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Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.


Papers
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TL;DR: In this paper, surface-functionalized periodic mesoporous organosilica (PMO) hollow spheres are successfully synthesized by using a hybrid silica precursor, 1,2-bis(trimethoxysilyl)ethane (BTME), and five precursors with different functional groups (−SH, −NH2, −CN, −C═C, −benzene) as well as surfactants, fluorocarbon and cetyltrimethylammonium bromide, combining a new vesicle and liquid
Abstract: Surface-functionalized periodic mesoporous organosilica (PMO) hollow spheres are successfully synthesized by using a hybrid silica precursor, 1,2-bis(trimethoxysilyl)ethane (BTME), and five precursors with different functional groups (−SH, −NH2, −CN, −C═C, −benzene) as well as surfactants, fluorocarbon and cetyltrimethylammonium bromide, combining a new vesicle and liquid crystal “dual templating” technique. Different disruption effects on the final mesostructure are observed following the order of −SH from 3-mercaptopropyltrimethoxysilane (MPTMS) > −benzene from (trimethoxysilyl)benzene (TMSB) ∼ −C═C from vinyltrimethoxysilane (VTMS) > −NH2 from 3-aminopropyltriethoxysilane (APTES) > −CN from 3-cyanopropyltriethoxysilane (CPTES). The particle size, cavity size, and wall thickness of these hollow spheres can be adjusted by changing the amount of precursors or surfactants applied. In terms of providing better control over surface properties of products and giving more uniform surface coverage of functional...

70 citations

Journal ArticleDOI
TL;DR: Powered by renewable electricity, the electrochemical conversion of CO2 to liquid fuels and valuable chemicals is a meaningful approach to enable carbon cycling and tackle environmental issues as mentioned in this paper. But, it is not suitable for indoor air quality.
Abstract: Powered by renewable electricity, the electrochemical conversion of CO2 to liquid fuels and valuable chemicals is a meaningful approach to enable carbon cycling and tackle environmental issues An

69 citations

Journal ArticleDOI
TL;DR: The centrosymmetric crystal in the space group of C2 and the non-polar molecule indicate that the ML property is not related to the piezoelectric effect in the crystal; alternatively, a strong static electric interaction may be responsible for its ML phenomenon.

69 citations

Journal ArticleDOI
TL;DR: A comprehensive distribution of iSNVs during this outbreak and along the EBOV genome is presented and it is revealed that VP40 is the most conserved gene during this outbreaks and thus it would be an ideal therapeutic target.
Abstract: Since 2013, West Africa has encountered the largest Ebola virus (EBOV) disease outbreak on record, and Sierra Leone is the worst-affected country, with nearly half of the infections. By means of next-generation sequencing and phylogeographic analysis, the epidemiology and transmission of EBOV have been well elucidated. However, the intra-host dynamics that mainly reflect viral–host interactions still need to be studied. Here, we show a total of 710 intra-host single nucleotide variations (iSNVs) from deep-sequenced samples from EBOV-infected patients, through a well-tailored bioinformatics pipeline. We present a comprehensive distribution of iSNVs during this outbreak and along the EBOV genome. Analyses of iSNV and its allele frequency reveal that VP40 is the most conserved gene during this outbreak, and thus it would be an ideal therapeutic target. In the co-occurring iSNV network, varied iSNV sites present different selection features. Intriguingly, the T-to-C substitutions at the 3′-UTR of the nucleoprotein (NP; positions 3008 and 3011), observed in many patients, result in the upregulation of the transcription of NP through an Ebola mini-genome reporting system. Additionally, no iSNV enrichment within B-cell epitopes of GP has been observed. Deep sequencing of samples from patients infected with Ebola virus during the latest West Africa outbreak reveals intra-host single nucleotide variations, including events that modulate the expression of the gene encoding the viral nucleoprotein.

69 citations

Journal ArticleDOI
Weitao Li1, Ming Li1, Yijian Liu1, Dengyu Pan1, Zhen Li1, Liang Wang1, Minghong Wu1 
30 Mar 2018
TL;DR: In this paper, a simple, superfast, and scalable strategy that obtains graphene quantum dots (GQDs) within 3 min under microwave irradiation (MA-GQD) was introduced.
Abstract: Here we introduce a simple, superfast, and scalable strategy that obtains graphene quantum dots (GQDs) within 3 min under microwave irradiation (MA-GQDs). The MA-GQDs exhibit excellent fluorescence quantum yields up to 35% in the optimum reaction condition. The MA-GQDs with single-crystalline and few-layer structure can reach the visible region with the longest absorption wavelength at 700 nm. Moreover, these ultrabright-fluorescence and stable MA-GQDs as a phosphor and fluorescence probe could be efficiently applied in white light-emitting diodes and cell-imaging fields. The developed pathway to GQDs can provide unambiguous and remarkable insights into the design of high-fluorescence and few-defect GQDs, and expedite the applications of GQDs.

69 citations


Cited by
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08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations