37 research outputs found
Poisson valuations
We study Poisson valuations and provide their applications in solving
problems related to rigidity, automorphisms, Dixmier property, isomorphisms,
and embeddings of Poisson algebras and fields.Comment: 47 page
Weighted Poisson polynomial rings
We discuss Poisson structures on a weighted polynomial algebra defined by a homogeneous element , called a potential. We
start with classifying potentials of degree degdegdeg
with any positive weight (deg, deg, deg) and list all with
isolated singularity. Based on the classification, we study the rigidity of
in terms of graded twistings and classify Poisson fraction fields of
for irreducible potentials. Using Poisson valuations, we
characterize the Poisson automorphism group of when has an
isolated singularity extending a nice result of
Makar-Limanov-Turusbekova-Umirbaev. Finally, Poisson cohomology groups are
computed for new classes of Poisson polynomial algebras.Comment: 37 page
Implementing a new fully stepwise decomposition-based sampling technique for the hybrid water level forecasting model in real-world application
Various time variant non-stationary signals need to be pre-processed properly
in hydrological time series forecasting in real world, for example, predictions
of water level. Decomposition method is a good candidate and widely used in
such a pre-processing problem. However, decomposition methods with an
inappropriate sampling technique may introduce future data which is not
available in practical applications, and result in incorrect
decomposition-based forecasting models. In this work, a novel Fully Stepwise
Decomposition-Based (FSDB) sampling technique is well designed for the
decomposition-based forecasting model, strictly avoiding introducing future
information. This sampling technique with decomposition methods, such as
Variational Mode Decomposition (VMD) and Singular spectrum analysis (SSA), is
applied to predict water level time series in three different stations of
Guoyang and Chaohu basins in China. Results of VMD-based hybrid model using
FSDB sampling technique show that Nash-Sutcliffe Efficiency (NSE) coefficient
is increased by 6.4%, 28.8% and 7.0% in three stations respectively, compared
with those obtained from the currently most advanced sampling technique. In the
meantime, for series of SSA-based experiments, NSE is increased by 3.2%, 3.1%
and 1.1% respectively. We conclude that the newly developed FSDB sampling
technique can be used to enhance the performance of decomposition-based hybrid
model in water level time series forecasting in real world
Grape Seed Proanthocyanidin Extract Prevents Ovarian Aging by Inhibiting Oxidative Stress in the Hens
Oxidative stress is an important inducement in ovarian aging which results in fecundity decline in human and diverse animals. As a potent antioxidant, grape seed proanthocyanidin extract (GSPE) was investigated to ameliorate chicken ovarian aging in this study. Firstly, ovarian antioxidant capacity of hens at different ages (90, 150, 280, and 580 days old) was compared to elucidate its age-related changes. Subsequently, a D-gal-induced (2.5 mg/mL) aging ovarian model was established and the cultured ovarian tissues were treated with GSPE at 5 μg/mL for 72 h to evaluate the putative attenuating effects of GSPE on ovarian aging. Meanwhile, ovaries of D280 (young) and D580 (old) were treated with GSPE for 72 h in culture to verify the protective effects of GSPE on natural aging ovary. The results showed that GSPE could rescue the antioxidant capacity decline by increasing the antioxidase activities and their gene expression in either D-gal-induced or natural aging ovaries. Moreover, GSPE could maintain the homeostasis between cell proliferation and apoptosis in the D-gal-induced and natural aging ovaries, as well as alleviate D-gal-induced nucleus chromatin condensation in the ovarian granulosa cells. In conclusion, GSPE treatment can effectively prevent the ovarian aging process in hens by reducing oxidative stress
Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes
Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Shan Ci Gu (Cremastra appendiculata) Against Non-Small Cell Lung Cancer
Background: In recent years, the incidence and mortality rates of non-small cell lung cancer (NSCLC) have increased significantly. Shan Ci Gu is commonly used as an anticancer drug in traditional Chinese medicine; however, its specific mechanism against NSCLC has not yet been elucidated. Here, the mechanism was clarified through network pharmacology and molecular docking.Methods: The Traditional Chinese Medicine Systems Pharmacology database was searched for the active ingredients of Shan Ci Gu, and the relevant targets in the Swiss Target Prediction database were obtained according to the structure of the active ingredients. GeneCards were searched for NSCLC-related disease targets. We obtained the cross-target using VENNY to obtain the core targets. The core targets were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins database, and Cytoscape software was used to operate a mesh chart. R software was used to analyze the Gene Ontology biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The core targets and active compounds were molecularly docked through Auto-Dock Vina software to predict the detailed molecular mechanism of Shan Ci Gu for NSCLC treatment. We did a simple survival analysis with hub gene to assess the prognosis of NSCLC patients.Results: Three compounds were screened to obtain 143 target genes and 1,226 targets related to NSCLC, of which 56 genes were related to NSCLC treatment. Shan Ci Gu treatment for NSCLC involved many BPs and acted on main targets including epidermal growth factor receptor (EGFR), ESR1, and SRC through signaling pathways including the endocrine resistance, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathways. Shan Ci Gu might be beneficial for treating NSCLC by inhibiting cell proliferation and migration. Molecular docking revealed that the active compounds β-sitosterol, stigmasterol, and 2-methoxy-9,10-dihydrophenanthrene-4,5-diol had good affinity with the core target genes (EGFR, SRC, and ESR1). Core targets included EGFR, SRC, ESR1, ERBB2, MTOR, MCL1, matrix metalloproteinase 2 (MMP2), MMP9, KDR, and JAK2. Key KEGG pathways included endocrine resistance, EGFR tyrosine kinase inhibitor resistance, ErbB signaling, PI3K-Akt signaling, and Rap1 signaling pathways. These core targets and pathways have an inhibitory effect on the proliferation of NSCLC cells.Conclusion: Shan Ci Gu can treat NSCLC through a multi-target, multi-pathway molecular mechanism and effectively improve NSCLC prognosis. This study could serve as a reference for further mechanistic research on wider application of Shan Ci Gu for NSCLC treatment