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  1. Article: Differential response of the soil nutrients, soil bacterial community structure and metabolic functions to different risk areas in Lead-Zine tailings.

    Liu, Zexun / Zhuang, Jiayao / Zheng, Kang / Luo, Chengcheng

    Frontiers in microbiology

    2023  Volume 14, Page(s) 1131770

    Abstract: Rapid growth in the mining industry has brought about a large formation of tailings, which result in serious destruction of the ecological environment and severe soil pollution problems. This study assesses soil nutrients, soil bacterial community and ... ...

    Abstract Rapid growth in the mining industry has brought about a large formation of tailings, which result in serious destruction of the ecological environment and severe soil pollution problems. This study assesses soil nutrients, soil bacterial community and soil microbes' metabolic function in heavily polluted areas (W1), moderately polluted areas (W2), lightly polluted areas (W3) and clean areas (CK) using 16S Illumina sequencing. The results of this study showed that compared with CK, a severe loss of soil nutrients and richness of OTUs (Chao1 and ACE indices) were observed with the aggravated pollution of tailings. The Chao1 and ACE indices in the W1 group decreased significantly by 15.53 and 16.03%, respectively, (
    Language English
    Publishing date 2023-09-12
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2023.1131770
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: The co-inoculation of

    Zhang, Wentao / Mao, Guohao / Zhuang, Jiayao / Yang, Hao

    Frontiers in microbiology

    2023  Volume 13, Page(s) 1079348

    Abstract: Currently, plant growth-promoting rhizobacteria (PGPR) microbial inoculants are heavily used in agricultural production among ... ...

    Abstract Currently, plant growth-promoting rhizobacteria (PGPR) microbial inoculants are heavily used in agricultural production among which
    Language English
    Publishing date 2023-01-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2022.1079348
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Enhancing remediation potential of heavy metal contaminated soils through synergistic application of microbial inoculants and legumes.

    Zheng, Kang / Liu, Zexun / Liu, Chao / Liu, Jiayi / Zhuang, Jiayao

    Frontiers in microbiology

    2023  Volume 14, Page(s) 1272591

    Abstract: Soil microorganisms play a crucial role in remediating contaminated soils in modern ecosystems. However, the potential of combining microorganisms with legumes to enhance the remediation of heavy metal-contaminated soils remains unexplored. To ... ...

    Abstract Soil microorganisms play a crucial role in remediating contaminated soils in modern ecosystems. However, the potential of combining microorganisms with legumes to enhance the remediation of heavy metal-contaminated soils remains unexplored. To investigate this, we isolated and purified a highly efficient cadmium and lead-tolerant strain. Through soil-cultivated pot experiments with two leguminous plants (
    Language English
    Publishing date 2023-09-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2023.1272591
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Drought stress impact on the performance of deep convolutional neural networks for weed detection in Bahiagrass

    Zhuang, Jiayao / Jin, Xiaojun / Chen, Yong / Meng, Wenting / Wang, Yundi / Yu, Jialin / Muthukumar, Bagavathiannan

    Grass and Forage Science. 2023 Mar., v. 78, no. 1 p.214-223

    2023  

    Abstract: Machine vision‐based weed detection relies on features such as plant colour, leaf texture, shape, and patterns. Drought stress in plants can alter leaf colour and morphological features, which may in turn affect the reliability of machine vision‐based ... ...

    Abstract Machine vision‐based weed detection relies on features such as plant colour, leaf texture, shape, and patterns. Drought stress in plants can alter leaf colour and morphological features, which may in turn affect the reliability of machine vision‐based weed detection. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for the detection of Florida pusley (Richardia scabra L.) growing in drought stressed and unstressed bahiagrass (Paspalum natatum Flugge). The object detection neural networks you only look once (YOLO)v3, faster region‐based convolutional network (Faster R‐CNN), and variable filter net (VFNet) failed to effectively detect Florida pusley growing in drought stressed or unstressed bahiagrass, with F1 scores ≤0.54 in the testing dataset. Nevertheless, the use of the image classification neural networks AlexNet, GoogLeNet, and Visual Geometry Group‐Network (VGGNet) was highly effective and achieved high (≥0.97) F1 scores and recall values (≥0.98) in detecting images containing Florida pusley growing in drought stressed or unstressed bahiagrass. Overall, these results demonstrated the effectiveness of using an image classification convolutional neural network for detecting Florida pusley in drought stressed or unstressed bahiagrass. These findings illustrate the broad applicability of these neural networks for weed detection.
    Keywords Paspalum notatum ; Richardia scabra ; color ; data collection ; drought ; forage and feed science ; geometry ; grasses ; image analysis ; leaf texture ; leaves ; neural networks ; water stress ; weeds
    Language English
    Dates of publication 2023-03
    Size p. 214-223.
    Publishing place John Wiley & Sons, Ltd
    Document type Article ; Online
    Note JOURNAL ARTICLE
    ZDB-ID 442646-0
    ISSN 1365-2494 ; 0142-5242
    ISSN (online) 1365-2494
    ISSN 0142-5242
    DOI 10.1111/gfs.12583
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 932: Show issues

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  5. Article: [Analysis of Soil Bacterial Community Structure and Ecological Function Characteristics in Different Pollution Levels of Lead-zinc Tailings in Datong].

    Liu, Ze-Xun / Zhuang, Jia-Yao / Liu, Chao / Zheng, Kang / Chen, Ling

    Huan jing ke xue= Huanjing kexue

    2023  Volume 44, Issue 7, Page(s) 4191–4200

    Abstract: A rapid rise in industrialization has led to the accumulation of copious mining waste, which has caused serious destruction of the ecological environment, resulting in severe pollution problems that need to be addressed urgently. In this study, altered ... ...

    Abstract A rapid rise in industrialization has led to the accumulation of copious mining waste, which has caused serious destruction of the ecological environment, resulting in severe pollution problems that need to be addressed urgently. In this study, altered soil bacterial communities in different polluted areas were analyzed using the Illumina high-throughput sequencing technique. The primary factors along with physical and chemical factors influencing the soil bacterial communities were also investigated, and the associated potential ecological functions were predicted. The results of these analyses indicated that aggravated pollution caused severe loss of tailing soil nutrients. A total of 14253 bacterial OTU was obtained from the soil samples. The total numbers of OTU in the heavily polluted area (W1), moderately polluted area (W2), lightly polluted area (W3), and clean area (CK) were 3240, 3330, 3813, and 3870, respectively. However, the soil OTUs decreased gradually with increasing pollution. In the α-diversity index analysis, the richness and evenness of the soil bacterial community were significantly decreased in the W1 group. A significant decrease in the Chao1, ACE, and Shannon indexes was also observed in the W1 group, whereas no significant difference was observed in the W3 group compared to the control. The dominant bacterial phyla identified in the soil were
    MeSH term(s) Soil ; Zinc ; Lead ; Environment ; Actinobacteria ; Betaproteobacteria ; Nitrogen
    Chemical Substances Soil ; Zinc (J41CSQ7QDS) ; Lead (2P299V784P) ; Nitrogen (N762921K75)
    Language Chinese
    Publishing date 2023-07-12
    Publishing country China
    Document type English Abstract ; Journal Article
    ISSN 0250-3301
    ISSN 0250-3301
    DOI 10.13227/j.hjkx.202209160
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Penicillium simplicissimum

    Zhuang, Jiayao / Liu, Chao / Wang, Xiaoxue / Xu, Tongxin / Yang, Hao

    Frontiers in microbiology

    2021  Volume 12, Page(s) 738734

    Abstract: It is found effective for phytoremediation of the guest soil spraying method by adding microbes to promote the growth of arbor leguminous plant on a high and steep rock slope. However, its underlying mechanisms remain elusive. Here, some experiments were ...

    Abstract It is found effective for phytoremediation of the guest soil spraying method by adding microbes to promote the growth of arbor leguminous plant on a high and steep rock slope. However, its underlying mechanisms remain elusive. Here, some experiments were conducted to explore the multifunctions of
    Language English
    Publishing date 2021-09-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2021.738734
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Soil bacterial communities of three types of plants from ecological restoration areas and plant-growth promotional benefits of

    Liu, Chao / Zhuang, Jiayao / Wang, Jie / Fan, Guohua / Feng, Ming / Zhang, Shutong

    Frontiers in microbiology

    2022  Volume 13, Page(s) 926037

    Abstract: Microbial-assisted phytoremediation promotes the ecological restoration of high and steep rocky slopes. To determine the structure and function of microbial communities in the soil in response to changes in soil nutrient content, the bacterial ... ...

    Abstract Microbial-assisted phytoremediation promotes the ecological restoration of high and steep rocky slopes. To determine the structure and function of microbial communities in the soil in response to changes in soil nutrient content, the bacterial communities of rhizospheric soil from three types of plants, i.e.,
    Language English
    Publishing date 2022-08-05
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587354-4
    ISSN 1664-302X
    ISSN 1664-302X
    DOI 10.3389/fmicb.2022.926037
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat

    Zhuang, Jiayao / Li, Xuehan / Bagavathiannan, Muthukumar / Jin, Xiaojun / Yang, Jie / Meng, Wenting / Li, Tao / Li, Lanxi / Wang, Yundi / Chen, Yong / Yu, Jialin

    Pest management science. 2022 Feb., v. 78, no. 2

    2022  

    Abstract: BACKGROUND: In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks ...

    Abstract BACKGROUND: In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS: The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION: CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.
    Keywords Triticum aestivum ; broadleaf weeds ; data collection ; image analysis ; pest management ; wheat
    Language English
    Dates of publication 2022-02
    Size p. 521-529.
    Publishing place John Wiley & Sons, Ltd.
    Document type Article
    Note JOURNAL ARTICLE
    ZDB-ID 2001705-4
    ISSN 1526-4998 ; 1526-498X
    ISSN (online) 1526-4998
    ISSN 1526-498X
    DOI 10.1002/ps.6656
    Database NAL-Catalogue (AGRICOLA)

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    In stock of ZB MED Bonn / Germany

    Z 2731: Show issues

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  9. Article: Effect of forest shelter-belt as a regional climate improver along the old course of the Yellow River, China

    Zhuang, Jia-Yao / Jin-Chi Zhang / Yangrong Yang / Bo Zhang / Juanjuan Li

    Agroforestry systems. 2017 June, v. 91, no. 3

    2017  

    Abstract: Up to now very few case studies have provided evidence of the effect of large regional increases in forest area on improving regional climate. This article is perhaps the first description of a unique positive case study of the increasing protection ... ...

    Abstract Up to now very few case studies have provided evidence of the effect of large regional increases in forest area on improving regional climate. This article is perhaps the first description of a unique positive case study of the increasing protection provided by reforestation in controlling a formerly disastrous climate, where gale days have decreased by 80 % per year, and maximum wind speeds of gales have decreased on average from 26 to 11 m/s, while overall average annual wind speed has decreased by 90 % near the ground surface when forest coverage has increased from 3 % in 1950s to 36.9 % in 2010s within 60 years, changing the long-term trend of sandstorms and desertification into a wetter climate where disastrous droughts are now rare despite a global megatrend of decreasing forest area and climate warming. The local climate has been improved by reducing the extreme highs in temperature, reducing the power and frequency of gales, and increasing the number of foggy days. Thus, we propose in arid and semi-arid regions, billions of trees may have a direct effect on improving regional climate, which is worth attention to more than just because of its function as a carbon sink.
    Keywords agroforestry ; carbon sinks ; case studies ; desertification ; drought ; forests ; global warming ; reforestation ; semiarid zones ; temperature ; trees ; wind speed ; China ; Yellow River
    Language English
    Dates of publication 2017-06
    Size p. 393-401.
    Publishing place Springer Netherlands
    Document type Article
    ZDB-ID 406958-4
    ISSN 1572-9680 ; 0167-4366
    ISSN (online) 1572-9680
    ISSN 0167-4366
    DOI 10.1007/s10457-016-9928-9
    Database NAL-Catalogue (AGRICOLA)

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  10. Article ; Online: Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat.

    Zhuang, Jiayao / Li, Xuehan / Bagavathiannan, Muthukumar / Jin, Xiaojun / Yang, Jie / Meng, Wenting / Li, Tao / Li, Lanxi / Wang, Yundi / Chen, Yong / Yu, Jialin

    Pest management science

    2021  Volume 78, Issue 2, Page(s) 521–529

    Abstract: Background: In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural ... ...

    Abstract Background: In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat.
    Results: The object detection neural networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels.
    Conclusion: CenterNet, Faster R-CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.
    MeSH term(s) Neural Networks, Computer ; Plant Weeds ; Seedlings ; Triticum
    Language English
    Publishing date 2021-10-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 2001705-4
    ISSN 1526-4998 ; 1526-498X
    ISSN (online) 1526-4998
    ISSN 1526-498X
    DOI 10.1002/ps.6656
    Database MEDical Literature Analysis and Retrieval System OnLINE

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