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79 results for “Liquidambar”
The dataset of Liquidambar orientalis for species distribution models
<p>The primary objective of this study was to predict the existing geographic range of <em>Liquidambar</em> <em>orientalis</em>, commonly known as the oriental sweetgum. To gain insights into the potential effects of climate change on the oriental sweetgum, the study employed species distribution models to project the model to future periods. Considering two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5), the ensemble modeling approach utilized the <em>biomod2</em> package in the R programming language to analyze the alterations in the spatial distribution of the species in forthcoming periods (namely, for the years 2035s, 2055s, and 2070s). </p>
The dataset of Liquidambar orientalis for species distribution models
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Liquidambar styraciflua (Hamamelidaceae) - leaf - showing orientation on twig
Image of Liquidambar styraciflua (Hamamelidaceae) - leaf - showing orientation on twig
Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - winter
Image of Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - winter
Liquidambar styraciflua (Hamamelidaceae) - twig - close-up winter terminal bud
Image of Liquidambar styraciflua (Hamamelidaceae) - twig - close-up winter terminal bud
Liquidambar styraciflua (Hamamelidaceae) - fruit - immature
Image of Liquidambar styraciflua (Hamamelidaceae) - fruit - immature
Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - view up trunk
Image of Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - view up trunk
Liquidambar styraciflua (Hamamelidaceae) - bark - of a large tree
Image of Liquidambar styraciflua (Hamamelidaceae) - bark - of a large tree
Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - general
Image of Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - general
Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - winter
Image of Liquidambar styraciflua (Hamamelidaceae) - whole tree (or vine) - winter
Liquidambar styraciflua (Hamamelidaceae) - whole tree - general
Image of Liquidambar styraciflua (Hamamelidaceae) - whole tree - general
Species TX OK Distribution Map FH Hosts Pityophthorina Araptus dentifrons Wood 1 1* MEX+NT 103 ph-my Milkweed vines Conophthorus echinatae Wood 1* SEUS 104 my-sp Pinus Conophthorus edulis Hopkins 1 SWNA 104 my-sp Pinus Dendroterus texanus Wood 1 MEX+NT 105 ph Jatropha dioica Pityoborus comatus (Zimmermann) 1 1 SENA 105 xm Pinus Pityophthorus annectens LeConte 1 1 SE+SW 106 ph Pinus Pityophthorus arcanus Bright 1 SWNA 106 ph Pinus Pityophthorus barberi Blackman 1 SWNA 107 ph Pinus Pityophthorus brevis Blackman 1 SWNA 108 ph Pinus Pityophthorus confertus Swaine 1 SWNA 110 ph Pinus Pityophthorus confinis LeConte 1* SWNA 109 ph Pinus Pityophthorus confusus Blandford 1 SE+SW 110 ph Pinus Pityophthorus consimilis LeConte 1 1* SENA 111 ph Pinus Pityophthorus crassus Blackman 1 SWNA 112 ph Pinus Pityophthorus crinalis Blackman 1 1* SENA 109 ph Rhus Pityophthorus deletus LeConte 1 SWNA 111 ph Pinus Pityophthorus grandis Blackman 1 SWNA 113 ph Pinus Pityophthorus guatemalensis Blandford 1 SWNA 112 ph Quercus Pityophthorus lautus Eichhoff 1 1* SENA 112 ph Rhus, Toxicodendron Pityophthorus liquidambarus Blackman 1 SENA 113 ph Liquidambar Pityophthorus pulchellus Eichhoff 1 SE+SW 115 ph Pinus Pityophthorus pulicarius (Zimmermann) 1 1* SENA 114 ph-my Pinus Pityophthorus pullus (Zimmermann) 1 SENA 115 ph Pinus Pityophthorus schwarzi Blackman 1 SWNA 116 ph Pinus Pityophthorus schwerdtfegeri Schedl 1 SWNA 117 ph-my Pinus Pityophthorus scriptor Blackman 1 1 SENA 118 ph Rhus in Atlas and checklist of the bark and ambrosia beetles of Texas and Oklahoma (Curculionidae: Scolytinae and Platypodinae)
Species TX OK Distribution Map FH Hosts Pityophthorina Araptus dentifrons Wood 1 1* MEX+NT 103 ph-my Milkweed vines Conophthorus echinatae Wood 1* SEUS 104 my-sp Pinus Conophthorus edulis Hopkins 1 SWNA 104 my-sp Pinus Dendroterus texanus Wood 1 MEX+NT 105 ph Jatropha dioica Pityoborus comatus (Zimmermann) 1 1 SENA 105 xm Pinus Pityophthorus annectens LeConte 1 1 SE+SW 106 ph Pinus Pityophthorus arcanus Bright 1 SWNA 106 ph Pinus Pityophthorus barberi Blackman 1 SWNA 107 ph Pinus Pityophthorus brevis Blackman 1 SWNA 108 ph Pinus Pityophthorus confertus Swaine 1 SWNA 110 ph Pinus Pityophthorus confinis LeConte 1* SWNA 109 ph Pinus Pityophthorus confusus Blandford 1 SE+SW 110 ph Pinus Pityophthorus consimilis LeConte 1 1* SENA 111 ph Pinus Pityophthorus crassus Blackman 1 SWNA 112 ph Pinus Pityophthorus crinalis Blackman 1 1* SENA 109 ph Rhus Pityophthorus deletus LeConte 1 SWNA 111 ph Pinus Pityophthorus grandis Blackman 1 SWNA 113 ph Pinus Pityophthorus guatemalensis Blandford 1 SWNA 112 ph Quercus Pityophthorus lautus Eichhoff 1 1* SENA 112 ph Rhus, Toxicodendron Pityophthorus liquidambarus Blackman 1 SENA 113 ph Liquidambar Pityophthorus pulchellus Eichhoff 1 SE+SW 115 ph Pinus Pityophthorus pulicarius (Zimmermann) 1 1* SENA 114 ph-my Pinus Pityophthorus pullus (Zimmermann) 1 SENA 115 ph Pinus Pityophthorus schwarzi Blackman 1 SWNA 116 ph Pinus Pityophthorus schwerdtfegeri Schedl 1 SWNA 117 ph-my Pinus Pityophthorus scriptor Blackman 1 1 SENA 118 ph Rhus
Liquidambar styraciflua (Hamamelidaceae) - fruit - section or open
Image of Liquidambar styraciflua (Hamamelidaceae) - fruit - section or open
Liquidambar styraciflua (Hamamelidaceae) - bark
Image of Liquidambar styraciflua (Hamamelidaceae) - bark
Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neutral network method
<p><i><span>Aims:</span></i> This study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (<i><span>PI</span></i>) for <i><span>Liquidambar formosana</span></i><span>,</span> a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction.</p> <p><i><span>Methods: </span></i>Three-layered BP models with an architecture 4-10-1 <span>(four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) </span>were trained and tested using the Levenberg-Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development.</p> <p><i><span>Results: </span></i>The BP models with eight input combinations proved a satisfactory fit: the determination coefficients (<i><span>R</span></i><sup><span>2</span></sup>) and fitting accuracies (<i><span>Acc</span></i>) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear problems involved in transpiration. In addition, the BP models showed a bit better performance by adding <i><span>PI</span></i><i> </i><span>to</span> the input family. The best BP model was achieved taking air temperature (<i><span>T</span></i><sub><span>a</span></sub>), relative humidity (<i><span>RH</span></i>), average net radiation (<i><span>ANR</span></i>) and <i><span>PI</span></i> as the input and sap flux density (<i><span>v</span></i><sub><span>s</span></sub>) as the output, with maximum <i><span>R</span></i><sup><span>2</span></sup> and <i><span>Acc</span></i> as high as 0.95 and 90%, respectively.</p> <p><i><span>Conclusions: </span></i><span>The</span><i> </i><span>BP</span><i> </i><span>models with input combination of </span><i><span>T</span></i><sub><span>a</span></sub><sub><span>, </span></sub><i><span>RH, ANR </span></i><span>and</span><i><span> PI </span></i><span>mirrored very well measured daily variations in </span><i><span>v</span></i><sub><span>s</span></sub>. The results could be used to fine-tune sap flow estimation by <i><span>Liquidambar formosana</span></i>, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.</p>
Fig. 8 in Pentacyclic Triterpenes from the resin of Liquidambar formosana have anti-angiogenic properties
Fig. 8. The effect of compounds 1–5, 6, 11, and14 on VEGF-induced migration observed in HUVECs. Data are presented as mean ± SEM of at least three independent experiments. *P <0.05; **P <0.01; ***P <0.001 vs. VEGF-treated control.
Fig. 7 in Pentacyclic Triterpenes from the resin of Liquidambar formosana have anti-angiogenic properties
Fig. 7. The effect of compounds 1–15 on VEGF-induced mitogenesis observed in HUVECs. Data are presented as mean ± SEM of at least four independent experiments. *P <0.05; **P <0.01; ***P <0.001 vs. VEGF-treated control.
Fig. 9 in Pentacyclic Triterpenes from the resin of Liquidambar formosana have anti-angiogenic properties
Fig. 9. Inhibitory effects of compound 1 on the phosphorylation of VEGFR2, AKT, and ERK. (A)Western blot analysis of phospho-(B) VEGFR2, (C) AKT, (D) ERK1/2 levels in HUVECs treated with compound 1 at indicated concentrations. β-actin was used as a loading control. Data are presented as mean ± SEM from three independent experiments. *P <0.05, **P <0.01 vs. VEGFtreated control.
Fig. 7 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 7. Effects of compounds 5, 7 and 8 on expression of apoptosis related genes in SMMC7721 cells. (A). Representative Western blot results. *P <0.05 vs control. (B) RT-qPCR results of three independent experiments. *P <0.05 vs control.
Fig. 5 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 5. Induction of apoptosis by compounds 5, 7 and 8 in SMMC7721 cancer cells at 36 h *P <0.05 vs control.
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