academic papers/sites.

Understanding Earth Observation Events Using an IoT-enabled Sensor Bed and Machine Learning Models

Discipline: Computer Science

Project Supervisor: Dr Soumyabrata Dev (https://soumyabrata.dev/)

The world is being urbanized and industrialized at an increasingly accelerated rate and as a result, the earth’s environment has significantly deteriorated. World Health organization data indicates that the general conditions of our environment in most cities fail to meet safety guidelines [1]. Therefore, it is essential to continuously monitor and predict the various atmospheric conditions. This will prevent citizens being exposed to dangerous air quality conditions and provide citizens with warnings and health recommendations.

In this project, we aim to create an intelligent system that uses an Internet of Things (IoT) enabled sensor bed and exploits various machine learning techniques to monitor and forecast the earth’s parameters. The monitoring station will initially deploy a variety of IoT capable earth-observation sensors [2]. This sensor bed will record various atmospheric variables including temperature, pressure, humidity, solar irradiance, etc. It will also record the pollutant concentrations of PM 2.5, PM 10, SO 2, NO 2, amongst others. These sensors will constitute a wireless sensor network. These monitoring stations will gather the earth observation parameters. We will then transmit this data to the cloud server via a wireless network and a low-power IoT protocol. We will also develop a machine learning framework that can efficiently forecast [3] these variables from historical sensor recordings. These short-term forecasted values will greatly assist the meteorologists, remote sensing analysts, and researchers. Our developed framework will greatly assist the end users to obtain the statistical characteristics and forecasted values through AAA, or anytime, anywhere, and on any device, from the cloud server. Skills required: python, data analysis, machine learning, DIY hardware, sensor networks.

References:

[1] WHO, 2014. Air quality deteriorating in many of the world’s cities, Geneva: World Health organization.

[2] P. Dey, S. K. Chaulya, and S. Kumar. "Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system." Process Safety and Environmental Protection 152 (2021): 249-263.

[3] N. Danesi, M. Jain, Y. H. Lee, and S. Dev, Predicting Ground-based PM2.5 Concentration in Queensland, Australia, Proc. Progress In Electromagnetics Research Symposium (PIERS), 2021.

Federated Learning-based Secure Edge AI Platform Robust to Privacy Attacks

Discipline: Computer Science

Supervisor: Dr. Madhusanka Liyanage

Federated Learning (FL) is a privacy-preserved Machine Learning (ML) approach that has recently attracted significant interest in industry and research. Unlike centralized ML, local models are trained within client devices in FL. However, recent research has identified FL is vulnerable to numerous attacks on privacy that can essentially reveal sensitive information about FL participants and the integrity of the model, even though data is not directly shared. Edge devices especially will be more vulnerable to these attacks since they lack proper privacy mechanisms due to resource restrictions. Considering this, we propose a secure FL-based edge AI platform consisting of privacy protection using Multiparty Computation (MPC) and a novel time-based weight-sharing mechanism. We use the edge computing platform to partially compute gradients of local models, such that the computation costs at IoT devices will be reduced. Then the edge FL server can calculate regional FL aggregation in Trusted Execution Environment (TEE) [1]. This edge AI can split the weights and share them with other regional models to enhance privacy. Fig. 01 presents the high-level architecture of the project.

Figure 01: Architecture of the proposed FL Edge AI platform

Key learning outcomes involves:

Privacy enhancement for FL using MPC
Application of AI with GPU-accelerated IoT edge AI platform
Privacy attacks and secure model sharing in FL

Resources required may include:

Wireless IoT/mobile sensing devices (e.g.: Raspberry Pi, smart watch, sensor nodes )

GPU-based Edge AI development kit (e.g.: NVIDIA Jetson AGX Orin [2], Jetson Nano )

Remote GPU-enabled server

References:

[1] Volos, Stavros, Kapil Vaswani, and Rodrigo Bruno. "Graviton: Trusted Execution Environments on {GPUs}."

13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 2018.

[2] Nvidia Jetson Orin (no date) NVIDIA. Available at:

https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ (Accessed: November 11, 2022).

Analysis of Biofilm Formation by the Human Pathogen Campylobacter Jejuni

Discipline: Biomedical Science

Supervisor: Dr. Tadhg Ó Cróinín

Campylobacter jejuni is the most prevalent cause of foodborne bacterial gastroenteritis world-wide, but little is known about how this oxygen-sensitive microorganism can survive and persist in the largely aerobic environment it experiences on meat products. This project will focus on the ability of this microorganism to form biofilm which may be critical for its survival in aerobic environments and thus likely a key factor in allowing the pathogen to cause foodborne outbreaks. As part of a collaboration with UCDavis strains isolated from a variety of different sources will be compared using different biofilm assays which will measure the ability of these organisms to form biofilms on a variety of substrates. The aim of the project is to investigate whether strains isolated from specific sources may be more likely to have a greater ability to form biofilm and whether strains from different sources may have the ability to form biofilms of different compositions. Methods used will include those outlined in our recent paper outlining a high screening approach for the study of biofilm formation and composition.

Students interested in taking this project should be highly motivated and interested in the field of infection biology and understanding how bacterial pathogens cause disease.

Whelan MVX, Simpson JC, Ó Cróinín T. (2021) A novel high-content screening approach for the elucidation of Campylobacter jejuni biofilm composition and integrity. BMC Microbiol. Jan 4;21(1):2.

Association of Gut Barrier Dysfunction in Inflammatory Bowel Disease development

Discipline: Biomedical Science

Supervisor: Dr David Hughes (Cancer Biology and Therapeutics Lab, Conway Institute, UCD)

Gastrointestinal (GI) diseases such as Inflammatory Bowel Disease (IBD) are leading causes of chronic illness in many world regions. Although the causes of these diseases are complex, environmental factors, particularly obesity and lifestyle, are known to play a strong role. Recent compelling evidence, some of which my work has helped generate, suggest that commensal microbial dysregulation and exposures to microbial toxins are involved in common GI illness such as IBD and colorectal cancer. One recent hypothesis is that this occurs through inflammatory induced weakening of the protective gut mucosal barrier by obesity, dietary/lifestyle, and microbiome factors.

To help explore this hypothesis, the student will measure several biomarkers of gut barrier function and bacterial translocation (as biomarkers of microbial dysbiosis) by custom ELISAs, e.g., the bacterial products flagellin and lipopolysaccharide, and the proteins TLR5 – receptor for flagellin and Muc2 (Mucin) – integral to an intact barrier (which modulates the permeability of tight junctions between intestinal tract cells). This will be done in a series of blood samples from patients with IBD (n=130) and non-disease controls (n=180) to assess gut-barrier breakdown associated with IBD onset. If biomarker levels in serum in disease cases are statistically different from those in controls, it will indicate that the hypothesis is correct. Together, these findings will inform us of gut-barrier function in healthy and disease states, and how this may contribute to exposure of bacterial toxins from the gut. Blood-based detection of bacteria and gut-barrier health may allow novel screening strategies for IBD disease prevention, diagnosis, and management.

Investigating the Role of Disease-Causing Proteins in Motor Neurons

Discipline: Biomedical Science

Supervisor: Dr. Niamh O’Sullivan

My lab studies inherited forms of motor neuron disease, particularly hereditary spastic paraplegia (HSP). Individuals with HSP develop weakness in their legs leading to difficulties walking which is caused by degeneration of the very longest motor neurons. Extensive work in recent years has successfully identified many of the genetic causes underpinning HSP, but there are currently no treatments to prevent, cure or even to slow the course of these diseases. To address this, my lab uses cutting-edge genetic engineering to generate novel animal models of HSP in the fruit fly, in which we can study the molecular events underpinning this disorder. Recently, researchers in my lab have found that HSP-causing genes play a role in the organization of the endoplasmic reticulum (ER) network within motor neurons. The aim of this project will be to study how this impaired ER network contributes to neurodegeneration in motor neurons. The student will learn various techniques associated with molecular genetics, confocal microscopic image analysis and the assay of behavioral readouts.

Lab website: fniamhy.wixsite.com/osullivanlab

Beta-Amyloid Induced Regulation of UT-B Urea Transporters in an Astrocyte Cell Line

Discipline: Biomedical Science

Supervisor: Dr. Gavin Stewart

UT-B urea transporters play an important physiological role in various human tissues - including kidney, bladder, colon and brain. Increased levels of both urea and UT-B transporter expression have been widely reported in the aging mammalian brain, as well as in various human disease states such as Alzheimer’s. Our previous research has shown that the rat C6 astrocyte cell line is an appropriate model for investigating urea-induced increases in brain UT-B transporters. Importantly, beta-amyloid is a protein crucial in the progression of Alzheimer’s but its potential effects on UT-B are currently unknown. Utilizing C6 cell culture, RT-PCR and western blotting techniques, this project aims to investigate the effects of beta-amyloid protein on UT-B transporters and any cellular pathways involved.

Understanding the Role of Rab33B in Smith McCort Dysplasia

Discipline: Biomolecular and Biomedical Science

Supervisor: Prof. Jez Simpson

Smith McCort dysplasia (SMC) is a rare hereditary skeletal disease affecting the development of bones. Recently it has been found that mutations in the gene encoding the Rab33B protein seem to be the cause of the disease. Similar to other Rab proteins, Rab33B is involved in membrane trafficking, in particular in regulating cargo movement from the Golgi apparatus to the endoplasmic reticulum along the retrograde pathway. Interestingly, recent work has suggested an additional role for Rab33B in autophagy. While the role of genetic links between Rab33B dysfunction and SMC are clear, the situation at the cellular level has not yet been elucidated. Work in our own laboratory has revealed that mutations in the Rab33B protein, which lead to SMC, result in a change in localisation of the Rab33B protein such that it no longer associates with Golgi membranes. This project will seek to carry out further analysis of a number of critical amino acid residues in Rab33B, in an attempt to identify why such specific changes in the protein have such a radical effect on its cellular distribution and localisation.

This project will use a range of molecular biology and confocal fluorescence imaging techniques to aim at deepening our understanding of Rab33B function and interaction network in the context of Smith McCort dysplasia.

Analysis of Biostimulants for Barley Growth and Development

Discipline: Biology/Environmental Science

Supervisor: Dr Rainer Melzer

Crop production is currently dependent on synthetic fertilizers as the main source of plant nutrition. These are energy intensive to produce (i.e. nitrogen-based fertilizers require a total energy consumption equivalent to ± 2 % of world energy use) and the majority of phosphate (90%) and nitrogen fertilizers (45%) in the European Union are imported. Biostimulants have the potential to significantly reduce the application of traditional nitrogen fertilizers while simultaneously increasing crop stress resistance and grain quality. Therefore, it is key to study if biostimulants can have an impact on reducing synthetic fertilizer use.

In our research, we test whether biostimulants have an effect on barley growth and yield (physiological, agronomical and nutritional traits). You will be involved in glasshouse trials, laboratory analysis of grain quality, data collection and statistical evaluation.

Lab homepage: https://ucdflowerpower.org/

Flower Development in Cannabis Sativa

Discipline: Biology/Plant Genetics

Supervisor: Dr Rainer Melzer

Cannabis sativa (hemp) is a species that develops male and female flowers on separate individuals. This mode of reproductive development is very unusual among the flowering plants, the vast majority of which are bisexual. In addition, large variation in the flowering time can be found between different Cannabis cultivars. However, almost nothing is known about the genetic and morphological processes involved in flower development and flowering time control in hemp. In this project, you will be involved in ongoing research aimed at studying flowering time and sex determination in hemp. We employ molecular genetic and bioinformatic tools. This comprises DNA isolation, gene cloning, qPCR as well as genome and transcriptome analyses.

Lab homepage: www.ucdflowerpower.org

Establishing Hemp (Cannabis Sativa) as a Crop for the Future

Discipline: Biology/Plant Genetics

Supervisor: Dr Susanne Schilling

Cannabis, or hemp, is an extraordinarily versatile plant – it has been used for millennia as a source of fibre, oil and for medicinal purposes. However, because Cannabis has psychoactive effects, it has been widely banned throughout the last century. It is now receiving new attention as a sustainable crop for a carbon-neutral society and as an important source for modern medicinal products. However, genetics research on Cannabis is still in its infancy compared to many other crops.

In this project, we aim to characterize hemp phenotypically as well as genetically. We are employing plant phenotyping, different molecular biology techniques (e.g. DNA isolation, PCR, gene cloning), next generation sequencing (NGS) and bioinformatics analyses. You will be involved in ongoing research.

https://ucdflowerpower.org/

Biotechnology and Gene Editing for Cannabis Sativa (hemp)

Discipline: Biology/Plant Genetics

Supervisor: Dr Susanne Schilling

Cannabis, or hemp, is an extraordinarily versatile plant – it has been used for millennia as a source of fibre, oil and for medicinal purpose. However, because Cannabis has psychoactive effects, it has been widely banned throughout the last century. It is now receiving new attention as a sustainable crop for a carbon-neutral society and as an important source for modern medicinal products. However, biotechnology research on Cannabis is still in its infancy compared to many other crops.

In this project, we aim to establish transformation and gene editing for Cannabis sativa. We are employing cell culture as well as molecular biology techniques. You will be involved in ongoing research.

https://ucdflowerpower.org/

Depicting Personality Traits in Fallow Deer (Dama Dama) Fawns at Capture.

Discipline: Biology and Environmental Science/Animal Behavior

Supervisor: Dr Simone Cuiti

Inter-individual variability in animal behavior within wild populations is an important component influencing their resilience to external perturbations such as human disturbance, climate and habitat change. When such differences are consistent through time, we typically refer to them as personality traits. This project aims to test the hypothesis that behavioral traits recorded at capture (e.g. reaction to capture, vocalization, and behavior at release) in fallow deer fawns of Phoenix Park are highly repeatable over multiple captures and are a good proxy for their personality. In short, we expect bold fawns to display bold behaviors across multiple captures, and this may predict their life history. The student will join the Phoenix Park capture team during the fawning season of June 2023 and will collect behavioral data on more than 100 fawns. The Phoenix Park is the largest urban park in Europe; it can be easily reached by bus from UCD, and would give the student the opportunity to enjoy both the UCD campus and the field site located in the heart of Dublin. The student will learn how to capture, handle and release deer fawns in June, leaving July for data analysis. This is a unique opportunity for students to gain experience in the field of wildlife research and join an experienced team of 20 students and academic staff with long term experience in animal behavior.

Does the Smoothness of Concrete on Vertical Seawalls Influence Their Biodiversity

Discipline: Biology/Environmental Science

Supervisor: Dr Paul Brooks

The proliferation of artificial structures is increasing in marine environments due to continued human population expansion and requirements for coastal protection under climate change. Artificial structures such as concrete seawalls tend to support different and often less diverse assemblages of algae and invertebrate communities in comparison to natural structures. This may be influenced by the vertical nature and/or their lack of habitat structure or surface texture.

Researchers in the Ecostructure project (www.ecostructureproject.eu/) are developing and testing strategies for enhancing coastal artificial structures to increase the degree to which they support biodiversity. In this project you contribute to this research by characterizing the biodiversity on concrete seawalls of varying surface texture (e.g. rough vs smooth) at a number of sites along the Dublin Coast. Such findings will help to contribute to better informed eco-engineering designs to improve artificial structures.

Targeted RNA Extraction from Probiotic Bacteria

Discipline: Molecular Biology

Supervisor: Dr Paola Valentini

Probiotic bacteria influence our wellbeing in several ways. An increasingly recognized one is through the delivery of extracellular vesicles, tiny particles surrounded by a membrane and containing a diverse cargo of nucleic acids, proteins and peptides. Among these molecules are several species of RNA. Such RNA molecules likely have a role in how bacteria interact with the environment and with the host, as they are specifically encapsulated in the vesicles, the content of the latter being different from that of the intracellular milieu. However, the functions of most of them remain elusive. The aim of the project is to extract particular RNA molecules from a selected probiotic bacteria strain, using specific probes. The student will be trained in handling bacteria and RNA material and will learn basic molecular biology techniques, such as probe design, RNA extraction and reverse transcription PCR (RT-PCR). The student will also confront particular technical issues related to total RNA extraction from non-model bacteria.

Purification of Extracellular Vesicles from Probiotic Bacteria

Discipline: Molecular Biology

Supervisor: Dr Paola Valentini

However, the functions of most of them remain elusive. The aim of the project is to purify extracellular vesicles from selected probiotic bacteria strains and to extract extracellular RNA for further analyses. The presence of some particular RNA species in the extracted RNA will be confirmed by reverse-transcription PCR (RT-PCR). The student will be trained in handling bacteria, extracellular vesicles and RNA material and will learn basic molecular biology techniques, such as RT-PCR. The student will also have the chance to confront particular technical issues related to EVs purification and RNA extraction from non-model bacteria.

Preparation of RNA Nanoparticles

Discipline: Molecular Biology

Supervisor: Dr Paola Valentini

RNA therapeutics represent a fast growing field, boosted by the recent development of RNA based vaccines against Coronavirus. Notwithstanding their enormous versatility, the stability and targeting of RNA therapeutics represent common issues in the field, to address which several technical solutions have been developed. The aim of the project is to prepare different nanoparticle formulations for new potential RNA therapeutics developed at our lab. These nanoparticles will then be characterized by several techniques (dynamic light scattering, electron microscopy, etc). The student will be trained in handling RNA and in preparing and characterizing different types of lipid/polymer nanoparticles.

Optimizing Primer Selection for Detecting Bacteria Associated with Diverse Fungal Isolates

Discipline: Molecular Biology

Supervisor: Dr Olga Lastovetsky

Fungi, like plants and animals, do not exist in the environment as sterile individuals, and instead co-exist together with other microbes, such as bacteria. These bacteria can intimately associate with fungi with some bacteria living inside the fungi, termed “endobacteria.” The best-studied fungal-bacterial interactions are from plant-associated fungi, where they have been shown to influence the pathogenicity of fungi on plant hosts. However, endobacteria have also been described in fungi of medical importance, with some affecting virulence in human hosts. Previously considered biological oddities, endobacteria are actually common in fungi, with molecular tools being the main avenue for discovering new fungal-bacterial associations. Such tools depend on PCR with universal bacterial primers. However, the most commonly used primer sets do not often discriminate between fungal mitochondria and bacteria, giving false positives. The aim of this project is to identify a new set of primers that can be used for screening fungal isolates for the presence of bacteria.

Skills: fungal and bacterial cultivation, DNA extraction, PCR, sequencing, simple bioinformatic analysis.

Exploring the Spectrum of Activity of Antibiotic Compounds Produced by Fungal Endophytes of Wild Barley Relatives

Discipline: Biology/Environmental Science

Supervisor: Dr Olga Lastovetsky

Plants are populated by fungal endophytes, which live asymptomatically inside plant tissues such as leaves and roots and can play important roles in plant biology. We have shown that fungal endophytes isolated from wild barley relatives can produce compounds with activity against the barley fungal pathogen Ramularia collo-cygni. Such compounds have the potential to be developed into new biopesticides. This project will explore the level of activity of these novel compounds against other cereal pathogens as well as a broad collection of bacteria and fungi. This project has the potential to contribute to the development of a novel biopesticide against cereal pathogens.

Skills: fungal and bacterial cultivation, statistical analysis of data, introduction to metabolomics.

The Microbiome of Fungi: How Often are Bacteria Found in Close Association with Fungal Endophytes?

Discipline: Biology/Environmental Science

Supervisor: Dr Olga Lastovetsky

Fungal endophytes live asymptomatically inside plant leaves and roots and can have important effects on plant biology. However, fungi themselves do not exist in the environment as sterile individuals, and instead, co-exist together with other microbes, such as bacteria. These bacteria can intimately associate with fungi with some bacteria living inside the fungi, termed “endobacteria.” Endobacteria are common in fungi and can have overarching effects on their biology including reproduction, metabolism, growth and virulence. Despite being common, endobacteria of fungi are relatively understudied and this project will aim to determine how often fungal endophytes of wild grasses form associations with endobacteria and determine the roles of these endobacteria in fungal biology.

Skills: bacterial and fungal cultivation, DNA extraction, PCR, microscopy.

Chemical Synthesis of Drug-Like Building Blocks Using Continuous Flow Reactors

Discipline: Organic Chemistry/Chemical Engineering

Supervisor: Dr Marcus Baumann, School of Chemistry

We wish to offer this unique summer research project that will provide a talented student with the opportunity to acquire novel laboratory skills in a project at the interface of organic chemistry, medicinal chemistry and chemical engineering. The synthesis and spectroscopic characterisation of a small collection of drug-like building blocks will be studied. This will involve using bespoke continuous flow reactors available in the newly established UCD Flow Chemistry lab.

Specifically, we will be using a light-driven continuous flow reactor to generate target molecules. Continuous flow chemistry is a novel and exciting addition to the chemist’s toolbox that allows to perform chemistry in a safer, more effective and highly reproducible manner yielding readily scaled and automated processes that are highly desirable in academia and industry alike. This approach will avoid the isolation of unstable intermediates and provides a powerful route into important bioactive structures. Using light as a traceless reagent in combination with in-line purification techniques will result in a green and sustainable technology that highlights the power of modern chemical synthesis in delivering important drug-like structures. The ability to automate this process is highly advantageous as it circumvents tedious downstream processing to yield clean products.

The successful student will be embedded in our international research group and gain new skills in chemical synthesis, purification, spectroscopic characterisation as well as the use of modern flow reactor technology. For an example of a past project that was subsequently published, please see: https://doi.org/10.1016/j.tetlet.2021.153522

Phosphorus-Based Cations as Main Group Catalysts

Discipline: Chemistry

Supervisor: Dr Tom Hooper

Catalysis produces over $500 billion worth of products worldwide each year and homogeneous catalysts are used to produce a huge range of compounds, from large quantities of feedstock chemicals to complex drug molecules. Many of these catalysts are based on rare and expensive transition metals, the supplies of which are limited. Developing catalysts based on main-group elements will make catalytic processes less expensive and more sustainable. Phosphorus is an excellent candidate as a catalytic center because it can adopt different coordination numbers and oxidation states and its NMR active nucleus provides an excellent handle to directly interrogate reactions. Phosphenium cations have 2 substituents and phosphorus can interact with substrate molecules in a similar way to metals.

The aim of this project is to synthesize and characterize a range of phosphenium cations with varying steric and electronic properties. This is done by manipulation of the ligand backbone through organic synthetic methods with the phosphenium center introduced using standard air sensitive methods (Schlenk line and glove box techniques). These potential catalysts will be tested for reactivity towards small molecules (H2, NH3, CO2 etc.) and organic substrates (alkenes, alkynes, furans, carbonyls etc.) with emphasis on reversible binding and reactivity. Onward reactivity towards the functionalization of these substrates will be targeted with the aim of regeneration of the catalytic center. In situ analysis of these reactions will be performed by multinuclear NMR spectroscopy, mass spectrometry and reaction kinetics studies.

Study of Molecular Probes of Non-Canonical DNA Towards New Therapeutics

Discipline: Chemistry

Supervisor: Prof. Susan Quinn

As the body’s information repository, DNA provides the essential role of programming all biological functions. This information is translated through numerous molecular interactions. Proteins and small molecules bind to DNA through a variety of modes that include groove binding, electrostatic interactions and intercalation. The nature of these interactions is influenced by structure of the molecules and the secondary structure of DNA, which in addition to the common B-DNA form can also adopt other non-canonical forms. Two important structures are guanine rich quadruplex and cytosine rich imotif DNA. Quadruplex DNA comprise stacked tetrads of four guanine bases while imotif DNA is an arrangement of alternating interdigitated C:C+ base pairs formed between hemi-protonated cytosines. The presence of quadruplex and i-motif DNA structures in the cell has been confirmed by fluorescence microscopy. The binding interactions of small molecules to these structures is of interest due to their potential as therapeutic targets. The quadruplex structure is also of interest as it is formed from human telomere DNA. In this project we aim to investigate the binding of metal complexes to distinguish the different structures, which is essential to resolving their biological roles. The project allows for spectroscopic measurements and DNA binding studies which will provide experience across a range of diverse techniques including circular dichroism, UV-visible and emission spectroscopy. This will build on our extensive experience in this field [Nature Chemistry 2015, 7, 961, Chem. Sci., 2017,8, 4705-4723, Chem. Commun., 2020,56, 9703-9706, Chem. Eur J. 2020, DOI: 10.1002/chem.202002165]

https://sites.google.com/site/sjquinngroup/

Development of Nanoparticle-Based Therapies

Discipline: Chemistry/Biomedical Science

Supervisor: Prof. Susan Quinn

We are interested in preparing gold nanoparticle systems to complement radiotherapy treatment for resistant cancers [ACS Appl. Nano Mater. 2020, 3, 3157]. This relies on the fact that X-ray irradiation of AuNPs releases electrons that form reactive oxygen species that can kill cells [Sicard-Roselli, C. et al. Small 2014, 10, 3338–46] see (1) below. Nanoparticles have a high surface area which allows them to carry molecules that can bind to their surface. We have developed methods to prepare composite particles using polystyrene nanoparticles with high loadings of Au NPs and demonstrated uptake in cells [Chem. Commun., 2016,52,14388-14391] (see 2 below) and to enhance radiotherapy towards radiation resistant breast cancer cells [ACS Applied Nano Materials 2020, 3, 4, 3157-3162]. In this project the aim is to replace polystyrene spheres with tiny glass beads in the form of biocompatible silica (SiO 2 ) nanoparticles to prepare gold nanoparticle composites. Silica particles also have the advantage that we can load their transparent structure with luminescent molecules to act like tiny light bulbs and allow them to be tracked in cells. Furthermore, over time silica are degraded by enzymes in the body. The low toxicity, high surface area and ease of functionalisation of silica nanoparticles makes them attractive systems for cellular imaging, diagnostics and therapeutics. The project will involve metal nanoparticle synthesis, spectroscopic characterisation, composite preparation and characterisation as well as assaying the stability of the composites formed to biological media.

https://sites.google.com/site/sjquinngroup/

Design, Synthesis and Characterization of Super Catalysts for Safe Hydrogen Storage

Discipline: Chemistry

Supervisor: Dr. Andrew Phillips

Greenhouse gas emissions have increased exponentially since the onset of the Industrial revolution due to the burning of fossil fuels with best estimates of average global warming around 1.5 °C. Therefore an immediate switch to green fuel sources is necessary. An up-and-coming replacement medium is hydrogen gas, a zero-carbon emission fuel, however, this gas is explosive in air and requires large heavy-duty tanks to be stored. This issue is overcome by releasing on-demand H 2 stored within chemical compounds. Ammonia borane, H 3 N-BH 3, is very promising in terms of safety and low toxicity. Using bifunctional ruthenium-based catalysts, the dehydrogenation of ammonia borane occurs under ambient conditions, and the high purity H 2 directly fed into a proton exchange membrane to generate an electric current. This project is focused on the development of highly efficient dehydrogenation catalysts based on new ligand designs incorporating the highly efficient ruthenium metal supported by an a,a-diimine ligand. This multi-disciplinary project involves the synthesis of ligands and the preparation of transition metal complexes, using NMR and X-ray crystallography to determine the catalyst structure and also learning how to measure H 2 output. Moreover, a lead catalyst is to be tested in a new prototype reactor for generating clean carbon-free energy. The student will have the opportunity to use their complex to generate H 2 in a prototype generator as a part of a test drone.

Electrophilic Fluorination of Piperdeines and Their Use to Approach the Synthesis of Fluorofebrifugine

Discipline: Chemistry/Biomedical Science

Supervisor: Dr. Paul Evans

Febrifugine 1 is a naturally occurring compound of interest based on its anti-parasitic activities. More recently an analogue termed halofuginone has been shown to affect protein synthesis in mammalian cells and has been part of a clinical trial aimed at improving symptoms in patients of Duchenne muscular dystrophy.¹ We (and others) have found that 1 undergoes interconversion into isofebrifugine 2 in a process that is likely to proceed via a retro-forward conjugate addition process followed by hemiketal formation.²

As shown in the scheme below, we have recently worked on the conversion of a series of N protected piperideines 3 into their corresponding 2-allyl-3-fluoro N-protected piperidines 4 (where PG = protecting group e.g. Boc, Cbz, Ts etc.).³ Depending on the identity of the N protecting group up to 95:5 of the cis- to the trans-diastereomer can be found.

Where we want to proceed with this chemistry in the context of this specific project is to use the products (4) to prepare the 3-fluoro version of febrifugine, i.e compound 5. Thus, this project will initially re-investigate our synthesis of fluoro piperidine 4 and then work at converting it to cisfluorofebrifugine 5. Since natural febrifugine exhibits a trans-juxtaposition of the 2,3-substutuents on the piperidine ring we will then use 5 to investigate the cis to trans isomerisation process. This process is akin to the interconversion between 1 and 2 shown above but avoids the complication of the hemiketal formation.

References:

N. P. McLaughlin, P. Evans and M. Pines, The Chemistry and Biology of Febrifugine and Halofuginone, Bioorg. Med. Chem., 2014, 22, 1993.
S. Smullen and P. Evans, Asymmetric Syntheses of Febrifugine, Halofuginone and their Hemiketal Isomers, Tetrahedron, 2017, 73, 5493.
P. Fischer, M. Morris, H. Müller-Bunz and P. Evans, Synthesis and Structural Elucidation of 1,2- Disubstituted 3-Fluoropiperidines, Eur. J. Org. Chem., 2020, 1165.

Physics of Collective Behavior

Discipline: Physics

Supervisor Assoc. Prof. Vladimir Lobaskin

While it is impossible to predict the behavior of a person or an animal using only basic physics laws, motion of a large crowd or a flock can be predicted quite accurately. Large scales make details and motives of individual behavior unimportant, while the symmetry of motion and interactions starts playing the central role. We can use language and methods of physics to characterize and predict self-organization in systems of active agents (the generic name for animals, robots, or humans). In this project, we will study mechanisms of dynamic self-organization using various models of collective behavior, and apply methods of non equilibrium statistical physics and condensed matter physics to describe opinion dynamics or flocking. Applications to recent social and political movements (political crises, COVID) may be considered.

Knowledge of basic programming (e.g. Python) and data processing (plotting, evaluating statistics) will be necessary.

Recent publications on the topic:

Romensky, M., Spaiser, V., Ihle, T., Lobaskin, V. Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data. R. Soc. Open Sci. 2018, http://doi.org/10.1098/rsos.171935
2. M. Romensky, V. Lobaskin, T. Ihle, Tricritical points in a Vicsek model of self-propelled particles with bounded confidence, Phys. Rev. E 90, 063315 (2014)
M. Romensky, D. Scholz, V. Lobaskin, Hysteretic dynamics of active particles in a periodic orienting field, J. R. Soc. Interface 12, 20150015 (2015)

Advanced Models of Bio-Nano Interactions

Discipline: Physics

Supervisor Assoc. Prof. Vladimir Lobaskin

Composite nanoparticles and graphene nanoplatelets are currently used in various applications ranging from cosmetics, medicine and food to electronics and smart materials. Due to large surface-to-volume ratios they may be extremely active and sometimes harmful to living organisms. We are trying to understand the molecular mechanisms of bio-nano interactions and predict nanoparticle activity in biological environments. We use multiscale models including molecular modeling, condensed matter theory, and machine learning to construct models of nanoparticle protein corona. The specific applications include toxicity screening and designing of drug delivery engines. The project will combine molecular modeling and machine learning methods to construct a competitive adsorption model and predict the protein corona and binding of antibodies to nanoparticles.

Recent publications on the topic:

I. Rouse, D. Power, E. G. Brandt, M. Schneemilch, K. Kotsis, N. Quirke, A. P. Lyubartsev, V. Lobaskin, First principles characterisation of bio-nano interface, Phys. Chem. Chem. Phys. 2021, 23, 13473-1348261.
Alsharif, S.A.; Power, D.; Rouse, I.; Lobaskin, V. In Silico Prediction of Protein Adsorption Energy on Titanium Dioxide and Gold Nanoparticles. Nanomaterials 2020, 10, 1967.
3. Power D, Rouse I, Poggio S, Brandt E, Lopez H, Lyubartsev A, Lobaskin V. A multiscale model of protein adsorption on a nanoparticle surface, Modelling Simul. Materials Sci. Eng. 27:084003 (2019)

Biomedical Assays Using Green Technology

Discipline: Physics

Supervisor: Dr James Rice

The development and testing of assays using green substrate designs will be undertaken. The use of biomaterial-based substrate materials with fabricated nano features will be assessed for their potential to support fluorescence-based assays. The project will require students with an interest in biochemistry and/or chemistry. The project centers around preparing and testing assays using pre-fabricated substrates. This project aims to facilitate students undertaking this project to learn aspects of biomedical assay design and nanomaterials.

Investigating a Novel Therapeutic Strategy for Alzheimer’s Disease

Discipline: Physics/Neuroscience

Supervisor: Prof. Brian Rodriguez (Physics), Dr Derek Costello (Neuroscience)

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease worldwide. The AD brain is characterized by the accumulation of the toxic peptide β-amyloid (Aβ), leading to uncontrolled inflammation and subsequent deterioration in neuronal integrity. Hippocampal neurons are particularly vulnerable to Aβ, resulting in the progressive cognitive decline characteristic of the disease. There is no cure for AD, and current treatments at best alleviate the early-stage symptoms. Recent attempts to develop disease-modifying strategies have targeted individual facets of the pathology, yet show minimal clinical efficacy. This highlights the importance of identifying a multi-factorial approach to target both the disease symptoms and progression.

Our collaborators based at TU Dublin have developed a suite of coumarin-derived small molecule agents, with anti-oxidant and metal-chelating activity. We have already identified one of these agents, ‘L4’, as a robust inhibitor of the neuroinflammation associated with AD. Our recent evidence further indicates that L4 can protect neurons from oxidative damage and cell death; a primary feature of AD. This collaborative and interdisciplinary project will further assess the impact of L4 on AD-related pathology in vivo. Using a transgenic C. elegans model of AD, we will examine whether L4 can restore Aβ-induced behavioral dysfunction and oxidative stress. Moreover, using atomic force microscopy, we will determine whether L4 may offer therapeutic benefit through modulation of Aβ aggregation in vivo.

Development and characterisation of aligned collagen matrices to model pancreatic cancer

Discipline:Biomedical Science

Supervisor: Prof. Brian Rodriguez and Dr Stephen Thorpe

Background: Pancreatic cancer claims the lives of approx. 600 people in Ireland each year, equivalent to 11 every week. Despite its relatively low prevalence, pancreatic cancer is the 4th most lethal of human cancers with a 5-year survival rate of less than 9%, and it is predicted to become the 2nd leading cause of cancer-related mortality by 2030. Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic cancer. A hallmark of PDAC is an abundant and organised collagenous extracellular matrix (ECM) which impedes immune cell accessibility, promotes cancer aggression by facilitating metastasis, and provides therapy resistance by acting as a barrier to drug infiltration.

The aim of this project is to develop and characterise a fibrous collagen-based ECM with mechanical properties and fibrous architecture mimicking PDAC tumour tissue.

Methods: Aligned collagen matrices will be created using either extrusion-based 3D printing or fluid shear. Collagen organisation will be assessed using polarised light microscopy and atomic force microscopy (AFM). Mechanical properties will be assessed using AFM. The ability of pancreatic cancer cells to remodel collagen in these hydrogels will be assessed.

Anticipated results: It is hypothesised that fibrous collagen alignment and hydrogel stiffness will encourage metastasis in pancreatic cancer cell lines. This 3D hydrogel system will provide a tool to facilitate a better understanding of the processes through which extracellular matrix properties influence PDAC progression.

The following information is vetted and provided by the American Association of Collegiate Registrars and Admissions Officers (AACRAO) on the Electronic Database for Global Education (EDGE).

Letter Grade	Percentage	Ranking	U.S. Equivalent
A+/A/A-	70 - 100%	First Class Honours	A
B+/B/B-	60 - 69%	Second Class Honours Upper	B+
C+/C/C-	50 - 59%	Second Class Honours Lower	B
D+	45 - 49%	Third Class Honours	C+
D/D-	40 - 44%	Pass	C
F	0 - 39%	Fail	F