Abstract
Pulmonary hypertension (PH) is a hemodynamic condition describing elevated pulmonary artery pressure. To date, right heart catheterism is the gold standard diagnostic test for PH, but it is an invasive and expensive procedure. Deep learning (DL) techniques applied to heart sounds have previously shown promising performances for PH screening. In this work, we analyze the impact of different input representations for PH detection with convolutional neural networks (CNNs). We found that considering each heartbeat as an independent input yielded systematically lower performance than considering the recordings as a whole: preserving the information about the variability over the heartbeats is key. Time-domain feature maps outperformed handcrafted features and combining the time- and frequency-domain proved consistently most effective. Reducing the number of heartbeats to 30 did not affect the performance, and even reducing to 10 beats preserves the diagnostic value. The proposed analysis moves one step further the applicability of DL for PH detection from heart sounds in the clinical practice. © 2025 IEEE Computer Society. All rights reserved.

Abstract
The European Union (EU) Digital Services Act (DSA) has introduced a novel set of rules for online platforms and search engines, with significant implications for the Recommender Systems community. Through its data access mechanisms, the DSA invites researchers to request both publicly available and private data from Very Large Online Platforms (VLOPs) and Very Large Search Engines (VLOSEs) - those with more than 45 million active recipients in the EU - to investigate systemic risks associated with the dissemination of illegal content, risks to the exercise of fundamental rights, and negative effects on electoral processes, public health, and gender-based violence. This tutorial is aimed at researchers who are interested in submitting such data access requests and will provide them with the knowledge to do so by introducing the relevant definitions and provisions of the DSA, and addressing the most important procedural steps to obtain data access and will provide attendees with a comprehensive understanding of the DSA's data access implications for RecSys research. The tutorial targets researchers, practitioners, and students in understanding current developments in online platform regulation in Europe and their impact on RecSys research.

Abstract
Purpose: To aggregate, interpret, and synthesize findings from qualitative studies on patients' experiences with chemotherapy-induced peripheral neuropathy (CIPN). Methods: A qualitative metasynthesis was conducted following the thematic synthesis approach of Thomas & Harden. A systematic literature search was performed in MEDLINE, CINAHL, Psychology and Behavioral Sciences Collection, and Scopus, including studies published up to December 2024. Two researchers independently conducted the screening and data extraction. They also independently evaluated the quality of the included studies. The data from these studies were then thematically analyzed and synthesized using Dorothea Orem's model. Results: Eighteen studies were included. Four main categories were identified: (1) Physical and Functional Impact of CIPN, (2) Emotional and Psychological Impact, (3) Coping Strategies and Self-management, and (4) Support and Barriers to Health. The findings revealed distinct self-care deficits related to functional limitations, emotional distress, and coping challenges. Utilizing Orem's Nursing Theory of Self-Care Deficit, these deficits were mapped onto different levels of nursing intervention, ranging from compensatory support to educational and self-management strategies, emphasizing an action-oriented approach in patient care. Conclusions: This metasynthesis highlights the complex and multidimensional effects of peripheral neuropathy on the lives of cancer patients. Applying Orem's model underscores the critical role of nurses in addressing healthcare system gaps, functional impairments, and long-term adaptation challenges to enhance supportive care for individuals suffering from CIPN.

Abstract
Radon (Rn-222) is a unique atmospheric tracer, since it is an inert gaseous radionuclide with a predominantly terrestrial source and a short half-life (3.8232 (8) d), enabling quantification of the relative degree of recent (< 21 d) terrestrial influences on marine air masses. High quality measurements of atmospheric radon activity concentration in remote oceanic locations enable the most accurate identification of baseline conditions. Observations of GHGs under baseline conditions, representative of hemispheric background values, are essential to characterise long-term changes in hemispheric-mean GHG concentrations, differentiate between natural and anthropogenic GHG sources, and improve understanding of the global carbon budget.The EU-funded project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) will establish world-leading high-quality atmospheric measurements of radon activity concentration and of selected GHG concentrations (CO2, and CH4) at a remote oceanic location, the Eastern North Atlantic (ENA) facility, managed by the Atmospheric Radiation Measurement (ARM) programme (Office of Science from the U.S. Department of Energy), located on Graciosa Island (Azores archipelago), near the middle of the north Atlantic Ocean. These observations will provide an accurate, time-varying atmospheric baseline reference for European greenhouse gas (GHG) levels, enabling a clearer distinction between anthropogenic emissions and slowly changing background levels. NuClim will also enhance measurement of atmospheric radon activity concentration at the Mace Head Station, allowing the identification of latitudinal gradients in baseline atmospheric composition, and supporting the evaluation of the performance of GHG mitigation measures for countries in the northern hemisphere.The high-quality nuclear and GHG observations from NuClim, and the resulting classification of terrestrial influences on marine air masses, will assist diverse climate and environmental studies, including the study of pollution events, characterisation of marine boundary layer clouds and aerosols, and exploration of the impact of natural planktonic communities on GHG emissions. This poster presents an overview of NuClim, outlines the project objectives and methodologies, and summarises the relevant data products that will be made available to the climate community.Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515.

Abstract

Abstract
This study presents an explainable content-based image retrieval system for predicting post-surgical aesthetic outcomes in breast cancer patients, comparing state-of-theart vision transformers, convolutional neural networks, and B-cos architectures. Results show that vision transformers, particularly GC ViT and DaViT, outperform convolutional neural networks and B-cos architectures, achieving an adjusted discounted cumulative gain of up to 80.18%. This superior performance is attributed to their ability to model long-range dependencies while effectively capturing local information. Bcos networks underperform (64.28-70.19% adjusted discounted cumulative gain), likely due to oversimplified feature alignment unsuitable for clinical tasks. Explainability analysis using Integrated Gradients reveals that models primarily focus on breast regions but occasionally attend to irrelevant features (e.g., arm positioning, leading to retrieval errors and highlighting a semantic gap between learned visual similarities and clinical relevance. Future work aims to integrate anatomical segmentation and ensemble learning methods to enhance clinical alignment and address attention inaccuracies. Clinical Relevance-The content-based image retrieval system developed in this study aids clinicians by supporting surgical outcome prediction in breast cancer patients and streamlining the traditionally time-intensive task of manually identifying similar reference images for patient consultation. © 2025 IEEE.