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.

Comparison of toxicity of benzene metabolite hydroquinone in hematopoietic stem cells derived from murine embryonic yolk sac and adult bone marrow.

Abstract: Benzene is an occupational toxicant and an environmental pollutant that potentially causes hematotoxicity and leukemia in exposed populations. Epidemiological studies suggest an association between an increased incidence of childhood leukemia and benzene exposure during the early stages of pregnancy. However, experimental evidence supporting the association is lacking at the present time. It is believed that benzene and its metabolites target hematopoietic stem cells (HSCs) to cause toxicity and cancer in the hematopoietic system. In the current study, we compared the effects of hydroquinone (HQ), a major metabolite of benzene in humans and animals, on mouse embryonic yolk sac hematopoietic stem cells (YS-HSCs) and adult bone marrow hematopoietic stem cells (BM-HSCs). YS-HSCs and BM-HSCs were isolated and enriched, and were exposed to HQ at increasing concentrations. HQ reduced the proliferation and the differentiation and colony formation, but increased the apoptosis of both YS-HSCs and BM-HSCs. However, the cytotoxic and apoptotic effects of HQ were more apparent and reduction of colony formation by HQ was more severe in YS-HSCs than in BM-HSCs. Differences in gene expression profiles were observed in HQ-treated YS-HSCs and BM-HSCs. Cyp4f18 was induced by HQ both in YS-HSCs and BM-HSCs, whereas DNA-PKcs was induced in BM-HSCs only. The results revealed differential effects of benzene metabolites on embryonic and adult HSCs. The study established an experimental system for comparison of the hematopoietic toxicity and leukemogenicity of benzene and metabolites during mouse embryonic development and adulthood.

To form AIE product with the target analyte: a new strategy for excellent fluorescent probes, and convenient detection of hydrazine in seconds with test strips

Abstract: A new hydrazine probe was designed, which demonstrates ultrafast “turn-on” response towards hydrazine both in solution and as gas, due to the excellent AIE (aggregation-induced emission) characteristic of the yielded products. Excitedly, the corresponding fabricated test strips can report the presence of trace hydrazine as low as 10 nmol/L in aqueous medium in seconds conveniently, with very high selectivity in the pH value ranging from 3.0 to 14.0.

Enhanced thermoelectric performance through synergy of resonance levels and valence band convergence via Q/In (Q = Mg, Ag, Bi) co-doping

Abstract: The temperature-dependent evolution of heavy-hole valence band contribution to the Seebeck coefficients of SnTe-based thermoelectric materials is revealed in situ by neutron and synchrotron powder diffraction. The additional carriers with high effective mass are created in a heavy-hole valence band above 493 K, which contribute to the electrical transport, and lead to a significant enhancement of the Seebeck coefficient at high temperature. In addition, remarkably improved electrical transport properties are achieved through the synergetic effects of the resonance levels, the valence band convergence, and the carrier concentration optimization by co-doping with Mg & In, Ag & In and Bi & In. Significant reduction in the lattice thermal conductivity is obtained by multiscale phonon scattering over a wide spectrum via atomic point defects, nanoscale elongated screw dislocations with random directions, and the microscale grain boundaries caused by sintering. As a result, a high figure of merit, ZT, of ∼1 at 873 K is obtained for the Mg0.015In0.015Sn0.97Te sample.

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.