IAS-LAB PUBLICATIONS
The Economic Burden, Epidemiological Insights, and Treatment Patterns of Wilson’s Disease: A Real-World Study in Italy
Authors: Sciattella Paolo; Scortichini Matteo; Cazzagon Nora; Loudianos Georgios; Zuin Massimo; Battezzati Pier Maria; Maggiore Giuseppe; Grieco Antonio; Baglione Eugenio; Senzolo Marco; Mazza Silvia; Della Corte Claudia; Tortora Annalisa; Di Dato Fabiola; Matarazzo Margherita; Iorio Raffaele
Journal: DRUGS-REAL WORLD OUTCOMES
Published: 2025
DOI: 10.1007/s40801-025-00506-w
Volume: 12 Pages: 391-398
Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; KJS-6941-2024; PGU-9738-2026; C-9225-2017; KKS-3168-2024; JQO-3317-2023; ABB-9156-2021; HNI-9120-2023; O-9495-2015; H-4394-2019; MXW-4784-2025; B-4650-2017; B-4348-2013; AHB-3798-2022; GFK-2340-2022; FDK-8162-2022; AAY-1704-2020; Z-4828-2019; DVE-7652-2022; PCC-0635-2025; B-9633-2012; AAV-1857-2021; PHP-8037-2026; FXZ-5996-2022; GEH-7593-2022; OIQ-4966-2025; O-8727-2015; ETW-6961-2022; FZO-1254-2022; FYJ-9637-2022; CDR-2303-2022; CEY-5520-2022; C-4378-2014; IUT-7926-2023; IUQ-9509-2023; GBF-1843-2022; EKV-4052-2022; DVB-2560-2022; EOE-6462-2022; L-8385-2017; OBC-0525-2025; IAE-2305-2023; HWT-5982-2023; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; E-8021-2017; IVA-4275-2023; OZD-6988-2025; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; DVC-6323-2022; NHE-3385-2025; PGG-2427-2026; AGZ-3259-2022; A-2693-2010; PCA-2324-2025; ERD-3189-2022; HLX-2021-2023; HKB-2933-2023; PWI-2374-2026; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CNP-7538-2022; CPC-6980-2022; AAR-6622-2021; IBV-9243-2023; CQF-5798-2022; I-5515-2016; DXA-1952-2022; HPT-5858-2023; GBB-1832-2022; CQR-5759-2022; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; AAT-5867-2020; GZL-0460-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; CYT-5449-2022; AAF-6025-2021; OOP-8239-2025; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; HUJ-7899-2023; DFQ-7859-2022; DFY-8508-2022; U-7309-2018; MWK-2416-2025; AAX-3485-2021; D-1300-2016; GNG-7078-2022; FNC-4379-2022; DFC-8070-2022; FLD-9518-2022; MWC-3186-2025; DVP-3997-2022; FNA-5485-2022; KJY-7272-2024; DWT-4779-2022; FNB-0821-2022; ABB-2322-2020; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; OYX-8116-2025; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; JVJ-6571-2024; FXG-6905-2022; H-1761-2016; DXM-5348-2022; GBG-9412-2022; DPD-7597-2022; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; EAA-4768-2022; LGB-5701-2024; L-8068-2014; PYK-2395-2026; MQB-6975-2025; DZM-7523-2022; GDK-6495-2022; T-7378-2018; AAB-2503-2019; GCB-5227-2022; HNI-8187-2023; GCA-5567-2022; FCD-8153-2022; JCG-3503-2023; NNO-6919-2025; MTT-8732-2025; DZU-8266-2022; EAZ-0566-2022; O-9396-2015; AAO-6325-2021; MKL-0317-2025; DTO-7937-2022; LWL-2178-2024; CMV-6954-2022; CDE-1189-2022; DUJ-9002-2022; GBY-6621-2022; LRV-2049-2024; FJX-8996-2022; GGM-6223-2022; FVK-3262-2022; P-2194-2018; DYK-4428-2022; ECX-7840-2022; AAH-3743-2019; DTU-2081-2022; HPI-3910-2023; FXV-4290-2022; ELC-7230-2022; A-7379-2017; IYE-9818-2023; DXH-1132-2022; L-6160-2017; KTI-3074-2024; CGZ-3153-2022; EPI-1133-2022; JNZ-6253-2023; EQF-3895-2022; GBB-5111-2022; O-9391-2015; QDC-1960-2026; KDL-3231-2024; S-1204-2016; HOH-0341-2023; AAQ-1509-2021; ISJ-4889-2023; EQT-2114-2022; CNT-5485-2022; L-2472-2017; ETR-0407-2022; L-6378-2014; IDQ-0489-2023; AAW-1061-2020; EZB-5943-2022; ETN-0093-2022; KSN-3481-2024; GBD-4336-2022; DTP-1685-2022; GBY-7028-2022; EVC-7104-2022; CRZ-8120-2022; PBH-2493-2025; EXD-3015-2022; CQL-4862-2022; MWU-0619-2025; DWN-8747-2022; FYO-9802-2022; ABB-8257-2020; KLF-9653-2024; NNI-3312-2025; GCU-3708-2022; KGG-1931-2024; KNK-3731-2024; NIQ-3499-2025; HRR-2616-2023; ITW-2356-2023; GEC-5455-2022; HRX-7202-2023; Q-5758-2017; GYG-7175-2022; DBI-3005-2022; GAV-5026-2022; IZP-8032-2023; FZY-7746-2022; GQU-8893-2022; DWL-3001-2022; DWN-4354-2022; ABA-3922-2020; JHF-8266-2023; MVA-1492-2025; GFP-2203-2022; D-1237-2017; FMN-9310-2022; Z-3406-2019; GXE-4405-2022; FQI-9285-2022; DXO-2849-2022; AAI-1245-2021; DNL-3219-2022; DYG-8551-2022; HXX-2997-2023; GCY-0967-2022; LQA-8898-2024; DWT-7233-2022; C-6308-2008; GEP-1274-2022; DRO-1214-2022; R-3469-2017; GCB-1754-2022; ABC-3828-2020; JCM-8241-2023; JAN-6167-2023; DXU-7894-2022; EAO-6360-2022; MLN-0990-2025; MLR-9932-2025; GDF-8239-2022; EBV-8310-2022; JEZ-2766-2023; IRI-6836-2023; CDU-7975-2022; JSZ-6163-2023; ECF-2024-2022; A-9058-2016; KCV-5780-2024; IOX-4199-2023; CEW-0728-2022; 58817124800; 56261663500; 57189593362; 58817722400; 9334587700; 57202215260; 56426999100; 6602409206; 55929371000; 57203047758; 54924573500; 7003910265; 57200793436; 55538241000; 57325670900; 56033190100; 7005317106; 35494536400; 56949991000; 7006440295; 57193523315; 57222380960; 7004408758; 57204700965; 57022106200; 35957375500; 57414680000; 14629998500; 56176939800; 8651648800; 57220414927; 24482926400; 57225389323; 35421870300; 7102960752; 6506892241; 6602293713; 36614022800; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 57193414472; 57090221700; 7004614794; 24439181000; 55948641800; 56260193000; 55543336500; 37121732700; 6507398813; 55757270100; 8856476200; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202453111
The Euclid mission is generating a vast amount of imaging data in four broadband filters at a high angular resolution. This data will allow for the detailed study of mass, metallicity, and stellar populations across galaxies that will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. Here, we investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity, and age. We generated noise-free synthetic high-resolution (100 pc × 100 pc) imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images were generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We used a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a ≤0.130 dex scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but they still contain significant information that originates from underlying correlations at a sub-kiloparsec scales between stellar mass surface density and stellar population properties. As a corollary, we show that TNG50 follows a spatially resolved mass-metallicity relation that is consistent with observations. Due to its relatively low computational and time requirements, which has a time-frame of minutes without dedicated high performance computing infrastructure once it has been trained, our method allows for fast and robust estimates of the stellar mass surface density distributions of nearby galaxies from four-filter Euclid imaging data. Equivalent estimates of stellar population properties (stellar metallicity and age) are less robust but still hold value as first-order approximations across large samples.
Volume: 695
Keywords: Galaxies: general; Galaxies: photometry; Methods: statistical;
Euclid: The Early Release Observations Lens Search Experiment
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; CGD-2351-2022; JAX-2768-2023; CKU-5761-2022; H-4394-2019; DXZ-7810-2022; PGG-2427-2026; DWU-8294-2022; D-1237-2017; OMN-3792-2025; FYJ-4908-2022; EPI-1133-2022; AGZ-3259-2022; LHK-9354-2024; DVB-8405-2022; AFJ-2074-2022; MDG-9557-2025; GMD-3106-2022; ILM-3517-2023; GBU-8717-2022; DWQ-9372-2022; MYW-7907-2025; AAC-7835-2021; JBR-8488-2023; DUZ-7625-2022; MWC-3186-2025; F-3052-2014; PCC-0635-2025; LXV-7382-2024; HFV-0042-2022; GEI-1893-2022; FTV-5671-2022; DRO-1214-2022; FXH-0557-2022; AAN-1908-2021; IUS-5192-2023; DYG-8338-2022; GFN-8936-2022; NLB-5121-2025; GCJ-5104-2022; GCV-8309-2022; H-2913-2012; GCS-2631-2022; AAD-3011-2021; O-9495-2015; DWB-0787-2022; DXA-1243-2022; GEH-7593-2022; GBG-9412-2022; DYF-3433-2022; OTJ-2099-2025; FYH-4361-2022; L-2472-2017; FYH-7305-2022; MYL-2765-2025; NKL-3434-2025; MMS-5823-2025; FHL-5547-2022; DWN-4354-2022; MYS-2354-2025; JNB-1152-2023; IRI-1547-2023; EFH-6710-2022; GBC-8404-2022; FZO-1254-2022; FYJ-9637-2022; DUY-3094-2022; CEY-5520-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; LXX-3952-2024; IUQ-9509-2023; GBF-1843-2022; DVB-2560-2022; L-8385-2017; OBC-0525-2025; LUN-9319-2024; CJD-7824-2022; HWT-5982-2023; CJL-9982-2022; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; IVA-4275-2023; OZD-6988-2025; B-4650-2017; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; NHE-3385-2025; GBN-8818-2022; NKT-5952-2025; A-2693-2010; PCA-2324-2025; CNE-2384-2022; HKB-2933-2023; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; HPT-5858-2023; GBB-1832-2022; DWB-6758-2022; GWP-3456-2022; GQH-6424-2022; CQR-5759-2022; DWK-1716-2022; JGR-4365-2023; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; HRW-8595-2023; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; AAT-5867-2020; GZL-0460-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; CYT-5449-2022; DUU-4676-2022; B-8502-2016; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLM-0394-2022; MTO-5925-2025; DWZ-6747-2022; HUJ-7899-2023; DFQ-7859-2022; U-7309-2018; MWK-2416-2025; AAX-3485-2021; D-1300-2016; GNG-7078-2022; DFC-8070-2022; FLD-9518-2022; DVP-3997-2022; FNA-5485-2022; KJY-7272-2024; DWT-4779-2022; KSI-9422-2024; KNP-2716-2024; DJO-8166-2022; MNN-0179-2025; FNB-0821-2022; ABB-2322-2020; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; OYX-8116-2025; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; JVJ-6571-2024; FXG-6905-2022; H-1761-2016; NKY-6871-2025; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; NES-1075-2025; EAA-4768-2022; LGB-5701-2024; L-8068-2014; PYK-2395-2026; MQB-6975-2025; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; NLM-0551-2025; MTT-8732-2025; O-9396-2015; DTO-7937-2022; JMK-1133-2023; DYK-4428-2022; NLL-3428-2025; MNN-3541-2025; 58090912100; 57210265918; 55731742600; 55929371000; 58911394800; 8856476200; 7801607411; 7101983827; 58544914400; 57190942170; 6602458029; 59636105400; 59317606600; 55862177400; 55668778200; 59317261300; 57322480400; 57473067700; 7202097638; 55539553700; 57188806951; 36126412600; 57207846185; 57193489486; 7005525798; 6701593679; 56949991000; 6603351766; 56153006200; 57199319204; 55932248600; 22836264500; 57211567711; 7003485288; 13204971700; 12786945200; 7005244190; 7401938216; 55822387500; 57191290632; 10738797800; 8270263200; 57218513471; 6602409206; 7003267532; 57192921002; 57204700965; 8833942000; 57206423651; 57188639623; 57221950386; 55976971800; 57213830231; 7004666481; 57193558463; 57958980000; 57205730219; 6701685211; 58419113700; 58666622600; 58202351900; 57535859700; 6701409861; 14629998500; 56176939800; 8651648800; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202451868
We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the 0.7 deg2 Euclid Early Release Observations data towards the Perseus cluster using both the high-resolution IE band and the lower-resolution YE, JE, and HE bands. Each extended source brighter than magnitude 23 in IE was inspected by 41 expert human classifiers. This amounts to 12 086 stamps of 1000 × 1000. We found 3 grade A and 13 grade B candidates. We assessed the validity of these 16 candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling, and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full 14 000 deg2 of the Euclid Wide Survey should contain 100 000+-7030000000 galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of 170 000 discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range 000 . 68 < θE < 100 . 24, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small-Einstein-radius systems. Whilst it is implausible to visually inspect the full Euclid dataset, our results corroborate the promise that Euclid will ultimately deliver a sample of around 105 galaxy-scale lenses.
Volume: 697
Keywords: galaxies: clusters: individual: Perseus; gravitational lensing: strong; methods: data analysis; methods: observational;
Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy
Authors: Mastrantoni Luca; Garufi Giovanna; Giordano Giulia; Maliziola Noemi; Di Monte Elena; Arcuri Giorgia; Frescura Valentina; Rotondi Angelachiara; Orlandi Armando; Carbognin Luisa; Palazzo Antonella; Miglietta Federica; Pontolillo Letizia; Fabi Alessandra; Gerratana Lorenzo; Pannunzio Sergio; Paris Ida; Pilotto Sara; Marazzi Fabio; Franco Antonio; Franceschini Gianluca; Dieci Maria Vittoria; Mazzeo Roberta; Puglisi Fabio; Guarneri Valentina; Milella Michele; Scambia Giovanni; Giannarelli Diana; Tortora Giampaolo; Bria Emilio
Journal: NPJ BREAST CANCER
Published: 2025
DOI: 10.1038/s41523-025-00727-w
Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials.
Volume: 11
Conversion Ability of Immunotherapy in Hepatocellular Carcinoma: Insights from the International Converse Study
Authors: AREA MIN. 06 - Scienze mediche; Non assegn; AREA MIN. 03 - Scienze chimiche; LIVER CANCER###2235-1795; Goal 3: Good health and well-being###25122; NVU-1059-2025; ODU-4058-2025; JFA-3201-2023; AAA-6985-2022; NRG-7376-2025; N-4884-2016; E-4136-2016; K-9027-2016; ETI-8054-2022; DVX-2977-2022; OKL-0925-2025; JXN-9765-2024; GBI-3050-2022; ODT-3691-2025; MNP-2390-2025; EAA-0713-2022; CTC-6453-2022; GWQ-5807-2022; JLL-4417-2023; NBP-8708-2025; ACF-6633-2022; CIF-8523-2022; EKD-3321-2022; PCI-1657-2025; ABU-7442-2022; E-2873-2017; AAL-4707-2020; NVU-5037-2025; AAA-5966-2019; LRC-4609-2024; LJL-8863-2024; B-1253-2019; CDD-1531-2022; DZE-2417-2022; GAX-9838-2022; ONI-9192-2025; GFI-6538-2022; GBX-0618-2022; JAS-4656-2023; JAE-9905-2023; HDN-8476-2022; DYG-1391-2022; DXZ-0364-2022; FZO-1269-2022; DKZ-9331-2022; AAJ-1461-2020; AAI-3301-2021; AAB-9010-2019; DWE-5897-2022; DZG-2198-2022; LRB-6334-2024; GPG-4717-2022; MEC-3675-2025; EMW-3214-2022; GFJ-8359-2022; LSJ-1295-2024; CTL-8875-2022; K-1325-2018; IAZ-8837-2023; CMV-5205-2022; GLS-8971-2022; NNR-6045-2025; LQJ-8211-2024; DLZ-1607-2022; FZH-0936-2022; IAR-5511-2023; E-1120-2012; AAX-3013-2021; HXI-6162-2023; DVI-6747-2022; LKW-4755-2024; CFF-4678-2022; AAW-6583-2020; OKP-8212-2025; AAB-7784-2019; MZZ-5336-2025; DTK-9313-2022; FUU-1830-2022; DKU-9582-2022; HSM-5150-2023; ETY-6019-2022; FUX-0969-2022; CRN-8826-2022; AAC-1142-2022; C-5224-2017; DMO-7443-2022; KZP-1983-2024; ABD-2759-2021; JUF-2562-2023; MUO-2309-2025; CDW-1641-2022; LFV-9926-2024; IQR-8787-2023; PCH-8152-2025; 7103385675; 57192647681; 22978623500; 57205570340; 7003859199; 6602480388; 35316450500; 25642049500; 59664061700; 55533314700; 7409872158; 8664424100; 35399280200; 59722080200; 56068319400; 6602872506; 58450192500; 57223875522; 57223031784; 59507208500; 57203097714; 57323265500; 57217312225; 55405948300; 57201443418; 23479552000; 7006292400; 7101699492; 56516762300; 56494141600; 57217138368; 57045491200; 7202067754; 47961522200; 57202577641; 57203908984; 59743389500; 35339851600; 7005105898; 55338330900; 23991339200; 7003959009; 7004364046; 7004103098; 58676486300; 8888138300; 24778880000; 24079621100; 35180464500; 57209858034; 57223434900; 58161784700; 6602978959; 55853885600; 7005920149; 55312125500; 57246900700; 15923933100; 35378208900; 58554753100; 57192201029; 54788065400; 8619722900; 57237152900; 36854891300; 57196972311; 7101771483; 57204044185; 6603879680; 56348477100; 59557307700; 57117920800; 60126828300; 7004428252; 55839825600; 60127864400; 57194525584; 6507767839; 57204688931; 6506102320; 6603008052; 6603675919; 57224857907; 57126311200; 6701515092; 59454560300; 55280001900; 57193399387; 8621248500; 60127714400; 57211341552; 7003673653; 57218222462; 7004918276
Journal: LIVER CANCER
Published: 2025
DOI: 10.1159/000547792
Pages: 1-22
Keywords: Conversion; Hepatocellular carcinoma; Immunotherapy; Surgery;
Elliptical ejecta of asteroid Dimorphos is due to its surface curvature
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; CUU-8183-2022; HLR-3780-2023; GGW-7139-2022; GAH-5074-2022; ESD-3744-2022; QBH-6137-2026; HKN-0500-2023; HZI-2517-2023; P-6476-2015; I-4902-2012; HLK-1269-2023; CTM-4947-2022; DYN-1984-2022; IFG-8963-2023; ISQ-0634-2023; GHK-8431-2022; DWV-9440-2022; FWP-2241-2022; CFS-3628-2022; DYP-6030-2022; FZX-2971-2022; A-9759-2012; GCQ-2889-2022; GFF-8225-2022; DWN-9574-2022; FZZ-0535-2022; DDJ-7308-2022; FVV-6858-2022; DUQ-2065-2022; I-7475-2015; F-5384-2015; DWU-0981-2022; GDY-4101-2022; EUN-3723-2022; HUL-9562-2023; JCE-4157-2023; B-7744-2016; F-4568-2015; DUL-6195-2022; ENM-7589-2022; GDE-9626-2022; AAJ-3985-2021; DUL-3415-2022; DWP-0733-2022; HGD-4524-2022; GGD-2860-2022; FEG-0617-2022; CAJ-2883-2022; DVO-9054-2022; N-5574-2018; GDH-3986-2022; DKW-3609-2022; KIB-5109-2024; D-4408-2016; J-6191-2012; PEV-1372-2025; AAC-4090-2021; HNS-2166-2023; DTA-9438-2022; FAQ-9880-2022; AAN-2497-2020; I-7029-2015; AAS-8419-2020; MKD-3733-2025; HGV-4184-2022; FJY-5851-2022; HLS-6942-2023; HSK-4888-2023; JCM-2837-2023; DHC-3887-2022; GGB-5197-2022; L-9058-2014; F-9818-2010; GDD-3436-2022; GGN-2463-2022; AAO-5357-2020; NATURE COMMUNICATIONS###2041-1723; 55826845600; 57208445497; 7004318097; 7004640341; 57074031000; 57203736332; 7004011265; 6701831440; 55533357700; 8889572200; 57214791438; 37072319500; 36701832200; 7004189587; 57208049670; 58146735800; 6504168821; 57216933235; 25636752900; 55471974400; 55669241300; 23990264300; 57194858670; 24823039400; 57193622827; 23989081200; 55849158600; 55449295000; 7003331332; 36879186600; 6602709696; 7402075077; 7003564619; 8889756900; 24314530700; 55976970100; 7004387103; 23995780300; 23020214400; 57372292200; 6701612015; 6602999700; 6602330014; 8124291100; 56365949500; 57205446071; 56452875500; 55597712400; 16319102800; 54883873200; 35313993500; 9741589500; 57202131090; 55493499600; 35785529800; 58192237300; 55397558400; 57214805190; 57981671400; 6603871165; 59098028700; 38561464400; 56152199100; 7006528435; 57807063200; 55920339500; 57214806320; 7004712183; 58546708400; 8305008500; 57216737338; 6701471393; 57198080053; 57191593736; 56287163900; 12647223300
Journal: NATURE COMMUNICATIONS
Published: 2025
DOI: 10.1038/s41467-025-56010-w
Kinetic deflection is a planetary defense technique delivering spacecraft momentum to a small body to deviate its course from Earth. The deflection efficiency depends on the impactor and target. Among them, the contribution of global curvature was poorly understood. The ejecta plume created by NASA’s Double Asteroid Redirection Test impact on its target asteroid, Dimorphos, exhibited an elliptical shape almost aligned along its north-south direction. Here, we identify that this elliptical ejecta plume resulted from the target’s curvature, reducing the momentum transfer to 44 ± 10% along the orbit track compared to an equivalent impact on a flat target. We also find lower kinetic deflection of impacts on smaller near-Earth objects due to higher curvature. A solution to mitigate low deflection efficiency is to apply multiple low-energy impactors rather than a single high-energy impactor. Rapid reconnaissance to acquire a target’s properties before deflection enables determining the proper locations and timing of impacts.
Volume: 16
High-speed Boulders and the Debris Field in DART Ejecta
Authors: Farnham Tony L.; Sunshine Jessica M.; Hirabayashi Masatoshi; Ernst Carolyn M.; Daly R. Terik; Agrusa Harrison F.; Barnouin Olivier S.; Li Jian-Yang; Kumamoto Kathryn M.; Syal Megan Bruck; Wiggins Sean E.; Bjonnes Evan; Stickle Angela M.; Raducan Sabina D.; Cheng Andrew F.; Glenar David A.; Lolachi Ramin; Stubbs Timothy J.; Fahnstock Eugene G.; Amoroso Marilena; Bertini Ivano; Brucato John R.; Capannolo Andrea; Cremonese Gabriele; Dall'Ora Massimo; Della Corte Vincenzo; Deshapriya J. D. P.; Dotto Elisabetta; Gai Igor; Hasselmann Pedro H.; Ieva Simone; Impresario Gabriele; Ivanovski Stavro L.; Lavagna Michele; Lucchetti Alice; Marzari Francesco; Epifani Elena Mazzotta; Modenini Dario; Pajola Maurizio; Palumbo Pasquale; Pirrotta Simone; Poggiali Giovanni; Rossi Alessandro; Tortora Paolo; Zannoni Marco; Zanotti Giovanni; Zinzi Angelo; Dall’Ora Massimo; Deshapriya J.D.P.; Lavagna Michèle
Journal: PLANETARY SCIENCE JOURNAL
Published: 2025
DOI: 10.3847/PSJ/addd1a
On 2022 September 26 the Double Asteroid Redirection Test (DART) spacecraft collided with Dimorphos, the moon of the near-Earth asteroid 65803 Didymos, in a full-scale demonstration of a kinetic impactor concept. The companion Light Italian Cubesat for Imaging of Asteroids (LICIACube) spacecraft documented the aftermath, capturing images of the expansion and evolution of the ejecta from 29 to 243 s after the impact. We present results from our analyses of these observations, including an improved reduction of the data and new absolute calibration, an updated LICIACube trajectory, and a detailed description of the events and phenomena that were recorded throughout the flyby. One notable aspect of the ejecta was the existence of clusters of boulders, up to 3.6 m in radius, that were ejected at speeds of up to 52 m s−1. Our analysis of the spatial distribution of 104 of these boulders suggests that they are likely the remnants of larger boulders shattered by the DART spacecraft in the first stages of the impact. The amount of momentum contained in these boulders is more than 3 times that of the DART spacecraft, and it is directed primarily to the south, almost perpendicular to the DART trajectory. Recoil of Dimorphos from the ejection of these boulders has the potential to change its orbital plane by up to a degree and to impart a non-principal-axis component to its rotation state. Damping timescales for these phenomena are such that the Hera spacecraft, arriving at the system in 2026, should be able to measure these effects.
Volume: 6
Evaluation of aggrephagy markers in myofibrillar myopathies
Authors: Iannibelli Eliana; Ruggieri Alessandra; Maruotti Antonello; Salerno Franco; Cheli Marta; Carnazzi Alessandra; Nicolini De Gaetano Lucia; Riolo Giorgia; Bortolani Sara; Riguzzi Pietro; Vianello Sara; Merlonghi Gioia; Bello Luca; Garibaldi Matteo; Filosto Massimiliano; Previtali Stefano Carlo; Tasca Giorgio; Vattemi Gaetano; Tonin Paola; Pegoraro Elena; Gibertini Sara; Maggi Lorenzo
Journal: ACTA NEUROPATHOLOGICA COMMUNICATIONS
Published: 2025
DOI: 10.1186/s40478-025-02041-9
Myofibrillar Myopathies (MFMs) are a growing group of muscular disorders genetically determined, whose diagnosis is based on histological features as myofibrillar degeneration, Z-disk disorganization and protein aggregates’ accumulation. Protein aggregates that do not fit the proteasome’s narrow pore are targeted for removal via a specialized form of autophagy, called aggrephagy. Our study aims to investigate the potential pathogenic role of aggrephagy in 52 muscle samples from an Italian MFM multicentric cohort. We measured, the percentage of positive areas of key aggrephagy proteins by immunofluorescence staining, of sequestosome 1 (p62/SQSTM1), Neighbor of BRCA1 Gene 1 (NBR1), and ubiquitinated proteins (FK2) in 11 DES-, 6 DNAJB6-, 5 FLNC-, 18 MYOT- and 12 TTN-mutated patients. We showed that all aggrephagy markers are increased in these patients, regardless of the mutated genes, suggesting a possible common pathomechanism; no positive signal was found in healthy, age-matched controls. We analyzed the association between positivity levels of these markers, measured as percentage of positive areas, and selected clinical features utilizing generalized linear mixed models with gamma distribution as the probability model and center-specific random effects to better capture possible heterogeneity across participating centers. Our findings indicate significant associations between levels of p62, NBR1, and FK2 with age at biopsy (p62 and NBR1 p-values < 0.001, FK2 p-value < 0.05), age of onset (p62 and NBR1 p-values < 0.001, FK2 p-value < 0.01) and disease severity through Walton & Gardner-Medwin (WGM) score at biopsy (all p-values < 0.001) and at the last visit (all p-values < 0.05). Noteworthy, the aggrephagic pathway is mostly activated in MYOT-mutated patients compared to the other subgroups. Moreover, the association between aggrephagy and WGM score at biopsy is stronger in this subgroup. Overall, our study emphasizes the role of aggrephagy in MFMs across all patients, and its association with specific clinical parameters.
Volume: 13
Keywords: Clinical association; Genetic rare diseases; Myofibrillar alterations; Protein aggregation;
The Effect of User Learning for Online EEG Decoding of Upper-Limb Movement Intention
Authors: Ceradini Matteo; Tortora Stefano; Micera Silvestro; Tonin Luca
Journal: IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS
Published: 2025
DOI: 10.1109/TMRB.2025.3537663
Electroencephalography (EEG) based brain-computer interfaces (BCIs) offer a promising way for individuals with motor impairments to control prosthetic or rehabilitation devices. Accurately decoding movement intention (MI) is crucial for translating subjects’ motor execution plans into action. Common challenges in EEG-based BCIs include performance discrepancies, often requiring frequent recalibration of decoding algorithms. The objective of this study was enhancing BCI decoding performance of upper-limb MI identification by exploiting both machine and subjects’ learning and maintaining stable decoding algorithms. Significant performance improvements were observed across most subjects from the first to the last session of the experiment. Some subjects also demonstrated stable performance without requiring any model recalibration between sessions. All subjects achieved high efficacy in online decoding of movement intention, as reflected in improvement of the F1 score from 0.58±0.26 in the first session, to 0.84±0.13 in the final session. We emphasize the critical importance of allowing users sufficient time to improve their performance in BCIs for upper-limb MI decoding. Unlike existing studies, we specifically evaluate the effect of stable decoding strategies in online and longitudinal BCI sessions, which are key to achieving more reliable and effective BCIs.
Volume: 7 Pages: 633-641
Keywords: Brain-computer interface; EEG; movement intention; online decoding; user learning;
OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation
Authors: Rahman Muhammad Rameez ur; Simonetto Piero; Polato Anna; Pasti Francesco; Tonin Luca; Vascon Sebastiano
Journal: COMPUTER VISION-ECCV 2024 WORKSHOPS, PT XII
Published: 2025
DOI: 10.1007/978-3-031-92591-7_23
Open vocabulary 3D object detection (OV3D) allows precise and extensible object recognition crucial for adapting to diverse environments encountered in assistive robotics. This paper presents OpenNav, a zero-shot 3D object detection pipeline based on RGB-D images for smart wheelchairs. Our pipeline integrates an open-vocabulary 2D object detector with a mask generator for semantic segmentation, followed by depth isolation and point cloud construction to create 3D bounding boxes. The smart wheelchair exploits these 3D bounding boxes to identify potential targets and navigate safely. We demonstrate OpenNav’s performance through experiments on the Replica dataset and we report preliminary results with a real wheelchair. OpenNav improves state-of-the-art significantly on the Replica dataset at mAP25 (+9pts) and mAP50 (+5pts) with marginal improvement at mAP. The code is publicly available at this link https://github.com/EasyWalk-PRIN/OpenNav.
Volume: 15634 Pages: 372-387
Keywords: 3D Object Detection; Assistive Robotics; Open Vocabulary Navigation; Smart Wheelchair;