At the University of Malta we are extremely proud of our students’ achievements while studying and furthering their knowledge with us. As such, we have done our research to best bring forth some of the many projects and dissertations done in the past months in a way we could share them with the whole community during the month of September.
While there were a lot to choose from, we had to make some very difficult decisions to shortlist just 7 finalists who ultimately made it to our Facebook and Instagram profiles!
In alphabetical order, here’s our choice of 7 dissertations for this year, including the title, the student’s name and a comment from each:
- Andrea Mula
'Permittivity measurements of blood’
Comment:
“The aim of my dissertation was to use electromagnetic fields to diagnose clinical conditions and study different pharmacological processes using the permittivity of blood.”
- Aurora Attard Coleiro
‘Sensation seeking and aesthetic preferences in the context of a supermarket e-commerce website'
Comment:
“My curiosity, and perhaps yours, spans across multiple areas so I spiced up my dissertation by combining two distinct interests of mine: Psychology & ICT.
This led me to explore how the sensation seeking personality trait affects what websites one finds aesthetically pleasing, which led to some interesting findings such as male sensation seekers liking abstract art in websites better than low sensation seekers.”
- Daniel Gafa'
‘Are Maltese Politics Ideological or Personalised? A Sociological Analysis’
Comment:
“My dissertation analysed personalism and the role of ideologies in Maltese politics. After interviewing political figures from different parties, it was found that albeit the negative sides of personalism there are also positive aspects and although the main parties are more ideologically open, the small ones are more principled.”
- Daniel Vella
‘Few-Shot Learning for Low Data Drug Discovery’
Comment:
“Machine learning techniques are typically data hungry, which is in stark contrast to the limited data for new disease targets. In this project, we explore meta-learning techniques to teach a machine learning model to "learn how to learn", similar to the ability observed in humans who can learn quickly from just a few examples (think of a child who sees a cat for the first time and can easily classify further encounters with such an animal and classify it).
Our proposed machine learning network (Prototypical Networks), making use of embeddings created through graph convolutional networks, achieve better results than the state-of-the-art on the Tox-21 dataset. In our experiments, we classified molecules as active or inactive/decoys within a previously unseen experimental assay, using only 1-10 molecule examples from this assay, using a model trained on related (but not identical) assays.”
- Desireè Sant
‘Characterisation of p53 isoforms and expression in colorectal cancer cell lines’
Comment:
“I am Desireè Sant and I have recently graduated from the Bachelor of Science (Honours) in Medical Biochemistry course with a first class. In my third and final year of studies I conducted laboratory research under my supervisor Prof. Therese Hunter and co-supervisors Dr Marita Vella and Dr Laura Grech.
With colorectal cancer (CRC) being the second leading cause of cancer-related mortality and the incomplete characterisation of the expression p53 isoforms being a considerable challenge facing precision oncology, the hypothesis behind my thesis was created.
My thesis focused on using western blotting techniques to characterise p53 isoforms and determine whether the pattern of expressed p53 isoforms differs among CRC cell lines and between different CRC subtypes. By conducting this research, crucial information, currently unknown to the scientific community, was identified which may aid in improving current precision targeted oncotherapy treatment strategies for CRC.”
- Raina Marie Seychell
‘Evaluation of small-molecule compounds as modulators of mitochondrial interactions with the human islet amyloid polypeptide (hiAPP)’
Comment:
“Type-2 Diabetes Mellitus (T2DM) is a metabolic disorder that impacts many millions worldwide. In our study, we identified small-molecule therapeutics that help keep crucial insulin-producing cells in the pancreas alive by protecting their mitochondria against damage by a toxic hormone, called amylin. We hope this strategy might offer a new effective therapy for type-2 diabetics.
The research project was carried out for my undergraduate dissertation in B.Sc. (Medical Biochemistry), under the supervision of Prof. Neville Vassallo (Dept. of Physiology & Biochemistry). The project was funded by the University of Malta (PHBR) and done in collaboration with the Max Planck Institute of Biophysical Chemistry, Göttingen, Germany.”
- Ryan Pace
‘Plan automation with motion control’
Comment:
“Automation technology in manufacturing is continuously advancing to boost productivity. Continuous advances in technology aims to significantly decrease human error while increasing flexibility in the process control. In my dissertation, a plant automation system was designed.
The aim of the project was to continuously cut pieces from a material according to the length specified by the operator while the material was constantly moving on a conveyor belt system.”
You too can be part of this list! Study today and #ShineAtUM!
