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Article ; Online: A comparative study on intra-annual classification of invasive saltcedar with Landsat 8 and Landsat 9

Li, Ruixuan / Wang, Le / Lü, Ying

International Journal of Remote Sensing. 2023 Mar. 19, v. 44, no. 6 p.2093-2114

2023

Abstract: The rapid expansion of exotic saltcedar along riparian corridors has dramatically altered the landscape structure and ecological function of riparian habitats in the western United States. The development of accurate and reproducible mapping methods with ...

Abstract	The rapid expansion of exotic saltcedar along riparian corridors has dramatically altered the landscape structure and ecological function of riparian habitats in the western United States. The development of accurate and reproducible mapping methods with remote sensing plays an indispensable role in the timely monitoring of saltcedar, re-evaluating its ecological functions, and establishing effective control measures. The utmost challenge for achieving this goal is manifested as the lack of time series of remote sensing images to capture the saltcedar phenology adequately. To this end, the newly available Landsat 9 images, combined with its counterpart of Landsat 8, offer a precious opportunity to compensate for the temporal image shortage. To understand Landsat 9 in the saltcedar classification and to discover helpful information for its application, this study presents the first attempt to classify saltcedar using intra-annual Landsat 8 and Landsat 9 images. We adopted two machine learning algorithms, support vector machine (SVM) and random forest (RF), to compare the performance of Landsat 9 and Landsat 8 for intra-annual saltcedar classification. In addition, we investigated the respective contribution of each spectral band to the overall performance and identified the optimal time window for saltcedar classification. The results indicated that the difference in classification performance between Landsat 9 and Landsat 8 was insignificant. The shortwave infrared bands associated with both Landsat 8 & 9 have contributed most to the process of saltcedar identification. Image acquired in July, November, and December yielded better results than other months for saltcedar classification. It is concluded that Landsat 8 & 9 constellation has the potential to refine saltcedar classification accuracy on larger spatial and temporal scales.
Keywords	Landsat ; Tamarix ; comparative study ; ecological function ; landscapes ; phenology ; support vector machines ; time series analysis
Language	English
Dates of publication	2023-0319
Size	p. 2093-2114.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	1497529-4
ISSN	1366-5901 ; 0143-1161
ISSN (online)	1366-5901
ISSN	0143-1161
DOI	10.1080/01431161.2023.2195573
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Effect of Reduced Graphene Oxide on Microwave Absorbing Properties of Al

Wang, Shuo / Zhang, Weiran / Zhang, Yong / Zhao, Jinqiang / Li, Ruixuan / Zhong, Yujie

Entropy (Basel, Switzerland)

2024 Volume 26, Issue 1

Abstract: The microwave absorption performance of high-entropy alloys (HEAs) can be improved by reducing the reflection coefficient of electromagnetic waves and broadening the absorption frequency band. The present work prepared flaky irregular-shaped ... ...

Abstract	The microwave absorption performance of high-entropy alloys (HEAs) can be improved by reducing the reflection coefficient of electromagnetic waves and broadening the absorption frequency band. The present work prepared flaky irregular-shaped Al
Language	English
Publishing date	2024-01-10
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e26010060
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Article ; Online: Renalase mediates macrophage-to-fibroblast crosstalk to attenuate pressure overload-induced pathological myocardial fibrosis.

Fu, Ru / You, Nana / Li, Ruixuan / Zhao, Xiexiong / Li, Yihui / Li, Xiaogang / Jiang, Weihong

Journal of hypertension

2024 Volume 42, Issue 4, Page(s) 629–643

Abstract: A potential antifibrotic mechanism in pathological myocardial remodeling is the recruitment of beneficial functional subpopulations of macrophages or the transformation of their phenotype. Macrophages are required to activate molecular cascades that ... ...

Abstract	A potential antifibrotic mechanism in pathological myocardial remodeling is the recruitment of beneficial functional subpopulations of macrophages or the transformation of their phenotype. Macrophages are required to activate molecular cascades that regulate fibroblast behavior. Identifying mediators that activate the antifibrotic macrophage phenotype is tantamount to identifying the button that retards pathological remodeling of the myocardium; however, relevant studies are inadequate. Circulating renalase (RNLS) is mainly of renal origin, and cardiac myocytes also secrete it autonomously. Our previous studies revealed that RNLS delivers cell signaling to exert multiple cardiovascular protective effects, including the improvement of myocardial ischemia, and heart failure. Here, we further investigated the potential mechanism by which macrophage phenotypic transformation is targeted by RNLS to mediate stress load-induced myocardial fibrosis. Mice subjected to transverse aortic constriction (TAC) were used as a model of myocardial fibrosis. The co-incubation of macrophages and cardiac fibroblasts was used to study intercellular signaling. The results showed that RNLS co-localized with macrophages and reduced protein expression after cardiac pressure overload. TAC mice exhibited improved cardiac function and alleviated left ventricular fibrosis when exogenous RNLS was administered. Flow sorting showed that RNLS is essential for macrophage polarization towards a restorative phenotype (M2-like), thereby inhibiting myofibroblast activation, as proven by both mouse RAW264.7 and bone marrow-derived macrophage models. Mechanistically, we found that activated protein kinase B is a major pathway by which RNLS promotes M2 polarization in macrophages. RNLS may serve as a prognostic biomarker and a potential clinical candidate for the treatment of myocardial fibrosis.
MeSH term(s)	Mice ; Animals ; Myocardium/pathology ; Cardiomyopathies ; Myocytes, Cardiac/metabolism ; Macrophages ; Fibroblasts/pathology ; Fibrosis ; Ventricular Remodeling ; Mice, Inbred C57BL ; Monoamine Oxidase
Chemical Substances	renalase (EC 1.4.3.4.) ; Monoamine Oxidase (EC 1.4.3.4)
Language	English
Publishing date	2024-01-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	605532-1
ISSN	1473-5598 ; 0263-6352 ; 0952-1178
ISSN (online)	1473-5598
ISSN	0263-6352 ; 0952-1178
DOI	10.1097/HJH.0000000000003635
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Article ; Online: CCL2 regulated by the CTBP1-AS2/miR-335-5p axis promotes hemangioma progression and angiogenesis.

Li, Ruixuan / Liu, Ying / Liu, Jianfeng / Chen, Bo / Ji, Zhongjie / Xu, Aixia / Zhang, Tianhua

Immunopharmacology and immunotoxicology

2024 , Page(s) 1–10

Abstract: Context:: Objective:: Methods:: Results:: Conclusion: ...

Abstract	Context: Objective: Methods: Results: Conclusion:
Language	English
Publishing date	2024-04-15
Publishing country	England
Document type	Journal Article
ZDB-ID	807033-7
ISSN	1532-2513 ; 0892-3973
ISSN (online)	1532-2513
ISSN	0892-3973
DOI	10.1080/08923973.2024.2330651
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Article ; Online: Affective computing of multi-type urban public spaces to analyze emotional quality using ensemble learning-based classification of multi-sensor data.

Li, Ruixuan / Yuizono, Takaya / Li, Xianghui

PloS one

2022 Volume 17, Issue 6, Page(s) e0269176

Abstract: The quality of urban public spaces affects the emotional response of users; therefore, the emotional data of users can be used as indices to evaluate the quality of a space. Emotional response can be evaluated to effectively measure public space quality ... ...

Abstract	The quality of urban public spaces affects the emotional response of users; therefore, the emotional data of users can be used as indices to evaluate the quality of a space. Emotional response can be evaluated to effectively measure public space quality through affective computing and obtain evidence-based support for urban space renewal. We proposed a feasible evaluation method for multi-type urban public spaces based on multiple physiological signals and ensemble learning. We built binary, ternary, and quinary classification models based on participants' physiological signals and self-reported emotional responses through experiments in eight public spaces of five types. Furthermore, we verified the effectiveness of the model by inputting data collected from two other public spaces. Three observations were made based on the results. First, the highest accuracies of the binary and ternary classification models were 92.59% and 91.07%, respectively. After external validation, the highest accuracies were 80.90% and 65.30%, respectively, which satisfied the preliminary requirements for evaluating the quality of actual urban spaces. However, the quinary classification model could not satisfy the preliminary requirements. Second, the average accuracy of ensemble learning was 7.59% higher than that of single classifiers. Third, reducing the number of physiological signal features and applying the synthetic minority oversampling technique to solve unbalanced data improved the evaluation ability.
MeSH term(s)	Emotions ; Humans ; Learning ; Machine Learning
Language	English
Publishing date	2022-06-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0269176
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Article ; Online: New Advances in High-Entropy Alloys.

Zhang, Yong / Li, Ruixuan

Entropy (Basel, Switzerland)

2020 Volume 22, Issue 10

Abstract: Exploring new materials is an eternal pursuit in the development of human civilization [ ... ]. ...

Abstract	Exploring new materials is an eternal pursuit in the development of human civilization [...].
Language	English
Publishing date	2020-10-15
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e22101158
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Article ; Online: Optimization scheme of wind energy prediction based on artificial intelligence

Zhang, Yagang / Li, Ruixuan / Zhang, Jinghui

Environ Sci Pollut Res. 2021 Aug., v. 28, no. 29 p.39966-39981

2021

Abstract: Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting the ... ...

Abstract	Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China’s Changma wind farm and Spain’s Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.
Keywords	algorithms ; artificial intelligence ; memory ; models ; power generation ; prediction ; uncertainty ; wavelet ; wind farms ; wind power ; wind speed ; China ; Spain
Language	English
Dates of publication	2021-08
Size	p. 39966-39981.
Publishing place	Springer Berlin Heidelberg
Document type	Article ; Online
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-021-13516-2
Database	NAL-Catalogue (AGRICOLA)

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Article: Insights into the impacts of bioturbation by multiple benthic organisms on the bioavailability and toxic effects of perfluorooctane sulfonate in sediment

Wu, Zihao / Li, Ruixuan / Zhang, Yanfeng / Zhu, Lingyan

Journal of hazardous materials. 2021 Oct. 15, v. 420

2021

Abstract: Sediment is an important reservoir for perfluorooctane sulfonate (PFOS) in the environment, which likely poses adverse effects to benthos. In this study, the impacts of bioturbation of three benthic organisms, i.e. Chironomus kiiensis, Hyalella azteca ... ...

Abstract	Sediment is an important reservoir for perfluorooctane sulfonate (PFOS) in the environment, which likely poses adverse effects to benthos. In this study, the impacts of bioturbation of three benthic organisms, i.e. Chironomus kiiensis, Hyalella azteca and Limnodrilus hoffmeisteri, on the release of PFOS from sediment were investigated, and the toxic effects of PFOS to C. kiiensis were explored in the presence of one or two of the other benthic organisms. Among the three organisms, C. kiiensis displayed the weakest effect on the distribution of PFOS between sediment and water (P＞0.05). The bioturbation of H. azteca and L. hoffmeisteri distinctly facilitated the suspension of sediment, leading to the enhanced amount of suspended particulate matter (SPM) and the flux of PFOS from sediment to SPM. Consequently, the concentrations of PFOS in the overlying water and pore water decreased significantly. Moreover, both H. azteca and L. hoffmeisteri affected the survival of C. kiiensis, and its mortality increased from 2.8% to 100% and 41.7% respectively. This study provides insights into the influences of bioturbation on the bioavailability of PFOS in sediments, and is helpful for accurately assessing the transport, toxicity and potential risks of PFOS in sediments.
Keywords	Chironomus ; Hyalella azteca ; Limnodrilus hoffmeisteri ; benthic organisms ; bioavailability ; bioturbation ; mortality ; particulates ; perfluorooctane sulfonic acid ; sediments ; toxicity
Language	English
Dates of publication	2021-1015
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1491302-1
ISSN	1873-3336 ; 0304-3894
ISSN (online)	1873-3336
ISSN	0304-3894
DOI	10.1016/j.jhazmat.2021.126675
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Article: Causality of gut microbiome and hypertension: A bidirectional mendelian randomization study.

Li, Yihui / Fu, Ru / Li, Ruixuan / Zeng, Jianwei / Liu, Tao / Li, Xiaogang / Jiang, Weihong

Frontiers in cardiovascular medicine

2023 Volume 10, Page(s) 1167346

Abstract: Background & aims: The pathogenesis of hypertension involves a diverse range of genetic, environmental, hemodynamic, and more causative factors. Recent evidence points to an association between the gut microbiome and hypertension. Given that the ... ...

Abstract	Background & aims: The pathogenesis of hypertension involves a diverse range of genetic, environmental, hemodynamic, and more causative factors. Recent evidence points to an association between the gut microbiome and hypertension. Given that the microbiota is in part determined by host genetics, we used the two-sample Mendelian randomization (MR) analysis to address the bidirectional causal link between gut microbiota and hypertension. Methods: We selected genetic variants ( Results: At the genus level, our MR estimates from gut microbiome to hypertension showed that there were 5 protective factors Conclusion: Altered gut microbiota is a causal factor in the development of hypertension, and hypertension causes imbalances in the intestinal flora. Substantial research is still needed to find the key gut flora and explore the specific mechanisms of their effects so that new biomarkers can be found for blood pressure control.
Language	English
Publishing date	2023-05-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2781496-8
ISSN	2297-055X
ISSN	2297-055X
DOI	10.3389/fcvm.2023.1167346
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Article ; Online: Optimization scheme of wind energy prediction based on artificial intelligence.

Zhang, Yagang / Li, Ruixuan / Zhang, Jinghui

Environmental science and pollution research international

2021 Volume 28, Issue 29, Page(s) 39966–39981

Abstract	Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned by many countries. However, due to the great volatility and uncertainty of natural wind, wind power also fluctuates, seriously affecting the reliability of wind power system and bringing challenges to large-scale grid connection of wind power. Wind speed prediction is very important to ensure the safety and stability of wind power generation system. In this paper, a new wind speed prediction scheme is proposed. First, improved hybrid mode decomposition is used to decompose the wind speed data into the trend part and the fluctuation part, and the noise is decomposed twice. Then wavelet analysis is used to decompose the trend part and the fluctuation part for the third time. The decomposed data are classified. The long- and short-term memory neural network optimized by the improved particle swarm optimization algorithm is used to train the nonlinear sequence and noise sequence, and the autoregressive moving average model is used to train the linear sequence. Finally, the final prediction results were reconstructed. This paper uses this system to predict the wind speed data of China's Changma wind farm and Spain's Sotavento wind farm. By experimenting with the real data from two different wind farms and comparing with other predictive models, we found that (1) by improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted. (2) According to the different characteristics of component data, the combination method is selected to predict modal components, which makes full use of the advantages of different algorithms and has good prediction effect. (3) The optimization algorithm is used to optimize the neural network, which solves the problem of parameter setting when establishing the prediction model. (4) The combination forecasting model proposed in this paper has clear structure and accurate prediction results. The research work in this paper will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.
MeSH term(s)	Artificial Intelligence ; Energy-Generating Resources ; Neural Networks, Computer ; Reproducibility of Results ; Wind
Language	English
Publishing date	2021-03-25
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/s11356-021-13516-2
Database	MEDical Literature Analysis and Retrieval System OnLINE

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