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Article: A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Zhao, Yapeng / Yin, Xiaozhe / Fu, Yan / Yue, Tianxiang

Environmental science and pollution research. 2022 Mar., v. 29, no. 12

2022

Abstract: Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the ... ...

Abstract	Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.
Keywords	Landsat ; data collection ; ecosystems ; pollution ; prediction ; reflectance ; research ; species diversity ; vegetation ; China
Language	English
Dates of publication	2022-03
Size	p. 17878-17891.
Publishing place	Springer Berlin Heidelberg
Document type	Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-021-16973-x
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Global future population exposure to heatwaves.

Wang, Yuwei / Zhao, Na / Yin, Xiaozhe / Wu, Chaoyang / Chen, Mingxing / Jiao, Yimeng / Yue, Tianxiang

Environment international

2023 Volume 178, Page(s) 108049

Abstract: The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect poses multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research ...

Abstract	The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect poses multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects such as oversimplification of human exposure to heatwaves and neglect of perceived temperature as well as actual body comfort, resulting in unreliable and unrealistic estimates of future results. In addition, little research has performed comprehensive and fine-resolution global analyses in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) considering urban expansion at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (approximately 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (approximately 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves.
MeSH term(s)	Humans ; Cities ; Hot Temperature ; Urbanization ; Urban Population ; Climate Change
Language	English
Publishing date	2023-06-20
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2023.108049
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Article: Understanding Quasi-Static and Dynamic Characteristics of Organic Ferroelectric Field Effect Transistors.

Ke, Hanjing / Liang, Xiaoci / Yin, Xiaozhe / Liu, Baiquan / Han, Songjia / Jiang, Shijie / Liu, Chuan / She, Xiaojian

Micromachines

2024 Volume 15, Issue 4

Abstract: Leveraging poly(vinylidene fluoride-trifluoroethylene) [(PVDF-TrFE)] as the dielectric, we fabricated organic ferroelectric field-effect transistors (OFe-FETs). These devices demonstrate quasi-static transfer characteristics that include a hysteresis ... ...

Abstract	Leveraging poly(vinylidene fluoride-trifluoroethylene) [(PVDF-TrFE)] as the dielectric, we fabricated organic ferroelectric field-effect transistors (OFe-FETs). These devices demonstrate quasi-static transfer characteristics that include a hysteresis window alongside transient phenomena that bear resemblance to synaptic plasticity-encapsulating excitatory postsynaptic current (EPSC) as well as both short-term and long-term potentiation (STP/LTP). We also explore and elucidate other aspects such as the subthreshold swing and the hysteresis window under dynamic state by varying the pace of voltage sweeps. In addition, we developed an analytical model that describes the electrical properties of OFe-FETs, which melds an empirical formula for ferroelectric polarization with a compact model. This model agrees well with the experimental data concerning quasi-static transfer characteristics, potentially serving as a quantitative tool to improve the understanding and design of OFe-FETs.
Language	English
Publishing date	2024-03-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2620864-7
ISSN	2072-666X
ISSN	2072-666X
DOI	10.3390/mi15040467
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Article ; Online: A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling.

Zhao, Yapeng / Yin, Xiaozhe / Fu, Yan / Yue, Tianxiang

Environmental science and pollution research international

2021 Volume 29, Issue 12, Page(s) 17878–17891

Abstract	Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.
MeSH term(s)	Algorithms ; Biodiversity ; China ; Ecosystem ; Machine Learning
Language	English
Publishing date	2021-10-21
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/s11356-021-16973-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PPARα at the crossroad of metabolic–immune regulation in cancer

Zeng, Wenfeng / Yin, Xiaozhe / Jiang, Yunhan / Jin, Lingtao / Liang, Wei

The FEBS Journal. 2022 Dec., v. 289, no. 24 p.7726-7739

2022

Abstract: Rewiring metabolism to sustain cell growth, division, and survival is the most prominent feature of cancer cells. In particular, dysregulated lipid metabolism in cancer has received accumulating interest, since lipid molecules serve as cell membrane ... ...

Abstract	Rewiring metabolism to sustain cell growth, division, and survival is the most prominent feature of cancer cells. In particular, dysregulated lipid metabolism in cancer has received accumulating interest, since lipid molecules serve as cell membrane structure components, secondary signaling messengers, and energy sources. Given the critical role of immune cells in host defense against cancer, recent studies have revealed that immune cells compete for nutrients with cancer cells in the tumor microenvironment and accordingly develop adaptive metabolic strategies for survival at the expense of compromised immune functions. Among these strategies, lipid metabolism reprogramming toward fatty acid oxidation is closely related to the immunosuppressive phenotype of tumor‐infiltrated immune cells, including macrophages and dendritic cells. Therefore, it is important to understand the lipid‐mediated crosstalk between cancer cells and immune cells in the tumor microenvironment. Peroxisome proliferator‐activated receptors (PPARs) consist of a nuclear receptor family for lipid sensing, and one of the family members PPARα is responsible for fatty acid oxidation, energy homeostasis, and regulation of immune cell functions. In this review, we discuss the emerging role of PPARα‐associated metabolic–immune regulation in tumor‐infiltrated immune cells, and key metabolic events and pathways involved, as well as their influences on antitumor immunity.
Keywords	beta oxidation ; cell growth ; cell membrane structures ; energy ; homeostasis ; immunosuppression ; lipids ; macrophages ; neoplasms ; phenotype
Language	English
Dates of publication	2022-12
Size	p. 7726-7739.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	REVIEW
ZDB-ID	2173655-8
ISSN	1742-4658 ; 1742-464X
ISSN (online)	1742-4658
ISSN	1742-464X
DOI	10.1111/febs.16181
Database	NAL-Catalogue (AGRICOLA)

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Article: Exposure models for particulate matter elemental concentrations in Southern California

Yin, Xiaozhe / Franklin, Meredith / Fallah-Shorshani, Masoud / Shafer, Martin / McConnell, Rob / Fruin, Scott

Environment international. 2022 Apr. 12,

2022

Abstract: Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations that are able to capture small-scale spatial variability ... ...

Abstract	Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations that are able to capture small-scale spatial variability near sources. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM₀.₂), accumulation mode fine (PM₀.₂₋₂.₅), and coarse (PM₂.₅₋₁₀) particulate matter concentrations were used to develop spatiotemporal regression, random forest, extreme gradient boosting and neural network models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.
Keywords	calcium ; carbon ; copper ; dust ; environment ; iron ; land use ; meteorology ; particulates ; regression analysis ; roads ; sodium ; soil ; traffic ; urban areas ; vanadium ; zinc ; California
Language	English
Dates of publication	2022-0412
Publishing place	Elsevier Ltd
Document type	Article
Note	Pre-press version
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2022.107247
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: In vitro

Zeng, Wenfeng / Yin, Xiaozhe / Jiang, Yunhan / Jin, Lingtao / Liang, Wei

STAR protocols

2021 Volume 2, Issue 1, Page(s) 100361

Abstract: Exosomes that contain various signaling molecules, such as proteins, nucleotides, metabolites, and lipids, are important for intercellular communication. Dendritic cells (DC) are central regulators of anti-tumor immunity but can be suppressed by tumor- ... ...

Abstract	Exosomes that contain various signaling molecules, such as proteins, nucleotides, metabolites, and lipids, are important for intercellular communication. Dendritic cells (DC) are central regulators of anti-tumor immunity but can be suppressed by tumor-derived exosomes (TDEs) in the tumor microenvironment. Here, we describe a step-by-step protocol for TDE isolation and evaluation of TDEs on DCs both
MeSH term(s)	Animals ; Cell Communication/genetics ; Cell Communication/immunology ; Dendritic Cells/immunology ; Exosomes/genetics ; Exosomes/immunology ; Mice ; Mice, Transgenic ; Neoplasms, Experimental/genetics ; Neoplasms, Experimental/immunology ; Tumor Microenvironment/genetics ; Tumor Microenvironment/immunology
Language	English
Publishing date	2021-03-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	2666-1667
ISSN (online)	2666-1667
DOI	10.1016/j.xpro.2021.100361
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PPARα at the crossroad of metabolic-immune regulation in cancer.

Zeng, Wenfeng / Yin, Xiaozhe / Jiang, Yunhan / Jin, Lingtao / Liang, Wei

The FEBS journal

2021

Abstract	Rewiring metabolism to sustain cell growth, division, and survival is the most prominent feature of cancer cells. In particular, dysregulated lipid metabolism in cancer has received accumulating interest, since lipid molecules serve as cell membrane structure components, secondary signaling messengers, and energy sources. Given the critical role of immune cells in host defense against cancer, recent studies have revealed that immune cells compete for nutrients with cancer cells in the tumor microenvironment and accordingly develop adaptive metabolic strategies for survival at the expense of compromised immune functions. Among these strategies, lipid metabolism reprogramming toward fatty acid oxidation is closely related to the immunosuppressive phenotype of tumor-infiltrated immune cells, including macrophages and dendritic cells. Therefore, it is important to understand the lipid-mediated crosstalk between cancer cells and immune cells in the tumor microenvironment. Peroxisome proliferator-activated receptors (PPARs) consist of a nuclear receptor family for lipid sensing, and one of the family members PPARα is responsible for fatty acid oxidation, energy homeostasis, and regulation of immune cell functions. In this review, we discuss the emerging role of PPARα-associated metabolic-immune regulation in tumor-infiltrated immune cells, and key metabolic events and pathways involved, as well as their influences on antitumor immunity.
Language	English
Publishing date	2021-09-04
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2173655-8
ISSN	1742-4658 ; 1742-464X
ISSN (online)	1742-4658
ISSN	1742-464X
DOI	10.1111/febs.16181
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Article ; Online: Estimating traffic noise over a large urban area: An evaluation of methods.

Fallah-Shorshani, Masoud / Yin, Xiaozhe / McConnell, Rob / Fruin, Scott / Franklin, Meredith

Environment international

2022 Volume 170, Page(s) 107583

Abstract: Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven ... ...

Abstract	Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.
Language	English
Publishing date	2022-10-13
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2022.107583
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Article ; Online: Exposure models for particulate matter elemental concentrations in Southern California.

Yin, Xiaozhe / Franklin, Meredith / Fallah-Shorshani, Masoud / Shafer, Martin / McConnell, Rob / Fruin, Scott

Environment international

2022 Volume 165, Page(s) 107247

Abstract: Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a ...

Abstract	Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM
MeSH term(s)	Air Pollutants/analysis ; Air Pollution/analysis ; Environmental Monitoring/methods ; Particulate Matter/analysis ; Vehicle Emissions/analysis
Chemical Substances	Air Pollutants ; Particulate Matter ; Vehicle Emissions
Language	English
Publishing date	2022-04-18
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	554791-x
ISSN	1873-6750 ; 0160-4120
ISSN (online)	1873-6750
ISSN	0160-4120
DOI	10.1016/j.envint.2022.107247
Database	MEDical Literature Analysis and Retrieval System OnLINE

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