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  • 1
    Online Resource
    Online Resource
    Therapeutic Efficacy of Autologous Platelet Concentrate Injection on Macular Holes with High Myopia, Large Macular Holes, or Recurrent Macular Holes: A Multicenter Randomized Controlled Trial
    MDPI AG ; 2021
    In:  Journal of Clinical Medicine Vol. 10, No. 12 ( 2021-06-21), p. 2727-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 10, No. 12 ( 2021-06-21), p. 2727-
    Abstract: We aimed to evaluate the anatomical and functional outcomes of pars-plana vitrectomy (PPV) with or without autologous platelet concentrate (APC) injection in patients with recurrent macular holes (MHs), large MHs, or MHs with high myopia. This multicenter, prospective, interventional randomized controlled trial was conducted from March 2017 to April 2020. Participants were randomly allocated to a PPV group or a PPV+APC group. All participants underwent standard 25-gauge PPV, and eyes in the PPV+APC group underwent PPV with intravitreal APC injection before air-gas exchange. A total of 117 patients were enrolled (PPV group: n = 59, PPV+APC group: n = 58). Hole closure was achieved in 47 participants (79.7%) in the PPV group and 52 participants (89.7%) in the PPV+APC group. There were no between-group differences in the anatomical closure rate or functional outcomes including best-corrected visual acuity, metamorphopsia, pattern-reversal visual evoked potential, or Visual Function Questionnaire-25 score. The use of APC injection does not improve the anatomical and functional outcomes of surgery for large MHs, recurrent MHs, or MHs with high myopia. The adjunctive use of APC can be considered in selected cases because it is not inferior to conventional MH surgery, is relatively simple to perform, and is not affected by the surgeon’s skill.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm10122727
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662592-1
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  • 2
    Online Resource
    Online Resource
    For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks
    MDPI AG ; 2020
    In:  Electronics Vol. 9, No. 11 ( 2020-11-02), p. 1829-
    Details
    In: Electronics, MDPI AG, Vol. 9, No. 11 ( 2020-11-02), p. 1829-
    Abstract: Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics9111829
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662127-7
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  • 3
    Online Resource
    Online Resource
    Survival Prediction Using Transformer-Based Categorical Feature Representation in the Treatment of Diffuse Large B-Cell Lymphoma
    MDPI AG ; 2023
    In:  Healthcare Vol. 11, No. 8 ( 2023-04-19), p. 1171-
    Details
    In: Healthcare, MDPI AG, Vol. 11, No. 8 ( 2023-04-19), p. 1171-
    Abstract: Diffuse large B-cell lymphoma (DLBCL) is a common and aggressive subtype of lymphoma, and accurate survival prediction is crucial for treatment decisions. This study aims to develop a robust survival prediction strategy to integrate various risk factors effectively, including clinical risk factors and Deauville scores in positron-emission tomography/computed tomography at different treatment stages using a deep-learning-based approach. We conduct a multi-institutional study on 604 DLBCL patients’ clinical data and validate the model on 220 patients from an independent institution. We propose a survival prediction model using transformer architecture and a categorical-feature-embedding technique that can handle high-dimensional and categorical data. Comparison with deep-learning survival models such as DeepSurv, CoxTime, and CoxCC based on the concordance index (C-index) and the mean absolute error (MAE) demonstrates that the categorical features obtained using transformers improved the MAE and the C-index. The proposed model outperforms the best-performing existing method by approximately 185 days in terms of the MAE for survival time estimation on the testing set. Using the Deauville score obtained during treatment resulted in a 0.02 improvement in the C-index and a 53.71-day improvement in the MAE, highlighting its prognostic importance. Our deep-learning model could improve survival prediction accuracy and treatment personalization for DLBCL patients.
    Type of Medium: Online Resource
    ISSN: 2227-9032
    URL: Article
    DOI: 10.3390/healthcare11081171
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2721009-1
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  • 4
    Online Resource
    Online Resource
    Is Only the Dedicated Lane for Automated Vehicles Essential in the Future? The Dedicated Lanes Optimal Operating System Evaluation
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 18 ( 2022-09-14), p. 11490-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 18 ( 2022-09-14), p. 11490-
    Abstract: As new mobility called automated vehicles (AVs) appears on the road, positive effects are expected, but in fact, unexpected adverse effects may arise due to the mixed traffic situation with human-driven vehicles (HVs). Prior to the commercialization of AVs, a preliminary review and preventive measures are required, and among them, the interaction between the existing vehicle and the new mobility and the interaction with the infrastructure must be considered. Therefore, we propose (i) the positive–negative effect of introducing AVs in a mixed traffic situation and (ii) the optimal operation plan for the dedicated lane for AVs. First, the effect of introducing AVs considering the interaction between vehicles in the mixed traffic situation showed mostly positive such as speed increase, delay time reduction, and capacity increase. However, in a 75% Market Penetration Rate (MPR) environment of all levels of Service (LOS), the effect was diminished compared to the previous MPR. This is contemplated to be the result of a conflict caused by the operation of some HVs (including heavy vehicles) behavior as obstacles in the situation where most of the vehicles on the road are AVs. Based on the previous result, we deployed the dedicated lane to resolve the negative effect in the 75% MPR environment and proposed an optimal operation strategy for the AVs dedicated lane from the perspective of operational efficiency for a more feasible operation. Given the 75% MPR, the Mixed-Use operation strategy of High-Occupancy Vehicles (HOV) and AVs is ascertained as the most suitable operation strategy. This implies that even in the era of AVs, the influence of other vehicles (e.g., heavy vehicles, other mobility) must be considered. This study is significant by considering the negative effects of the introduction of AVs and presenting an optimal operation strategy for dedicated lanes, and it can expect to be used as a new strategy as part of the Free/Expressway Traffic Management System (FTMS) applicable in the era of autonomous driving.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su141811490
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 5
    Online Resource
    Online Resource
    An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul
    MDPI AG ; 2021
    In:  Sustainability Vol. 13, No. 16 ( 2021-08-20), p. 9351-
    Details
    In: Sustainability, MDPI AG, Vol. 13, No. 16 ( 2021-08-20), p. 9351-
    Abstract: The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su13169351
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2518383-7
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  • 6
    Online Resource
    Online Resource
    Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 15 ( 2022-08-05), p. 9680-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 15 ( 2022-08-05), p. 9680-
    Abstract: Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA’s Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs’ capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su14159680
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 7
    Online Resource
    Online Resource
    Comparative Pharmacodynamics of Three Different Botulinum Toxin Type A Preparations following Repeated Intramuscular Administration in Mice
    MDPI AG ; 2022
    In:  Toxins Vol. 14, No. 6 ( 2022-05-25), p. 365-
    Details
    In: Toxins, MDPI AG, Vol. 14, No. 6 ( 2022-05-25), p. 365-
    Abstract: Botulinum neurotoxin type A (BoNT/A) causes muscle paralysis by blocking cholinergic signaling at neuromuscular junctions and is widely used to temporarily correct spasticity-related disorders and deformities. The paralytic effects of BoNT/A are time-limited and require repeated injections at regular intervals to achieve long-term therapeutic benefits. Differences in the level and duration of effectivity among various BoNT/A products can be attributed to their unique manufacturing processes, formulation, and noninterchangeable potency units. Herein, we compared the pharmacodynamics of three BoNT/A formulations, i.e., Botox® (onabotulinumtoxinA), Xeomin® (incobotulinumtoxinA), and Coretox®, following repeated intramuscular (IM) injections in mice. Three IM injections of BoNT/A formulations (12 U/kg per dose), 12-weeks apart, were administered at the right gastrocnemius. Local paresis and chemodenervation efficacy were evaluated over 36 weeks using the digit abduction score (DAS) and compound muscle action potential (CMAP), respectively. One week after administration, all three BoNT/A formulations induced peak DAS and maximal reduction of CMAP amplitudes. Among the three BoNT/A formulations, only Coretox® afforded a significant increase in paretic effects and chemodenervation with a prolonged duration of action after repeated injections. These findings suggest that Coretox® may offer a better overall therapeutic performance in clinical settings.
    Type of Medium: Online Resource
    ISSN: 2072-6651
    URL: Article
    DOI: 10.3390/toxins14060365
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518395-3
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  • 8
    Online Resource
    Online Resource
    A Plant-Derived Maternal Vaccine against Porcine Epidemic Diarrhea Protects Piglets through Maternally Derived Immunity
    MDPI AG ; 2023
    In:  Vaccines Vol. 11, No. 5 ( 2023-05-09), p. 965-
    Details
    In: Vaccines, MDPI AG, Vol. 11, No. 5 ( 2023-05-09), p. 965-
    Abstract: Newborn piglets are susceptible to a highly contagious enteritis caused by the porcine epidemic diarrhea virus (PEDV), associated with high levels of mortality worldwide. There is pressing need for a rapid, safe, and cost-effective vaccine to safeguard pigs from getting infected by PEDV. PEDV belongs to the coronavirus family and is characterized by high levels of mutability. The primary goal of a PEDV vaccine is to provide immunity to newborn piglets through vaccination of sows. Plant-based vaccines are becoming more popular because they have low manufacturing costs, are easily scalable, have high thermostability, and a long shelf life. This is in contrast to conventional vaccines which include inactivated, live, and/or recombinant types that can be expensive and have limited ability to respond to rapidly mutating viruses. The binding of the virus to host cell receptors is primarily facilitated by the N-terminal subunit of the viral spike protein (S1), which also contains several epitopes that are recognized by virus-neutralizing antibodies. As a result, we generated a recombinant S1 protein using a plant-based vaccine platform. We found that the recombinant protein was highly glycosylated, comparable to the native viral antigen. Vaccination of pregnant sows at four and two weeks before farrowing led to the development of humoral immunity specific to S1 in the suckling piglets. In addition, we noted significant viral neutralization titers in both vaccinated sows and piglets. When challenged with PEDV, piglets born from vaccinated sows displayed less severe clinical symptoms and significantly lower mortality rates compared to piglets born from non-vaccinated sows.
    Type of Medium: Online Resource
    ISSN: 2076-393X
    URL: Article
    DOI: 10.3390/vaccines11050965
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2703319-3
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  • 9
    Online Resource
    Online Resource
    The Detection of Black Ice Accidents for Preventative Automated Vehicles Using Convolutional Neural Networks
    MDPI AG ; 2020
    In:  Electronics Vol. 9, No. 12 ( 2020-12-18), p. 2178-
    Details
    In: Electronics, MDPI AG, Vol. 9, No. 12 ( 2020-12-18), p. 2178-
    Abstract: Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive design” for accidents is continuously required. Accordingly, the “preventive design” that prevents accidents in advance is continuously required to enhance the safety of AVs. Specially, black ice with characteristics that are difficult to identify with the naked eye—the main cause of major accidents in winter vehicles—is expected to cause serious injuries in the era of AVs, and measures are needed to prevent them. Therefore, this study presents a Convolutional Neural Network (CNN)-based black ice detection plan to prevent traffic accidents of AVs caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, we augmented image data and learned road environment images. Tests showed that the proposed CNN model detected black ice with 96% accuracy and reproducibility. It is expected that the CNN model for black ice detection proposed in this study will contribute to improving the safety of AVs and prevent black ice accidents in advance.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics9122178
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662127-7
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