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  • MDPI AG  (144)
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  • MDPI AG  (144)
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  • 1
    Online Resource
    Online Resource
    The Histological, Effectoromic, and Transcriptomic Analyses of Solanum pinnatisectum Reveal an Upregulation of Multiple NBS-LRR Genes Suppressing Phytophthora infestans Infection
    MDPI AG ; 2020
    In:  International Journal of Molecular Sciences Vol. 21, No. 9 ( 2020-05-01), p. 3211-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 21, No. 9 ( 2020-05-01), p. 3211-
    Abstract: Utilization of disease resistance components from wild potatoes is a promising and sustainable approach to control Phytophthora blight. Here, we combined avirulence (Avr) genes screen with RNA-seq analysis to discover the potential mechanism of resistance in Mexican wild potato species, Solanum pinnatisectum. Histological characterization displayed that hyphal expansion was significantly restricted in epidermal cells and mesophyll cell death was predominant, indicating that a typical defense response was initiated in S. pinnatisectum. Inoculation of S. pinnatisectum with diverse Phytophthora infestans isolates showed distinct resistance patterns, suggesting that S. pinnatisectum has complex genetic resistance to most of the prevalent races of P. infestans in northwestern China. Further analysis by Avr gene screens and comparative transcriptomic profiling revealed the presence and upregulation of multiple plant NBS-LRR genes corresponding to biotic stresses. Six NBS-LRR alleles of R1, R2, R3a, R3b, R4, and Rpi-smira2 were detected, and over 60% of the 112 detected NLR proteins were significantly induced in S. pinnatisectum. On the contrary, despite the expression of the Rpi-blb1, Rpi-vnt1, and Rpi-smira1 alleles, fewer NLR proteins were expressed in susceptible Solanum cardophyllum. Thus, the enriched NLR genes in S. pinnatisectum make it an ideal genetic resource for the discovery and deployment of resistance genes for potato breeding.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms21093211
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2019364-6
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Chebulic Acid Prevents Hypoxia Insult via Nrf2/ARE Pathway in Ischemic Stroke
    MDPI AG ; 2022
    In:  Nutrients Vol. 14, No. 24 ( 2022-12-19), p. 5390-
    Details
    In: Nutrients, MDPI AG, Vol. 14, No. 24 ( 2022-12-19), p. 5390-
    Abstract: Excessive reactive oxygen species (ROS) production contributes to brain ischemia/reperfusion (I/R) injury through many mechanisms including inflammation, apoptosis, and cellular necrosis. Chebulic acid (CA) isolated from Terminalia chebula has been found to have various biological effects, such as antioxidants. In this study, we investigated the mechanism of the anti-hypoxic neuroprotective effect of CA in vitro and in vivo. The results showed that CA could protect against oxygen-glucose deprivation/reoxygenation (OGD/R) induced neurotoxicity in SH-SY5Y cells, as evidenced by the enhancement of cell viability and improvement of total superoxide dismutase (T-SOD) in SH-SY5Y cells. CA also attenuated OGD/R-induced elevations of malondialdehyde (MDA) and ROS in SH-SY5Y cells. Nuclear factor-E2-related factor 2 (Nrf2) is one of the key regulators of endogenous antioxidant defense. CA acted as antioxidants indirectly by upregulating antioxidant-responsive-element (ARE) and Nrf2 nuclear translocation to relieve OGD/R-induced oxidative damage. Furthermore, the results showed that CA treatment resulted in a significant decrease in ischemic infarct volume and improved performance in the motor ability of mice 24 h after stroke. This study provides a new niche targeting drug to oppose ischemic stroke and reveals the promising potential of CA for the control of ischemic stroke in humans.
    Type of Medium: Online Resource
    ISSN: 2072-6643
    URL: Article
    DOI: 10.3390/nu14245390
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518386-2
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  • 3
    Online Resource
    Online Resource
    Classifying Mountain Vegetation Types Using Object-Oriented Machine Learning Methods Based on Different Feature Combinations
    MDPI AG ; 2023
    In:  Forests Vol. 14, No. 8 ( 2023-08-11), p. 1624-
    Details
    In: Forests, MDPI AG, Vol. 14, No. 8 ( 2023-08-11), p. 1624-
    Abstract: Mountainous vegetation type classification plays a fundamental role in resource investigation in forested areas, making it necessary to accurately identify mountain vegetation types. However, Mountainous vegetation growth is readily affected by terrain and climate, which often makes interpretation difficult. This study utilizes Sentinel-2A images and object-oriented machine learning methods to map vegetation types in the complex mountainous region of Jiuzhaigou County, China, incorporating multiple auxiliary features. The results showed that the inclusion of different features improved the accuracy of mountain vegetation type classification, with terrain features, vegetation indices, and spectral features providing significant benefits. After feature selection, the accuracy of mountain vegetation type classification was further improved. The random forest recursive feature elimination (RF_RFE) algorithm outperformed the RliefF algorithm in recognizing mountain vegetation types. Extreme learning machine (ELM), random forest (RF), rotation forest (ROF), and ROF_ELM algorithms all achieved good classification performance, with an overall accuracy greater than 84.62%. Comparing the mountain vegetation type distribution maps obtained using different classifiers, we found that classification algorithms with the same base classifier ensemble exhibited similar performance. Overall, the ROF algorithm performed the best, achieving an overall accuracy of 89.68%, an average accuracy of 88.48%, and a Kappa coefficient of 0.879.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f14081624
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2527081-3
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  • 4
    Online Resource
    Online Resource
    Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 22 ( 2021-11-09), p. 10508-
    Details
    In: Applied Sciences, MDPI AG, Vol. 11, No. 22 ( 2021-11-09), p. 10508-
    Abstract: Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app112210508
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 5
    Online Resource
    Online Resource
    Crop Mapping and Spatio–Temporal Analysis in Valley Areas Using Object-Oriented Machine Learning Methods Combined with Feature Optimization
    MDPI AG ; 2023
    In:  Agronomy Vol. 13, No. 10 ( 2023-09-24), p. 2467-
    Details
    In: Agronomy, MDPI AG, Vol. 13, No. 10 ( 2023-09-24), p. 2467-
    Abstract: Timely and accurate acquisition of crop planting areas and spatial distribution are deemed essential for grasping food configurations and guiding agricultural production. Despite the increasing research on crop mapping and changes with the development of remote sensing technology, most studies have focused on large-scale regions, with limited research being conducted in fragmented and ecologically vulnerable valley areas. To this end, this study utilized Landsat ETM+/OLI images as the data source to extract additional features, including vegetation index, terrain, and texture. We employed the Random Forest Recursive Feature Elimination (RF_RFE) algorithm for feature selection and evaluated the effectiveness of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Rotation Forest (ROF)—for crop extraction. Then, based on the optimal classifiers, the main crops in the Huangshui basin for the years of 2002, 2014, and 2022 were extracted. Finally, the transfer matrix, the gravity center model, and the Standard Deviation Ellipse (SDE) model were used to analyze the spatio—temporal changes of crops over the past 20 years in the Huangshui basin. The results showed that the spectral, vegetation index, and terrain features played a crucial role in crop extraction. Comparing the performance of the classifiers, the ROF algorithm displayed superior effectiveness in crop identification. The overall accuracy of crop extraction was above 86.97%, and the kappa coefficient was above 0.824. Notably, between 2002 and 2022, significant shifts in crop distribution within the Huangshui basin were observed. The highland barley experienced a net increase in planting area at a rate of 8.34 km2/year, while the spring wheat and oilseed rape demonstrated net decreases at rates of 16.02 km2/year and 14.28 km2/year, respectively. Furthermore, the study revealed that highland barley exhibited the most substantial movement, primarily expanding towards the southeast direction.
    Type of Medium: Online Resource
    ISSN: 2073-4395
    URL: Article
    DOI: 10.3390/agronomy13102467
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2607043-1
    SSG: 23
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  • 6
    Online Resource
    Online Resource
    Vegetation Type Classification Based on 3D Convolutional Neural Network Model: A Case Study of Baishuijiang National Nature Reserve
    MDPI AG ; 2022
    In:  Forests Vol. 13, No. 6 ( 2022-06-10), p. 906-
    Details
    In: Forests, MDPI AG, Vol. 13, No. 6 ( 2022-06-10), p. 906-
    Abstract: Efficient and accurate vegetation type extraction from remote sensing images can provide decision makers with basic forest cover and land use information, and provides a reliable basis for long-term monitoring. With the development of deep learning, the convolutional neural network (CNN) has been used successfully to classify tree species in many studies, but CNN models have rarely been applied in the classification of vegetation types on larger scales. To evaluate the performance of CNN models in the classification of vegetation types, this paper compared the classification accuracy of nine dominant land cover types in Baishuijiang National Nature Reserve with four models: 3D-CNN, 2D-CNN, JSSAN (joint spatial–spectral attention network) and Resnet18, using sentinel-2A data. Comparing the difference in classification accuracy between the direct use of raw sentinel images and fused feature indices sentinel images, the results showed that adding feature indices can improve the overall accuracy of the model. After fusing the characteristic bands, the accuracy of the four models was improved significantly, by 5.46–19.33%. The best performing 3D-CNN model achieved the highest classification accuracy with an overall accuracy of 95.82% and a kappa coefficient of 95.07%. In comparison, 2D-CNN achieved an overall accuracy of 79.07% and a kappa coefficient of 75.44%, JSSAN achieved an overall accuracy of 81.67% and a kappa coefficient of 78.56%, and Resnet18 achieved an overall accuracy of 93.61% and a kappa coefficient of 92.45%. The results showed that the 3D-CNN model can effectively capture vegetation type cover changes from broad-leaved forests at lower elevation, to shrublands and grasslands at higher elevation, across a range spanning 542–4007 m. In experiments using a small amount of sample data, 3D-CNN can better incorporate spatial–spectral information and is more effective in distinguishing the performance of spectrally similar vegetation types, providing an efficient and novel approach to classifying vegetation types in nature reserves with complex conditions.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f13060906
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527081-3
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  • 7
    Online Resource
    Online Resource
    Integrative Metabolomic and Transcriptomic Analyses Reveal Metabolic Changes and Its Molecular Basis in Rice Mutants of the Strigolactone Pathway
    MDPI AG ; 2020
    In:  Metabolites Vol. 10, No. 11 ( 2020-10-26), p. 425-
    Details
    In: Metabolites, MDPI AG, Vol. 10, No. 11 ( 2020-10-26), p. 425-
    Abstract: Plants have evolved many metabolites to meet the demands of growth and adaptation. Although strigolactones (SLs) play vital roles in controlling plant architecture, their function in regulating plant metabolism remains elusive. Here we report the integrative metabolomic and transcriptomic analyses of two rice SL mutants, d10 (a biosynthesis mutant) and d14 (a perception mutant). Both mutants displayed a series of metabolic and transcriptional alterations, especially in the lipid, flavonoid, and terpenoid pathways. Levels of several diterpenoid phytoalexins were substantially increased in d10 and d14, together with the induction of terpenoid gene cluster and the corresponding upstream transcription factor WRKY45, an established determinant of plant immunity. The fact that WRKY45 is a target of IPA1, which acted as a downstream transcription factor of SL signaling, suggests that SLs contribute to plant defense through WRKY45 and phytoalexins. Moreover, our data indicated that SLs may modulate rice metabolism through a vast number of clustered or tandemly duplicated genes. Our work revealed a central role of SLs in rice metabolism. Meanwhile, integrative analysis of the metabolome and transcriptome also suggested that SLs may contribute to metabolite-associated growth and defense.
    Type of Medium: Online Resource
    ISSN: 2218-1989
    URL: Article
    DOI: 10.3390/metabo10110425
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662251-8
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  • 8
    Online Resource
    Online Resource
    Evolution of the Chenglingji–Datong Channel in the Middle and Lower Reaches of the Yangtze River and Its Drivers
    MDPI AG ; 2023
    In:  Water Vol. 15, No. 8 ( 2023-04-11), p. 1484-
    Details
    In: Water, MDPI AG, Vol. 15, No. 8 ( 2023-04-11), p. 1484-
    Abstract: In recent years, the water–sand composition of the Yangtze River channel has changed due to the influence of human factors, especially the construction of water reservoirs such as the Three Gorges Project. Changing water–sand conditions have a long-term impact on the shaping of the river channel morphology in the middle and lower reaches of the Yangtze River, and the erosion retreat of local river sections has caused great harm to embankment projects. This paper focuses on the river evolution mechanism of the river channel from Chenglingji to Datong in the middle and lower reaches of the Yangtze River over the past 31 years. Landsat remote sensing images from 1989–2019 were used to extract and interpret water bodies, river shorelines, and central bars in the study area using the Modified Normalized Difference Water Index (MNDWI) combined with visual interpretation. We used near analysis to study the morphological evolution characteristics of the river, the channel, and selected typical river reaches for comparative analysis. We found out that the overall change in river morphology between 1989 and 2019 was small in the horizontal direction, but the local area changed significantly. Considerable scouring occurred in the vertical direction. Combining hydrological and meteorological data, we investigated the effects of the Three Gorges Dam, instream sand mining, boundary conditions, vegetation cover on both sides of the riverbanks, and aspects of storm flooding in the watershed on the evolution of the river. The study indicated that the geological conditions on both sides of the river, the implementation of the bank protection project, and the improvement of vegetation cover on both sides of the river have made the riverbanks more resistant to scouring. However, heavy rainfall floods, the operation of the Three Gorges Reservoir, and sand mining activities in the river channel make the river channel more susceptible to scouring. Based on the calculation of the slope change rate of the accumulated volume, it was found that the runoff is mainly influenced by precipitations, while the sand transport is mainly affected by human activities. This study shows that natural and anthropogenic activities affect the equilibrium state of the river’s water and sediment to varying degree.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w15081484
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2521238-2
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  • 9
    Online Resource
    Online Resource
    SAW Classification Algorithm for Chinese Text Classification
    MDPI AG ; 2015
    In:  Sustainability Vol. 7, No. 3 ( 2015-02-27), p. 2338-2352
    Details
    In: Sustainability, MDPI AG, Vol. 7, No. 3 ( 2015-02-27), p. 2338-2352
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su7032338
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2518383-7
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  • 10
    Online Resource
    Online Resource
    Development of a Monoclonal Antibody to Pig CD69 Reveals Early Activation of T Cells in Pig after PRRSV and ASFV Infection
    MDPI AG ; 2022
    In:  Viruses Vol. 14, No. 6 ( 2022-06-20), p. 1343-
    Details
    In: Viruses, MDPI AG, Vol. 14, No. 6 ( 2022-06-20), p. 1343-
    Abstract: The CD69 molecule, as an early activation marker of lymphocytes, is often used to assess the activation of cellular immunity. However, for pigs, an anti-pig CD69 antibody is not yet available for this purpose after infection or vaccination. In this study, a monoclonal antibody (mAb) against pig CD69 was produced by peptide immunization and hybridoma technique. One mAb (5F12) showed good reactivity with pig CD69 that was expressed in transfected-HEK-293T cells and on mitogen-activated porcine peripheral blood mononuclear cells (PBMCs) by indirect immunofluorescence assay and flow cytometry. This mAb did not cross-react with activated lymphocytes from mouse, bovine, and chicken. Epitope mapping showed that the epitope recognized by this mAb was located at amino acid residues 147–161 of pig CD69. By conjugating with fluorochrome, this mAb was used to detect the early activation of lymphocytes in PRRSV- and ASFV-infected pigs by flow cytometry. The results showed that PRRSV infection induced the dominant activation of CD4 T cells in mediastinal lymph nodes and CD8 T cells in the spleen at 14 days post-infection, in terms of CD69 expression. In an experiment on ASFV infection, we found that ASFV infection resulted in the early activation of NK cells, B cells, and distinct T cell subsets with variable magnitude in PBMCs, spleen, and submandibular lymph nodes. Our study revealed an early event of lymphocyte and T cell activation after PRRSV and ASFV infections and provides an important immunological tool for the in-depth analysis of cellular immune response in pigs after infection or vaccination.
    Type of Medium: Online Resource
    ISSN: 1999-4915
    URL: Article
    DOI: 10.3390/v14061343
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2516098-9
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