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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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Journal ArticleDOI
TL;DR: The theoretical results demonstrate that the electron deficient boron materials, such as α-B12 and γ-B28, can bond strongly with CO2 due to Lewis acid-base interactions because the electron density is higher on their surfaces and are predicted to be good candidates for CO2 capture.
Abstract: Capturing and sequestering carbon dioxide (CO2) can provide a route to partial mitigation of climate change associated with anthropogenic CO2 emissions. Here we report a comprehensive theoretical study of CO2 adsorption on two phases of boron, α-B12 and γ-B28. The theoretical results demonstrate that the electron deficient boron materials, such as α-B12 and γ-B28, can bond strongly with CO2 due to Lewis acid–base interactions because the electron density is higher on their surfaces. In order to evaluate the capacity of these boron materials for CO2 capture, we also performed calculations with various degrees of CO2 coverage. The computational results indicate CO2 capture on the boron phases is a kinetically and thermodynamically feasible process, and therefore from this perspective these boron materials are predicted to be good candidates for CO2 capture.

36 citations

Journal ArticleDOI
TL;DR: In this article, the effect of composition, including SiO 2 and impurity defined to contain K 2 O, Na 2 O and Fe 2 O 3, on sinter-crystallization and properties of the low temperature co-fired α-cordierite glass-ceramics was investigated.
Abstract: This article investigates effect of composition, including SiO 2 and impurity defined to contain K 2 O, Na 2 O, Fe 2 O 3 , etc., from K-feldspar, on sinter-crystallization and properties of the low temperature co-fired α-cordierite glass–ceramics. Increasing impurity content from 5.72 wt% to 9.16 wt% leads to enhanced crystallinity, formation of leucite and more pores but the crystallinity and porosity decreased with a further increase to 10.8 wt%. The main impurity K 2 O is critical for formation of α-cordierite and leucite. Only α-cordierite was precipitated from the glasses with different SiO 2 contents but an increase of SiO 2 content slightly improves their densification. The impurity and SiO 2 contents greatly affect the properties of glass–ceramics. Notably, some glass–ceramics from K-feldspar show high densification at low temperature, low dielectric constant (6–8), low loss (about 0.005), appropriate linear CTEs (4.32–5.87 × 10 −6 K −1 ) and flexural strength (above 100 MPa), all of which meet the requirements of LTCC substrates.

36 citations

Journal ArticleDOI
TL;DR: In this article, a water-soluble graphene sheet was prepared via chemical reduction of graphene oxide (GO) in the presence of polyacetylene bearing a quaternary ammonium pendant, with a relatively low feed ratio of 1/3 (Pac/GO, w/w).
Abstract: Water-soluble graphene sheet was prepared via chemical reduction of graphene oxide (GO) in the presence of polyacetylene bearing a quaternary ammonium pendant (Pac), with a relatively low feed ratio of 1/3 (Pac/GO, w/w). The non-covalent functionalization of graphene by Pac was mainly based on the electrostatic attraction and π–π interaction, the resultant material G–Pac showed good solubility in water with the concentration of 0.28 mg mL−1. In virtue of the unique sp2-conjugated structure of graphene, it displayed a prominent optical limiting response, which could be potentially used in photonic or optoelectronic devices to protect human eyes or optical sensors from damage by intense laser irradiation.

36 citations

Proceedings ArticleDOI
01 May 2020
TL;DR: An attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time while takes little computational costs is proposed.
Abstract: The real-time segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, it is still a challenging task to implement deep learning models to do real-time segmentation for surgical instruments due to their high computational costs and slow inference speed. In this paper, we propose an attention-guided lightweight network (LWANet), which can segment surgical instruments in real-time. LWANet adopts encoder-decoder architecture, where the encoder is the lightweight network MobileNetV2, and the decoder consists of depthwise separable convolution, attention fusion block, and transposed convolution. Depthwise separable convolution is used as the basic unit to construct the decoder, which can reduce the model size and computational costs. Attention fusion block captures global contexts and encodes semantic dependencies between channels to emphasize target regions, contributing to locating the surgical instrument. Transposed convolution is performed to upsample feature maps for acquiring refined edges. LWANet can segment surgical instruments in real-time while takes little computational costs. Based on 960x544 inputs, its inference speed can reach 39 fps with only 3.39 GFLOPs. Also, it has a small model size and the number of parameters is only 2.06 M. The proposed network is evaluated on two datasets. It achieves state-of-the- art performance 94.10% mean IOU on Cata7 and obtains a new record on EndoVis 2017 with a 4.10% increase on mean IOU.

36 citations

Journal ArticleDOI
TL;DR: A novel mechanism for sevoflurane preconditioning-induced tolerance to focal cerebral ischaemia is suggested and involved TREK-1 channels, a two-pore domain K+ channel and target for volatile anaesthetics, in vitro and in vivo.
Abstract: Background Preconditioning with volatile anaesthetic agents induces tolerance to focal cerebral ischaemia, although the underlying mechanisms have not been clearly defined. The present study analyses whether TREK-1, a two-pore domain K + channel and target for volatile anaesthetics, plays a role in mediating neuroprotection by sevoflurane. Methods Differentiated SH-SY5Y cells were preconditioning with sevoflurane and challenged by oxygen–glucose deprivation (OGD). Cell viability and expression of caspase-3 and TREK-1 were evaluated. Rats that were preconditioned with sevoflurane were subjected to middle cerebral artery occlusion (MCAO), and the expression of TREK-1 protein and mRNA was analysed. Neurological scores were evaluated and infarction volume was examined. Results Sevoflurane preconditioning reduced cell death in differentiated SH-SY5Y cells challenged by OGD. Sevoflurane preconditioning reduced infarct volume and improved neurological outcome in rats subjected to MCAO. Sevoflurane preconditioning increased levels of TREK-1 mRNA and protein. Knockdown of TREK-1 significantly attenuated sevoflurane preconditioning-induced neuroprotective effects in vitro and in vivo . Conclusions Sevoflurane preconditioning-induced neuroprotective effects against transient cerebral ischaemic injuries involve TREK-1 channels. These results suggest a novel mechanism for sevoflurane preconditioning-induced tolerance to focal cerebral ischaemia.

36 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