IAS-LAB PUBLICATIONS
Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers (Nature Genetics, (2020), 52, 3, (342-352), 10.1038/s41588-019-0557-x)
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Journal: NATURE GENETICS
Published: 2023
DOI: 10.1038/s41588-023-01317-x
Correction to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.
Volume: 55 Pages: 1078-1078
Euclid preparation: XXVI. the Euclid Morphology Challenge: Towards structural parameters for billions of galaxies
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; CIW-4665-2022; IWF-0947-2023; GBV-7145-2022; DVP-3997-2022; IVA-4275-2023; EDC-3693-2022; K-9464-2019; B-4348-2013; GDW-2905-2022; GBN-3512-2022; GWA-7849-2022; CTE-6491-2022; GRO-8426-2022; B-9902-2008; FZT-3187-2022; ABC-3716-2021; DWU-8294-2022; CUC-4357-2022; JWP-3376-2024; H-5733-2014; DQT-2192-2022; PCO-3236-2025; EPI-1133-2022; Y-9126-2019; DWO-2405-2022; KLD-3528-2024; DWY-3410-2022; A-5940-2015; GBD-1861-2022; DVE-7652-2022; H-4394-2019; EJM-8740-2022; FZO-1254-2022; CDR-2303-2022; DVO-2403-2022; C-4378-2014; GBF-1843-2022; EKV-4052-2022; L-8385-2017; J-2774-2019; M-2616-2015; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; B-4650-2017; EQA-1830-2022; GLW-3038-2022; H-8587-2015; DVC-6323-2022; B-4928-2015; GBN-8818-2022; LEQ-1557-2024; FZL-7353-2022; A-2693-2010; PCA-2324-2025; HKB-2933-2023; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; GAZ-3876-2022; GBB-1832-2022; DWB-6758-2022; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; ABF-1380-2020; FBV-0790-2022; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; DWQ-9372-2022; DXH-0671-2022; FFG-2233-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; AAA-1489-2019; GNG-7078-2022; DFC-8070-2022; AAB-4321-2020; KEK-6332-2024; AAW-4410-2021; DWT-4779-2022; DZP-5216-2022; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; IVG-7504-2023; FSY-2184-2022; ABC-8644-2021; DNY-0415-2022; AAR-4345-2020; K-4114-2015; OON-3882-2025; AFE-8548-2022; DXL-4304-2022; FVF-5606-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; DPD-7597-2022; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; NES-1075-2025; JAE-9097-2023; L-8068-2014; CFK-4637-2022; Q-2220-2015; T-7378-2018; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; ABD-6783-2021; EAZ-0566-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; HTG-8587-2023; CMV-6954-2022; GBY-6621-2022; GAU-7672-2022; B-8712-2017; FNC-4379-2022; FVK-3262-2022; O-9396-2015; CEY-5520-2022; AAZ-4907-2020; IUS-1317-2023; AAO-6325-2021; KTI-3074-2024; B-3004-2019; DTO-7937-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; PDR-7717-2025; ERD-3189-2022; CNT-5485-2022; ISC-4027-2023; AAW-1061-2020; COQ-7299-2022; CPC-6980-2022; DTP-1685-2022; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; ABB-8257-2020; JOZ-7589-2023; S-8590-2017; AAK-4578-2020; FYO-6165-2022; GZL-0460-2022; ITW-2356-2023; DWS-1040-2022; HRX-7202-2023; IZP-8032-2023; FZY-7746-2022; DWL-3001-2022; ABA-3922-2020; HUJ-7899-2023; U-7309-2018; DZP-0372-2022; FKW-0156-2022; Z-3406-2019; AAI-1245-2021; AAN-7016-2021; LXV-7382-2024; DYG-8551-2022; C-3470-2011; JCV-3612-2023; GCY-0967-2022; AAY-8788-2021; IWG-9653-2023; GEF-7978-2022; FXG-6905-2022; FXS-9180-2022; GBG-9412-2022; IYS-3498-2023; GCB-1754-2022; DXU-7894-2022; LXA-1722-2024; A-9058-2016; EDW-5764-2022; AAC-2083-2020; DYK-4428-2022; DXW-9679-2022; NKX-2133-2025; 57225065454; 55868798300; 23050749700; 14832846900; 24439181000; 57194413215; 36195346900; 7003910265; 57189300153; 58148393500; 56216916000; 24279354600; 57221803833; 7005656061; 8719314400; 57217712130; 7801607411; 57215682687; 57216343493; 15754453800; 57194419532; 36238621200; 6602458029; 36237905600; 7004293616; 36627225700; 57193558463; 57219746647; 55541304900; 35494536400; 55929371000; 6701390827; 14629998500; 8651648800; 24830335500; 24482926400; 7102960752; 6506892241; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 57193414472; 7004614794; 54924573500; 56260193000; 55959642400; 37121732700; 6507398813; 55757270100; 56018146100; 8856476200; 7004529134; 26663174300; 6603213706; 57212263363; 6506323808; 55978579700; 6601991850; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202245042
The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline of the Organisational Unit MER of the Euclid Science Ground Segment, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper focusses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes, DeepLeGATo, Galapagos-2, Morfometryka, ProFit and SourceXtractor++, on a sample of about 1.5 million simulated galaxies (350 000 above 5s) resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic Sérsic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (< 10% scatter on ideal Sérsic simulations) down to an apparent magnitude of about IE = 23 in one component and IE = 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5, respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the official Euclid Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.
Volume: 671
Keywords: Cosmology: observations; Galaxies: evolution; Galaxies: fundamental parameters; Methods: data analysis;
Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition (Nature Genetics, (2020), 52, 3, (306-319), 10.1038/s41588-019-0562-0)
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Journal: NATURE GENETICS
Published: 2023
DOI: 10.1038/s41588-023-01319-9
Correction to: Nature Genetics, published online 05 February 2020. In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper. Additional affiliations have been added for author Kathleen H. Burns (McKusick-Nathans Institute of Genetic Medicine, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University School of Medicine, Baltimore, MD, USA) and author Guillaume Bourque (Canadian Center for Computational Genomics, McGill University, Montreal, Quebec, Canada).
Volume: 55 Pages: 1080-1080
Author Correction: Patterns of somatic structural variation in human cancer genomes (Nature, (2020), 578, 7793, (112-121), 10.1038/s41586-019-1913-9)
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Journal: NATURE
Published: 2023
DOI: 10.1038/s41586-022-05597-x
In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional minor corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper.
Volume: 614 Pages: E38-E38
Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes (Nature, (2020), 578, 7793, (102-111), 10.1038/s41586-020-1965-x)
Authors: AREA MIN. 06 - Scienze mediche; NATURE###0028-0836; Goal 3: Good health and well-being###25122; GAW-7722-2022; IGV-0844-2023; F-2267-2016; GKQ-0335-2022; FVN-2824-2022; GHL-6006-2022; CVX-3737-2022; CTE-8718-2022; HGM-3834-2022; MRB-3963-2025; AAT-1226-2021; GEH-2398-2022; DWU-4099-2022; HQR-3924-2023; B-3777-2017; IGF-9394-2023; JGU-5477-2023; H-5859-2011; G-6261-2010; JGD-6876-2023; DVJ-2216-2022; CEU-1922-2022; GCZ-3563-2022; DVZ-4374-2022; ENX-7020-2022; IYG-6783-2023; DWG-2161-2022; CHT-7428-2022; EPB-5354-2022; ESF-7544-2022; B-3352-2014; B-7801-2009; DTM-9505-2022; HVO-2687-2023; MVQ-4462-2025; GCI-7047-2022; LLQ-9112-2024; GDX-1122-2022; ABG-4704-2021; FZY-3673-2022; ABB-8343-2020; HIR-4014-2022; HQF-9907-2023; M-2667-2013; IME-8582-2023; IVF-5829-2023; HQR-4379-2023; GAF-2079-2022; MYW-6714-2025; V-1738-2019; DEI-9877-2022; ABD-5852-2021; DVF-7129-2022; JOP-2884-2023; DQI-0934-2022; E-9821-2013; OVI-7023-2025; FVR-5229-2022; DVB-7032-2022; FQH-6752-2022; M-5283-2015; 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Journal: NATURE
Published: 2023
DOI: 10.1038/s41586-022-05599-9
Correction to: Nature Published online 5 February 2020 In the published version of this paper, the members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium were listed in the Supplementary Information; however, these members should have been included in the main paper. The original Article has been corrected to include the members and affiliations of the PCAWG Consortium in the main paper; the corrections have been made to the HTML version of the Article but not the PDF version. Additional minor corrections to affiliations have been made to the PDF and HTML versions of the original Article for consistency of information between the PCAWG list and the main paper. An additional affiliation has been added for Husen M. Umer (Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden). The affiliation for Erik Larsson has been changed from Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA to Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
Volume: 614 Pages: E40-E40
Euclid preparation: XXII. Selection of quiescent galaxies from mock photometry using machine learning
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; M-4118-2013; HNI-9120-2023; IWH-1605-2023; O-9495-2015; KLD-3528-2024; DWH-1491-2022; H-4394-2019; EAZ-0566-2022; M-4206-2013; Z-3026-2019; M-2616-2015; AAW-4410-2021; FZO-1254-2022; CDR-2303-2022; C-4378-2014; GBF-1843-2022; DVB-2560-2022; L-8385-2017; J-2774-2019; AAG-7753-2020; GBO-0318-2022; E-2727-2014; L-8237-2014; E-8021-2017; IVA-4275-2023; B-4650-2017; IRQ-6937-2023; GLW-3038-2022; H-8587-2015; B-4348-2013; DVC-6323-2022; B-4928-2015; GBN-8818-2022; PGG-2427-2026; FZL-7353-2022; A-2693-2010; PCA-2324-2025; GBB-1963-2022; EUO-2530-2022; ETL-7525-2022; EUK-3820-2022; J-3686-2012; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; ITP-9423-2023; GAZ-3876-2022; JGR-4365-2023; GBB-1832-2022; DWB-6758-2022; GQH-6424-2022; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; FBV-0790-2022; CTZ-4163-2022; DWQ-9372-2022; OOP-8239-2025; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; FGD-1080-2022; DAV-9216-2022; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; FIV-3763-2022; DWC-8789-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; AAX-3485-2021; D-1300-2016; FNO-5530-2022; AAA-1489-2019; GNG-7078-2022; DFC-8070-2022; AAB-4321-2020; DVP-3997-2022; KEK-6332-2024; DWT-4779-2022; DZP-5216-2022; DKR-3047-2022; FNB-0821-2022; ABB-2322-2020; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; GBD-7573-2022; MTQ-2344-2025; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; K-4114-2015; OON-3882-2025; AFE-8548-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; KCY-7756-2024; DPD-7597-2022; OIQ-4966-2025; IZJ-2041-2023; GBV-4959-2022; I-8498-2012; J-5067-2012; EAA-4768-2022; L-8068-2014; CFK-7257-2022; CFK-4637-2022; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; HNI-8187-2023; GCA-5567-2022; Q-6715-2019; ABD-6783-2021; DZU-8266-2022; EIA-6036-2022; FYJ-9637-2022; O-9369-2015; GDW-2905-2022; GVS-7124-2022; GBY-6621-2022; FLK-4707-2022; B-8712-2017; FNC-4379-2022; FNU-9642-2022; GFA-3443-2022; P-2194-2018; AAQ-4773-2020; O-9396-2015; CEY-5520-2022; DVH-0096-2022; AAO-6325-2021; PDD-1794-2025; KTI-3074-2024; CHT-0596-2022; DTO-7937-2022; EPI-1133-2022; O-9391-2015; KDL-3231-2024; JFJ-3489-2023; OZD-6988-2025; HTG-8587-2023; KUC-6512-2024; CMH-0439-2022; AGZ-3259-2022; EQT-2114-2022; PDR-7717-2025; ERD-3189-2022; IDQ-0489-2023; ISC-4027-2023; AAW-1061-2020; COQ-7299-2022; CPC-6980-2022; DUJ-9002-2022; CRZ-8120-2022; C-2920-2017; PDR-5897-2025; DWN-8747-2022; ABB-8257-2020; S-8590-2017; GBV-7145-2022; GZL-0460-2022; ITW-2356-2023; FEB-7061-2022; DWS-1040-2022; HRX-7202-2023; GAU-7672-2022; FLM-0394-2022; ABA-3922-2020; HUJ-7899-2023; NNN-1702-2025; DZP-0372-2022; FMO-3603-2022; D-1237-2017; AAI-1245-2021; LXV-7382-2024; DYG-8551-2022; FTV-6637-2022; JUZ-7207-2023; C-3470-2011; JCV-3612-2023; GCY-0967-2022; GEF-7978-2022; FXG-6905-2022; GBG-9412-2022; GDD-3627-2022; FVK-3262-2022; IYS-3498-2023; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202244307
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15 000deg2 of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid IE, YE, JE, and HE photometry, in combination with multi-wavelength photometry from other large surveys (e.g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has been designed to have ‘sparsity awareness’, such that missing photometry values are informative for the classification. In addition, our pipeline is able to derive photometric redshifts for galaxies selected as quiescent, aided by the ‘pseudo-labelling’ semi-supervised method, and using an outlier detection algorithm to identify and reject likely catastrophic outliers. After the application of the outlier filter, our pipeline achieves a normalised mean absolute deviation of âÂ? ²0.03 and a fraction of catastrophic outliers of âÂ? ²0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey photometry with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey photometry with ancillary ugriz, WISE, and radio data; and (iii) Euclid Wide Survey photometry only, with no foreknowledge of galaxy redshifts. In a like-for-like comparison, our classification pipeline outperforms UVJ selection, in addition to the Euclid IE YE, JE HE and u IE, IE JE colour-colour methods, with improvements in completeness and the F1-score (the harmonic mean of precision and recall) of up to a factor of 2.
Volume: 671
Keywords: Galaxies: evolution; Galaxies: general; Galaxies: high-redshift; Galaxies: photometry; Methods: statistical;
WEAVE-StePS: A stellar population survey using WEAVE at WHT
Authors: Iovino A.; Poggianti B. M.; Mercurio A.; Longhetti M.; Bolzonella M.; Busarello G.; Gullieuszik M.; La Barbera F.; Merluzzi P.; Morelli L.; Tortora C.; Vergani D.; Zibetti S.; Haines C. P.; Costantin L.; Ditrani F. R.; Pozzetti L.; Angthopo J.; Balcells M.; Bardelli S.; Benn C. R.; Bianconi M.; Cassara L. P.; Corsini E. M.; Cucciati O.; Dalton G.; Ferre-Mateu A.; Fossati M.; Gallazzi A.; Garcia-Benito R.; Granett B.; Delgado R. M. Gonzalez; Ikhsanova A.; Iodice E.; Jin S.; Knapen J. H.; McGee S.; Moretti A.; Murphy D. N. A.; de Arriba L. Peralta; Pizzella A.; Sanchez-Blazquez P.; Spiniello C.; Talia M.; Trager S. C.; Vazdekis A.; Vulcani B.; Zucca E.; Poggianti B.M.; Haines C.P.; Ditrani F.R.; Benn C.R.; Cassarà L.P.; Corsini E.M.; Ferré-Mateu A.; García-Benito R.; González Delgado R.M.; Knapen J.H.; Mcgee S.; Murphy D.N.A.; Peralta De Arriba L.; Sánchez-Blázquez P.; Trager S.C.
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2023
DOI: 10.1051/0004-6361/202245361
Context. The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide valuable opportunities for forthcoming galaxy surveys through their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage. Aims. WEAVE is a new wide-field spectroscopic facility mounted on the 4.2 m William Herschel Telescope in La Palma. WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during its first five years of operations. It will observe galaxies using WEAVE MOS (∼950 fibres distributed across a field of view of ∼3 square degrees on the sky) in low-resolution mode (R ∼ 5000, spanning the wavelength range 3660-9590 Å). Methods. WEAVE-StePS will obtain high-quality spectra (S/N ∼ 10 Å -1 at R ∼ 5000) for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies, the majority selected at z ≥ 0.3. The survey goal is to provide precise spectral measurements in the crucial interval that bridges the gap between LEGA-C and SDSS data. The wide area coverage of ∼25 square degrees will enable us to observe galaxies in a variety of environments. The ancillary data available in each of the observed fields (including X-ray coverage, multi-narrow-band photometry and spectroscopic redshift information) will provide an environmental characterisation for each observed galaxy. Results. This paper presents the science case of WEAVE-StePS, the fields to be observed, the parent catalogues used to define the target sample, and the observing strategy that was chosen after a forecast of the expected performance of the instrument for our typical targets. Conclusions. WEAVE-StePS will go back further in cosmic time than SDSS, extending its reach to encompass more than ∼6 Gyr. This is nearly half of the age of the Universe. The spectral and redshift range covered by WEAVE-StePS will open a new observational window by continuously tracing the evolutionary path of galaxies in the largely unexplored intermediate-redshift range.
Volume: 672
Keywords: Galaxies: evolution; Galaxies: formation; Galaxies: general; Galaxies: star formation; Galaxies: statistics; Galaxies: stellar content;
How the CYBATHLON Competition Has Advanced Assistive Technologies
Authors: Jaeger Lukas; Baptista Roberto de Souza; Basla Chiara; Capsi-Morales Patricia; Kim Yong Kuk; Nakajima Shuro; Piazza Cristina; Sommerhalder Michael; Tonin Luca; Valle Giacomo; Riener Robert; Sigrist Roland; De Souza Baptista Roberto
Journal: ANNUAL REVIEW OF CONTROL ROBOTICS AND AUTONOMOUS SYSTEMS
Published: 2023
DOI: 10.1146/annurev-control-071822-095355
Approximately 1.1. billion people worldwide live with some form of disability, and assistive technology has the potential to increase their overall quality of life. However, the end users’ perspective and needs are often not sufficiently considered during the development of this technology, leading to frustration and nonuse of existing devices. Since its first competition in 2016, CYBATHLON has aimed to drive innovation in the field of assistive technology by motivating teams to involve end users more actively in the development process and to tailor novel devices to their actual daily-life needs. Competition tasks therefore represent unsolved daily-life challenges for people with disabilities and serve the purpose of benchmarking the latest developments from research laboratories and companies from around the world. This review describes each of the competition disciplines, their contributions to assistive technology, and remaining challenges in the user-centered development of this technology.
Volume: 6 Pages: 447-476
Keywords: assistive technology; competition; CYBATHLON; daily-life challenge; inclusion; people with disabilities; user-centered design;
Using Cluster Analysis to Overcome the Limits of Traditional Phenotype–Genotype Correlations: The Example of RYR1-Related Myopathies
Authors: Dosi Claudia; Rubegni Anna; Baldacci Jacopo; Galatolo Daniele; Doccini Stefano; Astrea Guja; Berardinelli Angela; Bruno Claudio; Bruno Giorgia; Comi Giacomo Pietro; Donati Maria Alice; Dotti Maria Teresa; Filosto Massimiliano; Fiorillo Chiara; Giannini Fabio; Gigli Gian Luigi; Grandis Marina; Lopergolo Diego; Magri Francesca; Maioli Maria Antonietta; Malandrini Alessandro; Massa Roberto; Mata Sabrina; Melani Federico; Messina Sonia; Mignarri Andrea; Moggio Maurizio; Pennisi Elena Maria; Pegoraro Elena; Ricci Giulia; Sacchini Michele; Schenone Angelo; Sampaolo Simone; Sciacco Monica; Siciliano Gabriele; Tasca Giorgio; Tonin Paola; Tupler Rossella; Valente Mariarosaria; Volpi Nila; Cassandrini Denise; Santorelli Filippo Maria; Matà Sabrina
Journal: GENES
Published: 2023
Thanks to advances in gene sequencing, RYR1-related myopathy (RYR1-RM) is now known to manifest itself in vastly heterogeneous forms, whose clinical interpretation is, therefore, highly challenging. We set out to develop a novel unsupervised cluster analysis method in a large patient population. The objective was to analyze the main RYR1-related characteristics to identify distinctive features of RYR1-RM and, thus, offer more precise genotype–phenotype correlations in a group of potentially life-threatening disorders. We studied 600 patients presenting with a suspicion of inherited myopathy, who were investigated using next-generation sequencing. Among them, 73 index cases harbored variants in RYR1. In an attempt to group genetic variants and fully exploit information derived from genetic, morphological, and clinical datasets, we performed unsupervised cluster analysis in 64 probands carrying monoallelic variants. Most of the 73 patients with positive molecular diagnoses were clinically asymptomatic or pauci-symptomatic. Multimodal integration of clinical and histological data, performed using a non-metric multi-dimensional scaling analysis with k-means clustering, grouped the 64 patients into 4 clusters with distinctive patterns of clinical and morphological findings. In addressing the need for more specific genotype–phenotype correlations, we found clustering to overcome the limits of the “single-dimension” paradigm traditionally used to describe genotype–phenotype relationships.
Volume: 14
Keywords: genotype–phenotype correlation; NGS; RYR1-related myopathies; unsupervised cluster analysis;
Brain signals in semi-immersive and immersive VR environments: a feasibility study
Authors: Arcobelli V.A.; Marcaccini K.; Hassaballah P.G.; Tonin L.; Tortora S.; Orlandi S.
Journal: 21101138614
Published: 2023
In recent years, the integration of virtual reality (VR) technology is seeing a significant increase and has the potential to greatly impact the future of healthcare. The use of VR systems in conjunction with electroencephalography (EEG) offers new possibilities in rehabilitation scenarios. This study focuses on comparing the feasibility of using a head-mounted display (HMD) while acquiring EEG data in both semi-immersive and fully immersive environments. The impact of an Oculus Quest 2 on the frequency domain of 64-channel EEG measurements recorded on healthy subjects was investigated by performing six different tasks, including a 3D video game. Results showed statistically significant differences in beta and gamma frequency bands with and without the use of the HMD. Also, significant differences were found in the occipital brain region while playing the 3D video game with the HMD vs desktop mode due to the introduction of the immersive visual feedback. Based on our preliminary findings, future studies will test the feasibility of combining HMDs and EEG headsets before running EEG brain-computer interface studies.
Keywords: electroencephalogram (EEG); human-computer interaction (HCI); rehabilitation; virtual reality (VR);