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.

Tribological properties of oleic acid-modified graphene as lubricant oil additives

Abstract: Liquid phase exfoliated graphene sheets were modified by oleic acid and dispersed in lubricant oils as additives. The tribological behaviours of graphene-contained oils were investigated using a four-ball tribometer. The lubricant with optimized graphene concentrations of 0.02–0.06 wt% showed enhanced friction and anti-wear performance, with friction coefficient and wear scar diameter reduced by 17% and 14%, respectively.

Self-polymerization of dopamine and polyethyleneimine: novel fluorescent organic nanoprobes for biological imaging applications

Abstract: The development of novel fluorescent nanoprobes has attracted great current research interest over the past few decades due to their superior optical properties and multifunctional capability as compared with small organic dyes. Although great advance has been made in the utilization of fluorescent nanoprobes for biomedical applications, development of novel fluorescent nanoprobes that possess good fluorescent properties, biocompatibility, biodegradability and water dispersibility through a convenient and effective route is still highly desirable. In this work, we reported for the first time that novel fluorescent organic nanoparticles (FONs) can be conveniently fabricated via self-polymerization of dopamine and polyethyleneimine at room temperature and in an air atmosphere within 2 h. These FONs exhibited strong green fluorescence, high water stability and excellent biocompatibility, making them highly potential for biological imaging applications. More importantly, due to the high reactivity of polydopamine, these FONs might also be further functionalized with other functional components through Michael addition or Schiff base reaction. Therefore the method described in this work would open new avenues for the fabrication of fluorescent nanoprobes for various biomedical applications.

Label‐Free Fluorescent Probing of G‐Quadruplex Formation and Real‐Time Monitoring of DNA Folding by a Quaternized Tetraphenylethene Salt with Aggregation‐Induced Emission Characteristics

Abstract: Biosensing processes such as molecular beacons require non-trivial effort to covalently label or mark biomolecules We report here a label-free DNA assay system with a simple dye with aggregation-induced emission (AIE) characteristics as the fluorescent bioprobe 1,1,2,2-Tetrakis[4-(2-bromoethoxy)phenyl]ethene is nonemissive in solution but becomes highly emissive when aggregated This AIE effect is caused by restriction of intramolecular rotation, as verified by a large increase in the emission intensity by increasing viscosity and decreasing temperature of the aqueous buffer solution of 1,1,2,2-tetrakis[4-(2-triethylammonioethoxy)phenyl]ethene tetrabromide (TTAPE) When TTAPE is bound to a guanine-rich DNA strand (G1) via electrostatic attraction, its intramolecular rotation is restricted and its emission is turned on When a competitive cation is added to the G1 solution, TTAPE is detached and its emission is turned off TTAPE works as a sensitive poststaining agent for poly(acrylamide) gel electrophoresis (PAGE) visualization of G1 The dye is highly affinitive to a secondary structure of G1 called the G-quadruplex The bathochromic shift involved in the G1 folding process allows spectral discrimination of the G-quadruplex from other DNA structures The strong affinity of TTAPE dye to the G-quadruplex structure is associated with a geometric fit aided by the electrostatic attraction The distinct AIE feature of TTAPE enables real-time monitoring of folding process of G1 in the absence of any pre-attached fluorogenic labels on the DNA strand TTAPE can be used as a K+ ion biosensor because of its specificity to K+-induced and -stabilized quadruplex structure

BSA‐Mediated Synthesis of Bismuth Sulfide Nanotheranostic Agents for Tumor Multimodal Imaging and Thermoradiotherapy

Abstract: Fabrication of ultrasmall single-component omnipotent nanotheranostic agents integrated with multimodal imaging and multiple therapeutic functions becomes more and more practically relevant but challenging. In this article, sub 10 nm Bi2S3 biocompatible particles are prepared through a bovine serum albumin (BSA)-mediated biomineralization process under ambient aqueous conditions. Owing to the ultrasmall size and colloidal stability, the resulting nanoparticles (NPs) present outstanding blood circulation behavior and excellent tumor targeting ability. Toward theranostic applications, the biosafety profile is carefully investigated. In addition, photothermal conversion is characterized for both photoacoustic imaging and photothermal treatment of cancers. Upon radiolabeling, the performance of the resulting particles for SPECT/CT imaging in vivo is also carried out. Additionally, different combinations of treatments are applied for evaluating the performance of the as-prepared Bi2S3 NPs in photothermal- and radiotherapy of tumors. Due to the remarkable photothermal conversion efficiency and large X-ray attenuation coefficient, the implanted tumors are completely eradicated through combined therapies, which highlights the potential of BSA-capped Bi2S3 NPs as a novel multifunctional nanotheranostic agent.

I and i

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 …

Fast parallel algorithms for short-range molecular dynamics

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.

Highly accurate protein structure prediction with AlphaFold

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.