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Institution

Xiamen University

EducationAmoy, Fujian, China
About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a review summarizes the recent advances of graphitic carbon nitride (g-C3N4) based nanocomposites modified with transition metal sulfide (TMS), including preparation of pristine g-C 3N4, modification strategies of g-c3n4, design principles of TMS-modified g-n4 heterostructured photocatalysts, and applications in energy conversion.

386 citations

Journal ArticleDOI
30 May 2018-Nature
TL;DR: The histidine-rich domain of cyclin T1 promotes phase separation into liquid droplets, which facilitates the hyperphosphorylation of the C-terminal domain repeats of RNA polymerase II.
Abstract: Hyperphosphorylation of the C-terminal domain (CTD) of the RPB1 subunit of human RNA polymerase (Pol) II is essential for transcriptional elongation and mRNA processing1–3. The CTD contains 52 heptapeptide repeats of the consensus sequence YSPTSPS. The highly repetitive nature and abundant possible phosphorylation sites of the CTD exert special constraints on the kinases that catalyse its hyperphosphorylation. Positive transcription elongation factor b (P-TEFb)—which consists of CDK9 and cyclin T1—is known to hyperphosphorylate the CTD and negative elongation factors to stimulate Pol II elongation1,4,5. The sequence determinant on P-TEFb that facilitates this action is currently unknown. Here we identify a histidine-rich domain in cyclin T1 that promotes the hyperphosphorylation of the CTD and stimulation of transcription by CDK9. The histidine-rich domain markedly enhances the binding of P-TEFb to the CTD and functional engagement with target genes in cells. In addition to cyclin T1, at least one other kinase—DYRK1A 6 —also uses a histidine-rich domain to target and hyperphosphorylate the CTD. As a low-complexity domain, the histidine-rich domain also promotes the formation of phase-separated liquid droplets in vitro, and the localization of P-TEFb to nuclear speckles that display dynamic liquid properties and are sensitive to the disruption of weak hydrophobic interactions. The CTD—which in isolation does not phase separate, despite being a low-complexity domain—is trapped within the cyclin T1 droplets, and this process is enhanced upon pre-phosphorylation by CDK7 of transcription initiation factor TFIIH1–3. By using multivalent interactions to create a phase-separated functional compartment, the histidine-rich domain in kinases targets the CTD into this environment to ensure hyperphosphorylation and efficient elongation of Pol II. The histidine-rich domain of cyclin T1 promotes phase separation into liquid droplets, which facilitates the hyperphosphorylation of the C-terminal domain repeats of RNA polymerase II.

386 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the impact of green technology innovations on carbon dioxide (CO2) emissions based on a data panel covering 71 economies from 1996 to 2012, and found that green technology innovation does not significantly contribute to reducing CO2 emissions for the economies whose income levels are below the threshold while the mitigation effect becomes significant for those whose incomes levels surpass the threshold.

385 citations

Journal ArticleDOI
TL;DR: Recent approaches of utilizing the borrowing SERS activity strategy mainly through constructing two types of nanostructures are presented and the Raman spectra of surface water, having small Raman cross-section, on several transition metals for the first time are obtained.

384 citations

Journal ArticleDOI
03 Apr 2020
TL;DR: GMAN as mentioned in this paper adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatiotemporal factors on traffic conditions, and proposes a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph.
Abstract: Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

384 citations


Authors

Showing all 50945 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Lei Jiang1702244135205
Yang Gao1682047146301
William A. Goddard1511653123322
Rui Zhang1512625107917
Xiaoyuan Chen14999489870
Fuqiang Wang145151895014
Galen D. Stucky144958101796
Shu-Hong Yu14479970853
Wei Huang139241793522
Bin Liu138218187085
Jie Liu131153168891
Han Zhang13097058863
Lei Zhang130231286950
Jian Zhou128300791402
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023248
2022942
20216,782
20205,710
20194,982
20184,057