Approximately 1–2 per 100,000 young athletes die from sudden cardiac death (SCD) and extreme exercise may be associated with myocardial scar and arrhythmias. Racehorses have a high prevalence of atrial fibrillation (AF) and SCD but the presence of myocardial scar and inflammation has not been evaluated. Cardiac tissues from the left (LAA) and right (RAA) atrial appendages, left ventricular anterior (LVAPM) and posterior (LVPPM) papillary muscles, and right side of the interventricular septum (IVS-R) were harvested from racehorses with sudden cardiac death (SCD, n = 16) or other fatal injuries (OFI, n = 17), constituting the athletic group (ATH, n = 33), and compared to sedentary horses (SED, n = 10). Horses in the ATH group had myocyte hypertrophy at all sites; increased fibrosis at all sites other than the LAA; increased fibroblast infiltration but a reduction in the overall extracellular matrix (ECM) volume in the RAA, LVAPM, and IVS-R compared to SED horses. In this horse model, athletic conditioning was associated with myocyte hypertrophy and a reduction in ECM. There was an excess of fibrocyte infiltration and focal fibrosis that was not present in non-athletic horses, raising the possibility of an exercise-induced pro-fibrotic substrate.

Summary The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies (“multiomic” strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.

Application-specific quantum computers offer the most efficient means to tackle problems intractable by classical computers. Realizing these architectures necessitates a deep understanding of quantum circuit properties and their relationship to execution outcomes on quantum devices. Our study aims to perform for the first time a rigorous examination of quantum circuits by introducing graph theory-based metrics extracted from their qubit interaction graph and gate dependency graph (GDG) alongside conventional parameters describing the circuit itself. This methodology facilitates a comprehensive analysis and clustering of quantum circuits. Furthermore, it uncovers a connection between parameters rooted in both qubit interaction and GDGs, and the performance metrics for quantum circuit mapping, across a range of established quantum device and mapping configurations. Among the various device configurations, we particularly emphasize modular (i.e. multi-core) quantum computing architectures due to their high potential as a viable solution for quantum device scalability. This thorough analysis will help us to: i) identify key attributes of quantum circuits that affect the quantum circuit mapping performance metrics; ii) predict the performance on a specific chip for similar circuit structures; iii) determine preferable combinations of mapping techniques and hardware setups for specific circuits; and iv) define representative benchmark sets by clustering similarly structured circuits.

The European Committee for Future Accelerators (ECFA) Early-Career Researcher (ECR) panel, which represents the interests of the ECR community to ECFA, presents in this document its initiatives and activities in the year 2023. This report summarises the process of the first big turnover in the panel composition at the start of 2023 and reports on the activities of the active working groups - either pursued from before or newly established. The overarching goal of the ECFA-ECR panel is to better understand and support the diverse interests of early-career researchers in the ECFA community and beyond.

This study explores the development prospects of tourism in predominantly industrial small-sized cities (SSCs), focusing on the integration of tourism into urban planning and sustainable practices. Using structural equation modeling (SEM) to analyze survey data from SSCs in Serbia and Russia, the research identifies key factors contributing to urban tourism sustainability. The analysis reveals the significant roles of environmental, economic, social, and cultural indicators in promoting sustainable urban tourism. The importance of inclusive development and community engagement is also highlighted, underscoring their impact on sustainability. The findings offer theoretical insights and practical recommendations for effectively incorporating tourism into urban planning to achieve comprehensive sustainability in SSCs.

Artificial intelligence tools significantly impact almost all domains of industry and science, including public relations. The rapid development and accessibility of large language and vision models have facilitated the relatively easy implementation of great tools. This accessibility has made it relatively simple to integrate powerful tools into various workflows, revolutionizing practices in fields like public relations. These tools enable public relations practitioners to devote more time to creative work by quickly solving time-consuming, repetitive tasks using AI tools. This paper has identified 16 AI tools tailored for PR and content creation that can improve efficiency and simplify their work processes. By leveraging AI’s capacity to analyse vast amounts of data, PR professionals can gain insights into audience behaviour and media trends, enabling more targeted and effective communication strategies. This paper explores the transformative potential of AI in public relations, highlighting how these tools are reshaping the landscape of communication, engagement, and content creation.

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

Running speed in the form of sprinting is one of the most important abilities that can significantly define performance success in many sports. From the perspective of genetically inherited motor functions, running speed can be classified as a primary phylogenetic human movement, manifested in the form of a “threesegment model” consisting of speed, power, and coordination. By comprehensively analyzing the general and partial predictive contributions of dynamic-kinematic parameters of running, speed-power abilities, and morphological characteristics, on a sample of 80 boys aged 10-12 years, it can be concluded that regardless of the choice of criteria, achieved maximal speeds (KVMAX) or results in children’s athletic sprint over 50 meters (KT50m), the same or related predictor variables contributed to the explanation. The variable running time for 20m from a flying start (KTLS20m) has the greatest predictive contribution (β=0.83, p<0.001) to explaining both criteria, which may indicate the importance of conducting this test in the identification and selection for athletic sprint. Additionally, the selection of tests to assess speed-power abilities is extremely important for the identification and selection for athletic sprint. It can be concluded that tests of horizontal and vertical jumps are significant for identification, as well as tests for assessing neuro-muscular excitation. Tests for assessing continuous horizontal jump are also important, although there is an impression that, in boys aged 10-12 years, coordinatively simpler tests should be used. In the analysis of morphological characteristics, variables that significantly contributed to the explanation of criteria at a partial level were body height, back skinfold, and ankle diameter, indicating that in the identification of talented individuals, it should be considered that elite sprinters are characterized by light bones, optimal muscle mass, and low levels of subcutaneous fat tissue.

In this article, we claim that syntactic objects undergoing ellipsis can be targeted by both narrow syntactic and PF operations. We base this conclusion on experimental evidence from the interaction between single conjunct agreement and verb-echo answers in South Slavic, which we show to be derived via verb-stranding VP ellipsis. Adopting the view that Vocabulary Insertion replaces Q-variables on lexical heads (Halle 1991) and ellipsis is a syntactic operation which deletes Q-variables (Saab 2022), we demonstrate that constituents properly included in the ellipsis site can undergo Internal Merge in the narrow syntax, and can participate in PF processes from the derived position. The interaction between ellipsis, Internal Merge and Agree-Copy that accounts for these patterns of data follows naturally within the Distributed Ellipsis approach.

Through resilience theory, this paper explores the integration and alignment of the United Nations sustainable development goals (SDGs) within Kosovo's National Development Strategy (NDS). It highlights how adaptability and strategic planning underpin sustainable development in emerging national contexts like Kosovo, offering a qualitative analysis to identify gaps and suggest improvements for SDG integration. Utilizing a qualitative analysis, this study identifies gaps and provides recommendations for better SDG integration within Kosovo's national development agenda. Data analysis involves the thematic coding of qualitative data and synthesis of case study findings by examining existing documents, strategies, and plans as manifestations of Kosovo's commitment to fostering resilience and achieving a sustainable future. Key insights include recommendations for enhancing governance, environmental protection mechanisms, and social inclusivity to achieve resilient and sustainable economic growth. The study contributes to the discourse on resilience theory in national sustainable development strategies amidst political uncertainty.

Numerous industrial parts, devices, and processes are designed to withstand the conditions that lead to cavitation erosion. Metallic, ceramic, and composite materials used for these conditions must achieve specific mechanical characteristics required to resist cavitation erosion. When molten metal or alloy flows and comes into contact with refractory material or coated furnace linings, cavitation erosion can occur. This phenomenon is particularly expected in metallurgy, especially in casting operations. Alumina-based refractories, specifically low cement castable (ALCC), are often used in furnace lining applications due to their superior properties, such as high refractoriness, thermal stability, and mechanical characteristics. Mullite is another refractory material frequently used in foundry lining applications. It can be utilized as a coating in casting processes, such as the Lost Foam process, which is a novel method for producing high-quality, cost-effective castings. These two refractory materials were chosen to study their behavior under cavitation conditions. An ultrasonic vibratory test with a stationary specimen (ASTM G-32) was used for experimental cavitation determination. The results of mass loss and surface morphological parameters of degradation revealed that ALCC samples eroded predominantly at the surface, while the mullite samples exhibited more significant degradation by depth.

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients. The genomic landscape of clear cell renal cell carcinoma (ccRCC) remains to be comprehensively characterised. Here, whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project was used to identify potential drivers and clinical correlations to inform the development of therapies.