IAS-LAB PUBLICATIONS
DARKO-Nav: Hierarchical Risk and Context-Aware Robot Navigation in Complex Intralogistic Environments
Authors: Stracca Elena; Rudenko Andrey; Palmieri Luigi; Salaris Paolo; Castri Luca; Mazzi Nicolo; Rakcevic Vasilije; Vaskevicius Narunas; Linder Timm; Bellotto Nicola; Schreiter Tim; Zhu Yufei; Quero Manuel Castellano; Napolitano Olga; Stefanini Elisa; Heuer Lukas; Magnusson Martin; Swikir Abdalla; Lilienthal Achim J.; Mazzi Nicolò; Castellano Quero Manuel; J. Lilienthal Achim
Journal: EUROPEAN ROBOTICS FORUM 2025
Published: 2025
DOI: 10.1007/978-3-031-89471-8_24
We propose a flexible hierarchical navigation stack for a mobile robot in complex dynamic environments. Addressing the growing need for reliable navigation in real-world scenarios, where dynamic agents and environmental uncertainties pose significant challenges, our solution decomposes this complexity into task planning, navigation, control, and safe velocity components. In contrast to the prior art, our system at every level incorporates diverse contextual information about the environment, anticipates navigation risks and proactively avoids collisions with dynamic agents.
Volume: 36 Pages: 155-161
Keywords: intralogistics; navigation in dynamic environments; predictive collision avoidance; risk-aware path planning;
Role of Pb in Portland Cement Hydration: New Insights from In-Situ Laboratory XRD
Authors: Liu Yikai; Dalconi Maria Chiara; Valentini Luca; Bellotto Maurizio Pietro; Molinari Simone; Artioli Gilberto
Journal: PROCEEDINGS OF THE RILEM SPRING CONVENTION AND CONFERENCE 2024, VOL 2, RSCC 2024
Published: 2025
DOI: 10.1007/978-3-031-70281-5_41
Ordinary Portland cement (OPC) is a ubiquitous construction material and has long been the most prevalent of all man-made concepts. However, the massive demand for OPC is responsible for approximately 7–8% of all anthropogenic CO2 emissions. Substituting OPC with industrial by-products presents a promising avenue for reducing clinker usage and aiding industry decarbonization. However, concerns arise regarding the presence of trace metals, particularly Pb, which can impede early hydration and degrade material properties. Understanding the kinetics of clinker phase dissolution in the presence of Pb is crucial for mitigating these issues. Conventional characterization methods may alter samples and fail to adequately capture underlying reaction mechanisms. To address this challenge, our study employs in-situ X-ray diffraction (XRD) to accurately assess Pb-OPC hydration kinetics in real time. Furthermore, we develop a geochemical model to quantify hydration reactions. This model supplements experimental findings, providing valuable insights into the proposed mechanisms. Overall, our work enhances the understanding of Pb-OPC interactions in cementitious materials, ultimately contributing to more efficient industrial by-product management and sustainable construction practices.
Volume: 56 Pages: 367-375
Keywords: cement hydration; geochemical modeling; in-situ XRD; lead; retardation;
Aluminothermic Recovery of Strategic Ferroalloys from Ladle Slag: An Integrated Thermodynamic and Experimental Approach
Authors: Disconzi Filippo; Bellotto Maurizio; Frazzetto Riccardo; Brunelli Katya; Ardit Matteo; Artioli Gilberto
Journal: MINERALS
Published: 2025
DOI: 10.3390/min15111121
Volume: 15
ConUDA: Confidence-Guided Pseudo-Label Sampling for Unsupervised Domain Adaptation in 3D LiDAR Semantic Segmentation
Authors: Li Wanmeng; Mosco Simone; Fusaro Daniel; Pretto Alberto
Journal: 21101337793
Published: 2025
DOI: 10.1109/ECMR65884.2025.11163385
Dense annotation of real 3D LiDAR point clouds for mobile robot applications remains challenging. Unsupervised Domain Adaptation (UDA) enables the segmentation of unlabeled real-world point clouds by leveraging labeled synthetic data. However, existing self-training-based UDA methods rely on fixed thresholds for pseudo-label selection, limiting adaptation performance. In this work, we address this limitation. We propose a novel UDA framework for 3D LiDAR semantic segmentation, centered on a confidence-guided pseudo-label sampling strategy (ConSamp). Specifically, ConSamp adopts a probabilistic sampling strategy in which pseudo-labels with higher confidence are more likely to be retained. Meanwhile, the sampling function itself evolves adaptively throughout training to respond to changes in confidence distribution. Experiments show that our model achieves strong performance on synthetic-to-real 3D LiDAR semantic segmentation tasks. In particular, results better than state-of-the-art methods have been achieved on two public 3D point cloud datasets: SemanticKITTI [1] and SemanticPOSS [2].
Spatio-Temporal Consistent Semantic Mapping for Robotics Fruit Growth Monitoring
Authors: Lobefaro Luca; Sodano Matteo; Fusaro Daniel; Magistri Federico; Malladi Meher V. R.; Guadagnino Tiziano; Pretto Alberto; Stachniss Cyrill
Journal: IEEE ROBOTICS AND AUTOMATION LETTERS
Published: 2025
Automatic fruit growth monitoring plays a vital role in advancing precision agriculture. Tracking the evolution of fruits over time is essential to monitor their development and optimize production. The ability to recognize fruits over periods of time, even with drastic scene changes, is a required capability of agricultural robots. This letter presents a system that allows long-term fruit tracking in 3D data. It generates instance-segmented 3D representations of plants at various growth stages over time, utilizing only consumer-grade RGB-D cameras installed on a mobile robot. Our approach first performs instance segmentation on each image in a sequence. Then, by exploiting geometric information and depth maps, we track the same instances throughout the sequence. We produce a 3D point cloud containing instances, exploiting odometry information and 3D semantic mapping. Once our robot performs a new recording at a different plant growth stage, it associates each fruit with the previously built 3D cloud and update the model. We validate the system in a real-world glasshouse environment in Bonn, Germany. Experimental results demonstrate that our system outperforms existing baselines even though it relies only on annotated images and operates at frame-rate, allowing the deployment on a real robot.
Volume: 10 Pages: 9470-9477
Keywords: Mapping; robotics and automation in agriculture and forestry;
Real-time Underwater Place Recognition in Synthetic and Real Environments using Multibeam Sonar and Learning-based Descriptors
Authors: Fusaro Daniel; Mosco Simone; Li Wanmeng; Pretto Alberto
Journal: 2025 IEEE INTERNATIONAL CONFERENCE ON SIMULATION, MODELING, AND PROGRAMMING FOR AUTONOMOUS ROBOTS, SIMPAR
Published: 2025
DOI: 10.1109/SIMPAR62925.2025.10979022
One of the biggest challenges in autonomous underwater navigation is the capability of the autonomous underwater vehicle (AUV) to localize itself, since common positioning systems (e.g., GPS or USBL), when available, can be unstable and very noisy. In this paper, we address the problem of place recognition in underwater synthetic and real environments, which is a key component in autonomous localization for robotics and navigation systems. In underwater scenarios, cameras are often subject to water turbidity and low-light conditions, making their use unreliable. Sonar data on the other hand is not affected by these limitations, but its interpretation is more challenging. In this paper we introduce a global descriptor for multibeam sonar images, to be compared with a database of sonar image descriptors acquired at known locations in sparsely structured environments. To enforce the similarity between descriptors computed from nearby poses, we introduce a novel loss that correlates the oriented-Intersection over Union (o-IoU) between pairs of sonar scans with the corresponding distances between their descriptors. A proxy image reconstruction loss has also been integrated for self-supervised adaptation to real data. Preliminary experimental results show that our method is able to localize an AUV in real-time in both synthetic and real environments by training it for localization using only synthetic sonar images.
Prevalence of rare missense TTN variants in a cohort of patients with cardiomyopathy
Authors: Bottillo Irene; Ciccone Maria Pia; Magliozzi Monia; Pilichou Kalliopi; Girotto Giorgia; Girolami Francesca; Cecconi Massimiliano; D'Argenio Valeria; Novelli Valeria; Coiana Alessandra; Formicola Daniela; Micaglio Emanuele; Tortora Giada; Gualandi Francesca; Petrucci Simona; Castori Marco; Resta Nicoletta; Vestri Anna Rita; Iascone Maria; Grammatico Paola
Journal: JOURNAL OF MOLECULAR AND CELLULAR CARDIOLOGY
Published: 2025
DOI: 10.1016/j.yjmcc.2024.12.004
Volume: 199 Pages: 46-50
Euclid preparation: LVIII. Detecting extragalactic globular clusters in the Euclid survey
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; HRK-2012-2023; JGX-3888-2023; GEH-7593-2022; FGL-6869-2022; ENM-3795-2022; KKP-9133-2024; DWB-0787-2022; EYY-4006-2022; LXV-7382-2024; GNE-1283-2022; MVR-8365-2025; HEO-6319-2022; FSV-8899-2022; H-4394-2019; ABB-9156-2021; GXI-6108-2022; JYY-9592-2024; ENE-3351-2022; EPI-1133-2022; CJC-9258-2022; B-4348-2013; DWO-2405-2022; EVW-7270-2022; GFK-2340-2022; Z-4828-2019; LBZ-7918-2024; DVE-7652-2022; GSC-5225-2022; KPF-2019-2024; LTE-2549-2024; LOZ-1470-2024; JNB-8974-2023; HVE-8025-2023; CEY-5520-2022; HZQ-9553-2023; IUT-7926-2023; EKV-4052-2022; CHO-3061-2022; HRV-6262-2023; JWI-9457-2024; LUN-9319-2024; HWT-5982-2023; HFL-6092-2022; E-2727-2014; DWB-9873-2022; L-8237-2014; MEO-0896-2025; IVA-4275-2023; JUX-7553-2023; B-4650-2017; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; DVC-6323-2022; LTU-6502-2024; LEQ-1557-2024; AGZ-3259-2022; FZL-7353-2022; A-2693-2010; EVT-3533-2022; CNE-2384-2022; HLX-2021-2023; KBZ-1983-2024; EUO-2530-2022; EUK-3820-2022; EVA-7948-2022; JRB-6526-2023; MOU-4416-2025; CQF-5798-2022; CQN-5681-2022; ITG-6579-2023; EUI-3706-2022; JWF-2506-2024; JUF-9810-2023; GWP-3456-2022; MUO-4676-2025; CQR-5759-2022; KER-9145-2024; JGR-4365-2023; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; HRW-8595-2023; FBE-0351-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; HPW-2820-2023; DWS-1040-2022; MNZ-8396-2025; FFG-2233-2022; FCS-1018-2022; DAV-9216-2022; CYT-5449-2022; GWA-7849-2022; KAK-4177-2024; B-8502-2016; DAV-8065-2022; DDB-6234-2022; HYH-6107-2023; A-2699-2012; MDN-2641-2025; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; DFY-8508-2022; MFB-4566-2025; MWK-2416-2025; HFG-7438-2022; D-1300-2016; DFZ-7309-2022; GNG-7078-2022; FNC-4379-2022; FLD-9518-2022; DFW-8877-2022; DHS-8142-2022; KEK-6332-2024; KJY-7272-2024; FOW-5617-2022; KSI-9422-2024; KNP-2716-2024; DJO-8166-2022; MNN-0179-2025; KFB-7397-2024; ABB-2322-2020; DKF-4281-2022; HTJ-4919-2023; IHG-7220-2023; DLB-6897-2022; KBT-5668-2024; MTQ-2344-2025; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; HPO-8234-2023; DNY-0415-2022; HTE-6970-2023; JMR-9144-2023; OON-3882-2025; DNX-4243-2022; MXB-9468-2025; GCT-2940-2022; IGN-7320-2023; JVJ-6571-2024; HFW-5845-2022; CDE-5677-2022; MDQ-9712-2025; DPD-7597-2022; JWG-7083-2024; IZJ-2041-2023; IZM-5556-2023; GBY-3944-2022; IHY-7449-2023; HRO-4465-2023; KKE-9686-2024; EAA-4768-2022; LGB-5701-2024; KFZ-7504-2024; L-8068-2014; DZM-7523-2022; GDK-6495-2022; T-7378-2018; MVR-7884-2025; ECA-8225-2022; DYT-7473-2022; HNI-8187-2023; ECZ-6053-2022; JCG-3503-2023; NBH-4743-2025; ABD-6783-2021; HQC-0143-2023; HWF-6506-2023; MXO-2726-2025; HZW-5449-2023; DVM-3959-2022; KBV-9584-2024; LWL-2178-2024; CMV-6954-2022; CDE-1189-2022; JUD-5049-2023; GBY-6621-2022; FJX-8996-2022; IZB-6495-2023; IAC-8042-2023; ECX-7840-2022; CEY-5704-2022; JHB-9875-2023; MNS-3973-2025; IYE-9818-2023; JHC-4470-2023; HIV-4758-2022; CHK-6722-2022; KTI-3074-2024; B-3004-2019; MLC-7865-2025; AFR-7693-2022; GBB-5111-2022; HZX-4069-2023; EOI-0533-2022; S-1204-2016; HOH-0341-2023; MLE-5291-2025; ISJ-4889-2023; CMX-4174-2022; MJF-0435-2025; JHG-8097-2023; L-6378-2014; IDQ-0489-2023; JTL-0752-2023; KSN-3481-2024; HQG-1163-2023; EYU-4413-2022; EVC-7104-2022; CRZ-8120-2022; C-2920-2017; CQL-4862-2022; NUD-5395-2025; CSH-1681-2022; ABB-8257-2020; NBI-5904-2025; JZW-7667-2024; KNK-3731-2024; AAK-4578-2020; HRR-2616-2023; JCW-4739-2023; GEC-5455-2022; HRX-7202-2023; DVZ-8147-2022; LYK-3518-2024; DDY-1012-2022; FZY-7746-2022; GQU-8893-2022; KND-8351-2024; DWN-4354-2022; KEZ-0532-2024; MVA-1492-2025; DGC-7489-2022; MKJ-0611-2025; D-1237-2017; HLL-5972-2023; JHX-6642-2023; GCF-0434-2022; GIM-5410-2022; HRS-4550-2023; ORK-4232-2025; GGM-6223-2022; MVC-4382-2025; JCV-3612-2023; MRW-3611-2025; GEF-7978-2022; FSR-7582-2022; DWT-7233-2022; MWU-5876-2025; NBF-7788-2025; INY-7970-2023; JCM-8241-2023; JAN-6167-2023; GDF-8239-2022; MDF-0079-2025; JEZ-2766-2023; MUC-7166-2025; KZQ-2739-2024; JSZ-6163-2023; NBR-5956-2025; IAD-4339-2023; IOX-4199-2023; AAW-3335-2020; Q-4575-2017; NBS-6714-2025; 57103783400; 6701673913; 57204700965; 7402297037; 10042256200; 6603939854; 7003267532; 36663730200; 6603351766; 57545939600; 57190443165; 57195321311; 8833942000; 55929371000; 57202215260; 55941325100; 56261663500; 57211815007; 6602458029; 6603186973; 7003910265; 7004293616; 7402054273; 55538241000; 7005317106; 59170382200; 35494536400; 6701705206; 57193617511; 18838264600; 57339831000; 56176939800; 8651648800; 57220414927; 24482926400; 57225389323; 6506892241; 6602293713; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 7006366735; 57193414472; 57090221700; 24439181000; 55948641800; 54924573500; 56260193000; 55543336500; 37121732700; 6507398813; 55757270100; 8856476200; 24069757200; 7004529134; 26663174300; 6603213706; 6602521535; 7006071419; 57212263363; 6506323808; 6601991850; 6602678698; 56181792800; 57200514857; 9639653200; 36627225700; 6506341877; 56592156500; 24173378000; 14630273900; 57206536839; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202450851
Extragalactic globular clusters (EGCs) are an abundant and powerful tracer of galaxy dynamics and formation, and their own formation and evolution is also a matter of extensive debate. The compact nature of globular clusters means that they are hard to spatially resolve and thus study outside the Local Group. In this work we have examined how well EGCs will be detectable in images from the Euclid telescope, using both simulated pre-launch images and the first early-release observations of the Fornax galaxy cluster. The Euclid Wide Survey will provide high-spatial resolution VIS imaging in the broad IE band as well as near-infrared photometry (YE, JE, and HE). We estimate that the 24 719 known galaxies within 100 Mpc in the footprint of the Euclid survey host around 830 000 EGCs of which about 350 000 are within the survey’s detection limits. For about half of these EGCs, three infrared colours will be available as well. For any galaxy within 50 Mpc the brighter half of its GC luminosity function will be detectable by the Euclid Wide Survey. The detectability of EGCs is mainly driven by the residual surface brightness of their host galaxy. We find that an automated machine-learning EGC-classification method based on real Euclid data of the Fornax galaxy cluster provides an efficient method to generate high purity and high completeness GC candidate catalogues. We confirm that EGCs are spatially resolved compared to pure point sources in VIS images of Fornax. Our analysis of both simulated and first on-sky data show that Euclid will increase the number of GCs accessible with high-resolution imaging substantially compared to previous surveys, and will permit the study of GCs in the outskirts of their hosts. Euclid is unique in enabling systematic studies of EGCs in a spatially unbiased and homogeneous manner and is primed to improve our understanding of many understudied aspects of GC astrophysics.
Volume: 693
Keywords: Galaxies: nuclei; Galaxies: star clusters: general; Space vehicles: instruments;
Euclid: A complete Einstein ring in NGC 6505
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; JAX-2768-2023; IGO-5191-2023; FZX-4882-2022; DBL-5256-2022; DXZ-7810-2022; IUS-5192-2023; GBC-8404-2022; CKU-5761-2022; HQA-9441-2023; DMU-8531-2022; NBI-9280-2025; NBA-8033-2025; CBK-5920-2022; CSY-1689-2022; MNN-3541-2025; AAD-3011-2021; CGD-2351-2022; FYJ-4908-2022; MGX-9029-2025; ISJ-4889-2023; AFJ-2074-2022; I-2511-2015; MDG-9557-2025; MVE-8410-2025; JGR-4365-2023; MMS-5823-2025; DFW-8877-2022; D-1237-2017; FSV-8899-2022; JMS-1539-2023; H-4394-2019; GHS-5944-2022; MQB-8395-2025; MXB-2135-2025; FZO-1254-2022; JNB-8974-2023; HVE-8025-2023; KKX-1153-2024; CEY-5520-2022; HZQ-9553-2023; EKA-7986-2022; IUT-7926-2023; LXX-3952-2024; IUQ-9509-2023; GNJ-2760-2022; CHO-3061-2022; HRV-6262-2023; JWI-9457-2024; LUN-9319-2024; CJD-7824-2022; HWT-5982-2023; HFL-6092-2022; E-2727-2014; L-8237-2014; MEO-0896-2025; E-8021-2017; IVA-4275-2023; JUX-7553-2023; B-4650-2017; ESG-5016-2022; HTG-8587-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; LTU-6502-2024; GLO-1082-2022; IUS-3917-2023; AGZ-3259-2022; FZL-7353-2022; A-2693-2010; EVT-3533-2022; CNE-2384-2022; HLX-2021-2023; KBZ-1983-2024; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; EVA-7948-2022; JRB-6526-2023; MOU-4416-2025; IBV-9243-2023; CQF-5798-2022; EVA-4097-2022; ITG-6579-2023; EUI-3706-2022; JWF-2506-2024; JUF-9810-2023; MUO-4676-2025; CQR-5759-2022; KER-9145-2024; GAX-2002-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; EYM-5386-2022; HRW-8595-2023; FBE-0351-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; HPW-2820-2023; FBM-0217-2022; JWD-0263-2024; DWS-1040-2022; MNZ-8396-2025; FFG-2233-2022; FCS-1018-2022; DAV-9216-2022; CYT-5449-2022; GWA-7849-2022; KAK-4177-2024; B-8502-2016; MDB-7431-2025; DAV-8065-2022; LYD-9061-2024; HYH-6107-2023; A-2699-2012; MDN-2641-2025; FIV-3763-2022; FLM-0394-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; DFY-8508-2022; MFB-4566-2025; MWK-2416-2025; HFG-7438-2022; D-1300-2016; GNG-7078-2022; FNC-4379-2022; DFC-8070-2022; FLD-9518-2022; DHS-8142-2022; KEK-6332-2024; KJY-7272-2024; FOW-5617-2022; KNP-2716-2024; MNN-0179-2025; KFB-7397-2024; ABB-2322-2020; DKF-4281-2022; HTJ-4919-2023; IHG-7220-2023; DLB-6897-2022; HTM-1531-2023; KBT-5668-2024; MTQ-2344-2025; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; HPO-8234-2023; DNY-0415-2022; HTE-6970-2023; JMR-9144-2023; OON-3882-2025; DNX-4243-2022; DNY-1328-2022; MVR-8365-2025; MXB-9468-2025; GCT-2940-2022; HUZ-7198-2023; IGN-7320-2023; JVJ-6571-2024; FXG-6905-2022; HFW-5845-2022; CDE-5677-2022; DPD-7597-2022; JWG-7083-2024; IZJ-2041-2023; GBY-3944-2022; IHY-7449-2023; HRO-4465-2023; KKE-9686-2024; EAA-4768-2022; LGB-5701-2024; L-8068-2014; GDK-6495-2022; T-7378-2018; MVR-7884-2025; ECA-8225-2022; HNI-8187-2023; ECZ-6053-2022; FCD-8153-2022; JCG-3503-2023; NBS-7222-2025; ABD-6783-2021; GGK-1011-2022; HQC-0143-2023; HWF-6506-2023; KBV-9584-2024; LYP-7992-2024; FQI-9285-2022; IAC-8042-2023; B-1966-2015; HWT-1959-2023; 57210265918; 57120105100; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202453014
We report the discovery of a complete Einstein ring around the elliptical galaxy NGC 6505, at z = 0.042. This is the first strong gravitational lens discovered in Euclid and the first in an NGC object from any survey. The combination of the low redshift of the lens galaxy, the brightness of the source galaxy (IE = 18.1 lensed, IE = 21.3 unlensed), and the completeness of the ring make this an exceptionally rare strong lens, unidentified until its observation by Euclid. We present deep imaging data of the lens from the Euclid Visible Camera (VIS) and Near-Infrared Spectrometer and Photometer (NISP) instruments, as well as resolved spectroscopy from the Keck Cosmic Web Imager (KCWI). The Euclid imaging in particular presents one of the highest signal-to-noise ratio optical/near-infrared observations of a strong gravitational lens to date. From the KCWI data we measure a source redshift of z = 0.406. Using data from the Dark Energy Spectroscopic Instrument (DESI) we measure a velocity dispersion for the lens galaxy of σ∗ = 303 ± 15 km s-1. We model the lens galaxy light in detail, revealing angular structure that varies inside the Einstein ring. After subtracting this light model from the VIS observation, we model the strongly lensed images, finding an Einstein radius of 2.″5, corresponding to 2.1 kpc at the redshift of the lens. This is small compared to the effective radius of the galaxy, Reff ∼ 12.″3. Combining the strong lensing measurements with analysis of the spectroscopic data we estimate a dark matter fraction inside the Einstein radius of fDM = (11.1-3.5+5.4)% and a stellar initial mass-function (IMF) mismatch parameter of αIMF = 1.26-0.08+0.05, indicating a heavier-than-Chabrier IMF in the centre of the galaxy.
Volume: 694
Keywords: Galaxies: individual: NGC 6505; Gravitational lensing: strong; Surveys;
Subtype distribution, clinical presentation, and molecular spectrum of neurofibromatosis type 1-associated breast cancer
Authors: Di Giosaffatte Niccolo; Daniele Paola; Petrizzelli Francesco; Iacovino Chiara; Canciani Chiara; Garau Maria Luisa; Santoro Claudia; Trevisan Valentina; Panfili Arianna; Cavone Stefania; Guida Valentina; D'asdia Maria Cecilia; Bernardini Laura; Majoreb Silvia; Ferraris Alessandro; Valiante Michele; Gensini Francesca; Radio Francesca Clementina; Tortorai Giada; Cassinae Matteo; Mielej Giuseppina; Priolok Manuela; Sirchial Fabio; Piccinno Ludovica; Flex Elisabetta; Zampino Giuseppe; Genuardi Maurizio; Nigro Vincenzo; Salviati Leonardo; Papi Laura; Grammatico Paola; Leoni Chiara; Piluso Giulio; Giustini Sandra; Mazza Tommaso; Upadhyaya Meena; Tartaglia Marco; Trevisson Eva; De Luca Alessandro; Di Giosaffatte Niccolò; D'Asdia Maria Cecilia; Majore Silvia; Tortora Giada; Cassina Matteo; Miele Giuseppina; Priolo Manuela; Sirchia Fabio
Journal: BREAST
Published: 2025
DOI: 10.1016/j.breast.2025.104618
Aim: To investigate clinical and molecular features of neurofibromatosis type 1 (NF1)-associated breast cancer (BC) in a large multicenter cohort. Methods: Clinical and histopathological data from 86 NF1 patients with BC (69 with molecular data) were collected, and 111 published cases were reviewed. NF1 variants were assessed in silico, and their distribution across neurofibromin domains was compared with the general NF1 population. Results: NF1 patients developed BC earlier than the general population (mean 49 years), with missense variant heterozygotes showing the earliest onset (43.9 vs. 49.5 years for truncating variants, p = 0.014). Tumors were frequently high-grade (49 %), HER2-enriched (31 %) or luminal B subtypes (31 %), with reduced luminal A (28 %) frequency. NF1+BC patients had more subcutaneous (p = 0.006) and plexiform neurofibromas (p 70 % of variants, with proline/arginine substitutions accounting for 83 % of missense variants (vs. 44 % in the general NF1 population, p = 0.0012). Conclusions: NF1-associated BC is characterized by earlier onset, aggressive tumor features, and distinct mutational patterns.
Volume: 84
Keywords: Breast cancer; Cancer predisposition; Dominant negative effect; Genotype-phenotype correlation; Neurofibromatosis type 1; Neurofibromin; NF1; Surveillance;