Dataset Information

Predicting suitable habitats of Melia azedarach L. in China using data mining.

ABSTRACT: Melia azedarach L. is an important economic tree widely distributed in tropical and subtropical regions of China and some other countries. However, it is unclear how the species' suitable habitat will respond to future climate changes. We aimed to select the most accurate one among seven data mining models to predict the current and future suitable habitats for M. azedarach in China. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906 M. azedarach locations were identified, and sixteen climate predictors were used for model building. The models' validity was assessed using three measures (Area Under the Curves (AUC), kappa, and overall accuracy (OA)). We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species' suitable habitats were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18 °C (DD > 18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18 °C (DD < 18). We projected that future suitable habitat of this species would increase under both the RCP4.5 and RCP8.5 scenarios for the 2011-2040 (2020s), 2041-2070 (2050s), and 2071-2100 (2080s). Our findings are expected to assist in better understanding the impact of climate change on the species and provide scientific basis for its planting and conservation.

SUBMITTER: Feng L

PROVIDER: S-EPMC9308798 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:MAPK/ERK kinases transmit signals from many growth factors/kinase receptors during normal cell growth/differentiation, and their dysregulation is a hallmark of diverse types of cancers. A plethora of drugs were developed to block this kinase pathway for clinical application. With the exception of a recently identified agent, EQW, most of these inhibitors target upstream factors but not ERK1/2; no activator of ERK1/2 is currently available. METHOD:A library of compounds isolated from medicinal plants of China was screened for anti-cancer activities. Three limonoid compounds, termed A1541-43, originally isolated from the plant Melia azedarach, exhibiting strong anti-leukemic activity. The anti-neoplastic activity and the biological target of these compounds were explored using various methods, including western blotting, flow cytometry, molecular docking and animal model for leukemia. RESULTS:Compounds A1541-43, exhibiting potent anti-leukemic activity, was shown to induce ERK1/2 phosphorylation. In contrast, the natural product Cedrelone, which shares structural similarities with A1541-43, functions as a potent inhibitor of ERK1/2. We provided evidence that A1541-43 and Cedrelone specifically target ERK1/2, but not the upstream MAPK/ERK pathway. Computational docking analysis predicts that compounds A1541-43 bind a region in ERK1/2 that is distinct from that to which Cedrelone and EQW bind. Interestingly, both A1541-43, which act as ERK1/2 agonists, and Cedrelone, which inhibit these kinases, exerted strong anti-proliferative activity against multiple leukemic cell lines, and induced robust apoptosis as well as erythroid and megakaryocytic differentiation in erythroleukemic cell lines. These compounds also suppressed tumor progression in a mouse model of erythroleukemia. CONCLUSIONS:This study identifies for the first time activators of ERK1/2 with therapeutic potential for the treatment of cancers driven by dysregulation of the MAPK/ERK pathway and possibly for other disorders.

Project description:These days, research in agriculture is focusing on the theme of sustainability along with protection of agriculture produce. Nanotechnology in the agriculture sector aims for the enhancement of agricultural produce and the reduction of pesticides through providing innovative agrochemical agents and their novel delivery mechanisms. The current investigation involved the green synthesis of silver nanoparticles (AgNPs) from the aqueous leaf extract of Melia azedarach by following a microwave-assisted method to control Fusarium oxysporum, the causal agent of tomato wilt. Biosynthesized Melia leaf extract (MLE)-AgNPs were characterized by UV-visible spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray (EDX) spectrometry, dynamic light scattering (DLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and zeta potential analysis. The intensity of the peak at 434 nm in UV-vis spectra, attributed to the surface plasmon resonance of MLE-AgNPs, changes with reaction parameters. TEM exhibits spherical shaped nanoparticles with an average particle size range from 12 to 46 nm. Efficient inhibition of F. oxysporum, the causal agent of tomato wilt, was achieved after exposure to MLE-AgNPs both in vivo and in vitro. In vitro studies exhibited repressed fungal mycelial growth with 79-98% inhibition as compared to the control. Significant increases in growth parameters of tomato seedlings were observed after treatment with biosynthesized nanoparticles as compared to F. oxysporum-infected plants grown without them under greenhouse conditions. Furthermore, SEM imaging was done to reveal the prominent damage on the cell wall of hyphae and spores after MLE-AgNP treatment. Propidium iodide (PI) staining of mycelium indicated the extent of cell death, causing irretrievable damage and disintegration of cellular membranes by altering the membrane permeability. Also, 2',7'-dichlorofluorescin diacetate (DCFH-DA) fluorescence specifies intracellular reactive oxygen species (ROS) production in F. oxysporum after treatment with MLE-AgNPs. The current investigation suggested that biosynthesized nanoparticles can revolutionize the field of plant pathology by introducing an environment-friendly approach for disease management and playing a potential part in agriculture industry. However, to date, little work has been done to integrate nanotechnology into phytopathology so, this area of research is in need of adoption and exploration for the management of plant diseases.

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Predicting suitable habitats of Melia azedarach L. in China using data mining.

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