IAS-LAB PUBLICATIONS
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; 8724245000; 36657273100; 8527480900; 57203599140; 9270789600; 14008117700; 6603519641; 7102513091; 7202555066; 7006022077; 16024707000; 6603380199; 55539553700; 55885669700; 35227493200; 37123976000; 6701865592; 6506922416; 55578049300; 56216916000; 7102120605; 6603602446; 7003825248; 6602251719; 6603205767; 6603770482; 56403356600; 36195926600; 14058603600; 10239419900; 14025617800; 6506385309; 36933808800; 56463558800; 55779479900; 13407562800; 7102174334; 56229506700; 14056466700; 7102846243; 55665939900; 7005525798; 14832846900; 7004208543; 35299820900; 7004629002; 55668172300; 8764087600; 35216145800; 7006833728; 14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 55337191500; 7402364894; 57225899623; 8915699600; 6603819159; 7004160690; 57219119015; 15129157800; 48663031800; 9533992100; 57218941481; 13407890400; 14063887300; 6506381727; 54797318800; 55845420026; 6602208520; 6602315420; 14823864100; 58502049600; 6701705322; 57220082325; 35314080800; 58937209900; 17436196900; 57203391123; 57191960842; 6701439004; 6602565951; 57191419742; 56286395400; 10244106400; 7101771030; 8042894900; 9333441800; 7004185737; 6602409206; 36905906400; 6603819488; 57198031424; 9244606800; 58696967900; 57202592808; 12809267200; 26326923900; 56512377200; 35194662000; 7003963996; 57203063840; 35387346400; 46461103400; 42260895600; 56818885600; 35789534500; 14820320500; 59421337500; 7006764136; 56134803400; 57214989073; 57193874792; 57219376526; 9337037600; 7103030457; 56653598400; 7007018277; 57216129429; 35112881300; 58127155500; 57199061795; 55976971800; 24074399500; 6701458135; 6602348000; 57218304683; 56239931500; 25723173900; 58073729200; 7004144883; 7003645652; 8703100100; 55944081300; 56242244500; 15131601400; 36674792500; 8833942900; 55741929700; 35239938700; 36966126400; 57218097629; 56153170500; 26642611400; 57213763435; 59008427700; 57210924350; 6508080858; 35490928600; 6701309093; 6701685211; 6603292899; 22951241500; 57845873200; 56383649900; 7101983827; 7006221760; 57218766355; 35422761600; 35463408300; 7003762062; 59146418100; 57220131178; 7101903552; 57203270249; 16031797900; 36495525900; 57222902516; 55672552800; 10642144300; 57201003368; 7005050491; 55435714500; 57194728774; 6603851717; 6701718244; 7004109829; 59800191100; 57132747000; 58483607200; 6506892358; 14632583100; 57215412075; 55158076000; 7004307582; 7404952697
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; 57189593362; 58521236800; 58911394800; 13204971700; 6701409861; 55731742600; 57219756656; 57216613693; 56487221600; 7404294404; 9234510400; 7801607411; 25951796800; 57218513471; 58090912100; 57190942170; 57203165830; 35112881300; 55668778200; 7006314412; 59317261300; 57218283726; 57203599140; 57958980000; 7005525798; 7101983827; 8833942000; 7003485288; 55929371000; 7005244190; 55822387500; 57191290632; 14629998500; 56176939800; 8651648800; 35241679100; 57220414927; 24482926400; 6505819655; 57225389323; 58849865400; 35421870300; 7102960752; 6602293713; 6701447926; 7004168457; 7004279376; 55543112300; 35117442400; 56592859600; 8316050500; 57193414472; 57090221700; 7004614794; 24439181000; 55948641800; 54924573500; 56260193000; 55543336500; 37121732700; 7003910265; 6507398813; 55757270100; 56018146100; 8856476200; 24069757200; 7004529134; 26663174300; 6603213706; 6602521535; 7006071419; 57212263363; 6506323808; 55978579700; 6601991850; 6602678698; 56181792800; 57200514857; 24587025200; 9639653200; 36645908500; 6506341877; 56592156500; 24173378000; 14630273900; 8724245000; 36657273100; 8527480900; 22979454100; 9270789600; 14008117700; 9337191600; 6603519641; 10240830400; 7102513091; 7202555066; 7006022077; 16024707000; 6603380199; 36663730200; 55539553700; 35425530800; 23485209600; 55885669700; 35227493200; 37123976000; 6701865592; 6506922416; 55578049300; 56216916000; 7102120605; 6603602446; 7005760971; 7003825248; 6603623670; 6603205767; 6603770482; 56403356600; 36195926600; 35070066100; 10239419900; 14025617800; 6506385309; 36933808800; 56463558800; 55779479900; 13407562800; 7102174334; 14056466700; 7102846243; 36542679900; 55665939900; 14832846900; 7004208543; 35299820900; 7004629002; 8764087600; 7006833728; 14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 55337191500; 7402364894; 57225899623; 35350466600; 57190443165; 8915699600; 6603819159; 6603044987; 7004160690; 57219119015; 56881732300; 15129157800; 48663031800; 57218941481; 13407890400; 14063887300; 54797318800; 55845420026; 6602208520; 6602315420; 14823864100; 58502049600; 57220082325; 58937209900; 17436196900; 57203391123; 57191960842; 6602565951; 57191419742; 59019204600; 56286395400; 10244106400; 7101771030; 56273947300; 8042894900; 9333441800; 6603819488; 58848684700; 7102775303; 56512377200; 7003963996; 35748664400; 8680454200
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;
Lithologies of Dimorphos revealed by boulder morphological classes
Authors: Tusberti F.; Pajola M.; Penasa L.; Lucchetti A.; Massironi M.; Munaretto G.; Beccarelli J.; Pozzobon R.; Rossi C.; Poggiali G.; Murdoch N.; Robin C. Q.; Duchene A.; Amoroso M.; Bertini I.; Brucato J. R.; Capannolo A.; Caporali S.; Ceresoli M.; Cremonese G.; Dall'Ora M.; Della Corte V.; Deshapriya J. D. P.; Dotto E.; Epifani E. Mazzotta; Gai I.; Gramigna E.; Hasselmann P. H. A.; Ieva S.; Impresario G.; Ivanovski S.; Manghi R. Lasagni; Lavagna M.; Lombardo M.; Modenini D.; Palumbo P.; Perna D.; Pirrotta S.; Rossi A.; Tortora P.; Zannoni M.; Zanotti G.; Zinzi A.; Buratti B.; Trigo-Rodrigez J. M.; Casajus L. Gomez; Robin C.Q.; Brucato J.R.; Corte V. Della; Deshapriya J.D.P.; Hasselmann P.H.A.; Trigo-Rodrigez J.M.
Journal: ICARUS
Published: 2025
DOI: 10.1016/j.icarus.2025.116744
The stony/Sq-type binary system (65803) Didymos consists of two rubble pile bodies, Didymos, the primary, and its moonlet Dimorphos. In 2022, the first planetary defense mission Double Asteroid Redirection Test (DART) reached Dimorphos and collected unprecedented high-resolution images of its surface. They revealed a variegated surface completely covered by stacked boulders and cobbles with different shapes and textures. Their morphological heterogeneity likely reflects the lithologies originally present within the Didymos and Dimorphos’ parent body, before its fragmentation. We present a lithologic study of Dimorphos’ surface, based on a morphological analysis of its boulders. Our approach considers each boulder’s 2D outline, perceived 3D shape, and surface texture characteristics. Based on these features, we identified two main morphological classes. A total of 178 boulders were classified as Angular morphology covering 38.5 % of the mapped area. This type is characterized by cohesive boulders with straight and angular outlines as well as sharp three-dimensional edges, which form well-defined sub-planar facets. Additionally, the texture of this kind of rocks show low-to-mid Roughness and the presence of lineations on some boulders. On the other hand, 210 boulders were categorized as Hummocky morphology, which covers 61.5 % of the area. Such morphology appears more friable and features boulders with rounded shaped and ragged and irregular 2D perimeter. Their 3D perceived shape appears as flat-to-curve, with rough and hummocky surface textures due to embedded clasts. We interpret the Hummocky morphology as breccia lithology. On the other hand, although Angular morphology can be associated with multiple potential lithologies, we interpret it as either an achondritic/igneous lithology or a highly metamorphosed chondritic lithology. The breccias are likely characteristic of the parent body’s outer layers, actively involved in impact and sedimentary processes. Conversely, the lithologies associated with the An morphology should represent deeper regions of the plantesimal, possibly exhumed by impacts. All these results will be further complemented by data from the Hera mission, which will arrive at (65803) Didymos system at the end of 2026.
Volume: 442
Keywords: Asteroids; Geological processes; Near-Earth objects; Satellites of asteroids; Surfaces;
Hepatitis C Eradication Improves Oncologic and Clinical Outcomes in Patients Treated With Atezolizumab Plus Bevacizumab
Authors: Stella Leonardo; Cabibbo Giuseppe; Celsa Ciro; Ciccia Roberta; Sparacino Alba; Piscaglia Fabio; Tovoli Francesco; Arleo Andrea; Stefanini Bernardo; Iavarone Massimo; D'Ambrosio Roberta; Cerrito Lucia; Pallozzi Maria; Santopaolo Francesco; Marra Fabio; Campani Claudia; Mazzarelli Chiara; Vigano Raffaella; Tortora Raffaella; Aghemo Alessio; Nicola Stella De; Pressiani Tiziana; Rimassa Lorenza; Bhoori Sherrie; Corallo Salvatore; Maiocchi Laura; Martini Andrea; Solda Caterina; Russo Francesco Paolo; Gasbarrini Antonio; Ponziani Francesca Romana; Viganò Raffaella; Soldà Caterina
Journal: LIVER INTERNATIONAL
Published: 2025
DOI: 10.1111/liv.70362
Background and Aims: Hepatitis C virus (HCV) is a key driver of hepatocellular carcinoma (HCC). However, the impact of HCV eradication on systemic therapy remains unclear. We aimed to assess the safety and efficacy of direct-acting antivirals (DAA) in patients treated with Atezolizumab plus Bevacizumab (AtezoBev). Methods: This retrospective multicentre study included patients with HCV-related unresectable/advanced HCC treated with AtezoBev between 2021 and 2024. Three groups of patients were compared: Group A (n = 22), concurrent DAA with AtezoBev; Group B (n = 95), antiviral therapy before AtezoBev; and Group C (n = 22), active infection. Results: Group A showed the longest median overall survival (42.8 months) compared to Group B (26.8 months; p = 0.03) and Group C (19.7 months; p = 0.01). Time to progression and progression-free survival were significantly prolonged in Group A versus Groups B and C. Moreover, Group A exhibited a higher disease control rate than the other groups. Post-DAA decompensation rates were significantly lower in Group A (4.5%) compared to Groups B (26.3%) and C (36.4%). Treatment-related adverse events of grade ≥ 3 were similar across groups. In the multivariate competing risk analysis with adjustment for time-dependent variables, achieving sustained virologic response during AtezoBev showed a protective effect against liver decompensation (sHR 0.02, p = 0.003) or tumour progression (sHR 0.14, p = 0.009), and was also associated with reduced mortality (HR 0.29, p = 0.005). Conclusions: Achieving a SVR during AtezoBev seems to improve oncologic outcomes and reduce liver decompensation in patients with unresectable/advanced HCC. An integrated therapeutic approach can optimise systemic treatment efficacy, particularly in patients eligible for conversion strategies. Trial Registration: Protocol ID: 5890.
Volume: 45
Keywords: cirrhosis; DAA; HCV; hepatocellular carcinoma; immunotherapy; liver decompensation; survival;
From simulations to observations: Methodology and data release of mock TNG50 galaxies at 0.3 < z < 0.7 for WEAVE-StePS
Authors: Ikhsanova A.; Costantin L.; Pizzella A.; Corsini E. M.; Morelli L.; Ditrani F. R.; Ferre-Mateu A.; Gabarra L.; Gullieuszik M.; Haines C. P.; Iovino A.; Longhetti M.; Mercurio A.; Ragusa R.; Sanchez-Blazquez P.; Tortora C.; Vulcani B.; Zhou S.; Gafton E.; Pistis F.
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202555132
Volume: 703