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Article ; Online: Energy-saving and emission-reduction potential of fuel cell heavy-duty trucks in China during the fuel life cycle.

Yan, Rui / Jiang, Zhijuan

Environmental science and pollution research international

2023 Volume 30, Issue 33, Page(s) 80559–80572

Abstract: Exploring alternative fuels and advanced vehicle technology is a crucial strategy for vehicle emission reduction. Fuel cell heavy-duty trucks (FC-HDTs) have a promising application prospect to alleviate the high energy consumption and emissions of road ... ...

Abstract	Exploring alternative fuels and advanced vehicle technology is a crucial strategy for vehicle emission reduction. Fuel cell heavy-duty trucks (FC-HDTs) have a promising application prospect to alleviate the high energy consumption and emissions of road freight, but their environmental performance during the fuel life cycle should be further studied. This study is aimed at evaluating the fossil fuel consumption and GHG emissions of FC-HDTs in China using the updated GREET model. The results show that (1) comparing various hydrogen production pathways, it is found that the coke oven gas (COG) pathway can provide the best environmental performance, while the energy consumption and greenhouse gas (GHG) emissions of the coal gasification (CG) and grid power water electrolysis (GPWE) pathways will be significantly decreased in the future. (2) Among the involved vehicles in China, FC-HDT with GVWR18 has the greatest energy-saving and emission-reduction potential. (3) The application of carbon capture and storage (CCS) technology in hydrogen production is conducive to improving the emission-reduction effect of FC-HDT while increasing its energy consumption slightly. The key to achieving upstream carbon neutrality is to optimize the hydrogen production structure and electricity mix, along with adjusting the hydrogen production process and transportation mode. Furthermore, the fuel economy and payload of the FC-HDT affect its environmental performance, indicating the importance of improving the technology of the drivetrain, fuel cell, and hydrogen storage tank.
MeSH term(s)	Motor Vehicles ; Vehicle Emissions/analysis ; Hydrogen ; Greenhouse Gases ; China ; Carbon ; Gasoline/analysis
Chemical Substances	Vehicle Emissions ; Hydrogen (7YNJ3PO35Z) ; Greenhouse Gases ; Carbon (7440-44-0) ; Gasoline
Language	English
Publishing date	2023-06-10
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-023-28085-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Author Correction: System based greenhouse emission analysis of off-site prefabrication: a comparative study of residential projects.

Guo, Yuliang / Shi, Enhui / Yan, Rui / Wei, Wenchao

Scientific reports

2024 Volume 14, Issue 1, Page(s) 6283

Language	English
Publishing date	2024-03-15
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-56778-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson's disease.

Zhang, Xiaoming / Yu, Can / Yan, Rui

Journal of biomedical informatics

2024 Volume 152, Page(s) 104624

Abstract: The relational triple extraction of unstructured medical texts about Parkinson's disease is critical for the construction of a medical knowledge graph. However, the triple entities in Parkinson's disease are usually complicated and overlapped, which ... ...

Abstract	The relational triple extraction of unstructured medical texts about Parkinson's disease is critical for the construction of a medical knowledge graph. However, the triple entities in Parkinson's disease are usually complicated and overlapped, which impedes the accuracy of triple extraction, especially in the case of rarely available corpus. Therefore, this study first builds a corpus about Parkinson's disease. Then, a tagging-based three-stage relational triple extraction model is proposed, named ParTRE. To enhance the contextual representation of sentences, the proposed model employs BiLSTM modules to capture fine-grained semantic information. Additionally, a conditional normalization layer is used so that entity pairs can be extracted accurately from two complementary directions. As for the imbalanced relationship categories, an adaptive loss function strategy based on focal loss is derived by assigning different weights to relationship categories and reducing the loss of easy-to-classify samples. The model performance is evaluated on the Parkinson's corpus and public datasets. The results indicate that the proposed model achieves an overall F1-score of 93.3 % on the Parkinson's corpus and comparable performance on public datasets compared with the state-of-the-art methods. Moreover, a satisfactory result is achieved by the proposed model on conquering the overlapped entities and imbalanced relationship categories. Owing to demonstrated availability and validity, the proposed method can be integrated with medical knowledge graphs and therefore benefits medical intelligence.
MeSH term(s)	Humans ; Parkinson Disease ; Language ; Semantics ; Knowledge
Language	English
Publishing date	2024-03-11
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2024.104624
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Article: Identification of key biomarkers in neonatal sepsis by integrated bioinformatics analysis and clinical validation.

Yan, Rui / Zhou, Tao

Heliyon

2022 Volume 8, Issue 11, Page(s) e11634

Abstract: Background: Neonatal sepsis (NS) is a systemic inflammatory response to severe pathogenic infections, and is a major cause of high morbidity and mortality in newborns. Currently, there is a lack of efficient diagnostic technology to accurately and ... ...

Abstract	Background: Neonatal sepsis (NS) is a systemic inflammatory response to severe pathogenic infections, and is a major cause of high morbidity and mortality in newborns. Currently, there is a lack of efficient diagnostic technology to accurately and rapidly diagnose NS, and the precise pathogenesis of NS has yet to be fully elucidated. The present study aimed to identify the optimal biomarkers in the progression of NS. Methods: The differentially expressed genes (DEGs) between NS and controls in the discovery datasets were screened. Gene set variation analysis (GSVA) was used to enrich the changes in biological functions and pathways in sepsis patients compared to healthy individuals. The differences in immune cell infiltration between these two groups were assessed using CIBERSORT. Furthermore, LASSO algorithm and ROC analysis were performed to identify and evaluate the gene signature. Results: A total of 85 upregulated and 40 downregulated overlapping DEGs were screened in sepsis samples. The GSVA results indicated that DEGs largely contributed to upregulated inflammation and metabolism-related processes, and suppressed adaptive immune responses in NS. Markedly lower infiltration of most types of immune cell was observed in sepsis patients, except for some innate immune cells. Moreover, 57 genes with AUC >0.9 in both discovery sets were selected and applied to a LASSO model. Using this model, a seven-gene signature was acquired, which was validated in the discovery and independent validation sets. Five genes among the gene signatures with optimal diagnostic performance were obtained and further validated in clinical samples using RT-qPCR. Finally, three genes SLC2A3, OSCAR, and CD3G were identified as key biomarkers for NS. Conclusions: Our findings will provide novel insights into the pathogenesis of NS, and the potential biomarkers may have promising application values for its early detection and therapeutic intervention.
Language	English
Publishing date	2022-11-15
Publishing country	England
Document type	Journal Article
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e11634
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Research on the signaling pathway and the related mechanism of traditional Chinese medicine intervention in chronic gastritis of the "inflammation-cancer transformation".

Yan-Rui, Wang / Xue-Er, Yan / Mao-Yu, Ding / Ya-Ting, Lu / Bo-Heng, Lu / Miao-Jie, Zhai / Li, Zhu

Frontiers in pharmacology

2024 Volume 15, Page(s) 1338471

Abstract: Objective: ...

Abstract	Objective:
Language	English
Publishing date	2024-04-18
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2587355-6
ISSN	1663-9812
ISSN	1663-9812
DOI	10.3389/fphar.2024.1338471
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Article ; Online: Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm.

Xu, Maoxuan / Yang, Feng / Fang, Yuanjin / Li, Fanruo / Yan, Rui

Sensors (Basel, Switzerland)

2024 Volume 24, Issue 2

Abstract: The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It ... ...

Abstract	The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.
Language	English
Publishing date	2024-01-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s24020379
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Article ; Online: Treatment outcomes in different types of patellar fracture using internal fixation with suture anchor.

Shi, Yin-Hu / Yan, Rui-Hai / Li, Yuan-Shen / Suo, Yan-Hui

Asian journal of surgery

2024

Language	English
Publishing date	2024-02-23
Publishing country	Netherlands
Document type	Letter
ZDB-ID	1068461-x
ISSN	0219-3108 ; 1015-9584
ISSN (online)	0219-3108
ISSN	1015-9584
DOI	10.1016/j.asjsur.2024.02.010
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Article ; Online: Identification of key biomarkers in neonatal sepsis by integrated bioinformatics analysis and clinical validation

Yan, Rui / Zhou, Tao

Heliyon. 2022 Nov., v. 8, no. 11 p.e11634-

2022

Abstract: Neonatal sepsis (NS) is a systemic inflammatory response to severe pathogenic infections, and is a major cause of high morbidity and mortality in newborns. Currently, there is a lack of efficient diagnostic technology to accurately and rapidly diagnose ... ...

Abstract	Neonatal sepsis (NS) is a systemic inflammatory response to severe pathogenic infections, and is a major cause of high morbidity and mortality in newborns. Currently, there is a lack of efficient diagnostic technology to accurately and rapidly diagnose NS, and the precise pathogenesis of NS has yet to be fully elucidated. The present study aimed to identify the optimal biomarkers in the progression of NS. The differentially expressed genes (DEGs) between NS and controls in the discovery datasets were screened. Gene set variation analysis (GSVA) was used to enrich the changes in biological functions and pathways in sepsis patients compared to healthy individuals. The differences in immune cell infiltration between these two groups were assessed using CIBERSORT. Furthermore, LASSO algorithm and ROC analysis were performed to identify and evaluate the gene signature. A total of 85 upregulated and 40 downregulated overlapping DEGs were screened in sepsis samples. The GSVA results indicated that DEGs largely contributed to upregulated inflammation and metabolism-related processes, and suppressed adaptive immune responses in NS. Markedly lower infiltration of most types of immune cell was observed in sepsis patients, except for some innate immune cells. Moreover, 57 genes with AUC >0.9 in both discovery sets were selected and applied to a LASSO model. Using this model, a seven-gene signature was acquired, which was validated in the discovery and independent validation sets. Five genes among the gene signatures with optimal diagnostic performance were obtained and further validated in clinical samples using RT-qPCR. Finally, three genes SLC2A3, OSCAR, and CD3G were identified as key biomarkers for NS. Our findings will provide novel insights into the pathogenesis of NS, and the potential biomarkers may have promising application values for its early detection and therapeutic intervention.
Keywords	algorithms ; bioinformatics ; biomarkers ; data collection ; gene expression regulation ; genes ; inflammation ; models ; morbidity ; mortality ; pathogenesis ; therapeutics ; Neonatal sepsis ; Diagnostic biomarker ; Immune cell infiltration ; Inflammatory response ; GSVA ; LASSO model
Language	English
Dates of publication	2022-11
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2022.e11634
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Simultaneous Determination of 16-Hydroxystrychnine and Demethylbrucine by LC-MS/MS in Rat Urine and Its Application to Pharmacokinetic Study.

Yan, Rui / Xia, Bin / Zhang, Shan

Drug metabolism and bioanalysis letters

2023

Abstract: Introduction: This paper aimed to establish a method to help investigate the combination mechanism of traditional Chinese medicine from the metabolic perspective.: Background: Semen Strychni has been a frequently used herb in clinics for a long time. ...

Abstract	Introduction: This paper aimed to establish a method to help investigate the combination mechanism of traditional Chinese medicine from the metabolic perspective. Background: Semen Strychni has been a frequently used herb in clinics for a long time. In traditional Chinese medical science, Semen Strychni always combinate with other herbs to modulate its nature of severe toxicity. However, the mechanism of the combination is still unclear. Previous research mostly focused on the components of the herbs. The metabolic processes of the main components of the Semen Strychni are also very important and have rarely been reported. Objective: This study tended to develop a rapid and sensitive high-performance liquid chromatographic- tandem mass spectrometric (HPLC-MS/MS) method for the determination of two major metabolites of Semen Strychni in rat urine. Methods: Chromatographic separation was carried out on a C18 column. Detection was performed by a selective reaction monitoring (SRM) mode via an electrospray ionization (ESI) source operating in the positive ionization mode. Analysis of analytes from rat plasma was carried out by protein precipitation using acetonitrile. Results: The assay was validated in terms of specificity, precision, recovery, etc. The intra- and inter-day precision values were less than 13.1%. The recoveries at three levels were more than 69.1%. The method was then used to study the kinetic profiles of the metabolites of Semen Strychni in rat urine for the first time. Conclusion: The results demonstrate that the established LC/MS method in this study is accurate and sensitive for the simultaneous determination of the two metabolites of Semen Strychni in rats' urine samples. This method could be a supplement to the plasma pharmacokinetics of Semen Strychni.
Language	English
Publishing date	2023-05-15
Publishing country	United Arab Emirates
Document type	Journal Article
ISSN	2949-6829
ISSN (online)	2949-6829
DOI	10.2174/1872312815666230427122212
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Exploiting noise as a resource for computation and learning in spiking neural networks.

Ma, Gehua / Yan, Rui / Tang, Huajin

Patterns (New York, N.Y.)

2023 Volume 4, Issue 10, Page(s) 100831

Abstract: Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are ... ...

Abstract	Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.
Language	English
Publishing date	2023-09-04
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2023.100831
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