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Article ; Online: Efficacy of denosumab against osteoporosis determined using quantitative computed tomography in treatment-naïve male patients with ankylosing spondylitis: case series of six patients.

Kim, S H / Lee, S-H / Song, R

2024 , Page(s) 1–3

Language	English
Publishing date	2024-03-26
Publishing country	England
Document type	Journal Article
ZDB-ID	121265-5
ISSN	1502-7732 ; 0300-9742
ISSN (online)	1502-7732
ISSN	0300-9742
DOI	10.1080/03009742.2024.2316960
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Book ; Online: LinkDID

Song, Rui

A Privacy-Preserving, Sybil-Resistant and Key-Recoverable Decentralized Identity Scheme

2023

Abstract: Decentralized identity mechanisms endeavor to endow users with complete sovereignty over their digital assets within the Web3 ecosystem. Unfortunately, this benefit frequently comes at the expense of users' credential and identity privacy. Additionally, ... ...

Abstract	Decentralized identity mechanisms endeavor to endow users with complete sovereignty over their digital assets within the Web3 ecosystem. Unfortunately, this benefit frequently comes at the expense of users' credential and identity privacy. Additionally, existing schemes fail to resist Sybil attacks that have long plagued Web3, and lack reasonable key recovery mechanisms to regain control of digital assets after loss. In this work, we propose LinkDID, a privacy-preserving, Sybil-resistant, and key-recoverable decentralized identity scheme that supports selective disclosure of credentials for arbitrary predicates while maintaining privacy for credentials and identities. Through an identifier association mechanism, LinkDID can privately and forcibly aggregate users' identifiers, providing Sybil resistance without relying on any external data or collateral from benign users. To enable key recovery, LinkDID permits users to establish proofs of ownership for identifiers with lost keys and request an update of corresponding keys from the decentralized ledger. We provide a detailed theoretical analysis and security proofs of LinkDID, along with an exhaustive performance evaluation that shows its ability to complete interactions in less than 10 seconds on consumer-grade devices. Comment: 20 pages
Keywords	Computer Science - Cryptography and Security
Subject code	303
Publishing date	2023-07-27
Publishing country	us
Document type	Book ; Online
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Article ; Online: Deletion of the prorenin receptor in the ureteric bud in mice inhibits Dot1/H3K79 pathway.

Song, Renfang / Yosypiv, Ihor V

Pediatric research

2024

Abstract: Background: The prorenin receptor (PRR) plays a critical role in ureteric bud (UB) branching morphogenesis. DOT1 Like (DOT1L), a histone methyltransferase specific for Histone 3 lysine 79 (H3K79), is important for differentiation of the UB-derived renal ...

Abstract	Background: The prorenin receptor (PRR) plays a critical role in ureteric bud (UB) branching morphogenesis. DOT1 Like (DOT1L), a histone methyltransferase specific for Histone 3 lysine 79 (H3K79), is important for differentiation of the UB-derived renal collecting duct cells. In this study, we tested whether DOT1L/H3 dimethyl K79 (H3m2K79) are regulated by PRR deletion in the UB and UB-derived collecting ducts in the embryonic mouse kidneys. Methods: Mutant Hoxb7 Results: DOT1L mRNA levels were decreased in mutant compared to control mice (0.68 ± 0.06 vs. 1.0 ± 0.01, p < 0.01). DOT1L and H3m2K79 immunostaining was reduced in the mutant vs. control kidneys (Dot1: 0.62 ± 0.03 vs. 1.0 ± 0.01, p < 0.05; H3m2K79: 0.64 ± 0.04 vs.1.1 ± 0.01. p < 0.05.). Western blot analysis revealed decreased H3m2K79 protein levels in mutant compared to control kidneys (1.0 ± 0.06 vs. 1.5 ± 0.02, p < 0.05). Conclusion: Targeted deletion of the PRR in the UB and UB-derived collecting ducts results in reduced DOT1L gene/protein and H3m2K79 protein expression in the embryonic mouse metanephroi in vivo. Impact: The role of histone methylation in mediating the effect of the prorenin receptor on the ureteric bud branching (UB) morphogenesis and urine acidification during kidney development is unknown. We demonstrate that histone H3 lysine (K) 79 dimethylation by methyltransferase Dot1 is reduced in the embryonic kidney of mice that lack the prorenin receptor in the UB lineage.
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	4411-8
ISSN	1530-0447 ; 0031-3998
ISSN (online)	1530-0447
ISSN	0031-3998
DOI	10.1038/s41390-024-03026-5
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Zs.A 564: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 373: Show issues

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Article ; Online: Enhancing two-stage object detection models via data-driven anchor box optimization in UAV-based maritime SAR.

Zhao, Beigeng / Song, Rui

Scientific reports

2024 Volume 14, Issue 1, Page(s) 4765

Abstract: The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. ...

Abstract	The high-altitude imaging capabilities of Unmanned Aerial Vehicles (UAVs) offer an effective solution for maritime Search and Rescue (SAR) operations. In such missions, the accurate identification of boats, personnel, and objects within images is crucial. While object detection models trained on general image datasets can be directly applied to these tasks, their effectiveness is limited due to the unique challenges posed by the specific characteristics of maritime SAR scenarios. Addressing this challenge, our study leverages the large-scale benchmark dataset SeaDronesSee, specific to UAV-based maritime SAR, to analyze and explore the unique attributes of image data in this scenario. We identify the need for optimization in detecting specific categories of difficult-to-detect objects within this context. Building on this, an anchor box optimization strategy is proposed based on clustering analysis, aimed at enhancing the performance of the renowned two-stage object detection models in this specialized task. Experiments were conducted to validate the proposed anchor box optimization method and to explore the underlying reasons for its effectiveness. The experimental results show our optimization method achieved a 45.8% and a 10% increase in average precision over the default anchor box configurations of torchvision and the SeaDronesSee official sample code configuration respectively. This enhancement was particularly evident in the model's significantly improved ability to detect swimmers, floaters, and life jackets on boats within the SeaDronesSee dataset's SAR scenarios. The methods and findings of this study are anticipated to provide the UAV-based maritime SAR research community with valuable insights into data characteristics and model optimization, offering a meaningful reference for future research.
Language	English
Publishing date	2024-02-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-55570-z
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Article ; Online: Impact of green technology innovation based on IoT and industrial supply chain on the promotion of enterprise digital economy.

Song, Ruilin / Hu, Hui

PeerJ. Computer science

2023 Volume 9, Page(s) e1416

Abstract: With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we ... ...

Abstract	With the gradual deterioration of the natural environment, a green economy has become a competing goal for all countries. As a trend of green innovation development, the digital economy has become a research hotspot for scientists. In this article, we study the supply chain management of enterprises in green innovation and digital economy development and complete the identification and demand prediction of warehouse goods through the Internet of Things (IoT) and artificial intelligence (AI). As the stuff meets the goods detection and storage, we employ an intelligent method to detect and classify the goods. The demand prediction analysis is carried out based on historical data on goods demand in the enterprise. The absolute error between the prediction result and the actual demand within 1 week is less than 30 goods by the particle swarm optimization-support vector machine (PSO-SVM) method used in this article. First, the goods identification task is completed based on video surveillance data using YOLOv4, and the recognition rate is as high as 98.3%. This article realises enterprises' intelligent supply chain management through the intelligent identification of goods and the demand forecasting analysis of goods in the warehouse, which provides new ideas for green innovation and digital economy development.
Language	English
Publishing date	2023-05-30
Publishing country	United States
Document type	Journal Article
ISSN	2376-5992
ISSN (online)	2376-5992
DOI	10.7717/peerj-cs.1416
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Article ; Online: Isometric Plantarflexion Moment Prediction Based on a Compartment-specific HD-sEMG-driven Musculoskeletal Model.

Zheng, Manxu / Lu, Pengcheng / Wu, Wen / Song, Rong

IEEE transactions on bio-medical engineering

2024 Volume PP

Abstract: Objective: electromyogram (EMG)-driven musculoskeletal models have been widely used to investigate human movements while existing EMG-driven models commonly neglect regional heterogeneity in anatomy and activation within a skeletal muscle. To consider ... ...

Abstract	Objective: electromyogram (EMG)-driven musculoskeletal models have been widely used to investigate human movements while existing EMG-driven models commonly neglect regional heterogeneity in anatomy and activation within a skeletal muscle. To consider neuromuscular compartment anatomy and activation, a subject- and compartment-specific EMG-driven model was developed for isometric plantarflexion moment prediction. Methods: the model was hill-type consisting of gastrocnemius medialis, gastrocnemius lateralis, and soleus around the ankle joint, and each muscle was discretised into four compartments. The moment arms of each compartment were determined using magnetic resonance imaging and the compartment activation was calculated based on high-density surface EMG signals. And the hill-type compartment parameters were tuned in a calibration process. The developed compartment-specific model and a generic EMG-driven model were examined by comparing their predicted net ankle moments with measurements obtained while subjects performed isometric plantarflexion tasks at different contraction levels. Results: compared to the generic EMG-driven model, the isometric plantarflexion moment prediction using the compartment-specific model was more accurate at all contraction levels, with the average prediction error decreasing from average 13.81% to 10.11%. The contraction of each compartment was found to be generally non-uniform at all contraction levels. Conclusion: the developed compartment-specific model enabled accurate prediction of isometric plantarflexion moment and the simulation of non-uniform muscular contraction, which is more physiologically appropriate than the existing EMG-driven models. Significance: the proposed compartment-specific formulation opens new perspectives for subject-specific musculoskeletal modelling, which has great potential in understanding regional characteristics of the neuromuscular activities.
Language	English
Publishing date	2024-02-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2024.3368021
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Article ; Online: Nearly Optimal Control for Mixed Zero-Sum Game Based on Off-Policy Integral Reinforcement Learning.

Song, Ruizhuo / Yang, Gaofu / Lewis, Frank L

IEEE transactions on neural networks and learning systems

2024 Volume 35, Issue 2, Page(s) 2793–2804

Abstract: In this article, we solve a class of mixed zero-sum game with unknown dynamic information of nonlinear system. A policy iterative algorithm that adopts integral reinforcement learning (IRL), which does not depend on system information, is proposed to ... ...

Abstract	In this article, we solve a class of mixed zero-sum game with unknown dynamic information of nonlinear system. A policy iterative algorithm that adopts integral reinforcement learning (IRL), which does not depend on system information, is proposed to obtain the optimal control of competitor and collaborators. An adaptive update law that combines critic-actor structure with experience replay is proposed. The actor function not only approximates optimal control of every player but also estimates auxiliary control, which does not participate in the actual control process and only exists in theory. The parameters of the actor-critic structure are simultaneously updated. Then, it is proven that the parameter errors of the polynomial approximation are uniformly ultimately bounded. Finally, the effectiveness of the proposed algorithm is verified by two given simulations.
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ISSN	2162-2388
ISSN (online)	2162-2388
DOI	10.1109/TNNLS.2022.3191847
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Article: Development of a predictive model for predicting disability after optic neuritis: a secondary analysis of the Optic Neuritis Treatment Trial.

Wei, Siqian / Du, Yi / Luo, Meifeng / Song, Ruitong

Frontiers in neurology

2024 Volume 14, Page(s) 1326261

Abstract: Objective: The present study aimed to develop a prediction model for predicting developing debilities after optic neuritis.: Methods: The data for this research was obtained from the Optic Neuritis Treatment Trial (ONTT). The predictive model was ... ...

Abstract	Objective: The present study aimed to develop a prediction model for predicting developing debilities after optic neuritis. Methods: The data for this research was obtained from the Optic Neuritis Treatment Trial (ONTT). The predictive model was built based on a Cox proportional hazards regression model. Model performance was assessed using Harrell's C-index for discrimination, calibration plots for calibration, and stratification of patients into low-risk and high-risk groups for utility evaluation. Results: A total of 416 patients participated. Among them, 101 patients (24.3%) experienced disability, which was defined as achieving or surpassing a score of 3 on the expanded disability status scale. The median follow-up duration was 15.5 years (interquartile range, 7.0 to 16.8). Two predictors in the final predictive model included the classification of multiple sclerosis at baseline and the condition of the optic disk in the affected eye at baseline. Upon incorporating these two factors into the model, the model's C-index stood at 0.71 (95% CI, 0.66-0.76, with an optimism of 0.005) with a favorable alignment with the calibration curve. By utilizing this model, the ONTT cohort can be categorized into two risk categories, each having distinct rates of disability development within a 15-year timeframe (high-risk group, 41% [95% CI, 31-49%] and low-risk group, 13% [95% CI, 8.4-17%]; log-rank Conclusion: This predictive model has the potential to assist physicians in identifying individuals at a heightened risk of experiencing disability following optic neuritis, enabling timely intervention and treatment.
Language	English
Publishing date	2024-01-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2023.1326261
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Article ; Online: Deep Learning-based Glaucoma Detection Using CNN and Digital Fundus Images: A Promising Approach for Precise Diagnosis.

Song, Ruiying / Wang, Hong / Xing, Yinghua

Current medical imaging

2024 Volume 20, Page(s) 1–18

Abstract: Background: Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking.: Objective: To develop an AI-based glaucoma detection method to reduce glaucoma- ... ...

Abstract	Background: Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking. Objective: To develop an AI-based glaucoma detection method to reduce glaucoma-related blindness and offer more precise diagnosis. Methods: Discusses various methods and technologies, including Heidelberg Retinal Tomography (HRT), Optical Coherence Tomography (OCT), and Fundus Photography, for obtaining relevant information about the presence of glaucoma in a patient. Additionally, it mentions the use of Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) for glaucoma detection. There are many limitations for existing methods as; Asymptomatic Progression, reliance on subjective feedback, multiple tests required, late detection, limited availability of preventive tests, influence of external factors. Results: Findings reveal promising outcomes in terms of glaucoma detection accuracy, particularly in the analysis of the RIM-ONE-r3 dataset. By scrutinizing 20 images from the Healthy, Glaucoma, and Suspects categories through fundus image recognition, our developed AI model consistently achieved high diagnostic accuracy rates. Conclusion Our study suggests that further enhancements in glaucoma detection accuracy are attainable by augmenting the dataset with additional labeled images. We emphasize the significance of considering various application parameters when discussing the integration of computer-aided decision/management systems into healthcare frameworks.
MeSH term(s)	Humans ; Deep Learning ; Glaucoma/diagnostic imaging ; Fundus Oculi ; Neural Networks, Computer ; Blindness
Language	English
Publishing date	2024-02-22
Publishing country	United Arab Emirates
Document type	Journal Article
ISSN	1573-4056
ISSN (online)	1573-4056
DOI	10.2174/0115734056257657231115051020
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Article ; Online: Optical Intracranial Self-Stimulation (oICSS): A New Behavioral Model for Studying Drug Reward and Aversion in Rodents.

Song, Rui / Soler-Cedeño, Omar / Xi, Zheng-Xiong

International journal of molecular sciences

2024 Volume 25, Issue 6

Abstract: Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS (eICSS), an electrode is surgically implanted into the medial forebrain ... ...

Abstract	Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS (eICSS), an electrode is surgically implanted into the medial forebrain bundle (MFB) in the lateral hypothalamus or the ventral tegmental area (VTA) in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the eICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of eICSS is the lack of cellular selectivity in understanding the neural substrates underlying this behavior. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models-dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable stimulation frequency-lever response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in oICSS have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel behavioral model in preclinical medication development for treating substance use disorders.
MeSH term(s)	Animals ; Self Stimulation ; Rodentia ; Reward ; Mesencephalon ; Medial Forebrain Bundle ; Electric Stimulation
Language	English
Publishing date	2024-03-19
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25063455
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