Coronary artery calcium (CAC) scoring plays a crucial role in the early detection and risk stratification of coronary artery disease (CAD). In this study, we focus on non-contrast coronary computed tomography angiography (CCTA) scans, which are commonly used for early calcification detection in clinical settings. To address the challenge of limited annotated data, we propose a radiomics-based pipeline that leverages pseudo-labeling to generate training labels, thereby eliminating the need for expert-defined segmentations. Additionally, we explore the use of pretrained foundation models, specifically CT-FM and RadImageNet, to extract image features, which are then used with traditional classifiers. We compare the performance of these deep learning features with that of radiomics features. Evaluation is conducted on a clinical CCTA dataset comprising 182 patients, where individuals are classified into two groups: zero versus non-zero calcium scores. We further investigate the impact of training on non-contrast datasets versus combined contrast and non-contrast datasets, with testing performed only on non-contrast scans. Results show that radiomics-based models significantly outperform CNN-derived embeddings from foundation models (achieving 84% accuracy and p<0.05), despite the unavailability of expert annotations.

MicroRNAs (miRs) are robust regulators of gene expression, implicated in most biological processes. microRNAs predominantly downregulate the expression of genes post-transcriptionally and each miR is predicted to target several hundred genes. The accurate identification and annotation of miR-mRNA target interactions is central to understanding miRs function and their therapeutic potential. However, computational target prediction is challenging due to imperfect complementarity of miRs with their targets and the growing volume and heterogeneity of experimental data present challenges in accessing, integrating, and analysing miR-target interaction information across biological contexts. This creates a need for integrated resources and intelligent query tools. We present the miRKat Suite, comprising miRKatDB, a comprehensive, curated database of predicted and validated miR-target interactions and associated annotations, and miRKatAI, a multi-agent system powered by large language models (LLMs) and LangGraph. miRKatDB integrates data from multiple publicly available sources, providing a comprehensive foundation for miR studies, including miR target genes and changes in levels of tissue expression previously reported. miRKatAI offers a natural language interface for complex querying of miRKatDB, facilitates grounded information retrieval from established sources in the field, and supports basic data visualisation. The miRKat Suite aims to accelerate miR research by streamlining data access, enhancing exploratory analysis, and supporting hypothesis generation.

This paper presents a novel rule-based approach for generating music by varying existing tunes. We parse each tune to find the Pathway Assembly (PA) [ 1], that is a structure representing all repetitions in the tune. The Sequitur algorithm [2 ] is used for this. The result is a grammar. We then carry out mutation on the grammar, rather than on a tune directly. There are potentially 19 types of mutations such as adding, removing, swapping or reversing parts of the grammar that can be applied to the grammars. The system employs one of the mutations randomly in this step to automatically manipulate the grammar. Following the mutation, we need to expand the grammar which returns a new tune. The output after 1 or more mutations will be a new tune related to the original tune. Our study examines how tunes change gradually over the course of multiple mutations. Edit distances, structural complexity and length of the tunes are used to show how a tune is changed after multiple mutations. In addition, the size of effect of each mutation type is analyzed. As a final point, we review the musical aspect of the output tunes. It should be noted that the study only focused on generating new pitch sequences. The study is based on an Irish traditional tune dataset and a list of integers has been used to represent each tune's pitch values.

Ireland senator weighs in on bill to'restrict' speech for the'common good.' (Credit: Houses of the Oireachtas, June 13, 2023) Ireland's government condemned the recent burning of a hotel meant to house 70 migrants outside of Galway in the west of the country as a suspected arson attack. "I am deeply concerned about recent reports of suspected criminal damage at a number of properties around the country which have been earmarked for accommodating those seeking international protection here, including in County Galway last night," Prime Minister Leo Varadkar said in a statement Sunday. "There is no justification for violence, arson or vandalism in our Republic. Garda [police] investigations are underway." The statement came in response to a fire that erupted Saturday night at the Ross Lake House Hotel in Rosscahill, County Galway, in the west of Ireland, destroying the building.

Consultant cardiologist Professor Faisal Sharif at GUH welcomed the addition of the CorPath Robotic Angioplasy as "a game changer". "We recently successfully completed the first case and, going forward, we will be performing these procedures regularly," he said. He said robotic innovations have come a long way in the last 10 years. "We in Galway are delighted to have performed the first robotic-guided coronary intervention in Ireland and the UK," he said. "The main advantage of robotics is that it is safe and very precise in stent placement. It allows the accurate placement for up to 1mm at a time."

Dr Anastasia Griva is exploring real-world phenomena in the AI and business analytics space, looking to answer questions that are important to society. Dr Anastasia Griva received her PhD in business analytics from the Athens University of Economics and Business three years ago. This was an industry-funded PhD and she worked closely with the retail sector, while establishing two AI and analytics start-ups. But academia was her dream and so she joined the University of Galway as a post-doc researcher. She applied successfully for a Marie Skłodowska-Curie fellowship through Lero, the Science Foundation Ireland research centre for software. After this, she obtained her first academic position as a lecturer, and she is now the programme director for the MSc in business analytics at the University of Galway.

Thousands of historical images from across Ireland are being brought to life in color for the first time, thanks to a new AI-led photo project. Combining digital technology with painstaking historical research, professors John Breslin and Sarah-Anne Buckley at the National University of Ireland, Galway, have been able to turn photos, originally shot in black in white, into rich color images. It includes portraits of key figures like Oscar Wilde and poet W.B. Yeats, as well as defining moments in history, like the Titanic setting sail from the Belfast shipyard where it was constructed. Yet, some of the most compelling photos depict everyday scenes -- people herding pigs, spinning wool or packed onto the back of horse-drawn carts. And while poverty is evident in pictures of barefoot villagers crowding around for a photo, or of Dublin's working-class tenement buildings, there are also well-to-do family shots and depictions of upper-class pastimes like fox hunting.

Many learning tasks involve multi-modal data streams, where continuous data from different modes convey a comprehensive description about objects. A major challenge in this context is how to efficiently interpret multi-modal information in complex environments. This has motivated numerous studies on learning unsupervised representations from multi-modal data streams. These studies aim to understand higher-level contextual information (e.g., a Twitter message) by jointly learning embeddings for the lower-level semantic units in different modalities (e.g., text, user, and location of a Twitter message). However, these methods directly associate each low-level semantic unit with a continuous embedding vector, which results in high memory requirements. Hence, deploying and continuously learning such models in low-memory devices (e.g., mobile devices) becomes a problem. To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams. We evaluate METEOR using two types of multi-modal data streams (i.e., social media streams and shopping transaction streams) to demonstrate its ability to adapt to different domains. Our results show that METEOR preserves the quality of the representations while reducing memory usage by around 80% compared to the conventional memory-intensive embeddings.

Online hate speech is a newborn problem in our modern society which is growing at a steady rate exploiting weaknesses of the corresponding regimes that characterise several social media platforms. Therefore, this phenomenon is mainly cultivated through such comments, either during users' interaction or on posted multimedia context. Nowadays, giant companies own platforms where many millions of users log in daily. Thus, protection of their users from exposure to similar phenomena for keeping up with the corresponding law, as well as for retaining a high quality of offered services, seems mandatory. Having a robust and reliable mechanism for identifying and preventing the uploading of related material would have a huge effect on our society regarding several aspects of our daily life. On the other hand, its absence would deteriorate heavily the total user experience, while its erroneous operation might raise several ethical issues. In this work, we present a protocol for creating a more suitable dataset, regarding its both informativeness and representativeness aspects, favouring the safer capture of hate speech occurrence, without at the same time restricting its applicability to other classification problems. Moreover, we produce and publish a textual dataset with two variants: binary and multi-label, called `ETHOS', based on YouTube and Reddit comments validated through figure-eight crowdsourcing platform. Our assumption about the production of more compatible datasets is further investigated by applying various classification models and recording their behaviour over several appropriate metrics.

Mathieu d'Aquin is a Professor of Informatics specialised in data analytics and semantic technologies at the Insight Centre for Data Analytics of the National University of Ireland Galway. He was previously Senior Research Fellow at the Knowledge Media Institute of the Open University, where he led the Data Science Group. In this interview, he speaks about research on semantic web technologies and specific application of web data technologies, which are two key areas of his work interest. You have been working for years on Semantic Web/Linked Data technologies. What will shape our future the most?