cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22664890
title: Supplementary Material for: Pilot study of a wearable hydration monitor in haemodialysis patients

description: Abstract Introduction We aimed to assess the validity and reproducibility of a wearable hydration device in a cohort of maintenance dialysis patients. Methods We conducted a prospective, single-arm observational study on 20 haemodialysis patients between January-June 2021 in a single centre. A prototype wearable infrared spectroscopy device, termed the Sixty device, was worn on the forearm during dialysis sessions and nocturnally. Bioimpedance measurements were performed 4 times using the body composition monitor (BCM) over 3 weeks. Measurements from the Sixty device were compared with the BCM overhydration index (litres) pre- and post-dialysis and with standard haemodialysis parameters. Results 12 out of 20 patients had usable data. Mean age was 52 ± 12.4 years. The overall accuracy for predicting pre-dialysis categories of fluid status using Sixty was 0.55 [ᴋ= 0.00; 95% CI -0.39 – 0.42]. The accuracy for the prediction of post-dialysis categories of volume status was low [accuracy = 0.33, ᴋ = 0.08; 95% CI = -0.13 to 0.3]. Sixty outputs at the start and end of dialysis were weakly correlated with pre- and post-dialysis weights (r = 0.27 and r = 0.27, respectively), as well as weight loss during dialysis (r= 0.31), but not ultrafiltration volume (r = 0.12). There was no difference between the change in Sixty readings overnight, and the change in Sixty readings during dialysis (mean difference 0.09 ± 1.5 kg), [t(39) = 0.38, p = 0.71]. Conclusion A prototype wearable infrared spectroscopy device was unable to accurately assess changes in fluid status during or between dialysis sessions. In the future, hardware development and advances in photonics may enable the tracking of interdialytic fluid status.
DOI: 10.17632/6rxxmv4vpz.1
title: Light calcium isotope anomaly observed in continental basaltic lavas: a mixed signal of recycled carbonate and fractionation during melting

description: Supplementary Text includes detailed text for batch melting model, melt–mantle interaction model and pressure effect on Ca isotope fractionation during melting.
Supplementary figures include Figs. S1–S6.
A combined supplementary Excel table is provided including Tables S1–S7.
DOI: 10.17632/3962t6d445.1
title: Dataset_creditworthiness

description: Financial and non-financial data of Russian companies
DOI: 10.17632/3962t6d445.2
title: Dataset_creditworthiness

description: Financial and non-financial data of Russian companies
DOI: 10.6084/m9.figshare.22651876.v1
title: Supplementary Material for: Malaria as Potential Etiology and Treatment Complicating Factor in DLBCL Patient: a Case Report

description: Malaria is known to be a significant risk factor and also a potential complicating factor during the treatment of lymphoid malignancy. There has not been a reported case of malaria reactivation that occurred weeks after cytotoxic chemotherapy completion, especially in non-endemic regions. Our patient was a 47-year-old man with a history of repeated falciparum malaria infection experiencing two months of progressive unilateral nasal blockage and recurrent anterior epistaxis, which was diagnosed as diffuse large B-cell lymphoma (DLBCL) through pathological examination. He was treated with six cycles of classical R-CHOP, resulting in complete remission. One month after remission, he experienced shivering, fever, sweating, and a return to normal temperature, which repeated irregularly for roughly one week. His laboratory result showed anemia, leukopenia and profound thrombocytopenia. Immunochromatographic testing (ICT) confirmed the diagnosis of falciparum malaria. This case was considered a relapse since our centre is not in the malaria-endemic region. He was cured with a combination of dihydroartemisinin-piperaquine and primaquine. Our case demonstrated the duality of malaria as potential etiology and treatment complicating factor in DLBCL.
DOI: 10.5281/zenodo.7615272
title: Garage1 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7615491
title: Garage2 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7615655
title: Garage3 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7615808
title: Garage6 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7642487
title: Office5 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7646546
title: Office6 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7648549
title: Office7 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7869819
title: Rümlang1 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7923665
title: Rümlang2 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7615271
title: Rümlang3 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.5281/zenodo.7925546
title: Rümlang3 Trajectory

description: Dataset of the CoRL 2021 Paper "Self-Improving Semantic Perception for Indoor Localisation" Abstract: We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. By combining continual learning with self-supervision, our robotic system learns online during deployment without external supervision. We conduct real-world experiments with robots localising in 3D floorplans. Our experiments show how the robot's semantic perception improves during deployment and how this translates into improved localisation, even across drastically different environments. We further study the risk of catastrophic forgetting that such a continuous learning setting poses. We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments. On average, our system improves by 60% in segmentation and 10% in localisation accuracy compared to deployment of a fixed model, and it maintains this improvement while adapting to further environments.
DOI: 10.6084/m9.figshare.22652086.v1
title: Supplementary Material for: Treatment Response of Sacrococcygeal Chordoma to Palliative Stereotactic Body Radiotherapy: A Case Report

description: Chordomas are rare and locally aggressive tumours that arise from embryonic remnants of the notochord, with a predilection for the skull base, mobile spine and sacrum. Sacral or sacrococcygeal chordomas can be particularly difficult to manage because of their large size at presentation and involvement of adjacent organs and neural structures. Although the recommended definitive therapy for such tumours is either en-bloc resection with or without adjuvant radiotherapy (RT) or definitive fractionated RT with charged particle therapy, older and/or less-fit patients may not necessarily be accepting of said approaches due to the potential morbidities and challenging logistic requirements. Here, we report a case of a 79-year-old male presenting with intractable lower limb pain and neurological deficits due to a large de-novo sacrococcygeal chordoma. The patient was successfully treated with a 5-fraction course of stereotactic body radiotherapy (SBRT) given with palliative intent, with complete relief of his symptoms achieved approximately 21 months after RT and without the development of any iatrogenic toxicities. In view of this case, ultra-hypofractionated SBRT may be a suitable option for the palliation of large de-novo sacrococcygeal chordomas for selected patients to reduce their symptom burden and improve their quality-of-life (QOL).
DOI: 10.17632/z29xx9pxh6.1
title: Supplementary Material for JAAD-D-23-00715 “Composite subcuticular subcutaneous buried suture”

description: Supplementary figure 1. A) Baseline photo of post-traumatic scar on forehead against relaxed skin tension lines; B) follow up at 3 weeks post-op
DOI: 10.6084/m9.figshare.22665196.v1
title: Supplementary Material for: Granulomatosis with Polyangiitis Complicated by Severe Exudative Retinal Detachment and Orbital Granuloma Successfully Controlled with Rituximab: A Case Report

description: We report a rare case of severe exudative retinal detachment with orbital granuloma associated with granulomatosis with polyangiitis (GPA). A 42-year-old man developed bilateral conjunctival hyperemia and eye pain 15 months before presenting to us. Because vitreous cells and retinal detachment were detected in his left eye, he was referred to us for further evaluation. The left eye showed scleral edema, cells in the anterior chamber and anterior vitreous, exudative retinal detachment, and elevated white subretinal lesions from the nasal to the inferior parts of the eye fundus. Orbital contrast-enhanced magnetic resonance imaging revealed a granulomatous lesion, retinal detachment, and fluid retention in the left eyeball. Comprehensive rheumatological evaluation revealed proteinase 3 anti-neutrophil cytoplasmic antibody positivity and a history of otitis media, leading to a GPA diagnosis. Methylprednisolone 1000 mg/day was administered intravenously for 3 days, followed by oral prednisolone and intravenous cyclophosphamide. Although the retinal detachment decreased, scleritis and choroidal detachment relapse were observed in the left eye after the fifth cyclophosphamide administration. After switching from cyclophosphamide to rituximab, the scleritis and choroidal detachment resolved. Remission was successfully maintained with biannual rituximab administration. In this case, we conclude that rituximab was important to re-induce and maintain remission after recurrence. Collaboration with a rheumatologist is essential for proper treatment in related cases. This is the first report of ultra-widefield and multimodal imaging for retinal detachment associated with GPA.
DOI: 10.6084/m9.figshare.22691863
title: Supplementary Material for: Expanding phenotype of SYT-1 related neurodevelopmental disorder: case report and literature review.

description: Introduction: Synaptotagmin-1 (SYT1), the predominant SYT isoform in the central nervous system, likely acts by promoting vesicle docking, deforming the plasma membrane via Ca2+-dependent membrane penetration. Case presentation: Here we describe a 21-year-old woman harbouring a novel variant in the SYT1 gene, who presents with a complex phenotype featuring severe intellectual disability, absent speech, behavioral abnormalities, motor stereotypies, dystonic posturing of her hands, a hyperkinetic movement disorder in her childhood, infantile hypotonia, sialorrhea, mild dysmorphic features, epilepsy, peculiar EEG findings, and severe scoliosis. Discussion: Based on our cases and literature review on the 22 previously described patients, we can confirm a complex neurodevelopmental disorder in which, unlike other synaptopathies, epilepsy is present in a subset of cases (including our patient: 5/23, 22%), although characteristic EEG changes are far more common (10/23, 43.5%). Our patient's age allows us to provide long-term follow-up data and thus to better delineate the SYT1-related clinical phenotype.