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  • 1
    Online Resource
    Online Resource
    Carbon catabolite repressor MoCreA is required for the asexual development and pathogenicity of the rice blast fungus
    Elsevier BV ; 2021
    In:  Fungal Genetics and Biology Vol. 146 ( 2021-01), p. 103496-
    Details
    In: Fungal Genetics and Biology, Elsevier BV, Vol. 146 ( 2021-01), p. 103496-
    Type of Medium: Online Resource
    ISSN: 1087-1845
    URL: Article
    DOI: 10.1016/j.fgb.2020.103496
    RVK:
    WA 15000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 1467659-X
    SSG: 12
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  • 2
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    Chemical Composition of the Essential Oils of Two Alpinia Species from Hainan Island, China
    Walter de Gruyter GmbH ; 2004
    In:  Zeitschrift für Naturforschung C Vol. 59, No. 3-4 ( 2004-4-1), p. 157-160
    Details
    In: Zeitschrift für Naturforschung C, Walter de Gruyter GmbH, Vol. 59, No. 3-4 ( 2004-4-1), p. 157-160
    Abstract: The essential oils of two Alpinia species, i.e. A. hainanensis and A. katsumadai, from Hainan Island, China were analyzed by using GC-MS. The major constituents in the leaf oil of A. hainanensis were ocimene (27.4%), β-pinene (10.1%), 9-octadecenoic acid (6.5%), n-hexadecanoic acid (5.8%), 9,12-octadecadienoic acid (5.4%), and terpinen (4.3%). The oil constituents obtained from the flowers of A. hainanensis were ocimene (39.8%), β-pinene (17.7%), terpinene (5.5%), p-menth-1-en-ol (4.9%), caryophyllene (4.9%), and phellandrene (4.4%). In A. katsumadai, the major constituents in the leaf oil were p-menth-1-en-ol (22.0%), terpinen (19.0%), 4-carene (9.1%), 1,8-cineole (8.3%), and camphor (5.6%). The major constituents in the flower oil were p-menth-1-en-ol (21.3%), 1,8-cineole (20.2%), terpinen (12.6%), phellandrene (7.0%), 4-carene (6.4%), and β-pinene (5.2%).
    Type of Medium: Online Resource
    ISSN: 1865-7125 , 0939-5075
    URL: Article
    DOI: 10.1515/znc-2004-3-402
    RVK:
    WA 15000
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2004
    detail.hit.zdb_id: 2078107-6
    SSG: 12
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  • 3
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    Function and prognostic value of N6‐methyladenosine‐modified RNAs in lung adenocarcinoma
    Wiley ; 2023
    In:  The Journal of Gene Medicine Vol. 25, No. 1 ( 2023-01)
    Details
    In: The Journal of Gene Medicine, Wiley, Vol. 25, No. 1 ( 2023-01)
    Abstract: Aberrant regulation of N6‐methyladenosine (m6A) modification is reportedly vital for cancer progression, including lung adenocarcinoma (LUAD). However, current studies mainly focus on the function and mechanism of m6A‐modified regulators, such as m6A writers (METTL3 and METTL14), erasers (ALKBH5 and FTO), and readers (YTHDF1 and YTHDF2). The landscape, function, and prognostic value of RNAs by m6A‐modified have not been fully clarified until now. Methods The present study identified 57 RNAs with significantly different m6A‐methylation levels in LUAD tissues using epitranscriptomic microarray analysis. Results Among the 57 RNAs, 28 and 29 were hypermethylated and hypomethylated, respectively. The m6A‐methylation level increased in mRNA and long non‐coding RNA (lncRNA) but decreased in small non‐coding RNA. After pathway enrichment analyses, RNA metabolism‐associated pathways such as nucleotide metabolism were enriched in total and m6A‐hypermethylated mRNAs. Furthermore, lncRNA networks were built using miRNet tools, revealing that the immune system was closed to m6A‐modified lncRNAs. To evaluate the prognostic value of mRNAs with hypermethylated or hypomethylated, we calculated the risk scores, and constructed signatures to predict the survival time of patients with LUAD using multicox regression analysis. In addition, hypermethylated‐mRNA and hypomethylated‐mRNA signatures were established. The survival plotter showed that these two signatures effectively predicted the survival time of patients with LUAD. Conclusions The results of the present study support the evidence for understanding the expression, function, and potential prognostic values of m6A‐modified RNAs, possibly promoting effective therapies for patients with LUAD.
    Type of Medium: Online Resource
    ISSN: 1099-498X , 1521-2254
    URL: Issue
    URL: Article
    DOI: 10.1002/jgm.v25.1
    DOI: 10.1002/jgm.3454
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2002203-7
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  • 4
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    Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19
    Proceedings of the National Academy of Sciences ; 2023
    In:  Proceedings of the National Academy of Sciences Vol. 120, No. 18 ( 2023-05-02)
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 120, No. 18 ( 2023-05-02)
    Abstract: The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (M pro ), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3′,5′-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.2301775120
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2023
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
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  • 5
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    Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence
    Springer Science and Business Media LLC ; 2020
    In:  BMC Genomics Vol. 21, No. 1 ( 2020-12)
    Details
    In: BMC Genomics, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2020-12)
    Abstract: Developing effective strategies for signaling the pre-disease state of complex diseases, a state with high susceptibility before the disease onset or deterioration, is urgently needed because such state usually followed by a catastrophic transition into a worse stage of disease. However, it is a challenging task to identify such pre-disease state or tipping point in clinics, where only one single sample is available and thus results in the failure of most statistic approaches. Methods In this study, we presented a single-sample-based computational method to detect the early-warning signal of critical transition during the progression of complex diseases. Specifically, given a set of reference samples which were regarded as background, a novel index called single-sample Kullback–Leibler divergence (sKLD), was proposed to explore and quantify the disturbance on the background caused by a case sample. The pre-disease state is then signaled by the significant change of sKLD. Results The novel algorithm was developed and applied to both numerical simulation and real datasets, including lung squamous cell carcinoma, lung adenocarcinoma, stomach adenocarcinoma, thyroid carcinoma, colon adenocarcinoma, and acute lung injury. The successful identification of pre-disease states and the corresponding dynamical network biomarkers for all six datasets validated the effectiveness and accuracy of our method. Conclusions The proposed method effectively explores and quantifies the disturbance on the background caused by a case sample, and thus characterizes the criticality of a biological system. Our method not only identifies the critical state or tipping point at a single sample level, but also provides the sKLD-signaling markers for further practical application. It is therefore of great potential in personalized pre-disease diagnosis.
    Type of Medium: Online Resource
    ISSN: 1471-2164
    URL: Article
    DOI: 10.1186/s12864-020-6490-7
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2041499-7
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  • 6
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    Identifying the critical states of complex diseases by the dynamic change of multivariate distribution
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 5 ( 2022-09-20)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 5 ( 2022-09-20)
    Abstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study, we propose a computational method, the direct interaction network-based divergence, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbac177
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
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  • 7
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    The single-sample network module biomarkers (sNMB) method reveals the pre-deterioration stage of disease progression
    Oxford University Press (OUP) ; 2022
    In:  Journal of Molecular Cell Biology Vol. 14, No. 8 ( 2022-12-26)
    Details
    In: Journal of Molecular Cell Biology, Oxford University Press (OUP), Vol. 14, No. 8 ( 2022-12-26)
    Abstract: The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods. In this study, a novel computational method, called single-sample network module biomarkers (sNMB), is presented to predict the pre-deterioration stage or critical point using only a single sample. Specifically, the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples. Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets, including acute lung injury, stomach adenocarcinoma, esophageal carcinoma, and rectum adenocarcinoma. In addition, it provides signaling biomarkers for further practical application, which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.
    Type of Medium: Online Resource
    ISSN: 1674-2788 , 1759-4685
    URL: Article
    DOI: 10.1093/jmcb/mjac052
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2500949-7
    SSG: 12
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  • 8
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    Online Resource
    Identifying the critical state of complex biological systems by the directed-network rank score method
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Vol. 38, No. 24 ( 2022-12-13), p. 5398-5405
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 24 ( 2022-12-13), p. 5398-5405
    Abstract: Catastrophic transitions are ubiquitous in the dynamic progression of complex biological systems; that is, a critical transition at which complex systems suddenly shift from one stable state to another occurs. Identifying such a critical point or tipping point is essential for revealing the underlying mechanism of complex biological systems. However, it is difficult to identify the tipping point since few significant differences in the critical state are detected in terms of traditional static measurements. Results In this study, by exploring the dynamic changes in gene cooperative effects between the before-transition and critical states, we presented a model-free approach, the directed-network rank score (DNRS), to detect the early-warning signal of critical transition in complex biological systems. The proposed method is applicable to both bulk and single-cell RNA-sequencing (scRNA-seq) data. This computational method was validated by the successful identification of the critical or pre-transition state for both simulated and six real datasets, including three scRNA-seq datasets of embryonic development and three tumor datasets. In addition, the functional and pathway enrichment analyses suggested that the corresponding DNRS signaling biomarkers were involved in key biological processes. Availability and implementation The source code is freely available at https://github.com/zhongjiayuan/DNRS. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btac707
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 9
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    Online Resource
    Chemical Composition of the Essential Oils of Clausena lansium from Hainan Island, China
    Walter de Gruyter GmbH ; 2004
    In:  Zeitschrift für Naturforschung C Vol. 59, No. 3-4 ( 2004-4-1), p. 153-156
    Details
    In: Zeitschrift für Naturforschung C, Walter de Gruyter GmbH, Vol. 59, No. 3-4 ( 2004-4-1), p. 153-156
    Abstract: The essential oils of wild Clausena lansium collected in Hainan Island, China were extracted from leaves, flowers, sarcocarps and seeds, and then analyzed by using GC/MS. The main constituents of the essential oils were: β-santalol (35.2%), bisabolol (13.7%), methyl santalol (6.9%), ledol (6.5%) and sinensal (5.6%) in the leaves; β-santalol (50.6%), 9-octadecenamide (17.2%) and sinensal (4.1%) in the flowers; β-santalol (52.0%), α-santalol (15.5%), farnesol (5.2%) and sinensal (4.0%) in the sarcocarps; and phellandrene (54.8%), limonene (23.6%), and p-menth-1-en-4-ol (7.5%) in the seeds.
    Type of Medium: Online Resource
    ISSN: 1865-7125 , 0939-5075
    URL: Article
    DOI: 10.1515/znc-2004-3-401
    RVK:
    WA 15000
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2004
    detail.hit.zdb_id: 2078107-6
    SSG: 12
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  • 10
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    A neurofibromatosis-1-regulated pathway is required for learning in Drosophila
    Springer Science and Business Media LLC ; 2000
    In:  Nature Vol. 403, No. 6772 ( 2000-2), p. 895-898
    Details
    In: Nature, Springer Science and Business Media LLC, Vol. 403, No. 6772 ( 2000-2), p. 895-898
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    URL: Article
    DOI: 10.1038/35002593
    RVK:
    TA 1000
    RVK:
    UA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2000
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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